Quantitative Backtesting and Validation Framework

Overview

• Research-oriented backtesting and validation for the development and evaluation of portfolios and systematic trading strategies
• Currently focused on U.S.-based equities and derivatives (primarily SPY-related assets)

Supported Asset Classes

• Stocks/ETFs

Supported Time Horizons

• Medium frequency (daily/hourly)
• Low frequency (1mo/3mo)

Supported Strategy Classes

• Time-series strategies
  • Mean reversion
  • Cointegration (statistical arbitrage)
  • Momentum/ trend following
• Cross-sectional strategies
  • Mean reversion
  • Momentum/ trend following

Methodology

• Walk-forward testing (train/validation/test periods)
• Functional framework for modular experimentation
• Implements regime classification
• Utilizes common portfolio constraints
• Vectorized operations (Pandas/NumPy)
• Multiprocessing (joblib)
• Bayesian optimization (optuna)
• Risk-adjusted performance metrics used for assessment
• Supports graphs for visualization along with HTML export (Plotly)
• Uses packages (scikit-learn, PyTorch) to allow applying learning methods to all parts of the testing pipeline
• Execution costs, slippage, turnover costs included.

Quickstart

Create the virtual environment

python -m venv .venv

Activate the virtual environment

Windows (PowerShell)

.venv\Scripts\Activate.ps1

macOS / Linux (bash)

source .venv/bin/activate

Install necessary modules

python -m pip install -e

Entry point to execute backtester

python -m run

Results

• Regime based strategy implementation of mean reversion, momentum trending, cross asset mean reversion, cross asset momentum trending
• Multi asset cointegration
• Learning based regime models
• Learning based parameter optimization

Results emphasize out-of-sample performance and regime stability rather than raw metrics optimization.

Future goals

• Implementation of a broader range of factor models
• More explicit Bayesian filtering with a detailed implementation of the Kalman filter and path dependent models
• Greater focus on volatility including the implementation of volatility targeting and classification of asset volatility

Repository Structure

• src : Core strategy logic
• docs : Successful portfolios' trading logs, equity curves, and related analysis tools
  • results: Human-readable results.
• tests: Example of package usage and tests of strategies.
• data: cache data storage
• artifacts: cache results storage

For a comprehensive discussion of strategy logic and methodology, see https://qgspinor.com/projects/alp_gen