plutus-pairtrading 1.1.1

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PLUTUS: Pair-Trading Toolkit

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
  • Table of Contents
  • Documentation
  • Key Features
  • Installation
  • Quick Start
  • Contribution
  • License

Documentation

The full documentation is available on GitHub Pages.

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:

pip install plutus-pairtrading

Note: Requires Python 3.10 or above.

Quick Start

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

  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 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 file for details.