Author: Fran S.

I love math, data science, and, also, financial markets. I love to write about system analysis, money management, risk, and math for traders. I hold a degree in Telecommunications Engineering by Univ. Autónoma de Madrid, and a Master in Business Administration and Marketing by Univ. de A Coruña.

Categories
Candlestick patterns Forex Daily Topic

Candlestick Trading Patterns IV – Long White Bodies

There are two kinds of price movements in the markets: Impulsive movements and corrective movements. The ideal impulsive action is characterized by a continuous rise or decline from the opening level to the closing one, this being the highest or lowest point of the period. The ideal corrective movement is described by a lateral movement in a short-range and close opening and closing levels.

There are two kinds of price movements in the markets: Impulsive movements and corrective movements. The ideal impulsive action is characterized by a continuous rise or decline from the opening level to the closing one, this being the highest or lowest point of the period. The ideal corrective movement is described by a lateral movement in a short-range and close opening and closing levels.

There are two kinds of price movements in the markets: Impulsive movements and corrective movements. The ideal impulsive action is characterized by a continuous rise or decline from the opening level to the closing one, this being the highest or lowest point of the period. The ideal corrective movement is described by a lateral movement in a short-range and close opening and closing levels.

Most trading candles can be separated into those two moves. When impulsive movement prevails, the candle shows a large body, and only visible traces of the corrective action are perceived as upper and lower wicks. Corrective-motion candles have a short body and relatively long wicks at one or both ends.

A white and large-bodied candle body is indicative of a bullish impulse, whereas a black and large-bodied one shows a bearish or selling impulse. Therefore, when one of these appears at a critical level showing the opposite direction to the prevailing trend, we have to take notice of it.

Long White Candle at a low price level

A single candlestick Is mostly not enough for a proper forecast. However, a large candlestick at the end of a severe drawdown is a warning sign that the trend might have ended. If the candlestick shows its low, touching resistance levels, that is a second clue for a reversal, and also serves as a confirmation of the support level.  A white candlestick bouncing off a trendline gives credibility to that line.

 

In the above chart, we see the retracement of the price touch the trendline and then bounce with a white candle, that might have served as a good entry point to trade long. Further up, we see that the price still obeys the line in the second retracement, in this case, with a candle with a large lower wick.

Long White body breaking resistance

A Long white-bodied candle breaking resistance levels are usually a good confirmation of that fact. As we see in the chart below, the price crossed the resistance level decisively and never looked back. This is the kind of confirmation for a bullish continuation traders need.

Long White Body as Support

A long white body sometimes is retraced to test the bulls. But, on the occasions, the price retraces all the previous candle’s advance, its body bottom acts as a support level to hold the price and maintain the trend alive. It is more common that a Fibonacci level of the candle’s retracement would stop the pullback. According to Mr. Nison, the middle of the candle body is a usual support zone.

Once the underlying trend is established, a suitable method to enter the trend is to buy at 50% retracement, with a stop-loss below the white body. That way, the risk of entry is halved while profiting from mild retracements.

Takeaway

A single white-bodied candlestick can depict great information value to a savvy trader. This impulsive candle warns about potential trend changes, confirms breakouts when breaking resistance levels, and acts as support during retracement periods, thus, also showing potential levels to jump in and profit from the newly discovered trend.

Categories
Candlestick patterns Forex Daily Topic

Candlestick Trading Patterns III – The Doji, The Most Critical Candle

The Doji

The Doji

The Doji

The Doji is a special candle, not only because of its striking appearance but also because it is one of the most vital signals in trading. This figure is so important that we need to understand it very well, as it is one of the safest trading signals when properly applied.

Fig 1 – A Doji on a chart

The Doji is characterized by having the open and close at the same level while standing out for its elongated upper and lower shadows. The figure of the Doji has a precise meaning. Buyers and sellers are in a state of mental indecision. The Doji is a powerful sign of trend change. The probability of a turn increases if in addition to the Doji:

  1. The next candles confirm the Doji’s signal
  2. The market is overextended
  3. The chart does not have many Doji.

The perfect Doji has the same open and close values. Nevertheless, if both levels are separated a few pips, and the candle can still be seen as a single line, it can be considered as Doji.

The Doji is a powerful signal to detect market tops. Steve Nison says that a dog is a sign of indecision by buyers, and an upward trend cannot be sustained by undecided traders. Nison also points out that, from his experience, the Doji loses some reversal potential during downtrends. That observation may apply to the stock market but is useless in pairs trading, as they are symmetric. In this case, a bullish trend of a pair is a bearish pare on the inverse pair and vice-versa. So a Doji will always have a similar meaning: The trend is compromised.  When trading commodities, indices, or stock ETFs the trader should take this into account, though.

In view that a Doji is such a powerful signal, it is better to act upon it. Better to attend a false signal than ignore a real one. Therefore, dojis are signals to close positions, since a Doji alone does not mean a price reversal.

The Northern Doji

The northern Doji is called a Doji that shows up during a rally. According to Mr. Nisson, ” The Japanese say that with a Doji after a tall white candle, or a Doji in an overbought environment, that the market is “tired.” Therefore, as said, a Doji does not mean immediate market reversal. It shows the trend is vulnerable.

 

FIg 2 – Down Jones Industrial Average showing northern Doji.

As we can see in the chart above, a Doji after a large candle, as in the first case, is followed by a gap and a drop to the base of a previous candle that surged after a gap.  The next Doji we see was an inside bar that just acted as a retracement and continuation. In the third case, we can see two Dojis, the second being a kind of hanging man with no head. In this case, we notice that the third bearish candle is the right confirmation of the trend reversal. It is not uncommon to observe tops depicting several small bodies, one of which is a Doji.

The Long-legged Doji

Fig 3 – Long-legged Doji in a SPY Daily chart.

We already know that a small body and long upper and lower shadows is called a high wave candle. If the figure doesn’t have a body is called “long-legged Doji,” and also called “rickshaw man.” As it happens with high-wave candles, it reflects great confusion and indecision.

Gravestone Doji

The gravestone Doji is the Doji that begins and ends at the low of the day. According to Stephen Bigalow, the Japanese name is set to represent “those who died in the battle.” Gravestone Dojis are a rarity.

Fig 4 – Long-legged Doji in the UK-100 Daily chart.

 

Dragonfly Doji

The Dragonfly Doji occurs when the price moves down since the open, and then it comes back and closes at the open. When it happens after an uptrend is a variant of a hanging man.

Fig 5 – Long-legged Doji in the DAX-30 Daily chart.

Conclusions

Dojis are important figures that warn trend reversals, especially if it happens at support or resistance levels.

Dojis need confirmation for trend reversals. When that happens, they create morning star and evening star formations. They also are followed by other small bodies, creating a flat top or bottom.

A safe precaution when encountering these figures while a trade is active is to close or reduce the position or, alternatively, tight the stops.

 

Sources:

Japanese Candlestick Charting Techniques, Second Edition, Steve Nison

Stephen Bigalow, Profitable Candlestick Signals

 

Categories
Candlestick patterns Forex Daily Topic

Test your knowledge about Candlesticks

After our discussion about short-bodied candlestick in our article

After our discussion about short-bodied candlestick in our article

After our discussion about short-bodied candlestick in our article

Candlestick Trading Patterns II – Everything you need to know about Single Candlestick Signals

Here you can test your newly acquired knowledge about the matter. If you haven’t read it, please do so before the quiz.

 

 

[wp_quiz id=”51631″]

 

 

Reference: The Candlestick Course – Steve Nison

Categories
Candlestick patterns Forex Daily Topic

Candlestick Trading Patterns II – Everything you need to know about Single Candlestick Signals

This article is to be dedicated to single candlestick key figures. The majority of patterns are created by more than one candle, but some particular candlestick shapes are key figures to gauge the market sentiment and spot reversals.

This article is to be dedicated to single candlestick key figures. The majority of patterns are created by more than one candle, but some particular candlestick shapes are key figures to gauge the market sentiment and spot reversals.

This article is to be dedicated to single candlestick key figures. The majority of patterns are created by more than one candle, but some particular candlestick shapes are key figures to gauge the market sentiment and spot reversals.

In every one of them we will deal with the following aspects:

  • Identification of the candlestick
  • Marker psychology interpretation
  • Criteria and use

Key Single Candlestick Figures:

  • Doji
  • Spinning top
  • High Wave Candlestick
  • Hammer
  • Hanging man
  • Shooting star

The Japanese traders call the real body “the essence of the price action.” A scientist might call it the Signal part of the message, while the shadows are the nose of the market. The relation between the body and the shadows delivers unique insights into the sentiment of the traders. Shadows show the fight between buyers and sellers to control the price. A large body and small shadows denote that one of the sides has won the battle during that interval. A short body with large shadows after an extended trend indicates the winning herd is losing steam.

Spinning tops and high wave candles

Fig 1 – Spinning tops and High Wave candles

A spinning top is a visual clue for a candle with a tiny body. The color of the body does not matter.  A spinning top without a body is called Doji, such as the second one in the figure above. The fourth one is very close to it too.

Market sentiment in spinning tops

A the smaller the body, the larger the fight between bulls and bears. It shows that no one had control of the price during this period, as the sellers pressure the price down and buyers up, a small body means no one could outweigh the other party. The demand is counteracted by fresh supply,  and vice-versa, so the market is unable to move.

High Wave Candles

Steve Nison also mentions a close relative to the spinning top, called High Wave Candle. High Wave candles also have very small bodies, but to qualify as High Wave, the formation must also have large shadows on both sides. Shadows need not be of the same size, but they must be large.

Market sentiment in a High Wave Candle

According to Mr. Nison, If indecision is the crucial sentiment on spinning tops, High Wave candles represent “downright confusion.” That is evident because, in the same period, the market goes from the euphory of an extended high to the fear of a large drop, and then to close very near to its opening value. That means total confusion.

Trends and spinning tops

A large white body is like a green light for bulls in an uptrend. A large red body is also a green light to sell. But finding a spinning top in an uptrend means that the buyers do not have the complete control of the price. Therefore, such tops are a warning sign that the trend might be ending. Spinning tops acquire more importance when the price is overextended or close to resistance levels.

Spinning tops during ranging markets do not have any power to warn a trend change, as these stages are too noisy, and filled with lots of small bodies, anyway. Therefore, spinning tops and high waves during horizontal channels have no trading value.

Hammers, Hanging Man, and Shooting stars

Three special cases of spinning tops are the Hammer, the Hanging Man, and the Shooting Star.

Hammer

Fig 2 – Hammer

The hammer has a small real body and a large lower shadow. It is the equivalent of a reversal bar.  The price went from the open to the bottom, then it recovered and closed near or at the high of the session. The color of the body has less importance, although a close above the open has more upside implications. The signal is confirmed with a followthrough candle next to it.

Criteria:
  • The occurrence is after a lengthy downward movement, and the price is overextended.
  • The real body is at the upper top of the trading range
  • The shadow must be two times the length of the body. The longer, the better.
  • No upper or just a tiny shadow
  • Confirmation with a strong bullish candle, next
  • A large volume on the candle confirms a bottom.

 

Hanging Man

Fig 3 – Hanging Man

The hanging man has a similar shape of the hammer, but it shows up after an uptrend. The Japanese named that way because it is similar to the head and body of a man hanging by the neck.

Criteria:
  • The occurrence is after a significant upward move, and/or the price overextended.
  • The body is at the upper end of the trading range.
  • The lower shadow at least two times the height of the body. The color is not essential, but a bearish finish is preferred. the longer the shadow, the better
  • Tiny or no upper shadow.
  • Confirmation with a large bearish candle
  • High volume on the candlestick is indicative of a potential blowoff.
Shooting star

Fig 4 – Shooting Star

The shooting star is a top reversal candlestick and is the specular image to the hanging man.  In the case of a shooting star, it began great for buyers, but after the euphory of new highs, it came to the deception of the selling pressure with no demand to hold the price.  The close happens at the lower side of the trading range. A bear candle next confirms the trend change.

Criteria:
  • The upper shadow should be two times the height of the body. The larger, the better.
  • The real body is at the bottom of the trading range.
  • Color is less important, although a  red candle implies more bearishness.
  • Almost no lower shadow.
  • A large volume would give more credibility to the signal.
  • A  bear candle next is the confirmation of the change in the trend.

 

Reference: Steve Nison: The Candlestick Course

Profitable Candlestick Trading, Stephen Bigalow

 

 

Categories
Candlestick patterns Forex Daily Topic

Candlestick Trading Patterns I – The Story

The Financial markets are an exciting place for many people, attracted by dreams of infinite wealth. However, these markets are one of the most complicated environments on earth. The fact that millions of people exchange assets in financial markets makes them very difficult to predict, as each of the participants has its own vision, interests, and objectives.

The Financial markets are an exciting place for many people, attracted by dreams of infinite wealth. However, these markets are one of the most complicated environments on earth. The fact that millions of people exchange assets in financial markets makes them very difficult to predict, as each of the participants has its own vision, interests, and objectives.

The Financial markets are an exciting place for many people, attracted by dreams of infinite wealth. However, these markets are one of the most complicated environments on earth. The fact that millions of people exchange assets in financial markets makes them very difficult to predict, as each of the participants has its own vision, interests, and objectives.
That is why traders are always investigating the best tools to allow them to detect market sentiment in every situation.

Fundamental versus Technical

In the past, fundamental analysis was the only tool that allowed investors to detect whether a value was overvalued or undervalued. That gave them the keys to future trends, and to be able to overtake other investors with less information.
Then, at some point, the theory arises that the analysis of price history shows everything necessary for an informed investment. According to this theory, launched by Charles Dow, the price is already included in the fundamental analysis, since the chart is the trace left by investors about the consensus value of the good.

That said, there is a consensus that fundamental analysis is still necessary to detect the macro trend and to position the buying and selling actions in favor of the primary trend, while technical analysis is essential to generate the timing of trading activities.

Fig 1- Old NY Stock Exchange price table and Average chart. Source (https://pix-media.priceonomics-media.com/blog/1230/image04.png)

Chartism was encouraged in the early 1970s and 1980s by the emergence of personal computers, which allowed graphs to be automatically generated, instead of manually drawn, and also analyzed in time frames shorter than the daily.

The OHLC Chart

The technical analysis popularized the use of OHLC graphs that not only indicated the closing value of each interval but also gave the opening, maximum, and minimum data. This allowed chartists to observe the range of movements of the period and obtain an assessment of the volatility.

Fig 2- OHLC Chart in its classical B&W style.

The use of OHLC charts was a big advancement in the analysis of the price action. Soon analysts began to define profitable patterns such as reversal bar, key reversal bar, Doble and triple tops and bottoms, head and shoulders pattern round bottoms, Cup and handle, and many more.

Candlestick Charts

A centuries-old hidden way to analyze the markets came from Japan helped by Steve Nison’s studies of candlestick charting methods. According to him, centuries back, Japanese merchants were at the bottom of Japan’s social scale, well below soldiers, artisans, and farmers. But a prominent merchant began rising in status by the XVIIth century. His name was Munehisa Homma. At that time rice was a medium of exchange. Feudal Lords would store it in Osaka’s warehouses to, then, exchange the receipts when it was convenient for them, thus, becoming a de-facto futures market. Homa’s trading techniques, which included analysis through a primitive form of candlestick charts to gauge the psychology of the marker would earn him an immense fortune.

Fig 3- Candlestick Chart in its modern colorful style.

The major advantage of a candlestick chart over an OHLC chart is the ability to assess at a glance the overall trend and, also many hints about the current sentiment or psychological mood of the trader collective. Color is key to assess the current trend. Also, large bodies signify genuine momentum, short bodies and large wicks mean indecision and fight between buyers and sellers to control the price action.

Candlestick Patterns

Many of the western analysis methods can be applied also to candlestick charts, but these Japanese charts have brought a brand new batch of new patterns to assess market turns and continuations.  We will try to cover most of them, including obviously all major trading candlestick patterns such as Morning and evening stars, haramis, engulfing, three soldiers, and so on.

To refresh your basic knowledge of candlesticks, we recommend the following articles:

https://www.forex.academy/all-you-need-to-be-introduced-to-trading-charts-part-1-line-bar-and-candlestick-charts/

https://www.forex.academy/facts-about-candlesticks-you-never-knew/

https://www.forex.academy/dissection-of-candlestick/

https://www.forex.academy/candlestick-charts-and-its-advantages-in-financial-trading/

 

 

Categories
Forex Daily Topic Forex Stop-loss & argets

Masteting Stop-Loss setting: How about using Kase Dev-Stops?

The stop-loss setting is a crucial component to the long-term success of a forex and crypto trader. The market forces cannot be adapted to the wishes of traders. Successful traders

The stop-loss setting is a crucial component to the long-term success of a forex and crypto trader. The market forces cannot be adapted to the wishes of traders. Successful traders

The stop-loss setting is a crucial component to the long-term success of a forex and crypto trader. The market forces cannot be adapted to the wishes of traders. Successful traders must accept that fact instead of fighting it for the sake of being right. “What cannot be cannot be, and, furthermore, it is impossible,” said some time ago, a well-known politician in a phrase that did not pretend to be comical. But it states a clear fact: Fight against the markets is like Don Quixote fighting Windmills.

In previous articles, we explained John Sweeney’s MAE method, and also average true range-based stop-loss settings. In this article, we are going to talk about Cynthia Kase’s Dev-Stops.

Cynthia Kase is a well-known and successful futures trader, speaker, and author of several books on trading and technical analysis. She conceded high importance to stop settings. Cynthia says something undeniable to most of us, Technical literature has mostly focused on entries, and almost nothing on entries besides some words on stop-loss or trailing stops. She says that this is like teaching how to drive a car but without explaining where the brake pedal and how to press it.

In her book “Trading with the Odds,” she explains that this situation is mostly due to greed and fear. Traders don’t like to lose, and most of them don’t know when to get out of a trade. Also, she explains that fear of losing causes people to hang on their losses in the hope the market will turn and recover them. Another explanation for this situation is that the beginning of technical analysis was on the stock market, and no company wants its stock downgraded from buy to hold or, worse, to sell. As opposed to Forex, only a handful of people make money shorting stocks, so exits are much less critical on the stock market.

Stops based on fear and greed

Most traders want to squeeze out the most of a trade. Therefore, they decided to use the highest possible leverage. To reduce the dollar risk, they desire to put it as close as possible to the entry-level. But, as said earlier, using obvious levels of support/resistance and set the stop order just two or three pips below is absurd. Better send your money directly to the charity, since they will make much better use of it than the institution that is going to collect your hard-earned money for free.

Risk is imposed by the market

The critical point is not to impose our conditions on the market, but read what the market is telling us in terms of Risk. In trading, Risk is proportional to volatility. Your dollar risk is the amount the price can move against you in a given interval, times your position size.

Volatility is measured using the Range and also by the standard deviation of prices on an annualized basis. One standard deviation of the price holds 68$ of all the potential price movement if we assume prices are dispersed in a gaussian distribution. That means that a price that goes against a trade by one standard deviation it will encompass 34% of the observations (the other 34% would go in the direction of your trade). The problem with using volatility is that a yearly measurement of the price variations does not help with sudden short-term volatility changes. That’s the reason for using ATR instead.

The concept of the threshold of Uncertainty

A trade is a bet on a market trend. We think a particular trend is in place. Ideally, the direction is a straight line between one initial level and a final level. If we think of the short-term price wiggles as random noise, we adapt our trade by placing our stops far enough away from the trend mean to include noise. The magnitude of the noise means we don’t want to exit at the minimum turn against the trade. The trader needs to devise a way to follow the trend while getting out when it ends. 

 The Kase Dev Stops

Using a fixed multiplier for the True Range is an initial approximation. In our article of true range, we used a fixed 2X multiplier to set our stop order away from the market noise. Kase’s Dev Stop uses what she calls the skew of the volatility, the measure at which a range can spike in the opposite direction as a multiplier of the range measure. That makes the Dev-stop an adaptative trailing stop. Dev Stops is a well-known indicator in TradingView. Also, it is available for downloading at the MQL5.com site for your Metatrader workstation. 

Chart 1 – Kase Dev-Stops in a GBPUSD 4H chart.

We can see in Chart 1 that four lines follow the price action. The first one is the mean line and the 1, 2, and 3 standard deviation (SD) lines of a two-bar reversal. As we can see, the 3rd standard deviation is seldom touched, being the 2-SD the conservative method, and the 1-SD the preferred aggressive method. In the case of using 1SD, it is advisable for a reentry plan, or create mental stops that would trigger if the close happens below the 1SD Dev-stop line.

As it should be the norm when learning a new method, it is strongly advisable to backtest it first to assess which SD line works better with your particular asset and objectives. Also, after backtesting your optimal solution, it is prudent to trade it using a demo account. There we could also assess the costs and benefits of the method by adding the brokerage costs.

Reference: Trading with the Odds, Cynthia A. Kase. 1996, The McGraw-Hill Companies Inc.

 

Categories
Forex Basic Strategies Forex Daily Topic

The Case for Average True Range-based Stop-loss Settings

Most traders are taught to use stop-losses based on critical levels. The basic idea is to spot invalidation levels based on previous low or high. The assumption is that by

Most traders are taught to use stop-losses based on critical levels. The basic idea is to spot invalidation levels based on previous low or high. The assumption is that by

Most traders are taught to use stop-losses based on critical levels. The basic idea is to spot invalidation levels based on previous low or high. The assumption is that by putting the stop a few pips below or above a support/resistance level will be enough to ensure the right trade will not be stopped out and just bad trades will be taken away.

The problem with that is that all participants in the market, including institutional traders, can see these levels. Institutional traders have lots of cash to play with, so they can push the price down to take all the buy-stop (or sell-stop) orders they see in their price book.

Key-level-based Stops

In the following example, we see the EUR(USD making a breakout after failing to break the previous high, on high volume. A perfect setup for a short trade. We then see the price moving down and then retracing and heading up to our stop-loss. We have been cautious and set it above the last top made on the 6th of November.

Nevertheless, the price kept moving inexorably up until the stop was taken. This is market manipulation at the highest level by institutions. Institutions have advanced tools to observe the depth of the order book, so they know the place and amount of the stops. Also, they have the liquidity necessary to move up the market, take all the liquidity at excellent prices, then continue south.

Chart 1 – EURUSD Key-level-Based Stop-loss placement

 

ATR-Based stops

If we look at the next chart, we see the same asset with the Average True Range indicator added. For this kind of stop-setting strategy, we need to detect the short term range. Therefore, we use a period of five for the ATR indicator. Next, we look at the peak set by the latest impulsive candlestick, which happened ten bars ago, 0.00168, which is about 17 pips. This figure gives us the expected 4-hour price movement for the current market volatility. The usual is to protect us against two times this figure, at least. In this case, we would need to move the stop-loss level 34 pips away from the entry point.

Chart 1 – EURUSD ATR-Based Stop-loss placement

It is wise to keep statistics of the ideal ATR multiplier, because as the number increases, it cuts our position size for the same dollar-risk amount, and also it reduces our Reward-to-risk ratio.

John Sweeney developed the general method of stop-loss placement. He called it the Maximum Adverse Execution method. The theory of it has been already described in our article Maximum Adverse Excursion, so we are not going to repeat ourselves here. Using  MAE delivers statistical-significant and tamper-proof stops, but it is a bit cumbersome. The use of ATR Stops is a simpler and second-best option instead of the foreseeable key-level-based stops.

 

 

Categories
Forex Daily Topic Forex Psychology

A Strategic Plan for Trade Management

I’ve already stated my view that most wannabe traders put their focus in technical analysis of the market and on trading signals, mostly provided by others, hopefully, more knowledgeable than

I’ve already stated my view that most wannabe traders put their focus in technical analysis of the market and on trading signals, mostly provided by others, hopefully, more knowledgeable than

I’ve already stated my view that most wannabe traders put their focus in technical analysis of the market and on trading signals, mostly provided by others, hopefully, more knowledgeable than themselves.

The issue is that any advice, no matter how good it is, is worthless to most of the beginners because the problem is 10% of the success as a trader is entries, 20% exits, including stops and targets, and 70% is the rest of overlooked themes. 

The overlooked themes, all of them has to do with the trader’s psychology:

  • Lack of a strategy
  • Overtrading
  • Not following the plan 
    • Skipping entries or exits
    • let losses grow to wait for a reversal
    • cut profits short, afraid of a reversal…

Every one of these subjects is critical, but if you make me choose, I’d say that overtrading is the worst evil that happens to a novice trader. Improper position sizing kills the majority of the Forex trading accounts. This trait is also linked to the cut profits short, let losses run character flaw, so let’s do create a basic strategic plan to help traders with a basic trade management plan.  

Emotional Risk

For the following plan to work, the trader needs to accept the risk. It is easy to say but challenging to do. Mark Douglas, in his book Trading in the Zone, explains that “To eliminate the emotional risk of trading, you have to neutralize your expectations about what the market will or will not do at any given moment or in any given situation.”

That is key. You cannot control the market. You can only control yourself. You need to think about probabilities. Create a state of mind that is in harmony with the probabilistic environment. According to Mark Douglas, a probabilistic mindset consists of accepting the following truths:

  1.  Anything can occur.
  2. To make money, there is no need to know what will happen next 
  3. It is impossible to be 100% accurate. Therefore there is a win/loss distribution for any strategy with a trading edge.
  4. An edge is just a higher probability of being right against a coin toss (if not, the coin toss would be a better strategy)
  5. Every moment in the market is unique. Therefore
  6. A chart pattern is just a very short-term approximation to a statistical feature, therefore less reliable than a larger data set pattern. We trade reliability for speed.

The idea is to create a relaxed state of mind, ultimately accepting the fact that the market will always be affected by unknown forces.

The Casino Analogy

Once that is understood and accepted, we can approach our trading job as if the trading business were casino bets. When viewed through the perspective of a probabilistic game, we can think that trading is like roulette or slot machines, where you, the trader, have a positive edge. At a micro level, trade by trade, you will encounter wins and loses but looked at a macro level, the edge puts the odds in your favor. Therefore, you know only need to manage the proper risk to optimize the growth of the trading account.

A plan to manage the trade

Lots of traders enter the Forex market with a rich-quick mentality. They open a trading account with less than 5,000 USD and think that due to leverage, they can double it week after week. This is not possible, of course, and they get burned within a month.

Our plan consists of three ideas

  • Profit the most on the winners, while let die the losers
  • let profit run, or even, pyramid on the gains.
  • Reach as soon as possible a break-even condition, for our mind to attain a zero-state as quickly as possible.

The Strategy and Exercise

Pick a forex pair.

Choose one actively traded pair. All major pairs fit this condition, but then choose the one that provides the best liquidity of your time zone.

Choose your favorite strategy, that you think it works and fits you.

The strategy must include the following components:

An Entry: The entry method should be precise. No subjective evaluations or decisions. If the market shows an entry, you have to take it. Of course, you can condition it with a reward-to-risk ratio filter, since this is an objective fact. Really, having a reward-to-risk ratio filter is quite advisable. A 3:1 ratio would be ideal, but 2:1, which is more realistic, can work as well.

A Stop-loss: Your methodology should define the level at which set your stop loss.

Timeframes: You need to choose a couple of timeframes: A short timeframe to create low-risk trades, and a longer timeframe to be aware of the underlying trend and filter out any signal that does not go with that trend.

Profit Targets: This is the tricky part. We will define at least three take profit points: One-third very-short, one-third defined bt the short-term timeframe, and the rest of the position specified using the longer-term timeframe.

The trade size: Choose a total trade size such that the entire initial risk is no more than 2 percent of your account. So if your account is $3,000, the total risk of the trade will be $60.

Accepting the risk. The smaller dataset needed to get any statistical information is 30. Therefore, you should accept the loss equivalent of 30X the average loss per trade. Think that to analyze and decide about changing any parameter, you must move in chunks of 30 trades.

How it works

 1.- Compute the trading size

      • Measure the pip distance between entry and stop-loss.
      • Compute the value in dollars of that risk
      • Calculate how many mini or micro-lots fit in that amount.

2.- Trade that size and mentally divide it into three parts

3.- take profits of1/3 of the position as soon as you get 5-6 pips profit or 10% of your main profit target. This will help you tame the risk if the trade is a short-term gainer that, next, tanks.

4.- As soon as you get a profit equivalent to the size of your risk (1:1), move your stop-loss to Break-even.

5.- Take profits of the second third of the position when your second target is hit

6.- Let the remaining 1/3 run until your third target (from the longer timeframe) trailed by your stop loss. Use a parabolic approach to the stop loss, as the risk-reward diminishes when approaching the target.

7.- Alternatively, use the profits of the last winning trade and add it to the risk of the following trade. That way, on a combination of two trades, you can gain 4X with a risk of just one trade since the added risk was money taken from the market.

8.- The next trade should start with the basic dollar risk, but computed over the newly acquired funds.

Reference: Trading in the Zone by Mark Douglas.

Categories
Forex Chart Basics

Unusual Candlestick Chart Types

In our previous article, we have seen the mainstream chart types, out of which the candlestick charts are the most prevalent in the current markets. But traders devised other ways

In our previous article, we have seen the mainstream chart types, out of which the candlestick charts are the most prevalent in the current markets. But traders devised other ways

In our previous article, we have seen the mainstream chart types, out of which the candlestick charts are the most prevalent in the current markets. But traders devised other ways to represent the price action in their search to get an edge over the rest of the traders. In this article, we are going to describe two popular variations of the basic candlestick chart.

Tick Charts

Tick Charts look similar to a candlestick chart, and every bar indeed is a real candlestick. But a Tick Chart does not depict a linear time scale. Instead, the chart moves to another bar every time a determined number of ticks have been reached.
And what is a tick? A tick is defined as one trade. So a 100-tick chart changes to a new candle every 100 trades, no matter its size.

Advantages of Tick charts

The main advantage of tick charts is that it allows spotting bars mostly populated by non-pro trades. Since every bar is made of the same number of trades, it is easy to detect bars with low volume, caused mainly through retail accounts. That allows pros to fade them and collect their money.

Tick charts homogenize the potential volatility on every bar because all bars represent the same number of trades. Therefore, it compresses low liquidity time segments into a few or one candlestick and expands hyperactive times into several candles. That way, amoving averages, and other indicators are more accurate. Also, the price action can be better appreciated, breakouts appear earlier, and chart cycles show up better.

 

Range Charts

Range charts are a convenient kind of chart. It also gets rid of the temporal method to move to the next bar. The idea of a range chart is to switch to a new bar once the chart has covered the assigned range. On the example supplied, the EURUSD is drawn using a 10-point range. Every candlestick covers ten pips and moves to the next bar. If the instrument is stuck inside a tight range, that candle may last for hours, until volatility comes back and the price creates a breakout.

Advantages of Range Charts

A range chart acts as a filter for ranging periods if the range size is adequately set, so trades can more easily avoid choppy market action, and only act on trendy segments.
Range charts also homogenize volatility.

Trends can be spotted more quickly as a result, and the trader can act on breakouts sooner than with regular candlestick charts. As happens with tick charts, indicators such as moving averages, MACD, and Stochastics work better with range charts.

The key to a proper range setting is to see when a relevant range starts a trend. It is easy to experiment until the adequate range hides most of the sideways action and takes away these harmful periods of inactivity. Looking at the average true range indicator on the timeframe of reference can help with the decision. The style of the trade should be taken into account. A scalping trading style calls for shorter ranges than a 4-hour trader.

To trade using range charts, we can add trend lines, averages, and other indicators. Range charts, as said, are excellent charts for the early entry of breakouts. Finally, range charts are very handy to spot momentum, so trade strategies based on volatility work better using them.

 

Categories
Forex Chart Basics

All you need to be introduced to Trading Charts – Part 1: Line, Bar, and Candlestick Charts

Why Technical Analysis?
The expression “technical analysis” originated from the belief that price action is all that is required to make sound trading decisions. Fundamental analysts believe that fundamental or

Why Technical Analysis?
The expression “technical analysis” originated from the belief that price action is all that is required to make sound trading decisions. Fundamental analysts believe that fundamental or

Why Technical Analysis?

The expression “technical analysis” originated from the belief that price action is all that is required to make sound trading decisions. Fundamental analysts believe that fundamental or structural influences are already incorporated in the history of the price. The concept of price action analysis is credited to Charles Dow, the author of the Dow theory, around 1900.

Starting from there, TA began to rise in importance to traders. The idea that price movement discounts all new information seemed rational. Concepts such as price trending, price confirmation, support, resistance, divergence, and volume confirming price started to emerge.

TA practitioners believe that the current price represents the instantaneous consensus of value. It’s the cost at which someone is ready to buy and a different person to sell. That agreement depends on the different beliefs persons hold about the prevailing market situation. A potential seller believes that odds the price continuing moving up are minimal or that it will surely go down shortly. Opposing this view, a buyer, maybe trading in a different timeframe, might think it is the right place for the asset to start an uptrend. There’s a third category of people: Traders that are expecting to detect another price level to make a decision.

Charts

Traders using technical analysis record prices in charts. Since thousands of transactions happen every minute, chartists accumulate the market action in packets called timeframes. The x-axis registers the passing of time, while the Y-axis register the prices. Usually, volume bars are added at the bottom of the graph.

When traders and investors had to draw the charts on graphical paper, the usual was to use a daily timeframe and follow the daily closings. With the advent of personal computers and dedicated charting packages, we can find charts from sub-minute timeframes to hours, days, weeks, and months. Precisely, the MetaTrader 4 platform allows timeframes of 1 min, 5 min, 15 min, 30, min, 1 hour, 2, hours, 4 hours, one day, one week and one month.

Line charts

The most basic chart is the line chart. Line charts connect the ending price of every frame with a line.

Fig 1 – Line chart of the Bitcoin in a daily timeframe.

Bar charts

Line charts are useful to see trends but lack the information about how volatile was the session. To record this kind of information, chartists decided to draw vertical bars in every time segment, showing four critical elements: The open (O), the high (H), the Low (L), and the Close(C) prices of every segment of trading activity. That’s why sometimes they are called OHLC charts.

Fig 2 –  The same Bitcoin segment of history in a daily bar chart.

As we already stated, every bar is composed of four prices. The Open price is shown as a horizontal mark on the left side of the bar. A close price is depicted as a horizontal mark on the right side of the bar. The high is the highest point of the bar, and the Low price is the lowest part of a bar. The Close is the most crucial level, followed in importance by the Open, and then the high and the low.

Fig 3 – Bar anatomy.

The most probable price path for the bar shown above is the price moving from Open to High, then descending to the Low and finally having the strength to close higher. But we don’t know for sure. It might have moved from open to low, from there to a high to descend to the closing level, finally.  What we know for sure is that the sellers had the strength to drive down the price.

Candlestick charts

Candlesticks are a relatively new way to draw charts. They were introduced to the western world by the work of Steve Nison on the Japanse charting and trading methods.

They use the same four points, OHLC, but a body of the candle is formed between the Open and the Close. The rest of the price action, beyond that range, is left as a line called wick or shadow.

Fig 4 –  The same Bitcoin segment of history in a daily candlestick chart

 Traditionally a bullish candle was drawn hollow or white, while the bearish candle is drawn in black. Now we can assign any color to it. On figure 4, the upward candlesticks are depicted in turquoise, and the red candles denote descending prices.

Candlestick charts are much more graphical, and traders can see immediately if the trend is up or down. During the uptrend seen in fig 4, the turquoise color is prevalent, while the color shifted to red in the downtrend that followed.

Fig 5 –  The candlestick Anatomy

On candlestick charts, the Open and close prices are deducted by the context. The ascending candlestick moves from a lower open to a higher close, while the descending one moves from a higher open to a lower close.

The next article will be dedicated to introducing other forms of charting, such as Renko, three-line break, and point and figure.

 

Categories
Forex Basics Forex Daily Topic

The Babe Ruth Syndrome

In his book More than you know, Michael J. Mauboussin tells the story of a portfolio manager working in an investment company of roughly twenty additional managers. After assessing the

In his book More than you know, Michael J. Mauboussin tells the story of a portfolio manager working in an investment company of roughly twenty additional managers. After assessing the

In his book More than you know, Michael J. Mauboussin tells the story of a portfolio manager working in an investment company of roughly twenty additional managers. After assessing the poor performance of the group, the company’s treasurer decided to evaluate each manager’s decision methods. So he measured how many of the assets under each manager outperformed the market, as he thought that a simple dart-throwing choice would produce 50% outperformers. This portfolio manager was in a shocking position because he was one of the best performers of the group while keeping the worst percent of outperforming stocks.

When asked why was such a discrepancy between his excellent results and his bad average of outperformers, he answered with a beautiful lesson in probability: The frequency of correctness does not matter; it is the magnitude of correctness that matters. 

Transposed to the trading profession, The frequency of the winners does not matter. What matters is the reward-to-risk ratio of the winners.

Expected-Value A bull Versus Bear Case.

Since a combination of both parameters will produce our results, how should we evaluate a trade situation?

Mauboussin recalls an anecdote taken from Nassim Taleb’s Fooled by Randomness, where Nassim was asked about his views of the markets. He said there was a 70% chance the market had a slight upward movement in the coming week. Someone noted that he was short on a significant position in S&P futures. That was the opposite of what he was telling was his view of the market. So, Taleb explained his position in the expected-value form:

Market events Probability Magnitude Expected Value
Market moves up 70% 1% 0.700%
Market moves down 30% -10% -3.000%
Total 100% -2.300%

  As we see, the most probable outcome is the market goes up, but the expected value of a long bet is negative, the reason being, their magnitude is asymmetric. 

Now, consider the change in perception about the market if we start trading using this kind of decision methodology. On the one hand, we would start looking at both sides of the market. The trader will use a more objective methodology, taking out most of the personal biases from the trading decision. On the other hand, trading will be more focused on the size of the reward than on the frequency of small ego satisfactions.

The use of a system based on the expected value of a move will have another useful side-effect. The system will be much less dependent on the frequency of success and more focused on the potential rewards for its risk.

We Assign to much value to the frequency of success

Consider the following equity graph:

 

Fig 1 – Game with 90% winners where the player pays 10 dollars on losers and gains 1 dollar on gainers

This is a simulation of a game with 90% winners but with a reward-to-risk ratio of 0.1. Which means a loss wipes the value of ten previous winners.

Then, consider the next equity graph:

Fig 1 – Game with 10% winners where the player pays 1 dollar on losers and gains 10 dollars on gainers

A couple of interesting conclusions from the above graphs. One is that being right is unimportant, and two, that we don’t need to predict to be profitable. What we need is a proper method to assess the odds, and most importantly, define the reward-to-risk situation of the trade, utilizing the Expected Value concept,

By focusing on rewards instead of frequency of gainers, our strategy is protected against a momentary drop in the percent of winners.

The profitability rule

P  > 1 / (1+ R)  [1]

The equation above that tells the minimum percent winners needed for a strategy to be profitable if its average reward-to-risk ratio is R.

Of course, using [1], we could solve the problem of the minimum reward-to-risk ratio R required for a system with percent winners P.

R > (1-P)/P    [2]

We can apply one of these formulas to a spreadsheet and get the following table, which shows the break-even points for reward-to-risk scenarios against the percent winners.

We can see that a high reward-to-risk factor is a terrific way to protect us against a losing streak. The higher the R, the better. Let’s suppose that R = 5xr where r is the risk. Under this scenario, we can be wrong four times for every winner and still be profitable.

Final words

It is tough to keep profitable a low reward-to-risk strategy because it is unlikely to maintain high rates of success over a long period.

If we can create strategies focused on reward-to-risk ratios beyond 2.5, forecasting is not an issue, as it only needs to be right more than 28.6% of the time.

We can build trading systems with Reward ratios as our main parameter, while the rest of them could just be considered improvements.

It is much more sound to build an analysis methodology that weighs both sides of the trade using the Expected value formula.

The real focus of a trader is to search and find low-risk opportunities, with low cost and high reward (showing positive Expected value).

 

Appendix: The Jupyter Notebook of the Game Simulator

%pylab inline
Populating the interactive namespace from numpy and matplotlib
%load_ext Cython
from scipy import stats
import warnings
warnings.filterwarnings("ignore")
The Cython extension is already loaded. To reload it, use:
  %reload_ext Cython
from scipy import stats, integrate
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
import numpy as np
%%cython
import numpy as np
from matplotlib import pyplot as plt

# the computation of the account history. We use cython for faster results
# in the case of thousands of histories it matters.
# win: the amount gained per successful result , 
# Loss: the amount lost on failed results
# a game with reward to risk of 2 would result in win = 2, loss=1.
def pathplay(int nn, double win, double loss,double capital=100, double p=0.5):
    cdef double temp = capital
    a = np.random.binomial(1, p, nn)
    cdef int i=0
    rut=[]
    for n in a:
        if temp > capital/4: # definition of ruin as losing 75% of the initial capital.
            if n:
                temp = temp+win
            else:
                temp = temp-loss        
        rut.append(temp)
    return rut
# The main algorithm. 
arr= []
numpaths=1 # Nr of histories
mynn= 1000 # Number of trades/bets
capital = 1000 # Initial capital

# Creating the game path or paths in the case of several histories
for n in range(0,numpaths):
    pat =  pathplay(mynn, win= 1,loss =11, capital= cap, p = 90/100)
    arr.append(pat)

#Code to print the chart
with plt.style.context('seaborn-whitegrid'):
        fig, ax = plt.subplots(1, 1, figsize=(18, 10))
        plt.grid(b = True, which='major', color='0.6', linestyle='-')
        plt.xticks( color='k', size=30)
        plt.yticks( color='k', size=30)
        plt.ylabel('Account Balance ', fontsize=30)
        plt.xlabel('Trades', fontsize=30)
        line, = ax.plot([], [], lw=2)
        for pat in arr:
            plt.plot(range(0,mynn),pat)
        plt.show()

References:

More than you Know, Michael.J. Mauboussin

Fooled by randomness, Nassim. N. Taleb

 

 

Categories
Forex Daily Topic Forex Money Management

Why Compounding is such a Powerful Tool

Novice traders put their focus on how much leverage brokers are offering as a crucial part of their decision process to choose the right brokerage account. But in fact, as

Novice traders put their focus on how much leverage brokers are offering as a crucial part of their decision process to choose the right brokerage account. But in fact, as

Novice traders put their focus on how much leverage brokers are offering as a crucial part of their decision process to choose the right brokerage account. But in fact, as we saw in our previous article, Things you should Know about Leverage, Drawdown and Risk , there is no need for leverages above 30X, and in 99% of the cases, leverage is the rope with which traders hang themselves. 

In this article, we will show that compounding is a terrific tool to boost the growth of a trading account.

In our article To Reinvest or Not Reinvest, That is the Question, we discussed the properties needed for a trading system to be suitable for reinvestment strategies. 

Unhappily, 95% of the traders fail. The main reason for failure is discovered in the final section of the article. The article unveils that the growth factor of any investment strategy is called the Geometric Mean (G).

The Geometrical Mean  G = TWR^(1/N)  has two operators.

N the number of trades registered on the system, so it is just a normalization factor to equate all systems no matter how many trades were recorded.

Thus, TWR is the key. TWR means Total Wealth Return and is 

TWR = SUM(1+%Ri) 

where %Ri is the individual percent gains and losses.  

TWR is a product of (1 + the individual gains). If the gain is negative (a loss), this particular factor will decrease the total value of the account by the percent that was lost. If the gain is positive, it will increment it by the percent gained.

As an example, let’s say that 1,000 USD is the initial amount in a trading account, and two trades were performed. One lost 10%, and the other gained 15%. Which is the final TWR of this account, and which is the final balance?

TWR = (1- 0.1)*(1+0.15) = 0.9 *1.15 = 1.035

Final balance = TWR * Initial Capital

Final Balance = 1.035 * 1,000 = 1,035 USD

In this simple example, we clearly see that a system with large losses greatly hurts the growth of the investment.

For instance, if the first loss were 50% then

TWR = (1-0.5) * (1+0.15) = 0.5*1.15 = 0.575

and Final Balance would be =575 USD

Of course, 100% loss would mean the account wiped out and no second chance to make it grow. Therefore, it seems wise to concentrate on steady and continuous gains and cut losses short.   Let’s explore the long-term performance of low-risk position sizing strategies.

It is evident that a loss limit of 1 percent of the trading capital on each trade looks much better than a ten percent loss in our TWR equation.  The issue here is how it performs as generator of growth.

As a first approximation of what we could achieve, let’s look at an equity curve that might seem boring. It is the equity curve of a 26% yearly continuous growth. That was the rate of return Berkshire Hathaway gave his shareholders. 

Fig 1 – 10-year chart of risk-free 26%/year returns 

In this chart, we show ten years of 26% compounding interest. With an initial balance of $10,000, the final capital is $128,173.33 for a capital appreciation of 1,182%. Not a bad feat!

Well, let’s look at what this strategy does in 40 years:

Fig 2 – 40-year chart of a risk-free 26%/year returns 

We can see that in 40 years, this strategy converts $10,000 into 287,818 million dollars and a total growth of 1.88 million percent.

I know, waiting forty years is too much for the instant-satisfaction generation. Maybe we don’t need to wait so long. But before going further, let’s see the properties of this curve. On the 10-year graph, we can see that it takes 400 months to reach $500,000 and just 100 more months to go from 0.5 to almost 3 million. Compounding needs patience and perseverance because its power comes from the accumulation of past gains. 

A Trade sizing system for the faint-hearted

We are going to use a Forex discretionary system that was used live by a friend. Since we don’t have 40 years of history for it, we will extrapolate it with resampling with replacement to simulate its performance. The system will take three daily trades 20 days per month.

To refresh our memory, the following are the relevant statistical data of the system:

STRATEGY STATISTICAL PARAMETERS : 
 Percent winners         : 58.74%
 Profit Factor           : 1.74
 mean Reward Ratio       : 1.22

 Sample Statistics:      
 Mathematical Expectation : 0.0628
 Standard dev            : 0.4090

 Mathematical Expectation using Bootstraping, Samples=100K, confidence limit 99%: 
 Expectation interval   low : -0.01          high : 0.1776

The main parameters are 58% winners and an average reward to risk ratio of 1.22.

The following chart is one year of simulated performance of this system using a 1 percent risk on every trade:

Fig 3 -1-year chart of 1% risk Model Forex System

The figure also shows all the relevant information needed. We see that the system was able to move the initial balance from $10,000 to $13,729.25 for a total profit of 37.29 percent and a max drawdown of just 3.87%. That system beats Berkshire Hathaway Inc, by a fair margin.

Let’s see what it can do in 20 years:

Fig 4 -20-year chart of 1% risk Model Forex System

We can see that this modest system can produce $91.8 million in 20 years with no more than 6.3% drawdown.

We have shown here that there is no need for high leverages and drawdowns to be successful and rich in the long run.  But to show you the power of compounding, let’s show here the 20-year equity curve using a 2 percent risk to observe that drawdowns above 20% are not needed at all and that high leverages are totally unnecessary.

Fig 5 -20-year chart of 2% risk Model Forex System

This system gets the insane amount of $663 billion in 20 years with just a 12% drawdown, and the first 100 million is reached before year 10, starting with only $10,000.

We could go even to 3 percent. I did the numbers, and still, the drawdown is below 20% for insane theoretical profits. Of course, the system would break down well before such amounts could be traded.

The key idea is to show that insane risks are not the way to richness. The way to wealth is compounding with a controlled risk state of mind.

Categories
Forex Daily Topic Forex Money Management

Things you should Know about Leverage, Drawdown and Risk

Novice traders usually prefer to focus on trade ideas and strategies, believing that the path to success is the knowledge about entries and exits. But in a trading environment with

Novice traders usually prefer to focus on trade ideas and strategies, believing that the path to success is the knowledge about entries and exits. But in a trading environment with

Novice traders usually prefer to focus on trade ideas and strategies, believing that the path to success is the knowledge about entries and exits. But in a trading environment with leverage, risk management plays a crucial role. This article tries to show why.

Key points

 In trading, There are two key points a trader must care and make sure:

  1.  That his strategy is good
  2. Risk management trough proper position sizing

Good Strategies and Bad strategies

The first thing to consider is the quality of the trading system or strategy. There are risk management ideas that might convert a losing system into a winner if the problem was that stop-loss settings were wrong, But no position sizing can change a losing strategy into a winning one. Therefore, the first thing a trader should care about is for his system to have a positive edge.

In statistical terms, the strategy should have a positive expectation. If not, the trader should analyze it, find the weak points, and modify it for profitability. Once the system is profitable, it can be traded. Finally, depending on its quality, the system will make grow the trading account fast or slow, and, also, its growth can be optimized through position sizing.

Strategy basic Statistical 

To analyze a trading strategy, we need to normalize its trades to a basic unit and, then extract its four main statistical parameters:

  • Percent winners
  • Mean reward-to-risk Ratio
  • Mathematical expectation
  • Standard Deviation of the expectation.

For example, the system we are going to use as an example in this article shows the following parameters:

STRATEGY STATISTICAL PARAMETERS : 

  •  Nr. of Trades: 143.00
  •  Percent winners: 58.74%
  •  Mean Reward Ratio: 1.22
  •  Mathematical Expectation: 0.0887
  •  Standard dev: 0.4090

It is not a really good system, but it’s tradeable. The Mathematical expectation says that the system, using a basic unit of risk of one dollar, is able to extract a mean of 8.87 cents per dollar risked on every trade. Therefore, the system has an 8.87 cents edge against the market, which is 8.87%.

Drawdown

You can see that here, we did not show the drawdown as a parameter to consider. That is because drawdown is dependent on position sizing. The parameter we can compute, though, is the losing streak, which is the number of continuous losses we could expect based on the percent of losses. As we know, the percent of losers is 1-percent winners. Therefore, in this case, Percent losers = 41.26%

With that information, we can create a probabilistic curve of a losing streak of size N, such as the one here. But the trade size is what is going to define the drawdown parameter.

Fig 1 – Losing Streak Probability Curve

Leverage and Drawdown

Forex is a leveraged trading environment, and many brokers offer its customers the ability to go up to 500:1, meaning traders can use up to 500x the size of its trading account to open positions. But is it wise to get that leveraged? Let’s do an experiment using the above-mentioned system.

As said above, the system has been taken from a real trader and is a good, although not brilliant system. But it is a real no-hype system that can be traded what we want to test. For this test, we will always start with a balance of $10,000 and will increase the trade size using the same trade segment. 

Leverage = 1

Fig 2 – 0.1 Lots per trade

Using a leverage of one, we see that the system shows a max drawdown of 10.4 percent, and the final equity after 143 trades is a bit more than $11.600, which is 16% growth.

Leverage = 5

Fig 3 – 5X Leverage

Using 5X leverage, we notice that the Max Drawdown went to 39.58%, and the final equity ended up at $18,400.00 for an 84% profit.

Leverage = 10

Fig 4 – 10X Leverage

If the trader dares to go to 10X leverage, he must endure close to 61% drawdown for the opportunity to receive 168% profit and a total equity of $26,800 at the end of a 143-trade cycle.

Leverage = 20

Fig 5 – 20X Leverage

Leverage 20X is even wilder. The trader has to withstand up to 83.4% drawdown for a gain of 336.00 % profit.  The question is when to stop? Will a 40X leverage be even better for the profit-hungry trader?

Leverage = 40

Fig 6 – 40X Leverage

We can see that at some point, the risk is too much, and a profitable system, with the wrong risk and size management, can be converted into a very fast losing system and wipe the entire account.

As we see here, a 40X leverage is wild enough to wipe an entire account using a very profitable trading system. We must understand that up to one point, increasing the leverage will increase risk while decrease profitability.

As a summary, let’s see the plot of several account histories with increasing leverage

Fig 7 – 40X Leverage

This time we have plotted the histories on a semi-log scale to be aware of the enormous scale of the drawdowns. On the graph, we can see that the most critical moment of the histories happens at about trade Nr. five or six, which crashes all accounts above 30X leverage. But, if we take this event aside, we can see that to reach its destination traders must endure four more events when they lose close to 80% of their initial funds. We must take into account that at the moment of these events happening, there is no way to know when will they stop and start recovering the funds back.

A Propper Attitude Towards Risk

Position sizing and risk management are the tools traders have to accomplish their trading objectives, but it has to be done correctly.

We first need to set the daily, weekly, or monthly profitability of the trading strategy. Let keep using the previous example.  We know that the system has a mean of 8.87 cents per dollar risked.  Let’s suppose the system has an average of six daily trades. Then, the profitability of this system is $0.53 daily, and $10.64 monthly per dollar risked.

From the losing streak curve, we see that it is wise to be prepared for a max streak of, at least eight losing trades.  Then, we define our comfort zone for drawdowns. Let say we are bold and wanted to risk up to 40% of the capital. To accomplish this, we divide the max 40% drawdown by our defined max losing streak of 8, and the result will be the maximum percent risked on every trade. In this case, Risk per trade = 5%. (That is a huge of risk, we do not recommend more than 1%, by the way).

Now, if your current account balance were $10,000,  the risk per position should be 5% * 10,000, = $500. With that information, we can see that the system would deliver $5,320 monthly, on average.

If we were to double this amount, we would need to double the account balance or wait roughly two months until the profits reached the $10K mark.

The concept of applying a trade size proportional to the account balance helps traders to apply compounding growth to their accounts, while automatically reducing the trade size while in a losing streak on a dollar basis. More on compound growth will be developed in a future article.

 

Categories
Forex Daily Topic

To Reinvest or Not Reinvest, That is the Question

That is one key issue when trading. Should I stick with the same trade size, or is it better to compute trade size based on the account balance?

In his

That is one key issue when trading. Should I stick with the same trade size, or is it better to compute trade size based on the account balance?

In his

That is one key issue when trading. Should I stick with the same trade size, or is it better to compute trade size based on the account balance?

In his book “The mathematics of money management, Ralf Vince answers that question simply and elegantly, so let’s follow Ralph’s steps to dissect this topic.

No Easy Answer

The question of reinvestment or not can’t be answered directly. Let’s see an example where a wining system becomes a loser by reinvest.

Table 1 – System A

In System A, we have two trades. In the first trade making 50% and -40% in the second one, for a total profit of 10%. If we take the same sequence and reinvest, the system loses 10%.

Table 2 – System B

Using System B, we see there is a gain of 15%, followed by a loss of 5% for a total of 10% gain. This system also nets 10% without reinvestment, but it continues being a winner with reinvestment.

Changing the order of the sequence does not alter the final result, provided none of the trades leads the account to a broke (because then the second trade would not be happening). You can make your own calculations, but multiplication is commutative, isn’t it?: 

 A*(1 +0.15)* (1 -0.05) = A*(1 -0.05)*(1 +0.15)

Geometrical Mean: Key to Qualify Systems under Reinvestment

Let’s add two one-point winners to system A, and two one-point losers to system B.

Table 3 – System A

Table 4 – System B

Now we will take as a reference a typical bank account paying one point per period. 

Table 5 – System C

We already know that system B is the optimal one for reinvesting, but, let’s see which parameter defines the optimal system to fulfill our objective to maximize profits under reinvestment. How could we determine which system is the best for reinvesting, given we had only the information of its non-reinvesting performance?

By percentage of winners, system C will be the winner, by average trade or by total dollars the winner is system A. Risk/reward or lowest drawdown is not the answer. If that was the answer, then we should move our money to a bank account.

 We know system B has the right mix of profitability and consistency, and systems A and C lack one of these properties. So how to measure this mix?

According to Vince, the right formula is the Geometric Mean, which is the Nth root of what he calls “Terminal Wealth Relative” (TWR), where N is the number of trades. TWR is the cumulative amount we would obtain if the initial amount of the trading account were 1 instead of 100. 

TWR = (1+%R1)*(1+%R2)*… (1+%RN)

Where %Ri is the percent returns on each trade.  

A simpler formula to express TWR is:

TWR = Final Capital / Starting Capital 

      And these are the  TWR of our systems :

Table 6 – TWR   

    By taking the square roof of the N trades we obtain

Geometric Mean (G) = TWR^(1/N)

G = (Final Cap/Starting Cap)^(1/Nr of trades)

     Let’s see the G of our sample systems  

  Table 7 – Geometric Mean   

From table 7 we can see that the best performer in terms of geometric Mean is System B.

Final words

The Geometric Mean is the growth factor per trade. A system with the highest G is the system that makes the most profit and grows the fastest on a reinvestment basis. 

A G less than one means the system is not profitable and would lose money when using reinvestment.

If we obtain a G = 0, it means we went broke because anything multiplied by zero is still zero, and G is a multiplicative function. Any big losing trade will have a powerful effect in G. That is a mathematical way of saying, “cut your losses short.”

As Ralf Vince put it: “in trading, you are only as smart as your dumbest mistake.“

Reference: The Mathematics of Money Management, Chapter 1.

The examples of this article were taken from Ralph Vince’s book, although the formulas were checked and computed using an Excel spreadsheet.

Categories
Forex Assets

All you need before trading the EUR/USD Pair

The EUR/USD pair tracks the exchange rate of the Euro against the US Dollar. Since this pair represents a combination of the two stronger economies in the world, it is

The EUR/USD pair tracks the exchange rate of the Euro against the US Dollar. Since this pair represents a combination of the two stronger economies in the world, it is

The EUR/USD pair tracks the exchange rate of the Euro against the US Dollar. Since this pair represents a combination of the two stronger economies in the world, it is the most traded asset in Forex, and, therefore, the one with higher liquidity and less spread and slippage.

The value assigned represents how many US dollars are needed to buy a single EUR. That is, the quote is presented as 1 euro per x US dollars. For example, the current value is 1.1079, which means a trader needs to use 1.1079 dollars for every Euro he is willing to buy.

EUR/USD SPECIFICATIONS

LOT SIZE 100,000 EUR
MAGIN CURR. EUR
DIGITS 5
PIP VALUE $10
MIN TRADE SIZE 0.01 LOTS
MAX TRADE SIZE 1000 LOTS
AVERAGE 24H  VOLUME $575 BILLION

 

Spread

Spread is the difference between the bid and the ask prices. The EUR/USD spread varies depending on the account type. 

ECN: 0.3 pip

STP: 1 pip

Fees

The broker charges a fee per lot on ECN accounts, and usually, no fee on STP accounts The usual fee on an STP broker is from 6 to 10 pips per round trip and lot. Other

Slippage: Slippage is the difference between the trader’s intended price and the real price he received from the broker. It depends on the current volatility at the moment of the order. Slippage can be in favor of or against the trader.

Depending on the broker’s execution speed, slippage can be as low as 0.5pip or as high as 3 pips. 

Note:  Slippage happens twice: At the open and the close of a position.

Trading Ranges:

The following trading range tables measure the min, average, and max volatility of the asset at different timeframes.  Range figures usually multiply by the square root of two for every doubling of the timframe. That is, if the hourly timeframe volatility is 1, its 2h timeframe will show 1.41 on the same date. Trading ranges are useful tools to assess the risk. If the hourly volatility of the EURUSD is 20 pips, it means a potential $200 gain or loss in an hourly time span ( 20 pips + $10 value per pip).

The values shown depict ranges occurring at the moment of the creation of this document. The trader should assess the actual values at the moment of his trading activity.

        EUR/USD PIP RANGES  

MIN AVERAGE MAX
1H 5.9 10.4 26
2H 8.5 14.5 37
4H 13 22.1 49
D 45 64 114
W 119 160 210
M 290 537 918

  

Procedure to assess Pip Ranges

  1. Add the ATR indicator to your chart
  2. Set the period to 1
  3. Add a 200-period SMA to this indicator
  4. Shrink the chart so you can assess a large time period
  5. Select your desired timeframe
  6. Measure the floor level and set this value as the min
  7. Measure the level of the 200-period SMA and set this as the average
  8. Measure the peak levels and set this as Max.

EURUSD Cost as a percent of the Trading Range

To compute the costs, we add the trading fee, an average slippage value x 2 converted to pips, and we calculate what percent represents the min, average, and max of the ranges, assuming a range represents the amount of potential profit for one unit of time.

ECN MODEL ACCOUNT

ECN MIN AVERAGE MAX
Total 3.3 1H 55.93% 31.73% 12.69%
Slippage 2 2H 38.82% 22.76% 8.92%
Spread 0.3 4H 25.38% 14.93% 6.73%
Trading_Fee 1 D 7.33% 5.16% 2.89%
W 2.77% 2.06% 1.57%
M 1.14% 0.61% 0.36%

 

STP MODEL ACCOUNT

STP MIN AVERAGE MAX
Total 3.5 1H 59.32% 33.65% 13.46%
Slippage 2 2H 41.18% 24.14% 9.46%
Spread 1.5 4H 26.92% 15.84% 7.14%
Trading_Fee 0 D 7.78% 5.47% 3.07%
W 2.94% 2.19% 1.67%
M 1.21% 0.65% 0.38%

 

Best EUR/USD timeframe for trading

From the above charts, we see that hourly charts show a very high cost on entries with low volatility ( the Min column) therefore to trade these timeframes, traders need to spot the surges in volatility and be right most of the time to compensate for the 50%+ costs.

Intraday traders’ best timeframe is, definitively 4H, although the should optimize the costs using proper assessment of the volatility.

In both cases, strategies that take away slippage using limit orders would dramatically reduce costs and improve the results.

As an example, these are the results if we take away slippage using limit orders in entries and exits on an ECN account.

ECN MIN AVERAGE MAX
Total 1.3 1H 22.03% 12.50% 5.00%
Slippage 0 2H 15.29% 8.97% 3.51%
Spread 0.3 4H 10.00% 5.88% 2.65%
Trading_Fee 1 D 2.89% 2.03% 1.14%
W 1.09% 0.81% 0.62%
M 0.45% 0.24% 0.14%

 

We can see a percentual reduction of over 50% in costs, compared to market orders with slippage.

Categories
Forex Daily Topic Forex Psychology

Know The Two Systems Operating inside Your Head

In the introduction of his book, “Thinking fast and slow,”  Daniel Kahneman presents a face with an expression similar to the following image as an example of your mind

In the introduction of his book, “Thinking fast and slow,”  Daniel Kahneman presents a face with an expression similar to the following image as an example of your mind

In the introduction of his book, “Thinking fast and slow,”  Daniel Kahneman presents a face with an expression similar to the following image as an example of your mind working in automatic mode. 

By looking at the image, you’ll experience what is called intuitive thinking. In a fraction of seconds, you’ll notice it is a brown-haired young woman (not an old one, not a man or any other animal or object), and you instantly know she is upset. You feel also she is going to start saying harsh words in a loud voice. All that came to your mind automatically and without effort. It merely happened without you intending to do that assessment.

This is an example of what Dr. Kahneman calls System One.

Now look at this: 

28 x 13

Looking at it, you knew it is a multiplication immediately, but the result does not come to your mind instantly. You know you can solve it with paper and pencil or in your head, but you need to make a conscious effort to do it, and the solution comes slowly. If you engage in the process of solving it, you’ll experience the slow thinking process as you follow the steps you’ve learned to solve a multiplication operation. Dr. Kahneman describes this process as “deliberate, effortful and orderly.”

This is what Dr. Daniel Kahneman calls System Two.

System One is in charge of automatic activities such as 

  • Detecting if an object is distant or near
  • Finding the source of a sound
  • Complete the phrase “piece of c..”
  • Change the facial expressions
  • detect a warning or a hostile voice
  • Read 
  • drive a car
  • understanding a language

System One includes innate abilities. We are “programmed” to interpret the reality that surrounds us, recognize objects, focus our attention, and avoid dangers. System One also learns by the association of ideas, and also learn skills such as reading, driving a car, or pattern recognition, such as a chess player or a trader do.

The operations of System Two have one common characteristic: they require deliberate attention, and the process can be disturbed by a loss of concentration.

Here are examples extracted from the book:

  • Focus on the voice of a single person in a noisy room
  • look for a woman with white hair
  • trying to identify a surprising sound
  • telling someone your phone number
  • Count the number of times a word appears in a page

The Interaction between both Systems 

The usual situation when we are awake is that System One and Two both are active. According to Kahneman’s book, System One runs automatically while System Two works in “low-effort” mode, in which almost no effort from its part is needed. System one sends summary information to system two, and System Two has the final word.

Under this scenario, System One continually creates “suggestions” for System Two: impulses, feelings, intuitions, impressions, and intentions. When confirmed by System Two, these impressions become beliefs, and impulses turn into voluntary actions.

It is usual, under normal circumstances, that everything moves placidly. Under these situations, System Two adopts the suggestions sent by System One with small modifications, if any. We usually believe in our impressions and act on our desires.

When System One finds something it cannot solve, it asks for the help of System Two, as in the process of multiplying 28×13. We can feel this whenever we are surprised. That’s the activation of System Two. Surprises activate and orient our attention. That can be lifesaving. A hole in the road, a tiger, appearing 100 meters from you.

 

System two has been taught by our evolution to trust System One, as he is generally quite good at what it does: modeling familiar situations, short-term predictions, and initial reactions to challenges and dangers.

The Conflict

One limitation of System One is it cannot be switched off. Therefore, sometimes, there is a conflict between System One’s automatic reaction and System Two’s intention of control. Under uncertainty situations, System one triggers primary reactions such as fear or greed that System Two is used to believe and act upon. Even when the case does not call for such an automatic response, as usually happens when trading the Forex markets, System Two has a hard time to take control of the situation.

Since System One works in automatic mode, it cannot be turned off at will. Therefore errors due to intuitive thinking are very difficult to prevent. Also, biases cannot be avoided because System two is not aware of them, and when these biases are known, only by a System Two’s deliberate effort can be overcome. In the trading world that translates into people selling at the bottom and buying at the top. These people are making decisions based on the impressions generated by System One. Thus, System Two is inadvertently dominated by System One’s beliefs.

Final Words

If you find yourself reflected by the above scenario, you should establish the steps to break the dominance of your System One. 

  • Define yourself as a Soldier when trading. A soldier only obeys, never thinks. That is the task of your other self: The Planner. Plans are rational and are to be done before the trade opens, not during a live trade. After the trade is open, a soldier executes the plan decided by the Planner.
  • Start trading using risk sizes that do not trigger your primary fears 
  • Make a rational plan and build the discipline to follow it. You’re a soldier.
  • Before the market opens, rehearse trade situations from beginning to end. Establish how you’re going to react based on your trading plan when taking losses or profits. Visualize it in your mind. Look at your mental monitor screen and see the price moving and you making the planned decisions.
  •  Write down your feelings during the open trades. Check for inner conflicts, explain to yourself why you do what you’re doing.
  • Create a log of trade results, also annotating the maximum adverse and also maximum favorable excursions.
  • Grade your trades from 0 to 5 or 10 based on the percentage of the total possible profit you obtained.
  •  After your trading session ended, analyze the performance of your system in regards to the entry point, stop-loss, and profit target placements, and modify these parameters for the next session. But never change them while trading.
  • Compute your system’s performance and analyze if it is still performing as planned or there is a deviation from its past performance.
Categories
Forex Basics

Supply, Demand and Liquidity as Drivers of Prices

Markets are “places” where people and institutions exchange assets. It may be stock shares, commodities, grain, livestock, or currencies, but all markets behave similarly. Buyers and sellers look for the

Markets are “places” where people and institutions exchange assets. It may be stock shares, commodities, grain, livestock, or currencies, but all markets behave similarly. Buyers and sellers look for the

Markets are “places” where people and institutions exchange assets. It may be stock shares, commodities, grain, livestock, or currencies, but all markets behave similarly. Buyers and sellers look for the best possible price. A buyer seeks to buy at the cheapest possible price, while the seller wants to sell at the highest price.

How prices move

If we order buyers and sellers by the price they are willing to accept, we could see some buyers are bidding an amount very close to the price sellers are asking, and from there, the distance grows in a kind funnel-like shape.

For a sell to occur, one of them must cross the bridge and accept the other side’s price. Also, when a seller moves and takes the ask price for the first time, the “Last price” moves down a little. If another seller does the same, there might be other buyers willing to buy at the same level or not. If there are more buyers at that level, the next seller who takes the ask does not create an additional downward movement. If all buyers disappear from this level, the seller should accept a worse price, moving the asset down, or hold until a buyer takes his bid.

Conversely, if a buyer takes a bid price for the first time, the price of the asset moves up. If other buyers get in and deplete this level from sellers, they should buy at higher prices or wait till a seller takes his ask price.

Supply and Demand

Demand

The demand for an asset decreases as the price increases. The rate of that decline depends on the need for the asset and also on the perceived future value of the asset.

Supply

The supply increases as the price increases. The rate of increment depends mostly on the sellers’ belief about the future price growth of the security.

Equilibrium

Supply and demand are what drives prices up and down. If there are an equal number of buyers and sellers, the price stays at one level and is said to be in equilibrium.

When there are more buyers than sellers, the price moves up until a new equilibrium is reached. Conversely, if the number of sellers is higher, the price moves down until sellers and buyers get the new equilibrium.

Fair Price

The equilibrium is the result of a consensus about the fair price of the security, but fair price changes with the passage of time. The change in fair price may come from technical factors ( overbought-oversold levels, pivot points, breakouts), economic reports such as interest rates, GDP, manufacturing, nonfarm Payrolls, and inflation, or unexpected news events. The new price does not manifest itself in a single and swift price movement because that price is not known at the time. That’s the reason for the appearance of trends.

Liquidity

Liquidity is the term used to define the number of buyers and sellers present in the market.

In a very liquid market, the number of buyers and sellers is vast. Large-sized orders do not affect the price much. Also, bid and ask prices get closer to each other because buyers and sellers compete among themselves to offer their best bid and ask prices. That means spread tightens.

A market with low liquidity shows a scarcity of buyers and sellers. The size and number of operations are tiny, and one small order can produce significant price variations up or down. Also, usually, spreads widen because there is less competition among participants. Low liquidity may cause market manipulations since it is easy to drive prices up or down.

Liquidity does not depend only on the market in question. It changes with the time of the day. For instance, the EURUSD shows less liquidity during the Tokyo session. Then it grows when the European exchanged opens, and it maximizes at the open of the US session. Finally, it fades after Europe closes its markets and traded volume declines further after the US closes.

Final words

  • Supply and demand drive the prices up or down until an equilibrium is reached.
  • The equilibrium breaks by a change in the perception of value by the parties trading it.
  • High liquidity is the key factor for tightening spreads and making markets flow without price manipulation.
Categories
Forex Daily Topic Forex Risk Management

How Be Sure your Trading Strategy is a Winner?

To evaluate, the quality of a strategy is an old quest, and its answer has to do with gambling theory, although it can apply to any process in which the

To evaluate, the quality of a strategy is an old quest, and its answer has to do with gambling theory, although it can apply to any process in which the

To evaluate, the quality of a strategy is an old quest, and its answer has to do with gambling theory, although it can apply to any process in which the probability of profits is less than 100%. Of course, the first measure to know if our system is winning is when the current portfolio balance is higher than in its initial state. But that does not give very much information.

A better way might be to record winners and losers, and have a count of both so that we could apply some stats. It would be interesting to know the percentage of winners we get and how much is won on average. That also applies to losers.

We could try to find out if our results are independent of each other or they are dependent.

Finally, we could devise a way to obtain its Mathematical expectancy, which would show how profitable the strategy is.

Outcomes and probability statements

No trader is able to know in advance the result of the next trade. However, we could estimate the probability of it to be positive.

A probability statement is a figure between zero and one specifying the odds of the event to happen. In simple terms,

Probability = odds+ / ( odds+  +  odds – )

On a fair coin toss game: odds of heads (against, to one) = 1:1

probability Fair coin toss = 1/(1+1)

= 0.5

Probability of getting a Six on a dice:

odds = 5:1 – five against to one

Probability of a Six = 1/( 1+5) = 0.16666

We can also convert the probability into odds (against, to one) of occurring:

Odds = (1/ Probability) -1

As an example, let’s take the coin-toss game:

Odds of a head = 1/0.5 -1 = 2-1 =1:1

That is very handy. Suppose you have a system on which the probability of a winner is 66 percent. What are the odds of a loser?

System winners= 0.66 so -> System losers = 0.34

loser odds = 1/0.34 – 1 = 2 -> about 2:1.

That means, on average, there is one loser for every two winners, which means one loser every three trades.

Independent vs. Dependent processes

There are two categories of random processes: Independent and dependent.

A process is independent when the outcome of the previous events do not condition the odds of the coming one. For example, a coin toss or a dice throwing are independent processes. The result of the next event does not depend on previous outcomes.

A dependent process is one where the next outcome’s probability is affected by prior events. For example, Blackjack is a dependent process, because when cards are played, the rest of the deck his modified, so it modifies the odds of the next card being taken out.

This seems a tedious matter, but it has a lot of implications for trading. Bear with me.

What if we acknowledge our trades are independent from each other?

If we consider that our trades are independent, then we should be aware that the previous results do not affect the next trade, since there is no influence between each trade.

What if we know our system shows dependency?

If we know that our system’s results are dependent, we could make decisions on the position size directed to improve its profitability.

As an example, let’s suppose there is a very high probability that our system gets a winner after a loser, and also a loser after a winner. Then we could increase our trade size every time we get a loser, and, also, reduce or just paper-trade after a win.

Proving there is dependency on a strategy or system is very difficult to achieve. The best course of action is to assume there is none.

Assuming there is no dependency, then it is not right to modify the trade size after a loser such as martingale systems do since there is no way to know when the losing streak will end. Also, there is no use in trading different sizes after a winning or losing trade. We must split the decision-making process from trade-size decisions.

Mathematical expectancy

The mathematical expectancy is also known as the player’s edge. For events that have a unique outcome

ME = (1+A)*P-1

where P is the probability of winning, and A is the amount won.

If there are several amounts and probabilities then

ME = Sum ( Pi * Ai)

The last formula is suitable to be applied to analytical software or spreadsheet, but for an approximation of what a system can deliver, the first basic formula will be ok. Simply set

A = average profit and

P = percent winners.

As an example, let’s compute the mathematical expectancy of a system that produces 40% winners and wins 2x its risk.

ME = (1+2)*0.4 -1

ME = 3*0.4 -1

ME = 0.2

That means the system can produce 20 cents for every dollar risked on average on every trade.

Setting Profit Goals and Risk

Using this information, we can set profit goals. For instance, if we know the strategy delivers a mean of 3 trades every day – 60 monthly trades- The trader can expect, on average, to earn (60 * 0.2)R, or  12* R, being R his average risk.

If the trader set a goal of earning $6,000 monthly he can compute R easily

12*R = $6,000

R= $6000/12 = $500.

That means if the trader wants a monthly average of $6,000, he should risk $500 on every trade.

Final Words

On this article, we have seen the power of simple math statements, used to help us define the basic properties of our trading system, and then use these properties to assess the potential profitability of the strategy and, finally, create a simple plan with monthly dollar goals and its associated trade risk.

 

Categories
Forex Daily Topic Forex Risk Management

Basic Math Skills Traders Needs – Average and Chevyshev’s Inequality Explained!

Most of the people wanting to profit from the Financial Markets think that the secret to success lies in knowing the price turns to start a new trend and also

Most of the people wanting to profit from the Financial Markets think that the secret to success lies in knowing the price turns to start a new trend and also

Most of the people wanting to profit from the Financial Markets think that the secret to success lies in knowing the price turns to start a new trend and also detects when to get out of the trade. They might be right if there were a mathematical formula or crystal ball to show us the right timing. But the truth is the Financial Markets are chaotic and random. Thus, there is no sure way to be right.

The good news is that we don’t need to be right, but be profitable. That can be achieved by taking small losses when the trade goes against us and let profits run when the trade goes as we projected. And the key knowing if we are on the right track is measurements and analysis.

This article deals with how to extract information out of a set of results by computing an average. And also, by measuring the deviation from the norm extract wisdom hidden in the data collection.

Averages

Averages have the purpose of determining the typical value or center of a set. For instance, the mean profit achieved in a month or a year. We assume that the majority of the samples are located near the average, and, also, that the number of cases away from it decreases with the distance to the average.

The computation of an average is simple. We add all elements and divide them by the number of items in the set.

As an exercise, if we have a collection of trading results X, with elements x1 = $1, x2=$-1 and x3=$3 which is our average profit?

Average of X (M) = (x1+x2+x3 )/3 = (1+(-1) +3)/3 = 3/3 = 1 dollar.

The Sample Standard deviation (SD)

It is interesting also to measure how far could we expect the following trades to be away from this mean. There are several ways to measure errors, but the most used is the Standard Deviation. SD for short.

Computing the standard deviation is a bit more complicated than the average, but not much.

1.- We take the difference between the mean and every element, creating a new set of differences.

dxi = M-xi 

2.- Differences may be negative or positive, so we square them to get dxi^2, creating a set of squared differences.

dxi2 = dxi^2

3.- We add all elements of this last set and divide by its n-1, the number of items minus one. This result is called variance

Var = Sum(dxi2)/(n-1)

4.- Take the square root of the variance.

SD = √ Var

let’s do it with our example:

1.- dx1 = 1-1 =0;  dx2 = 1-(-1) =2; dx3 = 1-3 =-2

2.- dx1^2 = 0; dx2^2 =4; dx3^2 =4

3.- Var = (0+4+4)/(3-1) = 8/2 = 4

4.- SD = √4 = 2

After that, we can conclude that our system’s future performance will be one dollar plus or minus 2 dollars.

The Standard Deviation can be thought of as the average of the dispersion of the results.

Chebyshev’s Inequality

Once getting these results, we know a bit about our trading system. Chebyshev’s inequality gives us another handy piece of information. It addresses the question of how many of our samples will lie within a certain distance from its mean.

There are many classes of probability distributions. One of them is the Normal Distribution, with its typical Gauss or bell curve. The Normal distribution is very nice indeed, and many physical phenomena conform with it, such as the length of people or the distance from the target on a dart game. Unhappily, trading results do not conform to it.

The good news is that the Chebyshev’s inequality works with a wide variety of distributions, and guarantees that no more than a certain fraction of values can be farther away than a certain distance from the mean.

Specifically:

No more than 1/k^2 values can be farther away than k* SD

We can say it the other way around:

At least 1-1/k^2 of the values of a distribution are within k*SD from its mean. If we create a spreadsheet using these formulas we get:

Table 1 – Chebyshev’s Inequality

This table provides a lot of information.  We see, for instance, that only 11.11% of the trades are farther than 3 SD from its mean.

  • That means close to 90% of the profit on future trades in the above calculation will be between -5 and 7 dollars.
  • Also, 75% of them will lie within 2 SD – between -3 and 5 dollars.

Since it can be applied to most of the distributions, we could use it with prices. That way, we could determine how far a price is away of its mean and assess overbought and oversold conditions with statistically relevant tools.

Final words

  • Knowing how to compute averages and the standard deviation will help traders quantify and qualify their performance.
  • It is interesting to know how to find the odds for a value to be at a determined distance from the mean value of the distribution.

 

Categories
Forex Daily Topic Forex Range

Hidden Wisdom Behind Range Measures

People coming to the Forex markets usually learned new vocabulary. The first special words they learn maybe are, margin, profit, risk-reward, and candlestick. Perhaps, afterward, they learn new concepts such

People coming to the Forex markets usually learned new vocabulary. The first special words they learn maybe are, margin, profit, risk-reward, and candlestick. Perhaps, afterward, they learn new concepts such

People coming to the Forex markets usually learned new vocabulary. The first special words they learn maybe are, margin, profit, risk-reward, and candlestick. Perhaps, afterward, they learn new concepts such as Volatility. Also, along with other technical indicators, they get to know one study called Average True Range. However, later, they forget about it since they usually consider it unimportant.

The Average True Range (ATR) is one way to measure Volatility. Volatility is, as we know, a measure of risk. Therefore, ATR can be used as an estimate of our risk. This measurement is essential for us as traders, especially if we are trading on margin. And I’ll explain why.

 

What tells the Range?

A range is a measure of the price variation over a period of time. It is measured between the High and the Low of a bar or candlestick. For instance, the range of figure 1 below (a 4H chart) is 357.9 points. If each point/lot were worth $1, a short position started at the Low of the bar would have lost $357.9 in four hours on every lot traded. Conversely, a long position would get this amount of profit.

True Range

True range is similar to a normal Range, but it takes into consideration possible gaps between bars. That happens a lot in assets that do not trade all day. Not always the close of a session matches the open of the next one. A gap may form. A True range accounts for that by considering gaps as part of the range of the bar if the gap is not engulfed by the range.

Average True Range

As we can see, in the figure above, every bar’s range varies depending on the particular price action on the bar. Some bars are impulsive and move considerably. Other bars are corrective, and their range is short.

Therefore, to measure the average price range an average is taken, usually, the 14-period, although traders can change it. Below we show the 10-bar ATR of the Bitcoin.

On this figure, we see that the ATR gets quite high at some point on the left of the figure, and it slowly decreases in waves. That is normal. Assets move in a series of increasing and decreasing volatility waves, which describes the interests and power of buyers and sellers.

Average True Range and Risk.

Retail traders usually have small pockets. The first measure a retail trader should know is how much his account would endure in the event of an adverse excursion.

As an example, let’s examine the EURUSD daily chart. Observing the 10-ATR indicator in the chart below, we see that the maximum level on the chart is 0.01053 and the minimum value is 0.00664. Since we want to assess risk, we are only interested in the maximum value.

Let’s assume that we wanted to trade long one EURUSD contract at $1.1288 and that, on average, our trade takes one day to complete. How much can we assume the price would move in a single day?

If we take the 0.01053 as its daily range value and multiply it by the value of a lot ($100K) we see that the EURUSD price is expected to move about $1,053 per day. We don’t know if that will be in our favor or not, but from the risk perspective, we can see that to be on the safe side we would need at least $1,053 of available margin for every lot traded.

If the average trade, takes 4 or 8 hours instead, we should set the timframe to 4H or 8H and proceed as we did with the daily range.

For not standard durations, we could use the following rule: For each doubling in time, the average range grows by a factor of the square root of 2.

That is handy also to compute the right trade size. Maybe we do not have the required margin level, but just one fourth. Thus, if we still wanted to trade the asset, we should trim down our bet size to one-quarter of the lot.

How much time our stop-loss will endure?

Based on ATR figures, we could assess the validity of a stop-loss level. If the stop-loss size is too short compared to the ATR, it might be wrongly set.

What profits to expect?

We could assess that as well, on average, of course. If the dollar range of an asset is $1,000 in a 4-hour span, we can expect that amount on average in four hours, and $1.410 (√2 * $1,000) on an 8-hour lapse.

Deciding which asset to trade

We could use the True Range to assess which asset is best for trading. Let’s suppose, for instance, that you are undecided about trading Gold (XAU) and Platinum (XPT). So let’s examine them.

Gold:

Spread: 3.2

$Spread cost: $32

Digits: 2

contract size: 100

MAX Daily ATR: 16, $ATR: $1600

Spread cost as Percent of the daily range: 2%

Platinum:

Spread: 12.9

$Spread cost: $129

Digits:2

Contract Size: 100

Max Daily ATR: 22, $ATR $2,200

Spread cost as Percent of the daily range: 5.86%

After these calculations, we can see that it is much wiser to trade Gold, since the costs slice only 2% of the daily range, while Platinum takes almost 5% of the range as costs before break even.

 

Categories
Crypto Market Analysis

Daily Crypto Update, Oct 01 – Reversal Day!

Yesterday was a reversal day. Bitcoin went down from $8,054 to touch $7,701 to swiftly reverse its path and close above $8,300. Other cryptocurrencies followed. The heatmap

Yesterday was a reversal day. Bitcoin went down from $8,054 to touch $7,701 to swiftly reverse its path and close above $8,300. Other cryptocurrencies followed. The heatmap

Yesterday was a reversal day. Bitcoin went down from $8,054 to touch $7,701 to swiftly reverse its path and close above $8,300. Other cryptocurrencies followed. The heatmap here testifies the 24H advances, with Bitcoin, BCH, BSF, ETH, and XRP  up more than 7% over its previous session. Market capitalization went up to $224.8 billion with a traded volume of $33.4 Billion.

Fig- 1 Market Cap and Traded Volume

Fig 2 –  24-hour Heat Map

 

The News Front

Industry giants Coinbase, Bitrex, Kraken, Anchorage, and others join to form the Crypto Rating Council to qualify on how likely a coin is likely to be regulatory compliant and characterize tokens into currency, commodity, security or something else. The notable exception is Binance, which seems it has been excluded. Source: beincrypto.com.

SEC announced it had settled an agreement with Block.one the company behind EOS to settle the charges for raising billions in an ICO, back in 2017.  Block.one agreed to pay a “civil penalty” of $24 million. Source trustnodes.com.

Cardano is partnering with New Balance shoemaker to authenticate the company’s premium line of sports shoes. Cardano can produce blockchain.based data that consumers and stores can trust. Source: dailyhodl.com.

Bitpay achieves Service Organization Control (SOC2) compliance. That certification means BitPay is certified for confidentiality, security, privacy, processing integrity, and availability.  Source: cointelegraph.com.

 

Technical Analysis

Bitcoin


After creating a double bottom, yesterday bitcoin made a reversal day. The price made a kind of harami in the 4H chart and continue moving up to cross the Bollinger line mean and then the +1SD line. By crossing that line and then moving near the +1SD line, we should assume a new upward trend is initiated. MACD also confirms the bullish phase of the bitcoin.

Right now the price has bounced off of the $8,524 level, which touches the 200-day moving average on the daily chart. That means it is a tough resistance level to cross. If crossed, the price will need to fight the 8.800 level with is June’s 02 topping area. Right now BTC may need to consolidate near the Bollinger Mean line before continuing with the trend.

Ethereum


Ethereum has confirmed its bullish leg up by making another higher high and higher low. MACD and Bollinger bands are in agreement, obviously since indicators lag the price action. Currently, the price was rejected by the $185 resistance and is retracing some of the recent advances, as the price is overextended. We estimate that the price will retrace near its $177 support and, then continue its way up.  A breach of the $177 level ( on a closing basis) would imply less buying strength than anticipated.

Ripple


Ripple has made a sharp impulsive candle on strong volume, yesterday, and since then is making corrective candlesticks near the top of that range, the price hold by the 200-period MA. That is fine since this candle created a price overextension that now is being corrected. The MACD and Bollinger Bands confirm XRP has started a bullish trend, so buy the dip is the motto here. The chart shows the current key levels for this asset.

 

 

Categories
Forex Candlesticks Forex Daily Topic

Three Facts about Candlesticks you Never Knew About

Candlesticks are great because it makes trends visual at first glance. But most candlestick users stay just with that trait and don’t go more in-depth.

Of course, everybody knows some

Candlesticks are great because it makes trends visual at first glance. But most candlestick users stay just with that trait and don’t go more in-depth.

Of course, everybody knows some

Candlesticks are great because it makes trends visual at first glance. But most candlestick users stay just with that trait and don’t go more in-depth.

Of course, everybody knows some candlestick patterns such as Morning and Evening Stars, Haramis, Dojis and Shooting stars, but what’ is hiding inside the candlestick?. How to extract market sentiment from its shape or pattern?

So, let’s begin!

1 – Unwrapping a Candlestick

A candlestick is condensed information of the price action within its timeframe. The corollary is that if we go to a shorter timeframe, the candlestick now is a pattern of several candlesticks.

In the chart here we see the unwrapping of a 4H candle into 30-min parts

Three Facts about Candlesticks you Never Knew About

Chart 1 – 4H Hammer Candlestick unwrapped into 30-min candles.

 

We notice that the candle has one segment dominated by sellers and the other part controlled by buyers.

Which sentiment dominates in sellers at the bottom?

  • To the first class belong those traders who could no longer hold the pain of being long and close their position.
  • The second class is made of those who came late to the trend and sold believing the trend will last forever, or quite so.

Which sentiment dominates in the way back up?

  • Late sellers realized that they were in the losing side, so they needed to close their shorts. That meant, they have to buy, adding to the bullish fuel
  • Longs that were taken out of their position see frustrated how the price moves up without them. Hence, some of them retake their longs, while others don’t dare, afraid this is going to be another bull squeeze.

2.- Impulse or correction?

There are only two stages in the market: Impulses and corrections of previous impulses. So how to spot the price is in an impulsive or corrective phase?

Three Facts about Candlesticks you Never Knew About II

Chart 2 – Candlesticks: impulses and corrections.

Impulses break resistances and move with a clear direction. Impulses are what make trends. Corrections move in ranges, lack direction, and usually retraces some or all the advances of the previous impulse.  People usually think in trends as composed by many candlesticks or bars, but we now know that a single candlestick is composed by many shorter-timeframe candlesticks. Therefore, we cannot be surprised if we state that a trend can be made of a single candlestick. That applies also to corrective movements. A corrective movement can be summarised in a single candlestick.

How to know if a candle is impulsive or corrective?

To spot an impulse look for a candlestick with a large body and almost no wicks or shadows. To spot a corrective movement look for small-bodied candles with or without wicks ( usually with wicks).  Sometimes we find both characteristics in a candlestick. That may mean it is a combination of impulse and correction. That is ok since there is no law that forbids the start of a correction or impulse in the middle of the timeframe of a candle. Sorry, the universe is not perfect!

3.- Who is in control?

Once we know facts 1 and 2, we are in the position to spot who controls the price action: buyers or sellers.

One clue is, of course how the candlestick closes, but the other clue is where are and how long are wicks. If we spot several candlesticks with large lower wicks we could reason that the buyers are pushing the price above the bottom of the candlesticks. If wicks happen on top we could deduct the opposite: Sellers selling the rally.

Three Facts about Candlesticks you Never Knew About III

Chart 3 – Candlesticks: Wicks show who controls the price action

A downward trend with a lot of lower wicks is weak. That applies to an upward trend with lots of upper wicks.  Therefore, we can detect the market sentiment by just observing the wick appearance on the candlesticks.

 

Final words

So now we know that there is much more than just fancy colors and trend visualization. We have to inspect and pay attention to body and wicks, also called shadows by Steve Nison. The information provided by a single or a group or candlesticks is worth the time spent.

 

Categories
Crypto Market Analysis

Daily Crypto Update, Sept 30 – Bitcoin under $8,000 drives Crypto Assets Down!

Bitcoin breached the $8,000 early morning today, as the bearish sentiment keep persisting in the crypto sector. That lack of buyers is creating a pronounced bearish trend in the

Bitcoin breached the $8,000 early morning today, as the bearish sentiment keep persisting in the crypto sector. That lack of buyers is creating a pronounced bearish trend in the

Bitcoin breached the $8,000 early morning today, as the bearish sentiment keep persisting in the crypto sector. That lack of buyers is creating a pronounced bearish trend in the whole sector. This weekend, Bitcoin lost another 5% Bitcoin Cash(-5.55%), Binance Coin(-5.82%), Monero(-5.05%) and DASH(-6.2%) lead the loses.  The Market Capitalisation of the sector went further to $207.9 billion.

The heatmap below shows the price change of coins and tokens during the weekend.

The News Front

The Ukranian government is planning to legalize cryptocurrency. That is so according to a report published by an independent Ukranian news media. Currently, cryptocurrencies are not illegal in Ukraine. This step is a government move to regulate it and benefit via taxation. Source: The Block.

JP Morgan strategist Nikolaos Panigirtzoglou claims Bakkt’s launch of a physically-settled Bitcoin Futures contract was the reason for the Bitcoin 20% drop. Source: dailyhodl.com.

Denis Baykov has been fined the value of $7,000 by Russian authorities after mining bitcoin using a supercomputer able to petaflop speeds from an old Russian nuclear facility in Sarov, western Russia. Source beincripto.com.

Google has created a 50qbit computer able to execute in minutes what would have taken 20,000 yeats using a regular computer. Besides that, quantum computing is not a threat to crypto-assets, according to a news piece by bitcoinnews.com.

Technical analysis

 

Bitcoin


Today, bitcoin definitively broke the $8,000 support level and confirmed, also, the breach of the 200-day MA. On the daily chart, we see also the price has also broken the lower trendline of the descending wedge to the downside.

The next level to break is the $7,700 and, next, we could observe if the supply zone below $7,725 is able to hold prices and stop the downward evolution of the price, to, at least, experience a bounce.

 

Ethereum


Ethereum’s bounce ended, although the price has not broken the $166 support. We see the price moving slightly below the -1 Bollinger line, which means a downward pressure to prices. That, combined with the bitcoin weakness, makes us think ETH will continue descending to test $160 at least. But we can’t be surprised if $152 is reached in the coming days.

 

Ripple


Ripple seems to keep holding inside the range between 0.234 and 0.2468, besides the persistent BTC weakness. Today the price is losing 1% while bitcoin is -3.55% down. That shows there is some hidden buying power holding its price. The current sideways channel and technical indicators still show the price is in a downtrend, though. That and another BTC downward spike may force this token to break its support. The best course of action is to be in the sidelines while this is resolved.

 

Categories
Forex Daily Topic Forex Psychology

Two Mistakes Novice Traders Should Avoid

On this article we are going to discuss two mistakes novice traders should avoid to succeed in the financial markets. Reading a book about trading or a strategy article on

On this article we are going to discuss two mistakes novice traders should avoid to succeed in the financial markets. Reading a book about trading or a strategy article on

On this article we are going to discuss two mistakes novice traders should avoid to succeed in the financial markets. Reading a book about trading or a strategy article on a website makes trading the markets seem easy, But that is far away from the truth.

Judgmental errors

“We typically trade our beliefs about the market, and once we’ve made up our minds about those beliefs, we’re not likely to change them” – Van K. Tharp

 

Joe Novice comes to the markets, after reading a marvelous book explaining to him how to win easy money in the markets. The book has beautiful charts describing how. Joe has learned a lot from this book. Now he knows what bull and bear candlesticks are. He has learned to distinguish patterns. Head and shoulders, double tops and bottoms, the Morning Star and its counterpart the evening star. He also learned some handy indicators such as the Stochastics, the RSI, and the MACD. Finally, he has also get acquainted with the concepts of support, resistance, and breakout. He thought that was key to succeed

Prices Move faster in real-time than on a book illustration.

Joe founded his account with his first $1,000 to experience the exciting world of big wins. Then he downloaded his MT4 station to begin operating. He created the setup recommended in the book and started looking for major pairs and decided that for his scalping purposes, he should use 1-minute charts.

The first thing that surprises Joe is that prices are continually moving. He was switching from pair to pair on his laptop, but nothing happened until he left the chart and moved to another one. The price action seems to occur only when he wasn’t looking! That made him think that he must concentrate on just a couple of charts at a time.

Also, Joe had a hard time making decisions. For some reason, the strategy explained in the book only was evident after the fact. The right moment to trigger the trade seemed never to show. He was late to pull the trigger most of the time, and when not late, the moment to pull it did not appear right.

Representation Bias

How can a trader make money using patterns and levels everybody sees?

The fact is that all technical analysts are able to spot support and resistance levels. So why people make money trading breakouts? Or don’t they?

People believe in charts as if they were truths. They believe that charts represent the activity of the markets. In fact, bars or candlesticks are just approximations to that activity. The issue is that what we see is the representation of past action, but we do not see the reasons why the price arrived at that place.

What if most traders really didn’t have the right information to make decisions. What if only a handful of privileged traders owned that knowledge? Let’s suppose that these smart guys have the privilege to see where is the real supply zone. The zone where they’d do the worst harm to the herd of dumb traders. Wouldn’t it be logical that they tried to stretch the price to that zone to collect the best available prices, then turn back and move the price to the opposite side?

Under that assumption, the next day or week, another technical analyst would see the price extension and figure out where the stop-loss should be. It will reason the optimal place to be just below that zone. However, the fact is that there is an action-reaction phenomenon in the markets. The actions of the market participants change history. The market is an experiment, on which the scientist influences the result with his acts. If he were to trade the previous day, he might have decided the same way as did those who were that day in the market, and, so, would be wiped as the others.

So, how should we proceed?

The strategy should have clear rules of entry, stop-loss, and profit-taking

Traders should back-test the strategy and optimize some parameters. Then they should forward test it in a demo account or using one micro-lot.

After a list of 30 trades, the trader should have a minimum of data samples to approximate percent winners and reward to risk ratio: The two most critical parameters of any strategy. We do not talk about drawdown here, because drawdown is a dependent variable: it can be computed knowing the percent losers and changes with position size.

When deciding about stop-loss placement, Do not use pivot levels. These are already known to the sharks of the market, and will inevitably fail.  The best stop-loss placement is using the Maximum Adverse Excursion technique, a concept by John Sweeney.

Of course, to be able to use MAE, you should record your trades accurately, recording also the MAE information.

 

Categories
Forex Trading Strategies

The Connors & Raschke’s 80-20 Strategy

Introduction

 

The original Connors & Raschke’s 80-20 Strategy is an intraday strategy that was published in Street Smarts by Larry Connors and Linda Raschke.

It is based on

Introduction

 

The original Connors & Raschke’s 80-20 Strategy is an intraday strategy that was published in Street Smarts by Larry Connors and Linda Raschke.

It is based on

Introduction

 

The original Connors & Raschke’s 80-20 Strategy is an intraday strategy that was published in Street Smarts by Larry Connors and Linda Raschke.

It is based on the Taylor Trading Technique, which is a manual for swing trading. Taylor’s method was the result of the observation that the markets move within a cycle that is made up of a buy day, a sell day, and a sell short day. That setup was further investigated by Steve Moore ar the Moore Research Center.

Mr. Moore focused on days that closed in the top 10% of the range for the day. Then, he checked on for the percent of time next day prices exceeded the previously established high, and, also, for the percentage of times it also closed higher.

His results showed that when prices closed in the top/bottom 10% of its range, it had an 80-90% chance of following-through the next session, but only 50% of them closed higher/lower. This fact implied an excellent possibility of reversal.

Derek Gibson, said Connors, found out that the market has an even higher chance of reversing if the set-up bar opened in the opposite end of the range. That is, a candlestick with short wicks and a large body. Therefore this pre-condition was added. To create more opportunities, they lowered the percent of the daily range from 90 to 80, because it didn’t affect the system’s profitability.

Long Setups

  1. Yesterday, the asset opened in the top 20% and closed in the lower 20% of its daily range.
  2. Today the market must trade at least 5-15 ticks below yesterday’s low. This is a guideline.
  3. An entry buy stop is then placed at yesterday’s low, once the trade is being filled, and an initial protective stop near the low extreme of today’s action.

Move the stop to lock in profits. This trade is a day trade only.

 

Short setups

  1. Yesterday the asset opened in the bottom 20% and closed in the higher 20% of its daily range.
  2. Today the market must trade at least 5-15 ticks above yesterday’s high This is a guideline.
  3. An entry sell stop is then placed at yesterday’s high, after being filled, and an initial protective stop near the upper extreme of today.

Move the stop to lock in profits. This trade is a day trade only.

 

Example of a trade

 

The Connors & Raschke's 80-20 Strategy

Testing the Strategy

We tested this strategy using the backtesting capabilities of the Multicharts64 version 11 Platform.

The naked strategy, as is, in EURUSD, USDGPY, and USDCHF over a range of 17 years, were positive in all cases. Below the equity curves for the three pairs:

 

Examining the parameter map

 

The figure above shows the parameter maps of the USD_CHF and the EUR_USD pairs. We see that the return of the strategy increases as the parameters move to the 50% level, meaning that the importance of the starting and ending point (Open to Close) in the previous candlestick is not essential. The critical fact is the next day’s break above(below) the previous highs(lows) and the subsequent return to that level (False Breakout).

 

Example of  50-50 System with optimized stops and targets on the EURUSD

 

As we said, this is a 50-50 system, meaning that we don’t care in which part of the candle is the Open and Close. This is a simple false breakout system.

We see that the curve is quite good over its 17-year history. Starting with 10,000 dollars, the final equity reached $72,000, for a 6X profit figure.

 

Looking at the Total Trade Analysis table, we can observe that this system is also robust, with almost 40% winners and an average Ratio Win to Average loss ( Reward/risk) of 2.19.

 

The shuffled Trades Analysis shows that the system is very reliable, with a likelihood of small drawdowns, depicting a max consecutive loss of 16 trades.

 

The Net Profit distribution Analysis shows that there is a 75% probability of getting a 5X equity profit over 16 years and a 25% probability of getting a 7X profit figure.

 

Above is the Max Consecutive Losing Streak analysis, which shows that there is less than 10% probability of ending above a 16 losing streak. Although you think that a 16-losing-streak is terrible, it is not, but we need to be prepared psychologically to endure it. This figure is the one needed to help us conservatively decide our risk strategy.

As I already mentioned in other strategy analyses, you, as a trader, need to decide which percent of your equity you can lose without losing your temper. Many don’t like to lose any amount so they shouldn’t trade, because losing streaks are part of the trading job. Many would say 10% while others 50%. That figure has a close relation to the rate of growth of your trading account because it will decide the size of your position.

And here it comes the way to do it. Once we know the distribution of drawdowns of our trading system, we, as traders, want to minimize the probabilities that a losing streak goes beyond our max drawdown figure. This is an approximation, but its good enough to allow us to decide the best position size for our risk tastes.

Let’s say we are an average-risk trader, and we will be upset if we lose ¼ of our account. Using this trading system, and admitting a 10% probability of error, we would choose 16 as the losing streak to compute our size per trade.

Therefore, we divide 25% equity drawdown by 16, which is 1.56%. In this case, we must trade using a 1.56% risk on every trade. That means that the cost of a trade computed by the distance from entry to stop-loss levels, multiplied by the dollar pip risk and by the number of contracts should be 1.56% of your current equity balance.

Let’s simplify it using elementary math:

Percent Risk (PR) = MaxDD / Max_losing_Streak

Dollar Risk = PR x Equity_Balance

Dollar Risk = (Entry-Stop) x PipCost x Nr_of_Contracts

Let’s call Entry-Stop, Pips. And NC the Number of Contracts. Then the equation is:

Dollar Risk = Pips x PipCost x NC

Let’s move the elements from this equation to compute the Nr of contracts.

NC = Dollar_Risk / (Pips x PipCost)

 

That’s all. Every trade will be different, and the distance in pips from entry to stop loss will be different, but we can compute the number of contracts quickly:

Let’s do an example. Our current balance is right now $12.000, and we want to enter a trade with 20 pips of risk, and our cost per pip is $10 per lot. Which is my optimal size?

Our Dollar Risk is 1.56% of $12,000 or $187

NC = 187 / (20 x 10) = 0.93 lots, or 93 micro-lots.

 

Computing the Performance of the System

 

Now we want to know how much on average are we going to get, monthly, from this system. That is easily computed using the numbers above. We know that this system’s history is 205 months long, and it had 1401 trades, which is seven trades per month on average. Evidently, this system trades very scarcely, but we can hold a basket of assets. Thus, If we manage to get a basket of 10 holdings, including pairs, crosses, indices, and metals, we could trade 70 times per month. And those trades will not overlap most of the time if the assets are chosen uncorrelated.

Based on our risk profile and the average Reward-to-risk ratio, we know that our average winning trade will be 2.2 times our average losing trade.

So,

AvgWin = 411

AvgLoss = 187

Our winning percentage is 40%, so our losing one is 60%

Then on a 10-asset basket, there will be 28 winners and 42 losers monthly, then:

Gross Profit: 411*28 = 11,508

Gross Loss:  42*187 =  7,854

Average Monthly Net Profit =11,508 – 7,854 = 3,654

This is an average 30,4% monthly from a $12,000 balance. Not bad!

 

Note: The computations and graphs were done using Multicharts 11 trading platform.

 

 

Categories
Forex Trading Strategies

The Turtle Soup plus One System

Turtle soup
As Newton found out, an action carries its reaction. The market found a solution to profit from these anticipated turtle breakouts: Turtle Soup.

Larry Connors and Linda Bradford

Turtle soup
As Newton found out, an action carries its reaction. The market found a solution to profit from these anticipated turtle breakouts: Turtle Soup.

Larry Connors and Linda Bradford

Turtle soup

As Newton found out, an action carries its reaction. The market found a solution to profit from these anticipated turtle breakouts: Turtle Soup.

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

The method that Connors and Raschke 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 five 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 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 from mid-2006 up to Jan 2018.

As we observe above, the strategy’s percent winners are about 23%, but with a Risk/reward factor- which is the average win to the average loss ratio- of 3.77. Please note, also, that the average trade is rather small. That is the result of a short timeframe.

To achieve those values, we used a trailing stop of 0.22%

Main metrics of the naked Turtle Soup Donchian fading System, on the EUR-USD:

Conclusions:

The system is good. The reward to risk ratio is great, but it is hard to take just one successful trade every four trades.

The system could be much more profitable if we can assess when the asset is in a trading range, apply it only when this condition happens, and avoid trading it during prolonged trends. This can be done using a filter that allows only propper trades. For example, trading only when the ±1 STD Bollinger bands  (see figure below- BB bands in cyan) are shrinking in size, and its centerline goes flat. We should avoid the trade when the Bollinger bands start expanding with its center curving up or down. That would ensure a much higher success ratio.

 

How to use Metatrader 4 to trade this strategy:

The Donchian Channel indicator can be directly downloaded to your MT4 from:

https://www.mql5.com/es/market/product/4782

When clicking download, a pop-up window appears:

 

Clicking “Yes, I have MetaTrader 4,” the indicator installs directly in your trading platform.

To load the indicator to a chart, on your MT4, go to Insert -> Indicators -> Custom- Donchian Price Channels tfmt4:

Then, a popup window with the parameter selection appears:

 

And, finally, we get the desired channel surrounding prices:

 

A sell signal happens at the candle following a close price breaking the channel’s current upper border. A buy signal occurs at the candle following a closing price below the line of the current lower edge of the channel. See figure below three consecutive winners on a flat channel signaled by a Bollinger band contraction.

 

It’s not usual, but, from time to time, we can expect a streak of up to 10 losing trades. Thus, we have to apply adequate money management rules.

As an example, let’s say you don’t like to have a drawdown higher than 20% of your capital. Then, if you divide that figure by 10, that should be your maximum risk for a single trade. In this case, this is 2% of the current capital allotted to this strategy.

As a final note, the Turtle Soup and Turtle soup plus one are counter-trending systems profiting from false breakouts. Therefore, these systems work best in ranging markets. Bull or bear markets don’t fit well with its counter-trending nature. But they are a very good complement to trend following systems such as the Donchian channel breakout system or similar systems.

Categories
Forex Trading Strategies

The MACD Crossover Strategy

Moving Average Convergence Divergence: MACD
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

Moving Average Convergence Divergence: MACD
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

Moving Average Convergence Divergence: MACD

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. That is the fast MACD line.
  4. Compute the 9-period EMA of 3. That is the slow Signal line.

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

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 is12-26-9, that became the standard for commercial charting software.  This seems to indicate that Gerald Appel had a preference for getting in early in the trend and letting profits run.

My preference for intraday trading is 6-26-12, smoothing the signal line with a 12-period EMA. That reduces the indicator’s lag and having a faster signal that gets entries earlier, while a trailing stop keeps track of the trade at the exit side.

It’s not a good idea to optimize the 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 extended period EMA’s. Pivot

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 agreement, while the short period EMA represents a fresher consensus that is arising.

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 rises above the slow signal line, it means a bull cycle has begun. If the fast MACD line crosses under the slow signal, a corrective period has started.

On Fig. 1, we show an example of a EUR/USD 1H chart. There we may observe that pink – unproductive- areas are usually crossovers going against the trend, which happens in reactive segments with ranging price movement. In spite of these failures, a MACD crossover is an efficient way to detect a trend change.

Testing the MACD over 14-year historical data on  a 15-min chart of the EUR-USD

To test this system, we use two separate MACD modules. One for the long signals (MACD LE), and one for the short signals (MACD SE).

We will keep a constant 26-period MACD slow MA on both sides, and we will test the map space of the fast MA and the smoothing period to obtain the slow MACD signal.

Below, is shown on both, Long and short signals.

 

Based on the above figure, we see that the best performing combination is around 6,26,10 settings. Below we show its Equity Curve. That is the raw strategy, without stops nor targets.

 

It’s great to see this kind of curve with almost no modification because it shows this trading system is robust, and we know it will improve by merely adding a trailing stop. And we’ll get even more improvement if we add proper profit targets. Below, Both equity curves.

As we can see, there is a slight improvement in the results using targets. But it’s very small and might be a by-product of the optimization process rather than a real improvement.

I would recommend the use of this system just using a trailing stop, because, since this is a trend following system, it follows the well-tested advice “let profits run.” Thus, starting from now, we will present the system numbers using just trailing stops. As usual, we show the system with a constant one-lot trade.

The Total Trade Analysis table shows that the percent winners is at 34.23% and the Ratio Avg Win/ Avg Loss, which shows the reward-to-risk ratio is 2.17.

Main metrics of MACD, on the EUR-USD:

How to use Metatrader 4 to trade this strategy

To add a MACD indicator to your chart do the following:

After clicking the MACD on the menu window, another popup window with several tabs appears. In the Parameters tab, change Fast EMA to 6 and MACD SMA to 10. The Fast MACD line in this platform appears as a histogram, but it’s easy to spot MACD line crossing the fast line. See figure, below:

Categories
Forex Basics

Everything you should master to Detect Trends, and more!

Introduction
In chapter 1, we’ve set the foundations of market classification, what a trend is about, and the dissection of a trend in its several phases. Then we talked about

Introduction
In chapter 1, we’ve set the foundations of market classification, what a trend is about, and the dissection of a trend in its several phases. Then we talked about

Introduction

In chapter 1, we’ve set the foundations of market classification, what a trend is about, and the dissection of a trend in its several phases. Then we talked about its two dissimilar wave parts: an impulsive wave, followed by a corrective wave.  We dealt with support, resistance, and breakouts. Finally, we talked about channel contractions.

In this second chapter, we’ll learn the methods available in the early discovery of trends: Trendlines, moving averages, and Bollinger band channels.

Trendlines

A trendline is a line drawn touching two or more lows or highs of a bar or candlestick chart. The convention is to draw the line touching the lows if it’s an uptrend and the tops on a downtrend. Sometimes both are drawn to form a channel where the majority of prices fit.

As we see in Fig. 1 the trendline tends to draw resistance levels or supports where the price finds it difficult to cross, bouncing from there, although not always this happens. In Fig. 1 the first trendline has been crossed over by the price, and during the following bars, the slope of the downtrend diminished.  We saw, then, that the first trendline switched its role and now is acting as price support.

When the second trendline was crossed over by the price, a bottom has been created, and a new uptrend started. After a while trending up, we might note that we needed a second trend line to more accurately follow the new bottoms because the uptrend has sped up, and the first trendline is no longer able to track them.

Fig. 2 shows two channels made of trendlines, one descending and the other ascending. The trendline allows us to watch the volatility of the trend and the potential profit within the channel. The trend, as is depicted, has been drawn after it has been developing for a long lapse. Therefore, it’s drawn after the fact.  If we look at the descending channel, we observe that during the middle of the trend, the upper trendline doesn’t touch the price highs. So, this channel would look different at that stage of the chart.

I find more reliable the use of horizontal lines at support and resistance levels and breakouts/breakdowns at the end of a corrective wave. But, if we get a well-behaved trend, such as the second leg in fig 2, a channel might help us assess the channel profitability and assign better targets to our trades. If we use horizontal trendlines together with the trend channel (see Fig 2.b) it’s possible to better visualize profitable entry points and its targets, and, then, compute its reward to risk ratio.  The use of the Williams %R indicator (bottom graph) confirms entry and exit points.

Fig. 2b graph’s horizontal red lines show how resistance becomes the support in the next leg of a trend.

As a summary:

  • A trendline points at the direction of the trend and acts as a support or as a resistance, depending on the price trend direction.
  • If a second trendline is needed, we should pay attention if it shows acceleration or deceleration of the price movement.
  • If the price crosses over or crosses under the trendline, it may show a bottom or a top, and a trend change.
  • A trendline channel helps us assess the potential profitability and assign proper targets to our next trade.

Moving Averages (MA)

Note: At the end of this document, an Appendix discusses some basic statistical definitions, that may help with the formulas presented in this section, although reading it isn’t needed to understand this section.

Some centuries back, Karl Friedrich Gauss demonstrated that an average is the best estimator of random series.

Moving averages are used to smooth the price action. It acts as a low-pass filter, removing most of the fast changes in price, considered as noise. How smooth this pass filter behaves, is defined by its period. A moving average of 3 periods smoothens just three periods, while a 200-period moving average smoothens over the last 200 price values.

Usually, a Moving Average is calculated using the close of every bar, but there can be any other of the price points of a bar, or a weighted average of all price points.

Moving averages are computationally friendly. Thus, it’s easier to build a computerized algorithm using moving average crossovers than using trendlines.

Most Popular types of moving averages

Simple Moving Average(SMA):

The simple moving average is computed as the sum of all prices on the period and divided by the period.

The main issue with the SMA is its sudden change in value if a significant price movement is dropped off, especially if a short period has been chosen.

Average-modified method (AvgOff)

To avoid the drop-off problem of the SMA, the computation of an avgOff MA is made using and average-modified method:

Weighted moving average

The weighted moving average adds a different weight to every price point in the period of calculation before performing the summation. If all weights are 1, then we get the Simple Moving Average.

Since we divide by the sum of weights, they don’t need to add up to 1.

A usual form of weight distribution is such that recent prices receive more weight than former prices, so price importance is reduced as it becomes old.

w1 < w2 < w3… < wn

Weights may take any form, most popular being Triangular and exponential weighting.

To implement triangular weighting on a window of n periods, the weights increase linearly from 1 the central element (n/2), then decrease to the last element n.

Exponential weighting is an easy implementation:

EMAt = EMAt-1 + a x (pt – Et-1)

Where a, the smoothing constant, is in the interval 0< a < 1

The smoothing property comes at a price:  MA’s lags price, the longer the period, the higher the lag of the average. The use of weighting factors helps reducing it. That’s the reason traders prefer exponential and weighted moving averages: Reducing the lag of the average is thought to improve the edge of entries and exits.

Fig 3 shows how the different flavors of a 30-period MA behave on a chart. We may observe that the front-weighted MA is the one with a slope very close to prices, Exponential MA is faster following price, but Triangular MA is the one with less fake price crosses, along with simple MA: The catch is: We need to test which fits better in our strategy. The experience tells that, sometimes, the simpler, the better.

Detecting the trend using a moving average is simple. We select the average period to be about half the period of the market cycle. Usually, a 30 day/bar MA is adequate for short-term swings.

One method to decide the trend direction is to consider it a bull leg if the bar close is above the moving average; and a bear leg if the close is below the average.

Another method is to watch the slope of the moving average as if it were a trendline. If it bends up, then it’s a bull trend, and if it turns down, it’s a bear trend.

A third method is to use two moving averages:   Fast-Slow (Fast -> smaller period).

In this case, there are two variations:

  1. Moving average crossovers
  2. All the averages are pointing in the same direction.

As with the case of a single MA, a price retracement that touches the slower average is an opportunity to add to the position.

For example, using a 30-10 MA crossover: If the fast MA crosses over the slow MA, we consider it bullish; if it crosses under, bearish.

Using the method of both MA’s pointing in the same direction, we avoid false signals when the fast MA crosses the slow one, but the slow MA keeps pointing up.

When using MA crossovers, we are forbidden to take short trades if the fast MA is above the slow MA, but we’re allowed to add to the position at price pullbacks. Likewise, we’re not allowed to trade on the buy side if the fast MA is below the slow MA.

Using smaller periods, for instance, 5-10 MA, it’s possible to enter and exit the impulsive legs of a trend.  Then, the 10-30MA crossovers are used to allow just one type of trade, depending on the trend direction, and the 5-10 MA crossover is actually used as signal entry and exit (if we don’t use targets). In bull trends, for example, we may enter with the 5MA crossing over the 10MA, and we exit when it crosses under.

Bollinger Band Channel

We already touched channels that were made of two trendlines. There is another computationally friendly channel type that allows early trend detection and trading.

One of my favorite channel types is using Bollinger Bands as a framework to guide me.

A Bollinger Band is a volatility channel and was developed by John Bollinger, which popularized the 20-period, 2 standard deviations (SD) band.

This standard Bollinger band has a centerline that is a simple moving average of the 20-period MA. Then an upper band is drawn that is 2 standard deviations from the mean and a lower band that’s 2 standard deviations below it.

I tend to use two or three 30-period Bollinger bands. The first band is one SD wide, and the second one is two SD apart from the mean. A third band using 3 standard deviations might be, also, useful.

Fig 6 shows a very contracted chart with 3 Bollinger bands to show how it looks and distinguishing periods of low volatility.

During bull trends, the price moves above the mean of the Bollinger band.  During bear markets, the price is below the average line of the bands.

On impulsive legs of a trend, the price goes above 1-SD (or below on downtrends), and it continues moving until it crosses the 2-SD line, sometimes it even crosses the third 3-SD line. Price beyond 2 SDs is a clear sign of overbought or oversold. On corrective legs, the price goes back to the mean. During those phases volatility contracts, and is an excellent place to enter at breakouts or breakdowns of the trading range.

Below Fig. 7 shows an amplified segment of Fig 6, with volatility contractions circled. We may observe, also, how price moves to the mean, after crossing the 2 and 3 std lines.

 

Grading your performance

According to Dr. Alexander Elder, the market is testing us every day. Only most traders don’t bother looking at their grades.

Channels help us grade the quality of our trades. To do it, you may use two trendlines or some other measure of the channel. If you don’t see one, expand the view of the chart.

When entering a trade, we should measure the height of the channel from the bottom to its top.  Let’s say it’s 100 pips.  Suppose you buy at ¾ of the upper bound and sell 10 pips later. If you take 10 pips out of 100 pips, your trade quality is 10/100 or 1/10. How does this qualify?

According to Elder’s classification, any trade that takes 30% or more of a channel is credited with an A. If you make between 20 and 30%, your grade will be B. Between 10 and 20% you’re given a C and a D if you make less than 10%.  So, in this case, your grade is C.

Good traders record their performance. Dr. Elder recommends adding a column for the height of the channel and another column for the percentage your trade took out of the channel.

Monitor your trades to see if your performance improves or deteriorated.  Check if it’s steady or erratic.  The information, together with the autopsy of your past trades, helps you spot where are your failures: Entries too late? Are you exiting too soon? Too much time on a losing or an underperforming trade?  A trade against the prevailing trend?

 

The next chapter will be dedicated to chart patterns.

 

Appendix: Statistics Overview

Statistics is a branch of mathematics that gives us information about a data set. Usually, the data set cannot be described by an analytical equation because they come from unpredictable or random events. As traders, we need basic knowledge, at least, of statistics for our job.

We can express statistical data numerically and graphically. Abraham de Moivre, back in the XVII century, observed that as the number of events (coin flips) increased, the shape of the binomial distribution approached a very smooth curve. De Moivre thought that if he could find the mathematical formula for this curve, he could solve problems such as the probability of 60 or more heads out of 100 coin flips. This he did, and the curve is called Normal distribution.

This distribution plays a significant role because of the fact that many natural events follow normal distribution shapes.  One of the first applications of this distribution was the error analysis of measurements made in astronomical observations, errors due to imperfect measuring instruments.

The same distribution was also discovered by Laplace in 1778 when he derived the central limit theorem. Laplace showed the central limit theorem holds even when the distribution is not normal and that the larger the sample, the closer its mean would be to the normal distribution.

It was Kark Friedrich Gauss, who derived the actual mathematical formula for the normal distribution. Therefore, now, Normal distribution is also named as Gaussian distribution.

Although prices don’t follow a normal distribution, it’s is used in finance to extract information from prices and trading statistics.

There are two main measures we use routinely: The center of our observations and the variability of the points in our data set from that mean.

There’s one main way to compute the center of a set: the mean. But it’s handy to know also the median if the distribution isn’t symmetrical.

Mean: It’s the average of a set of data. It’s computed adding all the elements of a set and divide by the number of elements:

Mean = Sum(p1-Pn)/n

Median: The median is the value located in the middle of a set after the set has been placed in ascending order. If the set has a symmetrical distribution, the median and the mean are the same or very close to it.

The variability of a data set may be calculated using different methods. Two main ways are used in financial markets:

Range: The easiest way to measure the variability. The range is the difference between the highest and lowest data of a set. On financial data, usually, a variant of the range is calculated: Average true range, which gives the average range over a time interval of the movement of prices.

Sample Variance(Var): Variance is a measure of the mean distance of the data points around its mean. It’s computed by first subtracting the average from all points: (xi-mean) and squaring this value. Then added together and dividing by n-1.

Var = 𝝈2 =∑ (x-mean)2 / (n-1),

where ∑ is the symbol for the sum of all members of the set

By squaring (xi-mean), it takes out the negative sign from points smaller than the mean, so all errors add-up. The division by n-1 instead of n helps us not to be too much optimistic about the error. This measure increments the error measure on small samples, but as the samples increase, its result is closer and closer to a division by n.

If we take the square root of the variance, we obtain the standard deviation (𝝈 – sigma).

 Volatility: Volatility over a time period of a price series is computed by taking the annualized standard deviation of the logarithm of price returns multiplied by the square root of time expressed in days.

𝝈T = 𝝈annually √T

 

References:

New Systems and Methods 5th edition, Perry Kaufman

Trading with the Odds, Cynthia Kase

Come into my Trading Room, Alexander Elder

History of the Gaussian distribution http://onlinestatbook.com/2/normal_distribution/history_normal.html

https://en.wikipedia.org/wiki/Volatility_(finance)

Further readings:

Profitable Trading – Chapter 1: Market Anatomy

Profitable Trading Chapter III: Chart patterns

Profitable Trading – Computerised Studies I: DMI and ADX

Profitable Trading – Computerized Studies II: MACD

https://www.forex.academy/profitable-trading-computerized-studies-iii-psar/

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

Profitable Trading VIII – Computerized Studies V: Oscillators