Traders generally know how to describe the methods they use to enter and exit a trade. But when it comes to describing a method for determining the amount of capital to risk on a trade, few have a real solution. Some of them vaguely refer to experts who recommend risking 1-2% of capital on each trade. Others rely on intuition to determine when to increase their position on a particular trade, always risking different amounts. (Obviously, the vast majority of French and European traders have no concept of the subject, unlike their American counterparts).
Experienced traders know that having an efficient method of entering a position is just as important as having someone to decide how much to risk. A trader who risks too much increases his or her chances of not surviving long enough to realize the long-term profits of a sound trading strategy. Conversely, too little risk creates the possibility that a trading method will not utilize its full potential. So, while positive earnings expectation is a minimum requirement for successful trading, how you exploit this expectation will be the reason for your success or failure. This is actually one of the biggest challenges for the trader.
It is observed that at the same time traders reach a certain level of comfort with a system, they begin to realize that a supposed money management method is missing from their trading strategy.
There is no miracle formula for money management. In fact, different trading strategies and systems require different money management approaches. Additionally, one must always consider a trader’s ability to follow a money management method, taking into account their risk tolerance and other psychological factors. For example, strategies that focus on optimizing the amount of capital to be invested often result in periods of significant losses. Few traders are able to morally withstand periods of capital decline of 40, 50 or even 60%, which is not trivial in the case of certain aggressive strategies. As a result, it is important to match the trader’s risk tolerance with the largest theoretically probable loss periods.
The trader’s capital is also significant and can affect their ability to execute a strategy. Even in cases where it is preferable for a strategy to use a money management method, an undercapitalized trader may be unable to implement a strategy due to insufficient funds. In this situation, the trader is then unable to reap the potential profit. Aside from the success of a particular strategy on a trading method, 2 essential variables must be considered: the trader’s psychological preferences and their capital level. If one of these 2 factors does not support the money management strategy used, then it is unlikely that the trader will be able to effectively use the strategy. Although seemingly insignificant, this point cannot be emphasized enough. The trader must have enough confidence to stick to his strategy, even if positive results do not come immediately.
In the hope that this article helps you realize and evaluate the type of money management you use, we hope to have at least stimulated your imagination about the different ways to implement a strategy. In general, far too many traders use all their creativity for trading logic alone when they would benefit from spending some time sizing their positions in order to take maximum advantage of their position.
The topic is vast and beyond the scope of this article, but here are some template guidelines for you to determine position sizes.
In general, we can size trades with the following 6 position management models. (Where all parts are rounded to the nearest whole number).
1) Fixed batch
Trade a constant lot size with each trade (100 shares or 1 contract). This model never misses trades and stops trading if the capital falls below the required margin to trade a lot.
2) Fixed fraction
Adjust the lot size so that each trade allocates a fixed percentage of your capital.
LotSize = K * Large
Where K = Capital fraction/Initial Margin
This model never skips a trade, but stops trading if the fixed capital fraction (Capital*CapitalFraction) falls below the initial margin amount.
There are several variations of this model. All these variants simply determine the value of K in different ways. Example:
K = Fraction/InitialMargin
Trade 1 lot for each dollar amount of capital: K = 1 / dollar amount.
K = Fraction / PlusBigTab (Larry Williams)
Fixed risk (example, a constant money management stop) K = Fraction / Fixed risk amount.
Once you have determined the value of K among these variants, you can determine the parameter values for all other variants.
There are other variants that use a non-constant K value (where K is different for each trade). Two of these models are model #4 (percentage of risk) and #5 (percentage of volatility) described below.
Risk % uses a risk variable (initial stop).
Volatility % uses the market’s changing volatility to determine position sizes.
3) Fixed relationship
This model was popularized by Ryan Jones in his book “The trading game”.
It is calculated by adjusting the lot size as a function of the net result.
The formula is as follows:
LotSize = SquareRoot (2*NetProfit/delta + 1/4 + (InitialLotSize^2 – 1))
If LotSize < 1, then LotSize = 1 We use the input value of 10,000/delta, so that the input has the same scope as the other inputs. The input is then divided by 10,000 internally before calculating the number of items to trade. The “batch size” entry will be used by InitialBatchSize. This model never runs out of trades. He only stops trading when the capital becomes less than the necessary margin to trade a lot. Since the fix ratio model does not take capital into account, and that profit and capital loss depend on capital, depending on the initial capital, special attention should be paid to the InitialLotSize parameter to arrive at the optimal result and drawdown values.
4) % risk
Adjusts the lot size so that the total amount at risk (stop loss) for each trade is a fixed fraction of your capital.
LotSize = RiskFraction * Capital / Trading Risk
This model can skip trades or stop trading if the capital risk fraction becomes less than the risk or the initial stop loss to support entering a trade. If your input risk contains a constant value of risk (if you did not have risk data on a trade basis), then this model becomes a fixed fractional model. Its power comes from knowing the initial risk or stop loss size for each trade.
5) Volatility %
Adjusts the lot size so that market volatility (in dollars per lot, often measured by the average true range of the last 10 to 20 bars) is no greater than a fixed fraction of your capital.
Lot Size = Volatility Fraction * Capital / Volatility.
This model can skip trades or stop trading if capital volatility becomes less than market volatility.
This model also converts to a Fixed Fraction model if you have a constant input volatility value. The first 2 techniques are quite crude and commonly used by traders. The 3rd fixed ratio technique will usually provide the best performance without taking into account individual trade risk or market volatility (making this model rather aggressive for small accounts).
The last 2 techniques are more advanced and can generate more profit than the first 3 techniques for a given amount of withdrawal. The % Risk and % Capital models, while simple, provide the basics of capital preservation, risk control and conservatism that other models lack.
6) The optimal F
Described by Ralph Vince in his book “Portfolio Management Formulas”, it represents the ideal portion of capital that should be risked on each trade to maximize capital growth. Trade with sizes that are too small and you will make money too slowly; trade in sizes that are too large and you risk bankruptcy. Somewhere in the middle is an optimal part of the capital for risk.
Problem: With optimal F, the position sizes that exist to achieve maximum capital always involve huge risks that very few traders can absorb. Another problem is answered only on the given sequence of trades. Although the optimal fraction (using the fixed fraction model) would be the same if the trades had arrived in a different order, the outcome and write-down would be different. Applying the optimal F to a single sequence will not give you a complete picture of what your strategy can produce in terms of results and drawdown.
Solution: This is where Monte Carlo simulation can come in handy. Using it, we can regenerate these trades in random order hundreds of times, gather statistics on the results, and get a good idea of the variation in capital curve progression and drawdown that we can expect. We can then use these statistics to determine a more reliable optimal F for the trading system, have an acceptable result and an acceptable risk.
However, note that all traders use some form of money management. However, some of them are not even aware of the strategy or method they are using and determine entry positions with a ladle or even a yardstick! Others, on the other hand (the minority), use proven strategies to determine position sizes and when to add or liquidate positions according to their risk tolerance. Hopefully you are now in the latter category…