Price Action and Patterns

In this article we will create the zone recovery martingale forex trading strategy expert advisor (EA) in MetaQuotes Language 5 (MQL5) for MetaTrader 5 (MT5) step by step. The zone recovery martingale strategy is a common strategy that is oriented to counter loosing positions by opening opposite positions with a slightly greater trading volume that cancels the losing positions. It is basically a trend following strategy that does not care about the direction of the market, with the hope that at one point, the market will be trending either downwards or upwards and eventually particular targets will be hit.

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Even though trend movements only have two sides, uptrend and downtrend, sometimes there is a flat side where prices tend to stagnate as if there is almost no movement. Price action is usually combined with candle patterns to predict support and resistance based on price history.

In the previous article, we have laid the foundation for easy usage of the results obtained from optimization to build a ready-made EA with multiple instances of trading strategies working together. Now we do not have to manually enter the parameters of all used instances in the code or in the EA inputs. We only need to save the initialization string in a certain format to a file, or insert it as text into the source code so that the EA can use it.

So far, the initialization string has been generated manually. Now, finally, the time has come to start implementing the automatic formation of the EA initialization string based on the obtained optimization results. Most probably, we will not have a fully automated solution within the scope of this article, but at least we are going to make significant progress in the intended direction.

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Throughout the entire series, we addressed the topic of risk control several times. The concepts of a normalized trading strategy were introduced, the parameters of which ensure that a drawdown level of 10% is achieved during the test period. However, normalizing trading strategy instances, as well as groups of trading strategies, in this way can only provide a given drawdown over a historical period. We cannot be sure that the specified drawdown level will be observed when starting a test of a normalized group of strategies on the forward period, or launching it on a trading account.

Recently, the topic of risk management was considered in the articles Risk manager for manual trading and Risk manager for algorithmic trading. In these articles, the author proposed a programmatic implementation that controls the compliance of various trading parameters with pre-set indicators. For example, if the set loss level for a day, week or month is exceeded, trading is suspended.

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