Discussing the article: "Encoding Candlestick Patterns (Part 1): An Alphabetical System for Signal Detection"

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Check out the new article: Encoding Candlestick Patterns (Part 1): An Alphabetical System for Signal Detection.

We present a rule‑based alphabet for candlestick price action that maps measurable shape and direction to letter codes (A/a, H/h, E/e, G/g, D). The article shows an MQL5 implementation: classifying candles, building two‑bar sequences via permutations, and scanning charts with an indicator and alerts. Readers gain a practical template for objective pattern detection and systematic testing.

Candlestick price action remains one of the most widely used approaches for interpreting market behavior because it focuses directly on price movement rather than relying solely on lagging technical indicators derived from historical data. Many traders favor this method for its ability to reveal market sentiment, momentum, rejection, and volatility in real time. However, despite its popularity, candlestick analysis suffers from a major limitation: most patterns are described using subjective terms such as "long body," "small wick," "pin bar," or "marubozu." These descriptions often vary from one trader to another, leading to inconsistent pattern recognition, conflicting implementations, and unreliable backtesting results.

This challenge becomes even more significant when developers attempt to automate candlestick analysis in trading platforms such as MetaTrader 5 using MQL5. Without clearly defined mathematical thresholds, two programmers can implement the same candlestick pattern differently and obtain entirely different trading signals. As a result, strategy testing, optimization, and statistical evaluation become difficult to reproduce consistently.

This article addresses that problem by introducing an objective alphabetical encoding system for candlesticks based on measurable body-to-wick ratios. Instead of relying on subjective visual interpretation, each candle is transformed into a deterministic symbol using predefined quantitative rules. Bullish and bearish candle structures are paired into symbolic forms such as A/a, H/h, E/e, G/g, and D, creating a formal classification framework that can be processed programmatically.

Author: Daniel Opoku