Discussion of article "Useful and exotic techniques for automated trading"

In fact, this technique can be used not only for martingale, but also for any other strategies that have a sufficiently high trading frequency. In this example, I will use the metric based on the balance drawdown. Because everything related to balance is considered easier. Let us divide the balance chart into rising and falling segments. Two adjacent segments form a half-wave. The number of half-waves tends to infinity as the number of transactions tends to infinity. A finite sample will be enough for us to make the martingale a little more profitable. The following diagram explains the idea:

The figure shows a formed half-wave and the one that has just begun. Any balance graph consists of such half-waves. The size of these half-waves constantly fluctuates, and we can always distinguish groups of such half-waves on the chart. The size of these half-waves is smaller in one wave and is larger in the other. So, by gradually lowering the lots, we can wait until a half-wave with a critical drawdown appears in the current group. Since the lots of this critical drawdown will be minimal in the series, this will increase the overall average metrics of all groups of waves and, as a result, the same performance variables of the original test should also increase.

For implementation, we need two additional input parameters for the martingale:

• { DealsMinusToBreak } - the number of losing trades for the previous cycle, reaching which the starting lot of the cycle should be reset to the starting value
• { LotDecrease } - step for decreasing the starting lot of the cycle when a new cycle appears in the history of trades

These two parameters will allow us to provide increased lots for safe half-wave groups and reduced lots for dangerous half-wave groups, which should, in theory, increase the above mentioned performance metrics.