AlgoBacktest

A platform for developing and evaluating algorithmic trading strategies. Supports multi-asset portfolios with flexible signal generation and position sizing. Emphasizes robust backtesting, risk-aware design, and extensible architecture.

Features

• Modular Backtesting Engine
• Config-Driven Framework
• Multi-Asset Portfolio Support: Fetches data from yfinance
• Decoupled Signal & Execution Logic
• Time-Series Simulation Engine
• Performance & Risk Metrics

Installation

Prerequisites

• Python 3.x

Steps

1. Clone the repository:

   git clone https://github.com/dhruvi29/strategy-backtester
   cd strategy-backtester
   

2. Create and activate a virtual environment (recommended):

   python -m venv venv
   # On Windows:
   venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

Usage

Import the Engine class from engine.backtest_engine, initialize with your parameters, run the backtest, and retrieve performance metrics or plots.

Examples

Basic Backtest Setup

from engine.backtest_engine import Engine
import datetime

# Initialize the engine with custom parameters
engine = Engine(
    universe='NIFTY50',
    initial_capital=100000,
    start_date=datetime.date(2020, 1, 1),
    end_date=datetime.date(2023, 1, 1),
    interval='1mo'
)

# Run the backtest
engine.run_backtest()

# Get performance plot
performance_plot = engine.get_performance()

Running a Custom Strategy

The engine defaults to EqualWeightedStrategy, but you can pass a custom strategy instance.

Project Structure

• data/: Data handling modules
  • data_loader.py: Loads and validates financial data
  • securities.py: Defines security universes (e.g., NIFTY50)
• engine/: Core backtesting components
  • backtest_engine.py: Main engine for running backtests
  • portfolio.py: Portfolio tracking and trade execution
• strategies/: Trading strategy implementations
  • base_strategy.py: Base class for strategies
  • equal_weighted.py: Equal-weighted strategy example
• main.py: Entry point script (currently empty)

Technologies Used

• Python
• pandas
• yfinance
• matplotlib

Demo/Results

Below is a sample performance plot from a backtest using the equal-weighted strategy on NIFTY50 data from 2020-2023:

Interpretation: The plot illustrates the portfolio's value growth over time, demonstrating the strategy's performance against historical data. Peaks and valleys reflect market conditions and rebalancing effects.

Author

Dhruvi Doshi