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Task 0: Tutorial for Task 1: feat: Enhanced FinRL Task 1 solution with improved performance #1

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feat: Enhanced FinRL Task 1 solution with improved performance
xsa-dev Oct 10, 2025
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feat: Enhanced FinRL Task 1 solution with improved performance
xsa-dev Oct 10, 2025
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Merge remote-tracking branch 'origin/feature/finrl-task1-enhanced-sol…
xsa-dev Oct 10, 2025
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118 changes: 118 additions & 0 deletions Tutorials/FinRL_Contest_2023_Task_1_baseline_solution/RESULTS_SUMMARY.md
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# Task 1 - FinRL Contest 2023 Results Summary

## Overview
This project implements and compares different approaches for algorithmic trading using reinforcement learning on stock market data.

## Approaches Implemented

### 1. Baseline PPO Model
- **Algorithm**: Proximal Policy Optimization (PPO)
- **Features**: Standard technical indicators (MACD, Bollinger Bands, RSI, CCI, DX, SMA, VIX, Turbulence)
- **Configuration**: Standard PPO parameters

### 2. Enhanced PPO Model
- **Algorithm**: Proximal Policy Optimization (PPO) with enhanced features
- **Features**:
- All baseline features
- Additional moving averages (5, 10, 20, 50 periods)
- Price momentum indicators (5, 10 periods)
- Volume indicators (volume ratio)
- Volatility indicators (20-period standard deviation)
- **Configuration**: Same PPO parameters as baseline

## Results Comparison

### Baseline PPO Performance
- **Annual Return**: 19.55%
- **Sharpe Ratio**: 1.25
- **Max Drawdown**: -8.80%
- **Calmar Ratio**: 2.22
- **Volatility**: 15.30%

### Enhanced PPO Performance
- **Annual Return**: 25.55% (+6.00% improvement)
- **Sharpe Ratio**: 2.09 (+67% improvement)
- **Max Drawdown**: -5.70% (35% reduction)
- **Calmar Ratio**: 4.48 (+102% improvement)
- **Volatility**: 11.27% (26% reduction)

## Key Improvements

### 1. Enhanced Feature Engineering
- Added multiple time-frame moving averages for better trend analysis
- Implemented momentum indicators to capture price acceleration
- Added volume analysis for better market sentiment understanding
- Included volatility measures for risk assessment

### 2. Better Risk Management
- Significantly reduced maximum drawdown (35% improvement)
- Lower volatility while maintaining higher returns
- Improved stability metrics

### 3. Superior Performance Metrics
- 67% improvement in Sharpe ratio
- 102% improvement in Calmar ratio
- 6% absolute improvement in annual returns

## Technical Implementation

### Dependencies
- Python 3.10
- FinRL library
- Stable-Baselines3
- Pandas, NumPy, Matplotlib
- PyTorch (CPU version)

### File Structure
```
├── train.py # Baseline training script
├── test.py # Baseline testing script
├── simple_ensemble.py # Enhanced model implementation
├── train_data.csv # Training data with technical indicators
├── baseline_ppo_results.csv # Baseline results
├── enhanced_ppo_results.csv # Enhanced model results
├── enhanced_vs_baseline.png # Performance comparison chart
└── RESULTS_SUMMARY.md # This summary
```

### Usage
```bash
# Train baseline model
uv run python train.py

# Test baseline model
uv run python test.py --start_date 2019-01-01 --end_date 2020-01-01

# Train enhanced model
uv run python simple_ensemble.py --mode train

# Test and compare models
uv run python simple_ensemble.py --mode test
```

## Conclusion

The enhanced PPO model demonstrates significant improvements over the baseline approach:

1. **Better Returns**: 25.55% vs 19.55% annual return
2. **Lower Risk**: 35% reduction in maximum drawdown
3. **Better Risk-Adjusted Returns**: 67% improvement in Sharpe ratio
4. **More Stable**: Higher stability and lower volatility

The key to success was the addition of comprehensive technical indicators that provide the model with more nuanced information about market conditions, price momentum, and volatility patterns. This allows the enhanced model to make more informed trading decisions and better manage risk.

## Future Improvements

1. **Ensemble Methods**: Combine multiple models with different architectures
2. **Advanced Features**: Add more sophisticated technical indicators
3. **Market Regime Detection**: Implement regime-aware trading strategies
4. **Transaction Costs**: More realistic cost modeling
5. **Risk Constraints**: Add position sizing and risk limits

## Files Generated

- `baseline_ppo_results.csv`: Baseline model performance data
- `enhanced_ppo_results.csv`: Enhanced model performance data
- `enhanced_vs_baseline.png`: Visual comparison chart
- `plot.png`: Baseline model performance chart
- `results.csv`: Original baseline results
253 changes: 253 additions & 0 deletions Tutorials/FinRL_Contest_2023_Task_1_baseline_solution/baseline_ppo_results.csv
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