4.1 KiB
Neural Network Trading System
A comprehensive neural network trading system that uses deep learning models to analyze cryptocurrency price data and generate trading signals.
Architecture Overview
This project implements a 500M parameter neural network system using a Mixture of Experts (MoE) approach. The system consists of:
- Data Interface: Connects to real-time trading data from
realtime.pyand processes it for the neural network models - CNN Module (100M parameters): A deep convolutional neural network for feature extraction from time series data
- Transformer Module: Processes high-level features and raw data for improved pattern recognition
- Mixture of Experts (MoE): Coordinates the different models and combines their predictions
The system is designed to identify buy/sell opportunities in cryptocurrency markets by analyzing patterns in historical price and volume data.
Components
Data Interface
- Located in
NN/utils/data_interface.py - Provides seamless access to historical and real-time data from
realtime.py - Preprocesses data for neural network consumption
- Supports multiple timeframes and features
CNN Model
- Located in
NN/models/cnn_model.py - Implements a deep convolutional network for time series analysis
- Uses multiple parallel convolutional layers to detect patterns at different time scales
- Includes bidirectional LSTM layers for sequence modeling
- Optimized for financial time series data
Transformer Model
- Located in
NN/models/transformer_model.py - Uses self-attention mechanism to process time series data
- Takes both raw data and high-level features from the CNN as input
- Better at capturing long-range dependencies in the data
Orchestrator
- Located in
NN/main.py - Coordinates data flow between the models
- Implements training and inference pipelines
- Provides a unified interface for the entire system
Usage
Requirements
- TensorFlow 2.x
- NumPy
- Pandas
- Matplotlib
- scikit-learn
Training the Model
To train the neural network on historical data:
python -m NN.main --mode train --symbol BTC/USDT --timeframes 1h 4h 1d --epochs 100
Making Predictions
To make one-time predictions:
python -m NN.main --mode predict --symbol BTC/USDT --timeframe 1h --model_type cnn
Running Real-time Analysis
To continuously analyze the market and generate signals:
python -m NN.main --mode realtime --symbol BTC/USDT --timeframe 1h --interval 60
Model Architecture Details
CNN Architecture
The CNN model uses a multi-scale approach with three parallel convolutional pathways:
- Short-term patterns: 3x1 kernels
- Medium-term patterns: 5x1 kernels
- Long-term patterns: 7x1 kernels
These pathways are merged and processed through deeper convolutional layers, followed by LSTM layers to capture temporal dependencies.
Transformer Architecture
The transformer model uses:
- Multi-head self-attention layers to capture relationships between different time points
- Layer normalization and residual connections for stable training
- A feed-forward network for final classification/regression
Mixture of Experts
The MoE model:
- Combines predictions from CNN and Transformer models
- Uses a weighted average approach for signal generation
- Can be extended with additional expert models
Training Data
The system uses historical OHLCV (Open, High, Low, Close, Volume) data at different timeframes:
- 1-minute candles for short-term analysis
- 1-hour candles for medium-term trends
- 1-day candles for long-term market direction
Output
The system generates one of three signals:
- BUY: Indicates a potential buying opportunity
- HOLD: Suggests maintaining current position
- SELL: Indicates a potential selling opportunity
Development
Adding New Models
To add a new model type:
- Create a new class in the
NN/modelsdirectory - Implement the required interface (build_model, train, predict, etc.)
- Update the orchestrator to include the new model
Customizing Parameters
Key parameters can be customized through command-line arguments or by modifying the configuration in main.py.