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NN/README.md

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+# 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:
+
+1. **Data Interface**: Connects to real-time trading data from `realtime.py` and processes it for the neural network models
+2. **CNN Module (100M parameters)**: A deep convolutional neural network for feature extraction from time series data
+3. **Transformer Module**: Processes high-level features and raw data for improved pattern recognition
+4. **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:
+
+```bash
+python -m NN.main --mode train --symbol BTC/USDT --timeframes 1h 4h 1d --epochs 100
+```
+
+### Making Predictions
+
+To make one-time predictions:
+
+```bash
+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:
+
+```bash
+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:
+1. Create a new class in the `NN/models` directory
+2. Implement the required interface (build_model, train, predict, etc.)
+3. 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`.