Discussing the article: "MQL5 Trading Tools (Part 25): Expanding to Multiple Distributions with Interactive Switching"
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Check out the new article: MQL5 Trading Tools (Part 25): Expanding to Multiple Distributions with Interactive Switching.
In this article, we expand the MQL5 graphing tool to support seventeen statistical distributions with interactive cycling via a header switch icon. We add type-specific data loading, discrete and continuous histogram computation, and theoretical density functions for each model, with dynamic titles, axis labels, and parameter panels that adapt automatically. The result lets you overlay distribution models on the same sample and compare fit across families without reloading the tool.
Each statistical distribution describes a different pattern of uncertainty: discrete distributions like Poisson and the negative binomial describe count-based outcomes such as the number of trades hitting a target in a session, while continuous distributions like normal, Weibull, and gamma describe real-valued phenomena like returns, drawdown durations, or volatility levels. Interactive switching lets a trader overlay theoretical density functions on the same sampled data and quickly see which model best fits the observed histogram, without reloading the tool or changing hardcoded parameters. The framework uses a distribution type enumeration and a single dispatch point to route data loading, histogram computation, density calculation, and panel labeling. To add a new distribution, only a new loading function and a new enum entry are required.
In live trading, use the normal distribution to model daily return distributions and flag bars that fall beyond two standard deviations as potential mean-reversion entries. Switch to exponential to analyze time between significant price moves and calibrate stop distances. Use gamma or Weibull to model drawdown duration and assess recovery probability before sizing positions.
We will define an enumeration for all supported types, add dedicated input groups for each distribution's parameters, implement type-specific data loading functions that handle sample generation, histogram computation variants, and density calculations, and integrate a header switch icon with hover feedback and click handling to cycle through types. In brief, here is a visualization of the framework we will be building.
Author: Allan Munene Mutiiria