Investment Strategy Backtesting Framework

Overview

This project implements a comprehensive and extensible backtesting framework designed to evaluate and compare investment strategies on historical financial data. Developed as part of the M2 Quantitative Finance program at Université Paris Dauphine-PSL, this framework provides researchers, students, and practitioners with professional-grade tools for strategy development, testing, and analysis.

The framework adopts a modular, object-oriented architecture that balances ease of use with powerful functionality. Users can create custom strategies through either class inheritance or decorator-based approaches, test them against historical data with realistic transaction costs and slippage, and analyze results through comprehensive metrics and visualizations.

Features

Core Capabilities:

• Simple and intuitive API for strategy creation and testing
• Support for multiple data formats (CSV, Parquet, pandas DataFrame)
• Comprehensive performance metrics
• Multi-backend visualization support (matplotlib, seaborn, plotly)
• Realistic trading simulation with transaction costs and slippage
• Multi-asset portfolio support through dictionary-based position API
• Flexible rebalancing frequency configuration (daily, weekly, monthly)

Advanced Features:

• Walk-forward analysis for temporal validation
• Parameter optimization and robustness testing
• Strategy comparison and correlation analysis
• Benchmark-relative metrics (alpha, beta)
• Customizable risk-free rates and market benchmarks

Installation & Usage

Installation

Clone the repository and install the package:

git clone https://github.com/Antoine-Moniz/backtesting_framework.git
cd backtesting_framework
pip install -e .

For development with testing dependencies:

pip install -e ".[dev]"

Quick Start Example

import pandas as pd
from backtesting_framework import Backtester, Strategy
from strategies.buy_and_hold import BuyAndHoldStrategy
from strategies.moving_average_cross import MovingAverageCrossStrategy

# Load historical data (CSV, Parquet, or DataFrame)
data = pd.read_csv('data.csv')
backtester = Backtester(
    data=data,
    initial_capital=100000,
    transaction_cost=0.001,  # 0.1% per trade
    slippage=0.0005          # 0.05% slippage
)

# Create and test a strategy
strategy = MovingAverageCrossStrategy(short_window=10, long_window=30)
result = backtester.run_backtest(strategy)

# Display results
print(result.summary())
result.plot_performance(backend='matplotlib')

Creating Custom Strategies

Method 1: Class Inheritance

from backtesting_framework import Strategy

class RSIStrategy(Strategy):
    def __init__(self, window=14, oversold=30, overbought=70):
        self.window = window
        self.oversold = oversold
        self.overbought = overbought
        self.is_fitted = False
        self.rebalance_frequency = 'D'
    
    @property
    def name(self):
        return f"RSI Strategy ({self.window}, {self.oversold}, {self.overbought})"
    
    def fit(self, historical_data):
        self.is_fitted = True
    
    def get_position(self, data, positions):
        if len(data) < self.window + 5:
            return {'asset': 0.0}
        
        rsi = self.calculate_rsi(data['close'], self.window)
        
        if rsi < self.oversold:
            return {'asset': 1.0}  # Buy (oversold)
        elif rsi > self.overbought:
            return {'asset': 0.0}  # Sell (overbought)
        else:
            return positions  # Keep current position

Method 2: Decorator for Simple Strategies

from backtesting_framework import strategy_decorator

@strategy_decorator(name="Simple MA Strategy")
def simple_strategy(historical_data, current_position):
    if len(historical_data) < 10:
        return 0
    
    # Simple moving average logic
    short_ma = historical_data['close'].rolling(5).mean().iloc[-1]
    long_ma = historical_data['close'].rolling(10).mean().iloc[-1]
    
    return 1 if short_ma > long_ma else -1

Comparing Multiple Strategies

from backtesting_framework import compare_results

# Create multiple strategies
buy_hold = BuyAndHoldStrategy()
ma_cross = MovingAverageCrossStrategy(short_window=5, long_window=20)
rsi = RSIStrategy()

# Run backtests
result1 = backtester.run_backtest(buy_hold)
result2 = backtester.run_backtest(ma_cross)
result3 = backtester.run_backtest(rsi)

# Compare visually
compare_results(result1, result2, result3, backend='matplotlib')

For detailed examples including walk-forward analysis, parameter optimization, and advanced visualizations, see examples/example_usage.ipynb.

Project Structure

backtesting_framework/
├── backtesting_framework/
│   ├── __init__.py              # Package exports and initialization
│   ├── strategy.py              # Abstract Strategy class and decorators
│   ├── backtester.py            # Core backtesting engine
│   ├── result.py                # Results analysis and visualization
│   └── data_handler.py          # Data loading and validation utilities
│
├── examples/
│   ├── strategies/              # Built-in strategy implementations
│   │   ├── buy_and_hold.py
│   │   ├── moving_average_cross.py
│   │   └── mean_reversion.py
│   └── example_usage.ipynb      # Comprehensive usage demonstration
│
├── tests/
│   ├── test_backtester.py       # Backtester unit tests
│   ├── test_strategy.py         # Strategy tests
│   ├── test_result.py           # Results analysis tests
│   └── test_multi_asset.py      # Multi-asset support tests
│
├── pyproject.toml               # Package configuration and dependencies
├── README.md                    # Project documentation
└── LICENSE                      # MIT License

Methodology Summary

Object-Oriented Architecture

The framework implements core object-oriented programming principles:

• Abstraction: The abstract Strategy class defines the interface all strategies must implement, hiding implementation complexity behind a simple API.
• Inheritance: Custom strategies inherit from Strategy, gaining access to common functionality while implementing specific trading logic.
• Polymorphism: All strategies share the same interface (get_position, fit), allowing the Backtester to work with any strategy implementation.
• Encapsulation: Each class encapsulates its data and methods, exposing only necessary interfaces.

Backtesting Process

1. Data Preparation: Load and validate historical data (OHLCV format)
2. Strategy Initialization: Configure strategy parameters and training
3. Simulation Loop: Iterate through historical data, generating positions
4. Position Management: Apply transaction costs, slippage, and rebalancing
5. Metrics Calculation: Compute comprehensive performance statistics
6. Visualization: Generate interactive charts and comparison plots

Performance Metrics

The framework calculates 15+ performance metrics:

• Returns: Total, annualized, cumulative
• Risk Measures: Volatility, Value at Risk (VaR), maximum drawdown
• Risk-Adjusted Ratios: Sharpe, Sortino, Calmar
• Benchmark Analysis: Alpha, Beta, correlation
• Trading Statistics: Number of trades, win rate, transaction costs

Testing Strategy

Comprehensive test coverage includes:

• Unit Tests: Individual component validation
• Integration Tests: Cross-component interaction verification
• Multi-Asset Tests: Portfolio strategy validation
• Edge Cases: Boundary condition handling

Reports

Key Deliverables

• Technical Implementation: Complete source code with modular architecture

• Documentation: Comprehensive README, docstrings, and inline comments

• Example Notebook: Detailed Jupyter notebook (example_usage.ipynb) demonstrating:

  • Data loading from Yahoo Finance
  • Built-in strategy testing (Buy & Hold, MA Cross, Mean Reversion)
  • Custom strategy creation (RSI, Momentum, Simple ML)
  • Performance comparison and visualization
  • Walk-forward analysis and parameter optimization
  • Professional conclusions and insights

• Test Suite: Unit and integration tests covering all components

• Package Configuration: Professional pyproject.toml enabling pip installation

Performance Results

Testing on Apple (AAPL) stock from 2020-2023 yielded:

Strategy	Total Return	Sharpe Ratio	Max Drawdown	Trades
Momentum	151.55%	1.21	-51.35%	1005
Simple ML	19.06%	0.29	-32.37%	1005
RSI	2.47%	0.02	-34.82%	1005

Results demonstrate the framework's capability to differentiate strategy performance and provide actionable insights for investment decision-making.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors / Contact

M2 Quantitative Finance - Université Paris Dauphine-PSL

• Mariano BENJAMIN
• Noah CHIKHI
• Antoine Moniz

For issues, discussions, or contributions, please open an issue or pull request on the project's GitHub page