# 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