Want to read this in Chinese? Click here
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 |
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)
| Feature 📰 | Status |
|---|---|
| Engine (backtesting + performance analysis) | ⭐ Released on May 30, 2021 |
| Optimizer | ⭐ Released on Jun 7, 2021 |
| Rebalancing | ⭐ Released on Jun 27, 2021 |
| Risk manager | ⭐ Released on Jul 5, 2021 |
| Sandbox | ⭐ Released on Jul 17, 2021 |
Full documentation (website)
Full documentation (PDF)
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)A portfolio can be rebalanced for either a specific time period or for specific dates using the rebalance option.
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)You can rebalance a portfolio by specifying a list of custom dates.
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)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.weightsOutput:
[0.0, 0.0, 0.0348, 0.9652, 0.0]
We can see that the allocation has been optimized.
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)
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:
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.
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!
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
You are welcome to contact us by email at santoshpassoubady@gmail.com or in Empyrial's discussion space
MIT