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Quant Trading Strategy Backtester

Quant Trading Strategy Backtester

A professional-grade quantitative trading backtesting engine built with Python and Streamlit. This application allows users to test technical analysis strategies on historical financial data with a modern, interactive dashboard.

Overview

The Quant Trading Strategy Backtester provides a robust platform for analyzing trading strategies against historical market data. It features a clean, intuitive interface and powerful analytics to help traders evaluate performance metrics and visualize results.

Features

• Data Fetching:
  • Real-time historical data for stocks (e.g., AAPL, MSFT) and crypto (e.g., BTC-USD, ETH-USD) via yfinance.
• Strategies:
  • Simple Moving Average (SMA) Crossover: Captures trends by comparing short-term and long-term moving averages.
  • Relative Strength Index (RSI): Identifies overbought and oversold conditions for mean reversion trading.
• Backtesting Engine:
  • Realistic Simulation: Accounts for initial capital and transaction costs (commissions).
  • Portfolio Management: Tracks cash, positions, and total equity over time.
  • Benchmark Comparison: Automatically compares strategy performance against a "Buy & Hold" strategy.
• Advanced Analytics:
  • KPI Metrics: Total Return, CAGR, Volatility, Sharpe Ratio, Max Drawdown.
  • Trade Analysis: detailed trade logs and Win Rate calculation.
• Visualization:
  • Interactive Plotly charts.
  • Candlestick charts with precise Buy/Sell markers.
  • Interactive Equity Curve and Drawdown analysis.

Project Structure

• app.py: The main Streamlit application entry point.
• backtest.py: Core backtesting engine class handling logic and portfolio tracking.
• strategies.py: Implementation of trading logic (SMA, RSI).
• data.py: Data fetching and cleaning utility.
• metrics.py: Financial performance calculations.
• plots.py: Visualization modules using Plotly.

Tech Stack

Category	Technology	Icon
Backend & Framework	Python 3.10+
Backend & Framework	Streamlit
Data & Analysis	yfinance
Data & Analysis	pandas
Data & Analysis	NumPy
Visualization	Plotly
Deployment	Streamlit Cloud
Version Control	GitHub

Installation & Usage

1. Clone the repository:

   git clone <repository_url>
   cd QuantTradingBacktester

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   streamlit run app.py

4. Navigate: Open your browser to the URL shown in the terminal (usually http://localhost:8501).

Disclaimer

Educational Use Only: This software is provided for educational and informational purposes only. It does not constitute financial advice. Trading in financial markets involves a high degree of risk and may result in the loss of your entire investment. The authors are not responsible for any financial losses incurred from using this software.