README.md

metadata

title: DSA
emoji: 🏆
colorFrom: pink
colorTo: indigo
sdk: streamlit
sdk_version: 1.44.0
app_file: app.py
pinned: false
short_description: Dendritic Stock Algorithm
license: mit

Dendritic Stock Algorithm (DSA)

The Dendritic Stock Algorithm represents a novel approach to financial prediction that bridges computational neuroscience with financial analysis. This model implements a self-organizing, hierarchical dendritic network that naturally forms fractal patterns at computational boundaries.

Model Description

The DSA differs fundamentally from traditional machine learning approaches to stock prediction in several key ways:

• Biological Inspiration: Based on actual dendritic computation in neurons rather than abstract neural networks
• Self-Organization: Dendrites grow and prune connections based on pattern recognition without explicit training
• Fractal Boundary Processing: Information processing happens primarily at interfaces between dendrite clusters
• Temporal Integration: Simultaneously processes past patterns, current inputs, and future predictions
• Critical State Operation: Naturally balances at the edge of chaos for optimal information processing

Intended Uses

This model can be used for:

• Stock market directional prediction
• Market regime detection (bullish/bearish/volatile/sideways)
• Multi-asset correlation analysis
• Visualization of complex market patterns
• Research into boundary-emergent complexity

Performance

When tested on AAPL stock data (2020-2025):

• Directional Accuracy: 57.02% (vs. 50% random baseline)
• Trading Return: 62.83% (vs. 33.34% buy & hold)
• Fractal Dimension: 1.987 (indicating rich boundary complexity)
• **Take things with grain of sand.

Limitations

• Requires sufficient market history (minimum 1 year recommended)
• Computational complexity increases with hierarchy levels
• Best suited for liquid assets with consistent trading patterns
• May struggle during unprecedented market conditions

Training Details

The system leverages:

• Stock price OHLCV data
• Technical indicators (RSI, MACD, Bollinger Bands, etc.)
• Currency correlations
• Sector performance

The dendrite growth process is unsupervised, with dendrites self-organizing based on detected patterns in the data.

Theory and Research

This work is based on the theory that complex information processing in natural systems occurs at boundaries between different computational regimes. These boundaries naturally develop fractal patterns that encode information more efficiently than traditional architectures.

The system operates at the "edge of chaos" (critical state), which maximizes information processing capacity - a principle observed in both biological neural systems and fundamental physics.