pybacktestchain-ev 0.1.2

Improved pybacktestchain library with interactive UI and features added (risk measures, trading strategies, multiple asset classes)

pybacktestchain-ev

Package for the Python Course based on pybacktestchain library by J. Imbet.

Overview

pybacktestchain-ev is a Python package designed to facilitate portfolio backtesting using various trading strategies. The package incorporates risk management tools, blockchain-based backtest storage, and a user-friendly interface built with Streamlit.

The package includes:

• A fully interactive Streamlit-based user interface (UI).
• Support for multiple asset classes (stocks, commodities, FX, fixed income, etc.).
• Customizable backtesting parameters and risk management tools.
• Pre-processing capabilities for data compatibility across all time zones.

Installation

You can install the package via pip:

$ pip install pybacktestchain-ev

Features

• Interactive UI: A Streamlit-based interface for configuring backtests, visualizing results, and analyzing performance and risk metrics.
• Portfolio Backtesting: Supports backtesting of standard trading strategies:
  1. Equal Weight
  2. Minimum Variance
  3. Maximum Sharpe Ratio
  4. Risk Parity
• Customizable Transaction Costs: Allows precise configuration of trading fees for realistic simulations.
• Risk Management:
  • Includes stop-loss and take-profit mechanisms with customizable thresholds.
  • Computes Value-at-Risk (VaR) and Expected Shortfall (ES) with user-defined confidence levels.
• Pre-Processing:
  • Handles data across various asset classes and ensures compatibility across time zones.
• Blockchain Integration: Stores backtest results in a tamper-proof blockchain for future reference.

Usage

Running the Streamlit Interface

To start the Streamlit interface:

$ streamlit run <your_app_file>.py

Configuring a Backtest

1. Select Tickers: Enter assets' tickers (comma-separated) to define the universe (supports all asset classes).
2. Set Parameters:
   • Initial cash amount.
   • Start and end dates for the backtest.
   • Choose a strategy:
     • Equal Weight
     • Minimum Variance
     • Maximum Sharpe Ratio
     • Risk Parity
   • Configure risk management:
     • Stop-loss and take-profit thresholds.
     • Transaction costs.
     • Confidence levels for VaR and Expected Shortfall.
3. Run the Backtest: Click the "Run Backtest" button.

Viewing Results

• Summary Statistics: Includes key metrics for the backtested portfolio.
• Visualizations:
  • Portfolio value and returns over time.
  • Distribution of returns.
  • Correlation heatmap of asset returns.
• Risk Metrics: Displays VaR and Expected Shortfall with selected confidence levels.
• Transaction Log: Provides detailed insights into executed trades.
• Blockchain Storage: Saves each backtest under a randomly generated name for future reference.

Blockchain Storage

Each backtest is saved in a blockchain file under a randomly generated name. This ensures results are tamper-proof and can be referenced later. Blockchain files are stored in the blockchain/ directory within the package folder.

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

pybacktestchain-ev was created by Enzo Volpato. It is licensed under the terms of the MIT license.

Credits

pybacktestchain-ev was created with cookiecutter and the py-pkgs-cookiecutter template.