algo_trading_book/converted_code/notebooks/README.md

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:

1. Momentum Persistence: Whether momentum effects exist in the data
2. Strategy Comparison: Relative performance of time series vs cross-sectional approaches
3. Statistical Significance: Whether observed returns are statistically meaningful
4. Risk Characteristics: Drawdown patterns and risk-adjusted returns
5. 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