Discussing the article: "Building a Trading System (Part 4): How Random Exits Influence Trading Expectancy"

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Many traders have experienced this situation, often stick to their entry criteria but struggle with trade management. Even with the right setups, emotional decision-making—such as panic exits before trades reach their take-profit or stop-loss levels—can lead to a declining equity curve. How can traders overcome this issue and improve their results? This article will address these questions by examining random win-rates and demonstrating, through Monte Carlo simulation, how traders can refine their strategies by taking profits at reasonable levels before the original target is reached.

A backtest was conducted on the GBPUSD pair using the H1 timeframe to evaluate the performance of the strategy under varying take profit conditions. During the test, each trade was executed using the same entry setup and fixed stop loss, while the take profit levels were randomly selected within the predefined range.

This approach produced a series of trades that, although identical in entry criteria and risk parameters, differed in their exit points. It is important to emphasize that even when trades share the same entry signals, varying exit conditions — in this case, random take profit levels — lead to distinct trading outcomes. This introduces diversity and dynamism into the Expert Advisor’s behavior, making it more adaptive to changing market conditions.

The results of the backtest are illustrated in Figures 16 and 17, which display the equity performance and trade distribution across the randomly generated take profit levels.

Figure 16: Random Take Profit level at Each Entry

Author: Daniel Opoku