empyrical computes performance and risk statistics commonly used in quantitative finance
Project description
Common financial return and risk metrics in Python.
Installation
empyrical requires Python 3.7+. You can install it using pip
:
pip install empyrical-reloaded
or conda
:
conda install empyrical-reloaded -c ml4t -c ranaroussi
empyrical requires and installs the following packages while executing the above commands:
- numpy>=1.9.2
- pandas>=1.0.0
- scipy>=0.15.1
- pandas-datareader>=0.4
- yfinance>=0.1.59
Empyrical uses yfinance to download price data from Yahoo! Finance and pandas-datareader to access Fama-French risk factors.
Usage
Simple Statistics
Empyrical computes basic metrics from returns and volatility to alpha and beta, Value at Risk, and Shorpe or Sortino ratios.
import numpy as np
from empyrical import max_drawdown, alpha_beta
returns = np.array([.01, .02, .03, -.4, -.06, -.02])
benchmark_returns = np.array([.02, .02, .03, -.35, -.05, -.01])
# calculate the max drawdown
max_drawdown(returns)
# calculate alpha and beta
alpha, beta = alpha_beta(returns, benchmark_returns)
Rolling Measures
Empyrical also aggregates returna nd risk metrics for rolling windows:
import numpy as np
from empyrical import roll_max_drawdown
returns = np.array([.01, .02, .03, -.4, -.06, -.02])
# calculate the rolling max drawdown
roll_max_drawdown(returns, window=3)
Pandas Support
Empyrical also works with both NumPy arrays and Pandas data structures:
import pandas as pd
from empyrical import roll_up_capture, capture
returns = pd.Series([.01, .02, .03, -.4, -.06, -.02])
factor_returns = pd.Series([.02, .01, .03, -.01, -.02, .02])
# calculate a capture ratio
capture(returns, factor_returns)
-0.147387712263491
Fama-French Risk Factors
Empyrical downloads Fama-French risk factors from 1970 onward:
import empyrical as emp
risk_factors = emp.utils.get_fama_french()
risk_factors.head().append(risk_factors.tail())
Mkt-RF SMB HML RF Mom
Date
1970-01-02 00:00:00+00:00 0.0118 0.0131 0.0100 0.00029 -0.0341
1970-01-05 00:00:00+00:00 0.0059 0.0066 0.0072 0.00029 -0.0152
1970-01-06 00:00:00+00:00 -0.0074 0.0010 0.0020 0.00029 0.0040
1970-01-07 00:00:00+00:00 -0.0015 0.0039 -0.0032 0.00029 0.0011
1970-01-08 00:00:00+00:00 0.0004 0.0018 -0.0015 0.00029 0.0033
2021-02-22 00:00:00+00:00 -0.0112 -0.0009 0.0314 0.00000 -0.0325
2021-02-23 00:00:00+00:00 -0.0015 -0.0128 0.0090 0.00000 -0.0185
2021-02-24 00:00:00+00:00 0.0115 0.0120 0.0134 0.00000 -0.0007
2021-02-25 00:00:00+00:00 -0.0273 -0.0112 0.0087 0.00000 -0.0195
2021-02-26 00:00:00+00:00 -0.0028 0.0072 -0.0156 0.00000 0.0195
Asset Prices and Benchmark Returns
Empyrical yfinance to download price data from Yahoo! Finance. To obtain the S&P returns since 1950, use:
import empyrical as emp
symbol = '^GSPC'
returns = emp.utils.get_symbol_returns_from_yahoo(symbol,
start='1950-01-01')
import seaborn as sns # requires separate installation
import matplotlib.pyplot as plt # requires separate installation
fig, axes = plt.subplots(ncols=2, figsize=(14, 5))
with sns.axes_style('whitegrid'):
returns.plot(ax=axes[0], rot=0, title='Time Series', legend=False)
sns.histplot(returns, ax=axes[1], legend=False)
axes[1].set_title('Histogram')
sns.despine()
plt.tight_layout()
plt.suptitle('Daily S&P 500 Returns')
Documentation
See the documentation for details on the API.
Support
Please open an issue for support.
Contributing
Please contribute using Github Flow. Create a branch, add commits, and open a pull request.
Testing
- install requirements
- "pytest>=6.2.0",
pytest tests
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for empyrical_reloaded-0.5.8-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5e4ca1dc963d00494c6042340f73fb8c78ad7a8ff81f7589d718e8303bdb8fd4 |
|
MD5 | 04ac0b02d943e267cd61b3d6edc31f4c |
|
BLAKE2b-256 | 570f14e7ed76ba6647274e2a4049ca3abd425590b9a16baefcd407301ea40ecb |