https://github.com/fox-techniques/plutus-pairtrading

PLUTUS: Python-based toolkit for performing pair-trading analysis
https://github.com/fox-techniques/plutus-pairtrading

cointegration-analysis feature-engineering pair-trading plotly python stationarity-test trading yahoo-finance-api

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PLUTUS: Python-based toolkit for performing pair-trading analysis

• Host: GitHub
• URL: https://github.com/fox-techniques/plutus-pairtrading
• Owner: fox-techniques
• License: mit
• Created: 2024-12-10T13:21:10.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-02-16T22:29:05.000Z (8 months ago)
• Last Synced: 2025-09-09T00:45:06.289Z (about 1 month ago)
• Topics: cointegration-analysis, feature-engineering, pair-trading, plotly, python, stationarity-test, trading, yahoo-finance-api
• Language: Python
• Homepage: https://fox-techniques.github.io/plutus-pairtrading/
• Size: 1.48 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: docs/CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: docs/CODE_OF_CONDUCT.md
  • Codeowners: .github/CODEOWNERS
  • Security: docs/SECURITY.md
  • Support: docs/SUPPORT.md

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README

          
# PLUTUS: Pair-Trading Toolkit

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[![GitHub](https://img.shields.io/badge/GitHub-plutus--pairtrading-181717?logo=github)](https://github.com/fox-techniques/plutus-pairtrading)

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

- Fetching and processing financial data.

- Conducting statistical tests (stationarity and cointegration).

- Performing feature engineering.

- Visualizing financial time-series data.

## Table of Contents

- [PLUTUS: Pair-Trading Toolkit](#plutus-pair-trading-toolkit)

  - [Table of Contents](#table-of-contents)

  - [Documentation](#documentation)

  - [Key Features](#key-features)

  - [Installation](#installation)

  - [Quick Start](#quick-start)

  - [Contribution](#contribution)

  - [License](#license)

  

## Documentation

The full documentation is available on **[GitHub Pages](https://fox-techniques.github.io/plutus-pairtrading/)**.

## 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 Test: Tests for trend stationarity.

   - Cointegration Tests

       - Engle-Granger Test: Identifies long-term equilibrium relationships.

       - Phillips-Ouliaris Test: 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.

## Installation

Install **Plutus Pair-Trading Toolkit** using pip:

```bash

pip install plutus-pairtrading

```

> **Note**: Requires Python 3.10 or above.

## Quick Start

Here’s a quick example to use **PLUTUS** pair-trading tookit.

```python

  import plutus_pairtrading.data_acquisitions as dacq

  import plutus_pairtrading.data_generations as dgen

  import plutus_pairtrading.data_visualizations as dviz

  # Fetch stock data for multiple securities

  AAPL_df = dacq.fetch_yahoo_finance_data("AAPL", start_date="2015-01-01", end_date="2021-01-01")

  MSFT_df = dacq.fetch_yahoo_finance_data("MSFT", start_date="2015-01-01", end_date="2021-01-01")

  GOOG_df = dacq.fetch_yahoo_finance_data("GOOG", start_date="2015-01-01", end_date="2021-01-01")

  TSLA_df = dacq.fetch_yahoo_finance_data("TSLA", start_date="2015-01-01", end_date="2021-01-01")

  # Combine the data into a single DataFrame

  stock_df = dacq.combine_dataframes([AAPL_df, MSFT_df, GOOG_df, TSLA_df])

  # Perform pair identification

  pairs_df = dgen.pairs_identification(

      data=stock_df,

      stationarity_method="ADF",

      cointegration_method="phillips-ouliaris",

      stationarity_significance_level=0.05,

      coint_significance_level=0.05,

  )

  # Display the identified pairs

  print(pairs_df)

```

## Contribution

We welcome contributions! Visit our [Github](https://github.com/fox-techniques/plutus-pairtrading) repository, and to contribute:

1. Fork the repository.

2. Create a branch (`git checkout -b feature/NewFeature`).

3. Commit your changes (`git commit -m 'Add NewFeature'`).

4. Push to the branch (`git push origin feature/NewFeature`).

5. Open a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/fox-techniques/plutus-pairtrading/blob/main/LICENSE) file for details.