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Enhance your MQL5 code by optimizing logic, refining calculations, and reducing execution time to improve back-test accuracy. Fine-tune parameters, optimize loops, and eliminate inefficiencies for better performance.

The development of our algorithmic trading strategy begins with a structured, methodical approach to pattern recognition and signal validation. At its core, the strategy employs a candlestick-based framework designed to identify high-probability reversal scenarios. For long positions, the logic systematically detects three consecutive bullish candles, followed by one or two corrective bearish candles, culminating in a confirming bullish candle at index 1 (the most recently closed bar).

Conversely, short positions are triggered by an inverse pattern: three consecutive bearish candles, succeeded by one or two retracement bullish candles, and finalized by a confirming bearish candle at index 1. This configuration ensures signals are validated only upon the formation of a new bar, aligning execution with confirmed price action rather than intra-bar fluctuations.

To operationalize this logic, the strategy’s architecture will prioritize modular code design and computational efficiency. First, helper functions will be implemented to abstract repetitive tasks, such as candlestick classification (bullish/bearish determination) and sequence validation (consecutive candle pattern checks). These functions will leverage MQL5’s native price data access methods, including `iOpen()` and `iClose()`, while minimizing redundant calculations through static variable caching.

Author: Hlomohang John Borotho