Categories
Forex Risk Management

Basics of Risk To Reward Ratio In Forex Trading

Introduction

The Risk to Reward Ratio is one of the most critical aspects of risk management in Forex trading. Traders with a clear understanding of what RRR is can improve his/her chances of making more profits. In this article, let’s discuss the fundamentals of Risk to Reward ratio with examples and also the ways through which it can be increased while taking your trades.

What is the Risk to Reward Ratio?

Before getting right into the topic, let’s define the meaning of ‘Risk’ here. Risk is the amount of money that a trader is willing to lose in a trade. If you have read our previous money management articles, we mentioned that a trader should not be risking more than 2-3% of their trading capital in each trade. It means when they find a trade setup, they should choose their position size in such a way that if the market hits their stop-loss, they lose a maximum of 2-3% of their trading capital.

Now, the Risk to Reward Ratio is simply the ratio between the size of your stop-loss to the size of your target profit. Let’s say your stop-loss is five pips away from your entry price and your target profit is ten pips away from the entry. In this case, your risk to reward ratio is 1:2 (5 Pips/ 10 Pips).

The larger the profit against the stop loss, the smaller the risk to reward ratio. Which means your risk is a lot smaller than your reward.

What is the recommended risk to reward ratio in the forex market?

Typically, a minimum of 1:1 or 1:2 RRR is recommended for novice traders. There are super conservative traders where they look for a minimum RRR of 1:5.

The risk to reward in every trade cannot be fixed as it varies depending on the market condition. For example, 1:3 or 1:5 RR ratio is achievable when the market is trending, and you enter the market at the right time. Whereas when the market is not very volatile, we should be happy with a risk to reward ratio of 1:1.

How to increase the risk to reward (RR) ratio?

🏳️ Raising target and putting stop-loss to breakeven

A trader can think of raising the target if the market moves to the initial take-profit quickly. This is because when the market moves so fast, it has the potential to move further, thereby increasing the profits.

🏳️ Finding trade setups from the larger time frame

Another way to increase the risk to reward (RR) ratio is by taking the strong trade setups from the higher time frames like daily, weekly, and monthly. We need to wait for such strong trade setups to form. Once formed, the price will move for hundreds of pips, and so we can have wide targets.

Final words

Higher the RRR, the better it is, and of course, higher RRRs are more challenging to achieve. So, do not forget to keep the expectations real and the risks appropriate. You do not have to avoid perfect trades just because the RRR is not as high as 1:5. Make sure to do proper risk management before placing a trade. Never trade with a risk to reward ratio that is too less and try to maximize it as much as possible. Cheers!

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 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 Harmonic

The Alternate Bat Pattern

Harmonic Pattern Example: Alternate Bat Bullish

The Alternate Bat Pattern

The Alternate Bat Pattern is another pattern by Scott M. Carney. This pattern comes from his second Volume Two in his Harmonic Trading series of books. He discovered this pattern roughly two years after (2003) his discovery of the Bat Pattern (2001). Carney wrote that ‘the origin of the alternate Bat pattern resulted from many frustrated and failed trades of the standard framework. The standard Bat pattern is defined by the B point that is less than a 0.618 retracement of the XA Leg.’ Essentially, with the Alternate Bat Pattern we observe an extension beyond the 88.6% level at D, where D moves slightly below X (in a bullish Bat) or above X (in a bearish Bat). I view Alternate Bats as classic and powerful bear traps and bull traps. And they are just plain nasty if you find yourself thinking that a new low means further downside movement and a continuation lower – but instead to you get whipsawed by a massive reversal.

 

Alternate Bat Elements

  • Whereas the 88.6% retracement is nearly singular to the Bat Pattern, the Alternate Bat Pattern utilizes the 113% retracement of XA to determine the endpoint.
  • B must be a 38.2% or less retracement of XA.
  • Minimum projection of 200%
  • The AB=CD pattern must be an extended AB=CD and often is a 161.8% level.
  • The pattern is potent when using a form of divergence detection, such as the Composite Index, to confirm the pattern.

 

Sources: Carney, S. M. (2010). Harmonic trading. Upper Saddle River, NJ: Financial Times/Prentice Hall.  Gilmore, B. T. (2000). Geometry of markets. Greenville, SC: Traders Press.  Pesavento, L., & Jouflas, L. (2008). Trade what you see: how to profit from pattern recognition. Hoboken: Wiley.

Categories
Forex Harmonic

The Bat Pattern

Harmonic Pattern Example: Bearish Bat

The Bat Pattern

The Bat Pattern is another harmonic pattern that was not identified by Gartley, but instead by the great Scott M. Carney – found in Volume One of his Harmonic Trading series (I believe that Mr. Carney’s work is essential in your trading library).

I am particularly grateful to Carney’s work because it was his work that introduced me to a very powerful Fibonacci retracement level: 88.6%. Previously, I have followed Connie Brown’s suggestions in her various books utilizing only the 23.6%, 50%, and 61.8% Fibonacci levels – the 88.6% is now a near-constant in my own analysis and trading. That particular level, the 88.6% level, is the primary level to reach with the Bat pattern.

One of the key characteristics of this pattern is the strength, power, and speed of the reversals that occur after a confirmed and completed pattern is verified. As a Gann based trader, this is the pattern I personally look for to identify the ‘confirmation’ swing in a new trend (the first higher low in a reversing downtrend and the first lower high in a reversing uptrend).

Bat Pattern Elements

  1. B wave must be less than the 61.8% retracement of XA – ideally the 38.2% or 50%.
  2. BC projection must be at least 1.618.
  3. The AB=CD pattern is required and is often extended.
  4. C has an expansive range between 38.2% and 88.6%.
  5. The 88.6% Fibonacci retracement is a defining and particular level to the Bat Pattern.
  6. The 88.% D retracement is the defining and exact limit of the end of this pattern.

Ideal Bullish Bat Conditions

  1. 50% retracement of XA.
  2. Exact 88.6% D retracement of XA.
  3. BC wave 200%.
  4. Alternate AB=CD 127% is required.
  5. C should be inside the 50% and 61.8% retracement range.

Ideal Bearish Bat Conditions

  1. B wave must be less than the 61.8% retracement of XA – ideally the 38.2% or 50%.
  2. BC projection must be at least 88.6%.
  3. BC projection minimum of 161.8% with the max extensions between 200% to 261.8%.
  4. AB=CD is required, but the Alternate 127% AB=CD is ideal.
  5. C wave retracement can vary between the 38.2% to 88.6% retracement levels.

 

 

Sources: Carney, S. M. (2010). Harmonic trading. Upper Saddle River, NJ: Financial Times/Prentice Hall.  Gilmore, B. T. (2000). Geometry of markets. Greenville, SC: Traders Press.  Pesavento, L., & Jouflas, L. (2008). Trade what you see: how to profit from pattern recognition. Hoboken: Wiley.