Information-Geometric Market Dynamics + ML

Information-Geometric Market Dynamics + ML (IGMD-ML)

Foreword: Beyond the Shadows on the Wall

If you have traded for any length of time, you understand the frustration of a perfect setup that fails, the whipsaw that stops you out just before a major move, or the persistent feeling that the price chart is only telling you half the story. For decades, technical analysis has focused on interpreting the shadows—the patterns left behind by price. We draw lines, apply indicators, and hope to derive future movement from this historical data.

But what if we could stop analyzing the shadows and instead measure the object casting them?

This script, Information-Geometric Market Dynamics (IGMD), introduces a new paradigm for market analysis. Its core premise is that the price chart is merely a one-dimensional projection of a much richer, higher-dimensional reality—an "information field" generated by the collective actions, beliefs, and emotions of all market participants.

This is not just another collection of indicators. It is a unified framework for measuring the geometry of this information field—its memory, its complexity, its uncertainty, its causal flows—and making high-probability decisions based on that deeper reality.

The IGMD Framework: A Multi-Kernel Approach

At the heart of IGMD are mathematical "kernels"—specialized engines that transform raw price data into meaningful measurements of abstract market properties. The framework's power lies in its ability to fuse the outputs of five distinct kernels, synthesizing their diverse perspectives into a single, coherent picture of the market's state.

The Five Core Kernels of Market Dynamics:

1. The Wavelet Kernel (The "Microscope"): Decomposes price into different frequency scales, separating short-term noise from the underlying market "thesis."

2. The Hurst Exponent Kernel (The "Memory Gauge"): Measures the market's "long-term memory" to determine if it is in a trending, mean-reverting, or random state.

3. The Fractal Dimension Kernel (The "Complexity Compass"): Quantifies the geometric complexity of the price path, acting as a primary filter for tradable vs. untradable conditions.

4. The Shannon Entropy Kernel (The "Uncertainty Meter"): Provides a pure measure of information and uncertainty, gauging market conviction and predictability.

5. The Transfer Entropy Kernel (The "Causality Probe"): Moves beyond correlation to measure the directed flow of information, assessing if a driver (like volume) is genuinely leading price.

Major Update: The Intelligence Layer & Machine Learning Integration

This version of IGMD introduces a significant advancement: an integrated machine learning (ML) engine that acts as an intelligent decision-making layer on top of the core five-kernel analysis. This is not a "black box" system but a transparent, adaptive filter designed to improve signal quality by learning from the market in real time.

How the ML Engine Works

The ML model processes the outputs from all five IGMD kernels and other market variables (like RSI and Volume) to build a comprehensive, multi-dimensional understanding of the current market state.

Core Technology: The engine uses an online logistic regression model. "Online" means it learns and updates its parameters with every new bar of data, allowing it to adapt continuously to changing market dynamics without needing to be retrained.

Non-Linear Pattern Recognition: To capture the market's complex behavior, the model projects the kernel data into a higher-dimensional space using Random Fourier Features (RFF). This technique allows a linear model to recognize highly intricate patterns that would otherwise be invisible.

Probabilistic Filtering: The ML engine’s primary function is to act as a final confirmation filter. For every signal generated by the core IGMD system, the ML model calculates a probability score—its confidence that the price will move in the predicted direction. Signals are only displayed if they pass this confidence check.

Key Features of the ML Engine

Automated Regime Filter: The ML engine uses the Fractal Dimension and Shannon Entropy kernels to identify choppy, unpredictable markets. During these periods, the system automatically pauses new signal generation to help preserve capital.

Adaptive Confidence Threshold: To optimize performance, the ML engine features an optional self-adjusting confidence threshold. This system tracks its own rolling accuracy and adjusts its selectivity accordingly, becoming more cautious in uncertain periods and more opportunistic when its accuracy is high.

Feature Importance Monitoring: The dashboard displays which of the core IGMD features (e.g., Wave, Hurst, Entropy) the ML model is currently relying on most. This provides valuable insight into the market's character and what is driving the model's decisions.

Advanced Adaptation: The Reinforcement Learning Bandit (Experimental)

For advanced users, this version includes an experimental feature based on a Multi-Armed Bandit, a concept from reinforcement learning. When enabled, this system can automatically switch between different parameter presets (e.g., Conservative, Balanced, Aggressive) based on the current market regime. It learns over time which preset performs best under specific conditions by balancing the exploitation of known successful strategies with the exploration of others.

Fusion & Interpretation: The Field Score & Enhanced Dashboard

The Field Score: The outputs of the five kernels are fused into a single, comprehensive "Field Score" ranging from -1 (maximum bearish alignment) to +1 (maximum bullish alignment). This remains the ultimate at-a-glance metric for the market's net state.
The Enhanced Dashboard: Your mission control has been upgraded to include the ML engine's analysis. Alongside the core kernel readouts, you can now monitor:
ML Status: See if the model is active, warming up, or disabled.
ML Probability: View the model's real-time confidence for a bullish move.
Regime Status: Instantly know if the market is "Trending," "Normal," or "Choppy (Paused)."
Top Feature: Identify the most influential IGMD kernel according to the ML model.
Signal Status: See the final, ML-vetted signal.

Mastering the Controls: A Guide to the Inputs
The inputs menu gives you full control over the IGMD and ML engines.

🤖 Machine Learning Engine:
Enable ML Probability Model: The master switch for the entire ML layer.
Prediction Horizon: Set how many bars ahead you want the ML model to predict. This should align with your trading style.
ML Confidence Threshold: The minimum probability required for the ML model to approve a signal. This is your primary tool for adjusting signal quality versus frequency.
Pause in Choppy Regimes: Enable or disable the automated filter that stops trading in unfavorable conditions.
Auto-Adjust Threshold: Allow the system to self-optimize its confidence threshold based on recent accuracy.

🎰 Adaptive Parameter Bandit:
Enable Parameter Bandit: Activate the experimental reinforcement learning agent to manage strategy presets automatically.

Reading the Battlefield: On-Chart Visuals

In addition to the established pattern boxes, RR rails, and signal markers, a new visual element has been added:

ML Rejection Markers (✗): An orange '✗' will appear on the chart when the core IGMD system identifies a potential setup, but the ML model's confidence is below your defined threshold. This provides crucial feedback, showing you which signals were intelligently filtered out by the intelligence layer for having a lower probability of success.

A Methodological Distinction: What Sets IGMD Apart**

What sets this framework apart is its foundational approach. Instead of relying on traditional technical indicators, IGMD is architected as a multi-disciplinary engine that fuses concepts from signal processing, chaos theory, and information theory. It moves beyond analyzing simple price action to measure the market's underlying "information field"—quantifying its memory, complexity, and causal flows into a single, unified score. The integrated machine learning layer builds on this foundation, creating a transparent, adaptive filter that learns from market conditions in real-time. This combination of a fused, multi-dimensional analysis with a live, probabilistic intelligence layer offers a more dynamic and nuanced perspective than static, single-purpose indicators.

Development Philosophy & A Final Word

This script was designed to answer a single question: "What is the market *really* doing?" The addition of a transparent, adaptive machine learning layer is the next logical step in this pursuit—using computational intelligence to navigate the vast amount of information the core kernels provide.

This tool is offered for educational and analytical purposes and does not constitute financial advice. Its goal is to elevate market analysis from interpreting flat shadows to measuring the rich, geometric reality of the market's information field.

As the great mathematician Benoit Mandelbrot, father of fractal geometry, noted:
"Clouds are not spheres, mountains are not cones, coastlines are not circles, and bark is not smooth, nor does lightning travel in a straight line."

Neither does the market. IGMD is a tool designed to help navigate that beautiful, complex, and fractal reality.

— Dskyz, Trade with insight. Trade with anticipation.