Discussing the article: "Using PSAR, Heiken Ashi, and Deep Learning Together for Trading"

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Check out the new article: Using PSAR, Heiken Ashi, and Deep Learning Together for Trading.

This project explores the fusion of deep learning and technical analysis to test trading strategies in forex. A Python script is used for rapid experimentation, employing an ONNX model alongside traditional indicators like PSAR, SMA, and RSI to predict EUR/USD movements. A MetaTrader 5 script then brings this strategy into a live environment, using historical data and technical analysis to make informed trading decisions. The backtesting results indicate a cautious yet consistent approach, with a focus on risk management and steady growth rather than aggressive profit-seeking.

The results of the Expert Advisor (EA) provide an insightful look into its performance during the backtesting period. Starting with an initial deposit of $10,000, the strategy achieved a modest total net profit of 17 units, indicating that while it did generate profits, they were relatively small in proportion to the initial investment. The balance curve reflects this gradual growth, showing a steady, albeit slow, upward trajectory over time.

One of the standout metrics here is the Profit Factor of 4.39. This is a solid number, suggesting that for every unit of risk taken, the strategy earned 4.39 units of reward. It implies that the EA was effective at maximizing profits compared to its losses. The Recovery Factor of 4.48 further supports this, showing that the strategy was able to recover from drawdowns effectively, which is a positive sign for its robustness. However, it’s worth noting the Sharpe Ratio, which sits at 5.25. While this ratio is relatively high and generally indicates good risk-adjusted returns, the overall small profit suggests the strategy might have been taking very minimal risks, leading to the limited absolute profit.

Author: Javier Santiago Gaston De Iriarte Cabrera