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.
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) | |
Optimizer | |
Rebalancing | |
Risk manager | |
Sandbox | |
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.
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 |
This project follows the all-contributors specification. Contributions of any kind are welcome!
Contact
You are welcome to contact us by email at [email protected] or in Empyrial's discussion space
License
MIT