S&P SmallCap 600 Niche Index Inclusions Trading Strategy Backtest

A comprehensive Python backtesting framework for analyzing the "Niche Index Inclusions" trading strategy targeting newly added S&P SmallCap 600 stocks.

Strategy Overview

Target: S&P SmallCap 600 newly added companies
Universe: US stocks with market cap between $500M - $8B
Entry: Buy at closing price on announcement day
Exit Rules:

• ✅ Profit Target: Sell at +15% gain
• ❌ Stop Loss: Sell at -7% loss
• ⏰ Time Exit: Sell after 45 trading days

Project Structure

sp600_backtest/
├── data/
│   └── sp600_additions.csv     # Historical S&P 600 addition events
├── src/
│   ├── __init__.py
│   ├── config.py               # Strategy configuration
│   ├── data_loader.py          # Data fetching and preprocessing
│   ├── backtest_engine.py      # Core backtesting logic
│   └── performance_analyzer.py # Performance metrics and visualization
├── results/                    # Output files (generated)
├── notebooks/                  # Jupyter notebooks for analysis
├── requirements.txt           # Python dependencies
├── main.py                    # Main execution script
└── README.md                  # This file

Installation

1. Clone or download this project
2. Install dependencies:

   pip install -r requirements.txt

Data Requirements

Critical: S&P 600 Historical Additions Data

You need a CSV file at data/sp600_additions.csv with the following columns:

• ticker: Stock symbol
• company_name: Company name
• announcement_date: When addition was announced (YYYY-MM-DD)
• effective_date: When it became effective (YYYY-MM-DD)

Sources for this data:

• S&P Dow Jones Indices press releases
• Bloomberg Terminal
• Refinitiv/Eikon
• FactSet
• Specialized financial data providers

Note: The framework includes sample data for demonstration, but you must replace it with actual historical data for meaningful results.

Usage

Quick Start

python main.py

The framework will:

1. Load S&P 600 additions data
2. Filter stocks by market cap criteria
3. Fetch historical price data via yfinance
4. Run the backtesting simulation
5. Generate comprehensive performance analysis
6. Export results to the results/ directory

Configuration

Modify strategy parameters in src/config.py:

class BacktestConfig:
    PROFIT_TARGET = 0.15      # 15% profit target
    STOP_LOSS = 0.07          # 7% stop loss
    HOLDING_PERIOD = 45       # Max days to hold
    INITIAL_CAPITAL = 100000  # Starting portfolio
    TRANSACTION_COST = 0.001  # 0.1% per trade
    START_DATE = datetime(2010, 1, 1)
    END_DATE = datetime(2024, 12, 31)

Key Performance Metrics

The framework calculates comprehensive metrics including:

Return Metrics

• Total Return & CAGR
• Sharpe, Sortino & Calmar Ratios
• Maximum Drawdown

Strategy Validation

• Win/Loss/Neutral Rates (key for Monte Carlo validation)
• Average returns by outcome type
• Profit factor and consecutive streaks

Risk Analysis

• Volatility and drawdown analysis
• Rolling performance metrics
• Monthly return heatmaps

Output Files

Results are exported to the results/ directory:

• detailed_trade_log.csv: Every trade with entry/exit details
• daily_portfolio_values.csv: Daily portfolio performance
• performance_metrics.csv: Summary statistics
• Interactive plots and visualizations

Monte Carlo Validation

This backtest is designed to validate assumptions used in Monte Carlo simulations:

Expected Strategy Performance:

• Win Rate: ~40-50%
• Loss Rate: ~20-30%
• Neutral Rate: ~20-40%

The framework compares actual results against these expectations.

Important Considerations

Data Quality

• Survivorship Bias: yfinance may not include delisted stocks
• Lookahead Bias: Framework ensures only historical data is used
• Market Cap Filtering: Validates eligibility at announcement date

Limitations

• Transaction costs are simplified (fixed percentage)
• No consideration for liquidity or market impact
• Limited to US market hours trading simulation
• Requires manual data sourcing for S&P 600 additions

Professional Use

For production backtesting:

• Use professional data providers (Bloomberg, Refinitiv)
• Include comprehensive corporate actions handling
• Add more sophisticated transaction cost models
• Implement portfolio constraints and risk management

Advanced Usage

Custom Analysis

from src.config import BacktestConfig
from src.backtest_engine import BacktestEngine
from src.performance_analyzer import PerformanceAnalyzer

# Run custom backtest
config = BacktestConfig()
config.PROFIT_TARGET = 0.20  # 20% target
# ... run backtest with custom parameters

Jupyter Notebooks

Use the notebooks/ directory for:

• Interactive data exploration
• Custom visualizations
• Sensitivity analysis
• Strategy variations

Troubleshooting

Common Issues:

1. No trades executed: Check if S&P 600 data file exists and has valid date ranges
2. Price data failures: yfinance rate limits - add delays or use cached data
3. Market cap filtering issues: Some tickers may lack fundamental data

Performance Tips:

• Cache price data for multiple runs
• Use date range filtering to reduce data volume
• Consider parallel processing for large datasets

License

This backtesting framework is provided for educational and research purposes.

Contributing

Contributions welcome! Areas for improvement:

• Additional data source integrations
• Enhanced risk management features
• Portfolio optimization capabilities
• Alternative exit strategies

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Disclaimer: This framework is for research purposes only. Past performance does not guarantee future results. Always validate results with multiple data sources and consider transaction costs, market impact, and other real-world factors before implementing any trading strategy.