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https://github.com/santoshlite/Empyrial
An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎
https://github.com/santoshlite/Empyrial
backtesting finance fintech futures investment investment-analysis investment-portfolio portfolio-analysis portfolio-management portfolio-optimization python quant quantitative-analysis quantitative-finance stock stock-data stock-market
Last synced: about 2 months ago
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An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎
- Host: GitHub
- URL: https://github.com/santoshlite/Empyrial
- Owner: santoshlite
- License: mit
- Created: 2021-03-08T12:25:49.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-08-26T15:52:53.000Z (3 months ago)
- Last Synced: 2024-09-17T15:11:17.656Z (about 2 months ago)
- Topics: backtesting, finance, fintech, futures, investment, investment-analysis, investment-portfolio, portfolio-analysis, portfolio-management, portfolio-optimization, python, quant, quantitative-analysis, quantitative-finance, stock, stock-data, stock-market
- Language: Python
- Homepage: https://empyrial.gitbook.io/empyrial/
- Size: 91 MB
- Stars: 917
- Watchers: 30
- Forks: 124
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- License: license.txt
- Code of conduct: code_of_conduct.md
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README
# By Investors, For Investors.
[](https://colab.research.google.com/drive/1NqTkkP2u1p1g8W8erU-Y-rSSVbPUDvq2?usp=sharing)
Want to read this in **Mandarin 🇨🇳**? Click [**here**](README_CN.md)
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](#installation) |
| 2. [Features](#features) |
| 3. [Documentation](#documentation) |
| 4. [Usage example](#usage) |
| 5. [Download the tearsheet](#download-the-tearsheet) |
| 6. [Contribution and Issues](#contribution-and-issues) |
| 7. [Contributors](#contributors) |
| 8. [Contact](#contact) |
| 9. [License](#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](https://osxdaily.com/2014/02/12/install-command-line-tools-mac-os-x/)._
_Note: Windows users will need to install C++. ([download](https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=16), [install instructions](https://drive.google.com/file/d/0B4GsMXCRaSSIOWpYQkstajlYZ0tPVkNQSElmTWh1dXFaYkJr/view))_
## Features
| Feature 📰 | Status |
| -- | ------ |
| Engine (backtesting + performance analysis) | :star: [Released](https://github.com/ssantoshp/Empyrial/releases/tag/1.2.4) on May 30, 2021 |
| Optimizer | :star: [Released](https://github.com/ssantoshp/Empyrial/releases/tag/1.3.6) on Jun 7, 2021 |
| Rebalancing | :star: [Released](https://github.com/ssantoshp/Empyrial/releases/tag/1.5.0) on Jun 27, 2021 |
| Risk manager | :star: [Released](https://github.com/ssantoshp/Empyrial/releases/tag/v1.7.3) on Jul 5, 2021 |
| Sandbox | :star: [Released](https://github.com/ssantoshp/Empyrial/releases/tag/v1.9.1) on Jul 17, 2021 |
| Support for custom data | :star: [Released](https://github.com/ssantoshp/Empyrial/releases/tag/v2.1.3) on Aug 12, 2023 |
## Documentation
[Full documentation](https://empyrial.gitbook.io/empyrial/) (website)
## Usage
### Empyrial Engine
```py
from empyrial import empyrial, Engine
portfolio = Engine(
start_date = "2018-08-01",
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)
```
### Use custom data
See doc [here](https://empyrial.gitbook.io/empyrial/using-custom-data) to learn how to do this.
### 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```
```py
from empyrial import empyrial, Engine
portfolio = Engine(
start_date = "2018-08-01",
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.
```py
from empyrial import empyrial, Engine
portfolio = Engine(
start_date = "2018-08-01",
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.
```py
from empyrial import empyrial, Engine
portfolio = Engine(
start_date = "2018-08-01",
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](https://empyrial.gitbook.io/empyrial/optimization/global-efficient-frontier)
- ```"MEANVAR"```: **Mean-Variance** [Example](https://empyrial.gitbook.io/empyrial/optimization/mean-variance)
- ```"HRP"```: **Hierarchical Risk Parity** [Example](https://empyrial.gitbook.io/empyrial/optimization/hierarchical-risk-parity)
- ```"MINVAR"```: **Minimum-Variance** [Example](https://empyrial.gitbook.io/empyrial/optimization/minimum-variance)
```py
from empyrial import empyrial, Engine
portfolio = Engine(
start_date = "2018-08-01",
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](https://empyrial.gitbook.io/empyrial/risk-management/max-drawdown)
- **Take Profit**: ```{"Take Profit" : 0.4}``` [Example](https://empyrial.gitbook.io/empyrial/risk-management/take-profit)
- **Stop Loss**: ```{"Stop Loss" : -0.2}``` [Example](https://empyrial.gitbook.io/empyrial/risk-management/stop-loss)
```py
from empyrial import empyrial, Engine
portfolio = Engine(
start_date = "2018-08-01",
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.
```py
from empyrial import get_report, Engine
portfolio = Engine(
start_date = "2018-08-01",
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](https://docs.github.com/en/get-started/quickstart/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](https://github.com/ssantoshp/Empyrial/issues/new/choose).
Smaller improvements (e.g., document improvements, bugfixes) can be handled by the Pull Request process of GitHub: [pull requests](https://github.com/ssantoshp/Empyrial/pulls).
* To contribute to the code, you will need to do the following:
* [Fork](https://docs.github.com/en/get-started/quickstart/fork-a-repo#forking-a-repository) [Empyrial](https://github.com/ssantoshp/Empyrial) - Click the **Fork** button at the upper right corner of this page.
* [Clone your own fork](https://docs.github.com/en/get-started/quickstart/fork-a-repo#cloning-your-forked-repository). 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](https://help.github.com/articles/syncing-a-fork/)*
* [Create a Pull Request](https://github.com/ssantoshp/Empyrial/pulls) 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](https://allcontributors.org/docs/en/emoji-key)):
[](#contributors-)
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](https://github.com/all-contributors/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**](https://github.com/ranaroussi) for the [**Quantstats library**](https://github.com/ranaroussi/quantstats)
- [**Robert Martin**](https://github.com/robertmartin8) for the [**PyPortfolioOpt**](https://github.com/robertmartin8/PyPortfolioOpt)
## Contact
You are welcome to contact us by email at **[email protected]** or in Empyrial's [discussion space](https://github.com/ssantoshp/Empyrial/discussions)
## License
MIT