empyrial 2.0.2

An Open Source Portfolio Management Framework for Everyone 投资组合管理

By Investors, For Investors.

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Empyrial is a Python-based open-source quantitative investment library dedicated to financial institutions and retail investors, officially released in March 2021. Already used by thousands of people working in the finance industry, Empyrial aims to become an all-in-one platform for portfolio management, analysis, and optimization.

Empyrial empowers portfolio management by bringing the best of performance and risk analysis in an easy-to-understand, flexible and powerful framework.

With Empyrial, you can easily analyze security or a portfolio in order to get the best insights from it. This is mainly a wrapper of financial analysis libraries such as Quantstats and PyPortfolioOpt.

Table of Contents 📖
1. Installation
2. Features
3. Documentation
4. Usage example
5. Download the tearsheet
6. Contribution and Issues
7. Contributors
8. Contact
9. License

Installation

You can install Empyrial using pip:

pip install empyrial

For a better experience, we advise you to use Empyrial on a notebook (e.g., Jupyter, Google Colab)

Note: macOS users will need to install Xcode Command Line Tools.

Note: Windows users will need to install C++. (download, install instructions)

Features

Feature 📰	Status
Engine (backtesting + performance analysis)	:star: Released on May 30, 2021
Optimizer	:star: Released on Jun 7, 2021
Rebalancing	:star: Released on Jun 27, 2021
Risk manager	:star: Released on Jul 5, 2021
Sandbox	:star: Released on Jul 17, 2021

Documentation

Full documentation (website)

Full documentation (PDF)

Usage

Empyrial Engine

from empyrial import empyrial, Engine

portfolio = Engine(
    start_date = "2018-06-09", 
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    weights = [0.2, 0.2, 0.2, 0.2, 0.2],  # equal weighting is set by default
    benchmark = ["SPY"]  # SPY is set by default
)

empyrial(portfolio)

Calendar Rebalancing

A portfolio can be rebalanced for either a specific time period or for specific dates using the rebalance option.

Rebalance for Time Period

Time periods available for rebalancing are 2y, 1y, 6mo, quarterly, monthly

from empyrial import empyrial, Engine

portfolio = Engine(
    start_date = "2018-06-09", 
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    weights = [0.2, 0.2, 0.2, 0.2, 0.2],  # equal weighting is set by default
    benchmark = ["SPY"],  # SPY is set by default
    rebalance = "1y"
)

empyrial(portfolio)

Rebalance for Custom Dates

You can rebalance a portfolio by specifying a list of custom dates.
⚠️ When using custom dates, the first date of the list must correspond with the start_date and the last element should correspond to the end_date which is today's date by default.

from empyrial import empyrial, Engine

portfolio = Engine(
    start_date = "2018-06-09", 
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    weights = [0.2, 0.2, 0.2, 0.2, 0.2],  # equal weighting is set by default
    benchmark = ["SPY"],  # SPY is set by default
    rebalance = ["2018-06-09", "2019-01-01", "2020-01-01", "2021-01-01"]
)

empyrial(portfolio)

Optimizer

The default optimizer is equal weighting. You can specify custom weights, if desired.

from empyrial import empyrial, Engine

portfolio = Engine(
    start_date = "2018-01-01",
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    weights = [0.1, 0.3, 0.15, 0.25, 0.2],   # custom weights
    rebalance = "1y"  # rebalance every year
)

empyrial(portfolio)

You can also use the built-in optimizers. There are 4 optimizers available:

• "EF": Global Efficient Frontier Example
• "MEANVAR": Mean-Variance Example
• "HRP": Hierarchical Risk Parity Example
• "MINVAR": Minimum-Variance Example

from empyrial import empyrial, Engine

portfolio = Engine(
    start_date = "2018-01-01",
    portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"],
    optimizer = "EF",
    rebalance = "1y"  # rebalance every year
)

portfolio.weights

Output:

[0.0, 0.0, 0.0348, 0.9652, 0.0]

We can see that the allocation has been optimized.

Risk Manager

3 Risk Managers are available:

• Max Drawdown: {"Max Drawdown" : -0.3} Example
• Take Profit: {"Take Profit" : 0.4} Example
• Stop Loss: {"Stop Loss" : -0.2} Example

from empyrial import empyrial, Engine

portfolio = Engine(
    start_date = "2018-01-01",
    portfolio= ["BABA", "PDD", "KO", "AMD","^IXIC"], 
    optimizer = "EF",
    rebalance = "1y",  # rebalance every year
    risk_manager = {"Max Drawdown" : -0.2}  # Stop the investment when the drawdown becomes superior to -20%
)

empyrial(portfolio)

Empyrial Outputs

Download the Tearsheet

You can use the get_report() function of Empyrial to generate a tearsheet, and then download this as a PDF document.

from empyrial import get_report, Engine

portfolio = Engine(
      start_date = "2018-01-01",
      portfolio = ["BABA", "PDD", "KO", "AMD","^IXIC"],
      optimizer = "EF",
      rebalance = "1y", #rebalance every year
      risk_manager = {"Stop Loss" : -0.2}
)

get_report(portfolio)

Output:

Stargazers over time

Contribution and Issues

Empyrial uses GitHub to host its source code. Learn more about the Github flow.

For larger changes (e.g., new feature request, large refactoring), please open an issue to discuss first.

• If you wish to create a new Issue, then click here to create a new issue.

Smaller improvements (e.g., document improvements, bugfixes) can be handled by the Pull Request process of GitHub: pull requests.

• To contribute to the code, you will need to do the following:

• Fork Empyrial - Click the Fork button at the upper right corner of this page.

• Clone your own fork. E.g., git clone https://github.com/ssantoshp/Empyrial.git
  If your fork is out of date, then will you need to manually sync your fork: Synchronization method

• Create a Pull Request using your fork as the compare head repository.

You contributions will be reviewed, potentially modified, and hopefully merged into Empyrial.

Contributors

Thanks goes to these wonderful people (emoji key):

Brendan Glancy 💻 🐛

Renan Lopes 💻 🐛

Mark Thebault 💻

Diego Alvarez 💻🐛

Rakesh Bhat 💻

Anh Le 🐛

Tony Zhang 💻

Ikko Ashimine ✒️

QuantNomad 📹

Buckley ✒️💻

Adam Nelsson 💻

Ranjan Grover 🐛💻

This project follows the all-contributors specification. Contributions of any kind are welcome!

Credit

This library has also been made possible because of the work of these incredible people:

• Ran Aroussi for the Quantstats library
• Robert Martin for the PyPortfolioOpt

Contact

You are welcome to contact us by email at [e-mail] or in Empyrial's discussion space

License

MIT