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