Discussing the article: "Defining your Edge (Part 4): Applying Isotonic Regression and PNN Price-Forecasting in an Expert Advisor"
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Check out the new article: Defining your Edge (Part 4): Applying Isotonic Regression and PNN Price-Forecasting in an Expert Advisor.
We consider the methods with which Isotonic Regression calibrates raw RSI, Stochastic and price-action signal scores into probabilities that are sorted, while a separate Probability based Neural Network evaluates similar historical market states. This article uses both approaches in a ready-made MQL5 custom signal class that is compatible with MQL5 Wizard and provides up to 7 selectable entry modes. Reproducible tests compare isotonic-only signals with the combined Isotonic-PNN model to assess whether the network adds useful information beyond the simpler baseline.
Oscillators are usually presented as holistic trading tools: When the RSI gets to an extreme, or if the Stochastic presents a cross, the trader acts. When using automated systems though processing these patterns to determine whether they deserve an entry is not as easy as it may seem initially. The RSI could be bullish at 65 in one context and overbought in another, while a Stochastic crossover can mark either transition or noise. In these situations, neither indicator quantifies how much confidence to attach to the signal.
Thus, the custom signal that we examine in this article treats this as a calibration problem. We engage seven interpretations of RSI, Stochastic, and some price action to initially generate a bounded directional score. Isotonic Regression then maps that score onto an empirically ordered probability. An alternative Probability based Neural Network poses the question of whether the current multidimensional state looks like past bullish or bearish states.
The separation of labor is very simple: the isotonic regression tests if stronger raw signals are worth increasingly more confidence; the PNN evaluates if the internal composition of a signal looks like historic useful cases. More layers tend to imply more ways of overfitting; therefore, our goal here leans towards rapid proofing instead of "proof of superiority".
Author: Stephen Njuki