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gogo2/README_LAUNCH_MODES.md
Dobromir Popov 310f3c5bf9 wip
2025-05-24 09:59:11 +03:00

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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

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

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

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

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

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

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:

python main_clean.py --mode test
# Expected output: Feature matrix (2, 20, 26) with 26 indicators

Start scalping dashboard:

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

Prepare CNN training data:

python main_clean.py --mode cnn
# Prepares multi-symbol, multi-timeframe matrices

Setup RL training environment:

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)

🚀 VSCode Launch Configurations

1. Core Trading Modes

Live Trading (Demo)

"name": "Live Trading (Demo)"
"program": "main.py"
"args": ["--mode", "live", "--demo", "true", "--symbol", "ETH/USDT", "--timeframe", "1m"]
  • Purpose: Safe demo trading with virtual funds
  • Environment: Paper trading mode
  • Risk: Zero (no real money)

Live Trading (Real)

"name": "Live Trading (Real)"
"program": "main.py" 
"args": ["--mode", "live", "--demo", "false", "--symbol", "ETH/USDT", "--leverage", "50"]
  • Purpose: Real trading with actual funds
  • Environment: Live exchange API
  • Risk: High (real money)

2. Training & Development Modes

Train Bot

"name": "Train Bot"
"program": "main.py"
"args": ["--mode", "train", "--episodes", "100"]
  • Purpose: Standard RL agent training
  • Duration: 100 episodes
  • Output: Trained model files

Evaluate Bot

"name": "Evaluate Bot"
"program": "main.py"
"args": ["--mode", "eval", "--episodes", "10"]
  • Purpose: Model performance evaluation
  • Duration: 10 test episodes
  • Output: Performance metrics

3. Neural Network Training

NN Training Pipeline

"name": "NN Training Pipeline"
"module": "NN.realtime_main"
"args": ["--mode", "train", "--model-type", "cnn", "--epochs", "10"]
  • Purpose: Deep learning model training
  • Framework: PyTorch
  • Monitoring: Automatic TensorBoard integration

Quick CNN Test (Real Data + TensorBoard)

"name": "Quick CNN Test (Real Data + TensorBoard)"
"program": "test_cnn_only.py"
  • Purpose: Fast CNN validation with real market data
  • Duration: 2 epochs, 500 samples
  • Output: test_models/quick_cnn.pt
  • Monitoring: TensorBoard metrics

4. 🔥 Realtime RL Training + Monitoring

Realtime RL Training + TensorBoard + Web UI

"name": "Realtime RL Training + TensorBoard + Web UI"
"program": "train_realtime_with_tensorboard.py"
"args": ["--episodes", "50", "--symbol", "ETH/USDT", "--web-port", "8051"]
  • Purpose: Advanced RL training with comprehensive monitoring
  • Features:
    • Real-time TensorBoard metrics logging
    • Live web dashboard at http://localhost:8051
    • Episode rewards, balance tracking, win rates
    • Trading performance metrics
    • Agent learning progression
  • Data: 100% real ETH/USDT market data from Binance
  • Monitoring: Dual monitoring (TensorBoard + Web UI)
  • Duration: 50 episodes with real-time feedback

5. Monitoring & Visualization

TensorBoard Monitor (All Runs)

"name": "TensorBoard Monitor (All Runs)"
"program": "run_tensorboard.py"
  • Purpose: Monitor all training sessions
  • Features: Auto-discovery of training logs
  • Access: http://localhost:6006

Realtime Charts with NN Inference

"name": "Realtime Charts with NN Inference"
"program": "realtime.py"
  • Purpose: Live trading charts with ML predictions
  • Features: Real-time price updates + model inference
  • Models: CNN + RL integration

6. Advanced Training Modes

TRAIN Realtime Charts with NN Inference

"name": "TRAIN Realtime Charts with NN Inference"
"program": "train_rl_with_realtime.py"
"args": ["--episodes", "100", "--max-position", "0.1"]
  • Purpose: RL training with live chart integration
  • Features: Visual training feedback
  • Position limit: 10% portfolio allocation

📊 Monitoring URLs

Development

Production

  • Live Trading Dashboard: Integrated in trading interface
  • Performance Metrics: Real-time P&L tracking
  • Risk Management: Position size and drawdown monitoring

🎯 Quick Start Recommendations

For CNN Development

  1. Start: "Quick CNN Test (Real Data + TensorBoard)"
  2. Monitor: Open TensorBoard at http://localhost:6006
  3. Validate: Check test_models/ for output files

For RL Development

  1. Start: "Realtime RL Training + TensorBoard + Web UI"
  2. Monitor: TensorBoard (http://localhost:6006) + Web UI (http://localhost:8051)
  3. Track: Episode rewards, balance progression, win rates

For Production Trading

  1. Test: "Live Trading (Demo)" first
  2. Validate: Confirm strategy performance
  3. Deploy: "Live Trading (Real)" with appropriate risk management

⚡ Performance Features

GPU Acceleration

  • Automatic CUDA detection and utilization
  • Mixed precision training support
  • Memory optimization for large datasets

Real-time Data

  • Direct Binance API integration
  • Multi-timeframe data synchronization
  • Live price feed with minimal latency

Professional Monitoring

  • Industry-standard TensorBoard integration
  • Custom web dashboards for trading metrics
  • Real-time performance tracking

🛡️ Safety Features

Pre-launch Tasks

  • Kill Stale Processes: Automatic cleanup before launch
  • Port Management: Intelligent port allocation
  • Resource Monitoring: Memory and GPU usage tracking

Real Market Data Policy

  • ✅ No Synthetic Data: All training uses authentic exchange data
  • ✅ Live API Integration: Direct connection to cryptocurrency exchanges
  • ✅ Data Validation: Quality checks for completeness and consistency
  • ✅ Multi-timeframe Sync: Aligned data across all time horizons

✅ Launch configuration - Clean, modular mode selection ✅ Professional monitoring - TensorBoard + custom dashboards
✅ Real market data - Authentic cryptocurrency price data ✅ Safety features - Risk management and validation ✅ GPU acceleration - Optimized for high-performance training