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Common financial return and risk metrics in Python.

Installation

empyrical requires Python 3.9+. You can install it using pip:

pip install empyrical-reloaded

or conda from the conda-forge channel

conda install empyrical-reloaded -c conda-forge

empyrical requires and installs the following packages while executing the above commands:

Note that Numpy>=2.0 requires pandas>=2.2.2. If you are using an older version of pandas, you may need to upgrade accordingly, otherwise you may encounter compatibility issues.

Optional dependencies include yfinance to download price data from Yahoo! Finance and pandas-datareader to access Fama-French risk factors and FRED treasury yields.

Note that pandas-datareader is not compatible with Python>=3.12.

To install the optional dependencies, use:

pip install empyrical-reloaded[yfinance]

or

pip install empyrical-reloaded[datreader]

or

pip install empyrical-reloaded[yfinance,datreader]

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 return and 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:

Note: requires optional dependency pandas-datareader - see installation instructions above.gst

import pandas as pd
import empyrical as emp

risk_factors = emp.utils.get_fama_french()

pd.concat([risk_factors.head(), risk_factors.tail()])

Mkt - RF
SMB
HML
RF
Mom
Date
1970 - 01 - 02
00: 00:00 + 00: 00
0.0118
0.0129
0.0101
0.00029 - 0.0340
1970 - 01 - 05
00: 00:00 + 00: 00
0.0059
0.0067
0.0072
0.00029 - 0.0153
1970 - 01 - 06
00: 00:00 + 00: 00 - 0.0074
0.0010
0.0021
0.00029
0.0038
1970 - 01 - 07
00: 00:00 + 00: 00 - 0.0015
0.0040 - 0.0033
0.00029
0.0011
1970 - 01 - 0
8
00: 00:00 + 00: 00
0.0004
0.0018 - 0.0017
0.00029
0.0033
2024 - 03 - 22
00: 00:00 + 00: 00 - 0.0023 - 0.0087 - 0.0053
0.00021
0.0043
2024 - 03 - 25
00: 00:00 + 00: 00 - 0.0026 - 0.0024
0.0088
0.00021 - 0.0034
2024 - 03 - 26
00: 00:00 + 00: 00 - 0.0026
0.0009 - 0.0013
0.00021
0.0009
2024 - 03 - 27
00: 00:00 + 00: 00
0.0088
0.0104
0.0091
0.00021 - 0.0134
2024 - 03 - 28
00: 00:00 + 00: 00
0.0010
0.0029
0.0048
0.00021 - 0.0044

Asset Prices and Benchmark Returns

Empyrical use yfinance to download price data from Yahoo! Finance. To obtain the S&P returns since 1950, use:

Note: requires optional dependency yfinance - see installation instructions above.

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')
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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

pytest tests