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PLUTUS: Pair-Trading Toolkit

PLUTUS Flyer

Python PyPI - Version PyPI Downloads License: MIT GitHub

PLUTUS is a Python-based toolkit for performing pair-trading analysis. This project is designed for educational purposes and provides analysis tools for:

  • Fetching and processing financial data.
  • Conducting statistical tests (stationarity and cointegration).
  • Performing feature engineering.
  • Visualizing financial time-series data.

Key Features

1. Data Acquisition

  • Fetch historical financial data using Yahoo Finance API.
  • Store and manage time-series data in a structured format.
  • Combine and preprocess data for analysis.

2. Statistical Tests

  • Stationarity Tests

    • Augmented Dickey-Fuller Test (ADF) tests whether a time series is stationary.
    • Phillips-Perron Test (PP) handles autocorrelations and heteroskedasticity.
    • KPSS tests for trend stationarity.

  • Cointegration Tests

    • Engle-Granger identifies long-term equilibrium relationships.
    • Phillips-Ouliaris handles residual-based cointegration testing.
    • Johansen Test detects multiple cointegration vectors.

3. Feature Engineering

  • Compute periodic returns (daily, weekly, monthly).
  • Apply logarithmic and exponential transformations.
  • Calculate correlation matrices and filter securities based on thresholds.
  • Identify cointegrated pairs for pair trading.

4. Data Visualization

  • Plot financial time-series data.
  • Generate dual-axis plots for comparing securities.
  • Visualize correlation matrices.
  • Plot autocorrelation and partial autocorrelation.

Explore the documentation to learn how to customize and make the most of PLUTUS Pair-Trading Toolkit for your project!