Discussing the article: "MQL5 Trading Tools (Part 26): Integrating Frequency Binning, Entropy, and Chi-Square in Visual Analyzer"
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Check out the new article: MQL5 Trading Tools (Part 26): Integrating Frequency Binning, Entropy, and Chi-Square in Visual Analyzer.
In this article, we develop a frequency analysis tool in MQL5 that bins price data into histograms, computes entropy for information content, and applies chi-square tests for distribution goodness-of-fit, with interactive logs and statistical panels for market insights. We integrate per-bar or per-tick computation modes, supersampled rendering for smooth visuals, and draggable/resizable canvases with auto-scrolling logs to enhance usability in trading analysis.
Frequency binning divides a price range into equal-width intervals and counts how many closes fall into each bin. Dense bins indicate acceptance zones, while sparse bins indicate impulsive transitions. Shannon entropy quantifies how evenly spread the counts are across bins: a perfectly uniform distribution produces maximum entropy, while a histogram dominated by one or two bins produces low entropy, signaling strong clustering and potentially tradeable structure. The chi-square test formalizes this by comparing observed bin counts against the expected counts under a uniform distribution, producing a test statistic that rises as clustering intensifies, with degrees of freedom equal to the number of bins minus one.
In the market, use the frequency histogram to identify the highest-density bin as the current value area — price returning to that bin after a deviation is a mean-reversion candidate. Monitor entropy across sessions: a drop in entropy after a range period signals emerging structure and a potential directional breakout setup. Use the chi-square statistic as a filter — high values confirm non-random clustering worth trading, while values near zero suggest a random walk where frequency-based strategies lose edge.
We load recent closing prices and bin them into user-defined intervals. We then compute relative frequencies, entropy (−∑p·log p), and the chi-square statistic. Results are displayed on a canvas with a statistics panel and a supersampled auto-scrolling log panel (per-bar or per-tick). In a nutshell, here is what we intend to achieve.
Author: Allan Munene Mutiiria