| Name | plutus-pairtrading JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | None |
| upload_time | 2025-01-10 00:10:15 |
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| author | FOX Techniques |
| requires_python | <4.0,>=3.10 |
| license | MIT |
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# PLUTUS: Pair-Trading Toolkit
[](https://pypi.org/project/irene-sankey/)
[](https://pypi.org/project/plutus-pairtrading/)
[](https://pepy.tech/projects/plutus-pairtrading)
[](https://github.com/fox-techniques/plutus-pairtrading/blob/main/LICENSE)
[](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.
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"description": "# PLUTUS: Pair-Trading Toolkit\n\n[](https://pypi.org/project/irene-sankey/)\n[](https://pypi.org/project/plutus-pairtrading/)\n[](https://pepy.tech/projects/plutus-pairtrading)\n[](https://github.com/fox-techniques/plutus-pairtrading/blob/main/LICENSE)\n[](https://github.com/fox-techniques/plutus-pairtrading)\n\nPLUTUS is a Python-based toolkit for performing pair-trading analysis. This project is designed for educational purposes and provides tools for:\n\n- Fetching and processing financial data.\n- Conducting statistical tests (stationarity and cointegration).\n- Performing feature engineering.\n- Visualizing financial time-series data.\n\n\n## Table of Contents\n\n- [PLUTUS: Pair-Trading Toolkit](#plutus-pair-trading-toolkit)\n - [Table of Contents](#table-of-contents)\n - [Documentation](#documentation)\n - [Key Features](#key-features)\n - [Installation](#installation)\n - [Quick Start](#quick-start)\n - [Contribution](#contribution)\n - [License](#license)\n \n## Documentation\n\nThe full documentation is available on **[GitHub Pages](https://fox-techniques.github.io/plutus-pairtrading/)**.\n\n## Key Features\n\n**1. Data Acquisition**\n\n - Fetch historical financial data using Yahoo Finance API.\n - Store and manage time-series data in a structured format.\n - Combine and preprocess data for analysis.\n\n**2. Statistical Tests**\n - Stationarity Tests\n\n - Augmented Dickey-Fuller Test (ADF): Tests whether a time series is stationary.\n - Phillips-Perron Test (PP): Handles autocorrelations and heteroskedasticity.\n - KPSS Test: Tests for trend stationarity.\n\n - Cointegration Tests\n\n - Engle-Granger Test: Identifies long-term equilibrium relationships.\n - Phillips-Ouliaris Test: Handles residual-based cointegration testing.\n - Johansen Test: Detects multiple cointegration vectors.\n\n**3. Feature Engineering**\n\n - Compute periodic returns (daily, weekly, monthly).\n - Apply logarithmic and exponential transformations.\n - Calculate correlation matrices and filter securities based on thresholds.\n - Identify cointegrated pairs for pair trading.\n\n**4. Data Visualization**\n\n - Plot financial time-series data.\n - Generate dual-axis plots for comparing securities.\n - Visualize correlation matrices.\n - Plot autocorrelation and partial autocorrelation.\n\n\n## Installation\n\nInstall **Plutus Pair-Trading Toolkit** using pip:\n\n```bash\npip install plutus-pairtrading\n```\n\n> **Note**: Requires Python 3.10 or above.\n\n\n## Quick Start\n\nHere\u2019s a quick example to use **PLUTUS** pair-trading tookit.\n\n```python\n import plutus_pairtrading.data_acquisitions as dacq\n import plutus_pairtrading.data_generations as dgen\n import plutus_pairtrading.data_visualizations as dviz\n\n # Fetch stock data for multiple securities\n AAPL_df = dacq.fetch_yahoo_finance_data(\"AAPL\", start_date=\"2015-01-01\", end_date=\"2021-01-01\")\n MSFT_df = dacq.fetch_yahoo_finance_data(\"MSFT\", start_date=\"2015-01-01\", end_date=\"2021-01-01\")\n GOOG_df = dacq.fetch_yahoo_finance_data(\"GOOG\", start_date=\"2015-01-01\", end_date=\"2021-01-01\")\n TSLA_df = dacq.fetch_yahoo_finance_data(\"TSLA\", start_date=\"2015-01-01\", end_date=\"2021-01-01\")\n\n # Combine the data into a single DataFrame\n stock_df = dacq.combine_dataframes([AAPL_df, MSFT_df, GOOG_df, TSLA_df])\n\n # Perform pair identification\n pairs_df = dgen.pairs_identification(\n data=stock_df,\n stationarity_method=\"ADF\",\n cointegration_method=\"phillips-ouliaris\",\n stationarity_significance_level=0.05,\n coint_significance_level=0.05,\n )\n\n # Display the identified pairs\n print(pairs_df)\n```\n\n## Contribution\n\nWe welcome contributions! Visit our [Github](https://github.com/fox-techniques/plutus-pairtrading) repository, and to contribute:\n\n1. Fork the repository.\n2. Create a branch (`git checkout -b feature/NewFeature`).\n3. Commit your changes (`git commit -m 'Add NewFeature'`).\n4. Push to the branch (`git push origin feature/NewFeature`).\n5. Open a Pull Request.\n\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](https://github.com/fox-techniques/plutus-pairtrading/blob/main/LICENSE) file for details.\n\n",
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