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+# 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
+```bash
+pip install numpy pandas matplotlib seaborn scipy jupyter
+```
+
+### Running the Notebook
+```bash
+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