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

Key dates

PyPI data
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  • Released:
Latest release

1 maintainer

PyPI data
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Avatar for vksga from gravatar.com vksga

Credits

Author: Varun

License

Proprietary

Requires

Python <3.15, >=3.11

Provides Extra

transformer notebook test dev all

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