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Forex Educational Library

Profitable Trading VIII – Computerized Studies V: Oscillators

Introduction

Markets behave as a fair-price searching machine. When there is no consensus about value, caused by an economic event or some other news that might affect the current fair price, market forces launch a new trend, which starts moving the price toward another “fair” level.

And the rest of the time, when the price is already at a consensus “fair price”, what happens? Does it stay at a single price point until a new event shakes it?

Nature hates stillness and rest. It seeks movement -Any kind of movement- and the markets do, as well. Therefore, as we all acknowledge, price does not stay still just because everybody on earth thinks this is the fair price. The fact that there are zillions of market participants, every one of them with its own opinions, makes it impossible that a fair price exists at all. As Jesse Livermore stated, the two most significant market forces are greed and fear, and, consequently, they exert their pressure on prices, too.

Therefore, when a market lacks impulse to continue a trend, it tends to make oscillatory price changes, although the fact that traders are using different time frames, price targets, and stops, makes this oscillation quite complex, with multiple cycles blended on an intricate and, potentially, noisy pattern.

Science has been dealing with waves and cycles for long. Almost everything in science deals with cycles and oscillations, therefore, cycles are a part of markets that may be handled with precise scientific accuracy, limited only by the noisy nature of prices.

To conclude, Markets mainly behave in two interlinked modes: trend mode and cyclic mode. Those two states may blend with each other on a higher timeframe, though.

We’ve already dealt with computerized studies to help traders find and trade a trend. In this article, we’re going to analyze several computerized studies which might assist us during the cyclic phase of the market, when the markets are not trending.

Slow Stochastics

The Stochastics oscillator was developed by George Lane, who teach it during his investment seminars since 1950. According to Lucas and LeBeau on his book “Computer Analysis of the Futures Markets”, Mr. Lane has been perfecting the use of stochastics for trading over many years, and he is able to make it work well in almost any market situation.

The Stochastics Oscillator, came from the observation that closing prices tend to appear near the high of the range during uptrends and near the low of the range in downtrends.

This oscillator measures where the close is, relative to the range of prices over the latest period. The %K line comes from a simple formula, which makes sure the signal is always between zero and 100:

There is a %D line, which is called slow stochastic and is computed by applying a three-day moving average to the %K line.

By convention, an overbought market is one that led its stochastics lines – %K and %D – above the +80 level; while a market is in oversold condition when they are below the 20 level.

The basic way to use Stochastics is by acting at %D and %K crossovers when this happens at those extremes, and when the %D line crosses – over or under- these price triggers. For example, when %D crosses under the 80 level it indicates a sell signal, and when it crosses over the 20 level, it’s a buy signal.

Periods

The standard period for the Stochastic oscillator is 14, but, according to Lucas and LeBeau, George Lane used an adjusted value of about 50% of the perceived market main cycle. Those authors said in their book that they had tested several periods, and a range between 9 and 12 were the best performing ones, as these were the best compromise between speed and validity, producing the minimum quantity of false signals.

What this tells us is that we need to experiment with the period of the signal and back-test it, to get an optimal figure for the current market we are treading.

According to Alexander Elder, if you intend to use Stochastics as your sole signal it’s better to choose a longer-term period, while in combination with other signals a shorter period is preferable.

Signals against the trend

As said at the beginning, the use of this kind of oscillator is best suited to a cyclic phase of the market. When we detect a trend, the Stochastic oscillator does not perform so well, especially when the signal is against the main trend.

Fig. 2 shows a stochastics used in a downtrending market (NZD/YSD). We may observe that, mostly, good signals come from the overbought side of the market that trigger signals with the trend, even when those appear before %K and %D approaches the saturated region. This is common with strong trends. The market doesn’t reach the overbought ( or oversold) level before returning to the primary trend. The only potentially profitable buy signal comes at the end of this long downtrend when the oversold condition permeates several time frames.

LeBeau and Lucas said in his book: “Remember: The trader who coined the phrase ‘the trend is your friend’ was not using stochastics”.

Divergences

Several authors agree about the fact that divergence between prices and stochastics is one of the most powerful signals.

A bullish divergence occurs when a price makes a new low and the stochastic fails to do so, drawing a higher low.

A bearish divergence arises then prices are making new highs but stochastics lines draw a lower low.

A word of caution about divergences: It may work or not. I don’t know for sure. What’s sure is its performance is very challenging to test. I’d rather like those signals that I’m able to back-test, such crossovers, departures from an overbought or oversold level,  etc.

Knees and Shoulders

When %K has crossed over %D and, then, pulls back, but, without another %D crossing to the downside, and, next, it resumes its up movement, Mr. Lane calls it a knee. If the movement is from overbought to the downside, he calls it a shoulder. According to Mr. Lane, it shows a continuation with strength.

Anticipating a crossover

There are people that remark when %D flattens, call it a hinge. Also, there’s a warning hook, when both %K and %D turns at an extreme but still don’t cross.

According to LeBeau and Lucas, those observations focus, mainly, on anticipation rather than reliance on the signals, and they don’t recommend them. It’s better to wait for a crossover.

Bear and bull setups

Another unique tool by George Lane.

A bear setup happens when prices make a series of higher highs and higher bottoms, but the Stochastics oscillator produces a pattern of lower lows when prices are rising. This pattern indicates there will be a top soon.

Bull setups are the specular pattern to bear setups, indicating that a bottom will happen soon.

Williams %R

Williams Percent R is a momentum indicator developed by Larry Williams, very similar to the Stochastic indicator, but in this case, it computes the level of the closing price in relation to the highest high of the period, instead of the lowest low, and it doesn’t depict a smoothed %D line.

 

Therefore, this oscillator moves from -100 to 0. Values below -80 are oversold levels while from -20 to 0 are overbought levels.

Some charting packages shift these values to positive 0 to 100 by adding 100 to the formula. In this case, oversold levels are between 0 and 20, and overbought condition happens from 80 to 100.

%R is noisier than Stochastic %D, but with less lag, so together with a confirming pattern,  it usually allows for a better reward to risk ratio and tends to show more trade opportunities than Stochastic does.

 

This indicator is very good at detecting oversold conditions at an uptrend, or overbought levels at a downtrend, therefore, it’s well suited as a signal, to add to a position or enter a new one on pullbacks.


Advanced topics:

Adaptive Stochastics Indicator

John Ehlers introduced the idea of an adaptive indicator in his book “Cycle Analytics for traders.” Ehlers proposes to, first, find the dominant cycle first, and then use half of that cycle as the period for the stochastic calculation.

So, the adaptive Stochastics starts by computing the dominant cycle using an autocorrelation periodogram, which Ehlers describes in chapter 8 of his book (I will refer the interested readers to check it).

In his book, Ehlers showed the complete algorithm, as well (although written in easy-language, it may be transposed to any language). The main steps are:

  • A low pass roofing filter to eliminate unwanted noise from the price data
  • The periodogram calculation
  • The instantaneous period is used to compute the current value of %D.
  • No %K is computed.

As we observe from fig. 8, the adaptive stochastic is much less noisy and it adapts to the dominant cycle.

Center Of Gravity

John Ehlers describes this oscillator in chapter 5 of his book “Cybernetic analysis for stocks and futures, cutting edge DSP technology to improve your trading”. He states that this study is unique because his smoothing has virtually zero lag, therefore, enabling a definite identification of turning points at the same time.

The center of gravity of an object is, basically, a weighted center of its mass, a balance point. In a trading environment, we can define a kind of rule of weights on an observation window. A fast and upward moving price shifts the center of prices to the right, while a downward move, shift it to the left.

The following formula computes CG:

CG = ∑i=0 to N (xi +1) * Pricei / ∑ i = 0 to N   Pricei

Fig 9 shows the EUR/USD 10-minute chart with Center of Gravity. As we clearly see, the CG is almost free of noise, so a signal can be picked directly from its crossovers if they happen relatively far from its zero line.

Cyan and pink boxes on Fig.9 show the result of scalping on a 10-min EUR/USD chart using CG crossovers. Nothing is perfect in trading, but we clearly observe that CG crossover signals catch the turning points with accuracy, allowing a highly probabilistic approach to scalping.

Stochastizing indicators

Stochastizing indicators is another development by John Ehlers, which he introduced in his book Cybernetic analysis for stocks and futures.

We may “stochastize” any indicator by computing its value in comparison with its lowest value of a period. Below, is an example of Stochastics RSI, much easier to use than in its original form.

The example in Fig. 9 shows entries and exits following the trend, discarding those against it, depicting high accuracy and early signals that allow for good reward-to-risk ratios. If we take the complete bull and bear signals, the stochastic RSI signal is still quite reliable. Just the trades against the trend do not present such good profits, but do not show significant losses either.

 Inverse Fisher Transform Percent R

The idea of a transform is to map some domain into another domain. The Inverse Fisher transform maps an indicator, %R in this case, into another kind of domain that allows us to alter its probability density function (PDF).

Market prices don’t fit on a Gaussian PDF, the familiar bell-shaped normal distribution, instead, prices have fat tails, meaning that large bull and bear events are more probable than a normal distribution allows. The Fisher transform can be applied to prices, such that it makes the resulting distribution nearly Gaussian.

The following equation defines the Fisher transform:

y = 0.5 x ln [ (1+x)/(1-x)]

This function compresses prices, to cut the fat tails, making the resulting price distribution Gaussian.

The inverse Fisher transform, instead of being compressive, is expansive.  Its equation is:

x = (e2y – 1) / (e2y +1)

The shape of the transfer function of an inverse Fisher transform is a kind of sigmoid function. The resulting output has a higher probability of it being +1 or -1. This -almost saturated- function makes the inverse fisher transform behave like the output function of an artificial neuron. The resulting shape shows trend changes very early.

To conclude, we have seen that the use of advanced signal processing is a way to improve classic indicators, for them to show less lag or behave in ways not achieved by conventional means.

To do so, we are required to do a bit of programming, to translate the algorithm into our trading platform. It may be useful to do an internet search because there are lots of free translations of popular indicators to major trading platforms.

This article section was just, the starting point for those with interest in advanced indicators.

A very good article, including MT5 code, is https://www.mql5.com/en/articles/288, where you may get actual code to implement the ideas sketched here.

 


References:

Computer Analysis of the Futures Markets, LeBeau and Lucas

Cycle Analytics for Traders and Cybernetic analysis for stocks and futures, cutting edge DSP technology to improve your trading,  by John Ehlers.

Charts created using MT4 and Multicharts trading platforms.

 

©Forex.Academy
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Globalization and its Risks

Abstract

Globalization is an inevitable process that has been accelerated with the current means of communication and transportation, which help to trade more due to better access to foreign products. Globalization is not only economic, it also covers social and cultural aspects. But since globalization is a process that involves several aspects of society many people see it as a threat to local customs and producers, so they prefer to put some barriers to this process. But it is a process already in development that will be almost impossible to stop so the best thing that each country can do is take advantage of this for their benefit by being more competitive.

 

Globalization is a process that has spread to different areas in all countries, from cultural to economic aspects. For many economists, globalization has allowed the economic development of many countries to be driven by dependence on international trade and technological advances that not only stay in one country but also expand rapidly as well as new knowledge. But beyond the benefits of globalization, there are many people who question the effect that globalization has on different cultures in the world.

For many people, the values of their culture are very important to value and exchange them for more global ones, which is why some people are still reticent to see a total globalization of all aspects. There is a great debate that persists over time about how, and how much, politicians should intervene in this process of globalization and preserve their own identity or how much the economy of foreign goods should be protected.

Globalization has been formally defined as the phenomenon of acceleration and intensification of economic interaction between people around the world, companies, and governments of the countries involved in this effect, although today there is no country that has not been influenced for globalization. But globalization not only involves economic interactions, political and cultural effects have also been evident, which has led to the study of all areas of society and to encourage debate about the positive and negative effects of globalization.

The globalization of the production and distribution of goods and services has been well received by some people who can now access foreign goods of better quality and in some cases at better prices than local production. But the opponents of globalization show their concern about the local producers of each country and their standards of living after the expansion of international trade. In addition to increasing trade and allowing access to new products, globalization allows the exchange of cultural features, customs such as music and other determinants of daily life such as music and literature.

Exposure to the exchange of goods and services brings changes in the customs, values and local traditions of a country. This is also a point of debate since some believe that globalization eliminates the traditions of a country making it universal but at the cost of the loss of knowledge and traditional customs of a country which is not positive for some aboriginal and indigenous communities that can see this change in their traditions as something threatening.

One of the criticisms, in general, is the Americanization of culture and economy. The most powerful companies belong to the United States, as well as the richest people and in turn, many traditions and festivities that come from North America are exported and celebrated in other countries. This has been a high point and a great debate since many people, especially in underdeveloped and poor countries, see this Americanization as a threat, criticizing consumerism and certain values that are handled in that country. But this leader of globalization, as seen in the United States, has several elements that make it the leader of said globalization.

The first element that makes the United States one of the predominant countries in recent decades is the size of its market which represents about 300 million people. This makes it attractive for multinationals as well as generating economies of scale which means that as companies increase their production costs per unit produced are reduced making their operation more profitable. An important aspect of this large market is the wealth of the population since there are countries with similar or larger population sizes such as China and India, but the wealth between these three nations is not compared since the United States has about 25% of global production.

In addition to the importance of the United States market is the element of language because most people in North America speak English and it is a universal language that allows transactions to be made easily. It is not the only country that has contributed to the process of globalization, but it is one of the predominant countries in this process. The following graph shows the magnitude of the US market in urban areas

US market in urban areas

Graph 22. Urban population. Data taken from the World Bank.

The people who are in favor of globalization argue that contrary to what some people think, globalization supports local cultures through technology by allowing better communications through computer telephones and other elements that facilitate the exchange of opinions. In addition, they preach that through television and other visual artifacts, cultural elements can be displayed so that people who see it within each country feel more identified with their culture.

Another positive aspect of globalization is the new awareness of people that are currently being created – better educated with a broader knowledge and a global notion of different cultures. This type of people are the drivers of current economic processes, they have a more cosmopolitan than rational thinking, speak several languages, travel internationally and they have more developed skills with better education standards, so they have better living standards than the people who lived before.

Globalization in the economy is the most known and studied fact by people since it is vital to understand how this effect has an impact on people’s daily lives. Economic globalization involves the reduction or elimination of business and trade barriers between countries, which will encourage exports and imports to increase in each country. Therefore, globalization is the economic unification of different countries including the different types of investments that occur in each country. It is crucial that each country is seen as a piece of a puzzle and that each country will be very important for the good economic development of all countries.

Globalization is not a new phenomenon. It goes back more than  2000 years, during which time trade, traditions, ideas, and other elements have been exchanged between different countries and empires. In the last 200-300 years this unifying process has accelerated, leading to greater specialization in the production of goods and greater interdependence among economies. But what is not clear is that the pace of unification of the economies has not been constant, but has increased rapidly since the end of the Second World War. The following graph shows how trade in goods and services has increased in recent years, where globalization has accelerated.

 Exports of goods and services

Graph 23. Exports of goods and services. Data taken from World Bank.

 

Some of the consequences that globalization has left are:

  • The relaxation of government controls to let the market act independently thanks to the forces of supply and demand.
  • Efficiency in means of transport and communication which helps to make business faster and easier.
  • New technologies that contribute to the quality of life of people. Technological advances have been achieved thanks to the dynamism of trade and skilled labor which has contributed to the progress being made less and less time.
  • Greater dynamism in investments since resources can be easily moved from one country to another without the need to be in several countries to make such investments.
  • Better quality of life for a large part of the population. A better quality of life is achieved with globalization and international trade, consumers can acquire a greater variety of products of good quality and at better prices due to competition between multinationals and local companies.

In addition, globalization reduces labor costs because companies can be in the country that is most beneficial in terms of costs, where there is specialization depending on production and the intensity of capital used. That is why lower prices can be seen in goods and services since with globalization countries have managed to reduce their costs by specializing in countries with economies of scale and with good competitiveness in terms of costs.

Another consequence of globalization is access to the natural resources of different countries. This is an advantage for countries that do not have access to good resources due to their geographic location that determines the climate and their access to seas or not. In addition, there are countries that have better access to mineral resources such as oil or coal. Globalization intervenes in the fact that the multinationals depending on the location and resources of each country decide to locate themselves in a certain area to extract these resources, but also generating benefits for the local communities. The negative aspect of globalization at this point is to what extent it is positive to extract and exploit natural resources of a foreign country since it can destroy nature and many projects will be made for political interests rather than the possible economic benefits.

At this point in the article, we will analyze a determinant of investments in the globalized world that currently exists. Country risk is one of the most important variables when analyzing how risky investment is in a country given its political and economic factors and its relationship with other countries. The formal definition is the risk of an investment due to specific factors common to a specific country. It can be analyzed as the average risk of investments made in a country.

A country can see its risk increase if a country faces difficulties in paying debt acquired as bonds. The factors that may affect the payment of a country’s debt depend on economic, social, political conditions or in some cases due to natural disasters such as earthquakes. Any assessment of the risk of each country will express the level of probability of suffering a loss in the value invested and depending on the level of that country risk the investors will demand a return on an investment since in the finances if greater risks are incurred, it will be claimed better returns that demonstrate that it is a good idea to invest in that asset. In the following graph, you can see the percentage of country risk that some places had in 2016.

Market Risk Premium

Graph 24. Fernandez P., Fernandez I. and Ortiz A. (2016, May 9). Market Risk Premium Used In 71 Countries In 2016. Retrieved November 17, 2017, from http://www.valuewalk.com/2016/05/market-risk-premium-used-71-countries-2016-survey-6932-answers/

Country risk analysis is a predominant component of the specialized risk management departments of banks, insurance companies, risk rating agencies, and financial system regulators. In some cases, country risk includes the risk of payment transfers, confiscation and expropriation, and risks of internal and external wars.

The debtors of these investments can be stated sovereigns, or they can be private agents such as banks and companies that issue debt to obtain financing for their commercial operations. In general, any country or private agent can issue debt and be subject to country risk assessment and each agent that issues debt will be analyzed differently because almost no company or state will face the same determinants in the risk of their investment.

The country risk can be materialized to the extent that there may be a payment crisis and the result of this is the non-payment of the debt by the agents, whether private or state. This debt default occurs especially in countries with high debt or without good access to external debt. Also in cases where the debtor is a state agent, it is determined the number of foreign reserves that a country has since these can be used as payment by the creditors at a specific time. In the following graph, you can see the external reserves of different countries.

Total reserves. Data taken from World Bank

Graph 25. Total reserves. Data taken from World Bank.

©Forex.Academy

 

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Profitable Trading (VII) – Computerized Studies: Bands & Envelopes

Introduction

We have already dealt, briefly, with bands and channels. On this article, we will try to develop a bit more on bands and envelopes, as it is quite important in trading.

The history of trading bands is quite long. Let’s first define what we mean by a band.  John Bollinger defines bands as bands constructed above and below some measure of central tendency, which need not be symmetrical.

We called it envelopes when they are related to the price structure, with a kind of symmetry, but not following or associated with a central point reference -for example, moving average envelopes of highs and lows.

A Brief History of Envelopes and Bands

The earliest mention, according to John Bollinger, comes from Wilfrid LeDoux in 1960, who copyrighted the Twin-Line Chart, that connected monthly highs and monthly lows. You may google “Wilfrid LeDoux Twin-line chart” to see this type of chart.

At about the same time, Chester W. Keltner published the Ten-Day Moving Average Rule in his 1960 book How to make money in commodities. Keltner computed what he called the typical price by the simple formula: (H+L+C)/3 for a given period as a center line, and a 10-day moving average plus and minus a 10-period average of the daily range.

In the 60’s Richard Donchian took another approach. He let the markets show its envelopes via his four-week rule. A buy signal happens if the price exceeds the 4-week high, and a sell signal if the price falls under the 4-week low. This 4-week rule was turned into envelopes connecting the 4-week highs and the 4-week lows.

Back in the 70’s, J.M Hurst published The Profit Magic of Stock Transactions. Mr. Husrt interest was in cycles, so in his book, he presented “constant width curvilinear channels” to emphasize the cyclic character of the stock price movements.

In the early 80’s William Smith, of Tiger Software presented a black-box system: The Peerless Stock Market Timing. The system used a percentage band based on moving averages.

Concurrently, in the early 80’s Marc Chaikin and Bob Brogan developed the first adaptive band system, called Bomar Bands. They were designed to contain 85% of the price action over the latest 12 months (250 periods). That was a significant improvement to the former bands, as the width of the band grew or shrunk depending upon the market action.

A mention should be made to Jim Yates that in the early 80’s developed a method, using the implied volatility from the options market. He determined whether the security was overbought or oversold concerning market expectations. Mr. Yates showed that implied volatility could be used as part of a framework to make rational investing decisions. The framework consisted of six zones (bands) based on the implied volatility, with a specific strategy to be followed in each band.

John Bollinger, who at that time was trading options and met Mr. Yates, took this idea in the late 80’s to find a method to automate the band’s width based on the current mean volatility. He realized that volatility was directly related to the standard deviation of prices and that this method would provide a superior way to draw its bands, which he named Bollinger bands.

Bands formed by moving averages

There are several envelope types in trading. The simplest is the moving average envelope with percentage bands, n% away from the central average (Fig 1.a), that shows a 10-bar EMA with 0.2% bands.

Usually, the beginning of a trend is pointed out by prices touching or breaking one of the bands that agrees with a new change in the slope of the moving average.

The variations on this theme are countless. We could favor the volatility in the direction of the trend by using asymmetric bands.

Envelopes on highs and lows

Another possibility is using envelopes on highs and lows (Fig 1.b). Bands will show prices contained within the band at sideway channel price movements and will indicate the beginning of a trend with a breakout or breakdown.

An uptrend is in place as long as the price doesn’t close below the channel. The reverse holds true on downtrends.

To assess if this method improves its results against entries on a usual moving average breakout, Perry J. Kaufman (see reference) did a study over a 10-year span, using both methods, in two very different markets: Eurodollar interest rates and the S&P 500.

The results he presented on page 320 of his book is shown in table 1. We may observe that this method is slightly worse for the Eurodollar market, although the 40-period variant is quite similar. Just the opposite happened in the S&P 500, where, using a 40-period envelope MA turned a losing system into a winner, and the 20-period variant has improved a lot.

The conclusion is that bands might be a worthy entry method, but we need to find the right parameters for the market we try to trade.

Keltner Channels

Chester Keltner, a famous technical trader at the time, presented his 10-day moving average rule in his 1960 book How to Make Money in Commodities. This was a simple system that used a channel whose width is defined by the 10-day range.

The algorithm to compute the channel, as is done today is:

  1. Set the MA period n. Usually 20.
  2. Set a period m to calculate the average range. Usually 10.
  3. Set a multiplier q.
  4. Compute the typical daily price: (high+low+close)/3
  5. Compute AR the n-day average of the typical daily price.
  6. Compute MA the m-day average of the daily range.
  7. Compute the upper and lower bands by performing:

Upper band = q x AR + MA

Lower Band = q x AR – MA

  1. Buy when the market crosses over the upper band and sell when it crosses under the lower band.

The original system was always in the market, but on actual computer testing, it doesn’t show any effectiveness. The system is buying on strength and selling on weakness, so it may happen that, at the time of entry, the price has already traveled too much and It’s close to saturation.

Today, traders have modified the concept to better cope with current markets.

  • Instead of buying the upper band, they sell it, and vice versa. The reasoning is selling the strength and buying the weakness because markets are usually trading in ranges. The disadvantage is not being able to catch a significant trend.
  • The number of days is modified. Some systems use a three-day average with bands around that average.
  • Many systems are using a lower timeframe for entries. If the market hits an upper band, the system waits for the shorter timeframe to hit a lower band to act.

Fig 4 shows a Keltner channel system using a 3-period channel and a 10-period moving average.

The slope of the 10-period average defines if the trend is up or down.

A Sell takes place when the price hits the upper band, and the MA is pointing down (selling at resistance).  A buy is open when the price hits the lower band, and the 10-period MA is pointing up (buy at support). As Fig. 3 shows, the system forbids trading against the trend, avoiding reactive legs.

A short exit occurs if price crosses over the 10-period MA; while a crossing below it is a sign to close a long position.

Bollinger Bands

Bollinger bands are another type of volatility band, as is the Keltner channel, but the measurement of the volatility isn’t the daily range, but the standard deviation of prices for the period in place.

John Bollinger used the 20-period standard deviation (STD), and the upper and lower bands separated 2 STD’s from a central 20-period SMA. He explains that two STD’s distance from the mean will contain 95% of t e price action and that the bands are very responsive, since the standard deviation calculation is computed using the squares of the deviations from the average, so the channel contracts and expands rapidly with volatility changes.

Bollinger bands are commonly used together with another technical study. Some people use it with RSI. But, I think there’s another technical study much better suited as its companion.

Bollinger bands with two moving regression line crossovers were introduced to me by Ken Long on a seminar that took place in Raleigh, NC, back in 2013. He calls his system the Regression Line Crossover (RLCO), but, many charting packages don’t have a native moving linear regression study, so, he presented, too, a very close alternative to it: The 30-10-5 MACD study.

Bollinger band framework

Ken Long called the ±1 bands the river. The stream of prices is represented by the dragon, a 10-period -0.2std width- Bollinger band, which he calls that way due to its shape resembling those Chinese moving dragons, so common in their celebrations. Ken uses 30-period BB, but I don’t see any gain using this period. In fact, it makes more difficult to spot sideways channels because a 30-period BB is less responsive to volatility changes, so I recommend using the standard 20-period.

The dragon moves from side to side of the river, sometimes beyond. When it crosses it and travels along the upper side, the trend is up. If it moves to the lower side and keeps moving on that side, a downward leg has started.

Sideways channels are distinguished by a shrink in volatility that is clearly visible, particularly in ±1 bands, as Fig 5 shows (1, 3, 4, and 5). Those places are excellent entry points when the dragon breaks out (4), up or down, or if it starts moving next to a riverside, as in (1 and 3). You could be able to earn your monthly pay by just trading this formation.

We should be ready to close a failed breakout, as in b, and c; and willing to reverse direction when the candle, or the dragon, cross again the river because failure to continue is a clear indication to trade the other side, although, sometimes we got fooled twice. Twice is not that much. (read my essay “Trading, a different view”)

Trends are distinguished, also by an expansion of the river and its “wetlands”. And, if the price goes away from the dragon and travels to the 2nd and 3rd lands (a and d), then the price movement is well over-extended, and, for sure, it will travel back to the mean of the river. Anyway, an automatic stop and reverse trade isn’t advisable at these points. You should carefully assess the reward to risk situation, considering that the mean of the river also moves up or down with the flow.

This two-spike pattern, at the edge of ±3 bands (a and d), is quite important, as it’s a warning that we should take profits right away, it marks the peak of the trend, at least for a while.

As said earlier, Prices, after crossing bands 2 and 3, will move back searching the mean.  Usually, this is a continuation pattern. Price goes to the vicinity of the river mean and resumes the trend, as in 2.

Trading this setup together with MACD crossovers is very revealing. When we use this framework, we know beforehand a lot about the actual state of the market. At 1 we know we cannot trade long unless the dragon shifts sides. We, know, also, that the price stream is heading to the downside and the bands are expanding. All this points to a short side entry.

At a, we knew that prices have gone crazy, and the second white candlestick confirms that the downward move has paused, at the minimum. At b, and c we got fooled twice, but, as somebody said, crocodiles live in great rivers!

We, also, know the price condition relative to a well established statistical framework that shows 98% of prices enclosed within ±3 STD bands, and 95% of them within ±2 STD bands. The framework is a visual indication of overbought and oversold, but within a framework that quantifies the concepts.

Finally, by just looking, we are able to assess the reward to risk situation anytime. If our risk is 1STD and we expect to get 2 STD’s out of a trade, based on its position on this framework, then we have a 2:1 Reward to risk situation. We don’t need to spend time calculating. It’s visual and fast. You’re able to jump faster into any trade situation!

A practical trade scalping-like exercise

Trading Bollinger bands using the MACD, this way is a beauty, as the MACD tells the direction of the trade and the Bollinger band tells where the price is, relative to its mean.

If we look at fig. 6, point 1, we observe that, on the previous bars, the price has made a bottom, by touching the -2 band four times and in the last one, a Doji was formed. Meanwhile, the MACD sang a buy, loud and clear, so it was a buying opportunity at the breakout of the highs.

Since we wanted fast and dirty profits we set our target at the piercing of the +2 band for an excellent 3:1 reward to risk trade.

The beauty of the Bollinger is that price when on a trend, tends to go back to its mean, touch it and back away from it, so at point 2 we’ll take a re-entry for a nice 2.5:1 Reward/risk trade. And this happened a third time at point 3. We were nimble, so we took no chance and close the trades, again, at the bar piercing the +2 band.

At point 4 we saw a sideways channel, that is quite observable with Bollinger bands, as noticeable shrinkage of the three bands. This is a good trade to take when a breakout occurs. So, we took it and the market fooled us for a small loss on the trade.

Anyhow, the MACD crossover and the price crossing was a sign to stop and reverse(5), and that we did! Usually, a failed breakout and, then, a breakdown with the slope of the Bollinger band turning south is an excellent entry point. This time it didn’t fail for a big trade with almost 4:1 reward/risk. We let profits run this time because the price had broken down from its support and MACD wasn’t in oversold territory. We exited when it showed signs of bottoming, as seen in the graph.

At (6) we observed another sideways channel, so we took its breakout. This time it didn’t fail, but the trend wasn’t strong enough to touch the +2 band and we sold on weakness, when MACD crossed under. We might have taken a re-entry there but the MACD wasn’t pretty, Overall, 5 winners and 1 small loser. Not bad!

Donchian Channels

Richard Donchian was a pioneer of systematic trading. He was the author of one of the first channel breakout systems. When we draw lines connecting those breakouts – highest highs to highest highs and lowest lows to lowest lows- a channel is formed.  Fig 7 shows a 20 bar Donchian breakout channel of the EUR/USD 1-hour chart.

As a new high is made the upper band moves higher, while the lower band stays at the same level, until a new 20-day low comes out, creating the stairway pattern we observe in fig 5. Sometimes, the upper band goes up while the lower goes down. This is caused by a big outside bar.

According to Perry Kaufman, in 1971, Playboy’s Investment guide reviewed Donchian’s 4-week rule as a “childishly simple” way to invest.

The Donchian trading method was as follows:

  1. Go long (and cover short positions) when the current price is higher than the high of the latest 4 weeks
  2. Sell short (and close long positions) if the current price falls below the low of the latest 4 weeks.

This system is amazingly simple and effective, even today, after more than 50 years of it being public knowledge. The Donchian 4-week rule system complies with three basic rules of trading:

  • Follow the trend
  • Let profits run
  • Limit losses

The third rule is just relatively accomplished. In high volatility markets, where the highest high is far away from its lowest low, there is a risk problem that the system doesn’t solve.  In the early years trading with this system, most systems were focused on maximum returns, without regard to risk. Now the usual way is to adapt the trade size to the market risk.

Testing the N-BAR Rule

The widespread use of software platforms for trading, incorporating some kind of programming allows for simple back-testing and optimization of trading ideas.

In the case of a Donchian system, we need just one input parameter: N, the number of bars that define a breakout. Fig 8, below, shows a naked N-day breakout system parameter optimization. In this case, we’ll use different N parameters for long trades and for the short ones.


The graph shows a relatively smooth hill, with three main tops, that graphs the Return on Account achieved by the strategy. The Return on Account is a measure that weights profits against max drawdowns, so it’s an excellent mix to choose for when optimizing our systems.

All tops seem the right places for our parameter settings, so we chose the widest one, which seems to be more stable with time. The best parameters using return-on-account as metric are Longs: 65, Shorts: 55, resulting in the following equity curve (Fig 9).

The curve is typical of breakout systems. The system is robust, but the equity curve isn’t pretty.

A Montecarlo simulation of the system (fig 10) shows its robustness, but its wild nature, as well.

Fig 11 shows the Net profit distribution, which shows an orderly shape, being its mean about $14.000 on a single contract trade, for an average max-drawdown of $5.500.

My guess was that this entry system might improve a lot if we use MAE stops (from John Sweeney’s Maximum Adverse Execution concept). Let’s see what we get by adding them.

Fig 12 and 13 show the system behavior by adding MAE-type stops together with an appropriate trail stop. No targets added as this would spoil the spirit of the system.

We observe a slightly better and tighter smoke cloud, and the distribution analysis of the profit shows a 30% increase in the mean profit.

Below the distribution analysis of max drawdowns of the original and modified systems, showing that the MAE stops not only improved profits, but it lowered the system’s average max drawdown to about $4.800, a 13% improvement.

To conclude we may assert that Donchian channel breakouts work. Its mean risk to reward is 2:1 and it depicts 38% profitable trades. It’s is a robust and reliable system, although very difficult to trade.

Diversification and risk

To overcome those long and deep drawdowns, there is just one solution: To trade a basket of uncorrelated markets with risk-adjusted position sizing, so no single market holds a significant portion of the total risk.

To show you how a basket of uncorrelated stock may reduce overall risk and smooth the equity curve, let´s discuss the concept of portfolio variance.

As a simple situation let’s consider the total variance on a 2-position portfolio, which can be calculated using the following equation:

𝜎2 = w1 𝜎12 + w2 𝜎22 + 2 w1 w2 𝜎1𝜎2*cov12

Where w1 and w2 are the weights for each market, and cov12 is a quantity proportional to the correlation (ρ) between those assets.

In fact, cov12 = ρ1,2 * σ1 * σ2

Then, if the covariance is zero, then the variance of a portfolio with n assets is:

𝜎2 = w1 𝜎12 + w2 𝜎22 + … + wn 𝜎n2

To observe the effect of diversification, let’s assume that we have equal weights on five uncorrelated markets with an equal risk of $10, compared to investing just one of the markets with its full $10 risk.

Thus, to spread our risk we divide our position by five on each market, therefore, now we are exposed to a $2 risk in each market. Then, the total risk would be, again, 10, if the markets were perfectly correlated with each other, as is the case of a single asset. But, if they were totally uncorrelated, the expected combined risk would be computed using the above equation:

Risk =√ (5 x 22) = √20 = 4.47

So, for the same total market exposure, we’ve lowered our risk by more than half. Of course, there are no totally uncorrelated positions in the markets, and, sometimes, all markets move in sync with each other, but this is the way to reduce risk and smooth our equity curve as much as we can: By diversifying and making sure the correlation between our assets is as low as it may possibly be.

The Turtles

The Donchian breakout system is part of the history of systems trading, and it’s the subject of an amazing story worth a Hollywood movie.

“We’re going to raise traders like they raise turtles in Singapore”, said Richard Dennis to his friend Will Eckhardt. They wanted to end a long debate about whether a trader should be borne or could be raised.

Richard Dennis believed that anyone with the proper training and coaching could become a successful trader, while Eckhardt thought a trader needed to be born with special traits. So, the Turtles were born!

The full story at the link, below:

(https://www.huffingtonpost.com/zaheer-anwari/the-turtle-traders_b_1807500.html )

Turtle soup

As Newton found out, an action carries its reaction. To profit from those foreseeable turtle breakouts the market found a solution: Turtle soup.

Larry Connors and Linda Bradford Raschke wrote a beautiful book called Street Smarts, filled with a lot of ideas to swing trade.

Two of the ideas explained in their book trade against the Turtle pattern: The main concept is: If the 20-day Donchian breakout commonly used by the Turtles is just 38% profitable, trading against it should be about 62% profitable, by detecting and profiting from failed breakouts.

The method that Connors and Rashcke propose, looks to identify those times when a breakout fails and jump aboard to catch a reversal. By the way, this strategy can be traded in all markets and time frames.

The Turtle Soup rules for long positions (the inverse goes for short positions):

  1. The market must make a 20-period low. The lower the better
  2. The previous low must have happened four periods earlier
  3. After the market fell below the 20-period low, we place an entry buy stop 5 ticks above the previous day low.
  4. If the buy stop is filled, buy a stop-loss some tics under the current period low.
  5. Use trailing stops, as the current position is moving profitably.
  6. Re-entry rule: if you’re stopped out, you may re-enter at your original entry price if this happens in the next two bars.

Turtle soup plus one

This strategy is identical to the Turtle Soup, except it happens one day or bar later.

This strategy is more conservative, as it waits for the current bar to end, and sets the buy stop at the same place, but one bar later.

To show that two radically different ways to trade are both valid, I’ve tested this strategy. Let’s see how it behaves.

As we observe in Fig 15, the strategy is about 44% percent profitable, higher than a Donchian breakout, but far away from the theoretical 62%. Anyway, this strategy is very good, its equity curve( fig 16.b) nicer than the Turtles, and its Montecarlo cloud (fig 16.a) much thinner than the one shown in the original Turtle strategy, a sign that the variance of results is much better and more adapted to swing and day-trading. This is in agreement with its drawdown, which is more than 50% smaller than that on the Turtle strategy.

But a word of caution here. The red Montecarlo line in fig 16.a is an equity path with a segment depicting a large drawdown. The corollary here is, even using a smoother strategy, we need to have the psychological strength to accept such drawdown. This also proves that diversification is key in reducing market risk.

 


References:

John Bollinger on Bollinger Bands, John Bollinger

Ken Long Seminar on RLCO framework

Trading Systems and Methods. Fifth Edition, PERRY J. KAUFMAN

The Ultimate trading guide, John R. Hill, and George Pruitt

Quantitative trading strategies, Lars Kestner

Street Smarts, Larry Connors, Linda Raschke

Parameter testing and graphs, including Montecarlo analysis, was done in a Multicharts 11 Trading Platform.

©Forex.Academy
Categories
Forex Educational Library

Cycles and Economic Oscillations

Abstract

The economy of any country has 4 main phases: Depression, recovery, boom, and recession. In each economic cycle, there will be industries that will perform better than others, depending on which variables are growing the most, what is the situation and what are the policies taken by the central bank and the government. For investors, it is important to know in what economic cycle the economy is to make decisions about what assets to buy. In addition to these economic phases, the economy of the most important countries such as the United States, China, and some of the Europeans, can affect the cycles of other economies that can be called emerging and underdeveloped.

The economy of the countries is not stable in any case, they always have oscillations that are called economic cycles. The economic cycles are the recurrent increases and decreases in the economic activity of each country and therefore also the economic sectors are facing these fluctuations in the economy. These oscillations do not occur in the same way or in its magnitude since in each period the factors that affect an economy are different. In some periods of time, its most important sectors will stop growing due to internal or external factors, shocks from external economies or natural disasters, causing the magnitude and effect in the economy to be different over time.

There are four recognizable phases in the economic cycles experienced by all economies:

  • Depression or crisis: The lowest point of the economic cycle. When the economy is in this phase it is common that the main indicators of the economy are at low rates such as employability, consumption, the price of goods decreases or remains stable (low inflation rates) and the gross domestic product of a country is far from its potential. Given this situation, it is normal for the profitability margins of companies and banks to decrease during this point of the economic cycle.
  • Recovery: Phase in which the behavior of the economy begins to improve, there is a phase of better economic growth, better levels of employment, more positive margins, greater expansion of the domestic product and an increase in the price index in response to a better demand for goods and services by consumers and businesses.
  • Boom: The highest point of the business cycle. When the economy is at this point, production is in its potential, or in some cases, above. Because the economy is at its best there is full employment and as production is at its highest point with the lowest possible unemployment there is no possibility of better economic growth. In addition, since there is full employment, wages are higher due to the low supply of labor and consumption is also largely due to these higher wages
  • Recession or contraction: It’s the Economic phase where growth stops or slows down.  Production, investment, trade, and employment rates, as well as the wages of people, stalls or, even, decreases. The margins of the companies drop again, and the revenues of the government diminish. If the recession or contraction occurs in a severe and prolonged way, it may lead the economy to a crisis, signaling the beginning of a depression.

Economic cycles can be calculated by analyzing some economic variables. The most used are the gross domestic product of a country, inflation, and unemployment among others. Keep in mind that some variables are pro-cyclical, and this means that they increase when the economy grows and decrease when the economy decreases such as GDP and inflation and there are other variables that are countercyclical which grow when the economy gets worse such as unemployment. There are others that are acyclic, and this indicates that they are not very correlated with the behavior of the economy.

As there was mentioned earlier, economic variables are never constant, they are always fluctuating around a trend. These fluctuations are common in all the economies of the world, but there are always facts that explain why these variables fluctuate in a different way in time. It is strange to see two periods in time that have the same economic factors developing so the recessions are not the same as the booms because they are generated by different factors.

Fluctuations lack periodicity, but they are recurrent. In addition, when growth is above the trend, the variables are correlated so that when the economy starts to grow, investment also increases, which magnifies booms or recessions because one variable affects the others. What the economists have found is that among all the variables, the most volatile is the investment over the product and the consumption due to the risk aversion of many investors and the expectations they have. When the economy begins to show signs of stagnation, investors are the first to change assets and invest in another country.

For the world economy, it is important to follow the economic cycle of countries that drive global growth, such as the United States, the countries belonging to the European Union and China. When these economies enter phases of depression or recession this affects the other economies because in a world as globalized as the current one if a country does not grow to its potential this will affect its domestic consumption and end up affecting products that are exported to these countries. This is the reason why global growth since 2007 has not been very good in general because there are countries that are not growing at what they should according to their potential. In the next part of the article, we will explain how the current economic cycle of the United States and then the countries of the European Union are.

During much of the 1990s, the United States economy recorded an expansionary phase that ended with the economic crisis of 2000 to 2001 generated by several factors including the crisis of technology companies. The boom of these years was also due to the formation of capital in the high technology sector, where such was the increase in investment that boosted the domestic product which in turn boosted consumption and employment. But such was the euphoria of the investors, that overvaluation was generated in the United States stock market of the main companies added to a great appreciation of the dollar. This appreciation of the dollar allowed increasing the purchasing power of the citizens which generated imbalances and causing a deficit in the current account of the balance of payments, increasing the indebtedness and high prices of some assets.

The exhaustion of the expansion was manifested by drastic drops in the margins of the companies that ended up affecting the stock market indexes and this frightened away the investors, which drastically reduced the investment. To respond to this crisis, the monetary authorities used fiscal policies (higher spending, lower taxes) and expansive monetary policies (nominal interest rates and low real rates to encourage consumption). These measures allowed consumption to replace investment as the engine of the economy but created the basis for the crisis of 2007.

The measures to recover from the economic cycle of depression in the United States economy raised the debt of government households while companies began to see their margins improve due to lower debt costs and higher domestic consumption. The dollar was also depreciated due to the decrease in rates, which encouraged exports, but this effect was paralyzed when in 2004 monetary policy stopped the depreciation. The crisis of 2007 was a subject exposed in the article Problems and crisis in the economy.

After the crisis of 2007 and its continued depression, the recovery phase began in 2009. Since 1949, the United States economy has had 11 expansion phases and 11 recession phases. The expansion phases have had an average of 21 consecutive quarters growing. The longest phase of expansion has been of 39 quarters that was from 1991 to the year 2000 with a growth in that period of 43% of the domestic product. The most significant growth was 53.7% between 1961 and 1969. On the other hand, the shortest economic cycle lasted 4 quarters between 1980 and 1981.

The current economic cycle of the United States is above average in its growth phase, growing close to 32 consecutive quarters since the third of 2009, but the growth has not been of the same magnitude as the most euphoric episodes of the past. Although each economic cycle is different, and patterns are not always repeated, there are indications that global economic cycles are slower in their recovery if the origin of the crisis was a financial crisis like the one in 2007 where several banks went bankrupt and others were weak in their balance sheets and this indicates why the growth has not been the same as in other periods of time. The growth rate has not been very high in the United States since domestic demand (Consumption and Investment) has been weak in several quarters showing the consequences of the crisis.

In conclusion, the United States is in a period of boom economic cycle and some analysts predict that as the period of growth is so long, it is likely that in 2018 or 2019 the economy stops growing and enters a period of recession in its economic cycle.

For many investors, economic cycles are a useful tool used to allocate their resources in booming or growing economies or know which sectors can grow if a country is in recession. Clearly all sectors are affected by crises, but there are some sectors that do better during these difficult times than others that depend more on the economic cycle as dependent sectors of consumption that is one of the most affected.

Now we will analyze which economic cycle Europe is in. The indicator of economic sentiment has increased in recent quarters in the euro area, which shows that investors’ expectations are positive on the general growth of the eurozone. As can be observed in the following graph, the indicator of economic sentiment is correlated with the behavior of the gross domestic product, so it is expected that in the following quarters the eurozone will continue to grow, showing that they are booming in their economic cycle.

Graph 36. (2017, October) European Business Cycle Indicators. Retrieved December 3, 2017. From https://ec.europa.eu/info/sites/info/files/economy-finance/tp019_en.pdf.

If the eurozone is analyzed by sectors, it can be seen in the figures that the industry and services sectors grew while construction and consumption have not shown higher growth, which indicates that these variables are stable. In the following graph, you can see the confidence that exists about the growth of the industry in the European area.

Graph 37. (2017, October) European Business Cycle Indicators. Retrieved December 3, 2017. From https://ec.europa.eu/info/sites/info/files/economy-finance/tp019_en.pdf.

During the third quarter, the highest levels were seen in all sectors for approximately six years. The country where you see the best performance of the economy, with even better expectations, is in Italy. Then comes France, Poland, Holland, and Spain. The expectations about Germany and England are not so good, and their indicators have remained stagnant.

The central bank’s forecasts were raised in one of its last meetings in the third quarter on production and prices in the eurozone. If we compare these forecasts with their expectations in the previous quarters has seen improvement in these which indicates that the expectations are positive and indicates that the countries of the eurozone are in their growth or boom phase.

The only exception with negative expectations is England, where the index of economic sentiment is negative. In other countries, there has been an increase in the utilization capacity in manufacturing for five consecutive quarters. In the services sector, there are positive expectations because positive symptoms are seen in domestic demand so in the following quarters should increase production, added to employment, investment and therefore the sectors that are related to these indicators.

In conclusion, the main economic zones of the world are in an expansive phase of their economies, such as the United States and the European zone, so other countries such as emerging countries and others have positive prospects, but some analysts predict that in next few years the US cycle may end and enter a period of recession which would affect the growth of the countries that trade more with this country. The most important thing is to understand that all economies move in these cycles and economic oscillations so entering a recession is not serious if governments take counter-cyclical measures to reactivate the economy. Another factor to consider is the change of the Chinese economic model, a fact that can also affect some countries that saw China as an importer of raw materials such as coal and oil. As the trajectory of the economies of China, the United States and the eurozone follow, they will determine world growth in the coming years.

©Forex.Academy

Categories
Forex Educational Library

China and its Economic Predominance

Abstract

Throughout history, there have always been countries that stand out for their developments and their economy. From the Second World War until recent years, the United States stood out for having one of the largest domestic productions of goods and services in the world. But in the last three decades, China has shown high rates of economic growth over any other country which generated a convergence towards developed countries. With this economic development has also come a social development but that is not enough because many people are still in poverty. This great growth was based on exports from its most developed sectors, manufacturing, and agriculture. But its growth policy has changed in the last ten years, since its leaders wanted to base growth on greater domestic consumption and that most of the population would benefit from this change, which had consequences not only in China but many countries that they traded with this country.

China has emerged in recent years as a super economic power fighting in recent years for being the most important country in economic terms. For some people, China is the most important country in terms of production, and for other analysts, this country still does not reach the first place. Regardless of that ranking, China is the largest exporter in the world and therefore has the highest national reserves in the world. The global crisis of 2009 interrupted the steady growth rate that China had had thanks to its exports, which showed the country’s dependence on its trading partners.

As a result of the global economic slowdown and its consequent slowdown in trade, China’s growth slowed to less than 7% in 2015, one of the worst performances of that economy in the last two decades. In 2016, the slowdown in the economy continued to grow at relatively low rates for that economy. The result of this was the growing indebtedness of the companies reaching very high levels compared to the rate of China’s gross domestic product. Domestic consumption was also affected in addition to a devaluation of the yuan against the dollar which ended up causing a flight of capital that deeply affected the economy of the Asian country.

Despite the economic development and its high growth rates in recent decades in China, there are still many challenges related to the problem of population aging, reduction of the workforce and large differences in the quality of life between the city and the city. countryside. It is true that poverty has declined, but it is still a high rate of around 10% that translates into 120 million poor people, which worries investors and the government because to continue with an optimal path of growth, poverty is an obstacle that has not yet been overcome.

China’s economy is very diversified, but the manufacturing and agricultural sectors predominate. Agriculture employs about a third of the active population and contributes about 9% of the gross domestic product. This is a consequence of China being the most populated country in the world and for that reason, it is one of the main agricultural producers and consumers in the world. In terms of livestock, China is also the largest market in the world in its production to respond to the needs of its population. Mining is also an important sector of the Chinese economy since it has good access to these resources and has large reserves of coal, gold, iron, oil, and gas.

The manufacturing and construction sectors contribute almost half of the production in China. The Asian country has become one of the favorite destinations for the transfer of global manufacturing units because of the amount of labor that makes it cheaper in compared with other countries, although as mentioned before it has been reducing the supply of labor what has been reflected with an increase in the salaries.

But to better understand how China became a super economic power that competes with the United States to be the country with the highest production in the world, it is necessary to understand the relationship between politics and the economy in the Asian country. China is a socialist republic with a one-party system governed by the communist party that has as its principles or aims to maintain a stable and high rate of growth and social stability that allows having a good coexistence within its population. The following graphs show the gross domestic product per capita and nominal respectively between the United States and China.

Chinese Economy Growth Rate

Graph 26. Gross domestic product per capita, PPP. Data taken from World bank

Chinese Economy Growth Rate

Graph 27. Gross domestic product. Data taken from World Bank

In 1993 a Marxist economy with liberal laws and policies was introduced. In 2001, China entered the world trade organization and with that began the investment in private property, establishing state interests in own assets and new contract laws. By making a structural analysis of the Chinese economy you can see certain clear characteristics that differentiate it from others and that is why it has achieved economic success:

  • High savings rate: In China, there is a savings rate of 51% and an investment rate of 43%. The economies that save the most are the economies that grow the most in the long term since with this saving, investments are financed without having to resort to bank loans. In the case of China, the high savings rate can also be explained due to the uncertainty of private companies and citizens to access bank financing since this type of financing has public companies as a priority.
  • Excess capacity: China being governed by a single party has had certain extreme policies such as the regulation of prices in public services and in some cases on land, which has promoted different sectors such as manufacturing and heavy industry reduce their costs and focus more the profits to invest, but has been pushed to the limit so there is excess capacity due to excessive investment.

Another explanation that China converged to the level of the developed countries was its decision to open its borders in 1960 with the free world moving out of the sphere in which it found itself with the Soviet Union. When China decided to open its economy to the entire world, it modified its economic and political system, as previously mentioned, by leaning towards a one-party system.

Beginning in the 1980s, China changed its economy from self-sufficiency to an export-based economy, being a key change for the growth rates it would reach years later. But China knowing that not only could depend on exports due to the excessive dependence of other countries, so it was also building its internal consumption sector so that in the future it would have more alternatives to base its growth.

The Chinese economy is essentially industrial. In the early 1970s, agriculture and livestock accounted for about 30% of China’s gross domestic product. In recent years this has changed, and the most predominant sector is construction and services. Although the primary sector has lost weight in the economy, it continues to provide employment to around 40% of the population, being a very important sector for the Chinese economy. There are also large deposits of minerals, so in 2007 it was the third-largest mineral extraction country in the world.

One of the main products extracted by China is coal and it is the third country that imports most oil in the world, so when China’s economy has problems in its growth it stops demanding oil, affecting the price of this and its producers like Russia, the United States and members of the OPEC.

Analyzing the growth of China’s domestic product in recent years, the industrial sector continues to grow, but not at high rates as it did previously, so we can observe a convergence of China with advanced economies as this is a sign of the consolidation of the Asian country as a world power because the countries that see their industry growing at high rates are usually developing countries.

Another figure that demonstrates China’s current dominance in the economy, as well as the convergence of its economy to advanced economies, is its weight in world production, representing 17% of world production surpassing United States production, which by 2014 represented the 16% of world production. This has been a positive surprise for economists who predicted 30 years ago that China would remain in the group of low middle-income economies. It is important to clarify that China has a higher gross product than the United States in total, but if the per capita gross domestic product is analyzed, the United States continues to have more production than China due to the poverty that exists in China.

 

An important test that faced the Chinese authorities was the global recession of 2007 where Chinese exports fell between 15% and 18% generating unemployment of 23 million people but managed to recover their growth path, unlike other countries that still have persistent problems since 2007. For some economists is a mystery that is worth studying because China has managed to survive the last major global economic crisis maintaining high growth rates being a country that has tried not to monetarily intervene much the economy.

But in recent years the authorities have tried to take China towards a new economic growth model. The growth model of the Chinese economy that has been based on exports, the development of industry and investment in about 40 years could become a model based on domestic consumption and services, but as mentioned in the paragraph previous always thinking about better standards of life of the population. A clear example of this change in the economic model is the decline in China’s exports of goods and services, as shown in the following graph.

Chinese Economy

Graph 28. Exports of goods and services. Data taken from World Bank.

The main news has always been based on the growth and predominance of China, but little has been said about social development. China has improved the living standards of its population as few countries have done in history. But the Chinese authorities want to change their economic model to maintain a balanced and sustained growth in the long term. That is why we have seen some adjustments in the world economy today as the crisis of the mining energy sectors worldwide. Oil and other raw materials have suffered serious price crises that have affected the economies of countries such as Venezuela, Russia, Colombia and other countries that received most of their income from these sectors of extraction of raw materials.

China’s current figures are 40% of the domestic product is dedicated to investment, industrial production represents about 50% of production, but services and consumption have low rates compared to developed countries, group to which China has already entered in recent years due to the behavior of its economy.

The structure of the economy that has given positive results to the Chinese economy has caused some friction with other countries since China has produced more than it has consumed and the excess of this has gone to exports. But many countries believe that the exchange rate in relation to the yuan is undervalued so that exports are more competitive than it should be, and this leads to China having trade Surpluses.

The change that the authorities want is to increase the weight of consumption in the economy to the detriment of savings and investment, decrease the weight of exports offset by domestic demand and an increase in the weight of services in domestic production to the detriment of the Industrial production. To support these changes and to encourage greater consumption, China must continue promoting social development, especially in education and health to reduce these items and increase population savings rates.

For economists the fiscal structure must also change to encourage consumption, that is, they must seek taxes on other items than private consumption to reach the objective in the economic transition. If the business tax is increased, the fiscal effect will be compensated so that imbalances in the national accounts are not generated. Likewise, the financial system must refocus its credits since to sustain growth before they were based more on loans for public companies and investments, but with the change of model, they should focus more on loans to people to increase their consumption.

The exchange rate also plays a fundamental point in the change of economic model. As mentioned above, the exchange rate favors the yuan, which is undervalued, which favors exports, but ends up affecting the consumption of people, since to consume foreign goods it becomes more expensive. If the exchange rate is appreciated, this generates an income effect, increasing the purchasing power of consumers and reducing the competitiveness of products of Chinese origin.

But these changes have been taking place since 2010. Since that year, imports have grown more than exports, the trade surplus has been falling and the yuan has appreciated, but to a lesser extent than the other effects. There has also been a clear interference by the government in the economy to generate structural change such as increases in the minimum wage, subsidies for consumption and a progressive extension of the social security system.

All this structural change of the Chinese economy will influence the whole world mainly of the companies and countries that benefited from the Chinese industrialization and the products that it was exporting. Raw materials will also be affected as one of the largest importers in the world is changing its model which will drag prices down. But new developing sectors will also be opened and new opportunities in financial services, consumer goods and other sectors that are related to the new economic model will be opened.

For foreign companies, the main reason to invest in China was the low production costs due to a large amount of labor, but currently, the attractiveness is in the size of its market. But with the change of model, new conflicts have also been generated with foreign companies in China, where the government has tried to favor local companies, following the restrictions of foreign investments in numerous sectors, which is why the fear of economic nationalism has also arisen of the Chinese state.

But here the question arises whether, with the change of model and the emergence of some global problems of this change, China managed to maintain those high growth rates. The consensus among economists is that the good performance of the Chinese economy will continue but not the rates that had been registered for more than 30 years. The factors on which China’s economic growth has been based will continue to operate in the long term, such as:

  • Abundant and qualified workforce.
  • Growth model open to foreign relations.
  • Greater liberalization of the economic system.
  • Political and social stability.
  • Process of gradual and prudent reform that avoids a strong shock in the economy and its agents.

In conclusion, China has been a unique case in the development of economies due to its political model that has allowed the development of economic policies based on the industrialization of the country, a large number of exports and important social development but still lacking to improve because of a large number of people who are poor. This is seen in the domestic product per capita where China has not managed to surpass the United States as the largest economy in the world because there are still millions of people who are not considered in economic development. China is currently in an economic transition where domestic consumption is the priority to continue with the social process that it has already achieved in the past.

©Forex.Academy

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Designing a Trading System (III) – The Toolbox Part 2

Trading simulator

The trading simulator is the part of the trading platform that takes the programmed rules of a trading system and computes the simulated paper-trades along a time interval. The reason for its existence is the speed, efficiency, and accuracy with which it can perform thousands of computations.

Forex traders usually get MT4 for free when opening a trading account, and MT4 includes what it calls a “strategy tester” which is accessible by clicking a magnifying glass button or Ctrl-R.

Simulator outputs

All trading simulators generate outputs containing a considerable amount of information about the performance of the system on a particular market. Data about gross profit, net profit, maximum drawdown, percent winners, mean and max profit and loss, mean reward to risk, return on account, etc. should be present in a general report.

In well-designed simulators, the output is presented as a several page report, with text and graphics depicting relevant information of the system.

Summary reports

An example of a summary report of the MT4 Strategy Tester is shown in Fig. 1.

MetaTrader has a summary report of average quality, but since it’s widely used let’s discuss its main components. This summary report shows the minimum needed to realize if our strategy is worthwhile or junk. The main parameters are shown below.

  • Initial deposit: The amount of initial paper money account. It only matters as the initial reference for testing.
  • Total net profit: The difference between “Gross profit” and “Gross loss”
  • Gross profit: The sum of all profits on profitable trades
  • Gross loss: the sum of all losses on unprofitable trades
  • Profit factor: The percent ratio between the gross profit and the gross loss: A fundamental metric.
  • Expected payoff: The monetary expectancy of the system. The average monetary value of a trade. One of the most important values. It should be greater than zero for a positive expectancy.
  • Absolute drawdown: The largest loss that is below the initial deposit amount.
  • Maximal drawdown: The largest drawdown took from a local maximum of the balance. It’s essential, because an otherwise good strategy might be useless if it has several 50%+ drawdowns. You need to set your mark on the maximum allowed level you’re able to tolerate.

Even if the system’s results are below your mark, it might be optimum to perform Monte Carlo permutations (preferably more than 10.000) to study the probability of those drawdowns closely. Please be aware also that this value changes with position size, so it will increase as you increase your position and, consequently your risk.

  • Relative drawdown: The maximum percent loss relative to the account balance at the local maximum. The same as above but percentwise.
  • Total trades: Total number of trades. It is important to get at least 100 trades to get a statistically good approximation. This value is of interest, also, when comparing systems. When multiplied by the expected payoff it should return the total net profit value.
  • Short positions (won %): The number and percent profitable on short positions. It shows how good the system is in short positions. You should watch if there is an asymmetry when compared to long positions and analyze why this might occur.
  • Long positions (won %): The number and percent winners on long positions.
  • Profit trades (% of total): The total number and percent of profitable trades. Although technically it’s not that important to get high values on this parameter, as long as there is a good profit factor, this value psychologically is important. For example, many trend-following systems have no more than 35% winners. Therefore, you should decide if you’re able to accept just one winner out of three or you’re more comfortable with higher values at the expense of less reward-to-risk ratios on trades.

Percent profits allow us to compute the probability of a winning streak, and, when multiplied by the average profit it shows the likelihood of a monetary streak.

The probability of a winning streak of length n  is  the %Profit to the power of n:

Probability of an n-Winning Streak:

PWn  = %Profit n

Then, the expected profit on an n-streak of winners is

Expected profit_S on = PWn * average profit trade.

Below the probability curve of an n run-up streak in a system with 58% profits.

 

  • Loss trades (% of total): The number and percent of unprofitable trades. This value is computed subtracting 1- %Profits. Besides the psychological effect on traders, it allows us to directly compute the probability of a losing streak in the same way as in the winner streak case

Probability of an n-Losing streak 

PLn = %Lossn

We should remember that

%Loss = 1 – %Gain

Therefore, the probability of a loss on a 45% gainers system is 55%.

And the expected loss on that streak will be:

                        Expected loss_S = PLn * Average loss trade

Below the distribution on a system with 48% losers

 

Thus, using these two metrics, %Profit and %Loss, we can build a distribution curve of run-ups and drawdowns, and build a graph, as an alternative to a full Monte Carlo simulation, so we could get a more in-depth insight of what to expect of the system in term of run-ups and drawdowns.

Above, the distributions of run-ups and drawdowns of the same system, normalized to the risk taken called R, that matches the average loss.

These figures were performed by a simple algorithmic computation using Python and it’s plotting library matplotlib.

Let’s say that, in this case, our average loss is 500 €. And we are using a system on which, according to fig 2d, there is a 2.5% likelihood of hitting a 5xR drawdown. Therefore, if we have a 10,000 € account, there is a 2,5% chance that it will reach a 2500 € loss, or 25% drawdown. If we increase our risk to 1,000 € per trade, we’ll end up with 50% drawdown. Moreover, if we don’t like a 25% max drawdown, we should reduce the size of our trades to set the risk according to the max drawdown we are willing to accept.

As we see, this kind of analysis is much richer than a mere maximum drawdown measure, because we know the actual probability of a drawdown length and its monetary size, and is linked to the maximum allowed risk.

  • Largest profit trade: The largest profitable trade, we need to analyze large trades and evaluate if they are accidental outliers. We should evaluate their contribution to the profit curve. If for example, our profit balance is due to a small number of random outliers, and the rest of the profitable trades are just scratching the break-even mark we should be cautious about the future profitability of the system.
  • Largest loss trade: The largest losing trade. Largest losing trades give us hints about the positioning of our stop-loss levels. If we get sporadic but large losses, we need to check if we have bad historical data or if we suffer from gaps or spikes that needed to be corrected.
  • Average profit trade: The result of dividing the total profit by the number of profitable trades. An important metric to be used in conjunction with the statistical method described above.
  • Average loss trade: The result of dividing the total loss by the Nr. of losing trades. The average loss is a description of out mean risk per trade. We must be aware of the implications of that figure. We need to be prepared for more than 5 consecutive losses, and its associated risk, as previously discussed.
  • Maximum consecutive wins (profit expressed as money): the longest streak of profitable trades and its total profit.
  • Maximum consecutive losses (loss expressed as money): The longest streak of unprofitable trades and its total loss. As on the previous point, these values are better analyzed using the statistical method described above.

Other convenient parameters might have been handy (but not included):

  • Standard deviation of the expected payoff: This value is only computable by exporting the results list and performing the computation on a spreadsheet or Python notebook.
  • Win/loss ratio: The ratio of the mean winner to the mean loser, easily computed since those two values are shown.
  • Sharpe ratio or similar quality metric: Computed from Expected payoff and its Standard deviation.
  • T-statistics, that may help determine if the system has some statistical validity or it’s close to a zero-mean random system. It’s also important to know if the system delivers positive or negative skewed results.

These statistics can be obtained by saving the strategy report and using Excel to compute them, or using Python. In both cases, we need to build a small script that takes a bit of effort the first time but will reward us with a continuous stream of subtle details that no simulator shows.

The optimization procedure on the MT4 strategy tester uses a complete back-tested approach. There is no out of sample testing, so to avoid false expectations, it might be good to perform the optimization procedure using only a chunk of the historical data available and, after optimizing, running the optimized system in another chunk of the data series to get out of sample results.

Graph

This tab shows the equity balance graphic on a back-test (in this case a free EA: Headstrong Free, after optimizing it from 2011 to 2013). Below, the optimized EA behavior in out-of-sample data. Close to a random system.

 

 

Trade by trade reports

The “results” tab shows a trade by trade report organized by date and time. That report presents the time, type, size, price profit and balance of every operation. Of course, open actions don’t show a profit.

By right-clicking on any part of the report, you can save it on file for future use or further analysis using Excel or Python.

Data burnt

One major issue with data testing is that if we test on all the data available we “burn” it. That means that any posterior retest will be a bit more curve-fit. The issue is that while we go from a fair system to a great one, we introduce a small change here and there, and when back-testing that variation, we select the best performers and discard others. As the number of back-tests increases using the same data, a hidden curve fitting is emerging.

Theoretically one should perform a test using a data set and, after a change on a parameter, make a new test using another set, but, in practice, we end up testing all our strategy variants on a single dataset. That’s the reason we need to be cautious.

So, what’s the best way to use our data so it won’t burn too much? Kevin J. Davey says he uses just a portion of all the data he has, large enough to get statistically valid results. He also says he makes a random selection of the portion to be used. That way he uses new data after a change in parameters and makes sure his historical database is used minimally.

In the next issue, we’ll deal with entry evaluation. Let’s remember that we still are in the “limited testing” stage. The idea of entry evaluation is to assess the validity of an idea as an entry signal. That we’ll discuss, as said, in the next article of this series.

 


References:

Building Winning Algorithmic Trading Systems, Kevin J. Davey

Encyclopedia of Trading Strategies, Jeffrey Owen Katz, and Donna L. McCormick

 Graphs were done using custom Python 3 software and also, from MT4, trading platform.

 ©Forex.Academy
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Profitable Trading – Computerized Studies II: MACD

Introduction

Moving Average Convergence-Divergence, MACD, was developed in 1979 by Gerald Appel as a market timing tool, and it’s an advanced derivation of moving averages.

MACD consists of two exponential moving averages (EMA’s) of different periods that are subtracted, forming what is called the fast line. There is a second, slow line, that’s a short-period moving average of the fast line.

How to compute the standard MACD:

  1. Compute the 12-period EMA of prices (usually the closing price).
  2. Compute the 26-period EMA of prices.
  3. Subtract 2. from 1. Its result is the fast MACD line.
  4. Compute the 9-period EMA of 3. Its result is the slow Signal line.

Almost any charting software allows a user to modify these periods and the price type (Open, High, Low, Close, or an average of all four).

According to LeBeau and Lucas, on their classic book “Computer analysis of the Futures Market” Gerald Appel had two setups: One for the entry side and another one for the closing side.

The entry side is 8-17-9, while the relatively slower closing side is 12-26-9, which became the standard for commercial charting software.  That seems to indicate that Gerald Appel favored getting in early in the trend and, then, holding into winners a bit more and let profits run.

My preference for intraday trading is 12-26-6, smoothing the signal line with a 6-period EMA. That reduces the indicator’s lag, producing earlier entries, but keeping the short-term and long-term EMAs distance.

It’s not a good idea to optimize MACD for a particular market, but, I think that a quicker formula is suitable for less than average volatility markets, and those with higher volatility require more prolonged  EMA’s period.

Interpretation:

Price represents the consensus of value at a particular moment. A moving average is an average consensus over a period of time. The long-period EMA on a MACD reflects the longer-term consensus, while the short one represents a fresher consensus that is emerging.

The subtraction of the moving averages that shapes the fast MACD line reveals shifts in the short-term opinion in comparison to the longer term (older) view.

Signal Crossovers

The usual MACD signal is a crossover between the fast MACD line and the signal line. When the fast MACD line moves above the slow signal line, it means a bull cycle has begun. If the fast MACD line crosses under the slow signal, a corrective cycle has started.

We have to be cautious to trade naked MACD crossovers because during quiet periods MACD crossovers deliver numerous false signals that might drive us into a streak of consecutive losing trades.  But we have on hand several interpretations of this indicator that will help us get better use of it.

In Fig. 1 we present an example of a EUR/USD 1-hour chart. There, we may observe that pink – unproductive- areas are usually crossovers going against the trend, which take place in reactive trend segments with sideways price movement. In spite of those failed entries, MACD crossovers are an efficient way to spot trend changes.

Overbought-Oversold indicator:

MACD can be used to spot when the market is overbought or oversold. As an example, on the EUR/USD, when MACD lines are above +0.0012 (using MT4) prices are close to overbought. Conversely, when MACD lines cross below -0.0012 the market may be close to a short-term low. Thus, under those two conditions, stops should be tightened and take partial profits.

Crossovers above/below those levels are worthwhile as entries. On the contrary, crossovers that happen in the band between zero and any of those levels are usually irrelevant, if occurring against the current trend. But MACD crossovers that go with the trend, confirmed by, for instance, a breakout on support or resistance, shall be considered.

A powerful signal happens when there’s a crossover against the previous trend that fails and then a crossover with the trend takes place (see fig 2, points E and F).

The overbought or oversold MACD levels shall be assessed for each market and condition, monitoring them from time to time, to get good results. The reason is that automatic MT4 level adjustment depends on the volatility and price levels; thus, the resulting MACD values might show shockingly different values.  For instance, watching today’s USD/JPY MACD study, the optimum levels for overbought and oversold levels are at about ± 0.2.  This issue seems annoying, but it’s not, as this is solved easily by observing the latest extreme conditions and checking the MACD level where they took place.

As an example, let’s observe Fig.2 MACD behavior on the USD/JPY. On A, the crossover to short the USD is close to the zero line, so it’s rejected. There we missed a profitable short. Then on B, we got a buy signal that is at oversold levels, so we take it. Then we reach C, but the crossover doesn’t happen in overbought levels. Therefore, we keep our position, and hold it up to D, closing it and reversing. This resulted in a 2X reward compared to an exit at C. Our short position wiggled but we hold it and price reached point E, and there we exit it since the MACD signal wiggled at oversold levels. At point F we observed an evident breakout with a MACD crossover that’s in the rejection band, but goes with the current trend, so we take it. Finally, at G we exit.

MACD trend lines

A variation of the crossover signals is obtained using trend lines. By drawing lines parallel to the signal lines, we obtain early entry points, in advance of MACD crossovers. According to LeBeau and Lucas, MACD crossovers that are preceded by, or in sync with, a trend line crossover tend to be more relevant. I haven’t found any evidence of that on intraday charts, Nevertheless, since all indicators present unavoidable delays that hurt profits, I think worth studying the use of a trend line parallel to the MACD signal.

I think this method is an interesting addition, especially on sideways channels in conjunction with the concept of overbought-oversold MACD, and the complement of profit targets at congestion areas.

Fig 3 shows an example, taken from a very choppy sideways channel in the USD/JPY 1 hourly chart from 28-Sept-2017 until 10-Oct-2017. The green highlighted areas show the profitable trades and its magnitude. There’s only one pink shaded segment, which corresponds to a failed trade. That’s a huge feat! Just one loser out of 11 trades on a very choppy channel, that has been the first choppy area I’ve found. No cherry-picking at all.

Just to avoid after the fact selection, on this example we didn’t take any crossover outside overbought and oversold areas that went against the current trend. The only criticism of this exercise might be trade Nr. 1 that was profitable because we didn’t set any stop.

However, even if we accept that trade as a loser, the numbers are quite sound: One very profitable trade (4), four good trades (3,5,7, and 11), two average trades (6 and 9), two scratch trades (2 and 10) and two losing trades (1 and 8).

I find this method worthwhile for just the exits, as well, if the break of the line happens in overbought or oversold areas.

MACD Histogram

MACD histogram displays the difference between the MACD line and the Signal line as a histogram: vertical bars whose lengths correspond to that difference. When a MACD crossover happens, this corresponds to a zero crossing of the MACDhist. Positive histogram values correspond to the MACD line above the signal line, and negative values below the signal line.

MACDhist = MACD line – Signal line

When the difference increases, meaning the trend has momentum, the corresponding lines are larger. Conversely, when it’s decreasing, bars get shorter, giving early warning of the potential weakness of the trend.

Thus, positive and negative peaks on the MACD histogram corresponds to the maximum momentum of the trend, and a retreat from the maximum values shows the shift in sentiment that sooner than later might stop the trend.

Consequently, it’s best to trade in sync with the slope of the MACD histogram, as it shows the dominant group: Bulls if raising bears if decreasing.

A corollary to this statement is: On an open trade if the MACD histogram decreases, tighten your stops. It doesn’t mean that the trend is going to reverse but it might, especially if prices are on a sideways channel.

New peaks and valleys:

When a new record peak on the MACD histogram is reached during an uptrend, it shows that the current trend is healthy and that prices are, likely, to continue moving up. A New record valley during a downtrend means that prices most likely will retest the recent low or keep moving down.

Dr. Elder has a helpful analogy to the MACD histogram: “MACD-Histogram works like headlights on a car—it gives you a glimpse of the road ahead. Not all the way home, mind you, but enough to drive safely at a reasonable speed.

Divergences

According to several authors, divergences are among the most valuable signals in MACD, especially in sideways price channels.

A divergence happens when we spot a new high or low in the price, but it isn’t followed by the corresponding high or low on the MACD lines.

bullish divergence

This pattern happens, as might be obvious, at the end of a downward trend, and is a bottom indicator.

Please, note that the histogram has crossed the center line at point b. Point c may appear on the positive side, as in here, or at the negative side, but making a higher valley than at point a. For the pattern to be called divergence, the crossing of the zero line must happen. If it doesn’t happen, it’s not a bullish divergence.

The divergence in the MACD histogram is reinforced by a signal line divergence, as well. Such combined pattern is rare – the usual is just a histogram divergence with the MACD lines not following higher lows-, and it shows a higher likelihood that the coming trend would be strong.

Bearish divergence

A bearish divergence is a specular pattern to the bullish divergence, so it happens in uptrends. Price has reached a new high, roll back and then move up to a higher high, without a confirmation of a MACD histogram higher high. As in the previous case, the B point has to cross the zero line, in this case to the negative side.

Fig. 6 show a triple bearish divergence in which a middle top failed to continue going down. When the second big high at C isn’t able to make a new high on the MACD-Hist and neither does on the MACD signal, then a bearish divergence is confirmed.

According to Alexander Elder, “missing the right shoulder” divergences in which the second peak at c fails to cross the zero line, are rare, but producing strong downward moves.

Conclusion

MACD is a versatile study that helps traders spot trend reversals early on, allowing them to trade with the trend.

The combination of MACD entries with sensible stops and targets, together with some market filter that forbids trading during congestion areas can make for a simple and robust trading system, that’s a bit more sophisticated than simple MA crossovers and with potentially better overall performance.

 


References:

The New Trading for a Living, Alexander Elder

Computer Analysis of the Futures Markets, Lucas and LeBeau

©Forex.Academy

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Designing a Trading System (II) – The Toolbox Part 1

Introduction

The first issue of this series on trading systems finishes with a chart flow as in Fig 1, that sketches the steps needed for a good design of a trading system.

There are other ways around, of course, for example, from limited testing going directly to paper trading or small-sized trading, but this larger flow makes sure that any system that is profitable at the end of this pipe will be performing close to what’s expected.

Moreover, the designer and trader will be much more confident trading it and will be aware of what to expect in terms of returns, percent gainers, and drawdowns.

Of course, this a long process, taking months to a year, or even more to accomplish. That’s not an easy way of doing it, but, as Kevin Davey says “That’s how it’s supposed to be. Think about it for a second – if it were easy to find a strategy don’t you think others would have already found it and exploited it?”

Kevin Davey says that for him, strategy development is like a factory, a pipeline of ideas that get developed along the refining process until its output, as garbage or as a gold nut. Therefore, we need to keep our factory running all the time, filling it with new preliminary ideas.

That said, we have to build that factory, first. Throughout this article, we’ll deal with the factory building problem, therefore, we’re going to explore Fig 1 chart flow and find out what’s needed on each stage of the pipeline.

Trading ideas

Kevin Davey on his book(see reference below) gives a very good list of things to consider about idea gathering:

  • Keep an updated list of ideas. Whenever you jump on a trading idea or something that intrigues you, write it down on your list
  • Look for ideas: Ideas are around us in books, web pages, internet forums, and magazines.
  • Dumb ideas are those never tested. Absolutely everything might be a good idea. Even the craziest idea might end as a good system.
  • If you make a mistake while coding (or in your script if you aren’t coding), test it anyway. Penicillin discovery was accidental.
  • If your idea results in a bad system, try the opposite: switch buy and sell signals and see what happens. Although it doesn’t always work, it might.
  • Plan to test one to five strategies per week. With this amount of inputs, it may take six months, but you’ll end up with a pack of good trading systems.
  • Find other traders and offer to swap ideas and strategies with them. Take what others have and build strategies around those ideas.

Limited testing

Goal setting

Before going on into either a limited or an extended testing, we need to define the specific objectives our system must accomplish.

A list of the items might be:

Likes:

  • The possible markets the system is going to trade
  • The Time-frame or time-frames applicable
  • Minimum volatility to trade
  • Allowed time intervals
  • Purely automatic, Automatic entries and manual on exits or vice-versa.
  • Reward-to-risk desired interval (aiming for 1.5:1 at least is a good starting point)
  • Minimum percent of gainers we need to be comfortable with.
  • Minimum quality criteria for the system:
  • maximum allowed coefficient of variation
  • Minimum quality metrics (Sharpe, Sortino, SQN …)
  • Maximum drawdown length

Dislikes:

  • Trading on strength / Trading on weakness
  • More than X trades a day / less than X trades a day
  • More than 5 losers in a row
  • Targets too large / short

The idea

We take an idea from our list of ideas and we decide about how to develop the following details:

  • Testing and trading platform
  • Data needed
  • market direction
  • timeframe or bar size
  • trading intervals (optional)
  • Entry rules
    • Allow long and allow short signals
    • trigger long and short signals
  • Exit rules
    • Long and short stops
    • trail stops
    • volatility stops
    • Profit targets
  • Algorithm programming of decision chart flow in manual execution

Testing, optimizer, and trading platform

Developing a system by hand is possible, but it takes a lot of effort, and at least, it needs some kind of programming to perform the Monte Carlo testing, and fine-tune the position size to our particular needs and tastes.

Metatrader

Currently, the most popular system for the forex markets is Metatrader. It uses a C++ variant called MetaQuotes Language MQL. The latest version is MT-5, although MT-4 is still used by many forex brokers.

The following are the key features of the MQL-5 language:

  • C++ syntax
  • Operating speed close to that achievable with C++
  • Wide range of built-in features for creating technical indicators
  • Open-Cl support for fast parallel executions for optimization tasks without the need to write parallel code.
  • Wide variety of free code for indicators and strategies with a strong mlq4 and mlq5 community.

Python

Python is a terrific high-level programming language with tons of features and a huge number of libraries for anything you can imagine, from database to scientific, data science, statistics, and machine learning, from logistic regression to neural networks.

Python has a specific library to backtest and optimize MT4 libraries and expert advisors.

Python-Metatrader can also be bridged to Meta Trader using ZeroMQ free software distributed messaging. (http://zeromq.org/intro:read-the-manual)

https://www.youtube.com/watch?v=GGOajzvl860

Python enables the implementation of different kinds of strategies, compared to those developed by typical technical analysis, although there are technical analysis libraries in Python. As an example, Python makes it easy to develop statistically-based strategies, for example, stats-based pivot points.

The implementation of Monte Carlo permutation routines takes less than 4 lines of code, And Python has a lot of machine learning functionality, including parallel GPU-enabled neural network libraries, such as TensorFlow, a terrific deep learning neural platform by Google. https://www.tensorflow.org

There exists a large community of quants developing stats-based strategies in Python, and lots of free code to start from, so ZeroMQ is a serious alternative to direct design in MQL.

One of the most popular Python packages is the Anaconda distribution. Anaconda incorporates Jupyter Notebooks, an interactive Python web-based environment that allows development of Python apps as if it were a notebook (Fig. 3). Anaconda includes, also, an integrated software development IDE called Spyder (fig. 4)

Anaconda distribution may be found at https://www.anaconda.com/download/

Data

Forex historical data bars can be acquired for free from the broker for major pairs crosses, and exotic pairs, with more than 5 years of one minute, via MT’s the historical data center (F2). Starting from the minute bars, other timeframes can be recreated easily

Tick and sub-minute data aren’t available for free, so we should consider if the idea needs data resolutions beyond the minute.

Forex traders are fortunate in that their data is already continuous through time. Futures trading needs to combine several contracts on a continuous contract because futures contracts expire every three months.

The problem is that the expiring contract prices are slightly different from the starting prices of the new contract because future prices are affected by interest rates since its value is computed taking into account the cost of the money, and this cost varies as the distance to its expiration approaches.

If you need to build your own continuous contract, there are several ways. The simplest is just to put them side by side and let the gap appear, but, obviously, this is wrong, so the second easier way is to back-adjust the price in the old contracts, by subtracting the distance in points from the current contract’s starting price to the ending price of the latest expiring contract. That’s ok, but you may end up with negative prices if you are doing that a lot of times. The other problem with this method is that you lose the actual historical price values and, even worse, the original percent variations.

The best method, in my opinion, is to convert prices to ratios using the formula:

Price change = Closei – closei-1 / closei-1

Then go back to the first price of the current contract and perform the conversion back from there on the historical data series.

Data Cleansing

Historical data are rarely perfect. Spurious prices are more common than most think. Also, it may be desirable to get rid of price spikes that, although correct, are so unusual that they should be ignored by our system. Therefore, it may be desirable to include a data cleansing routine that takes away unwanted large spikes.

A way to do that is to check each bar in the price history and mark as erroneous any bar if the ratio of its close to the prior close is less than a specified fraction, or greater than the specified fraction’s reciprocal. All marked erroneous dates won’t be used to compute any variable, indicator or target, or they can be filled with the mean values of the two neighboring bars.

Data normalization

The common way to design a trading system does not involve any price normalization or adjustment, besides what is needed to create a continuous contract in the futures markets.

There are two kinds of technical studies, those whose actual value at one bar is of key importance in and of itself, and those whose importance is based on their current value relative to recent values. As examples of the first category, we may consider PSAR, ATR, Moving averages, linear regression, and pivot points.  Examples of the second category are stochastics, Williams %R, MACD, and RSI.

The reason to adjust the current value of an indicator to recent values is to force the maximum possible degree of stationarity on it. Stationarity is a very desirable statistical property that improves the predicting accuracy of a technical study.

There are two types of price adjustment: Centering and scaling. Centering subtracts the historical median from the indicator. Scaling divides the indicator by its interquartile range.

Centering

There are several ways to achieve centering. One of them is to apply a detrending filter to the historical price series. The other way is to subtract the median of some bar quantity, for example, 100 to 200 bars.

Scaling

Sometimes centering the variable may destroy important information, but, we may want to compensate for shifting volatility. It may happen that a volatility value that is  “large” in one period might be “medium” or even “short” in another period, thus, we may want to divide the value of the variable by a measure of its recent value range. The interquartile range is an ideal measure of variation because it’s not affected by outliers as a classical standard deviation would be.

The formula to do that is:

Scaled_value = 100 * CDF [ 0.25 * X/(P75 – P25) ] -50

Where CDF is the standard normal Cumulative Density Function

X is the unscaled current value

P75 and P25 are, respectively, the 75  and 25 percentile of the historical values of the indicator.

Spectral Dilation

John Ehlers coined the term “spectral dilation” to signal the effect of the fractal nature of the markets and its profound effect on almost all technical indicators.

The solution he proposes to get rid of this effect is a roofing filter.  A roofing filter is composed of a high pass filter that only lets frequency components whose periods are shorter than 48 bars pass in. The output of the roofing filter is passed through a smoother filter that passes components whose periods are longer than 10 bars.

Market direction

We must decide whether we are going to trade both directions indistinctly or if we should define a permission rule for every direction.

It seems straightforward the need to define a trend-following rule, but it’s not. Sometimes that rule doesn’t help to improve performance. On the contrary, it forbids perfectly valid entry signals and it hurts profits and other system metrics.

To assess the efficacy of a market trending signal, the right way is to test it after having tested the main entry signal. We should weight any possible improvement, but focused return on account, not merely in an increase in total profits.

Timeframe or bar size

Many traders don’t pay attention to this variable, although it plays an important role in the performance of a system. Timeframe length is really an important issue when developing a trading system because it will define a lot of things:

  • The length of the timeframe is inversely proportional to the number of trade opportunities available on a given period.
  • Length is proportional to the time it takes to close a trade.
  • It’s also connected to the mean achievable profit. An hourly bar would offer a channel width much wider than a 5-minute bar. Since trading costs are a fixed amount for every trade (spread + commissions) timeframe length is proportional to the ratio Rewards/costs.
  • It directly connects with risk. A longer timeframe needs longer stops, so we should lower our position size for the same monetary risk

The forex three basic categories are:

  • Midterm: from 2-hour bars to a couple of days. It may be used with swing trading or similar techniques.
  • Short term: From 15 minutes to 1-hour bars.
  • Very short term: from seconds to 15 min-bars.

Time frames of popular strategies:

  • Scalping: It’s a very short-term strategy. From seconds to a few minutes to complete a trade. It usually takes less than 5 bars to complete a trade.
  • Day trading: from very short-term to short-term. From minutes, up to 1-hour bars, although the most popular are 5, 10 and 15 minute-bars. It takes from 3-5 minutes up to hours to complete a trade. Open trades are closed before traders stop their trading session.
  • Range trading: This type of strategy doesn’t rely on a time frame but on a range breakout. So, its length depends on the range size. A small range takes less to be crossed through, while a large range may take 30 minutes or more. The most useful range will be that one that transition on average every 3-6 minutes.

It is not advisable to choose very short time frames. There, the market noise is very high and the timing for entries and exits is much more critical. The most critical parameter of a trading system is profitability with low variance. Profit objectives can be easily achieved using proper position size, but not over-trading in shorter time frames. Those very short time frames are only good for your broker.

General rules for entries

Once we have an entry rule we need translating it into a computer language. Computer languages are a formal and unequivocal description of a set of rules to do a task. If we need to test our idea in 5 years of one-minute data we surely will need an advanced trading platform such as MT5 or MT4 at least. But, even if we aren’t going to do that we still need to formalize our rules.

To do that a good solution is to implement our system in some pseudo code. This is, even, advisable as a first step before programming it to real code. Python users are very fortunate because Python code matches perfectly pseudo code. An example of pseudo code might be:

# Pseudo code for a simple 2-MA strategy using the slope instead of the crossover.

Inputs:

minRR = 1.5

PeriodLong = 15

PeriodSHort = 5

NN = 1

StopLong = MinLow(Low, 10) - 3 pips # it takes the low point of the last 10 bars - 3 pips

StopShort = MaxHi(High,10) + 3 pips # it takes the max of the last 5 bat
TargetLong = MaxHi(High, 20) -3 pips # Target is max of the last 20 bars – 3 pips
TagetShort = MinLow(Low, 10) + 3 pips # Target is the min of the last 10 bars + 3 pips
# Go long if both moving averages are pointing up

if Long_avg[0] > Long_avg[1]  and Short_avg[0] > Short_avg[1]:  
         GoLong = True
         GoSort = False
         RR = (TargetLong –Price) / (Price-StopLong)

# Go short if both moving averages are pointing down

if Long_avg[0] < Long_avg[1]  and Short_avg[0] < Short_avg[1]:  
         GoLong = False
         GoSort = True
         RR = (Price - TargetShort) / (StopShort - Price)

If Golong and RR>minRR:
         Buy NN contracts at Price, limit

If Goshort and RR >minRR:
         Sell Short NN contracts at Price Limit

# Stops and targets

If myposition > 0:
         Sell at StopLong
         Sell at TargetLong

If myposition < 0:
         Buy to cover at StopShort
         Buy at TargetShort

Bullet points for creating good entries:

  • KISS principle applies: The “Keep it simple stupid” principle devised by the US Navy. If you can’t explain it in simple terms you’ll have a problem while converting it to rules or code.
  • Limit the parameter number: The higher the number the higher the probability of overfitting. If you have just 3 entry parameters, then with exit and filter parameters you’ll end up with 8 to 10 that need to be optimized. Try to limit that to no more than two.
  • Think differently. For example, MA crossovers have been used extensively. If you want to try them, think on how to innovate using them, for example, using MA’s against MA crossover users by fading the signal as a kind of scalping and look what you get.
  • Use a single rule first, test it and continue adding another one and observe its effect in performance, so you’ll know if it works or is junk.

General comments on exits

Exits seem to be the poor relative in the trading family. Most people pay little attention to exits. They seem to believe that a good entry is all that matters. But I’ll tell you something: Exits can turn a lousy entry strategy into a decent or good system, and the contrary applies: it can turn a good entry strategy into a loser.

Stop loss define the risk: The distance from entry to stop-loss together with the size of our position are the variables needed to compute the monetary risk of a trade.

Profit target to entry define the reward, and with reward and risk, we can define our reward to risk ratio. This ratio and the average percent of winners is all we need to make a rational decision to pull or not to pull the trigger on a particular trade.

If for example, our system’s average percent winners are 50%, and the risk is two times bigger than the expected reward, it’s foolish to enter that trade. We’d require that the reward was at least a bit bigger than the risk to have an edge.

The usual exit methods are:

  • Technical-based stops: Stops below supports on long entries and above resistance on short entries
  • MAE Stops. Maximum adverse execution is a concept devised by John Sweeney. The main idea is: If our entries have an edge, then there will be a behavior on good trades different to bad trades. Then if we compute the sweet spot where the good trade almost never reaches, this is the right place to set our stop (In following articles, we will develop on this idea).
  • Break-even stops: At some point when the trade is giving profits, many traders move the stop loss to break-even. This is psychologically appealing, but it may hurt profits. Moving the stops to break-even should be based on statistically sound price levels that, not on gut feeling, and should be based upon its goodness, not to make us more comfortable.
  • Trail stops: As the trade develops in our favor, the stop-loss is raised/lowered to a new level. Trail stops may be linear or parabolic.
  • Profit Targets. Profit targets really are not stops. Exits on targets are usually accomplished using limit orders. As with the MAE stops, we should test the best placement for profits statistically instead of fixed money targets.

 

©Forex.Academy


References:

Building Winning Algorithmic Trading Systems, Kevin J. Davey

Computer Analysis of the Futures Markets, Charles LeBeau, George Lucas

Statistically sound Machine Learning for Algorithmic Trading of Financial Instruments, Aronson and Masters

 ©Forex.Academy
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Maximum Adverse Excursion

INTRODUCTION

What is the MAE

Maximum Adverse Excursion (MAE) is a method of analysis for automatic or discretionary trading systems that allow us to objectively improve the overall operating result by positioning stops based on the statistical analysis of the development of operations from its inception to its closure.

When we study a trading system, we often find losses. Sometimes these losses are recurrent and lead us to reject a system or some of its rules. Here, the proposal is that instead of doing this, we approach the problem in another way.

As a method of improving results, John Sweeney proposes a statistical system rather than a technical one. We will not rely on indicators or the behavior of complicated logarithms, but on the statistical study differentiated from winning and losing operations. If our entry method is good, the course of price in winning trades is different from the behavior of the price of losing trades. We will not rely on indicators or the behavior of complicated logarithms, but on the statistical study differentiated from winning and losing operations.

Let’s analyze the winning trades and, above all, those that ended in losses. Are there any common features in them? Can we detect any pattern that makes us think we are in front of something usable?

There is a truth that in any trading system we must accept inescapably. At some point, we have to cut our losses. Of the many methods used for this purpose, the most common of all is price action when it distances itself from the meaning of our trade.

THE METHOD

We will track the price path during positive trades and along those that end in losses. The idea is to check the typical route of each of them and in this way, find the best way to place the system stop to achieve a better risk to reward ratio.

We will call “excursion” to the price range traveled by the price from our entrance to its end. We will distinguish the two possible directions:

Maximum Favorable Excursion: (MFE) It is the biggest advance of the price from our entrance to the exit.

Maximum Adverse Excursion: (MAE) It is the maximum retreat of the price from our entrance until the closing.

Steps

  1. Define our input and output rules.
  2. Record how much the price has moved from our entry to our departure both for and against the trade.
  3. Separate the winning trader’s data from losers into a table.
  4. Order the losers for lost categories.
  5. Check which patterns follow the price on losing trades and learn to recognize it.
  6. Set the stop according to the recognized pattern. If it behaves like a loser trade, acknowledge that we have been wrong and assume the losses.

Let’s see an example of how this methodology works.

Graph of a system without stops that operates on the DAX

In the vertical axis, we can see the maximum gap of each trade before its closure (MFE). The horizontal displacement represents the maximum adverse excursion (MAE) produced before its closing.

Given the graph, it appears as relevant the level of 0.15% – 0.20% as a limit. This translates into losing trades less than 0.15% -0.20% before ending losses. We can then cut losses at 0.28% (X-axis). It is also appreciated that the vast majority of winning trades retraced less than 0.10%.

The statistics of this system before making changes are as follows.

Based on the above results, we set the stop at 0.105% of retraction, and now it looks like this.

The statistics are as follows:

Statistics have improved. First, the maximum loss has been reduced from -1425 € to -237 € as well as the average loss from -195.47 to -167.24, and the profit-loss ratio has improved from 1.81 to 2.13.

By applying a trailing stop to the system, we can improve some statistical data that will help in the general computation. The use of this type of stop is delicate because it is very easy to be touched by a momentary price retreat. However, its use at a sufficiently loose distance can prevent a trade that is very advanced from becoming lost. In our case, the average loss on the losing trades improve from 167 to 158, and the percentage of winning trades increases slightly in both the long and short sides. It would read as follows:

Now to advance the robustness of the system, let’s look at the Maximum Favorable Excursion (MFE) by looking at the following graph.

We see the effect of the Trailing Stop. The trades advance far beyond the point at which they finally close. This is normal using this type of stop. Adjusting them further usually leads to a worsening of the final result of the set.

In the following chart, we see the effect of setting the Trailing Stop 50% closer.

Although it seems better, the total gain is worse, and the drawdown increases.

You can then compare the two options statistics, which leave no doubt.

Statistics with optimal Trailing stop:

Statistics with Trailing stop a 50% more adjusted

Maximum Favorable Excursion

The MFE is found by monitoring the maximum reached during positive operations. Often, we find that our trades end very far from this point. Obviously, our goal must be to get them to stop as close as possible to the MFE. In each particular case, we cannot expect to always close at the absolute maximum. In this case, the methods of traditional technical analysis based on indicators often betray us by getting us out of the market ahead of time or, on the contrary, keeping us while the point of maximum profit goes away.

By searching the MFE, we can detect the most likely area that the winning trades will reach. In this way, we should place our profit target at a point that’s the most probable, statistically speaking. This won’t make our system jump to an absolute possible maximum profit. However, it simplifies the system and makes it more robust by shortening the exposure time to the market.

Finally, a sample of the effect of adding a profit target, taking as reference the MFE. In this case, we separate the behavior of the MFE in the bearish bullish trades since, in the case of indexes and stocks, the market does not usually show symmetry. Experience tells us that there are different characteristics between bull markets and bear markets, although there are authors who question it.

We put a TP (Take Profit) of 27 on bullish and 33 points for the bearish.

What you see is a performance improvement. Although the improvement in net profit does not seem too important, the maximum streak of losses decreases a lot. This procedure reduces the capital necessary for its implementation, and therefore, there is a substantial improvement in percentage profitability. It also increases the net profit and especially the Profit Factor.

Minimum Favorable Excursion

Another concept to evaluate is the Minimum Favorable Excursion. It is to detect the minimum point of advance from which it is unlikely that the price returns on our entrance. This will allow us to move our stops to break even, upon reaching this area and prevent it from ending up in losses.

Conclusion

The statistical method proposed by the MAE and MFE study is revealed as a fully valid system for the study and improvement of any trading system. The use of Maximum Favorable Excursion charts gives us a way to distinguish winning trades from losing trades since they behave differently.

We can see very quickly if there are exploitable behaviors. The behavior of losing trades has its own patterns. It just advances a tiny amount in favor of the entry and then moves against it, and this allows to act consequently by cutting the losses at the right spot where statistically winning trades didn’t reach.

On the other hand, the traditional use of pivots as a reference to locate stops usually leads to losses, since it is the first place where the market seeks liquidity as it minimally weakens a trend.

Finally, the MFE allows us to put the trade into break-even at the right time or even add positions in a favorable environment. It also facilitates the tracking of profit targets. In any case, it is a highly recommended study method to improve automatic or manual systems.

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Changes in the Energy Model

 Abstract

The current energy model is based on fossil fuels which can be extracted quickly and throughout history have been abundant resources on earth. But with an increase in population and its consequent demand for energy, energy sources have been reduced which has led to the prices of coal, oil and other raw materials fluctuating drastically, leading to booms and falls that have affected many economies because their main source of income is the exploitation of these resources. As has happened throughout the history of mankind with changes in demand, energy models change. At present, there are problems in the environment caused by the exploitation of fossil fuels, which has led many analysts and economies to rethink on which energy model their economies should be based.

 

The conventional energy model has sustained economic growth since the first industrial revolution for over 150 years and has been based on the extraction and combustion of non-renewable and finite fossil fuels such as coal, oil and natural gas. With globalization, the distribution and exploitation of these resources have been achieved through multinationals that allow countries that do not have good reserves of fossil energy sources to access these resources through imports.

But this conventional energy model has some drawbacks such as the generation of wealth but distributed unevenly where multinationals benefit mainly, the impact on the environment and the emission of polluting gases, the externalization of environmental and social costs. As the extraction sector requires so much capital, it is a sector with a high entry barrier where some companies cannot compete due to high-value auctions of mines and wells where these resources are located, which generates a high concentration of business and this ends up generating a concentration of wealth in a small group of companies.

The consequences of this way of generating energy, such as the acceleration of resource depletion, social and economic imbalances, inefficiency in the way energy is generated and climate change has generated a debate about whether it is time to change the energy model by one friendlier with the environment and one more inclusive with the local communities that generate wealth more equitably. The problem to change the energy model is the vast amount of energy consumption that depends on conventional sources, so it will not be easy to change from one model to another.

The main source of energy consumption in the world is non-renewable sources. The forecasts of the International Energy Agency speak of an oil demand of 116 million barrels per day for the year 2030 so with some forecasts it is believed that by 2050 there would be no more reserves. The same occurs with coal and natural gas that is estimated that the reserves will not reach more than 250 and 75 years respectively. It is for this reason that in the last years until 2014 the prices of raw materials had increased in an accelerated way showing that trend of reserves. What happened after 2014 was a decrease in demand in most commodities due to a change in the Chinese economic model as discussed in the article China and its economic predominance and the unconventional extraction of oil. In the next graph, you can see the price of gas and oil in the last years.

Graph 29. McGuire A. (2009, July 2). This Chart Compares Oil Price History to Natural Gas Price History. Retrieved November 25, 2017. From https://moneymorning.com/2015/07/02/this-chart-compares-the-oil-price-history-to-natural-gas-price-history/

The OPEC response to the crisis in commodity prices was to reduce the supply of oil to increase oil prices that reached below 40 dollars per barrel in 2016 when years before the crisis of 2007 came to be above $ 120 per barrel. OPEC had its beginnings in 1949 when Venezuela entered into talks with Iran, Iraq, Saudi Arabia, and Kuwait to exchange views and improve communication between countries regarding the oil market. Already in 1959, the need for cooperation became more evident because the multinationals that were in the producing countries began to pay less unilaterally to the producer countries in royalties which increased their profit margins but affected the countries that had the deposits.

After this behavior of the multinationals where they reduced the price of oil extracted in the countries with reserves to pay fewer royalties, but increased the price of refined oil, made the countries act forming a congress of producing countries where it was determined that this could not happen unilaterally if not that there should be negotiations prior to any decision. Such was the indignation of the producing countries that the Organization of the Petroleum Exporting Countries was constituted in 1960 by the governments of Venezuela, Kuwait, Saudi Arabia, Iran, and Iraq. The first thing OPEC did was to establish fixed royalty rates and the price that sold their product.

But the unrest continued due to the unfair behavior of the international oil companies, so they tried to establish state oil companies to compete. This was the first measure that was taken by the producing countries to control oil prices. From this measure and the OPEC interference in oil prices, many economists began to consider the oil market as an oligopoly where there are very few oil suppliers due to the power exercised by this organization and a high level of demand. But there is also another group of analysts who think that despite the existence of OPEC there are other factors that make the market competitive such as the reduction of reserves, the uncertainty of extraction of some countries and technological and environmental aspects.

What is true is that historically OPEC has not caused oil crises nor have generated sudden growth in oil prices. There may be news about the behavior of member countries of this organization that have an impact on financial markets and some volatility in assets linked to the price of oil. The drastic increase in oil prices between 2004 and 2008 was not due to OPEC’s organizational policies but to the uncertainty about the production capacity of the members.

The causes of the 2014 crisis in the oil market were mainly due to three factors. The first factor is the development of the extraction of unconventional hydrocarbons such as Fracking, which has allowed non-OPEC countries such as the United States to increase their production in an accelerated manner. In just six years the United States production increased close to 90% so that producing countries that found in this country as a net buyer of fuels now see it as competition from other markets such as Asia. In the following graphic, you can see a summary of how fracking works

Graph 30. (2017, March 17). Fracking. Retrieved November 25, 2017. From http://www.goveonline.com.au/fracking/

The second factor that determined the crisis of recent years was the decision of OPEC to maintain its production unaltered so as not to affect the market share of each country that was a member of the organization. This factor was added to the first, so the offer increased considerably.

And the third factor was the weak demand for raw materials including oil due to the change of the Chinese economic model and the weak demand of European countries, all products of the crisis of 2007 that affected the growth of most countries in the world and his recovery was very slow.

The winners of this oil crisis were the importing countries of oil and other raw materials since the low costs of these energies increased the purchasing power of the households generating an impulse in the local consumption of each country. The losing countries obviously were the exporting countries due to the reduction in extraction royalties and therefore the revenues of the governments. Given the above, there were taxation pressures and some countries entered crises like Venezuela that needed higher prices to be able to carry a huge social expenditure.

The prospect of the future of raw materials is complicated. In 2017 prices have recovered slightly but far from reaching the prices reached by the raw material before the crisis thanks to the reduction of supply by the countries belonging to the OPEC. Given the unstoppable proliferation of unconventional extraction platforms, the evolution of the supply will depend on the next agreements that the members of the organization can reach to control production.

Currently, although the OPEC countries are complying with the cuts promised in the offer, the number of new platforms in the United States is such that prices have seen prices above $ 50 a barrel, but not beyond this point due to the low production costs that non-conventional producers need to have benefits in their activity. Fracking is a technique used to extract oil and gas that has been impregnated with rocks and is difficult to obtain with conventional methods. For this purpose, water and chemicals that expel hydrocarbons to the surface are injected into the subsoil.

The measures taken by OPEC have had a negative aspect in their interests since as the price has risen with the agreements they have reached, the opening of wells in the United States and other non-member countries that previously were not profitable has been propitiated, but with the new prices, they generate economic benefits.

But as has happened throughout history since industrial development, energy transitions occur when the behavior of people changes. In the first epochs of mankind in the underdeveloped agrarian economies, the basic needs of food were satisfied by simple forms of agriculture that had an energy source, solar energy. As the economies develop, they become more complex and depending on these changes, the uses and sources of energy also change.

Each stage of economic development has been accompanied by an energy transition from one source to another. That is why, in the 21st century, we are seeing a transition in the energy sources from fossil fuels to renewable energy sources and this change is mainly due to the concern about environmental impacts, the limits in the reserves of fossil fuels, prices of raw materials and technological changes. The lifetimes of fossil fuels could be extended thanks to the technological innovations that are more efficient and make the extraction activity more profitable, but what could be decisive when changing the energy model is the concern about the impact on the environment that humans have caused.

Because much of the capital invested and the infrastructure of the economic system of the main modern economies are based on the extraction and use of fossil fuels, the total change of infrastructure for new technologies will not be easy or cheap. The financial and private markets will play a crucial role in this process due to the large amount of money that is needed as well as the help of the governments of each country.

Up to a certain point renewable energy are unlimited since their sources are provided by nature and you can have a daily supply such as solar energy and wind energy that has no limit. But not all countries are the most suitable to have any type of energy since they also depend on their geographical location. In some states of the United States, solar energy is more appropriate than in Africa and the Middle East. Unlike other countries in Europe, South America and part of the United States that should implement wind energy.

What is certain is that all countries in the world have some renewable energy resources, although the availability and cost of use are variable. But after the economies achieve the coupling to new technologies renewable energies will help to save money due to lower costs since it is a constant source of energy. They will also compensate for the effect that unemployment generates due to the disuse of non-renewable energies since in the transition new types of employment will have to be created, specializing in clean energies.

In conclusion, we are in a time of change where the rethinking of the energy model is not abnormal. This has happened periodically throughout history since energy models correspond mainly to the needs of people and the technologies available in its time. The current situation of the extraction sectors is complicated due to two main factors, which are the new global awareness of environmental care and the appearance of new technologies such as Fracking. We are in a moment of transition where society has considered the cost-benefit of the use of non-renewable energies and it’s clear that the cost generated using these sources is quite high to continue with the current model.

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Forex Designing a Trading System (I) – Introduction to Systematic Trading

Trade and money

Trade is a concept that began with the advent of the Homo Sapien, some 20K years back. People, back then, traded their spare hunting pieces for new arrows, spears, or something else he had no time to make himself because he was hunting mammoths.

So, the first questions about what a fair deal was, and, also, how to measure and count things, began.

Trade and agriculture were significant factors that drove the Cro-Magnon men from the caves into civilization.  Everyone started specializing in what they were good at, and people had time to think and test new ideas because they didn’t need to spend time hunting.

Trade brought accounting, the concept of numbers and, finally, mathematics. Ancient account methods were discovered more than 10k years ago by Sumerians in Mesopotamia (the place between two rivers). Also, Babylonians and Egyptians gave value to accounting and measuring the results of their work and trade activities.

Sumerians were the first civilization where agricultural surpluses were big enough that many people could be freed from agricultural work, so, new professions arose, such merchants, home builders, book-keepers, priests, and artists.

The oldest Sumerian writings were records of transactions between buyers and sellers. Money started being used in Mesopotamia as early as 5,000 B.C.  in the form of silver rings. Silver coins were used in Mesopotamia and Egypt as early as 2,600 B.C.

Accounting and money are interlinked. Money was (and still is) the standard way to define the value of products and services, accounting is the method to keep track of earnings, loses and costs and evaluate the use of resources and time.

Currency trading is nothing but a refinement of the concept when several types of currencies are available in an interlinked civilization. Currency trading is the way to search the fair value of currency in relation to other currencies. To traders, accounting is the way to measure the properties and value of their trading system.

Automated versus discretionary

There are several reasons why we’ll need an automated trading system. First of all, people always trade using a system, even when they think they don’t. People trade their beliefs about the market, so their system is their beliefs.

The problem with a system based on just beliefs is that, usually, greed and fear contaminate those beliefs, and, thus, the resulting system behaves like a random system with a handicap against the trader.

Facts

Economic information translates gradually to price changes. Future events aren’t instantly discounted on price.  This is the reason for the existence of trends.

Leverage produces instability in the markets because participants don’t have infinite resources to hold to a losing position. That’s one reason for the cyclic nature of all markets and their fat tail probabilistic density distribution of returns.

There is a segmentation of participants by their risk-taking potential, objectives, and time-frames

The market reacts to new strategies. A new strategy has diminishing returns as it spreads between market participants.

The Market forgets at a long timescale. The success rate of market participants is less than 10%, so there is a high turnover rate. A new generation of traders rarely learn the lessons of the previous generation.

There is no short-term link between price and value.

The market as a noisy structure

All these facts make the markets chaotic, with a fractal-like structure of price paths, a place with millions of traders trading their beliefs, but everyone with a different timeframe and expectations.

This is ok, as no trade is possible if all market participants have the same viewpoint, but the result of hundreds of thousands of beliefs and viewpoints is that the market is as noisy as a random coin flip, as we observe in Fig. 1, reproducing three paths with 200 coin-flip bets, that closely resembles the paths of a currency or futures market.

Contradictory strategies may be both profitable or both losers

On my article about trading using bands, profitable trading VII, I’ve described two totally opposed systems: one was a Donchian breakout system, and the other was The Turtle Soup plus one. Both made money and both traded opposite to each other. That shows that opposing beliefs produce profitable systems.  Trading is not a competition to decide who’s right and who’s wrong. Trading is a business, and the goal of a trading system is to make money, not being right. In fact, none of these two systems were right even 50% of the time, but both were profitable.

Some time ago I developed a mechanical system and was so wrong that I saw almost a continuous downward equity curve. Nice! I thought. This system is so bad that if I switched it from buy to sell and vice-versa it might result in a great system!

Wrong! The reverse system was almost equally bad. That’s the nature of the markets. Nothing is easy or straightforward.

Too much freedom is dangerous

The marketplace is an open environment where a trader can freely choose entry time, direction, the size of her trade and, finally when to exit. No barrier nor rule forces any constraint on a trader. Such freedom to act is the difference between trading and gambling, but it’s a burden to discretionary traders, especially new to this profession because they tend to fall victim to their biases.

The main biases that a novel trader suffers are two: The need to be right and the belief in the law of small numbers. These two biases combined are the main culprits for the trader’s bias to take profits short and let losses run. The need to be right is also the culprit for the trader’s inclination to prefer high-frequency of winners instead of high-expectancy systems. For more on this read my article Trading, a different perspective.

To counteract this unwanted behavior, we’d need to restrict the trader’s freedom by forcing strict entry and exit rules that guarantee a proper discipline and ensure the expected reward to risk, and, at the same time, avoiding emotionally driven trades.

Discretionary versus systematic

The probability of a discretionary trader to succeed is minimal, mainly because, without a set of rules to enter and to exit, the trader immediately fall into the trap of the law of small numbers and starts doubting his system, usually with a substantial loss, as a consequence of considerable leverage.

Most successful traders develop and improve a trading strategy using discipline and objectivity, but this cannot be qualified as a systematic system because its rules are based on their beliefs about the state of the market, their mental state, and other unquantifiable factors.

A systematic trading approach must, conceptually, include:

  • Entry and exit rules should be objective, reproducible, and solely based on their inputs.
  • The strategy must be applied with discipline, without emotional bias.

Basically, a systematic strategy is a model of the behavior of one or several markets. This model defines the decision-making process based on the inputs, without emotional or belief content.

Trading can only be successful using a systematic strategy. If a trader does not follow one, trades will not be executed consistently, and there will be the tendency to second-guess signals, take late entries and premature exits.

Personal adaptation

Developing our own trading system not only gives us confidence in its results, but we’d be able to adapt it to our personal preferences and temperament. We don’t like suits that don’t fit well. A system is like a suit. A perfectly good system for one trader might be impossible to trade for another trader. For instance, a trader might not be comfortable trading on strength while another one cannot trade on weakness. A trader may hate the small losses produced by tight stops, so he favors a system with wide stops. That’s why we need to adapt the system to fit us.

The need to measure and keep records

Measurements allow distinguishing good systems from bad ones. There are several parameters worth measuring, that, later, will be dealt with, but the final objective of measurements is to determine if the trading idea behind the system is worthwhile or useless.

Measurements allow finding where the trading system is weak and optimizing the inadequate parameter to a better value. For instance, we might observe that the system experiences sporadic large losses or that it faces too many whipsaws. Measurements allow searching the sweet spot where stops do its duty to protect our capital while preserving most of the profitable trades.

When the system is already trading live, we might use measurements to adapt it to the current conditions of the market, for example to an increase or decrease of volatility. Measurements help us detect when a system no longer performs as designed, by analyzing the current statistical performance against its original or reference.

Discretionary strategies allow measurements, as well, and, sometimes help to improve a particular parameter, as well, particularly stop-loss position, but, how can this help with entries or exits if they are discretionary or emotionally driven?

Measurements, finally, will help us assess proper risk management and position sizing, based on our objectives and the statistical properties of the developed system. This concept will be developed later on, but my previous article Trading, a different perspective, mentioned above, deals with this theme.

Information

In the discretionary trading style the forex information is categorized into the following areas:

  1. Macroeconomic
  2. Political
  3. Asset-class specific
  4. News driven
  5. Price and volume
  6. Order flow or liquidity driven

In the systematic trading style the information is taken from Price and volume, although lately, some systems are also using sentiment analysis, by scanning the social networks (especially Twitter) and news, with machine learning algorithms. Order flow is left to systems designed for institutional trading because retail forex participants don’t have this information.

In this context, systematic trading simplifies information gathering, by focusing mostly on price and its derivatives: averages and technical studies.

Key Features of a sound system

The essential features a system must accomplish are:

Profitability: The model is profitable in a diversified basket of markets and conditions. To guarantee its profitability over time, we should add another feature: Simplicity. Simple rules tend to be more robust than a large pack of rules. With the later, often, we get extremely good back-tested results but this outcome is the result of overfitting, therefore, future results tend to be dismal.

Quality: the model should show a statistical distribution of returns differentiated from a random system. There are several ways to measure this. The Sharpe Ratio and the Sortino Ratio are two of them. Basically, quality measures a ratio between returns and a measure of the variation of those returns, usually this variation is the standard deviation of returns.

Risk Management and position sizing algorithms: Models are executed using position sizing and risk management algorithms, adapted to the objectives and risk tastes of the trader.

It is desirable that the algorithm for entries and exits be separated from risk management and position sizing.

Sharpe Ratio (SR) and other measures of quality

Sharpe Ratio

Sharpe ratio is a measure of the quality of a system, and is the standard way for the computation of risk-adjusted returns.

SR =ER/ STD(R)

SR = Sharpe ratio

R = Annualized percent returns

ER = Excess returns = R – Risk-free rate

STD = Standard deviation

Sortino Ratio

The Sortino Ratio is a variation of the Sharpe Ratio that seeks to measure only the “bad” volatility. Thus, the divisor is STD(R-) where R- is linked solely to the negative returns. That way the index doesn’t punish possible large positive deviations common in trend following strategies.

Sortino Ratio = ER/SRD(R-)

Coefficient of variation(CV)

The coefficient of variation is the ratio of the standard deviation of the expected returns (E), which is the mean of returns, divided by E. It’s a measure of how smooth is the equity curve. The smaller the value, the smoother the equity curve.

CV = STD(E)/E

SQN

The inverse of CV multiplied by the square root of trades is another measure of the quality of the system. It’s a measure of how good it is in comparison with a random system.

SQ = √N x E/STD(E)

Another, very similar measure of quality comes from Van K. Tharp’s SQN:

SQN = 10*E/STD(E), if the number of samples is more than 100 and

SQN = SQ if the number of samples is less than 100.

The capped SQ value allows comparing performance when the number of trades differs between systems.

Calmar Ratio

CR = R% /Max Drawdown%

It typically measures the ratio over a three year period but can be used on any period, and it’s mental peace index. How stressing the system is, compared to its returns.

CR shrinks as position size grows, so it can be a measure of position oversize if it goes below 5

Defining the parts of the problem

To finally produce a good trading system we need to identify, first, the elements of the problem, and at each one of them find the best solutions available. There are many solutions. There’s no question of right or wrong. The optimal solution is the one that fit us best.

1.    Identify the tradable markets and its features

The forex market is composed of seven major pairs and 16 crosses. The trader should decide about the composition of his trading basket in a way to minimize the correlation of the currently opened trades.

Liquidity is an important factor too. Especially noteworthy is to detect and avoid the hours when liquidity is low, and define a minimum liquidity to accept a trade.

Volatility is also necessary information that needs to be quantified. Too much volatility on a system designed in less volatile conditions may fail miserably. Especial attention to stop placement if they don’t get automatically adjusted based on the current volatility or price ranges.

We should be careful with news-driven volatility. We must decide if we trade that kind of volatility or not, and, if yes, how to fit that into our system.

2.    Identifying the market condition

A trading signal works for a defined market condition. A trend following signal to buy does not work in sideways or down trending markets.

Identify regression to a mean. Usually, mean-reverting conditions happen in short timeframes, due to the spread of trading robots, liquidity availability, and profit taking. Regression to the mean markets merits special entry and exit methods, using some kind of bands or channels, for instance.

Identify oversold and overbought: it is crucial to detect overbought and oversold conditions to avoid trades when it’s too late to get in.

Finally, we must find a way to include all this information in the form of a set-up or trading filter.

3.    The concept and yourself

There are lots of ways to trade a market, but not every one of them will fit all traders. An example of a tough system for myself would be the Donchian breakout system. A robust and simple trend following system, but I know I wouldn’t be comfortable with 35% winners because I know that this means 12% chance of having a streak of 5 losers.

Therefore, to know what fits you, you should try to know yourself and your available time for trading.

Conceptually there are two kinds of players: Those who need frequent small gains and accepts sporadic substantial losses (premium sellers) and those who are willing to pay insurance for disasters, taking periodic small losses in search of large gains (premium buyers).

A premium seeker prefers a hit-and-run style of trading: scalping, mean-reverting, swing trading, support- resistance plays, and similar strategies. A premium seeker has a negatively skewed return distribution, as in Fig. 2:

A premium buyer is a trend follower, an early loss taker, a lottery ticket buyer, a scientist experimenting in search of the cancer cure. He is willing to be wrong several times in a row, looking for the big success: When he finds a trend, he jumps on it with no stops. If proven right, he adds to the position, pyramiding, as soon as new profits allow it. A premium buyer has a positively skewed return distribution, as in Fig. 3:

We notice that the most psychologically pleasing style comes from premium-sellers. The problem with this trading style is that it is not insured for the black-swan-type of risks. He is the risk insurer!

The premium buyer is like a businessperson. A person who is willing to assume the cost of the business for a proper reward. His problem is that he must endure continuous streaks of small loses.

An early loss taker is against the crowd instinct to take profits early, a habit with a negative expectancy, so it pays extra returns to be contrarian.

There is a mixed style where reward to risk is analyzed and optimized. It uses stop protection, while the percentage of winners is enhanced using profit targets.

The main concept for a sound system, in my opinion, is to make sure our distribution of returns has a positive skew. That can be accomplished if we make sure our system has a mean reward-to-risk ratio higher than 1:1, preferably greater than 2:1 but never less than 1:1, and by setting profit targets in tune with the market movements – placing them near resistance or support, and using trail stops.

4.    The law of active management

The Fundamental Law of Active Management was developed by Grinold and Kahn to measure the value of active management, expressed by the information ratio, using only two variables: Manager skill IC and the number of independent investment opportunities N.

IR = IC x √N

If two managers have the same investment skills but one has a more dynamic management, meaning N is higher than the other manager, its return will outperform the less dynamic manager.

This formula can be used in trading strategies, as well:  On two equally smart strategies, the one with more frequent trading will outperform the other. There’s a limitation, though, that this formula doesn’t address: The cost of doing business is more significant the higher the frequency of trading.

5.    Timeframes: Fast vs Slow

The trader’s available daily time has to be considered too. Does the trader have time to be on the screen the whole day or are they busy during work hours, hence, only able to dedicate just a couple of hours at noon to trading.

Unless the trader is willing to use a fully automated system, the time available to them dictates the possible timeframes. A trader who’s busy all day cannot trade signals that show on 1-hour timeframes but is instead forced to focus on swing trading signals that can be analyzed at noon. A full-time trader has all available time-frames at their disposal.

Well summarize here the classification made by Robert Carver on his book Systematic Trading:

·      Very Slow (average holding period: months)

Very slow systems tend to behave like buy and hold portfolios. Trading rules tend to include mean reversion to very long equilibrium such as relative value equity portfolios, that buy on weakness and sell on strength.

As a result of the law of active management, returns from dynamic trading diminish, the lower the trading frequency. Therefore, at large holding periods, the return tends to be poor, unless the skill at timing the market were top notch.

·      Medium (average period hours to days)

The law of active management gives us a clue that this timeframe gets more attractive results than a longer time-frame.

It’s adequate to part-time traders that can do swing trading, working in the evening, searching for signals to be used in hourly and daily charts. From a Forex perspective, these medium-speed timeframes are less crowded with traders. Therefore, strategies may work better than shorter timeframes.

·      Fast( from microseconds to one day)

The Sharpe ratios could be very high in these timeframes, an important portion of the raw returns ought to be spent on costs (commissions and spreads).

6.    Risk

Risk is a broad concept. There are several kinds of risk. The first type of risk is the trade risk. The risk you assume on a particular trade. That’s the monetary distance from entry to your stop loss multiplied by the number of contracts bought or sold.  It’s easy to assess and measure.

The second type of risk is the Potential drawdown a system may experience. This kind of risk is dependent on position size and the percent losers of the system, therefore, we can estimate it with certain accuracy.

Risk can be defined as the variability of results. It’s a statistical value that measures the mean value of the distance between results, and the mean of results and is called standard deviation. The point is that market volatility is shifting and, further, it’s different from asset to asset.

If a trader has a basket of tradable markets, there might happen that one asset is responsible for 60% of the overall volatility on her portfolio, because the position size in that particular asset is higher, compared with others or because its volatility is much higher.

The best way to reduce the overall risk is through diversification and volatility standardization.

Diversification:

To reduce overall risk, there is just one solution: To trade a basket of uncorrelated markets and systems, with risk-adjusted position sizing, so no single market holds a significant portion of the total risk.

Below is the equation of the risk of an n-asset portfolio when there’s no correlation:

𝜎 =√ ( w1 𝜎12 + w2 𝜎22 + … + wn 𝜎n2)

where 𝜎I is the risk on an asset, and wi is the weight of that asset on the basket.

Let’s assume that we hold a basket of equal risk-adjusted positions in 5 uncorrelated markets with a total risk of $10. Therefore, we have a $2 risk exposure on each market, and the correlated total risk would have been 10. But, if the assets were totally uncorrelated, the expected combined risk would be computed using the above equation, then:

Risk =√ (5 x 22) = √20 = 4.47

So, for the same total market exposure, we have lowered our risk by more than half.

Volatility standardisation

Volatility standardization is a powerful idea: It is the adjustment of the position size of different assets, so they have the same expected risk.

This allows having a balanced portfolio where each component has a similar risk. It means, also, that the same trading rule can be applied to different markets if applied with the same standardized risk.

Leverage

The forex industry is attractive for its huge amount of allowed leverage. A trader is allowed to control up to one million euros with a modest 10,000 euro account. That is heaven and hell at the same time. If a trader doesn’t know how to control her risk, he’s surely overtrading.

I recommend reading my small article on position size, but for the sake of clarity, let’s do an exercise.

Let’s compute the maximum dollar risk on a $10,000 account and a maximum tolerable drawdown of 20%, assuming we wanted to withstand 8 consecutive losses.

According to this, we will assume a streak of eight consecutive losses, or 8R.

20% x $10,000 = $2,000 this is our maximum allowed drawdown, and will be distributed over 8 trades, so:

8R = $2,000 = $250, therefore:

Our maximum allowed risk on any trade would be $250 or 2.5% of our running account.

By the way, that value is a bit high. I’d recommend new traders starting with no more than 0.5% risk while beginning with a new system. It’s better to pay less while learning.

The second part of the equation is to compute how many contracts to buy on a particular entry signal.

The risk on one unit is a direct calculation of the difference in points, ticks, pips or cents from entry point to the stop loss multiplied by the minimum allowed lot.

 

Consider, for example, the risk of a micro-lot of the EUR/USD pair in the following short entry:

 

Size of a micro-lot: 1,000 units

           Entry point: 1.19344

              Stop loss: 1.19621

 

We see that the distance from entry to stop loss is 0.00277

Then, the monetary risk for one micro-lot: 0.00277 * 1,000 = € 2.77
Therefore, the proper position size is €250 /€2.77= 90 micro-lots, or 9 mini-lots

Using this concept, we can standardize our position size according to individual risk. For instance, if the unit risk in the previous example were $5 instead, the position size would be:

PS = €250/5 => 50 micro-lots.

That way risk is constant and independent of the distance from entry to stop.

Finally, it’s better to use a percentage of the running capital instead of a fixed euro amount, because, that way, our risk is constantly adapted to our current portfolio size.

7.    The profitability rule

A trading system is profitable over a period if the amount won is higher than the amount lost:

∑Won -∑Lost >0    (1)

The average winning trade is the sum won divided by the number of winning traders Nw.

W =∑Won / N (2)

The average losing trade is then:

L =∑Lost / NL,  (3)  where NL is the number of losing trades.

Thus, equation (1) becomes:

WNwLNL, > 0    (4)

The number of losing trades is the total number of trades minus the winning trades:

NL = N – Nw     (5)

Therefore, substituting (5) and dividing by N, equation (4) becomes:

WNw / N – L(N-Nw) / N > 0     (6)

If we define P = Nw / N,  then (N-Nw) / N = 1-P,  and  (6) becomes:

WP– L(1-P) > 0   ->    W/L x P – (1-P) > 0    (7)

Finally, if we define a Reward to risk ratio as Rwr = W/L  Then we get

P > 1 / (1+ Rwr)     (8)

Equation 8 is the formula that tells the trader the minimum percent winners required on a system to be profitable if its mean reward to risk ratio is Rwr.

Of course, we could solve the problem of the minimum Rwr required on a system with percent winners greater than P.

Rwr  > (1-P)/P     (9)

8.    Parts of a trading system

In upcoming articles, we’ll be discussing all parts of a trading system extensively. Here we are just sketching a skeleton on which to build a successful system.

A trading system is composed of at least of a rule to enter the market and a rule to exit, but it may include the following:

  • A setup rule: A rule defines under which conditions a trade is allowed, for example, a trend following rule.
  • A filter rule: A filter to forbid entries under certain conditions, for example, when there is low volume, or high volatility, the overbought or oversold conditions are reached.
  • An entry rule, defined with price action, moving averages, MACD, Bollinger Bands and so on.
  • A stop-loss, to limit losses in case the trade goes wrong. Optionally a trailing stop.
  • A profit target: Profit target may be monetary, percent, based on supports or resistances, on the touch of a moving average or any other.
  • A position sizing rule. As mentioned before, it should make sure the risk is evenly and correctly set.
  • Optionally, A re-entry rule. The rule decides a re-entry if the stopped trade turns again on the original trade direction.

9.    Chart flow of the development of a trading system

In the next chapters of this series, we will develop on every aspect sketched in this introductory article.

 

 


References:

Professional Automated Trading, Eugene A. Durenard

Systematic Trading, Robert Carver

Profitability and Systematic Trading, Michael Harris

Computer Analysis of the Futures Markets, Charles LeBeau, George Lucas

Building Winning Algorithmic Trading Systems, Kevin J. Davey

Categories
Forex Educational Library

Trading Is An Adult Game (II): Mysterious & Engaging

Introduction

“If you wish to know the road, inquire of those who have traveled it” – old Japanese saying

(Quote from Steve Nison’s first book on Candlesticks)

On the first part of this series, we introduced technical analysis and the basics of charting. In this section, we’ll study candlestick patterns and look at ways to profit from them.

Reversal patterns

One of the most determinant skills a professional trader should have has to do with the ability to identify trend reversals as quickly and accurately as possible. In fact, reversal patterns do provide for the profitable trading setups by most candlestick masters.

Although reversal patterns do not always result in deep trend corrections, or in clear changes in market sentiment, they usually become a warning that a given trend is running out of steam (due to several reasons). Thus we as traders should start considering to either close our existing positions, to deleverage them or to tighten up risk (i.e. stop loss). Perhaps the most appreciated feature of reversal patterns is that they spot price areas for efficient entries, i.e., with an optimised risk-reward ration. However, we should not lose insight by forgetting the most inherent probabilistic nature of trading; put in differently, reversal patterns do increase our chances of determining successful entries, but we must add other factors to the equation such as risk-reward ratio, market sentiment, seasonality adjustments, etc.  Due to their relevance in nowadays trading literature, we shall carefully analyze trend reversal patterns.

We must be cautious to take positions against a prevailing trend if there is one. A rapid train isn’t going to reverse its direction easily. So, before strong trending markets, we should take only those signals matching the trend. Of course, on markets moving within a channel a pattern near the top or bottom of the channel is an ideal place for exit and reverse our position, provided the channel is broad enough to have a proper reward for our risk.

Before engaging in the study of the candlestick reversal patterns, let’s carry out a short discussion about a couple of indicators which may be used as confirmation of the main pattern.


Handy confirming indicators for its use together with candlestick patterns

I’ll be back… and back…and back…

Candlestick patterns are visually simple but sometimes variations of them appear in a lot of places, and not all of them should be taken into account as entry place. The accuracy of the candlestick patterns is highly enhanced when a confirmation signal is used with it. So in this section, we’ll present a couple of Indicators that may be useful as a companion because of its ability to show overbought or oversold places.

Handy confirming indicators

Stochastic Oscillator:

Two math for dummies, at $16.99 each: That’ll be $50.

The Stochastics Oscillator, developed by George Lane in 1950, came from the observation that closing prices tend to appear near the high of the range during uptrends and near the low of the range in downtrends.

This oscillator measures where the close is relative to the range of prices over a period of time. The %K line comes from a simple formula, which makes sure the signal is always between zero and 100:

There is a %D line, which is called slow stochastic and is computed by applying a three-day moving average to the %K line.

The usual way to be used when combined with a candlestick pattern is by taking action at %D and %K crossovers when this happens at an extreme.

Stochastic Oscillator:

Williams %R

Too much gear for this to be normal! Fuck that; we’re multitasking! (a Robin Williams liberal translation)

Williams Percent R is a momentum indicator developed by Larry Williams, which is very similar to the Stochastic indicator, but in this case, it shows the level of the Close in relation to the highest high of the period, instead of the lowest low, and it doesn’t depict a smoothed %D line.Williams %R

Therefore, this oscillator moves from -100 to 0. Values below -80 are oversold levels while from -20 to 0 are overbought.

Some charting packages shift these values to positive 0 to 100 by adding 100 to the formula. In this case, oversold levels are between 0 and 20, and overbought condition happens from 80 to 100.

%R is noisier than Stochastic %D, but with less lag, so together with the confirming candle pattern,  it allows for a better reward to risk ratio and tends to show more trade opportunities than Stochastic does.

 

We observe the excellent accuracy in sync of candlestick top and bottom patterns with the overbought and oversold levels pictured by the %R; and, also, the high reward to risk ratios that might have been achieved. Just one of the patterns (the piercing pattern, fourth from left to right) doesn’t present a good opportunity (therefore we won’t take the trade).

This type of good synch happens in horizontal channels mostly. When the trend is strong,  reliable signals only appear on pullbacks of the main trend.

Finally, the right chart is the continuation of the last signal from the left one. On the left, we had a good hammer, followed by a white candle, with %R in an oversold condition and rising. The left image shows the fate of this imaginary trade, which closes, rightly at the previous high, for a reward/risk of about Two. Then, another entry might have been taken reversing 100% of the last move with yet another 2:1 Reward to risk ratio.

That concludes our small digression about oscillators. The rest of the article will deal with Candlestick signals together with %R as my choice for companion oscillator.


Major candlestick signals

Japanese candlestick charts increase the level of information for the visually gifted trader. Each candle, in combination with its neighboring ones, reflects the psychological shifts in the investor’s sentiment.

  • Umbrella lines; hammer and Hanging man
  • The Doji Star
  • Engulfing patterns
  • Piercing patterns
  • Dark Cloud
  • Harami
  • Stars: Morning and Evening Stars
  • Kicker signal
  • Shooting star

Umbrella Lines

Give it to me, she yelled, I’m so fucking wet! …!

Umbrella lines are candles that show very long lower shadows and small bodies near the top of the trading range. This kind of candles is very interesting as it may be bullish or bearish, depending on price location. If it appears during a downtrend, it’s indicative of the end of it. In such places, the umbrella is tagged as a hammer. If it shows after a rally is called a hanging man.

Umbrella Lines

There are three differences between a hammer and a hanging man.

  • Trend: Hammers come after a downfall. A hanging man after a run-up.
  • Magnitude of the move: Hammers are valid even after a small drop. For a hanging man, the move should last longer.
  • Confirmation: A hanging man should be validated, while a hammer not.

A note to pairs and Forex Traders: Trading pairs makes umbrella candles kind of symmetrical. The stock asymmetry is tamed. A bull EUR/USD is a bear USD/EUR, so this confirmation stuff does not apply. We just need to realize that this kind signals work better when it goes with the prevailing trend, and need confirmation on the opposite direction.

Besides these patterns, we should always pay attention to the shadows of the candles. Shadows show the result of the fight between bulls and bears: If we see several consecutive candles with long upper shadows, although the trend is still up, those shadows are a sign that bears are starting to win.

Besides these patterns, we should always pay attention to the shadows of the candles. Shadows show the result of the fight between bulls and bears: If we see several consecutive candles with long upper shadows, although the trend is still up, those shadows are a sign that bears are starting to win.

On the other side, if there’s a downtrend, but long lower shadows with relatively small bodies start appearing, the continuation of the downtrend is under suspicion.

who is in command - forex academyUsually, by just paying attention to where the most longer shadows are drawn, we get the information of who’s in command, although not always this translates into a trend change, it just adds volatility. If we follow, as if it were our polar star, the proper reward to risk ratio and use the oversold/overbought indications set by %R, or Stochastics. we may survive those siren chants…

Hammer:

Honey, I’d really like to nail you…

An umbrella-like formation that’s present at support levels, signaling the end of a downward leg.

Pattern sentiment:

After a long time in a downtrend, the last bulls give up and sell. The latest bears take the byte and price go down on a climax of selling pressure; but then, there’s almost no one who hasn’t sold. Therefore bulls are the majority and prices start to climb back to the opening level. It may happen that short positions are being closed by traders realizing they were wrong, adding steam to the bull side. The longer the shadow, the weaker the position for the short side.

Sometimes, two or three consecutive hammers are drawing a double or triple bottom. Those are excellent signals of a trend change.

hammer - pattern sentiment

Criteria for trading hammers:

  1. The reward to risk must be higher than 2
  2. %R shows an oversold condition
  3. The lower shadow must be at least twice as long as its body
  4. The real body should be at the top of the range of the candle,  The color isn’t important, although a white body is more bullish.
  5. Almost no upper shadow.
  6. Large volume on the hammer bar.
  7. The entry on the next candle should be above the high of the hammer.
  8. A gap up is an enhancement, but not so much that spoils the reward to risk ratio below two.

Hanging Man:

Darling, suddenly, I feel quite vulnerable…

A hanging man has the same look as a hammer, but placed at the (hopefully) top of an ascending trend.

Criteria for trading Hanging Man:

  1. The reward be higher than 2x the risk
  2. % R is showing an overbought condition
  3. The lower shadow must be at least twice as long as its body
  4. The real body should be at the top of the range of the candle. The color isn’t important, although a black body is more bearish
  5. Almost no upper shadow.
  6. Large volume on the hammer bar.
  7. The entry point must be below the hanging man’s low.
  8. A gap up on that day and then a gap down is a strong signal
  9. Alternatively, a confirmation with a strong down day, or gap down, that goes below the hanging man’s low.
  10. Alternatively, wait for a failed test of the highs, forming a double top

Hanging Man

Hanging Man Pattern sentiment:

After a long uptrend, that day the price opens higher, but the bulls are hesitating and some traders take profits pushing on the bear side, so the price declines below the opening level. At the end of the session, the buyers start to move the price up again, to the opening level, or even higher.

This seems to demonstrate that the bulls are still in control, but it also shows that traders start taking profits, and even, short sellers are entering with more than reasonable reward to risk scenarios.

When the next candle moves below the previous low, bulls start to unload their positions at sell stops, adding fire to the downward pressure.

The Doji Star     

Stars can’t shine without darkness…
This pattern shows when the open and the close prices are the same, forming a horizontal line. That implies that bulls and bears are at an impasse. It’s an important alert when a trend has travelled
long.The Doji Star     

Perfect dojis hardly happen, and on intraday time frames, much less. Most of the cases are tiny hammers or small bodied candles, but when it happens, we should pay attention, and close a position at the violation of the low (or the high when shorts) of the doji candle.

Two special kinds of dojis justify being mentioned:

Gravestone Doji (Tohba)

The Gravestone is formed when open and close prices are the at low of the day. According to Stephen Bigalow, the Japanese analogy “it represents those who have died in the battle. The victories of the day are lost by the end of the day.”

It works better, according to sources, showing bottom reversals than tops. But it’s a significant indecision, with plenty psychological weight.

Gravestone Doji (Tohba)

Fig 8: Gravestone Doji

Dragonfly Doji (Tonbo)

Dragonfly dojis occur when the opening and closing prices happen, both, at the high of the day, and are hammers and hanging man variants.

Dragonfly Doji (Tonbo)

Fig 9: Dragonfly Doji

Engulfing patterns (Tsutsumi)

The issue isn’t penetration, but engulfing…(Amy Schumer)

The engulfing pattern is a major reversal pattern. Seen in a 2x timeframe
it may be pictured as an inverted hammer or doji.

The bullish engulfing candle opens lower than previous day’s close and closes higher than the previous open, engulfing the whole body of the previous one.bullish engulfing

The bearish engulfing is a mirror image of the first one.bearish engulfing

Fig 10a and 10.b: Engulfing patterns

Note: On intraday charts, the engulfing candle hardly opens lower/higher than the previous candle, but it must close higher/lower, engulfing the entire body of the previous candle.

Criteria to trade an engulfing formation:

  1. A reward two times the risk has been established
  2. The body of the bullish candle closes higher/lower than the open of the previous bear/bull candle.
  3. Prices have been in a trend and %R shows they are in oversold/overbought territory signal.
  4. The body of the engulfing candle is of opposite color, except when engulfing small bodied candles.
  5. A large body engulfing small bodies is a positive sign
  6. Large volume on the engulfing candle
  7. The body engulfs more than one body.
  8. An opening gap after the pattern (but not much of that 2:1 Reward is taken)

Pattern sentiment

After a decline of some proportion the price opens at or lower than the previous candle, but after testing or crossing the lows of the previous candle and taking all stops, the bulls take command and move the price up, and above last day’s open. That forces the bears to close positions, adding more fuel to the bull move. Now, the change in sentiment shifts and traders seek to test the highs of the previous bearish move.

Pattern sentiment

Piercing Patterns

Piercing is everything about holes… so you’re telling me you find them attractive!
Piercing pattern (bullish) and Dark cloud cover(bearish) are specular patterns between them, so for pairs trading, they are the same pattern, just on a reversed pair chart.

The piercing pattern is a two candle pattern. The first one is a bearish candle after a downtrend has traveled for some time. The second candle starts below the low of its neighbor, and it closes above the middle of it near or at the high of the range.Piercing Patterns

The condition that the open should be below the low of the previous bear candle hardly happens on intraday charts, therefore, with those, it’s enough that it starts at the previous open, closing above the middle of it, with a strong close.

Piercing Pattern sentiment:

Criteria to trade a Piercing Pattern

  1. A reward 2 times the risk has been evaluated
  2. %R shows oversold levels and moves up at the close of the white candle
  3. There had been a downward movement with a final long black candle
  4. the actual candle is white, crossed the middle of the black candle and closes at its highs
  5. A gap down on the white candle adds power to the signal
  6. The higher the close, the better
  7. A large volume is an enhancement.

Piercing Pattern sentiment:

After a continuous decline, the bearish sentiment is extreme. The last long candle shows a lot of selling activity. The next candle gaps down, continuing down for a while. Finally, everyone willing to sell has already sold and what’s left is traders thinking that this level might be a good price to buy. Short sellers start to close their positions, as well, so the price starts to go up. The end of the bar is strong, retracing half or more of the previous candle and closing at its highs. When the next candle continues the up-trend, late short positions losing money, start being closed and the move accelerates, therefore the downward move is questioned.

Criteria to trade a Dark Cloud Cover

Since Dark Cloud Cover is the specular image of a Piecing Pattern, just translate the piecing pattern criteria to its specular condition.

Harami

I’m never having babies. I hear they take nine months to download…(Liza Sabater 🇵🇷👸🏾 On Twitter: Retrieved from https://twitter.com/blogdiva/status/2753105115)

Steve Nison describes a Harami as a small real body that is contained within what the Japanese call “an unusual long black or white body”.

Harami is a Japanese word for “pregnant woman”. The large body, being the mother of the small one, the baby. It’s a sign of market lacking steam. As we see on the fig 14, haramis happen quite often during trends. Candles of profit taking, and testing. Short term trading has made this pattern almost unworthy.

Therefore a Harami, by itself, isn’t reason enough to take an opposite position to the main trend. More confirming evidence is required, such as a third candle taking the lows of the mother candle, depicting a kind of morning or evening star pattern.Harami

Morning/evening Stars.

I’m not bad, I just have a lousy publicist (Lucifer)

The Morning star is a bottom reversal signal. Looking at it in a longer time frame may be seen as a hammer, or, even, a Dragonfly Doji.  It’s composed of three elements. After a down leg, the last candle is a long, black, candle. Next candle is a gap down, and a small body is formed. The last day, there’s a gap up and a long white candle that closes near the open of the first long and black candle.Morning/evening Stars.

Note:  Intraday charts do not show gaps. Therefore, the second body may be the baby of a Harami or a small black body below the first black candle. It also may appear two or three small bodies and then the white candle. That is an indication that a bottom may have been reached. This rounded bottom is a nice place to enter after the long white candle crosses above their highs.

Criteria to trade a Morning Star.

  1. Morning Star patternThere had been a downtrend easily visible on the chart
  2. The %R is in oversold condition
  3. After a bottoming black candle, and one or more small bodies with lower tails, the last white candle closes near the opening of the black candle and in its top range
  4. A reward 2X the risk is spotted from the entry point to the last major top or resistance
  5. If we see higher volume than usual in the black and white candles, the better.
  6. If there is a long down tail in the small body the signal is stronger.

Market sentiment in a Morning Star pattern:

A strong correction has happened and long positions acquired during the down move started to think that they went wrong and close their positions. A selloff begins forming a long black body. Next day (or candle) there is a gap, but at those levels, due to supports being hit and that most of the selling ended, the price doesn’t move much. The third candle starts moving up, pushed by the bulls, that realize there is a huge reward to risk guaranteed by the previous small body. This up move forces bear traders to close their short positions, adding strength to the upward momentum.Morning Star trade

The Evening Star is an inverted Morning Star, so for forex, pairs traders, it’s exactly the same pattern on an inverted pair. For example, an evening star on the EUR/USD is a Morning Star on the fictitious USD/EUR.

Criteria to trade an Evening Star.

  1. There had been an uptrend easily visible on the chart
  2. The %R is in overbought condition
  3. After a long white candle and one or more small bodies with higher tails, the last black candle closes near the opening of the white candle and in its bottom range
  4. A reward 2X the risk is spotted from the entry point to the last major bottom or support
  5. If we see higher volume than usual in the black and white candles, the better.
  6. If there is a long upper tail in the small body the signal is stronger.

Market sentiment in an Evening Star pattern

The Evening Star is a signal that warns the top of an upward move has been reached. There is a first long white candle, showing buying exuberance, then a happy gap up that fails to go further; and, finally, a painful gap down and a long dark candle.  You may imagine the feelings of the trader who bought at that island body on the top watching a gap down and a large down-candle. That’s exactly the feeling the trader had when he traded short at the small body of the Morning Star and watched the gap up and a long white candle.  Thus, after that gap down, bulls give up their hope for the continuation of the trend and close their positions at a loss and increasing the selling pressure.

two profitable evening star trades

Kicker Signal (Keri Ashi)

I don’t need a kicker to win my super bowls… O God, please don’t kick me in the nuts!

Keri Ashi is a powerful signal, although it almost never appears on intraday charts, and when it does, its cause is a surprise news creating huge instantaneous volatility, whose Reward to risk ratio is too poor to trade at once. Likewise, due to the 24/7 nature of the currency trading, almost never shows on daily charts, as well; although it may appear in the futures market of that pair.Kicker Signal (Keri Ashi)

A bullish Kicker signal is made of a long black candle and a long white candle whose open is above the body of the previous black one.  The bearish kicker is its reverse: A long white candle followed by a long black candle that opens below the open of the white one. It’s a radical shift in beliefs of traders about the value if one of the members of the pair that triggers an instantaneous thrust in price.

Besides its rarity in currencies trading, the reward to risk is quite poor, and almost always is a consequence from a news event, so its value is linked to the event’s value as trend changer. Therefore, Kickers are worth only to recognize a major trend shift.

Shooting stars (Nagare Boshi)

Hey, Arthur, check it out: A shooting star. That’s a sure sign of good luck, my friend! (Anonymous Dinosaur)

We already talked about the value of the shadows, wicks or tails to assess who’s controlling the candle: the bulls or the bears. Shooting stars are small bodied-candles with long Shooting stars (Nagare Boshi)upper tails.  The Japanese named that way by its similitude to a shooting star. It shows that the bears have controlled the candle. It may be produced by profit taking or fresh short position, but the bullish sentiment weakened. It’s body color isn’t important. As a reversal signal, it must come after a rally. At tops, it’s usually part of the Evening Star formation, so a shooting star is a very good warning signal to close our position if the next candle travels below the low of the star’s low.

When a shooting star happens at a bottom is called Inverted Hammer, and shows evidence that the bulls started to get in, although, finally bears won. Thus, the downtrend is in question. Under this circumstance, our stops should be tightened or profit must be taken.

A positive candle, after it will picture a kind of morning star signal and may be traded similarly. On fig 20 there’s one example of an inverted hammer at the bottom of the downward leg.


Summary:

Candlestick charts show information that’s hidden in bar charts. Candlestick patterns are visually more evident than in bar style, but, mainly, the information shown is very similar.

The main characteristic of a good reversal signal is that in one, two, three -or more- candles price undoes the road taken by a long initial candle of opposite color at the end of a trend of a certain length. Thus, a candlestick pattern looks similar to a small bodied-candle with long shadow or tail, if observed on a longer time frame.

A long tail, then, is always worth paying attention to. It shows who won the battle in that particular candle and is an early warning of a reversal, especially near supports or resistance levels.

The use of Stochastics or Williams %R as companion indicator enhances the value of the pattern information and its probability as a winning trade.

As a trading philosophy, we should always weight the potential reward for the risk we take. Therefore, we should qualify the pattern by the reward it shows in comparison to its risk.


 

References:

  • Profitable Candlestick Trading, Stephen Bigalow
  • Japanese Candlestick Charting Techniques, Steve Nison

All Images were taken using Multicharts 11 trading platform, and MetaTrader 4

 ©Forex.Academy
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Forex Educational Library

Trading Is An Adult Game: Spicy & Risky

Introduction

In this series, we will explore the basic aspects of charts and indicators; what they are, how to use them, what they represent, etc. All of this with a humorous attitude, but with the depth and seriousness to make it a good reference for people who are new in trading.

So, let’s begin:

Mom, What’s Technical Analysis (TA)?

Honey, that’s too easy for me!  Fundamentals tell you a lot about love, but behavior tells if you’re wrong a lot sooner!

The term “technical analysis” (hereafter, “TA”) came from the idea that price movement is all that is needed to make a trading decision; that fundamental issues are incorporated into price history. The idea of price analysis is attributed to Charles Dow around 1900, the creator of the Dow theory. Kicking off from there, TA started to grow in importance to traders in the sense that price discounts all new information, including price trending, price confirmation, support, resistance, divergence, volume confirming price, etc.

To TA practitioners, the price represents a consensus of value. It’s the price at which somebody is willing to buy and another person ready to sell. That consensus depends on the different beliefs of the person on the market. The seller believes that price will go down in the future, while the buyer believes that it will go up. There’s a third category of traders: Traders that are waiting to see a price level to make a decision.

If an equivalent number of buyers and sellers collide, then price fluctuate around some consensus of value. When, due to some new information, that equilibrium is broken, price moves until a new equilibrium is found.

The historical record of prices is what draws chart patterns the investor or trader is willing to study, to determine the right way to enter a market.

Charts come in different flavors: line, bar, and candlestick chart are the most common; but there are other useful types, as well:

Technical Analysis and The Basics Of Charting

Fig 1. 5 min candlestick chart

Point: this chart type forgets the time. A new bar is created if a delta of price points is bettered. Very handy to get rid of the noise on tight price fluctuations.

10-point candlestick chart

Fig 2. 10-point candlestick chart

Ticks: It forgets time, as well. A new bar is created after a defined number of ticks.

80-tick candlestick chart

Fig 3. 80-tick candlestick chart

Changes: Also forgetting time, a new bar is made after a predetermined number of price changes.

80-changes candlestick chart

Fig 4. 80-changes candlestick chart (same segment as the tick chart)

Renko: This chart type ignores time and focuses on price changes that move further than a defined price delta.

10.point Renko chart

Fig 5. 10.point Renko chart (same segment as the tick chart)

Point and figure: It consists of X’s and O’s columns. X’s represent rising prices, while O’s, falling prices.

Point and figure chart

Fig 6. Point and figure chart (same segment as the tick chart)

Kagi: It draws lines of price action in columns that represent price movements. It’s a different way to represent what point and figure charts does.

Kagi chart

Fig 7. Kagi chart (same segment as the tick chart)

Three-line break: White and black blocks of varying heights. Using the closing price a white reversal block is added if price exceeds the previous 3-block  high, and a black block reversal is added is price reaches a new 3-block low. A new line is drawn when a new high or low is made.

Three-line break chart

Fig 8. Three-line break chart (same segment as the tick chart)


Daddy, What’s a Bar?

Honey, a bar is a nice idea, but don’t take it too seriously…

Bar anatomyFig 9: Bar anatomy

A bar is a summary graph of what happened in a time-lapse. It consists of four price fields, and, usually, there’s also a volume field that’s plotted below the price plot.

The four fields are important moments during the time-lapse the bar represents: The open price, the highest and the lowest prices, and the last price (close), summarized in four letters: OHLC

Bars can be created in any time frame. From one-second bars to hourly, daily, weekly, monthly,  or quarterly bars. A higher time frame bar is constructed using smaller time frame bar data. 1-minute bars from 30-second bars, or hourly bars from minute bars, for instance.

A single bar tells a lot of information. Price changes show the dominant beliefs and psychological frame of mind of the trader horde. Certain bar formations have special significance:

Reversal bar: The bar closes in the vicinity of the open, but the high or low is far away.  This single bar pattern represents a couple of opposing bars at a smaller timeframe:

Fig 10: Bullish reversal patternBullish reversal pattern

Fig 10: Bullish reversal pattern

Key Reversal bar: It’s a particular kind of reversal bar. A key reversal bullish bar consists of an opening below the previous close, and a close above the previous high.
It’s hardly seen in intraday charts. This combination is a strong confirmation that the downtrend has ended and an up-trend is beginning. Bearish reversals are similar, but opposite.

Key Reversal bar

Inside bar: It’s a bar that’s entirely inside the previous bar. It’s a contraction of volatility. Several of them together tighten prices on a horizontal channel:

Inside bar

Outside bar: It’s a bar whose range exceeds that of the previous bar. It shows an increase in volatility.

Outside bar

Exhaustion bar: A bullish exhaustion bar opens with a gap down and moves up and closes near its top with a long body bar, with a high volume. The gap may remain unfilled, but when there’s a lot of strength it gets filled as well. The volume confirmation and the pattern are a sign of the end of a down-trend leg. The bearish exhaustion is the opposite, starting with a gap up and traveling south.

Exhaustion bar


Mom, What’s a candlestick?

Sweetie, I’ve had a lot of fun using candlesticks! I’m sure you’ll enjoy them too when I explain to you how!

Candlesticks are a different way to depict the information present in a bar. It uses the Open, High, Low and Close of a bar to construct a figure that resembles a candlestick, that’s the explanation of its name. Candlesticks were widely used in Japan beginning a hundred years ago but were mostly ignored in western countries.

It was credited to Steve Nison its introduction to the western charts, and since then it has grown in popularity because it presented the same information as a bar chart, but it added visual information a bar couldn’t show.

A candlestick bar consists of a body part from open to close and two “shadows”, outside the body, up to the High and down to the Low price levels.Candlestick Chart

The traditional candle style was leaving the body of an ascending candle empty, and filled, if it belonged to a bearish candle. That was because charts were pencil-drawn on paper. With the advent of personal computers, charting software allows filling the body of a rising candle with a different color than on a falling candle.

The wicks, or shadows, are an important source of information when their length is similar or greater than the body. Several candlestick patterns contain large wicked candles, as we’ll see.

The Japanese say, according to Steve Nison, that the essence of price movement resides in the real body. We might say that the body contains the information part while the shadow is noise.  Observing a particular candle, we get a quick visual indication of who’s in command: bulls if there’s a green body or bears if it’s red. Another use of the candle body is a way to measure market momentum. A large body reveals momentum, while a small, or bodyless, show indecision or a fading of the momentum.

This kind of information warns about the health of a trend but is usually hidden in simple bar charts.small bodies chart

Of course, we’ll observe all patterns of a bar graph. In fig 15 there are some of them: A reversal candle, just past the dissected candle, an inside bar, and an outside bar are seen later, as well.

But Japan brought us a new batch of candlestick-based patterns with exotic and suggestive names.

Candlestick formations:

Spinning tops: Very small bodies, red or green that show a very tight trading range. It’s a sign of indecision- It may be a pause before continuing the trend or sign that the trend loses steam.

Doji: It’s a kind of spinning top, but with an extremely thin body and long wicks. It’s one of the most important single body candlestick signals. The longer the shadow, the higher its importance. Dojis are present in major tops and bottoms.

Candlestick formationsdoji

Together with an oversold indicator (oversold if it’s a doji at a potential bottom) is a very good place for a high reward to risk ratio entry.

Marubozu:  A long body candle with no shadows. Black marubozus are viewed as weak indicators. It usually appears as a final candle in a downtrend, followed by a long white candle, but If it appears after some small body candle it reveals the continuation of a trend.

White marubozus are extremely strong when appearing at the beginning of a trend change. If it appears after the trend has been for a while, it may show the last impulse of that trend and a is a warning sign of the end of that leg, or that a reversal is coming.

There are also two more varieties of marubozu: Closing Marubozu and opening marubozu.

A closing marubozu has a small shadow on the opening side and no shadow on the closing side. It shows momentum in the direction of the closing side.

marubozu

Opening Marubozus show shadows on the close side, but not on the opening side. Although it’s a strong signal, it’s not as strong as the closing marubozu.

In the next article of this series, we’ll deal with major and minor candlestick patterns.

 


References: 

Technical Analysis from A to Z, Steven B. Achelis

https://www.tradingsetupsreview.com/10-price-action-bar-patterns-must-know/

Japanese Candlestick Charting Techniques, Steve Nison

Profitable Candlestick Trading, Stephen W. Bigalow

All images were taken using Multicharts© v11 software.

©Forex.Academy

Categories
Forex Educational Library

Expectations about the Economy

Abstract

There are a great many intertwining variables that influence the current state of the economy at any given time; such as unemployment, wages, internal production, investments and many more. But with all of them, it is the expectations of people, and investors that generate certain effects, that explain the behavior of some of these variables and which will affect future conditions. For example, if people expect future interest rates to be higher, they will consume or ask for more loans in the present because in the future it will be more expensive to consume. But without rising interest rates, consumption varies today due to expectations. Another example is the expected interest rates in a country, depending on these expectations the investors decided where to place their capital without waiting for this to happen.

The economy in its short-and long-term models must consider the expectations of consumers, companies and all representative agents within an economy. To consider the subject of expectations first the interest rate variable must be introduced. Interest rates expressed in units of a national currency are referred to as nominal interest rates and these rates appear in newspapers and financial pages. Interest rates expressed in a basket of goods are called the real interest rate and are called that because it is beyond inflation and reflects the cost of acquiring goods that will be consumed by people, what is truly important.

There is an equation that establishes that the real interest rate equals (approximately) the nominal interest rate minus the expected inflation. That is why in some media, although they mention a decrease of the interest rates it is said that the interest rate is still contractionary or it may be that the nominal is lower one year than another, but that does not indicate that loans are cheaper than the previous year, so you should consider real interest rates. The interest rate directly affected by the monetary policy is the nominal interest rate. The interest rate that affects spending and production is the real interest rate. Given this difference, it will be possible to see in the news that they contradict the potential effects of monetary policy on the economy and financial markets. In the following graph, you can see the real interest rate of some countries.

Expectations about the economy

Graph 6 Real interest rate. Data were taken from the World Bank.

To summarize the issue of interest rates it should be clarified that the nominal interest rate indicates how many euros must be returned in the future to obtain a euro today and the real interest rate tells us how many goods must be returned in the future to obtain a good today. Nominal interest rates will affect investment decisions between bonds, stocks, and money, while the real interest rate will affect project investment decisions. In the short term, an increase in the growth of money leads to a decrease in both nominal interest rates and the real rate, in the medium term, an increase in the growth of money will not affect the real interest rate, but if it raises the nominal interest rate.

The bonds issued by a country are differentiated in two aspects: their risk of default and the time of their pay. There are some bonds that have better coupons than others, which are riskier by defaults and that ends up affecting the price of bonuses. But for economic purposes, this part of the article will focus on the bond term. Bonds with different times of paid have different prices and different interest rates that will be called yields. The yields of short-term bonds, usually a year or less, are termed short-term yields and, if the bonds are more than one year, they are called long-term yields.

The interesting thing about analyzing the bonds is to determine the curve of the yields and the relationship between short-term and long-term rates. The price of a one-year bond varies inversely with the nominal interest rate at one year that is in effect at the present. The price of a two-year bond depends both on the interest rate to a current year at the present, as well as the expected interest rate for the following year. The interesting thing about analyzing bond prices is that bond yields contain the same information about future interest rates as the bond yield curve fully reflects the agents ‘ expectations of the economy of a Country.

To begin the analysis, a term performance must be defined; The term yield of a bond to N years, or in other words the N years interest rate is the constant annual rate that makes the current price of the bond equal to the current value of the future interests generated by this. By examining the yields of the bonds at different times, we can deduce the expectations of the financial markets on future short-term interest rates. For example, if you want to see the expectations of the financial markets in one year you should observe two-year bonds which have included expectations about the interest rate that will be at the end of the year and observe bonds to a year of maturation.

When the yield curve has a positive slope is when long-term interest rates are higher than the short-term, financial markets expect short-term interest rates to increase in the future. When the yield curve has a negative slope long-term interest rates are lower than those of the short-term, but markets expect this situation to change and short-term interest rates fall in the future. To observe market expectations during the crises of 2000, 2007 and others, it is interesting to observe the curves of the bonds of the countries that reflect the expectations of the financial markets and the decisions that were expected to be taken by the banks Central. It is important to note that the interpretation of performance curves only focuses on expectations and in most cases the decisions of banks and market agents are unpredictable. In the following two graphs you can see the bonus yield curve. In the first graph, you can see a bond of Colombia and in the second is the types of curves of yields.

Curva de Rentabilidad TES Tasa Fija

Graph 7.   (2017, October 10) Curva de Rentabilidad TES Tasa Fija, retrieved October 10, from https://www.grupoaval.com/wps/portal/grupo-aval/aval/portal-financiero/renta-fija/tes/curva-rentabilidad

Estrategias con bonos

Graph 8. Roca E. (2013 September 23).  Estrategias con bonos. Retrieved October 10, 2017, from https://www.rankia.com/blog/erre/1963186-estrategias-bonos

Leaving the issue of bonds aside, the behavior and expectations of consumers and businesses will now be analyzed. These two market agents always respond to their expectations about the future. In economic models, you have a consumer who is extremely far-sighted about the future so consumers will always be thinking about what affects their consumption in the following periods. While not all consumers are like that, in reality, it is a simplification that helps to understand the formation of expectations and how they would respond to an external shock.

To understand consumption decisions, it is essential to take an intertemporal perspective because what a consumer spends and borrows today will impact their future consumption. It is assumed that individuals are rational but in the models, it is assumed that the individuals are identical to simplify the models. According to the theoretical models, consumers are very sensitive to variations in income. An explanation of this is that credit is not available to the whole population so if income disappears or decreases it will have an immediate effect on consumption as there would be liquidity restrictions. Or in another example, before the tax rate reduction is announced soon, consumers can anticipate a future increase in their income because consumption will increase at the present. These effects on consumption depend a lot on the type of agents that there are because in some countries there are more wealthy people and they do not have liquidity restrictions when compared with another country where much of the population have problems to access to credit or income is very low.

Consumption probably varies more than the current income. For example, if you have the expectations that the decrease in your income is permanent your consumption will fall in the same proportion. But if the consumer believes that the effect is transient, they will adjust their consumption less. In a recession, consumption does not adjust to the same magnitude in which the income decreases because when a rational consumer knows that it is a temporary shock and the economy will end up retaking its natural level of production. The same happens during the expansions since the rent can increase but it is not proportional to the increase of consumption because it is a momentary shock.

It should be considered that consumption probably varies, although the current income does not vary. Presidential elections, changes in Congress, or changes in people’s expectations of the performance of the economy or international relations can affect consumption without the income being affected. Even some recessions are exacerbated by people’s expectations of a crisis greater than that which exists

It will now be analyzed how companies make their decisions depending on the expectations they have. As mentioned earlier in this article investment decisions by companies depend on the real interest rate differently from the way people do in considering the nominal interest rate. Corporate decisions also depend on household consumption, sales, and expectations. A company when it is going to invest in machinery and capital to develop its activities more efficiently must make a comparison. The companies must first calculate the expected value of the benefits that the acquisition of that machinery would bring, and then compare this to the costs incurred in buying that machine.

In short, if the company believes in its expectations that the benefits in the future will be greater than the costs of its investment, then it will decide to invest. The higher the actual and expected real interest rate, the lower the expected value of benefits and this will reduce the investment the company makes. The sum of the real interest rate and depreciation is called the cost of capital use and they have adversely affected the investment decision of the companies.

If a company experiences an increase in sales that is believed to be permanent, the expected value of the benefits will also increase what will lead to an increase in investment. But it’s similar to what happens to consumption, the investment does not respond in the same magnitude as sales as the investment is not continuous as can be the consumption. Once a new technology has been implemented, the company has no incentive to continue investing beyond a certain equilibrium point. That is why it can be concluded that investment is much more volatile than consumption, although they respond in the same way to external factors such as recessions and economic booms.

If monetary expansion leads financial investors, businesses, and consumers to revise their expectations of future interest rates and future production, monetary expansion has an influence on economic output, but if the expectations do not change, central banks will not have good tools to affect production as they have small effects on the economy. If a change in monetary policy does not surprise the agents of the economy, expectations will not change and production along with other variables are not affected. The effects can be deeper or not in expectations, but it does not mean that expectations are random and erratic. Economists assume that there are rational expectations in their models and on this basis monetary policies are formulated.

Forex Academy

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Profitable Trading Chapter III: Chart patterns

Introduction

In the early days of technical analysis, charts were drawn by hand. Personal computers came much later. Traders and investors patiently drew their charts one day, hour or minute at a time. Indicators such as moving averages were calculated by hand. Sophisticated indicators- MACD, Bollinger bands- were mostly absent.

Under those conditions, traders focused on chart patterns for clues about market turns or continuations. Thus, patterns such as head and shoulders, triple bottoms, triangles, flags and so forth started being part of traders’ arsenal.

Actually, computer-oriented traders pay more attention to sophisticated indicators than to patterns, but there are many successful traders that sing praises to the good old method of pattern trading, because, they say, it’s faster than those lag-ish indicators.

One bar patterns

The easiest to recognize, of all chart patterns, is one bar patterns: Gaps, one bar reversal, spikes, inside and outside days.

Gaps and spikes

Gaps don’t occur in the 24/7 currency markets. Or do they? That depends on how we define a gap.

Gaps and spikes

And what is a gap: Technically it is a hole in the prices due to a price change that happened between the previous close and the open.

In order to make it extensive for its use in the currency markets lets define gap as a price shock. An extensive bar or a spike such as one that is caused by a news event.

Fig 1.a shows a gap on a daily chart of a stock. Fig 1.b shows the same gap on a weekly chart as an elongated candle. It would be an elongated candle on its daily chart, too, if its open were drawn near the previous close.

The important factor is the traders’ sentiment about the price shock, not the way we draw it on a chart.

Using our brand new definition, a gap in the currency markets is a spike, a huge candle, produced by a news event that might have been a hole if the market had been closed during that event.spike in the usd/gbp hourly chart

This pattern is the product of a large volatility jump, and it’s usually a local top or bottom. Thus, it’s often followed by a pullback, sometimes very large, as we may see in fig 1.d.

The majority of spikes show the direction of a future leg. It may be the same as the current leg or opposite to it. In any case, the gap is an indication of trend direction, at least mid-term. Although, the intelligent trader knew he must wait for a low-risk entry on a breakout or breakdown of the pullback leg.

Unexperienced traders must avoid trading events such as these. Money is made with method and discipline, not by greedy entries.spike with pullback fill

Reversal bars

 

Reversal barsA bullish reversal bar is a bar with its low making a new low but closing higher.  A bearish reversal is a bar where there’s a new high but with the closing lower. Those reversals aren’t significant unless in context with highly oversold or overbought situations.

 

A bullish key reversal is a bar with new lows and then making a high higher high, closing, also, higher than the previous bar’s
close. The bearish key reversal is its specular pattern.

bearish key reversal


Important reversal patterns

From lower to higher or vice versa.

The most important pattern for determining a trend reversal has already been dealt with. Since a trend reversal -for instance from bear to bull trend- is going from lower highs and lows to higher highs and lows, this is a pattern, and it’s one of most important for the detection of a trend change.

From lower to higher or vice versa

It’s possible that we may detect a failure to new lows, but we don’t start getting higher highs and lows. Thus, we have shifted from a downtrend to a sideways market, that may resume a downtrend or break up. We still don’t know. But the bear trend has stopped.

Head-and-Shoulders

It’s a top formation. The inverted Head-and-Shoulders is its specular counterpart as a bottom formation.

Head-and-Shoulders

Stages of a Head-and-Shoulders pattern

  1. A strong rally followed by a minor pullback forms the left shoulder.
  2. Another high-volume rally reaches a higher high and, then, pulls back to or near to the previous low
  3. A third rally that doesn’t make new highs with a downward leg that push prices below those two previous lows, to D
  4. Prices are below the neckline that acts as resistance. Prices go up a bit, touching the neckline but failing to hold and descending to fresh lows.

Breakout: The confirmatory stage is D, with prices below the neckline failing to go back up.

The importance of the volume:

The majority of the volume appears during A and B legs, the highest occurring during B rally, and greatly diminished volume during the formation of the right shoulder.

Fig.4.b shows an inverted head-and-shoulders bottom, where the neckline isn’t horizontal, and a typical volume pattern of stronger trading before B and lightening after it.

The importance of the volume

Broadening bottoms

Appearance:  Drawing trend lines at tops and bottoms of the formation it gives the impression of the silhouette of an ancient turntable’s horn.

Broadening bottoms

Identification:

Price Trend: declining.

Shape: Price seems an oscillating pattern with increasing amplitude.

Trend lines: The two trend lines across minor tops and bottoms diverge from each other. The upper trend line must be ascending or horizontal. The bottom one should be pointing down.

Touches: According to Thomas N. Bukowski’s book on patterns, a broadening bottom needs at least two minor highs and two minor lows to validate the pattern.

Volume: Volume follows price. When the price falls so does volume, and when price moves up volume increases, as well.

How to trade it: When the channel is wide enough, buy near the lower trend line and sell on weakness or when it touches the higher trend line. Also buy a pullback after the breakout or breakdown, if it fails.

Broadening tops

Appearance:  Similar to a widening bottom.

Broadening tops

Identification:

Price Trend: Ascending.

Shape: Price seems an oscillating pattern with increasing amplitude.

Trend lines: The two trend lines across minor tops and bottoms diverge from each other. The upper trend line must be ascending. The bottom line should be pointing down or horizontal.

Touches: According to Thomas N. Bukowski’s book on patterns, a broadening top needs at least two minor highs and two minor lows to validate the pattern.

Volume: Volume follows price. When the price falls so does the volume, and when price moves up volume increases, as well.

How to trade it: This formation may show a trend change, but sometimes is just a trend continuation. When the channel is wide enough, sell near the higher trend line and buy when it touches the lower trend line. Also, buy the breakdown from the lower trend line.

Broadening ascending wedges

Appearance: similar to a broadening top, but the lower trend line is, also, ascending.

Broadening ascending wedges

Identification:

Price Trend: ascending.

Trend lines: The top line is steeper but the lower one is also trending up.

Touches: It needs at least three distinct touches (or close to it) on each of the lines.

Breakdown: In the majority of times there is a breakdown.

How to trade it: As in the case of the other broadening tops, sell near the higher trend line. Also, sell if it fails to make a new high. Sell the breakdown, as well.

Broadening descending wedges:

Appearance: similar to a broadening bottom, but the higher trend line is, also, descending.

Broadening descending wedges

Identification:

Price Trend: It usually acts as consolidation of an upward trend.

Trend lines: Both head down, but the bottom line is steeper.

Touches: It needs at least two distinct touches on each line.

Breakout: In the majority of times there is a breakout.

How to trade it: As in the case of the other broadening bottoms, buy near the lower trend line. Also, buy if it fails to make a new low. Buy the breakout, as well.

Double bottoms

A double bottom is the first confirmation that the trend has stopped making new lower lows and lower highs. After a new low is made, the following bars draw a small rally to the recent highs.

Then, the price experiences a pull-down from that resistance level, to test the recent lows: The test resolves to the upside, breaking the recent resistance up to fresh highs, starting an upward trend.

Double bottoms

Identification:

Price Trend: downward.

Bottom shape: Both bottoms are at the same level or close to it. The shadow of the second low may be below the first low, but it closes above it or the next candle does it.

Confirmation: The double bottom is confirmed by a breakout of the resistance level of the formation.

How to trade it: Buy the breakout.

Double tops

Double topsA double top is the specular image of a double bottom. Price was on an uptrend and made a new local top. Then it pulled back to a local support level, and, after, it rallied again but failed to break the recent top and fell down, breaking down that local support to make fresh lows.

Price trend: upward.

Top Shape: both highs are at the same level or close to it.

Confirmation: The double top is confirmed when the support level is broken down.

How to trade it: Sell the breakdown.

Triple Bottoms

Triple bottoms are a form of oscillation. Not only present themselves after a long downward trend, but it’s usually three or more bottoms in sideways channels, or in reactive legs during an uptrend.

They are more reliable as a continuation pattern, on a bull trend than as counter-trend signal in bear markets.

Triple Bottoms

Price trend: Preferably upward

Bottoms: Three bottoms at similar levels. It helps that the second and third bottoms didn’t touch the first one.

Confirmation: Price breaks up the confirmation line.

How to trade: Wait for a pullback to support (confirmation line) and buy the second breakout.

Triple tops:

A Triple top is good trend continuation signal on a bear trend, after a pullback rally.

Triple tops

Appearance: Three distinct highs well separated. The peaks present sharp spikes.

Tops: The price variation between peaks is minor.

Confirmation: Prices must go below the lowest low in the formation.

How to trade: The breakdown risk is too high. Wait for a pullback to trade.

Rounding bottoms

Rounding bottoms and saucers are synonyms. This pattern, that’s supposedly trend reversal is so plagued by “surprise” failures that we hardly may call them “bottoms” at all. More usually, these formations appear during uptrends as a pullback, after which the trend resumes.

Rounding bottoms

Bottom Shape: The bodies on the downtrend candle get smaller, and then a bottom is formed (no fresh lows). Then a breakout happens with a tiny rally that holds the breaking level. Forming higher lows. A potential pattern of higher highs and higher lows emerges.

Confirmation: The second breakout to new highs, confirms the new leg.

How to trade it: Buy the first or the second breakout, after the small pullback. The second one is safer.

Rounding tops:

Yet another false pattern. Thomas Bulkowski writes about this pattern: “When is a top not a top? When it is a rounding top and prices break out upward 53% of the time. I like to refer to this pattern not as a rounding top, but as a rounding turn (RdT).

Rounding tops

Appearance: After a rally lost its steam, price moves on a pullback that erases most of it.  The last local bottom holds. A rally move proceeds up again.

Confirmation: Prices break the resistance of a confirmation line drawn at the high of the rounded formation.

How to trade it: To me, it’s a reactive leg in an uptrend, thus, if a new leg is confirmed by breaking a trend line drawn on the highs of the pullback segment, then a buy order may be entered at a breakout of recent highs. Taking the breakout of the confirmation line is too risky because we still don’t know if it will repeat the previous pullback by forming a sideways channel. Therefore, it’s better to wait for another pullback past the breakout of the confirmation line. See Fig. 13 for clarification.

Continuation patterns

Flags

A typical continuation pattern. After an impulsive leg a pause in the opposite direction. It’s usually a nice entry point after an impulsive leg.

Flags - Forex Academy

Appearance: Flags are fast oscillating symmetrical patterns with a downward slope in bull trends and upward one in downtrends. A typical corrective leg.

Volume: Volume fades, receding with the trend.

Confirmation: Breakout, during uptrends, or breakdown on downtrends.

How to trade it: Enter the breakout, following the previous trend. Do not enter if it’s in opposite direction to the trend, such as, in fig.14, the 4th ascending flag.

Pennants

Pennants are a flag variation, but with its trend lines converging.

Pennants

The fact that the volatility fades on a pennant, makes it an excellent spot for good reward to risk trades.

Appearance: Flags are fast oscillating converging patterns. The slope usually is contrary to the main trend, but it may be horizontal or in the same direction to the main trend.

Volume: Volume fades, receding with the trend.

Confirmation: Breakout, during uptrends, or breakdown on downtrends.

How to trade it: Enter the breakout, following the previous trend.

Triangles and Wedges:

Triangles and Wedges are like flags and pennants. The difference is the undulations are more noticeable and wide, so they take longer to develop. The difference between triangles and Wedges is a bit arbitrary: Triangles have lower highs and higher lows. Wedges may not because their tilt is higher, upward or downward.

They are reactive waves that, usually, end with the burst of another impulsive wave in the same direction as the previous one. Anyhow beware: All reactive waves, including wedges, are fights between bulls and bears, so its ending is uncertain.

There are upward and downward wedges. The fact that it usually breaks out in the opposite direction of its own is its reactive nature that opposes the main trend.

Wedge - Forex Academy

Appearance: Undulations with fading strength. Its upper and lower trend lines converge

Volume: Volume fades, receding with time.

Confirmation: Breakout, during uptrends, or breakdown on downtrends.

How to trade it: Enter the breakout, following the previous trend.

This covers all important chart patterns and formations available.

The main common feature is an ending with a breakout. To trade them we must evaluate carefully the reward-to-risk situation and, if not satisfactory, wait for the end of the first impulse and enter at the end of a continuation pattern – usually a flag or pennant.

There are plenty of variations of these patterns, and, sometimes, they’re very hard to spot. But if we keep our trendlines touching the local tops or highs on downward legs, and at the local lows during uptrends, we’ll be able to spot trend breakouts, judge where we are, and if it’s a good risk to reward, take appropriate actions. See fig 19.

Profitable Trading Chapter III: Chart patterns

Next chapter, well be dealing with computerized indicators, such as MACD, RSI, Stochastics, Williams %R, On balance Volume, Parabolic SAR a so on.

 


References:

Encyclopedia of Chart Patterns 2nd edition, Thomas N. Bulkowski

Trading systems and Methods 5th edition, Perry Kaufman

 

 ©Forex.Academy
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Forex Educational Library

Profitable Trading – Chapter 1: Market Anatomy

Introduction

In their pursuit of a profitable system or strategy, traders look at past behaviors as a forward indicator of prices.

Indeed, if the price movement is an objective manifestation of the average trader sentiment, then wherever in the future the same sentiment arises, a pattern might take place with a shape similar to the one drawn in the past.  From this belief, it follows that the study of patterns may be of help to find entry and exit points in our pursuit of profits.

There is a catch, though. The human brain is a natural pattern recognizing system. We see patterns everywhere. In ordinary life, that’s the way we recognize things: A round tire, a rectangular table, or a staircase shape. When random events enter into the equation, we continue seeing patterns, although not always, they may exist. Nowadays, Technical Analysis is evolving toward a more evidence-based framework, pushed by authors such as David Aronson, Robert Carver, and others.

I’ll deal with this framework in a future article series, but, throughout the following issues, we’ll deal with the standard TA framework as a foundation for the later. We will develop a base knowledge and the tools to trade the three different market types.

Please keep in mind that, here, we’re dealing with uncertain events, so nothing is 100% sure. Sometimes not even 50% sure. But our objective when trading isn’t being right, but profitable, so as long as our overall performance is positive, the technical signal is good.

Market classification

There are three basic market types:

  • Bull markets: broad ascending trends with sporadic retracements or sideways moves or channels.
  • Bear markets: broad, descending trends with sideways movements or strong and fast bear-trap retracements.

Of course, when trading currency pairs, a bearish market in one pair is a bullish market in its inverse pair, so the tools for bear and bull markets tend to be somewhat similar, and the situations encountered quite symmetric.

Sometimes, lateral markets aren’t perfectly horizontal. The main feature of a lateral market is its increased volatility and noise. The other trait is the seemingly cyclic nature of their movements.

Anatomy of a trend

In Fig 1, we may observe a bullish trend on the EUR/AUD, back in 2015. We may see that prices move in waves. However, the crest of a new wave goes higher than its preceding one. Similarly, its valley is more elevated than preceding valleys, as well.

So, a practical definition of an uptrend is a price pattern with higher highs and higher lows. Consequently, a bearish trend, or downtrend, is, then, a price pattern with lower highs and lower lows.

Trends happen on any time-frame. Even a single bar might be classified as a bull, bear, or lateral. A candlestick with a long white body and short shadows is, in fact, a bullish trend in its tiny time-frame while a long black candle with small shadows accounts as a bearish trend.

Lateral movements on a single bar happen when the candle presents a small body, or none at all, together with long upper and/or lower shadows.

Phases of a bullish trend:

The wavelike pattern, present in most trends, is the result of phases of accumulation, distribution, and sale that draw two different kinds of patterns: One impulsive and one corrective. Every one of these phases accounts as a trend (or lateral channel) in a shorter time frame, which might be composed, at the same time, by shorter trends with its own phases of accumulation, distribution, and selling.

Accumulation phase:

In the later stages of a wave valley, there is accumulation by smart traders who think there is an excellent opportunity with low risk, forming a support level. Sometimes, this support is briefly broken to the downside; stops are taken, and, then, the price back up, again, above support.

After this last trick to fool the weak hands, price starts to climb, slowly at first, faster as momentum grows. In the final moments of this phase, price moves quickly, with substantial price increases on higher volume.

Distribution phase:

In the final stage of an impulsive phase, selling begins by smart profit takers, while the price is still rising, then it reaches overbought levels. A kind of barrier seems to have been established: It’s called a resistance level.

At those levels, more traders are willing to sell than buyers can manage, so price stagnates. Latest bulls don’t have the strength to raise prices beyond that point, but this new leg high is higher than the one in the previous impulsive phase.

Selling phase:

Price moves in a declining channel. Traders that went long at its highs close at a loss. Thus, the price moves down. Then, price recovers as if a new leg up might happen, just to fade again a bit lower than before. Several push-pull phases take place, its pattern like a fading oscillation.

Price reached a new support, and a new accumulation phase begins. Usually, this support is at or near the high of the previous impulse high. This stage draws a corrective pattern.

Phases of a bearish trend

In stock and futures markets, there is a marked asymmetry between bull and bear markets. The former being orderly and, usually, less volatile, except at its beginning, while its ending depicts exuberance and extremely positive expectations. Conversely, bear markets depict much higher volatility, together with fast, bear-trap rallies.

Sell-offs drives prices down much faster than when they are rising. Bear markets tend to be short and fast, losing between 20 to 70% of its value. On stocks, it may lose up to 95% of its value, as it happened to some tech stocks back in 2000 or bank stocks in 2008.

Currency pairs, by its own nature -currency prices moving against each other- make bull and bear phases symmetrical. The discussion above may have been the same if we swap the pair. So A bearish trend in a currency pair has identical stages because it’s just a bullish trend looked from the short side.

What is essential to be aware of is that the impulsive pattern, be it up or down, is the one where we could make more profits with reasonable reward to risk ratios. And the corrective leg (2), the product of a selling phase, is harder to trade and presents more mediocre rewards for its risks.

Fig 2 shows this behavior. We may discover that the Reward to risk channel on the impulsive phase (1) is much broader than on the reactive leg (2) when traded to the short side.

On the impulsive leg, the potential reward is more than 2x its risk, while to the short side, on a corrective phase, it’s less than one, even in the ideal case of taking profits at the lowest low of the channel.

This is a good example of why we should never fight the trend. Instead, we might use a corrective leg to add to a position or entering near support, that is, near the bottom of the channel.

Upside down, this example applies to a bear trend. Here, the impulsive leg, of course, is downward.

There are trends where the channel is more extensive, and both phases, impulsive and corrective, are equally profitable. But those cases are comparable to a sideways market, so the same kind of strategies applies there.

Sideways channels

A sideways channel happens when the price oscillates between two levels that seldom move or move up or down very slowly.  If the channel is wide enough, it may offer trading opportunities, although, usually, volatility is higher, so it’s harder to trade.

Fig 3 shows a sideways channel that took place in the EUR/GBP from Nov. 2016 until Jun. 2017.  Here, we observe there’s a floor level and a ceiling level, where bounces occur.  On this sideways channel, we could split every leg and consider each leg as a bull or bear trend, and go to a shorter time frame to trade it.

But not always, this may be possible. Fig 4 shows the price behavior on the USD/CHF pair for the last five months, from the end of May 2017, till the beginning of October. We may observe that the high volatility that takes place in the last two legs makes it difficult to differentiate impulsive from corrective.  There we must switch to a shorter time frame in search of better behaved impulsive patterns.

A final word about time frames. We should use a higher-order time frame as our guide to decide which side to trade on the shorter frame, then mark support and resistance levels and potential entry points and stops to assess the reward and its risk.

Levels, breakouts, support, and resistance

I’ll tell you here my personal view about levels, support, resistance, and breakouts. People trade their beliefs about the markets. Price is continuously moving in a struggle of two sides, while a third side is watching.

The fight is between those who believe it’ll go up and those thinking it’s already too high and must go down. If the believers on one side are less than on the other hand, price moves against them until a new consensus is made where both parties have similar power.

At price levels where the power of bulls and bears is similar, the price cannot move up or down any longer, until one of the parties weaken or the other gets more strength. In the first case of a price going up and then stagnating, the level is called resistance. The case of a price falling and then stopping its downfall is called support.

On the occasions when the price is pushed beyond resistance, it’s called a breakout. If the price crosses a support level to the downside, it’s called a breakdown. Sometimes the breakout or breakdown is of short duration, price resuming to its previous levels after a few bars. In such cases, it’s called a failed breakout or a failed breakdown. Thus, the passing of time confirms the breakout or breakdown. As time goes, the strength of a support or resistance level increases.

Fig 5 shows a couple of support and resistance channels with two breakouts. Please note that on the second channel, supports are located at the peaks of the sideways channel that preceded the first breakout. This is quite usual, and a persistent pattern in trading charts. Current support levels were, first, resistance levels crossed by a price breakout, becoming supports. Similarly, current resistance levels were support places that were pierced down.

Support and resistance patterns are extraordinarily useful because of their rather good predictive value. Buying at support and selling at resistance is one of the better strategies around, and not only by its success rate but also because they are locations with excellent reward to risk ratio.

Channel contractions

Channel contractions are patterns sometimes called flags and sometimes pennants, wedges, triangles, etc. Although many books about trading patterns make a differentiation between them, they are the same corrective phase, after an impulsive leg.

The critical point to remember about this type of formations is that they are excellent places to trade following the breaking direction. We don’t care which one is it. Usually, it’s a trend continuation, but that doesn’t matter much because the second most important feature of a range contraction is that at those spots, the reward to risk is increased almost double compared to its beginning point.

Fig 6 shows an example of a channel contraction, where we will be able to observe at 1 the risk at its beginning and at 2 the risk at its end. We, also, are able to see that this type of formations is a trend continuation most of the time.

In the next issue, we’ll talk about trendlines, moving averages, and channels.

That’s all for today.

©Forex.Academy

 


References:

I took some ideas from Essential Technical Analysis, Tools, and Techniques to Spot Market Trends by Leigh Stevens. The rest is mine.

All charts are taken from the MT4 trading platform.

 

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Forex Educational Library

Let Profits Run

Introduction

Aspiring traders and the majority of people think that if they had a crystal ball telling them the right entry point or the perfect stock to pick, that would be the key to succeed in the financial markets and become filthily rich. Nothing could be further from the truth.

Of course, entries are important, but no entry would save the investor or trader from market volatility. Novice investors and traders without the knowledge about how and when to exit will be wiped from their perfect purchases at the worst moment for them.

Most people focus on a frequentist way of looking at trading. Van K. Tharp calls it “the need to be right.” This need to be right pushes the investor to cut gains short and let losses run in the hope of a reversal that doesn’t always happen.

Daniel Kahneman in his book “Judgment Under Uncertainty: Heuristics and Biases” says that giving a choice of experiencing a string of small losses or one single huge loss, people prefer the second one. It also happens that what people enjoy most are frequent, although modest, gains; a lethal combination that transforms any winning system into a loser.

1.    The mathematics of profitability

What you’ll read just below is the best-kept secret in the trading industry. The real holy grail of trading that explains the importance of letting profits run (achieving high Reward to risk ratios)

The critical feature of a good system isn’t the percentage of gainers, but its expectancy (expected value E).

Expectancy is the expected value of gainers (E+) less the expected value of losers (E-)

(E+) = Sum(G)/(n+) * %Gainers

(E-) = Sum(L)/(n-) * %Losers

Sum(G) is the total dollar gain in our sample history

Sum(L) is the total dollar loss in our sample history

n+ is the number of positive trades(Gainers)

n- is the number of negative trades(Losers)

The expectancy E then is:

E = (E+) – (E-)

If E is positive, the system is good. The higher E the better the system. If E is zero or negative, the system is a loser, even if the percentage of gainers were more than 80%.

There is another angle to this. If we switch our good-old brain to the right way a good trader should think -let profits run and cut losses short- we would be rewarded with high Reward to Risk ratios.

That is the right way to make our system resilient to a decrease in its percent of gainers.

For instance, a 2:1 RR sets the break-even point at 33.33 percent gainers.

Right! We need only one winner out of every three trades to break even.  A 2.5:1 RR will bring the BE point down to 28.5% and a 3:1 RR will get it to 25%: Just one winner out of four trades.

There is no need to struggle with being right! We just need to make sure we follow our system, cut our losses short and let profits run! We can be blind chickens searching for corn grains!

Letting profits run requires patient and discipline. Jack Schwager recalls a sentence by Jesse Livermore: “it was never my thinking that made the big money for me. It was my sitting”. According to Schwager, “that’s a very appropriate comment. It means that you don’t have to be a genius, you don’t have to be smarter than anybody else, but you do need the patience to stay in the correct position.

The “letting profits run” stuff seems to be aimed at long-term traders and nothing to do with short-term ones, but it’s false. Letting profits run is a way of thinking.

You may be an intra-day trader, but at least you’ll need a strategy or trading plan that uses some entry method and some profit target. In this context, letting profits run means allowing your trade develop until your profit target is reached (or close to it) and not letting your fears of losing trigger a premature exit compromising the Reward-to-risk of your system.

2.    Trailing the trend

The usual way to let profits run is using trailing stops, and there are different methods on where to put a trailing stop.

There are systems not using any explicit trail stop. On those, the close of a position is marked by the system’s indicators. A reverse signal marks the close of the previous position.

1.- Trailing stops

The usual way to follow a trend is by trailing it with a stop at some level below the price action in long trades or above it in shorts.

The use of very tight stops at the beginning of a trade, until it proves itself, may lower the risk at the expense of being caught early. In this case, a re-entry plan should be considered.

You should be aware that to catch big trends you’re going to give back 25-35% of the profits at least once in that move. One way to deal with this is to break the position into three different chunks and set different stops and targets for each.

The use of stops based on price and pattern are common. Price movement has to do with levels. Once a level is surpassed there is a good chance it’ll continue to the next level. If price takes out a level and then flips back, like a false breakout, it’s a definite sign to close.

2.- Volatility stops

Volatility stops are located to avoid the “noise” of the market, by putting them below the recent volatility. For example, a trailing stop below the level marked by 1.5 times the 14-period average true range (ATR) keeps us in the trend most of the time.

3.- Trailing stops based on chart patterns.

Some traders use trail stops based on the latest pullback. They use the distance of that pullback as his stop distance. We may also use a pattern of higher highs and lows to put stops just below the recent low (or high if short).

When a market is moving up vertically a very reliable strategy would be a stop below the low of two to three days back (the same strategy on highs apply to down-moving markets).

4.- Trail stops and profit targets

Let’s say we started with a $500 money management stop let’s call this risk R. We should have defined a logical target at least 2R.  As our trade develops in out favor we should consider moving our stop when we reach 1R: At that point, we are left with 1R potential profit, so we should at least move the stop to break-even.

As the price approaches our target, we must consider if the price momentum is improving or stalling, If the latter happens we should tight our stop to a level consistent with the principle of 2:1 reward to risk. For example, if what’s left is 0.5R, it’s unreasonable to risk 1R. The idea is to reassess the reward to risk ratio of what we think remains as open profit.

7.- Multiple methods

Two interesting methods are mentioned in Bruce Babcock’s book “The four cardinal principles of traders”:

Steve Briese, one of the traders, interviewed by the author, states that every trader should have a price objective. The objective shall be set based on the length of the trend. An oscillator of half the length of the trend can be used for this. When it becomes overbought on a long trade it’s advisable to exit a portion of the position, letting the rest ride the trend using a trailing stop.

Jake Bernstein spoke about a channel method to generate entries, which says, it will keep the trader on the right side of a trend.

That indicator consists of a moving average channel composed of a 10-bar MA of highs and an 8-bar MA of lows. The trend turns up when two successive bars are entirely above the top MA. It remains up until two consecutive price bars are below the bottom MA. The trade is kept until the indicator reverses.

Another way to lock profits would be, he says, to buy insurance in the form of options in the other direction (puts on longs, calls on shorts).

3.    key points

  • Let profits run is a crucial ability for a successful trader
  • We should pursue Reward to risk ratios (RR) greater than two rather than high frequency of gainers at the expense of high RR.
  • The use of trail stops is a standard method to let profits run, but an exit can be triggered, also, by a reversal signal or volatility in the opposite direction.
  • Trails come in different flavors: recent retracement point, latest higher low or high, the crossing of a moving average or the price moving out of the channel in the other direction.
  • It’s ok to set targets based on the price action, but the trailing stop must be moved to where RR is within the 2:1 concept. It doesn’t make sense to risk 2 to get just 1.
  • It is advisable to computer-test our idea of trailing to optimize it.

2.    conclusions and criticism

The concept of let profits run is nice, but a short-term currency trader must adapt it and test it, through back and forward tests, and make it his own system like a suit.

The use of trails stops must be carefully tested against fixed stops and targets, but the concept of Reward to risk ratios is Key.

A system with 2.5 – 3 :1 and 40% gainers is preferable than another one with a 1:1 ratio and 65% gainers because in real markets it’s usual that a system underperforms its back-tested values.  We see that the system with 2.5:1 RR has a lot of room to allow for underperformance and, still, being profitable.

The only drawback of low percentage gainers is longer losing streaks, but, as we said, we must train our mind to be committed to the plan, and accept losing streaks. To achieve the right state of mind, we should trade small at the beginning and let the system prove itself.

 


3.    appendix

Python code to play with Reward to risk and Percent gainers

The code takes % gainers (PG) and Reward to risk ratio(RR) as inputs and computes the expectancy(E). A Break-even point between % gainers and RR happens when E approaches zero.

appendix

The figure above shows the break-even (E=0) for a 3 to 1 reward to risk ratio that is at 25% gainers.

You could work the other way around and find the percentage needed for a Reward to risk ratio less than 1. (Below the one required for RR=0.5: PG=66.6% or 2 out of 3 winning trades shall be winners to BE, as is to be expected)

reward to risk game - forex academy

 

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Is The Trend Really Your Friend?

Introduction

In this document, we will discuss the principle of Trading with the trend.  

  1. We will dissect the “Trade with the trend” discussion in:
  2. What is a trend
  3. Methods traders use to identify and trade a trend
  4. Time-frames and risk
  5. Conclusions and criticism

1.    What is a trend

Bruce Babcock, in his classic “The four cardinal principles of trading,” states his firm belief that there must exist some price trend to profit from price movement in the markets.  Even, if somebody says he trades against the trend,” those ‘countertrend’ traders are really trend traders in a shorter time frame.

The key issue for him is whether we know what our trading time frame is, whether we have specific means of identifying the trend in that time frame.

According to Babcock, a trend seems to be some relatively persistent price movement– upward or downward- linked to a time frame.

Colin Alexander, one of his interviewees, shows the main captcha with trends: “Everybody will tell you that the trend is your friend, but unless you have a working definition of what a trend is, you have a real problem with putting the idea into practical effect. […] The practicality of using this concept requires that you have criteria for determining what a trend is for the purposes of trading.”

Also, Peter Brandt touches the point: “Trading with the trend is a wonderful idea conceptually, but it’s difficult to implement in everyday practical terms.” […] “Like everything else in trading, a trend is wonderfully identifiable in hindsight, but very difficult to grasp in real-time.”

James Kneafsey adds another bit to the puzzle. He says that, in theory, trading with the trend is a noble idea, but difficult in practice because markets are not trending all the time. He states that about one-third of the time markets are in choppy mode or minor trend and another third of the time there is a neutral or sideways move.

Glen Ring says that while trading with the trend is important, it’s not the key. It’s vital to adapt your trading style to your own personality, but the most forgiven way to trade in the way to expertise is with the trend, he said.

2.    Methods traders use to identify and trade a trend

The first thing a trader needs to do, then, is to find a methodology for a definition of trend that fulfils its purpose, for him, as trading signal at the earliest possible moment (usually when the eye still doesn’t see it) with a compromise between reliability and risk size; or, more precisely, between success rate and risk to reward ratio.

  • Single moving average

Using a single moving average, a trading signal appears when the price crosses over/under the average. Another method is to wait until the average points up/down.

Good MA periods to define a trend range from 20-60 bars.

  • Two or more moving averages

Two or more moving averages

In this case, there are two variations:

  1. Moving average crossovers
  2. All the averages are pointing in the same direction.

Typical periods are 10-25 for the fast MA, and 30-60 for the slower one.

As with the case of a single MA, a price retracement to touch the slower average is an opportunity to add to the position.

Moving averages on different timeframes

  • A pattern of higher highs and higher lows is a pattern of an uptrend while lower highs and lower lows reveal a downtrend.

higher highs and lows

 

  • Breakouts of a trading range

Oscillators, such as Stochastics or Williams %R, are used by some of those traders to tighten the trigger point to a lower risk. The problem with breakouts is the risk.

If we look at the above figure, it’s evident that going long at breakout point is riskier than going long at the bottom of previous retracements, as the distance from the entry to the level where the trend is negated is rather high (entry:108335, stop: 1.04495).

The way traders deal with this is to move to a lower time frame, in this case, intraday (hourly or smaller), and look for early signs of a breakout. The other way is using an oscillator that tells the trader the market is in oversold condition (in this case it’s the beginning of the EUR/USD uptrend) but ready to resume the main trend. Here the %R is beginning to move up (see below), breaking up the -80 level, hinting a short-term leg up.

Those points are good entries, with much less risk – about 50% of the breakout risk- that may be used to double the position size for the same dollar risk. It’s useful, also, to add to the position.

 

Breakouts of a trading range

 

  • The usage of Stochastics as a proxy for moving averages:

Michael Chisholm says he prefers the use of three stochastics in three timeframes: daily, weekly and monthly. When all of them are in agreement “that’s a powerful indicator”. The use of Stochastics, he says, allows him to grasp the different market cycles better.

The usage of Stochastics as a proxy for moving averages:

 

  • Trend lines: the use of trend lines at the highs or lows of the price formation.

Trend lines: the use of trend lines at the highs or lows of the price formation.

 

 

  • Linear regression channel.

Sometimes there’s no easy way to see where the price is moving. The use of the linear regression channel may help there.

Linear regression channel

In cases such as this, when there is a downward channel, the use of an oscillator is key to profiting from sub-trends, as we see in the above figure. This kind of trade uses the old principle of “buying the dips and selling the rallies.”

We may observe, also that a 50-day MA is rather useless. A shorter 10-day MA helps, though, because the channel is wide enough.

3.    timeframes and risk

The selection of the timeframe should be part of the process of money management, rather than just an entry rule.

Each chart pattern has an objective and stop-loss point where we know the pattern failed: That is the exit point on a losing trade. If the potential loss is higher than allowed by the account size, this particular time-frame isn’t tradable, or we should trade smaller. Then, a shift to a shorter timeframe, with lower dollar-risk, allows a trader to ramp up his position size.

4.    take away points

  • We need to assess the major trend direction to improve the chances of a trade being successful
  • Even if we trade counter-trend, it’s important to know the direction of the higher-order trend
  • The trend can be discovered using several methods, but the sooner we find it, the better
  • The use of oscillators helps us spot better (less risky) entry points, and also add-to-position points
  • The choice of timeframe is directly related to risk

5.    conclusions and criticism

When trading using short timeframes, it’s advantageous the use of oscillators or a trend channel to find better entry points at the end of minor corrections.

Sometimes it happens that, when a trend has been detected, the price has already traveled a long distance, reducing the Reward to risk ratio of the potential trade too much to be worth trading. The use of pivots to assess swing points may allow traders much better entry spots and profit targets.

Trend-following systems show very high Risk to reward trades (from 3 to 10), but usually, those come at the expense of less than 40% success rate. That means it’s prone to very large losing streaks (more than 5 are common, and sometimes it goes up to 20). Trend following requires a trader with a strong discipline to execute the entries and stop-loss trades; and a firm belief in the system or it will inevitably fail.

A way to deal with this is to split the trade into 3 lots, the first with a target close to the entry, the second with a target close to a resistance/support level and a third one trailed to let it run until is taken by price action.

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Fibonacci: Analysis of Common Errors

Analysis of errors

There are certain common errors that traders can make when they are using the analysis of Fibonacci.

  1. The first most common mistake that is made when traders use Fibonacci retracements is to start the range in the wrong direction. For example, if you want to find supports, you should start from high prices and go down, and the opposite process to find resistances. Also, when you select the high price and low price for the range, you should be sure that the confluence or support area is below the current price that closes the market, otherwise you would be finding a support which the market has already broken in the past.

how to use fibonacci on charts

If you use the Fibonacci tool like in the graph above to find supports, you will not have the correct supports or confluence zones and the market will be able to easily break these levels in the future because the market does not consider them important. The trader should find important levels of the market but levels that serve to project the future behavior of the market.

fibonacci stock charts

The graph above is taken on the EUR/USD. If you use the tool from high prices to bottom to find resistances, the analysis will be carried out erroneously and these supposed resistances will not be, and the market will continue its rally. This would be a costly mistake because the stop loss zone will be activated removing the trader from the market.

  1. Do not use high or low prices for ranges that have been reached by recent oscillations, because if this is done, the market will have exceeded the Fibonacci levels and this means that the market will have broken zones of resistances or supports so that the market could do it again.

fibonacci charts

forex charts with fibonacci

In these graphs can be observed that if high prices are in a range that has been touched by the market recently at some point, the market will break the resistance and the trade will be closed by a bad analysis running the point of stop loss.

  1. Another common error is to make the ranges for Fibonacci retracements, but without a fixed point. For example, each range has an initial and final point different from each other, so the confluence areas will not be the right ones or there will not even be confluence zones so many people could say that the Fibonacci-based analyses do not serve, but they don’t realize that the exercise is wrong. Below are two graphs that show this problem and why it will lead to erroneous conclusions.

stock charts fibonacci tools

charts with fibonacci

As seen in the graphs, if this were done, the trader would have areas of support and resistance that the market does not respect because it is not a true level so the trader will assume erroneous levels and will not be managing the risks adequately. To finish, it is important to mention that like all the tools, those that use the Fibonacci series can fail in some cases, where the market does not respect areas that the market should and continues its path without rebounding. But by using these tools, most of the time it will be managing the risk correctly and dramatically increase the chances of performing winning trades, so it is important to use all the tools mentioned to confirm that the market signals are correct.

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Short-Term Economics

Abstract

It is true that the long-term is more important than the short term in the economy, but there are variables that it is important to analyze in the short term such as the internal consumption of people, investment, the trade balance, the exchange rate, and others. When people are analyzing the short term, it can be concluded why an economy is behaving in a certain way since variables that are constantly changing are analyzed. Even fiscal and monetary policies are based more on the short term because they have to correct the failures that exist in economic cycles so that the crises are not so deep and that the booms do not turn out to be negative, as in some cases it has been presented euphoria in the stock market.

In an economy there are different agents such as companies, consumers or the state and each of them makes their decisions in different ways. Therefore, if someone wants to understand how the demand for goods behaves it is necessary to decompose the aggregate production (gross domestic product) from the point of view of the different goods produced and the different buyers. When GDP is decomposed several important components can be seen.

The first component of the gross domestic product is consumption. It represents the goods and services purchased by consumers ranging from food to luxury goods. In all countries, this is the most predominant component of the gross domestic product. The second component is the investment which considers the investment in capital and the residential investment. The third component is public expenditure which represents the goods and services purchased by the state, but this component does not consider the transfers made by the state nor the interest paid by the public debt. In the chart below, you can see the spending of three countries as a percentage of the gross domestic product.

Expense

Graph 11. Expense. Data taken from the World Bank

To find the purchase of domestic goods and services should be subtracted imports and added exports. The subtraction between exports and imports is called the trade balance. If exports are higher than imports, it is said that the country has a trade surplus. If exports are lower than imports, the country is said to have a trade deficit. With what has been mentioned so far, we have analyzed some sources of purchases or sales of goods and services. To analyze the production, it must be considered that companies may have inventories from previous years or that they do not sell all their products in the current year. The difference between goods produced and goods sold in a year is called inventory investment. This investment is usually small and can be positive or negative. The following two graphs show exports and imports respectively from four countries.

Exports of goods and services

Graph 12. Exports of goods and services. Data taken from the World Bank

 

Imports of goods and services

Graph 13. Imports of goods and services Data taken from the World Bank

 

Consumption decisions depend on many factors, but the main one is available disposable income after paying taxes and other compulsory expenses. It is normal that, if disposable income increases over a long period of time, the demand for goods increases. The effect of income on consumption is called the marginal propensity to consume and this effect may be different between countries. In conclusion, consumption depends on income and taxes.

As for public expenditure is determined by the fiscal policy of each state. It is directly affected by taxes since these are the main source of income for a state. As states do not behave as companies or consumers cannot establish rules about this component since it involves policy, so it is not easy to analyze how the fiscal policy of countries

The production depends on the demand which depends on the rent. So, an increase in public spending causes an increase in production and this leads to an increase in income-generating an impact on demand and this again affects production and so on. Given the above, it is important to analyze what effects each component of GDP has since all variables are related to each other. But despite the models that indicate how each variable affects production is impossible for governments to reach the level of production they want because there will always be external shocks, or the expectations of agents will not respond as expected.

In the next part of the article, we analyze the determinants of the demand for money. In the economy, there are several types of instruments such as bonds, shares and bank deposits. Also, people could decide to have all their wealth of money which would be more comfortable since they would not have to make transactions nor go to the bank, but that also means that they will not receive any interest in this so the decision to have cash or does not depend on the yields of the various instruments. The determinants of the demand for money will be our number of transactions and the interest rate. The interest rate serves to intervene in the economy by the central bank affecting consumer decisions.

The demand for money from the economy is no more than the sum of everyone’s money demands. For everyone, the demand for money increases in proportion as income increases and the demand for money has an inverse relationship with the interest rate. The higher the interest rate set by the central bank the higher the opportunity cost of having money in the pocket and the better it would be to have bonds or deposits in the bank.

The money supply will now be analyzed. In economies, there are two sources of supply. The first, bank deposits that are offered to the public and the second source is the cash offered by the central bank. When there is an increase in the national income this leads to the demand for money increases, but not the supply, which generates an increase in the interest rate to balance supply and demand. So, when the central bank increases the money supply generates a decrease in the interest rate to reach equilibrium in the money market by increasing the demand for money. It is important to keep in mind that these interest rates that are modified are in the short term.

In current economies, central banks change the money supply by buying or selling bonds in open market operations. If a central bank wants more liquidity in the economy, it buys bonds and pays them by creating money and if it wants to remove liquidity it sells bonds to collect money from the economy. With these open market operations, the economies succeeded in modifying the short-term interest rate. This short-term, as well as long-term rate, affects the investment of the economy as consumers and companies take these rates into account in their decisions. The higher the interest rate both short and long term will be more expensive for the company to borrow and will postpone this decision. In the equilibrium of the goods market, a rise in the interest rate will cause a decrease in production.

In current economies, governments and central banks combine fiscal and monetary policies to have deeper effects on the economy and take it along the path they believe is right, but they do not always agree on their policies. When there are recessions in the countries is when the banks and governments agree to get the economy out of this state. At other times when the bank takes an expansive monetary policy the government may have a contractive fiscal policy this depends on the expectations of each and what the objective of each agent.

After the implementation of these policies the adjustment in the economy is not immediately and each agent takes time to adjust their decisions, so when taxes rise consumption takes time to show the effects or the same when the monetary policy is changing when the central bank lowers its interest rates consumption does not respond immediately because the economy has transmission channels that are not updated in a quick way. In addition, in each country, the transmission time of the policies can vary because each consumer or company in each country acts differently so it cannot take an estimated transmission time globally.

Now we will discuss the trade balance issue. Nowadays the world is totally globalized and so is the economy. Globalization brought with it greater competition for local companies and specialization of economies, taking advantage of their competitive advantages thanks to their geographical location or climate. Exports represent important percentages in the domestic production of a country. Imports are also positive if consumer welfare is analyzed as they will have more variety of products and will be able to choose better goods. This decision of consumers and companies sometimes buying foreign and non-local goods obviously affect local production.

In the decision to buy inland or foreign goods is fundamental the price of goods in the choice of which to buy. The comparison of foreign and local prices is a relative price and is called the real exchange rate. This rate is the important one to know because the agents prefer local or foreign goods as it indicates the relative terms of exchange, but in goods not in currency. The real exchange rate is an index number i.e. it does not transmit direct information, but it can indicate whether in a country with the passing of time the goods became more expensive or not with respect to other countries what matters is the variation of this exchange rate.

A rise in the real exchange rate means a real appreciation and a reduction in the rate is called real depreciation. But globalization consists not only in the exchange of goods and services but also the opening of financial markets. This opening also affects the trade surplus or deficit of countries. If a country is in a trade deficit, it means that a country is buying more from the rest of the world than it sells to them and therefore must borrow from international agencies to balance its accounts. The investment decision of the financial markets will depend on the differential of the interest rate between countries, the political risk and the growth prospects of the regions among other risks. In the following chart, you can see the net inflow of capital at current prices in dollars.

Foreign direct investment, net inflows

Graph 14. Foreign direct investment, net inflows. Data taken from the World Bank

 

In conclusion, the real rate affects the composition of consumer spending on domestic and foreign goods, although in the first instance it should not affect the total level of consumption. The same can happen with investment, the real exchange rate can influence the decision of companies to buy local or foreign capital, and for the financial markets enter other variables that will decide which is preferred by the investors like the difference of interest rate.

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Fibonacci Complementary Tools

The analysis with rhythmic wave diagrams shows a relationship between the ratios so it will allow traders to further understand the relationship between them and help to make better decisions in trading. To understand the great contribution of the analysis of the harmonic proportions, it should be mentioned that the confluence zones are connected between them by harmonic relations. Also, if you want to know when the market is going to develop a strong movement in a bias you can also apply the harmonic proportions as a relevant indicator.

When the harmonic proportions are plotted, there are points where the harmonic intervals overlap and the market respects these points known as the diatonic Pythagorean scale; As the market respects the areas where different harmonic proportions coincide, this indicates that there are areas of confluence that weigh more than others so that the harmonic proportions will be a complement to the tools already studied, thus giving the trader the information on which major resistances and supports will be more important to the market.

A very important suggestion that must be considered by the trader is never to operate a single index, since in the market there are always assets that are correlated either proportional or inverse, so for certain assets there will be some other index that takes the lead and will serve to predict how the market behaves in the active trading. To see that assets can be correlated with an asset of interest you should analyze assets in the same sector, bonds, global indexes or raw materials.

fibonacci harmonic series

In the graph above the harmonic series is exhibited; There are analyses that apply these series to the market and in the points, that overlap series with others, will be areas that will be more important to the market, as not all areas of supports and resistances weigh the same for the market.

Retaking the Fibonacci analysis, there are certain tips that need to be considered. The highest or lowest prices are not always used to perform the analysis. The market gives the parameter to locate the range such as, the development of a rally in the market, very strong decision bars, gaps or certain technical patterns that indicate a change in the bias. If there are several signs of the market, it is better to draw several ranges to make sure that you are not leaving important areas without analysis, but considering that the ranges and their respective divisions must be areas respected by the market in the past because if this does not happen the drawn range will be irrelevant.

Other tools that serve to support the Fibonacci analysis are the oscillators like the RSI, but it is advisable to use them only when the market is in the confluence areas because otherwise it could give erroneous signals and is not reliable. If the market is reaching areas of confluence that are larger supports or resistances, oscillators will be able to give clues as to how the market behavior will be when it touches these points

Future price projections will be obtained by the projection of boxes where the important is the height of the boxes whose edges will be the ranges that were used for the Fibonacci analysis. If the market does not respect the first box of a small range, you should draw larger boxes that cover larger ranges and oscillations and their respective subdivisions that generate areas of confluence. This is the first step in creating future projections.

Using this Fibonacci analysis with the boxes starting from points of confluence to prices where a strong movement has been triggered and its subsequent subdivision with the Fibonacci ratios can be projected future prices in the oscillations that are formed. Many people do not know how to project in this way so they resort to Fibonacci expansions, a topic discussed in the article “Foundations of Fibonacci Extensions” (Foundations of Fibonacci Extensions) which is just as valuable and perhaps a little easier.

Another form of analysis is the Fibonacci channels. The channels are a variation to the Fibonacci retracements in which separate trendlines are drawn from a base price channel at distances provided by Fibonacci ratios. As a result, you get parallel trendlines that form channels used to estimate resistance and support zones as well as a Fibonacci retracement, only projected on a different axis. To create a parallel channel, it is observed first pivots that are confirmations of confluence zones that were analyzed in the horizontal axis, the lines of support or resistance in the channels must be respected where there are areas of confluence using the Fibonacci retracements. The pivot points will serve to create the channels are made in such a way that they respect important areas encountered previously.

The first step is to draw a channel where there are important pivots and based on this main channel are projected the Fibonacci percentages. This type of analysis also creates support areas and resistance and the knowledge becomes more robust because traders not only have the areas of horizontal confluence also have important areas diagonally. On many occasions, the intersections between different axes mark the entry point, but you should keep in mind that the stop loss points must be beyond the confluence zones and outside the Fibonacci channel.

Fibonacci channels

In the previous graph you can see some Fibonacci channels having as the basis of the channel the trend line of the market and then based on this project the Fibonacci ratios It’s important to have in mind that to be projected based on this trend line, the pivot points must have respected this trend line and the confluence areas that have been encountered with the Fibonacci retracements.

Fibonacci retracements

In this second graph, you can see the horizontal Fibonacci levels and the Fibonacci channels. As mentioned in this article, areas where support is intercepted or resistances with the channels will be areas that respect the market with greater determination and will be interesting areas to analyze. If you add this to the analysis with harmonic series, it will be a well-done analysis with enough tools to make no mistakes.

Summarizing, regardless of the asset, you should follow the same steps to analyze your important areas. First, it should be determined whether the areas you want to find are support or resistance zones, and depending on this you will proceed to stipulate the range to use Fibonacci retracements and other tools. There may be many ranges. They will always have the same starting point, but it will be carried at different price levels to obtain different ranges and several confluence zones. After identifying these areas, you should verify that the market has respected them in the past in different time horizons and be clear that the areas that have been found as major supports do not serve as resistance, that is another stage in the analysis. When you have identified the most relevant areas of the market it is advisable to shade them and eliminate those areas irrelevant to make the graph easier to interpret. Using the channels, retracements and Fibonacci expansion, oscillators, and Elliot’s principle, you will have the necessary tools to project the future direction of the market and thus make the best decisions of entry and exit.

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Long run Macroeconomics

Abstract

Long term is what really matters for economists. It is not enough a decade of continuous growth if an economy does not have adequate policies to perpetuate that growth. So, the models in general in macroeconomics are about the long term. As mentioned in other articles, the economy fluctuates permanently along a trend and it is normal to present crisis in certain periods of time. But to ensure production growth constantly over time, government policies must focus on variables such as savings rate, capital accumulation, human capital development among others that offer the ability to maintain stable growth.

In the long term of the economy, the interannual fluctuations of economic activity predominate. When recessions happen, causes consumers to be pessimistic and when the economy is expanding, consumers are optimistic and their behaviors demonstrate. But if you look to the past in long periods of time the panorama changes, and fluctuations are not important but the long-term growth is. What matters, in the long run, is the historical aggregate production, so the objective of this branch of macroeconomics is to determine which factors affect long-term growth, why some countries grow more than others and why there are more inequalities between countries. In the following graph, you can see the growth of some countries.

GDP PER CAPITA

Graph 1 GDP PER CAPITA. Data taken from the World Bank.

The reason why growth is important is that this determines the standard of living and determines whether it has improved this in time. Because of this, what matters to macroeconomics is not only aggregate production but production per person as this approximates each person’s standard of living. Economists do not ignore the fact of inequality, but studies and models try to approach reality so it is necessary to have these variables that even though they are not entirely true, they approximate to reality. At this point, there are several variables that try to measure the quality of life of people as their consumption, necessities unmet, among others, which measure the overall well-being

When you compare production per person you should adjust by purchasing power parity, in other words, the prices are adjusted in real terms to be able to compare a basket of goods that can be bought in each country, otherwise, these indicators would be affected by exchange rates. One of the great conclusions that can be seen after seeing the growth of different countries is that in general welfare in all countries has increased and in some countries, growths have converged at similar rates, but there are others in which the Growth seems to have stagnated as in Africa and some countries in South America

To analyze growth in countries, economists have used long-term models, initiated by Robert Solow at the beginning of 1950. The models consider aggregate production and try to have variables that affect production such as capital and workers in an economy. The way in which these two factors are related to the models is affected by technology, as an economy with a higher technology will be more efficient with the factors it has and thus will reach greater aggregate production. With such simplified models, graphics are very similar to reality, and it is also concluded that growth rates stop increasing in certain periods of time and by the characteristic of declining yields. What indicates declining yields of the factors is that the larger the accumulation of these factors, they cease to be so productive because the economy is saturated with these and are no longer as necessary as in the beginning.

With these concepts clear it is valid to ask yourself the question why an economy grows and which factors promote the growth. With long-term macroeconomic models, it is concluded that increases in worker output are due to increases in capital per worker, technological improvements or more skilled workers. The education is a very interesting explanation because it explains the great growth after the Second World War as technological innovations made production more efficient in all countries and the knowledge of people also had an expansion during this period. In conclusion, what determines long-term production is the relationship between production and capital as the amount of capital determines production. In graph 2, the gross capital formation is a variable of physical capital.

Gross capital formation

Graph 2 Gross capital formation. Data taken from the World Bank

Another important aspect of the long-term growth of economies is the saving rate of each economy. It has been seen in data that the most saving economies grow more in the long run and an example of this are the Asian economies that have high savings rates and thus grow more than the average of countries. Similarly, technological progress helps to grow constantly more than in the past. But here arises another concern, what determines the rate of technological progress? The answer is the projects that are carried out in an economy and the way the economy is organized, its rules and the institutions.

Governments can influence the saving rate in various ways. In the first place, they can change public savings, in other words, to have a surplus in the government budget. Moreover, governments can use taxes to influence private savings, for example, they can grant tax privileges to people who save to make more beneficial the saving. But at this point arises a problem and is that consumption suffers when there are higher rates of savings and the desire of an economy that grows is that people consume more so there should be a limit on savings rates because an economy with excessively high rates is also not ideal for economists. But if you take an economy with zero savings to invest in capital, the economy will have zero capital and consumption will also have the same value, so it is better than the saving rate is positive but not excessive as consumption will also be null and that is not the ideal of the economy. In graph 3, a gross saving rate can be observed in which the most developed countries are the ones with the highest savings rates.

Gross savings

Graph 3 Gross savings. Data taken from the World Bank

There is empirical evidence that most countries are below the optimum savings level and are therefore below their optimal capital level and their consumption is not the maximum they could get. But the savings rates that are considered in these initial models are only used to acquire physical capital, but as mentioned previously, economies have another capital that is also very important which is human capital. An economy that has many skilled workers will be much more productive than another that does not have the same types of workers. Human capital has increased as much as physical capital in the last two centuries. It is known that at the beginning of the first Industrial Revolution, 30% of the workers knew how to read and now that percentage is located at 95%. Graph 4 shows the difference in the rates of children enrolled in tertiary education.

Gross enrolment ratio

Graph 4 Gross enrolment ratio. Data taken from the World Bank

After having introduced the distinction within the capital of an economy it can be concluded that the level of production of an economy depends on physical capital, human capital, and technology present in an economy. An increase in the physical capital per worker and an increase in the average level of qualifications per worker would lead to an increase in production per worker. A problem is that the population today is so educated and the yields of this are also decreasing the most children now know how to read, write and have the possibility of going to college so it is no longer so representative the education as it was in the last century. Savings also influence human capital as an increase in savings in this capital increases production per worker.

Considering another important variable in long-term growth the technological progress will be exposed. Technological progress helps economic growth at least in the short term because it makes the economy more efficient and allows new objects to be produced at higher speeds. But it is not a permanent effect because after the economy is accustomed to these innovations, its effect on growth disappears. Technological progress reduces the number of workers needed to achieve a certain amount of production, in other words, it allows to produce more without having to increase the factors already exposed.

Technological progress has made great strides throughout history from finding sources of energy to the understanding of the human body. In modern economies, most of the technological progress comes from investment in research and development, which is commonly denoted (R&D). According to some estimates, countries allocate between 3% and 5% of GDP and modern companies allocate large resources to this in order to be at the forefront of the market. For a company to have incentives to invest in research and development there must be clear rules such as proprietary rights and patents that guarantee companies to receive returns on investment in R&D. In Figure 5, the difference in research between countries can be seen.

Researchers in R&D

Graph 5 Researchers in R&D. Data taken from the World Bank.

There is data showing that the recorded growth from 1950 to the present has been generated by the technological process rather than the accumulation of physical capital, but without the latter being negligible for economic growth. Throughout history it has been seen that the poorest countries have less physical capital initially but then converge their growth rates with the most developed because they implement the technological progress of the most advanced countries, in other words, they take advantage of the progress of developed countries and as the technological levels converge, the production per worker is also converging. This is one of the central ideas about technological progress, as the most advanced countries are on the technological frontier and must innovate more, while lagging countries can mimic the technology of the advanced countries and close the gap between them without having to innovate. While this occurs in some countries, not all are able to do so due to inefficient policies and institutions.

 

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Trading, a Different Viewpoint: It’s all about Market Structure, Risk and System Design

Introduction

Globalization, the internet and also the massive use of computers have contributed to the worldwide spread of trading of all kinds: Stocks, futures, bonds, commodities, currencies. Even the weather forecast is traded!

In recent years, investors have turned their attention to the currency markets as a way to achieve their financial freedom. Forex is perceived as an easy place to achieve that goal. Trading currencies don’t know about bear markets. Currency pairs simply fluctuate driven by supply and demand in cycles of speculation-saturation, explained by behavioral economic science and game theory.

It looks straightforward: buy when a currency rises, sell once it falls.

However, this idyllic paradise has its own crocodiles that are required to be dealt with. The novel investor gets into this new territory armed with her own beliefs, that fitted well in his normal life but it’s utterly wrong when trading. But the main issues relating to underperformance lies inside the mind of the trader. Some say the market is rigged to fool most traders, however, the reality is that traders fool themselves. A shift in their core beliefs – and in the way they think-  is critical for them to succeed.

The purpose of this, divided into three parts, is to boost awareness concerning three main issues the trader faces:

  • The true knowledge about the nature of the trading environment
  • The nature of risk and opportunity
  • The key success factors when designing a system

The trading environment

Usually, people approach the study of the market environment by focusing mainly on market knowledge: Fundamentals, world news, central banks, meetings, interest rates, economic developments, etc. At that point, they interminably analyze currency technicalities: Overbought-oversold, trends, support-resistance, channels, moving averages, Fibonacci and so on.

They think that success is identified with that sort of information; that a trade has been positive because they were right on the market, and when it’s not is because they were wrong.

Huge amounts of paper and bytes have been spent in books and articles about those topics, yet there’s a concealed reality down there not yet uncovered, despite the fact that it’s the primary driver for the failure of the majority of traders (the other one is over-leverage and over-trading).

It’s evident that all traders are aware of the uncertainty of doing currency trading. Yet, except for individuals proficient about probability distributions, I am tempted to state that not a single person really knew what is this about, when she decided to trade currencies (at least not me, by the way).

So, let’s begin. Everybody knows what a fair coin flip is, but what’s the balance of a fair coin flip game, starting 100€, after 100 flips if we earn 1€ when heads and lose 1€ when tails?

Many would say close to zero, and they might be right, but this is just one possible path:Fig 1: 100 flips of a fair coin flip game

There are other paths, for instance, this one that loses more than 20€:

Fig 2: 100 different fair coin flips

This second path seems taken from a totally different game, but the nature of the random processes is baffling, and usually fools us into believing those two graphs are made from different games (distributions) although they’re not.

If we do a graph with 1,000 different games, we’d observe this kind of image:

Fig 3: 1,000 paths of a fair coin flip 100 flips long

Below, a flip coin with a small handicap against the gambler:

Fig 4: 1,000 paths of an unfair coin flip

Finally, a coin flip game with a slight advantage for the player:

Fig 5: 1,000 paths of a coin flip with edge

And that’s the genuine nature of the beast. This figure above corresponds to a diffusion process and each path is called random walk. Diffusion processes happen in nature, likewise, for instance, as a billow of smoke ascending out from a cigarette or the spread of a droplet of watercolor in a glass filled with clean water.

Our first observation regarding fig 4 is that the mean of the smoke cloud drifts with negative slope, so after the 100 games, just about 1/3 of them are above its initial value; and we may observe that, even in the fair coin case, 50% of paths end in negative territory.

The game of a coin flip with an edge (fig 5) is the only one that’s a winner long term, although, short-term, it might be a losing game. In fact, before the first 20 flips, close to 50% of them are underwater, and at flip Nr. 100 about 35% of all paths end losing money.

If that game were a trading system, it would be a fairly good one, with a mean profit of 70% after 500 trades, but how many traders would hold it after 50 trades? My figure: only a 30% lucky traders. The rest would drop it out; even that long-term performance is good enough.

Below fig 6 shows a diffusion graph of 1500 bets of that game, roughly the number of trades a system that takes six daily bets produces in a year. We see that absolutely all paths end positive and the mean total profit is about 200%.

Fig 6: 1,000 paths of a coin flip with edge

By the way, the edge in this game is just a reward to risk ratio of 1.3:1, while keeping a fair coin flip.

Before going into explaining the psychological aspects of what we’ve seen so far, let me show you some observations we’ve learned so far, regarding this phenomena:

  • The nature of the random environment fools the major part of the people
  • There are unlucky paths: Having an edge is no guarantee for a trader’s success (short term).
  • A casino game and the market, as well, collect money from endless hordes of gamblers with thin pockets and weak hands because they have no profitable system or stop trading his system before it could manifest its long-term edge.
  • Casino owners know the math of gambling and protect themselves against volatility by diversification and a maximum allowed bet (only small gamblers allowed).
  • By playing several uncorrelated paths at the same time, we could lower the overall risk, as does the casino owner, but we’d still need an edge and a proper psychological attitude.
  • A system without edge is always a loser, long term.

The psychology of decisions taken under uncertainty

In 2002, Daniel Kahneman received the Nobel prize in economics “for having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty.”

Dr.Kahneman did most of this work with Dr. Amos Tversky, who died in 1966. Their studies opened a new field of economics: Behavioural finance. They called it Prospect Theory and dealt with how investors make decisions under uncertainty and how they choose between alternatives.

One of the behaviors studied was loss aversion. Loss aversion shows the tendency for traders to feel more pain when taking a loss than the joy they feel when taking a profit.

Loss aversion has its complementary conduct: fear of regret. Investors don’t like to make mistakes. Both mechanisms combined are responsible for their compulsion to cut gains short and let loses run.

Another conduct taken from Prospect theory is that individuals believe in the law of small numbers: The tendency of people to infer long-term behavior using a small set of samples. They suffer from myopic loss aversion by assigning excessive significance to short-term losses, abandoning a beneficial long-term strategy because of suboptimal short-term behavior.

That’s the reason people gamble or trade until an unlucky losing streak happens to them, and the main reason casinos and the markets profit from people. Those who lose early, exit because they had depleted their pockets or their patience. Lucky winners will bet until a losing streak wipes their gains.

To abstain from falling into those traps, we should develop a strategy and get the strength and discipline to follow it, instead of looking too closely at results.

In Decision Traps, Russo and Schoemaker, have an illustrative approach to point to the process vs. outcomes dilemma:

Fig 7: Russo and Schoemaker: Process vs Outcomes.

Results are important and they are more easily evaluated and quantified than processes, but traders make the mistake to presume that good results come from good processes and bad results came from bad ones. As we saw here, this may be false, so we should concentrate on making our framework as robust as possible and focus on following our rules.

– A good decision is to follow our rules, even if the result is a loss

– A bad decision is not following our rules, even if the result is a winner.

The Nature of risk and opportunity

To help us in the task of exploring and finding a good trading system we’ll examine the features of risk and opportunity.

We’ll define risk as the amount of money we are willing to lose in order to get a profit.  We may call it a cost instead of risk since it’s truly the cost of our operations. From now on, let’s call this cost R.

We define opportunity as a multiple of R. Of course, as good businesspeople, we expect that the opportunity is worth the risk, so we should value most those opportunities whose returns are higher than the risk involved. The higher, the better.

From the preceding section, we realize that the majority of new investors and traders tend to cut gains and let the losses run, in an attempt for their losses to turn into profits, caused by the need to be right. Therefore, they prefer a trading system that’s right most of the time to a system that’s wrong most of the time, without any other consideration.

The novel trader looks for ideas that could make their system right, endlessly back-testing and optimizing it. The issue is that its enhancements are focused in the wrong direction, and, likewise, most likely ending over-optimized. Thus, with almost certainty, the resulting system won’t perform well in practice on any aspect (expectancy, % gainers, R/r, robustness…)

The focus on probability is sound when the outcomes are symmetrical (Reward/risk =1); otherwise, we must take into account the size of the opportunity as well.

So, we’d like a frame of reference that helps us in our job.  That frame will be achieved using, again, the assistance of our beloved diffusion cloud. The two parameters we’ll toy with will be: percent of gainers and also the Reward to risk (or Opportunity to cost ratio).

Since the goal of this exercise is to expel the misconceptions of the typical trader, we’ll use an extreme example: A winning system that’s right just 10% of the time, however with an R/r =10. It isn’t too pretty. It’s simply to point out that percent gainers don’t matter much:

 Fig 8: A game with 10% winners, and R/r = 10.

Right! One 10xR winner overtakes nine losers.

The only downside employing a system with parameters like this is that there’s a 5% chance to experience 30 consecutive losses, something tough to swallow.

But there are five really bright ideas taken from this exercise:

  • If you find an nxR opportunity you could fail on average n-1 times out of n and, still be profitable. Therefore, you only need to be right just one out of n times on an nX reward to risk opportunity.
  • A higher nxR protects us against a drop in the percent of gainers of our system, making it more robust
  • You don’t need to predict price movement to make money
  • Repeat; You don’t need to predict prices to make money
  • If you don’t have to predict, then the real money comes from exits, not entries.
  • The search for higher R-multiples with decent winning chances is the primary goal when designing a trading system.

Below it’s a table with the break-even point winning rate against nxR

Fig 9: nxR vs. break-even point in % winners.

We should look at the reward ratio nxR as a kind of insurance against a potential drop in the percent of winners, and make sure our systems inherit that sort of safe protection. Finally, we must avoid nxR’s below 1.0, since it forces our system to percent winners higher than 50%, and that’s very difficult to attain combined with stop losses and normal trading indicators.

Now, I feel we all know far better what we should seek: Looking for what a good businessperson does: Good opportunities with reduced cost and a reasonable likelihood to happen.

That’s what Dr. Van K. Tharp calls Low-risk ideas. A low-risk idea may be found simply by price location compared to some recent high, low or long-term moving average. As an example, let’s see this chart:

Fig 10: EUR/USD 15 min chart.

Here we make use of a triple bottom, suggested by three dojis, as a sign that there is a possible price turn, and we define our trigger as the price above the high of the latest doji. The stochastics in over-sold condition and crossing the 20-line to the upside is the second sign in favor of the hypothesis. There has a 3.71R profit on the table from entry to target, so the opportunity is there for us to pick.

Here it is another example using a simple moving average 10-3 crossover, but taking only those signals with more than 2xR:

Fig 10b: USD/CAD 15 min chart. In green 2xR trades using MA x-overs. In red trades that don’t pass the 2xR condition

Those are simply examples. The main purpose is, there are lots of ideas on trading signals: support-resistance, MA crossovers, breakouts, MACD, Stochastics, channels, Candlestick patterns, double and triple tops and bottoms, ABC pattern etc. However, all those ought to be weighed against its R-multiple payoff before being taken. Another point to remember is that good exits and risk control are more important than entries.

Key success factors when designing a system

Yeo Keon Hee, in his book Peak Performance Forex Trading, defines the three most vital elements of successful trading:

  1. Establishing a well-defined trading system
  2. Developing a consistent way to control risk
  3. Having the discipline to respect all trading rules defined in point 1 and 2

Those three points are essential, however not unique. We need additional tasks to perfect our job and our results as traders:

  1. Using proper position sizing to help us achieve our objectives.
  2. Keeping a trading diary, with annotations of our feelings, beliefs, and errors, while trading.
  3. A trading record including position size, entry date, exit date, entry price, stop price, target price, exit price, the nxR planned, the nxR achieved; and optionally the max adverse excursion and max favorable excursion as well.
  4. A continuous improvement method: A systematic review task that periodically looks at our trading record and draws conclusions about our trading actions, errors, profit taking, stop placement etc. and apply corrections/improvements to the system.

First of all, let’s define what a system is:

Van K Tharp wrote an article [1] about the subject. There he stated that what most traders think is a trading system, he would call it a trading strategy.

To me, the major takeaway of Van K. Tharp’s view of what a system means is the idea that a system is some structure designed to accomplish some objectives. In reference to McDonald’s, as an example of a business system, he says “a system is something that is repeatable, simple enough to be run by a 16-year-old who might not be that bright, and works well enough to keep many people returning as customers”.

You can fully read this interesting article by clicking on the link [1]  at the bottom of this document. Therefore I won’t expand more on this subject. If I discussed it, it was owing to the appealing thought of a system, as some structure designed to accomplish some goals that work mechanically or managed by people with average intelligence.

In this section, we won’t discuss details concerning entries, stop losses and exits- that’s a subject for other articles- however. We’ll examine the statistical properties of a sound system, and we’ll compare them with those from a bad system, so we could learn something about the way to advance in our pursuit.

Let’s begin by saying that in order to make sure the parameters of our system are representative of the entire universe of possibilities, we’d need an ample sample of trades taken from all possible scenarios that the system may encounter. Professionals test their systems using a multiyear database (10+ years as a minimum); however, an absolute minimum of 100 trades is a must, though it’s beyond the lowest size I might accept.

The mathematics of profitability

The main key feature of a sound system isn’t the percentage of gainers, but expectancy E (the expected value of trades).

Expectancy is the expected value of winners (E+) less the expected value of losers (E-)

(E+) = Sum(G)/(n+)  x  %Winners

(E-) = Sum(L)/(n-)  x  %Losers

Sum(G): The total dollar gains in our sample history, excluding losers

Sum(L): The total dollar losses in our sample history, excluding winners

(n+): The number of positive trades(Gainers)

(n-): The number of negative trades(Losers)

The expectancy E then is:

E = (E+) – (E-)

Similarly, we can compute

E = SUM(trade results)/n

Where n = total number of trades,

So E is the normalized mean or total results divided by the number of trades n.

If E is positive the system is good. The higher the E is, the better the system is, as well. If E is zero or negative, the system is a loser, even though the percentage of gainers was over 80%.

Another measure of goodness is the variation of results. Dr. Chis Anderson (main consultant for Dr. Van K. Tharp on his book about position sizing) explains that, for him, expectancy E is a measure of the non-random (or edge) part of the trade, and that we are able to determine if that edge is real or not, statistically.

From the trade list, we can also calculate the standard deviation of the set (STD).

From the trade list, we can also calculate the standard deviation of the set (STD). That can be done in Excel or by some other statistical package (Python, R, etc.). This is a measure of the variability of those results around the mean(E).

A ratio of E divided by the STD is a good metric of how big our edge is, relative to random variations. This can be coupled quite directly to how smooth the equity curve is.

Dr. Van K Tharp uses this measure to compute what he calls the System Quality Number (SQN)

SQN = 100 x E / STDEV

Dr. Anderson says he’s happy if the STDEV is five times smaller than E, that systems with those kind of figures show drawdown characteristics he can live with. That means SQN >= 2 are excellent systems.

As an exercise about the way to progress from a lousy system up to a decent and quite usable one, let’s start by looking at the stats, and other interesting metrics, of a bad system- a real draft for a currencies system- and, next, tweak it to try improving its performance:

STATISTICS OF THE ORIGINAL SYSTEM:

Nr. of trades             : 143.00
gainers                   : 58.74%
Profit Factor             : 1.06
Mean nxR                  : 0.74
sample stats parameters:      
mean(Expectancy)          : 0.0228
Standard dev              : 1.6351
VAN K THARP SQN           : 0.1396

Our sample is 143 long, with 58.74% winners, but the mean nxR is just 0.74, therefore the combination of those two parameters results in E = 0.0228, or just 2,28 cents per dollar risked. SQN at 0.1396 shows it’s unsuitable to trade.

Let’s see the histogram of losses:

Histogram of R-losers

Fig 11: Histogram of R-losers (normalized to R=1)

Original system probability of profits of x R-size

Histogram of R-Profits

Fig 12: Histogram of R-Profits (normalized to R=1)

Diffusion cloud of 10,000 synthetic histories of the system:

Original system: Diffusion cloud 10,000 histories of 1,000 trades

Fig 13: Original system: Diffusion cloud 10,000 histories of 1,000 trades.

Histogram of Expectancy of 10,000 synthetic histories:

Expectancy histogram of 10,000 histories of 1,000 trades

Fig 14: Expectancy histogram of 10,000 histories of 1,000 trades. 50% of them are negative

We notice that the main source of information about what to improve lies in the histograms of losses and profits. There, we may note that we need to trim losses as a first measure. Also, the histogram of profits shows that there are too many trades with just a tinny profit. We don’t know what causes all this: Entries taken too early; too soon, or too late, on exits, or a combination of these factors. Therefore, we must examine trade by trade to find out that information and make the needed changes.

As a theoretical exercise, let’s assume we did that and, as a consequence of these modifications, we’ve reduced losses bigger than 2R by half and, also improved profits below 0.5R by two. The rest of the losses and profits remain unchanged.  Let’s see the stats of the new system:

IMPROVED SYSTEM STATISTICS:  
Nr. of trades             : 143.00 %
gainers                   : 58.74%
Profit Factor             : 1.99
mean nxR                  : 1.40 

sample stats parameters:       
mean(Expectancy)          : 0.4081
Standard dev              : 2.2007  
VAN K THARP SQN           : 1.8546

By doing this we’ve achieved an expectancy of 41 cents per dollar risked and an SQN of 1.53. It isn’t a perfect system, but it’s already usable to trade, even better than the average system:

Improved system: Diffusion cloud 10,000 histories of 1,000 trades

Fig 15: Improved system: Diffusion cloud 10,000 histories of 1,000 trades.

We may notice, also, on the histogram of Expectancies, below, that, besides owning a higher mean, all values of the distribution lie in positive territory. That’s an excellent sign of robustness and a good edge.

Expectancy histogram of 10,000 histories of 1,000 trades

Fig 17: Improved system: Expectancy histogram of 10,000 histories of 1,000 trades.

There are other complementary data we can extract that reveal other aspects of the system, such those below:

Trading System: It’s All About Market Structure, Risk & System Design

Trading System

Fig 17, for instance, shows that the system has a 40% chance of having 2 winners in a row, and 15% chance of 3 of them. Also, from fig 18, there’s a 60% chance of 2 loses in a row, 37% chance of 3 losers and 5% chance of a streak of 7 losers, so we must prepare ourselves against this eventuality by proper risk management.

Throughout this exercise, we’ve learned how to use our past trading information to analyze a system, decide what parts need to be modified, then perform the modifications, continue by testing it again using a new batch of results and observe if the new statistical data is sufficiently good to approve it for trading. Otherwise, a new round of modifications must be carried out.

Position Sizing

All figures and stats we’ve seen until now belong to an R-normalized system: It trades just a unity of risk per trade, without any position sizing strategy at all. That is needed to characterize the system properly. But the real value of a framework that allows this type of measurements is to use it as a scenario planning to experiment with different position sizing strategies.

We should remember, a system is a structure to achieve specific goals.

And position size is the method that helps us to achieve the financial goal of that system, at a determined financial maximum risk.

As an exercise, Let’s look at what this system may accomplish by maximizing position size without regard for risk (besides not going broke).

We’ll do it using Ralf Vince’s optimal f: The optimal fraction of our running capital. The computation of optimal f for this system was done using a Python script over 10,000 synthetic histories and resulted in a mean Opt f = 22%. To be on the safe side, we’ll use 75% of this value. That means the system will bet 16.77% of the running capital on every trade.

The result of the diffusion cloud will be shown in semi-log scale to make it fit the graph:

Diffusion cloud traded with Optimal f. y log scale

Fig 19: Improved system: Diffusion cloud traded with Optimal f. y log scale.

Below the probability curve of the log of profits, on a starting 10,000€ account, after 1000 trades:Trading System curve

Starting capital   :  1.0 e+4
Mean ending Capital:  2.54013596e+10
Min  ending Capital:  4.20930083e+02
Max  ending Capital:  3.94680594e+18

We see that there is 50% chance that our capital ends at 25,400 million euro (2.54 e+10) after 1000 trades, and a small chance of that figure is 3+ digits higher. Of course, the market will stop delivering profits much early than this. The purpose of the exercise is to show the power of compounding using position sizing.

Let’s see the drawdown curve of this positioning strategy:

Curve of Trading System

We observe there’s 80% chance our max drawdown being more than 75% and 20% chance of it being 90%, so this kind of roller-coaster isn’t for the faint heart!

Before finishing with this scenario, let’s look at a final graph:

trading curve

This graph shows the probability to reach 10x our initial capital after n trades. For this system, we observe that there’s a 25% chance (one out of 4 paths) that we could reach 10X in less than 80 trades and a 50% chance this happens in less than 150 trades.

That shows an important property of the optimal f strategy: Optimal f is the fastest way to grow a portfolio. The closer we approach optimal f the faster it grows. But as position size goes beyond the optimal fraction the risk keeps increasing but the profit diminishes, so there’s no incentive to trade beyond that point.

That property may suggest ideas about an alternative use the use of optimal f. If you think about a bit, surely, you’ll find some of them.

My goal with this exercise was to show that any average system can achieve any desirable objective.

Now let’s do another useful exercise. Let’s compute the fraction that fulfills a given objective limited by a given drawdown.

Let’s do a position sizing strategy for the faint-heart trader. He doesn’t wish more than 10% drawdown, accepting a 5% probability that drawdown goes to 15%. His primary concern is the risk, so he takes what the system could deliver within that small risk.

To find this sweet spot we need to try several sizes on our simulator using different fractions until that spot is reached. After a couple of trials, we find that the right amount for this system is to trade 1% of the running balance on each trade. Here we assumed that no other systems are used, and just one trade at a time. If several positions are needed, then the portfolio should be divided, or, alternatively, we must compute the characteristics of all systems combined.

Below, the main figures of the resulting system:

Starting Capital   :  10,000 | forex academy

Starting Capital   :  10,000
Mean ending Capital:  45,508
Min  ending Capital:  13,406
Max  ending Capital:  177,833

Trading System - Forex Academy

Mean drawdown: 9.58%
Max drawdown: 25.98%
Min drawdown: 4.13%

We observe, the system performs quite well for such small drawdown, with 100% of all paths more than doubling the capital, and a mean return of 455% after 1,000 trades.

Trading System - Forex.Academy

Finally, the figures for a bold trader who is willing to risk 30% of its capital, with just a small chance of more than 40%, are shown below.

FA

Starting Capital   :  10,000
Mean ending Capital:  710,455
Min  ending Capital:  19,890
Max  ending Capital:  37,924,312

FOREX TRADING SYSTEM

Mean drawdown: 26.43%
Max drawdown: 60.52%
Min drawdown: 11.97%

We observe that this positioning size is about 2.5 times riskier than the previous one. In the more conservative position sizing, we have a 5% chance of 15% max drawdown, while this one has a 5% chance of about 38% drawdown. But on returns we go from a mean ending capital of 45,500€ to a mean ending capital of 710,455€, surpassing by more than 10 times the returns of the first strategy.

This is common in position sizing compounding. Drawdowns grow arithmetically, returns grow geometrically.


Summary

Throughout this document, we’ve learned quite a bit about the three main aspects of trading.

Let’s summarize:

Nature of the trading environment

  • The nature of the random environment fools a majority of the people
  • New traders want to be right so they cut their profits while hanging on their losses
  • New traders are psychologically affected by the law of small numbers, and fail because they believe in the law of small numbers instead of being confident by the long-term edge of their system.
  • Having an edge is no guarantee for a trader’s success (short term) of you don’t have an edge and the discipline to follow your system.
  • By splitting the risk into several uncorrelated paths at the same time, we could lower the overall risk
  • A system without edge is always a loser long term.

The nature of risk and opportunity

  • If we look for nxR opportunities, we just need to succeed once every n trades be profitable.
  • A system with higher nxR is protected against a drop in the percent of gainers of our system, making it more robust.
  • We don’t need to predict price movement to make money.
  • If we don’t need to predict, then real money comes from exits, not from entries.
  • The search for higher R-multiples with decent winning chances is the primary goal when designing a trading system.

Key factors to look when developing a system

  • A system is a structure designed to accomplish specific goals that work automatically.
  • The three most vital elements of successful trading:
    • Establishing a well-defined trading system
    • Developing a consistent way of controlling risk
    • Having the discipline to respect all trading rules defined in point 1 and 2
  • Using proper position sizing helps us achieve our objectives.
  • Keeping a trading diary, with annotations of our feelings, beliefs, and errors, while trading.
  • It’s essential to keep a trading record
  • We need a continuous improvement method: A systematic review task that periodically looks at our trading records and draws conclusions about our trading actions, errors, profit-taking, stop placement, etc. and apply corrections/improvements to the system.
  • Position sizing is the tool to help us achieve our specific objectives about profits and risk.


References

[1] http://www.stockbangladesh.com/blog/what-is-a-trading-system-by-van-k-tharp/

Recommended readings:

Trade your way to your financial freedom, Van K. Tharp

Peak performance Forex Trading, Yeo, Keong Hee

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Forex Educational Library

Limitations On Monetary Policy

 Abstract

Macroeconomics currently has different models to analyze how markets behave, which relationship has different indicators and what effect the application of certain policies could have. But the problem with the theory is that it does not consider all the variables that really affect, and a principle of the models is the simplicity and because there are variables that behave differently than would be expected. This mainly occurs with the expectations of people because it is a variable that cannot be controlled and can behave very differently than would be expected.

In the last century, various models have been developed to explain economic behavior, but it is still far from perfection. When central banks see an increase in unemployment, they try to give a stimulus to the economy with the help of the interest rate or with an increase in the amount of money. But when those incentives are considered, the banks use macroeconomic models to see the possible effects of their measures, although there is no perfect model to indicate the real effects. The problem is that in the diversity of models that exist each one shows different magnitudes in the effects of the interventions so that the banks do not know with certainty what can happen in the economy.

Given this uncertainty about the effects of economic policy, some analysts believe that central banks should not intervene so much in the economy to avoid possible unwanted effects. In general terms, central banks should limit themselves to avoiding prolonged recessions, curbing dangerous expansions, and avoiding inflationary pressures. The higher the unemployment or the inflation the measures of the bank should be stronger but neither aspires to a perfect effect since this does not exist.

One of the main reasons why the effects of monetary policy are uncertain is a topic discussed in the article Expectations in the economy. Expectations and their interaction with central bank policies distort the effects of policies, because not only the variables of the present matter but the expectations of the future as well. For a central bank policy to be efficient and have the desired effects their policies must be credible by the agents of the economy to change the expectations of these.

But another problem facing monetary policy apart from the expectations of consumers, investors and other players, is the political interests of the authorities of the countries. Politicians do not always do the best for the economy as they always avoid making difficult decisions and always do what they are represented by votes. Another problem is the alternation in the power of different ideologies that do not allow economic and monetary policies to be lasting over time. All the above is to highlight the limitations of monetary authorities and because despite the development of economic theory it has not been possible to perfect predictions about the effects of monetary policy.

One of the issues that banks should deal with is inflation. Inflation has four costs recognized by economists:

  • An increase in inflation leads to an increase in the nominal interest rate and therefore the cost of opportunity to have money so that people prefer money in the bank and this leads to having to go more often to the bank.
  • Inflation generates fiscal distortions. For example, when a company must pay capital and income taxes if there is a high inflation rate it may end up paying more because of this effect.
  • Inflation variability generates that year after year no one knows exactly what the inflation rate will be affecting certain assets such as bonds.
  • Inflation causes an effect called monetary illusion that causes people to make systematic mistakes because they value different real and nominal purchasing power changes.

But inflation also has positive aspects. The creation of money by banks and which is the cause of inflation is a way in which the state can finance its spending, for example, it is an alternative to borrowing from the public or raise taxes. It is also positive because when an economy is in a recession the central bank can take expansionary measures and thus help the economy. But if the economy has 0 inflation, or it is very low, monetary policy will be hard to implement and therefore it will be difficult to return production to its natural levels by the bank. In many countries, the main objective of central banks is to achieve an inflation rate between a pre-determined range for each bank that is supposed to be good for the economy. In the following graph, you can see the inflation rate of different countries including Venezuela that is having serious problems in its economy.

Inflation, consumer prices

Graph 9. Inflation, consumer prices. Data taken from the World Bank.

Another way aside from the interest rate to control production fluctuations is the fiscal deficit. It is true that the fiscal deficit helps in the negative cycles, but it must also be considered that fiscal deficits have a negative impact on capital accumulation in the long term. For the debt not to advance continuously when there are expansions in the economy it should be intended to have fiscal surpluses to clean up national accounts. Due to the problems generated by an increase in the debt generated by the fiscal deficit, it is important that investors and citizens are monitoring the fiscal deficit of countries as this can end up affecting the growth in the medium and long-term.

As mentioned before the policy plays an important role in the monetary decisions and the course of the economies so that the healthiest thing for the countries would be to limit their interference in the decisions of the Central Bank and other policies aimed at limiting the fiscal deficits because to not lose support a government may be able to borrow beyond what is necessary and healthy by affecting long-term growth where those governments will no longer be.

European Union.

The next part of the article will be based on the monetary integration of Europe and the details of this integration. The decision to adopt a single currency such as the Euro is the most extreme way to set the exchange rate between countries in a region. When the central bank worries about the exchange rate and tries to fix a stable exchange rate there are many problems for the parity to remain so by unifying several countries under a currency these problems are eliminated as losses of reserves, but other problems arise. In the next graphic shows the members current of the European Union.

Top 30 maps and charts that explain the European Union

Graph 10. Buczkowski A. (2017, March 27). Top 30 maps and charts that explain the European Union. Retrieved October 16, 2017, from http://geoawesomeness.com/top-30-maps-charts-explain-european-union/.

The European countries managed to establish a single currency because throughout history they had had some concerns that led them to take this measure. The first factor is the openness of European economies since they are so exposed to trade, so they are also more exposed to fluctuations in the exchange rate than other countries in the world. The greater proportion of exports or imports in national income the economy is more sensitive to changes in the exchange rate. Secondly, Europe has historically great fluctuations that have led to economic crises and conflicts between them. And the last factor determining the monetary union in Europe was the common agricultural market. This market for its operation needed a stable exchange rate so that helped in the decision to unify the currency.

Then introducing the causes of Europe’s monetary union will now discuss how monetary policy is managed. Europe’s monetary policy is managed by the European Central Bank which, together with the central banks of all the Member States of the European Union, constitutes the European system of central banks. This system is independent of other institutions such as national governments. Monetary policy decisions are centralized in the European Central Bank so that European monetary policy is unique, but its application is decentralized in the national central banks who are responsible for carrying out operations of an open market in their countries.

The governing bodies of the European Central Bank are the Governing Council and the Executive Committee. The Governing Council is composed of the six members of the executive committee and the governors of the national central banks of the countries that are part of the eurozone. This body is responsible for monetary policy and sets out the guidelines for its implementation. The council normally meets twice a month on Thursdays. The executive committee implements monetary policy and gives instructions to the national central banks. The governing bodies of the European Central Bank agree on the policy to be implemented by voting and win the decision that has the most votes. The governors of the national central banks have the same weight in the votes regardless of the economic importance of each country.

The basic task of the European Central Bank is to manage the monetary policy of the European Union and the main priority of this policy is the stability of prices. The bank has established that price stability is defined as an inter-annual increase in euro-zone price indices of less than 2% but positive. Due to this, the central bank decides its corrective measures based on the deviations of the expected inflation with respect to the desired path. To achieve the price target, the central bank has two strategies, the first is related to the money supply and consists of announcing the benchmark for the growth of money. The second strategy consists of an economic analysis of different economic indicators such as economic activity, labor costs, financial assets among others and with this analysis are fixed the interest rates.

A major problem facing the central bank when making decisions is the economic fluctuations of each member country of the European Union. The asymmetric economic cycles have their origin in different specializations of each country and that causes asymmetric perturbations since the most developed sectors in each country face different offers and demands. There is evidence that, with the European Union, many regions have reduced the possibility of being affected by asymmetric shocks thanks to economic integration, but this effect has not been seen in all countries.

An important factor that reduced the costs of monetary union is the existence of interregional labor mobility, since if the demand for a product in one country is reduced and the labor force in another increase the labor force can move freely to the country where it has this more developed sector. But despite good labor mobility, some economists argue that Europe is not an optimal monetary union since country governors vote for measures favorable to their respective countries or otherwise the measures end up affecting more a country that is in a recession than a country that is well economically.

 

©Forex.Academy

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Labor Market and its Implications

Abstract

One of the aspects that matter most to people about economics is the labor market. It is of special interest because much of the population is active in this market, so it is important to analyze how it behaves, which variables are more important to determine wages. In addition, what problems imply some policies that in the first instance try to protect workers, but end up affecting it as it is the minimum wage. Likewise, unions represent certain frictions in the labor market that, consequently, have undesirable negative effects.

 

The labor market in the economy is one of the most important issues in the economy. To analyze how the job market behaves some concepts must be clear

  • Active population: Sum of people who are working or looking for work.
  • Inactive population: People who do not work and do not seek work.
  • Activity Rate: Active population/working-age population. In Europe, as well as most developed countries the rate of activity has increased over time due to the inclusion in women’s labor market, although this fact does not occur in all countries.
  • Unemployment rate: unemployed/active population. The unemployment rate can reflect two different realities. It can reflect an active labor market where there are large numbers of layoffs, but also of hiring. Or it can reflect a stagnant job market where there is little movement. To find out what is behind the aggregate unemployment rate, workers ‘ movements need to be analyzed and this data can be obtained from quarterly surveys conducted in the countries.

The following charts show the rates of participation of men and women respectively in the labor market

Labor market and its implications

Graph 15. Labor force participation rate, male. Data taken from the World Bank

Labor force participation rate female

 

Graph 16. Labor force participation rate, female. Data taken from the World Bank.

It’s important to keep in mind that many times the surveys carried out and the numbers given by the media do not reflect the reality, because in some cases people looking for work stop looking because they do not find and take these people as an inactive population and this reduces the unemployment rate which politicians use to say that the economy has a good performance which is not true. So, to have a complete picture of job creation and destruction and to know if it is true the unemployment rate is better to review workers ‘ movement surveys.

The job market like any other market has a price variable, which is the salary. Salaries are determined in many ways, sometimes they are determined by collective bargaining between companies and workers, but these negotiations do not cover all workers. Some negotiations are bilateral between a businessman and a private worker. Although in each country differs in the way wages are adjusted, a theory can be developed to explain how the salaries are determined.

According to economic theory, workers usually receive a higher salary than their reserve salary which is the minimum level at which workers are indifferent between working or not. That is, most people receive a salary that at least encourages them to work. Moreover, salaries depend on the situation of the labor market. When the unemployment rate decreases the wages increase because there are fewer job offers. Even when there is no collective bargaining between employers and workers, workers have some bargaining power that they can use to get better wages than their reserve wages

Some companies can pay higher salaries than others to encourage their employees or attract more skilled workers. In the negotiations the companies consider the costs of hiring an employee, the costs of firing him and how much it would cost him to maintain these. The more expensive it is for a company to replace its workers, it will be willing to pay more to keep them. But this is not the only reason to pay better salaries some companies want their workers to be more comfortable in their work and have better performance, so they decide to pay salaries that are called efficiency wages.

As previously mentioned the labor market situation also influences wages. When the unemployment rate is low it is harder to find skilled workers, so wages increase. The following charts show the unemployment rate of several countries and the growth of production.

Unemployment rate

Graph 17. Unemployment rate. Data taken from the World Bank

 

Gross Domestic Product

 

Graph 18. Gross Domestic Product. Data taken from the World Bank

Now it will be analyzed some variables that affect the wage negotiation. On the one hand, workers do not care about the level of nominal wages, but they do care about real wages as this indicates how many goods can be bought. On the other hand, companies do not care how much the salary they pay, but they care about the relationship between the salary they pay and the price of the goods they sell. As these variables are those that come into the decisions of the agents, this article must analyze what happens when they change. For example, if the price level increases in a certain proportion, workers will ask for an increase of at least the magnitude of the price increase as their purchasing power is reduced. Companies will be willing to increase maximum wages in the magnitude that increased the prices. But the decision to set wages will depend primarily on the bargaining power of each agent

Given the above, the existence of trade unions are important as they have more bargaining power than people individually. If there are unions, they will ask that the wage increase be at least the magnitude of the inflation. But if companies are the ones with the decision-making power, they will make the decision to raise wages less than inflation, as they earn a higher income by having better sales margins. Likewise, the existence of a minimum wage or unemployment insurance would alter the labor market.

If there is unemployment insurance in an economy, people will have a higher reserve salary because when they are unemployed they will have an income for a certain amount of time, so it will be harder for them to work for a low salary. This gives a little bit of bargaining power to the workers because if they don’t feel motivated by a company they will decide to quit and be unemployed for a while until they find a job that motivates them enough. Efficiency wages will also have to be higher because it is not so expensive to be unemployed for people.

Another variable affecting wage fixing is the existence of a minimum wage or protection of workers as it may be in some cases that the minimum wage is higher than the reserve salary of the people so that it will be positive for the workers but for companies will be a contracting barrier and in some cases it can increase the unemployment rate in the countries because if the minimum wage level is above the productivity of the workers and is greater than their reserve salary will be very expensive for companies to hire and decide to hire less or use machines that lower their production.

Now it will analyze the aggregate supply of goods and their relationship with the labor market. An increase in production causes an increase in employment. The increase in employment causes a decrease in unemployment and therefore in the unemployment rate, which causes nominal wages to rise as well since it is more difficult for companies to replace their workers. The rise in nominal wages causes prices to rise on the part of companies. These effects also affect the agent’s expectations if prices are expected to increase in a certain proportion in the future in the negotiations, the salary will be asked to increase in the same way.

In the demand when there is an increase in the price level this causes a reduction in production as it decreases the real quantity of the amount of money which leads to an increase in the interest rate which in turn leads to a decrease in the demand for goods and services. Production depends negatively on taxes and positively on the real amount of money in the economy and on public spending.

In some situations, in the short term, production is higher than the natural level of the economy, that is, the economy is producing beyond its capabilities. When this happens, the price level increases which leads to higher expected inflation and this ends up impacting wage negotiations. The above effect concludes with a decrease in the real amount of money which generates an increase in the nominal interest rate and this reduces the levels of production taking it to its natural level. It is normal that in the short term the growth of production is above or below the natural level, but in the long term, these imbalances are eliminated.

After analyzing the aggregate demand, the short and medium-term effects of an expansionary monetary policy will be analyzed. In principle, when a central bank emits more money to the economy, the amount of real money increases and therefore production does so in the short term. This leads to an increase in the price level which affects the price expectations. The effect of monetary expansion dissipates when production returns to its natural level thanks to the fact that price expectations adjust to this new scenario leading to higher wages in the short term.

On the other hand, inflation also affects the supply and aggregate demand for goods. An increase in expected inflation causes an increase in effective inflation since there are several channels of transmission of these expectations. A channel is the negotiation of contracts because if they expect a higher price level in the future they will set higher nominal wages, which in effect leads to higher prices in the future. Inflation is also related to unemployment since when there is an increase in the unemployment rate this causes a decrease in inflation. This occurs because an increase in unemployment causes a reduction in nominal wages, which directly leads to a reduction in the price level.

In conclusion, the labor market is very important for people because they are constantly involved in it. In some countries, there are rigidities in this market that end up affecting the whole economy as we saw in this article because it ends up affecting inflation and production. Unemployment insurance, a high degree of employee protection, forms of wage negotiations and the minimum wage are some of the rigidities that the labor market faces. When the inflation rate reaches a high level, inflation tends to be more variable and consequently, the agents of the economy are more reluctant to sign long-term contracts that are not flexible. That is why salary negotiations are done every year or even for shorter periods of time.

In the medium term, the growth of production is equal to the normal rate of growth, unemployment is equal to its natural rate and both variables are independent of the growth of the nominal amount of money. The growth of the nominal amount of money only affects inflation. That is, inflation is always a purely monetary phenomenon since, in the presence of other factors such as monopolies, unions, and strikes, fiscal deficits do not affect inflation unless they affect the nominal amount of money.

 

©Forex.Academy

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The Meaning of Cutting Losses Short

Abstract

In this article, we deal with the different ways and aspects of the stop-loss setting. A crucial task for a successful trader.

Introduction

What a price to pay for bad wisdom? Too young to know too much too soon… (Suzanne Vega)

The decision where to cut losses if a trade is not working should be part of the trade selection process for every trade, and should be assessed in connection to the potential profit; so the risk to reward should comply with our trading rules.

One key example of the need for cutting losses short (in other words having high reward to risk trades) was given by trader Glen Ring when interviewed by Bruce Babcock (The Four Cardinal Principles of Trading).

He had a month when he made eight trades, with seven winners and one loser, but the net result was a losing month, just because of a single big loss.

The opposite may hold: you might experience eight trades with seven losers and still be profitable just because your system’s mean reward-to-risk ratio is excellent and that winning trade erased the losses of the seven losers.

The key lesson is: Although psychologically, we need to be right, we must focus on a reward-to-risk ratio, not in the frequency of our gains.

In Glen’s words: “Having those small losses is what keep us in the game, keeps your position for when you do catch a trend or big move. But, it’s a law of numbers to me. If we can make enough controlled-loss trades, even a blind squirrel is going to find a nut once in a while.

The Stop-Loss Concept

The stop-loss concept is related to position size. Trend following’s main idea is to catch the big trend. Its rate of success is reduced -below 35%-, but with potential big reward to risk ratios.

There’s a small chance for a trader to have ten losses in a row. A trader that risk no more than 2% of its equity on a single trade experiences a 20% drawdown at the end of 10 consecutive losses, but may still keep following the rules. A trader risking 5-6% will be 50 or 60% down, and undoubtedly will lose perspective and may stop following the rules, even though the system hasn’t failed.
The main lesson is: Trade thin instead of big at the beginning, analyze your potential drawdowns in losing streaks as a mean to optimize your position size of your system.

Minimizing losses means that we are in control. Being in control is the difference between being a speculator and a gambler. Being a speculator means we can decide on the odds. Be in control about when to enter the market and when to exit. That can’t be done gambling.

We’ll discuss the several methods top traders use in their trading systems. They can be divided into the following categories:

  • Chart-based stops
  • Indicator stops
  • Entry method stops
  • Volatility stops
  • Money management stops
  • Account equity stops
  • Margin-based stops

 

1.    chart-based stops

Chart-based stops are those stops put near a meaningful point on a chart. This may be related to a chart pattern, trend line or pivot point that represents support or resistance.

Cutting losses short don’t mean unrealistic tight stops, though. It’s important to give latitude enough to let the trade work.

So, cutting losses short means to close a trade if, by our rules, has touched the stop point. But that point shall be placed according to the logic of the price movement.

Also, it’s wise to let a wide margin at the beginning of the trade using a small position, but, as the trade develops in our favor, we should move the stop higher and, optionally, add to the position.

What happens if the chart stop defines a trade that’s too risky? In the futures market, the minimum risk one can take is the one assumed by trading one contract. In the case of currency markets, this isn’t an issue, so the answer is: reduce your position to the level you have set in your trading rules. The number one rule is to protect our capital.

The chart method to set the stop has its detractors. In Babcock’s book already mentioned, Jake Bernstein says that John Granville used to say: “if it’s obvious, it’s obviously wrong.” “Let’s put the stop at the low of the day.” Ten thousand people are thinking the same way. The odds are that approach is not going to work.

How To Set Stop Loss In Options Trading

The Last Day Rule:

Peter Brandt, mentioned in Babcock’s book, has what he calls “the last day rule”. He applies it to breakout trades to reduce losses on failed breakouts.

The rule calls for a stop set at the opposite extreme of the last day of the previous range pattern. If the break is to the upside, he sets the stop to the low of the last day within the pattern. If to the downside, he uses the high of that last day.

The use of retracements, Fibonacci:

Some traders use retracements as places to start a trade using Fibonacci retracements. One way to place entries and stops is, for instance, entries at a 50% retracement and stops at 62%, that way we plan for a 50% potential profit with a 12% risk; more than 4:1 RR.

Moving to break-even:

One method that helps release stress and anxiety from the trader is to move the stop to the break-even point if and when the price has moved to a level that allows to do it.  Then the rest of the trade is a free ride. This has been recommended by many authors focused on trader’s psychology (Alexander Elder, Mark Douglas, Van K. Tharp).

2.    indicator stops

Indicator stop means setting the stop by virtue of an indicator, such as a moving average or momentum.  It’s not a chart-based stop since it’s computationally based.

Indicator stops seek to optimize the relationship between cutting losses short and not getting chopped up at the same time. That’s difficult to achieve without studying past trades for improvement. Indicator stops tries to optimize the relationship between cutting losses short and not getting chopped up at the same time. That’s difficult to achieve without studying past trades for improvement.

To optimize stops we need to back (or forward) test which is the stop distance beyond which there is are more money lost than gained. For more on this, I recommend John Sweeney’s concept of Maximum Adverse Execution. To optimize stops we need to back (or forward) test which is the stop distance beyond which there is are more money lost than gained. For more on this, I recommend John Sweeney’s concept of Maximum Adverse Execution.

The main idea of the MAE using Sweeney’s words is:

 “It turns out that if your trading rules are consistent and can distinguish between good and bad trades, then, over many experiences, you can measure how far good trades go bad and, usually, see at what point a trade is more likely to end badly than profitably. That is the point at which you stop and/or reverse.”

 

How To Set Stop Loss In Options Trading | Forex Academy

How To Set Stop Loss In trading

(figures taken from John Sweeney’s book)

3.    entry method stops

By entry method stops, it means some stop point that is set by the entry method. It may be a reverse entry signal, or it may occur as a result of the violation of some or all of the trade’s entry conditions.

“The same methodology that says enter the trade has to tell you when the trade is wrong. [..] If a market exceeds the price and time projection windows, then the trade is wrong” (Robert Miner)

Robert Miner has a price and time zone. If price breaks the zone or if the time window is reached without gains, he closes the position.

4.    volatility stops

Volatility stops are stops placed at a distance from the entry calculated as some percentage of recent or historical volatility. In general, volatility is measured as a price range computed over a time-lapse.

Stan Tamulevich, interviewed by Babcock for his book, uses the three to four-day volatility. If the market takes out the distance of the last day, he quits the trade. Usually even less than that. If the market takes out 50% of last day move ¡t enters in a danger zone.

Russell Wasendorf, another trader interviewed, sets his stop outside the range set by historical volatility. Short-term volatility increases don’t change his plan. His method is more concerned with not getting shaken off a potentially winning position rather than improve its short-term risk.

5.    money management stops

Money management stops mean fixed dollar amount stops. It’s a combination of stops and dollar risk management.

The two main advantages the author sees are:

  • If the purpose of stop-loss is to manage risk, a dollar stop is the most direct way to manage it.
  •  That kind of stops don’t go to obvious places, except by coincidence, so the risk to be whipsawed by the market is reduced.

6.    account equity stops

An Equity stop is based on a fixed percentage of the account equity. A variant of money management stop.

It’s a methodology that starts by defining in dollar terms what’s the risk allowed by the account’s rolling balance of the trader. If we assume 1% risk is set,  this leads to a dollar risk amount for that account balance.

Then the risk-dollar amount of the potential trade is computed. If it falls within the 1% risk the trade is taken, opening the number of contracts within the 1% risk rule.

If the loss is not within the 1% rule, the entry point must be adapted to bring it close enough to the exit point, so the risk is no more than 1%.

7.    margin-based stops

A variation of the previous type. Stops are calculated by taking a percentage of the exchange margin. This is specific to futures trading.

8.    main points to remember

  • Cutting losses short is the most important rule in a trading plan
  • The trader should be more concerned with the reward-to-risk ratios than with the percentage of winning trades.
  • Chart-based stops set stop points in the proximity of market bottoms/tops.
  • Indicator-based stops look to optimize the stop point using math and historical analysis of past trades.
  • Volatility stops try to keep stop points away from the volatility cloud.
  • Account-equity stops move the entry point of a trade to a place that complies with the percent risk rules of the account.

9.    conclusions and criticism

Stop-loss definition is a difficult task, but it has to be designed with care, as is the main concept to success in trading.

In Mr. Babcock’s book, the primary focus is the futures market, that presents a very poor atomization of the position. I mean, the minimum size allowed in the futures market is ONE contract so that the minimum risk would be the risk of that single contract from an entry point to a stop point. That makes it difficult to split the concept of “cutting losses” with the concept of “position size.” In the currency markets, this is not the case, as we can do it down to micro-lots, which makes it possible to do independent optimization of the two concepts.

I think a combination of Chart or volatility-based stops is the initial stage towards the definition of this task as part of a trading system. But a second step might be to optimize stops using John Sweeney’s MAE concept. For this, we might need a computerized analysis of our past trades, or a back-test, if the system rules can be automated.

We may design a continuous improvement process, by a careful annotation of the behavior of our current stops for further analysis in search of better places.

Regarding position sizing, this is a subject for another essay, it suffices to say for the moment that we could use the before mentioned rule: don’t to risk more than 1% of our current trading account balance, and if you’re starting trading, don’t risk more than 25% of that. There’s a Spanish popular wisdom sentence: ” En dinero y amistad, la mitad de la mitad” (about money and friendship divide by half and then by half).

We should remember that the primary goal of a trader is to survive.

 ©Forex.Academy
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Expectations about the Economy

Abstract

There are a great many intertwining variables that influence the current state of the economy at any given time; such as unemployment, wages, internal production, investments and many more. But with all of them, it is the expectations of people, and investors that generate certain effects, that explain the behavior of some of these variables and which will affect future conditions. For example, if people expect future interest rates to be higher, they will consume or ask for more loans in the present because in the future it will be more expensive to consume. But without rising interest rates, consumption varies today due to expectations. Another example is the expected interest rates in a country, depending on these expectations the investors decided where to place their capital without waiting for this to happen.

The economy in its short-and long-term models must consider the expectations of consumers, companies and all representative agents within an economy. To consider the subject of expectations first the interest rate variable must be introduced. Interest rates expressed in units of a national currency are referred to as nominal interest rates and these rates appear in newspapers and financial pages. Interest rates expressed in a basket of goods are called the real interest rate and are called that because it is beyond inflation and reflects the cost of acquiring goods that will be consumed by people, what is truly important.

There is an equation that establishes that the real interest rate equals (approximately) the nominal interest rate minus the expected inflation. That is why in some media, although they mention a decrease of the interest rates it is said that the interest rate is still contractionary or it may be that the nominal is lower one year than another, but that does not indicate that loans are cheaper than the previous year, so you should consider real interest rates. The interest rate directly affected by the monetary policy is the nominal interest rate. The interest rate that affects spending and production is the real interest rate. Given this difference, it will be possible to see in the news that they contradict the potential effects of monetary policy on the economy and financial markets. In the following graph, you can see the real interest rate of some countries.

Graph 6 Real Interest Rate. Data taken from the World Bank.

To summarize the issue of interest rates it should be clarified that the nominal interest rate indicates how many euros must be returned in the future to obtain a euro today and the real interest rate tells us how many goods must be returned in the future to obtain a good today. Nominal interest rates will affect investment decisions between bonds, stocks, and money, while the real interest rate will affect project investment decisions. In the short term, an increase in the growth of money leads to a decrease in both nominal interest rates and the real rate, in the medium term, an increase in the growth of money will not affect the real interest rate, but if it raises the nominal interest rate.

The bonds issued by a country are differentiated in two aspects: their risk of default and the time of their pay. There are some bonds that have better coupons than others, which are riskier by defaults and that ends up affecting the price of bonuses. But for economic purposes, this part of the article will focus on the bond term. Bonds with different times of paid have different prices and different interest rates that will be called yields. The yields of short-term bonds, usually a year or less, are termed short-term yields and, if the bonds are more than one year, they are called long-term yields.

The interesting thing about analyzing the bonds is to determine the curve of the yields and the relationship between short-term and long-term rates. The price of a one-year bond varies inversely with the nominal interest rate at one year that is in effect at the present. The price of a two-year bond depends both on the interest rate to a current year at the present, as well as the expected interest rate for the following year. The interesting thing about analyzing bond prices is that bond yields contain the same information about future interest rates as the bond yield curve fully reflects the agents ‘ expectations of the economy of a Country.

To begin the analysis, a term performance must be defined; The term yield of a bond to N years, or in other words the N years interest rate is the constant annual rate that makes the current price of the bond equal to the current value of the future interests generated by this. By examining the yields of the bonds at different times, we can deduce the expectations of the financial markets on future short-term interest rates. For example, if you want to see the expectations of the financial markets in one year you should observe two-year bonds which have included expectations about the interest rate that will be at the end of the year and observe bonds to a year of maturation.

When the yield curve has a positive slope is when long-term interest rates are higher than the short-term, financial markets expect short-term interest rates to increase in the future. When the yield curve has a negative slope long-term interest rates are lower than those of the short-term, but markets expect this situation to change and short-term interest rates fall in the future. To observe market expectations during the crises of 2000, 2007 and others, it is interesting to observe the curves of the bonds of the countries that reflect the expectations of the financial markets and the decisions that were expected to be taken by the banks Central. It is important to note that the interpretation of performance curves only focuses on expectations and in most cases the decisions of banks and market agents are unpredictable. In the following two graphs you can see the bonus yield curve. In the first graph, you can see a bond of Colombia and in the second is the types of curves of yields.

Graph 7.   Curva de Rentabilidad TES Tasa Fija 

Graph 8. Roca E.  Estrategias con bonos

Leaving the issue of bonds aside, the behavior and expectations of consumers and businesses will now be analyzed. These two market agents always respond to their expectations about the future. In economic models, you have a consumer who is extremely far-sighted about the future so consumers will always be thinking about what affects their consumption in the following periods. While not all consumers are like that, in reality, it is a simplification that helps to understand the formation of expectations and how they would respond to an external shock.

To understand consumption decisions, it is essential to take an intertemporal perspective because what a consumer spends and borrows today will impact their future consumption. It is assumed that individuals are rational but in the models, it is assumed that the individuals are identical to simplify the models. According to the theoretical models, consumers are very sensitive to variations in income. An explanation of this is that credit is not available to the whole population so if income disappears or decreases it will have an immediate effect on consumption as there would be liquidity restrictions. Or in another example, before the tax rate reduction is announced soon, consumers can anticipate a future increase in their income because consumption will increase at the present. These effects on consumption depend a lot on the type of agents that there are because in some countries there are more wealthy people and they do not have liquidity restrictions when compared with another country where much of the population have problems to access to credit or income is very low.

Consumption probably varies more than the current income. For example, if you have the expectations that the decrease in your income is permanent your consumption will fall in the same proportion. But if the consumer believes that the effect is transient, they will adjust their consumption less. In a recession, consumption does not adjust to the same magnitude in which the income decreases because when a rational consumer knows that it is a temporary shock and the economy will end up retaking its natural level of production. The same happens during the expansions since the rent can increase but it is not proportional to the increase of consumption because it is a momentary shock.

It should be considered that consumption probably varies, although the current income does not vary. Presidential elections, changes in Congress, or changes in people’s expectations of the performance of the economy or international relations can affect consumption without the income being affected. Even some recessions are exacerbated by people’s expectations of a crisis greater than that which exists

It will now be analyzed how companies make their decisions depending on the expectations they have. As mentioned earlier in this article investment decisions by companies depend on the real interest rate differently from the way people do in considering the nominal interest rate. Corporate decisions also depend on household consumption, sales, and expectations. A company when it is going to invest in machinery and capital to develop its activities more efficiently must make a comparison. The companies must first calculate the expected value of the benefits that the acquisition of that machinery would bring, and then compare this to the costs incurred in buying that machine.

In short, if the company believes in its expectations that the benefits in the future will be greater than the costs of its investment, then it will decide to invest. The higher the actual and expected real interest rate, the lower the expected value of benefits and this will reduce the investment the company makes. The sum of the real interest rate and depreciation is called the cost of capital use and they have adversely affected the investment decision of the companies.

If a company experiences an increase in sales that is believed to be permanent, the expected value of the benefits will also increase what will lead to an increase in investment. But it’s similar to what happens to consumption, the investment does not respond in the same magnitude as sales as the investment is not continuous as can be the consumption. Once a new technology has been implemented, the company has no incentive to continue investing beyond a certain equilibrium point. That is why it can be concluded that investment is much more volatile than consumption, although they respond in the same way to external factors such as recessions and economic booms.

If monetary expansion leads financial investors, businesses, and consumers to revise their expectations of future interest rates and future production, monetary expansion has an influence on economic output, but if the expectations do not change, central banks will not have good tools to affect production as they have small effects on the economy. If a change in monetary policy does not surprise the agents of the economy, expectations will not change and production along with other variables are not affected. The effects can be deeper or not in expectations, but it does not mean that expectations are random and erratic. Economists assume that there are rational expectations in their models and on this basis monetary policies are formulated.

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Foundations of Fibonacci Extensions

Introduction

A very popular application of Fibonacci deals with the projection of levels up to where price is expected to further develop. In the article “Foundations of Fibonacci Retracements” (https://www.forex.academy/training/technical-analysis/beginner/foundations-of-fibonacci-retracements/) we covered how to use Fibonacci to locate price areas where impulses could bounce in every wave of a given trend, as well as to its application for risk management (i.e. where to set the Stop Loss). In the following article, we will introduce readers to the basic Fibo extensions as a good set up to exit winning positions.

The first step of the analysis is to draw boxes on the price chart that represent the distances between the top of the range and the closest which Fibonacci retracement level, and from that point to the closest low or high and so on. Priority must be given to the height of the boxes and not to the width. Each box will be subdivided between the Fibonacci retracement levels to determine the percentage ratios within the boxes; these levels may be a guide to fix a target price. However, the problem with the projected boxes is that we won’t know exactly whether such subdivisions are resistances or major supports, i.e., we don’t know how likely it is for the price to reach such levels.

To establish the robustness of the boxes´ subdivisions, i.e., how strong price will be contained, the last range of the box should be subdivided if it is in a bearish trend market to the line ending the box using Fibonacci retracements. And the strategy would be to wait for these areas to respect the market and rebound the price or continue to fall. Using this strategy to draw boxes from the Confluence zone to the limit of the range either higher or lower and replicate this box in the opposite direction, and then subdivide the box into Fibonacci levels does not always ensure that there is a successful trade, so you should use more analytical tools; and the first step is to have the confluence areas analyzed before and from this, project the boxes.

In some cases, the boxes will not share the same edges as the confluence zone due to, for example, gaps between both boxes. To find out which prices are areas of resistance or larger supports, inner boxes of other boxes must be carried out between the current price and the nearest percentage that would be of 38.1% and in this inner box are made the subdivisions with the Fibonacci levels which create areas of confluence that show that zones are supports or greater resistance.

To make this concept clearer, two examples will be presented below. The first example will be NOK/SEK. It is a market with a bearish tendency so you want to find supports that the market could respect and bounce, or find supports to where the market could reach if you continue lowering. At the beginning of the confluence zones were determined and from there, the graph of the upper table was created. This box is then replicated at the bottom of the confluence zone and the Fibonacci levels are determined within this. Having these three boxes you can see the smaller supports that the market has before reaching the minimum price. To find out which of these are areas of larger supports would have to draw another box in the smaller one to have areas of confluence. After having the three boxes drawn, you can see a blue line where a major support is located because it is a zone of confluence and the market should respect these levels of prices.

Foundations of Fibonacci Extensions

The second example is the USD/CHF asset, which is at a similar stage to the first example. The procedure is the same, being the first step to find the confluence zones with the Fibonacci retracements because it gives more strength to the analysis of price projection to be achieved with the boxes.

Fibonacci Extensions

As mentioned in the previous articles, the market gives clues on where to draw the ranges and boxes that at the end show areas of confluence. Care should be taken with graphics that have gaps as this will generate the drawn boxes do not share the same edges.

 

Introduction to Fibonacci Expansion

Another method used by traders is the Fibonacci expansion and the objective of this tool is to determine the third oscillation. Unlike Fibonacci regressions, this tool uses the market fluctuations instead of the bias for its construction. With the Fibonacci expansion, traders will be able to obtain signals of how the waves evolve, and with the analysis of the waves could be projected where the end of the bias is and the following rebounds that will suffer the price.

The important percentages used by the tool are 0618, 1,382.1, 1.5 and 1,618; These percentages must be expanded along the graph to touch the axis and to verify that they are important areas that the market has respected in the past. Several ranges have created starting from the same starting point to find zones of confluence and thus to see zones that are resistances or supports. It is important to keep in mind that if you are going to trade on long horizons of time, you should observe more data from the past that will allow for a clearer picture.

As in the article “Foundations of Fibonacci Retracements”, with this type of analysis, if you want to find a major support, you must start from a high price and go down in the graph to a low price where there is a market signal that shows significance at this level. To know where the high price should be located, a market correction point should be chosen. After having a certain range for the analysis, a box is drawn from the higher price chosen to where the bias is triggered. Then a second range will be drawn starting from the highest point, which was used for the first box and lowered in the graph to where the Fibonacci ratios seem significant. When both boxes are subdivided into the Fibonacci ratios, there are confluence areas which represent larger supports or resistances, which will be added to the confluence areas analyzed above. The short-term confluence zones will serve in the long term and vice versa, so it`s suggested to observe different horizons of time to appreciate various important levels. Using the Fibonacci expansion as given by the various trading programs and programming correctly, the graph of the market should be seen in the following way where, as with Fibonacci retracements, areas of confluence should be found to know that Fibonacci ratios are higher supports for the market.

Forex Academy: Foundations of Fibonacci Extensions

Elliot Wave Principle

Elliot’s principle is based on the discovery of Ralph Elliot who discovered that the price of the action was not random, on the contrary, it follows a certain logic and order. Elliot saw the same patterns repeating in different cycles, which reflected the prevailing emotions of investors. The movements were called waves. The basic interpretation of Elliot’s principle is that every action has its reaction. When there is a bullet market there are 5 waves moving in the direction of the bias and after this is exhausted there is a three-wave movement. In the next graph, you can see the waves that compose a bull market. The waves numbers 1,3 and 5 are impulse waves and numbers 2 and 4 are correction waves. While the bias exemplified by the letters shows the correction of the whole upward bias.

Elliot Wave Principle

Having explained the principle of Elliot’s wave can be related to the Fibonacci expansion. As mentioned before, the Fibonacci expansion will allow projecting an objective price to where the market price will reach, the Fibonacci projections are calculated after an impulsive phase is produced with its subsequent correction. The projection of 0.618 and 1 is usually used for the waves 5 once the waves 3 and 4 are located, the projection of 1,618 is usually given in the waves 3 once we have located the waves 1 y2. Finally, the projections of 1 and 1,618 usually appear in corrective waves C when the waves A and B are already located.

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Foundations of Fibonacci Retracements

As it shall be further developed throughout this article, the first step when applying the Fibonacci method to construct support and resistance levels of a broader price trend is to identify and connect lower and higher points of wave impulses before a bullish move, and higher and lower points when the move is bearish.

It is important to notice that Fibo percentages of any range when taken from high to low and from low to high correspond to 100. For example, as it is shown in the graph below, if one connects the higher and lower price levels of a bearish wave, the distance between the highest point and the 61.8% retracement equals 38.2% retrace if the connection was to be made from the lowest to the highest points in an inverted symmetric wave. In other words, Fibonacci retracements determine portions of the wave that coincides to 100 when summed up, so that the high price is to 61.8% as the lowest price is to 38.2%. The observation described above has immense implications when attempting to predict the strength of price after a breakout of a given cluster in concordance with other analytical tools. For instance, a 61.8% retracement would indicate that the counter-move is deep enough for us to regard price as losing steam in a broader trend so that a break higher or lower is more likely to complete the 38.2% extension. After all, Fibo ratios are, like any other technical tools, instruments of price prediction and not written-in-stone dogmas.

Foundations of Fibonacci Retracements

The graph above is taken on the USD/CAD and shows the simplest process of connecting the highest and lowest price points of a bearish wave in order to determine resistance levels of a possible retracement move.

When applying Fibo techniques to determine support and resistance price levels, it is important to avoid the following very common mistakes:

  1. Not any High or Low should be plugged with every Low or High to construct resistance or support levels respectively. The reason for this is that;
  2. Not every time interval has the same robustness, i.e., faster time-frames could see the complete counter-move as profit-taking without negating the impulse of a broader trend while a smaller counter-move of a broader time-frame could also be the completing of a broader wave.
  3. Depending on the chosen time-frame of the graph, a wave can be completed or not, thus the Fibo levels are correctly or incorrectly constructed. For example, some authors suggest that it is best to build Fibonacci retracements levels in ranges that include strong price impulses; or ranges when a subsequent sub-wave marks the final thrust of the price.
  4. The time validity of any Fibo setup depends on both the underlying time-frame of the graph and the degree with which a charted range has developed. Put it differently, when considering price as a “living entity”, it is relevant to notice that every technical setup is valid to a certain point and time.

Consequently, constructing Fibo retracement is far more complex than simply connecting some random Highs with Lows or Lows with Highs. Albeit the popularity of Fibo techniques, mainly due to its predictability power, they are only a method that should, in fact, be combined (better to say, completed) with other techniques. The cornerstone of Fibo retracement (and extensions) relates to the degree of robustness of the resulting pivots in terms of the strength with which such price levels succeed in preventing price breakouts, i.e., how well they serve as thresholds that manage to “hold” price within a given range in time and frequency.

It is typically assumed that robust support and resistance levels are the results of using Fibo tools from different divisions and sub-divisions of a certain price range. In other words, any price wave (i.e., oscillation) can be circumscribed in broader waves when observing the chart in a slower time-frame. For example, an 800-pips wave on a monthly chart could easily contain 10 sub-waves on a daily chart and 100 mini-waves. By incorporating slower charts, i.e., wider ranges, we achieve more robustness to the analysis. Adding multiple time-frames allows us to transform simple price levels into clusters and when such areas prove to consistently contain price, they become pivots.

A multi-dimensional approach is belonging (in its simplest form) to a third analytical methodology known as relational. Every trader must print robustness to the price levels he constructs so that a faster and more frequent scalper would combine the 5, 15 and 60 minutes charts while a long-term and more fundamental oriented trader would consider the 4 hours, daily and weekly charts. That leads us to the assumption that there is no “correct” or “incorrect” way to apply Fibo when building up supports and resistance levels; a construction is better or worse depending on how much it fulfills the needs of the trader.

Fibonacci Retracements

In the graph, you can see how the Fibonacci retracements should be applied if you want to find market resistance. Also, you can see that the market respects on several occasions important Fibonacci percentages such as 38.2%, 50% and 61.8% over the monthly period.

In the second example, you can see how Fibonacci retracement should be plotted to find market supports. The range was drawn from the bottom to the top of a bullish trend before subdividing the range to find market supports.

Foundations of Fibonacci Retracements - FA

How to find the range to apply Fibonacci if the market is constantly expanding and shrinking continuously and the oscillations are not symmetrical? To start creating the ranges, there are occasions where the market gives the starting points to locate these ranges. For example, when there are gaps in a bearish market, you can locate the 50 Fibonacci percentage right in the middle of the gap trying to get it aligned. But it is not enough to align the line of 50% with the gap, it is necessary to observe that the high and low prices and the subdivisions are respected by the market in different horizons of time. For this, the subdivisions must be plotted to the axis so will be easier to see if the market has respected these points in its various oscillations. In addition, some points of the subdivision although they have not generated a rebound of the market, can be areas where it begins to develop a strong trend whether bullish or bearish.

After the correct range is selected, the question arises as to whether Fibonacci subdivisions within the range are major resistances or supports. To answer this, you must define a second range with the same starting point of the previous range, but with a different length. When these two ranges have been created, an entry goal will be generated that is called a confluence zone (i.e., price cluster). This area of confluence is formed when different Fibonacci ratios of several ranges overlap each other, showing that there are areas where Fibonacci levels fall at the same market price, it should be clarified that they are not necessarily the same percentages of the ranks.

The confluence areas are tipping points in the price and these areas determine higher supports or resistances. Already knowing the areas or prices that are major resistances or supports, it will be possible to project the direction of the market in the future because it will be known which areas will generate a rebound in the market, or if it is the case that the market breaks it will trigger a strong movement when the market continues its tendency without rebounding. With this knowledge of important points, you can put stop loss points if the market rally takes an unexpected direction or if it exceeds areas of confluence.

To be more confident which will be the correct direction of the market, we need to create a third rank with their respective subdivisions. When the third rank is made in the market, we must be sure that its subdivisions have been respected by the market in the past more than once to ensure that they are not minor resistances or supports. With the third range already made, it will be possible to see sometimes that there is more than one zone of confluence between the subdivisions of the ranks. A key element will be to locate the stop-loss outside of the confluence zones because if it is located between these will not achieve the goal of stopping the losses but possibly stop the gains if a rebound is generated in the areas of confluence. It is also important to mention that Fibonacci ranges should be larger when the trade is long term to be able to observe the entire market panorama than in a short-term position and therefore also the stop loss point will be farther away in long-term horizon operations.

For example, the following graph shows the EUR/USD with three ranges traced with Fibonacci retracements. As you can see, there is a confluence zone around the price 1,19949 since several of the subdivisions of the ranks coincide at this point, forming a major resistance as it began to trace from a low price to different high prices. Another example to see the stroke of the three ranges and their confluence zones is the next graph of the AUD/USD, where you can see several areas of confluence that will be major supports given the way the ranges were plotted from a high price to different low prices. Below the area of 0.89870, there is a major support as well as the level of 0.69470.

Fibonacci Retracements Chart

Fibonacci Chart

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Introduction to Fibonacci Analysis

Fibonacci analysis: A mathematical ground to construct support and resistance price levels.

One of the most complex aspects of trading certainly has to do with risk. How to measure and manage it regardless of any given set up or market environment or direction of the price (i.e., whether it is bullish or bearish). Potential profit and risk are thus correlated concepts; consequently, higher yields imply higher risks and vice versa. The main objective of any sound price analysis is then to maximize profit by keeping risk tightly under control; or, in practical terms, to preserve profit from running off in the long-run. However, the complete elimination of risk is nothing more than an illusion, what truly matters is to make it compatible with the preferred profit-generating setup, and it all starts with recognizing such risks and controlling them through the application of a wide range of tools. Despite the complexity of risk management, novices should not be discouraged; after all, even the most experienced traders still fail when applying their preferred tools, sometimes due to a know-it-all attitude, and because such tools are not comprehensive enough to account for all market conditions.

Fibonacci-based techniques are powerful enough to dedicate a complete set of articles; they will cover the conceptual basics, some easy-to-digest mathematical grounds, and of course, practical ways of applicability.

The Fibonacci sequence is an infinite sequence of natural numbers, which begins as follows –

0,1,1,2,3,5,8,13,21,34,55,89,144, 233, 377….

Starting with 0 and 1, the following numbers are the sum of their two predecessors. The golden ratio, on the other hand, is a universal law that explains that everything that grows and decays, evolves. Trading in financial markets is just one of the varied ways to apply the sequence. The golden ratio is a continuous geometric ratio resulting from a straight line between two number A and B. The straight line is divided into a segment longer than the other, and the total A + B segment has the same ratio to the more extended segment A as the long segment A has towards the shortest segment

Introduction to Fibonacci Analysis

The relationship would be written as follows: A + B: A: A: B. The golden ratio is an irrational number and the positive solution to this relationship is 1.61803398874989 … This relationship is found in some geometric figures and in nature, as for example in the shell of Nautilus, a species of mollusk.

Fibonacci

This golden relationship is viewed as an aesthetic and even divine character for some people. When a Fibonacci number is divided by its predecessor, a ratio is created, and, as those numbers become larger this ratio will approach the golden ratio.

The power of Fibonacci when added as an extra tool of market analysis lies in the fundamental observation that price does not move in a linear fashion; instead, it progresses in oscillations that can match natural ratios therein described. Put it differently, Fibo ratios describe the way price naturally moves in either direction, so that every new “step” higher or lower (regard such moves as waves) generates price clusters known as pivots; such “barriers” if you will follow patterns where levels are “stationary” areas usually categorized as supports and resistance if price has a bearish (low) or bluish (high) bias if you will.

Through the Fibonacci analysis and Phi ratios, you can test the laws that follow the market and therefore if we follow these principles (instead of going against them), we will be able to anticipate future movements of the market in a more precise fashion. Here is precisely where Fibonacci becomes relevant, either to predict retracement of extension price levels.

It comes as straightforward that Fibonacci belongs to what traders categorize as “technical analysis” as complementary to “fundamental analysis”. As for the retracement appliance of Fibo, the anticipated price levels are setbacks that refer to the possible support areas where buyers re-enter long positions if the dominant trend is bullish; or resistance levels where sellers re-enter short positions if the dominant trend is bearish. These levels are constructed by drawing ranges between the extreme points of the analyzed movement and the distance between the limits of the range there are subdivisions that will be vertical distances and the key percentages of this distance will be 38.2%, 50%, 61.8%, etc.

Why are we talking about retracements? Because in the technical analysis it is known that the prices of an asset move in “tendencies” (waves) either bullish or bearish, but that trend is not continuous; on the contrary, it has levels where the price stagnates due to such resistances or supports levels, simply because the market does not have completely vertical movements but oscillations within that trend.

Given the confirmation of a setback in the price of an asset, Fibonacci retracements will seek to calculate the magnitude of this movement. To achieve this, the Fibonacci tool available in the trading platforms is used and the percentages are applied which are obtained from the Fibonacci series on the magnitude of the previous trend. The percentage of 61.8% is known as the golden ratio and is the limit of the quotient that is obtained from the division of an element of the Fibonacci series with its previous number as the series tends to infinity. The percentage of 50 is equivalent to half the advance of the main trend and the percentage of 38.2 is the result of the subtraction of the unit and the percentage of 61.8%. The above can be seen reflected in the following graph, which shows how a regression would look depending on the Fibonacci percentage.

Fibonacci Analysis

Needless to say, this article introduces the very basis of how Fibonacci ratios can be applied as means to anticipate price retracements (or extensions) in the simplest form; such a tool is nothing but a mathematical approach to the natural ways price develops in any given trend. Being able to draw support or resistance levels by connecting the highest and lowest levels of a certain wave is, however, the first step of the process of charting. More advanced applications refer to the construction of price clusters by, for example, relating different waves from a varied array of time-intervals. To put it differently, Fibonacci is the keystone of more complex methods of technical analysis, but that will be the subject of other articles.

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Trading Glossary

 

200-day moving average

Is a popular technical indicator which investors use to analyze price trends. It is simply a security’s average closing price over the last 200 days.

Absolute advantage

is an advantage in production of certain goods or services some country has over the others due to peculiarities of climate, education, labour resources and other factors. If a country has an absolute advantage in certain industries, it can produce corresponding goods and services at a lower cost per unit, than competitors.

Account

Personal account opened with the company by a client. This account is used to offset the obligations of the client and dealer, resulting from the deals concluded under the present agreement.

Account history

A full list of completed transactions and non-trading operations of a certain trading account.

Accounting currency

Currency unit in which deposit/withdrawal operations are performed.

Adjustment

The decision of a central bank to adjust domestic economic policy aimed at eliminating imbalances of payments or determining an official exchange rate.

Adviser

A trading account management algorithm in a form of a program engineered in MetaQuotes Language 4, that sends requests and orders to the server via the client terminal (platform).

Agent

A brokerage firm is said to be an agent when it acts on behalf of the client in buying or purchasing of shares. At no point of time in the entire transaction the agent will own the shares.

American Depository Receipts

Introduced to the financial markets in 1927, an American depositary receipt (ADR) is a stock that trades in the United States and is denominated in US dollars but represents a specified number of shares in a foreign corporation. ADRs are also freely traded on European markets. ADRs can also be listed on major exchanges. It is a tool for raising capital on the US and international markets. ADRs can have different names depending on the requirements of a particular market.

American Stock Exchange (AMEX)

The stock exchange that had grown into the second largest securities market in the United States from a small securities house. Most of the securities listed on the AMEX are of small and medium sized companies. Its major indices are AMEX Major Market Index and AMEX Market Value Index.

Annual inflation      

Is the increasing of the overall prices for goods and services during the period of one calendar year.

APEC (Asia-Pacific Economic Cooperation)

Is a forum of 21 Asia-Pacific countries that seeks to promote free trade, investment liberalisation and facilitation across the region, as well as to foster economic growth and strengthen the Asia-Pacific community.

Arbitrage

The simultaneous purchase of an asset on one market and sale on another to profit from a temporary difference in the price.

Ask/Offer

The lowest price an owner is willing to sell the stocks.

Asset

Any item of economic value which is the subject of a trade on a financial market. An asset can be a currency pair with an aim to profit from a difference in its exchange rate.

At best

An order to a dealer or a broker to buy or sell a financial asset at the most desirable price available.

At the money

A situation at which an options strike price is identical to the price of the underlying securities. Options trading activity tends to be high when options are at the money.

Balance

The total financial result of all fully executed transactions and deposits/withdrawals to/from an account.

Balance of payments

Shows the balance of a country’s overseas payments and payments incoming from abroad over a certain period of time.

Bank Stress Test

Is a compulsory condition for high estimation of the risk management system under the Direction of the Central Bank of Russia dd. January 16, 2004 No 1379 U “On assessment of the financial stability of the bank with the purpose to confirm it as sufficient for participating in the deposit insurance system”.

Bar chart

A style of chart on which the top of the vertical line indicates the highest price of an asset for the certain period, while the bottom represents the lowest price. The closing price is displayed on the right side of the bar, and the opening price is shown on the left side.

Barrel

Is a unit of liquid volume which is used on international oil market. Price for basic oil brands is estimated in dollars per barrel.

Base currency

Currency unit in which an account, balances, commission fees and payments are nominated and calculated.

Base interest rate

The rate of interest used by commercial banks as a basis for their lending rates. Determined by a country’s central bank, the base interest rate has a direct impact on the national currency’s exchange rate. This makes monitoring its changes a useful indicator for Forex traders.

Basis points

They are 1/100th of a percent. This means 9% would be 900 basis point, and the difference between 9% and 0.5% interest would be 850 basis points.

Bear

Is the market participant who opens sell trades and believes that a currency exchange rate is about to fall.

Bear Market

A market in which stock prices are falling consistently.

Beta

It is a measurement of relationship between stock price of any particular stock and the movement of whole market.

Bid

Is the demand price; the price at which a market participant is willing to buy the base currency.

Bid price

The price at which an investor can sell an asset on financial markets. The bid price is a part of the formula which is used to calculate the expiry level of an asset.

Blue Chip Stock

Stocks of large, well-established and financially-sound companies which hold a record of consistently increasing rate of paying the dividends over decades to its stock holders. Blue chip stocks typically have a market capitalization in thousands of crores.

Board Lot

A standard trading unit as defined by the particular exchange board. Board lot size usually depends on the per share price. Common board lot size are 50, 100, 500, 1000 units.

Bollinger Bands

An indicator that allows users to compare volatility and relative price levels over a period of time. Made up of a Simple Moving Average (SMA), an Upper Band (SMA plus two standard deviations), and a Lower Band (SMA minus two standard deviations).

Bonds

It is promissory note issued by companies or government to its buyers. It speaks about the specified amount held for a specified time period by the buyer.

Brent

Is a standard oil brand which is sourced near the European shoreline in the North Sea. This brand is famous for low percentage of sulfur which makes it attractive for oil-processing companies. Brent is a pricing benchmark for almost 40% of all global oil brands. It is traded on international oil markets.

Broker/Brokerage Firm

A registered securities firm are called broker/brokerage firm. Broker’s acts as an advisor for purchase and sell of listed stocks, they do not own the securities at any point of the time. But they charge a commission for their service.

Bull

An investor who thinks the market will rise; purchases securities under assumption that they can be sold later at a higher price.

Bull Market

A market in which the stock price are increasing consistently.

Business Climate Indicator (BCI)

Is a business climate indicator of a particular country which is based on the responses of business representatives. Index data has a direct impact on the market.

Business Day

Monday to Friday, excluding public holidays.

Call Option

An option that is given to investor the right but not obligation to buy a particular stock at a specified price within a specified time period.

Capital account

Is the other component of the balance of payments directly related to the GNP. It represents the balance of public and private capital inflow and outflow as well as the amount of funds borrowed and lent.

Carry trade

Is a Forex trading strategy in which the profit is gained not from the price movement and closure of profitable position, but from holding position with a positive swap.

Cash

The currency of a nation in its physical form. Cash is money (notes or coins) that is in free circulation and that is used in the turnover of goods and services.

Central Bank of the Russian Federation

Is the primary bank of Russia, its main issuing monetary body. Together with the government, it develops and implements the monetary policy. The bank has broad powers. In particular, it is accredited to issue the national currency and regulate commercial banks. The central bank of the Russian Federation coordinates and regulates Russia’s entire credit system. It acts as economic regulator. The bank supervises lending institutions, grants and withdraws banking licences. The headquarters of the bank are in Moscow.

Channel Line

The channel line (or return line) is a useful variation of the trendline technique. Sometimes, prices trend between two parallel lines; the basic trendline and the channel line. When this is the case and when the analyst recognizes that a channel exists, this knowledge can be used to profitable advantage. Channel line can be projected by constructing a parallel line over the first peak.

Chart

Graphical representation of a particular currency’s price change over the given period of time. The chart types are: the line chart, the bar chart and the candlestick chart.

Chicago Board Options Exchange (CBOE)

Is the US options exchange located in Chicago. More than 2,200 companies 22 market indices and 140 market indices funds with the overall trade turnover of 1 billion contracts are listed in it.

Chicago Mercantile Exchange (CME)

The Chicago Mercantile Exchange (CME) is an American financial and commodity derivative exchange. Originally established as a non-profit organization, the CME is a platform for futures and options trading and is also a regulatory authority, collecting and releasing information about the market and ensuring orderly clearing and settlement of executed trades.

Clearing

The end of mutual settlements on a trade.

Client terminal

MetaTrader 4 or 5 software product that lets the client get information about financial market trades in real time mode (volume defined by the company), perform technical analysis of markets, operate, set/change/cancel orders and receive messages from the dealer and the company as well.

Close position

Is a fixation of profits or losses on an open position; removal of a pending order.

Close Price

The final price at which the stock is traded on a given particular trading day.

Closed transaction

Consists of two opposite trading operations of equal volume (the position opening and closing): buying followed by selling or selling followed by buying.

COMEX (Commodity Exchange)

Is a leading exchange market of valuable metals, department of New York commodity market. Futures contracts are concluded for any month, including the current month. Schedules for trading sessions of every futures contract are individual. Electronic trading session is hold daily from Sunday to Friday.

Commodities

Product used for commerce that are traded on a separate, authorized commodities platform. Commodities include agricultural products and natural resources.

Comparative advantage

Is an economic law referring to the ability of an individual or a country to demonstrate the best performance producing goods and services in the sphere they are the most competent, qualified or experienced.

Concealed Unemployment

Is not reflected in official statistics. It relies on data on how many people receive unemployment benefits in a country. Apart from this group of people, there are other categories of employable citizens: those who are almost out of work, but do not get any jobless benefits; those employed part-time; and those who have not claimed for any benefits for some reason.

Consolidation

Is a period during which a price action fluctuates in a certain range without establishing a trend either up or down. Consolidation is believed to be followed by a breakout in one direction or the other.

Consumer Confidence

Indicates how consumers feel about current economic conditions in their country. The index is calculated on the basis of a monthly survey. The survey respondents are asked to assess economic prospects of their country. The index does not tend to affect the markets much, because it provides a subjective vision of the economy, rather than a real picture. A rising index augurs well for the national currency.

Continuous Linked Settlement (CLS)

Is a global multi-currency payment system founded in 1997 by the largest brokerage Forex companies for cash remittances on currency deals.

Trading Contract specifications

General trading conditions (such as spread, lot size, minimal trading operation volume, trading operation volume increment, initial margin, lock margin etc.) for each instrument

Convergence

A situation in trading when the price of an underlying asset is moving to the price of a futures contract and vice versa.

Convertible Securities

A security (bonds, debentures, preferred stocks) by an issuer that can be converted into other securities of that issuer are known as convertible securities. The conversion usually occurs at the option of the holder, but it may occur at the option of the issuer.

Correction

A reverse price movement, or its sideways movement after the previous uptrend or downtrend weakened, which led to positions’ closing or profit taking by market participants as they considered the price too high or low for further trading.

Cost of Carry

The cost, usually quoted in terms of dollars or pips per day, of holding an open position.

Counter currency

The currency used as the second currency in a currency pair. It is also known as the quote currency. Units of the counter currency are expressed in terms of a single unit of a base currency.

CPI (Consumer Price Index)

Indicates changes in retail prices for goods and services the market basket comprises. The index is based on cost of food, clothes, education, housing and household utilities, leisure and entertainment, charges for health care services and transportation fares. The index is calculated monthly. It is a key measure of inflation in any country.

Credit rating

Measures the creditworthiness of an individual, firm, region, or a country. Ratings are calculated based on past and current financial performance of market participants as well as on the estimated amount they borrowed and obligations incurred. The estimates are supposed to provide potential lenders/investors with insight into probability of on time repayment.

Cross Currency

A pair of currencies traded in FOREX that does not include the U.S. dollar.

Cross rate

Is the currency exchange rate between two currencies, both of which are not the official currencies of the country in which the exchange rate quote is given in.

Currency basket

A selected group of currencies in which the weighted average is used as a measure of the value of a currency not included in the group. Depending on its purpose, the currency basket can include currencies in different relative amounts, a currency’s inclusion on a constant basis or changing depending on different Forex market factors.

Currency code

The three-letter alphabetic code, where the first two letters represent the country of a currency, while the third one stands for the name of the currency. The currency codes are specified by the International Organization for Standardization (ISO).

Currency intervention

Is a significant one-time and direct central bank’s intervention in the currency market and the currency rate implemented by means of buying and selling of large amount of foreign currency. Currency intervention is used to regulate the course of foreign currencies in the interest of the government.

Currency pair

Two currencies which make up a foreign exchange rate, for example, EUR/USD.

Currency risk

Potential adverse movements of currency rates.

Current account balance

Is one of the two components of the balance of payments referring to net revenue on exports minus payments for imports, net revenue on investments and net transfer payments.

DAX (Deutsche Akzien Index)

The benchmark index for the German equity market. It tracks the performance of 30 selected German blue chip stocks traded on the Frankfurt Stock Exchange. There is also the DAX 100 index, which includes reinvested dividends to show the overall return available to investors. The index was introduced in 1987. It is calculated using the Xetra technology.

Day trading

Trading on a financial market during one session within a day. This means that positions opened this day are kept overnight to the next day and the next trading session.

Dealing

Non-cash currency trading.

Dealing center

Company that provides access to the money market.

Debentures

A type of debt instrument that is not secured by physical assets or collateral. Debentures are backed only by the general creditworthiness and reputation of the issuer.A debenture is an unsecured form of investment.

Defensive Stock

A stock that provides a constant dividends and stable earnings even in the periods of economic downturn i.e. even in the extreme critical situations of the stock market these companies continue to pay the dividends at a constant rate.

Deflator

Is a statistical tool used to convert a national currency into inflation-adjusted national currency.

Delivery

A deal on Forex under which a currency is tendered to and received by the buyer.

Delta

The ratio that compares the change in the price of the underlying asset to the corresponding change in the price of a derivative. Sometimes referred to as the hedge ratio. It has a range from 0 to 1.

Demo account (Demonstration account)

Is a virtual trading account rendered by a broker to review and test the trading systems features on financial markets without the risk of losing real funds. As a rule, the demo account interface is similar to that of a live account.

Depreciation

A decrease in the value of a currency as a result of market forces.

Derivatives

Financial instruments with a price that is derived from one or more underlying assets (bonds, options, shares, futures contracts). Derivatives are often used for hedging and also for speculative purposes. Leverage is applied in buying these financial instruments, thus allowing traders to buy derivatives at a price much higher than they can afford.

Direct quote

A foreign exchange rate quoted as the domestic currency per unit of the foreign currency.

Discount rate

An interest rate charged to financial institutions for loans received from a country’s central bank.

Divergence

Is the difference between two correlative instruments. For instance, intermarket divergence happens when one of the markets reaches cyclical/session/history price extremes (highs or lows) when the correlated market does not do so. Divergence quite often signifies a price reversal; (it) is used by traders as a trading signal.

Diversification

Is the distribution of invested or loanable money assets among different investments with the purpose of smoothing out potential losses.

Dividend

A portion of the company’s earnings decided to pay to its shareholders in return to their investments. It is usually declared as a percentage of current share price or some specified value, usually decided by the board of directors of the company.

DJIA

Is Dow Jones Industrial Average index, the oldest stock index in the USA which is a simple arithmetic average of price growth of 30 top industrial U.S. companies’ shares.

Double Bottom(Top)

A charting pattern used in technical analysis. It describes the drop (rise) of a stock/index, a rebound (a drop), another drop (rise) to the same/similar level as the original drop (rise), and finally another rebound (drop.)

Double top

Is a classic pattern of technical analysis used to depict two consecutive price rises placed at the same level; it signifies a reversal.

Downtick

A stock market transaction at a price below the previous transaction.

Downtrend

Is the forex market term meaning gradual decline of a currency price at a certain period of time.

ECB (European Central Bank)

European Central Bank which carries out EU is monetary policy. Was established on June 1, 1998. The ECB headquarters are situated in Frankfurt am Main, Germany. The General Council of the European Central Bank includes representatives of all the EU countries. The ECB is independent from other EU authorities.

Economic Calendar

The date and time when each economic indicator will be released. Economic announcement may cause unanticipated price movement during these periods. Corresponding trading strategy: News Fade.

Economic integration

Is the development of stable relations among neighbouring countries into unification of economies. This process is usually accompanied by coordinated interstate economic policy.

Economy Watchers Survey

Assesses current economic conditions in a country.

Elliot Wave Theory

Asserts that crowd behavior moves in waves. Can be used to identify structure to price movements in financial markets. Elliot Waves come in two forms, impulse and corrective, and patterns made by them are analyzed to predict trends.

Equity

The secured part of the client account, including open positions, that is bound to the Balance and the Floating rate (profit/loss) by the following formula: Balance + Floating + Swap, i.e. the funds on the client account minus the current loss of the open positions, plus the current profit of the open positions.

ESM

Stands for European Stability Mechanism. It is a joint financial fund of the Eurozone countries replacing the two existing EU funding programmes.

 

ETF (Exchange Traded Fund)

Investment funds much like securities which serve as certificates for share portfolio. ETF may consist of securities of different companies and share funds. On legal basis, ETF is a type of share funds.

Euribor

Is a rate European banks use to borrow funds from peers for three months.

Exchange rate

The price of a nation’s currency in terms of another currency.

Expiry time

A predetermined deadline for a binary option which a trader can choose prior to opening a trade. At the time of a binary option expiration, the financial result of the trade is fixed and traders either receive the profit known in advance, or they suffer a loss in case of an incorrect forecast.

Export

Means delivering (or shipping) goods or capital abroad.

Face value

It is the cash denomination or the amount of money the holder of the individual security going to earn from the issuer of the security at the time of maturity. It is also known as par value.

Fan Principle

Sometimes after the violation of an up trendline, prices will decline a bit before rallying back to the bottom of the old up trendline (now a resistance line). Often, previously broken support lines become resistance and resistance lines become support. The term “fan principle” derives from the appearance of the lines that gradually flatten out, resembling a fan.

Federal Reserve Bank

Is a regional bank of Federal Reserve System of the USA. A network of 12 Reserve Banks carry out money management of the government.

Federal Reserve System

Is the U.S. banking system which fulfils functions of country’s Central Bank.

Fibonacci Retracements

Based on the fibonacci sequence and the golden ratio, one can find resistance and support points using these. To use them, start the retracements at the lowest point in a graph and drag it up to the highest point, or vice versa.

Financial instruments

Various types of financial market products, including securities, bonds, forex, futures, options, etc.

Financial Transaction Tax

Allows regulating financial markets, in particular, the market of derivatives.

Fitch Ratings

Is one of the largest rating agencies which provide analytical data of financial markets and estimates companies’ borrowing power.

Flag

Both flag and pennants represent brief pauses in a dynamic market move. They represent situations where a steep advance or decline has gotten ahead of itself, and where the market pauses briefly to “catch its breath” before running off again in the same direction. Both are treated together because they are very similar in appearance and only rarely produce a trend reversal. The flag resembles a parallelogram or rectangle marked by two parallel trendlines that tend to slope against the prevailing trend.

Flat

Is the situation on the currency market characterized by the absence of an uptrend or downtrend; also known as non-trend or sideways movement.

Flexible exchange rate

An exchange rate which fluctuates depending on the supply and demand of a currency in relation to other currencies on the forex market. It is also called floating exchange rate.

Floating profit/loss

Unrecorded gains/losses on the opened positions at current rates values.

Force major circumstances

Occurrences which could not be foreseen or prevented. These include: natural disasters; wars; acts of terrorism; government actions, actions of executive and legislative government authority, hacker attacks, and other unlawful acts toward servers.

Forecast

Assesment of future market conditions on Forex within a short-term, medium-term and long-term framework.

Forex (foreign exchange market)

Is a segment of the financial market for interbank currency exchange at non-fixed rates.

Forex indicator

Is a software-based analytical tool that can be used to receive and visualize additional information (or information transformed into more understandable format) about the price chart.

Forex trend

It occurs when the price moves in an identifiable direction over a specific period. There is an uptrend, downtrend and a sideways trend.

Forward contract

A contract that specifies the price and quantity of an asset to be delivered in the future. Although delivery is made in the future, the price is determined on the day of the transaction.

Free margin

Determines the state of an account. Calculated according to the formula: Equity – Margin = Free margin.

FSSS

Stands for Federal State Statistics Service of Russia.  It is a federal executive institute responsible for official statistical data on social, economic, demographic, and ecological conditions in Russia. In addition, FSSS controls and supervises state statistics in the Russian Federation.

Fundamental analysis

A popular way of forecasting price behavior on the forex market. The fundamental analysis is based on the analysis of the world’s leading economies’ indicators and reports on economic sectors released by major countries. The fundamental analysis also includes important political and financial news that can have impact on prices. Based on the analyzed information, experts say in what direction an asset’s price will move.

Futures contract

An agreement to buy or sell a predetermined amount of a financial asset or a commodity at a specific price on a specific date in the future. Futures contracts are characterized by strict conditions on the type and quantity of assets, involving only minor discrepancies. These contracts also have certain terms of payment of invoices or transportation costs.

Gap

A quick market move in which prices skip several levels without any trades occurring. Gaps usually follow economic data or news announcements. Is a break between prices which may happen as a result of sharp fluctuations on the market or during the weekends (between the closing price of one week and opening price of the other)

GDP (Gross Domestic Product)

Is a macroeconomic indicator measuring the market value of output produces within a year in all the economic sectors of a country, for consumption, exporting and hoarding, regardless of the producer’s nationality.

Hard currency

The term denoting a currency that can be easily exchanged (i.e. widely accepted around the world) with a stable rate. Hard currencies generally come from developed nations with strong economies. Because of its liquidity, a hard currency is considered to be a good investment instrument.

Hedge

A strategy or an attempt in reducing the risk of adverse price movements of assets.

Hedge fund

The funds that are managed much more aggressively than their mutual fund counterparts. The strategies applied for increasing the yield include trading with leverage, swaps, and arbitrage.

Hedged margin

Collateral necessary to cover open position.

Hedging

Operation that protects an asset or liability against a fluctuation in the foreign exchange rate.

HICP

Stands for Harmonized Consumer Price Index. HICP is an indicator of inflation, a single cornerstone in the statistical system for the EU countries. The index is particularly important in the periods of interest rate increases, as its growth provokes further monetary tightening and consequently the national currency upswing.

High-Frequency Trading, HFT

Is a type of trading which applies technical tools and computer algorithms (high-speed servers) to trade securities with high speed. In contrast to usual trading, High-Frequency Trading uses powerful computers to execute a greater number of trading operations. Usually, a computer analyzes the markets and executes operations based on its own trading strategy. The number of operations generated via High-Frequency Trading daily is counted in tens of thousands.

Hot Money

Large inflows of funds into countries with high interest rates that drive up their exchange rate.

IMF (International Monetary Fund)

International organization established by the UN and grants loans to member countries to meet balance of payments needs.

Import

Is delivery of goods or capital into the country from abroad.

Import substitution

Is fostering domestic production of goods rather than importing foreign goods.

Income Stock

A security which has a solid record of dividend payments and offers the dividend higher than the common stocks.

Index

A statistical measurement of change in the economy or security market. Indices have their own calculation methodology and are usually measured as a percentage change in the base value over the time.

Indicative quote

A quote serving for information about the current price of an asset. It is not used for making market orders.

Industrial Production Index

Is an economic indicator published by the Federal Reserve Board of the United States. It is based on the most significant categories of industrial goods. Since this index reflects changes in output of mineral resources, energy efficiency, gas and water usage as well as manufacturing production, it can be regarded as a GDP constituent.

Inflation

Is the process of increase in prices for goods and services. Due to inflation, a certain amount of money sufficient for purchase of some good or service at present will not be sufficient anymore in a while.

Initial margin

An amount necessary to open a position that serves as a trader’s guarantee of fulfillment of obligations towards a broker. The margin largely depends on leverage. The higher the leverage, the lower the margin required to open a position.

Initial Public Offering (IPO)

A company’s first issue of shares to general public. IPOs are issued by smaller, younger companies seeking funds for expansion and growth, but large companies also practice this to become publicly traded companies.

Insider

A person who belongs to a group or organization and has special knowledge about it unknown to ordinary traders and related to financial markets.

Instant execution

The mechanism of providing a client with quotes without prior request. Clients see live streaming forex rates of a dealer, based on which they can send an order to execute a trading operation at any time.

International division of labour

Implies that each country produces certain goods because it has everything necessary to produce them as compared to other countries. By specializing in suitable production, the country satisfies its own needs yet relying on foreign trade. Thus, international division of labour is a principle of the world economy where every country has its own specializations, exports the goods produces while importing the goods other countries specialize in.

International reserves

Refer to a country’s external highly liquid assets, namely gold and currency, controlled by the monetary authorities. International reserves are meant for financing the current account deficit, currency interventions etc.

International trade

Is a system of relations implying exchange of goods and services across international borders.

Internet Trading

Internet Trading is a platform with Internet as a medium. Internet trading execution takes place through order routing system, which will rout traders order to exchange trading system. Thus traders sitting in any part of the world can be able to trade using their brokers Internet Trading System. The Securities and Exchange Board of India (SEBI) approved Internet Trading in January 2000.

Intraday trade

Trade oriented at gaining profit within one day.

Investments

Assets purchased with the idea that they will provide income in the future or will be sold at a higher price for profit. However, an investment not always results in a financial gain, unless it is made in profitable projects or shares. So it is crucial to determine potential risks. As a rule, investing in high-yield assets is more risky.

IPO (Initial Public Offering)

Is the initial public offering of joint-stock company shares for selling.

ISM Service Index

Surveys supply managers in the field of services with an aim to trace changes in this field. The index tends to be sensitive to psychological factors, rather than an actual situation. New readings of the index are published at the beginning of every month at 10:00 EST (NY).

Lagging Indicator

Measurable economic variable that changes after the economy has begun to follow a particular path or trend; can confirm a trend, but cannot predict it.

Leading index

Shows an average of leading economic indicators such as Factory Orders, Initial Jobless Claims, Money Supply, Average Workweek, Building Permits, quotes of common stocks, Durable Goods Orders and Consumer Confidence. The leading index is believed to predict the pace of economic growth over the next six months. The index is calculated at the beginning of every month and released at 10:00 EST (NY).

Leverage

An amount of money a broker is ready to give to a trader to trade on financial markets with larger trading volume. Using the leverage, traders increase their deposits tenfold and even more. Leverage is expressed in the ratio between the trader’s own funds and those funds borrowed from the broker: 1:10, 1:100, 1:500, etc. If traders use the leverage, their own money serves as collateral.

Libor

Stands for London Interbank Offer Rate. Libor is a commonly recognized indicator of the cost of funds to banks. This is the rate that determines the cost of loans the world’s largest banks provide each other with on the London Interbank Exchange. Libor is one of the most widely used benchmarks for short-term interest rates. At present, it fixes rates for EUR, USD, GBP, JPY, CHF, CAD, AUD, DKK, and NZD. Libor is calculated for various borrowing periods, ranging from overnight to one year.

Light Sweet

Is a standard oil brand which is extracted in Texas (the USA). Due to low content of sulfur and relatively high output of useful products, it is used mainly for gasoline processing. This kind of oil is in high demand in the USA and China. Light Sweet Crude Oil is a pricing benchmark for world types of oil. It is traded on NYMEX (New York Mercantile Exchange).

Limit

Allows investor to set the minimum or maximum price at which they would like to buy or sell.

Limit Order

An order to buy or sell a share at a specified price. The order will be executed only at the specified limit price or even better. A limit order sets a minimum price the seller is willing to accept and maximum price the buyer is willing to pay for it.

Liquidation

Closing of an existing position by opening the opposite trade.

Liquidity

Is the degree to which an asset (currency, security, etc.) can be sold at the current market price. This term refers to market volatility and dynamics: a liquid market is a type of a market with large trading volumes where every trade incapable of making a significant impact.

Listed Stocks

The shares of an issuer that are traded on the stock exchange. The issuer has to pay fees to be listed in the stock exchange and abide by the regulations of the stock exchange to maintain listing privilege.

Locked positions

Positions of the same asset and volume opened on one account in opposite directions (buy and sell).

Long

Buying of a security with the expectation that asset will rise in value.

Long position

Is the buying of a financial instrument.

Loss

Is the fixed loss on the position.

Lots

Is a trading unit on the market; A micro lot is 1000 units of currency, and a mini lot is 10,000 units. If an account is based in dollars, a micro lot would be 1000$.  The standardized quantity of goods making up a transaction, exchange-traded securities, a certain amount of currency on Forex. Lot also represents the trade volume (position, order).

Lot Size

A quantity base currency in one lot, that is specified in the contract.

Low price

The lowest traded price for an underlying instrument for the specific period of time. On the stock market it is the lowest traded price for a security for one trading day.

M&A (Mergers and Acquisitions)

Is a buyout market. It is highly developed in the USA and in Euro area where in the history of every company information about its merging and absorption with other companies is stated.

MACD (Moving Average Convergence Divergence)

Momentum indicator that shows the relationship between two moving averages of prices. Calculated by subtracting 26 EMA from 12 EMA. Use with a 9 period EMA signal line. Generate trade signals when crossover or divergence between price and MACD appears.

Margin

The required equity which an investor must deposit to collateralize a position equal to 1% (when leverage = 1:100) of an open position deposit.

Margin level

The ratio of equity to margin expressed in percentage. The margin level shows existing risks enabling a trader to prevent them. Looking at the margin level, a trader realizes whether he has enough funds to further open traders and keep orders open. The margin level is calculated using the following formula: Margin Level = (Equity Necessary Margin) × 100%.

Margin trading

using borrowed money to buy securities, with the expectation of increasing profits. Margin trading can bring big returns, but is also risky

Market

A system with established rules of trade in financial assets or instruments, goods or services.

Market Capitalization

The total value in INR of all of a company’s outstanding shares. It is calculated by multiplying all the outstanding shares with the current market price of one share. It determines the company’s size in terms of its wealth.

Market opening

Trade opening after a weekend, holidays or after an interval between trading sessions

Market opening price gap

Either of the following situations:

– Market opening quote Bid is greater than market closing quote Ask.

– Market opening quote Ask is less than market closing quoteBid.

Market price

The last posted bid and ask prices for a given asset, currently valid on the market.

Market-Makers

Major banks and financial firms that pledge to provide liquidity by accepting the other side of a trade in a currency, security or futures contract.

MICEX (Moscow Interbank Currency Exchange)

Is a Russian stock exchange where papers of more than 700 issues with the total capitalization of 10 trillion rubles are trading.

Minimum deviation

An acceptable price range within one contract set by a stock exchange.

Money Flow Index

An indicator that analyzes the conviction in a trend through the price and volume of the security. MFI values range from 0 to 100. MFI below 20 suggests asset has been oversold, while MFI over 80 suggests asset has been overbought.

Mutual Fund

A pool of money managed by experts by investing in stocks, bonds and other securities with the objective of improving their savings. These experts will create a diversified portfolio from these funds.

NASDAQ (National Association of Securities Dealers Automated Quotation)

Is the US stock market where shares of more than 4,000 hi-tech companies with the total capitalization of $6 trillion are trading.

Necessary margin

The amount necessary to open the position of the needed volume. It is collateral a trader leaves on the account of a broker or a dealing center. The size of the necessary margin varies depending on the leverage used by the trader. The lower the leverage, the bigger the margin, and vice versa.

Net position

The difference between total open long and open short positions in a given asset held by an individual.

New York Stock Exchange (NYSE)

Is New York stock exchange where securities of more than 3,000 issues bodies are trading daily with the overall capitalization above $27 trillion.

Nikkei 225 Stock Average

Is Japanese stock market index which represents an average of the most widely quoted Japanese equities of 225 companies of the first section of Tokyo stock exchange.

Non-Farm Payroll (NFP)

Measure of the number of people employed in all activities except agriculture; released first Friday of each month.

Non-trading operation

Depositing or withdrawing funds from a trading account, or extending credit.

Normal market conditions

Condition of a market that meets the following requirements:

– absence of noticeable breaks in relation to the trading platform quotes.

– absence of rushing price dynamics; absence of significant price gaps.

North American Free Trade Agreement

Is the world’s largest free trade area which includes the USA, Mexico, and Canada. The area’s population makes up 406 million people while the total GDP is $10.3 trillion.

Obvious mistake

Opening/closing client positions or executing client order at a price that greatly differs from price quoted per instrument in present flow quoting at the moment of processing. Or some other dealer activity or inactivity that deals with mistaken determination of market prices at the present moment.

Odd Lot

A number of shares which are less than or greater than but not equal to the board lot size. For example, if the board lot size is 100 shares, an odd lot would be 95 or 102 shares. Usually odd lots are difficult for trading and it is not accepted easily in the market.

Offset

Offsetting is a liquidating of a buy or sell position by opening the equivalent position in the opposite direction. Thus, if a trader has short EUR/USD position, the offsetting position will be a long trade on the same instrument of the same volume.

Offshore

The term “offshore” is used to describe a territory or a nation that accumulates foreign capital by offering special tax incentives to companies based there.

One-sided Market

A market that has only potential sellers or only potential buyers but not both.

Open position

The result of the first part of a completed transaction; at the position opening, the client accepts the following liabilities:

– to execute the opposite operation of equal volume;

– to maintain equity not lower than 10% of the necessary margin

Operation Twist

When the fed will buys either short term or long term bonds and sells the opposite in an effort to shape the yield curve.

Option

A contract between two investors, under which one party buys or sells an underlying asset within a specified period of time at an agreed-upon price. Another party sells or buys the asset according to specified conditions. In other words, an option can be both a contract to buy and sell a trading instrument.

Option exercise level

The price level of an underlying asset at the moment when an option is exercised, i.e. at expiration time of a binary option. Accurateness of a financial forecast of a trader is determined by comparing an option’s exercise level and the buying price level.

Order Forex

An instruction that is sent to a broker to enter or exit a position at a specified price.

Order level

The price specified in the order.

Oscillators

Momentum indicators that show subtle reversals in non-trending prices, and also indicate short-term overbought or oversold conditions. MACD, RSI are examples.

Out-of-The-Money (OTM)

For call options, this means the stock price is below the strike price. For put options, this means the stock price is above the strike price. The price of out-of-the-money options consists entirely of “time value.”

Output

Is the volume of goods a company has produced or services it has provided. It is measured in real and value terms.

Overbought

Is the situation on the currency market in which the demand for an asset increases the asset’s value to a level that does not meet the fundamental factors. It is commonly believed that in the overbought zone traders should sell.

Overnight position

The position that a trader possesses at the end of a trading day. A trade that remains open until the next business day.

Oversold

Is the situation on the currency market in which the demand for an asset decreases the asset’s value to a level that does not meet the fundamental factors. It is commonly believed that in the oversold zone traders should buy.

Payout

A fixed amount a trader gets in case of the correct forecast of a binary option’s price movement. The payout is directly proportional to the size of investment in the binary option.

Pending order

The client instructs the dealer to buy or sell once the price reaches the order level.

Pennants

Represent brief pauses in a dynamic market move. They represent situations where a steep advance or decline has gotten ahead of itself, and where the market pauses briefly to “catch its breath” before running off again in the same direction. The pennant is identified by two converging trendlines and is more horizontal. It very closely resembles a small symmetrical triangle. An important requirement is that volume should dry up noticeably while each of the patterns is forming.

Pip

The smallest price increment in a currency. Often referred to as “ticks” in the futures markets. For example, in EURUSD, a move from .9015 to .9016 is one pip. In USDJPY, a move from 128.51 to 128.52 is one pip.

Pip cost

The cost per 1 pip move per unit traded of a currency pair. For example if the price of EUR/USD increased to 1.3498 (increased by a pip), and the pip cost was $0.10 per lot, and we were long 10 lots, we will gain 10 × ($0.10) × 1 = $1.

Pivot point

Is the key point of support/resistance level calculated by taking the average of an asset’s previous high, low, and closing price.

PMI (Purchasing Managers’ Index)

Is a leading indicator. Its reading is determined by polling manufacturing supply managers. The index provides insight into business trends and influence of the economy on price formation.

Portfolio

Holding of any individual or institution. A portfolio may include various type of securities of different companies operating in different sectors.

Positions Limit

Maximum number of futures and options contract that any individual investor can hold at any given point of time.

Pre-opening Session

The pre-open session is for duration of 15 minutes i.e. from 9:00 AM to 9:15 AM. In pre-open session order entry, modification and cancelation takes place.

Producer Price Index (PPI)

Measures changes in prices domestic producers receive for their output.

Price Earnings (P/E) Ratio

A valuation of companies last traded share price to its latest reported 12 months earnings per share. For example, if the last traded share price of any X company is USD 40 and earnings over a last 12 months per share is USD 2, then the P/E ratio of that X company is USD 20 (40/2)

Price prior to non-market quoting

Closing price of minute bar, prior to minute bar with non-market quoting. Price Gap – either of the following situations:

– Present quoting Bid is greater than prior quoting Ask;

– Present quoting Ask is less than prior quoting Bid.

Price transparency

Equal availability of quotes to all market participants.

Principal value

Is a trader’s initial capital, start-up capital.

Producer-Price Inflation

Is the increasing of the average prices for raw materials and consumer goods calculated for the base/reference period.

Profit

A financial gain that resulted from investing, or from a speculative operation that exceeds an amount of initial capital.

Put Option

An option that is given to investor the right to sell a particular stock at a stated price within a specified time period. Put option is purchased by those who believe that particular stock price is going to fall down than the stated price.

Pyramiding

Is a method of increasing a position size in which each new position is less (greater) than the previous one.

Quantitative Easing

A government monetary policy used to increase the money supply by buying government securities from the market. The U.S. Federal Reserve has been buying billions of dollars worth of bonds to keep interest rates low.

Quote Currency (Secondary/Counter Currency)

Second currency quoted in a currency pair in FOREX. In a direct quote, the quote currency is the foreign currency while in an indirect quote, the quote is the domestic currency.

Quote flow

A sequence of numerical data describing the price value of an instrument at a certain time period.

Quotes base

Information about the stream of quotes.

Range

The distance between levels of support and levels of resistance.

Rate

The price of the base currency in terms of the quote currency.

RBA

Stands for Reserve Bank of Australia. The bank is responsible for Australia’s monetary policy, works to maintain a strong financial system and issues the nation’s banknotes.

Real GDP

Shows production output and incomes in real terms.

Repo

A repurchase agreement where a seller agrees to buy assets back from a buyer at a predetermined price.

Resistance level

Highest channel’s borderline

Retracement

A reversal in the direction of a price movement or its pullback from a previous low or high.

Rising trend

Occurs, when every following value of the wave curve is higher than the previous rate value. The lows of the waves are connected with a straight line – the trend line.

Risk

A probable chances of investments actual returns will be reduced then as calculated. Risk is usually measured by calculating the standard deviation of the historical price returns. Standard deviation is directly proportional to the degree of risk associated.

Risk management

Means using a strict set of rules in trading in order to limit losses.

Rollover

The interest earned when going long or short a currency pair. For example, since Australia has a high interest rate and the US has a low one, going long on the AUD/USD can earn you a heft rollover. This is essentially the same idea as shorting US Treasuries and using the proceeds to invest in Australian Treasuries, earning the spread between the interest rate differential.

RSI (Relative Strength Indicator)

Using recent gains and losses to compute if a security is overbought or oversold. If above 0 line – bull, below 0 line – bear. Generate trading signals when divergence between price and RSI, or when RSI is above 70 or below 30 (might consider using 80-20 or FX).

RTS (Russian Trading System)

Conducts trades on the stock and derivative markets. Open 10:00 to 23:50 MSK (GMT+4). In December 2011, RTS merged with MICEX into MICEX-RTS. In 2012 the united stock exchange was renamed as OAO Moscow Stock Exchange (MSE).

S&P

Is one of the largest rating agencies which deal with analytical investigations of financial market. The company to one of the world’s most powerful rating agencies. S&P is also known as the creator and editor of the American stock index S&P500; and the Australian S&P200.

S&P 500

Is stock index of Standard & Poor’s rating agency which is comprised of 500 top publicly traded companies of the USA which securities can be found on the largest U.S. stock markets. It is a market-value-weighted index and one of the main indicators of the American economic climate.

Saucers

Although not seen as frequently, reversal patterns sometimes take the shape of saucers (or rounding bottoms). The saucer bottom shows a very slow and very gradual turn from down to sideways to up. It is difficult to tell exactly when the saucer has been completed or to measure how far prices will travel in the opposite direction. Saucer bottoms are usually spotted on weekly or monthly charts that span several years. The longer they last, the more significant they become (cf. spikes).

Scalping

Is the method of short-term trading that suggests a large number of positions opened during a trading day fixing small amounts of profit or loss.

Securities

A transferable certificate of ownership of investment in products such as stocks, bonds, future contracts and options which an individual holds.

Sentix Investor Confidence Sentix

Measures credibility of the Eurozone economy in the eyes of investors. The indicator is calculated on the basis of a survey held among investors and analysts. If the indicator rises, a foreign currency is converted into a national one when buying securities or non-financial assets, which pushes the national currency rate up. Readings of the indicator exceeding forecast bode well for the currency.

Server log file

File, created by the server, which records all requests and orders received from the client to a dealer, as well as the processing result, with 1 second accuracy

Short

Sale of a borrowed security with the expectation that the asset will fall in value

Slippage

Is the situation on the currency market in which a broker closes an opened position at a less profitable price than stated in the order. It happens amid price spikes when the level of position closure is broken too fast.

Soft Currency

A currency with a value that fluctuates due to its country’s political or economic uncertainty. They tend to be avoided by traders as a result of the instability.

Spikes

Spikes are quick downward reversal patterns. Spikes are the hardest market turns to deal with because the spike (or V pattern) happens very quickly with little or no transition period. They usually take place in a market that has gotten so overextended in one direction, that a sudden piece of adverse news causes the market to reverse direction very abruptly.

Spot Market

The spot market is the market for buying and selling currencies at current market prices.

Spot price

The current price in the marketplace at which a given asset can be bought or sold. The standard settlement time frame for spot transactions is two business days from the trade date.

Spread – Bid/Ask

The distance, usually in pips, between the Bid and Ask price. A tighter spread is better for the trader.

Square Forex

The term means that the buy positions and the sell positions on the same asset are equal. It is also used when there are no opened trades.

Stock Index

Represents a compilation of stocks to measure the value of a stock market sector.

Stock market

(Securities market) is a segment of the financial market where securities are traded.

Stock Split

An attempt to increase the number of outstanding shares of a company by splitting the existing shares. It is usually done to increase the availability of shares in the market. The usual split ratio is 2:1 or 3:1, i.e. one share is split into two or three.

Stop

An order to buy at the market only when a currency moves up to a specific price, or to sell at the market only when a currency moves down to a specific price. allows an investor to specify the particular price at which they would like to buy or sell

Stop loss

Is a pending order to close the losing position when it reaches a certain price.

Stop out

A forced closing of a position without the client’s consent and prior notification in the event of lack of funds to maintain the opened position.

Strike Price

The price at which the holder of an option can buy (in case of call option) or sell (in case of put option) the securities they hold when the option is executed.

Support & Resistance

A price level that the price historically had difficulty crossing below and above. The more times the support/resistance is checked, the more credible it is for future use. When the price level is penetrated, the roles of support and resistance reverse.

Swap

The amount of money deducted from or added to a client account for the overnight position.

Swaption

A futures or an option granting its owner the right to enter into an interest rate swap agreement by some specified date in the future.

Take Profit

A trading order that allows a trader to take profit when the price reaches a certain level. The order helps a trader to reduce risks. The take profit order for a chosen trading instrument will close the transaction automatically as soon as the price reaches the specified level. In an open trade, take profit can be set at any time.

Technical analysis

A popular way of forecasting a price behavior on the forex market. The technical analysis is based on the opinion that a price change in the past will be repeated in the future. Confirmation of previous patterns of market behavior are searched with the help of price chart analysis that detects certain graphical patterns that are interpreted as signs of a possible price move in one direction or another.

The Organization of the Petroleum Exporting Countries, the OPEC

Was established in 1960 by the world’s largest exporters in order to create common oil policy and ensure stable prices on global oil market.

Thin Market

A market in which there are comparatively low number of bids to buy and offers to sell. Since the number of transactions is low the prices are very volatile.

Tick

Minimum upward or downward movement in the price of a security.

Ticker

A unique identification number given to each open position or a pending order in a trading platform.

Time frame

Refers to the time period of the chard chosen to display the price move on the forex market. Time periods in trading platforms include: 1 minute (M1), 5 minutes (M5), 15 minutes (M15), 30 minutes (M30), 1 hour (H1), 4 hours (H4), 1 day (D1), and 1 month (MN).

Tom-Next

Short for tomorrow-next day. The process of moving the settlement value date on an open position forward from one business day after the trade date (tomorrow), to the next valid value date (next).

Trade balance

Is a difference between the monetary value of exports and imports over a certain period of time (for example, a year). It is calculated for both separate nations and groups of countries and includes both actual transactions and those carried out on credit.

Trade deficit

Is a negative trade balance. Merchandise trade balance of a country is a difference between the value of exports and that of imports over a certain period of time. Merchandise trade balance includes all the actual transactions and those carried out on credit.

Trade forecast

An outlook for future changes on the forex market. It is carried out through analysis of financial information and related research studies.

Trade operation volume

Number of lots multiplied by lot size.

Trader

Person, who trades currency on the Forex market in order to earn profit.

Trading account

A unique account of a trader with a unique number where all trading operations of a trader are displayed, including deposit and withdrawal of funds, all pending orders and complete history of an account.

Trading hours

Operating time of the world’s financial markets such as London, New York, Hong Kong and others. Trading hours of exchanges located in different parts of the world differ. In the context of the forex market, trading hours mean the time when transactions can be made – round the clock on weekdays.

Trading operation

An act of buying or selling any instrument performed by the client.

Trading platform

Software and technical facilities that provide the transmission of financial trading information in real time mode, execution of trading operations with account of mutual obligations between the client and the dealer, and control of conditions and restrictions. For the purposes of the present regulation, it consists of “Server” and “Client terminal”

Trading range

It occurs when currency rates on Forex move in a certain price corridor. The lower limit is formed on the support level, while the upper limit is located on the resistance level.

Trading session

The period of time wich is open for trading for both sellers and buyers, within this time frame all the orders of the day must be placed. Here all the orders placed in pre-opening sessions are matched and executed.

Trading strategy

A system of trading on the forex market based on a certain approach to market forecasting. The most widespread strategies include trading with the help of Bollinger bands, moving averages, breakthrough of resistance levels, etc.

Trailing stop

Is a pending stop loss order automatically moved at a specified distance from the current price.

Transaction

Trade operations where money resources move from base currency into quoting currency and vice versa.

Trend

Is the current general direction of a price movement for a substantial period of time. There is an uptrend (bullish trend), downtrend (bearish trend), and non-trend (flat or sideways movement).

Trendlines

Trendlines are created by linking one strongly identifiable support to another or one strongly identifiable resistance to another. This creates an uptrend or downtrend.

TSE (Tokyo Stock Exchange)

Consists of more than 2,500 Japanese and foreign largest companies that are members of this stock exchange. It takes the second place in the world by capitalization after New York exchange.

Turnover

Turnover is an aggregated cost of all executed trades in a specified time period.

Two-way quote

The type of a quote that gives both the bid and the ask price of an asset.

Unemployment Rate

Is calculated by dividing the number of the jobless citizens by the number of the employed or by the total number of people of a certain population category. In most cases, the unemployment rate is expressed as a percentage.

Unprofitable option

A term used in binary options trading. It is a contract, where the asset price at the moment of expiration unfavorably for a trader differs from the asset price at the moment of the option’s buying.

Unrealized gain/loss

It is a theoretical gain or a loss of opened positions calculated in accordance with current market prices as defined by a broker in its sole discretion. When the position is closed, unrealized gains or losses turn into realized gains or losses.

Uptick

New price that is higher than the previous one.

Usable margin

Is a part of deposit not involved in trading which can be used to open new positions (orders). It is denoted as “Free” in the trading platform.

Used margin

Is the blocked part of the deposit, which is used to cover potential losses on open orders. In the trading platform it is displayed in the Margin field.

Value date

A date on which FX trades settle, i.e. the date that the payments of each currency are made.

Variation margin

Additional amount of deposit you need to make to your trading account in order to maintain sufficient money for loss deduction after significant losses have taken place.

Volatility

Is instability, the measure of how much the market conditions, demand or prices change.

Yield

It is the measure of return on investments in terms of percentage. Stock yield is calculated by dividing the current price of the share by the annual dividend paid by the company for that share. For example, if the current price of the share is INR 100 and the dividend paid is INR 5 per share annually, then the stock yield is 5%.