XAUUSD Volatility Classifier

Overview

This is a PyTorch-based Binary Classifier designed to act as a pre-trade filter for an algorithmic trading system. It analyzes the last 5 OHLCV candles of an asset (like XAUUSD) and outputs a probability score predicting imminent market volatility.

How it Works

graph LR
    A[Data Engine<br/>5 OHLCV Candles<br/>25 Features] --> B(Layer 1: Linear 64 + ReLU)
    B --> C(Layer 2: Linear 32 + ReLU)
    C --> D(Output Layer: Linear 1 + Sigmoid)
    D --> E{Prediction Score}
    E -->|> 50%| F((EXPANSION))
    E -->|< 50%| G((CONSOLIDATION))
    
    style A fill:#6a329f
    style F fill:#073763
    style G fill:#660000

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Architecture

• Inputs: 25 features (5 sequential candles flattened into a 1D array).
• Network: Feed-Forward Deep Neural Network (Dense Layers).
• Activations: * ReLU for hidden layers to map non-linear price relationships.
  • Sigmoid for the output layer to bound predictions to a probability [0, 1].
• Loss Function: Binary Cross-Entropy (BCELoss).
• Optimizer: Adam (lr=0.01).

How to Run

1. Install dependencies: pip install torch numpy
2. Run the full pipeline: python test.py

Role in the Omni-Agent Ecosystem

This module prevents trend-following bots from executing during choppy, sideways consolidation periods by filtering out setups with a volatility probability below 50%.