105 lines
3.5 KiB
Markdown
105 lines
3.5 KiB
Markdown
# Trading Strategy Analysis Notebooks
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This directory contains Jupyter notebooks for analyzing and visualizing trading strategies from the algorithmic trading book.
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## Available Notebooks
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### 1. `momentum_trading_analysis.ipynb`
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**Comprehensive Momentum Trading Analysis - Chapters 6 & 7**
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This notebook implements and analyzes two key momentum trading strategies:
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#### 🔵 Time Series Momentum (Chapter 7)
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- **Strategy**: Compares current prices to historical levels (250-day lookback)
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- **Asset Class**: Treasury futures (TU contracts)
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- **Logic**: Long when price > price 250 days ago, short otherwise
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- **Holding Period**: 25 days with gradual position building
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#### 🔴 Cross-Sectional Momentum (Chapter 6)
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- **Strategy**: Kent Daniel style long-short equity momentum
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- **Asset Class**: Stock universe (up to 500 stocks)
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- **Logic**: Long top performers, short bottom performers based on 252-day returns
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- **Rebalancing**: Monthly with 20 stocks long, 20 stocks short
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#### 📊 Analysis Features
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**Performance Metrics**:
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- Annual returns, volatility, Sharpe ratios
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- Maximum drawdown and duration
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- Win rates and trading frequency
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- Risk-adjusted performance (Calmar ratio)
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**Statistical Testing**:
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- T-tests for significance
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- Bootstrap confidence intervals
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- Randomized market returns tests
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- Monte Carlo simulations
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**Visualizations**:
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- Cumulative return charts
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- Rolling Sharpe ratio analysis
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- Drawdown patterns over time
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- Return distribution histograms
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- Risk-return scatter plots
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- Monthly returns heatmaps
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**Risk Analysis**:
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- Value at Risk (VaR) calculations
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- Skewness and kurtosis analysis
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- Downside deviation metrics
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- Drawdown series visualization
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## Usage
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### Prerequisites
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```bash
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pip install numpy pandas matplotlib seaborn scipy jupyter
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```
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### Running the Notebook
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```bash
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cd converted_code/notebooks
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jupyter notebook momentum_trading_analysis.ipynb
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```
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### Data Requirements
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The notebook automatically attempts to load real market data from the converted CSV/JSON files in `../data/`. If real data is unavailable, it generates synthetic data for demonstration purposes.
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**Real Data Used**:
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- Treasury futures: `futures_20120813.csv`
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- Stock data: `stocks_20120424.csv`
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- Earnings data: `earnings.json`
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## Key Insights
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The analysis provides insights into:
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1. **Momentum Persistence**: Whether momentum effects exist in the data
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2. **Strategy Comparison**: Relative performance of time series vs cross-sectional approaches
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3. **Statistical Significance**: Whether observed returns are statistically meaningful
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4. **Risk Characteristics**: Drawdown patterns and risk-adjusted returns
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5. **Practical Implementation**: Trading frequency and portfolio turnover
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## Academic Context
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The strategies implemented follow the methodologies described in:
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- **Chapter 6**: Cross-sectional momentum in equity markets
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- **Chapter 7**: Time series momentum in futures markets
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The analysis includes proper statistical testing to validate the significance of momentum effects, following academic best practices for strategy evaluation.
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## Limitations and Disclaimers
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- Results may use synthetic data if real market data is unavailable
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- Transaction costs and market impact are not included
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- Past performance does not guarantee future results
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- Strategies may be subject to regime changes and capacity constraints
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## Next Steps
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For further research, consider:
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- Testing across different time periods and market regimes
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- Including realistic transaction costs
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- Implementing risk management overlays
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- Analyzing factor exposures and attribution |