142 lines
4.3 KiB
Markdown
142 lines
4.3 KiB
Markdown
# Trading System - Launch Modes Guide
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## Overview
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The unified trading system now provides clean, modular launch modes optimized for scalping and multi-timeframe analysis.
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## Available Modes
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### 1. Test Mode
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```bash
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python main_clean.py --mode test
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```
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- Tests enhanced data provider with multi-timeframe indicators
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- Validates feature matrix creation (26 technical indicators)
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- Checks data provider health and caching
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- **Use case**: System validation and debugging
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### 2. CNN Training Mode
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```bash
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python main_clean.py --mode cnn --symbol ETH/USDT
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```
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- Trains CNN models only
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- Prepares multi-timeframe, multi-symbol feature matrices
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- Supports timeframes: 1s, 1m, 5m, 1h, 4h
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- **Use case**: Isolated CNN model development
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### 3. RL Training Mode
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```bash
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python main_clean.py --mode rl --symbol ETH/USDT
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```
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- Trains RL agents only
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- Focuses on 1s scalping data
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- Optimized for short-term decision making
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- **Use case**: Isolated RL agent development
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### 4. Combined Training Mode
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```bash
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python main_clean.py --mode train --symbol ETH/USDT
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```
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- Trains both CNN and RL models sequentially
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- First runs CNN training, then RL training
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- **Use case**: Full model pipeline training
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### 5. Live Trading Mode
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```bash
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python main_clean.py --mode trade --symbol ETH/USDT
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```
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- Runs live trading with 1s scalping focus
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- Real-time data streaming integration
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- **Use case**: Production trading execution
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### 6. Web Dashboard Mode
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```bash
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python main_clean.py --mode web --demo --port 8050
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```
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- Enhanced scalping dashboard with 1s charts
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- Real-time technical indicators visualization
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- Scalping demo mode with realistic decisions
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- **Use case**: System monitoring and visualization
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## Key Features
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### Enhanced Data Provider
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- **26 Technical Indicators** including:
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- Trend: SMA, EMA, MACD, ADX, PSAR
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- Momentum: RSI, Stochastic, Williams %R
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- Volatility: Bollinger Bands, ATR, Keltner Channels
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- Volume: OBV, MFI, VWAP, Volume profiles
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- Custom composites for trend/momentum
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### Scalping Optimization
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- **Primary timeframe: 1s** (falls back to 1m, 5m)
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- High-frequency decision making
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- Precise buy/sell marker positioning
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- Small price movement detection
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### Memory Management
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- **8GB total memory limit** with per-model limits
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- Automatic cleanup and GPU/CPU fallback
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- Model registry with memory tracking
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### Multi-Timeframe Architecture
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- **Unified feature matrix**: (n_timeframes, window_size, n_features)
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- Common feature set across all timeframes
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- Consistent shape validation
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## Quick Start Examples
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### Test the enhanced system:
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```bash
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python main_clean.py --mode test
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# Expected output: Feature matrix (2, 20, 26) with 26 indicators
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```
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### Start scalping dashboard:
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```bash
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python main_clean.py --mode web --demo
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# Access: http://localhost:8050
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# Shows 1s charts with scalping decisions
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```
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### Prepare CNN training data:
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```bash
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python main_clean.py --mode cnn
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# Prepares multi-symbol, multi-timeframe matrices
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```
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### Setup RL training environment:
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```bash
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python main_clean.py --mode rl
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# Focuses on 1s scalping data
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```
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## Technical Improvements
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### Fixed Issues
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✅ **Feature matrix shape mismatch** - Now uses common features across timeframes
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✅ **Buy/sell marker positioning** - Properly aligned with chart timestamps
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✅ **Chart timeframe** - Optimized for 1s scalping with fallbacks
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✅ **Unicode encoding errors** - Removed problematic emoji characters
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✅ **Launch configuration** - Clean, modular mode selection
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### New Capabilities
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🚀 **Enhanced indicators** - 26 vs previous 17 features
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🚀 **Scalping focus** - 1s timeframe with dense data points
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🚀 **Separate training** - CNN and RL can be trained independently
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🚀 **Memory efficiency** - 8GB limit with automatic management
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🚀 **Real-time charts** - Enhanced dashboard with multiple indicators
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## Integration Notes
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- **CNN modules**: Connect to `run_cnn_training()` function
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- **RL modules**: Connect to `run_rl_training()` function
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- **Live trading**: Integrate with `run_live_trading()` function
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- **Custom indicators**: Add to `_add_technical_indicators()` method
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## Performance Specifications
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- **Data throughput**: 1s candles with 200+ data points
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- **Feature processing**: 26 indicators in < 1 second
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- **Memory usage**: Monitored and limited per model
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- **Chart updates**: 2-second refresh for real-time display
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- **Decision latency**: Optimized for scalping (< 100ms target) |