TradeSage 📈

A powerful and flexible trading strategy backtesting framework built in Python. TradeSage allows you to test trading strategies on historical data, analyze performance metrics, and visualize results.

🎯 Why I Built This

As someone passionate about algorithmic trading and quantitative finance, I wanted to create a comprehensive backtesting framework that demonstrates both the technical implementation of trading strategies and the analytical rigor required to evaluate their performance. This project showcases my ability to build production-ready financial software with proper risk management, performance metrics, and data visualization capabilities. It's designed to be both educational for learning trading concepts and practical for strategy development and testing.

🚀 Key Features

Modular Design with Strategy Plug-ins: Clean, extensible architecture allowing easy addition of new trading strategies
SMA Crossover Strategy with Buy/Sell Signals: Implements Simple Moving Average crossover logic with clear entry/exit signals
Custom Backtest Loop with Vectorized Returns: Efficient, vectorized backtesting engine for fast strategy evaluation
Advanced Performance Metrics: Comprehensive risk and return analysis including Sharpe ratio, maximum drawdown, and trade statistics
Visualized Cumulative Returns: Interactive charts comparing strategy performance vs market benchmarks
Flexible Data Input: Support for multiple data formats and sources (CSV, API integrations)
Command-Line Interface: Professional CLI for batch testing and automation
Jupyter Notebook Integration: Interactive analysis and strategy development environment

📁 Project Structure

TradeSage/
├── data/                    # Historical price data
│   └── AAPL_5y.csv         # Apple stock data (5 years)
├── notebooks/               # Jupyter notebooks for analysis
│   └── trade_analysis.ipynb # Interactive analysis notebook
├── plots/                   # Generated performance plots
│   └── backtest_result.png  # Equity curve visualization
├── tradesage/               # Main package
│   ├── __init__.py         # Package initialization
│   ├── main.py             # CLI entry point
│   ├── strategy.py         # Trading strategy implementation
│   ├── backtester.py       # Backtesting engine
│   ├── metrics.py          # Performance calculation functions
│   └── utils.py            # Utility functions (data loading, plotting)
├── requirements.txt         # Python dependencies
└── README.md               # This file

🛠️ Installation

Prerequisites

Python 3.8 or higher
pip (Python package installer)

Setup

Clone or download the project:
```
git clone <repository-url>
cd TradeSage
```
Install required packages:
```
pip install -r requirements.txt
```

Dependencies

The following packages are automatically installed:

pandas: Data manipulation and analysis
numpy: Numerical computing
matplotlib: Data visualization
yfinance: Yahoo Finance data downloader

📊 How It Works

Trading Strategy: SMA Crossover

The Simple Moving Average (SMA) Crossover strategy is a popular trend-following approach:

Calculate two moving averages:
- Short-term MA (e.g., 20-day)
- Long-term MA (e.g., 100-day)
Generate signals:
- BUY: When short-term MA crosses above long-term MA
- SELL: When short-term MA crosses below long-term MA
Execute trades:
- Buy/sell the asset based on signals
- Track portfolio value over time

Backtesting Process

Load historical data from CSV files
Calculate moving averages for the specified periods
Generate trading signals based on MA crossovers
Simulate trades with the specified initial capital
Calculate performance metrics:
- Cumulative return
- Sharpe ratio (risk-adjusted return)
- Maximum drawdown
- Total number of trades
Generate visualization of the equity curve

🎯 Usage

Basic Backtest

Run a backtest with default parameters:

python -m tradesage.main --data-file data/AAPL_5y.csv --short-ma 50 --long-ma 200 --initial-capital 100000 --output-plot plots/backtest_result.png

Command Line Options

Parameter	Description	Default	Example
`--data-file`	Path to CSV file with price data	Required	`data/AAPL_5y.csv`
`--short-ma`	Short moving average period	50	`20`
`--long-ma`	Long moving average period	200	`100`
`--initial-capital`	Starting portfolio value	100000	`50000`
`--output-plot`	Path to save equity curve plot	`plots/backtest_result.png`	`my_results.png`

Example Commands

Conservative Strategy (longer periods):

python -m tradesage.main --data-file data/AAPL_5y.csv --short-ma 100 --long-ma 300 --initial-capital 100000

Aggressive Strategy (shorter periods):

python -m tradesage.main --data-file data/AAPL_5y.csv --short-ma 10 --long-ma 50 --initial-capital 100000

Custom Output Location:

python -m tradesage.main --data-file data/AAPL_5y.csv --output-plot results/my_analysis.png

📈 Understanding Results

Performance Metrics

Cumulative Return: Total percentage return over the backtest period
- Positive: Strategy made money
- Negative: Strategy lost money
Sharpe Ratio: Risk-adjusted return measure
- Higher is better (typically > 1.0 is good)
- Measures return per unit of risk
Maximum Drawdown: Largest peak-to-trough decline
- Lower is better (e.g., -20% means worst decline was 20%)
- Important for risk management
Total Trades: Number of buy/sell signals executed
- More trades = more active strategy
- Consider transaction costs for high-frequency trading

Sample Output

Backtest Results:
  Cumulative Return: 0.1411
  Sharpe Ratio: 0.2333
  Max Drawdown: -0.3538
  Total Trades: 21.0000
Equity curve saved to plots/backtest_result.png

📊 Sample Output Visualization

Equity curve showing portfolio value over time with buy/sell signals marked

📊 Data Format

Required CSV Structure

Your CSV file should have the following columns:

Date: Date in YYYY-MM-DD format
Close: Closing price for each day
High: Highest price of the day (optional)
Low: Lowest price of the day (optional)
Open: Opening price of the day (optional)
Volume: Trading volume (optional)

Example CSV Format

Price,Close,High,Low,Open,Volume
Ticker,AAPL,AAPL,AAPL,AAPL,AAPL
Date,,,,,
2020-08-12,110.05,110.31,107.41,107.60,165598000
2020-08-13,112.00,113.00,110.95,111.43,210082000
...

Getting Data

You can download stock data using yfinance:

import yfinance as yf

# Download Apple data
aapl = yf.download('AAPL', start='2020-01-01', end='2024-01-01')
aapl.to_csv('data/AAPL_4y.csv')

# Download Tesla data
tsla = yf.download('TSLA', start='2020-01-01', end='2024-01-01')
tsla.to_csv('data/TSLA_4y.csv')

🔧 Customization

Modifying the Strategy

Edit tradesage/strategy.py to implement different trading strategies:

class MyCustomStrategy:
    def __init__(self, param1, param2):
        self.param1 = param1
        self.param2 = param2
    
    def generate_signals(self, data):
        # Your custom logic here
        signals = pd.DataFrame(index=data.index)
        signals['signal'] = 0  # 1 for buy, -1 for sell, 0 for hold
        return signals

Adding New Metrics

Extend tradesage/metrics.py to calculate additional performance measures:

def calculate_sortino_ratio(returns, risk_free_rate=0.0):
    """Calculate Sortino ratio (downside risk-adjusted return)"""
    excess_returns = returns - risk_free_rate
    downside_returns = excess_returns[excess_returns < 0]
    downside_std = downside_returns.std()
    return excess_returns.mean() / downside_std if downside_std != 0 else 0

📚 Jupyter Notebook Analysis

Open notebooks/trade_analysis.ipynb for interactive analysis:

Explore data structure and quality
Test different parameter combinations
Create custom visualizations
Perform statistical analysis

🚨 Important Notes

Risk Disclaimer

⚠️ This is for educational and research purposes only.

Past performance does not guarantee future results
Always do your own research before making investment decisions
Consider transaction costs, taxes, and market conditions
This is not financial advice

Limitations

No transaction costs: Real trading incurs fees and slippage
Perfect execution: Assumes trades execute at exact prices
No market impact: Large orders can affect prices
Historical data: Future market conditions may differ

🤝 Contributing

Feel free to contribute to this project:

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

📄 License

This project is open source and available under the MIT License.

🆘 Troubleshooting

Common Issues

"Missing column provided to 'parse_dates': 'Date'"

Check your CSV file format
Ensure the Date column exists and is named correctly

"KeyError: 'Close'"

Verify your CSV has a Close column
Check the data loading function in utils.py

"No module named 'tradesage'"

Make sure you're in the project root directory
Install dependencies: pip install -r requirements.txt

Getting Help

If you encounter issues:

Check the error message carefully
Verify your data format
Ensure all dependencies are installed
Open an issue with details about the problem

🎯 Next Steps & Future Enhancements

Immediate Improvements

Add support for multiple tickers - Portfolio backtesting across multiple assets
Implement position sizing - Kelly criterion, risk parity, and volatility targeting
Add stop-loss and take-profit mechanisms for better risk management
Include transaction costs - Realistic trading fees and slippage modeling

Advanced Features

Integrate with Alpaca or Interactive Brokers API - Live trading capabilities
Expand strategy library - RSI, MACD, Bollinger Bands, Mean Reversion, etc.
Machine Learning integration - LSTM, Random Forest, and ensemble methods
Real-time data feeds - WebSocket connections for live market data
Backtesting optimization - Genetic algorithms for parameter tuning

Production Features

Web dashboard - Interactive web interface for strategy management
Database integration - PostgreSQL for storing historical data and results
Cloud deployment - AWS/GCP deployment for scalable backtesting
API endpoints - RESTful API for external integrations
Performance monitoring - Real-time strategy performance tracking

Happy Trading! 📈💰

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
data		data
notebooks		notebooks
plots		plots
tradesage		tradesage
QUICKSTART.md		QUICKSTART.md
README.md		README.md
example.py		example.py
requirements.txt		requirements.txt

khushi-saxena/TradeSage

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