This project contains an interactive Jupyter notebook focused on analyzing and backtesting trading strategies using historical market data. It is designed to help traders and analysts evaluate strategy performance, visualize key indicators, and refine parameters for better decision-making.
- Analysis_and_Backtesting_V2.ipynb: Main notebook containing data preprocessing, strategy logic, and performance evaluation.
- data/: (Optional) Directory where input datasets like historical prices (CSV/JSON) are stored.
- output/: (Optional) Folder to store generated plots or backtesting results.
- 🧮 Technical indicator calculation (e.g., SMA, RSI, MACD)
- 📉 Strategy logic implementation (e.g., crossover strategy, signal-based logic)
- 📊 Backtesting engine with PnL evaluation
- 📈 Visualizations for signals, trades, and performance comparison
- 📂 Option to export results for further analysis
- Python 3.7+
- Pandas – data manipulation and time series handling
- NumPy – numerical computations
- Matplotlib & Seaborn – data visualization
- yfinance – fetching historical financial data
- TA (Technical Analysis) – built-in technical indicators (e.g., RSI, MACD, SMA)
- Jupyter Notebook – interactive coding and documentation
- Yahoo Finance API (via
yfinance)
- Custom logic implemented for:
- Signal generation (e.g., crossover, momentum)
- Trade simulation and portfolio management
- Performance evaluation (returns, drawdown, win rate)
- Backtrader or QuantConnect – full-fledged backtesting engines
- Plotly – interactive visualizations
- SQL/SQLite – data storage for larger datasets
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Install Requirements
Use a virtual environment (optional) and install required libraries:
pip install pandas numpy matplotlib seaborn yfinance ta