Discussing the article: "Price Action Analysis Toolkit Development (Part 83): Building a Reusable Double-Bottom Pattern Library"
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Check out the new article: Price Action Analysis Toolkit Development (Part 83): Building a Reusable Double-Bottom Pattern Library.
This recorder bridges visual labeling and systematic research in MQL5. After you position a rectangle, the EA extracts the candles it spans, derives candle geometry, normalizes OHLC values, assigns an ID, and saves the sample for reuse across sessions. The current category is the double bottom, and the module records the chosen sequence without enforcing a predefined pattern definition.
Chart patterns are easy to name and surprisingly difficult to describe in code. Two traders may point to the same section of a chart and agree that it resembles a double bottom, yet disagree about the precise starting bar, the acceptable difference between the lows, the position of the neckline, or the point at which the formation becomes valid. That ambiguity becomes a practical obstacle when we move from visual analysis to systematic research.
This part grew from a practical difficulty I encountered while preparing double-bottom examples. A chart image preserved what I had seen, but it did not preserve the candle sequence in a form that I could reload, measure and compare later.
Before an algorithm can recognize a pattern, it needs examples in a consistent data format. Each example must retain its original candle data. Percentage offsets reduce differences in absolute price scale, but do not by themselves make samples comparable across symbols, timeframes or market conditions.
At the starting point, the trader may recognize a double bottom visually but has no repeatable way to preserve its candles. Screenshots and informal notes do not directly provide normalized, searchable candle data that the Expert Advisor can reload. The immediate problem is a missing collection process.
By the end, the reader will have a six-file recorder. It selects formations, derives candle features, normalizes prices, stores CSV samples, and reloads them after a restart. The implementation moves from shared data structures through extraction, normalization, persistence and library management to the chart panel and Expert Advisor.
Author: Christian Benjamin