Hopfield Network Market Regimes - Neural Physics for Financial Analysis

NEURAL PHYSICS FRAMEWORK

HOPFIELD NETWORK ENERGY FUNCTION

E = -1/2 ΣΣ wᵢⱼ xᵢ xⱼ + Σ θᵢ xᵢ Where: - E = system energy - wᵢⱼ = synaptic weight matrix - xᵢ = neuron activation state - θᵢ = threshold bias

HEBBIAN LEARNING RULE

wᵢⱼ = (1/N) Σ xᵢ^μ xⱼ^μ Where: - N = number of patterns - μ = pattern index - xᵢ^μ = activation of neuron i in pattern μ

ENERGY MINIMIZATION DYNAMICS

xᵢ(t+1) = sign(Σ wᵢⱼ xⱼ(t) - θᵢ) Convergence to stable attractor states representing distinct market regime configurations.

MARKET REGIME ANALYSIS

Comprehensive visualization showing 5 distinct market regimes identified through Hopfield network pattern recognition with binary encoding of technical indicators.

Pattern matrix visualization showing the stable attractor states corresponding to Bull Market, Bear Market, Volatile, Consolidation, and Crisis regimes.

CORE CAPABILITIES

NEURAL PHYSICS APPROACH

Novel application of Hopfield networks to financial markets, treating market regimes as stable attractors in the network's energy landscape. Uses content-addressable memory for robust pattern recognition and regime classification.

BINARY PATTERN ENCODING

Technical indicators transformed into binary patterns using adaptive thresholding. RSI, MACD, Bollinger Bands, and volume metrics encoded as bipolar neurons for optimal Hopfield network processing.

FIVE MARKET REGIMES

Systematic identification of Bull Market, Bear Market, High Volatility, Consolidation, and Crisis regimes through unsupervised pattern clustering and energy landscape analysis.

REAL-TIME CLASSIFICATION

Fast regime identification through network relaxation dynamics. Input market conditions converge to nearest stable attractor within milliseconds, enabling real-time trading applications.

COMPREHENSIVE VISUALIZATION

Interactive dashboards showing regime transitions, pattern matrices, energy landscapes, and market behavior analysis with publication-quality plots for research and presentation purposes.

PRODUCTION DEPLOYMENT

Fully containerized with Docker, comprehensive testing suite, and scalable architecture. Includes Jupyter notebooks for interactive analysis and complete documentation for researchers and practitioners.

TECHNICAL SPECIFICATIONS

NETWORK TYPE

Hopfield Recurrent Neural Network

LEARNING RULE

Hebbian Learning

PATTERN ENCODING

Binary (-1, +1)

MARKET REGIMES

5 Distinct States

INPUT FEATURES

Technical Indicators

CONVERGENCE

Energy Minimization

DEPLOYMENT

Docker Container

DEMO

Jupyter Notebook