Discussing the article: "How to Detect and Normalize Chart Objects in MQL5 (Part 2): Collecting and Structuring Data from Complex Analytical Objects"
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Check out the new article: How to Detect and Normalize Chart Objects in MQL5 (Part 2): Collecting and Structuring Data from Complex Analytical Objects.
Manually drawn analytical object tools like Fibonacci tools, and Andrews Pitchforks are invisible to automated trading logic. This article extends a base detector to extract anchor points, level arrays, and geometric offsets from complex objects. You will implement a reusable collector that normalizes the raw chart data into structured memory arrays, ready for strategy decisions.
Part 1 of this series introduced a detector that identifies analytical objects (trendlines, horizontals, rectangles, Fibonacci tools) and returns their basic time‑price coordinates. However, complex objects such as Fibonacci retracements, channels, and Pitchforks carry richer information: multiple levels, slopes, and geometric relationships. The simple coordinate pairs are insufficient for algorithmic strategies that rely on specific retracement ratios, channel boundaries, or median line touches.
This article extends the detector to collect and structure data from complex analytical objects. You will learn how to extract Fibonacci level arrays (ratios and corresponding prices), capture channel anchor points, and parse Pitchfork geometry. The data is normalized into an extended structure that preserves all relevant metrics, enabling rule‑based decisions such as “enter at 0.618 retracement” or “alert when price touches the median line of a Pitchfork.”
The implementation covers OBJ_FIBO (retracement/extensions), OBJ_FIBOTIMES, OBJ_FIBOFAN, OBJ_FIBOARC, OBJ_CHANNEL (equidistant channel), and OBJ_PITCHFORK. The same pattern can be applied to cycles and Gann tools.
You will obtain two files:
Author: Clemence Benjamin