You don't need to understand any of that stuff to use the software. The geeks that made the software just had to say a few words so I had to let them. Again, if I were you I wouldn't even bother reading.
How to reach decent profits and at the same time protect your capital?
The task is to find the proper lot size for reaching decent profits, but at the same time protecting your capital from suffering a painful setback.
One can describe this task mathematically as finding the lot size which maximizes the profit subject to the constraint that we do not want to experience a capital setback greater than a predefined value.
In mathematics such problem is known as “constrained optimization” and can be solved by using the method of Lagrange multipliers. See the picture below:
Without any constraint for a strategy with a profit factor greater than 1, the solution for optimal lot problem is as follows:
That means the bigger the lot size, the greater the profit we will have. However, if constraints come into play the solution is as follows:
The difficulty here is to determine MaxDradown. The reason is that the maximum drawdown is not just the maximum loss you may phase in a single trade, or the loss you may suffer in the case you get maximum number of losses in a row. The maximum setback can also occur in the case you get losing trades within winning trades. Clearly speaking there are millions of combinations and you can only determine the maximum drawdown in terms of probability.
We will use a 3sigma-probability level which means that with 99.75% probability - almost 100% - we won’t reach the predefined drawdown.
For doing so we use the so-called Monte Carlo method.
Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results.
Monte Carlo methods are often used when simulating physical and mathematical systems. Because of their reliance on repeated computation and random or pseudo-random numbers, Monte Carlo methods are most suited for calculation by a computer.
Following steps are done:
A) For 1 lot do the following calculation
1) First we calculate the probability distribution for the outcome of a single trade
2) Then we simulate 10,000 samples, each one with 1,000 trades, based on the previously determined probability distribution for the outcome of a single trade
3) Calculate the probability distribution that a Max Drawdown occurs
4) Calculate the cumulative distribution function and determine the 3s point
5) We get the maximum drawdown value for trading 1 lot
=> now we need to adjust the lot size so that we get the maximum desired drawdown
B) As the maximum drawdown is proportional to the lot size, we can use the following trick. We calculate the needed factor for multiplying the max drawdown axis in order that the 3 sigma point falls as desired. With the same factor we multiply the 1 lot size and obtain the desired value.
Here is a general example how this could look like, when plotting the determined drawdown as function of profit factor and winning percentage rate (accuracy).
Ok, now that we now how to control the risk, it is about to determine which are proper risk values to trade with.
How to determine proper risk values?
If we have a 100,000$ trading account, then surely we can afford to suffer a 5,000$ setback, but this is not true when trading with a 10,000$ trading account. So taking a constant risk value is surely not the right solution. By the way, this is exactly what you do when you always use the same lot size.
The proper risk is highly dependent on the current balance, but not in the way as it is commonly used.
Out there you will find that the risk value is typically calculated as a percentage of the current balance. But by doing so you have a major weakness, which becomes clear in following scenario. Assume that you trade using a constant 20% risk value and that you were facing a good raw of trades, so that you initial capital grew from 10,000$ up to 40,000$. The higher your balance the greater risk you are taking – speaking in terms of absolute value. This is ok, as long as your good raw continues, but by following this money management system it only needs few bad trades to devastate your wins. But what is the problem? As your current balance grows up, you increase your lot size. But a certain point you will phase some losing trades, which due to the bigger lot size, will be overvalued and have a great negative influence on your balance.
Ok, what is then the right approach? Well, the right approach is in between both cases. So that on hand you are able to get decent profits by increasing moderately the lot size as your balance grows, but at the same time protect your reached profits from too great setbacks. Here an example how this could look like:
This can be mathematically described by using a power function with non-integer exponent:
Our system is continuously monitoring the performance values
The probability distribution is continuously recalculated once we enter new trades by using our portfolio of strategies.
Also the best lot size – by using the method explained before – is recalculated in order to adjust to the updated performance values.
So that one could speak of a self-adjusting lot size calculation method.
What is the benefit of doing these calculations constantly?
Well, we need to react to the market reality as fast as possible.
If a strategy A is performing extremely well in the last trades, then our Intelligent Money Management Function will increase the lot size for entering a trade based on that strategy.
But also if a strategy B is facing a bad period, then we will reduce the lot size, so that the negative effect on the overall performance is minimized.
We could go on and on about the logic behind the software however we are not sure that you are all that interested in such technical aspects.