Empyrical Reloaded

https://github.com/stefan-jansen/empyrical-reloaded
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Empyrical Reloaded is a Python library for calculating common financial return and risk metrics,...
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License:Apache-2.0
Update:4 months ago
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### Install empyrical-reloaded with pip or conda

Source: https://github.com/stefan-jansen/empyrical-reloaded/blob/main/README.md

Instructions for installing the empyrical-reloaded package using either pip or conda. It also lists the required and optional dependencies.

```bash
pip install empyrical-reloaded
```

```bash
conda install empyrical-reloaded -c conda-forge
```

```bash
pip install empyrical-reloaded[yfinance]
```

```bash
pip install empyrical-reloaded[datreader]
```

```bash
pip install empyrical-reloaded[yfinance,datreader]
```

--------------------------------

### Download and Visualize S&P 500 Returns using Empyrical and yfinance

Source: https://github.com/stefan-jansen/empyrical-reloaded/blob/main/README.md

This Python snippet demonstrates downloading historical S&P 500 returns using the empyrical library, which in turn uses yfinance to fetch data from Yahoo Finance. It then visualizes the time series and histogram of these daily returns using seaborn and matplotlib. This requires the 'yfinance', 'seaborn', and 'matplotlib' libraries to be installed.

```python
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')
```

--------------------------------

### Run Tests using pytest

Source: https://github.com/stefan-jansen/empyrical-reloaded/blob/main/README.md

This bash command executes the tests for the Empyrical Reloaded project using pytest. Ensure that the project requirements, including 'pytest>=6.2.0', are installed before running this command.

```bash
pytest tests
```

--------------------------------

### Download Fama-French risk factors using empyrical

Source: https://github.com/stefan-jansen/empyrical-reloaded/blob/main/README.md

Demonstrates how to download Fama-French risk factors using the empyrical package's utility function. This requires the optional pandas-datareader dependency.

```python
import pandas as pd
import empyrical as emp

risk_factors = emp.utils.get_fama_french()

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

--------------------------------

### Calculate max drawdown and alpha/beta using NumPy

Source: https://github.com/stefan-jansen/empyrical-reloaded/blob/main/README.md

Demonstrates how to calculate the maximum drawdown and the alpha and beta of a given set of returns against benchmark returns using NumPy arrays. Requires the empyrical package.

```python
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)
```

--------------------------------

### Calculate rolling max drawdown using NumPy

Source: https://github.com/stefan-jansen/empyrical-reloaded/blob/main/README.md

Shows how to compute the rolling maximum drawdown for a given array of returns using a specified window size. Requires the empyrical package and NumPy.

```python
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)
```

--------------------------------

### Calculate capture ratio using Pandas

Source: https://github.com/stefan-jansen/empyrical-reloaded/blob/main/README.md

Illustrates how to calculate a capture ratio using Pandas Series for both the primary returns and the factor returns. This function is part of the empyrical package.

```python
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)
```
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