# Trading System - Launch Modes Guide

## Overview
The unified trading system now provides clean, modular launch modes optimized for scalping and multi-timeframe analysis.

## Available Modes

### 1. Test Mode
```bash
python main_clean.py --mode test
```
- Tests enhanced data provider with multi-timeframe indicators
- Validates feature matrix creation (26 technical indicators)
- Checks data provider health and caching
- **Use case**: System validation and debugging

### 2. CNN Training Mode
```bash
python main_clean.py --mode cnn --symbol ETH/USDT
```
- Trains CNN models only
- Prepares multi-timeframe, multi-symbol feature matrices
- Supports timeframes: 1s, 1m, 5m, 1h, 4h
- **Use case**: Isolated CNN model development

### 3. RL Training Mode  
```bash
python main_clean.py --mode rl --symbol ETH/USDT
```
- Trains RL agents only
- Focuses on 1s scalping data
- Optimized for short-term decision making
- **Use case**: Isolated RL agent development

### 4. Combined Training Mode
```bash
python main_clean.py --mode train --symbol ETH/USDT
```
- Trains both CNN and RL models sequentially
- First runs CNN training, then RL training
- **Use case**: Full model pipeline training

### 5. Live Trading Mode
```bash
python main_clean.py --mode trade --symbol ETH/USDT
```
- Runs live trading with 1s scalping focus
- Real-time data streaming integration
- **Use case**: Production trading execution

### 6. Web Dashboard Mode
```bash
python main_clean.py --mode web --demo --port 8050
```
- Enhanced scalping dashboard with 1s charts
- Real-time technical indicators visualization
- Scalping demo mode with realistic decisions
- **Use case**: System monitoring and visualization

## Key Features

### Enhanced Data Provider
- **26 Technical Indicators** including:
  - Trend: SMA, EMA, MACD, ADX, PSAR
  - Momentum: RSI, Stochastic, Williams %R
  - Volatility: Bollinger Bands, ATR, Keltner Channels
  - Volume: OBV, MFI, VWAP, Volume profiles
  - Custom composites for trend/momentum

### Scalping Optimization
- **Primary timeframe: 1s** (falls back to 1m, 5m)
- High-frequency decision making
- Precise buy/sell marker positioning
- Small price movement detection

### Memory Management
- **8GB total memory limit** with per-model limits
- Automatic cleanup and GPU/CPU fallback
- Model registry with memory tracking

### Multi-Timeframe Architecture
- **Unified feature matrix**: (n_timeframes, window_size, n_features)
- Common feature set across all timeframes
- Consistent shape validation

## Quick Start Examples

### Test the enhanced system:
```bash
python main_clean.py --mode test
# Expected output: Feature matrix (2, 20, 26) with 26 indicators
```

### Start scalping dashboard:
```bash
python main_clean.py --mode web --demo
# Access: http://localhost:8050
# Shows 1s charts with scalping decisions
```

### Prepare CNN training data:
```bash
python main_clean.py --mode cnn
# Prepares multi-symbol, multi-timeframe matrices
```

### Setup RL training environment:
```bash
python main_clean.py --mode rl  
# Focuses on 1s scalping data
```

## Technical Improvements

### Fixed Issues
✅ **Feature matrix shape mismatch** - Now uses common features across timeframes  
✅ **Buy/sell marker positioning** - Properly aligned with chart timestamps  
✅ **Chart timeframe** - Optimized for 1s scalping with fallbacks  
✅ **Unicode encoding errors** - Removed problematic emoji characters  
✅ **Launch configuration** - Clean, modular mode selection  

### New Capabilities
🚀 **Enhanced indicators** - 26 vs previous 17 features  
🚀 **Scalping focus** - 1s timeframe with dense data points  
🚀 **Separate training** - CNN and RL can be trained independently  
🚀 **Memory efficiency** - 8GB limit with automatic management  
🚀 **Real-time charts** - Enhanced dashboard with multiple indicators  

## Integration Notes

- **CNN modules**: Connect to `run_cnn_training()` function
- **RL modules**: Connect to `run_rl_training()` function  
- **Live trading**: Integrate with `run_live_trading()` function
- **Custom indicators**: Add to `_add_technical_indicators()` method

## Performance Specifications

- **Data throughput**: 1s candles with 200+ data points
- **Feature processing**: 26 indicators in < 1 second
- **Memory usage**: Monitored and limited per model
- **Chart updates**: 2-second refresh for real-time display
- **Decision latency**: Optimized for scalping (< 100ms target)