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https://github.com/jasonstrimpel/volatility-trading
A complete set of volatility estimators based on Euan Sinclair's Volatility Trading
https://github.com/jasonstrimpel/volatility-trading
options options-trading python trading volatility volatility-trading
Last synced: 6 days ago
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A complete set of volatility estimators based on Euan Sinclair's Volatility Trading
- Host: GitHub
- URL: https://github.com/jasonstrimpel/volatility-trading
- Owner: jasonstrimpel
- License: gpl-3.0
- Created: 2014-06-14T21:04:23.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2024-10-21T17:00:05.000Z (4 months ago)
- Last Synced: 2025-02-06T19:06:23.628Z (13 days ago)
- Topics: options, options-trading, python, trading, volatility, volatility-trading
- Language: Python
- Homepage:
- Size: 7.77 MB
- Stars: 1,639
- Watchers: 84
- Forks: 381
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-systematic-trading - volest - A complete set of volatility estimators based on Euan Sinclair's Volatility Trading (Alpha Collections / General Alpha)
README
# volest #
## Learn how to apply this code to your own options trading ##
[Getting Started With Python for Quant Finance](https://gettingstartedwithpythonforquantfinance.com) is the cohort-based course and community that will take you from complete beginner to up and running with Python for quant finance in 30 days.
## [A complete set of volatility estimators based on Euan Sinclair's Volatility Trading](https://amzn.to/3UhddGP) ##
The original version incorporated network data acquisition from Yahoo!Finance
from `pandas_datareader`. Yahoo! changed their API and broke `pandas_datareader`.The changes allow you to specify your own data so you're not tied into equity
data from Yahoo! finance. If you're still using equity data, just download
a CSV from finance.yahoo.com and use the `data.yahoo_data_helper` method
to form the data properly.### Volatility estimators include: ###
* Garman Klass
* Hodges Tompkins
* Parkinson
* Rogers Satchell
* Yang Zhang
* Standard DeviationAlso includes
* Skew
* Kurtosis
* CorrelationFor each of the estimators, plot:
* Probability cones
* Rolling quantiles
* Rolling extremes
* Rolling descriptive statistics
* Histogram
* Comparison against arbirary comparable
* Correlation against arbirary comparable
* Regression against arbirary comparableCreate a term sheet with all the metrics printed to a PDF.
### Page 1 - Volatility cones ###
### Page 2 - Volatility rolling percentiles ###
### Page 3 - Volatility rolling min and max ###
### Page 4 - Volatility rolling mean, standard deviation and zscore ###
### Page 5 - Volatility distribution ###
### Page 6 - Volatility, benchmark volatility and ratio###
### Page 7 - Volatility rolling correlation with benchmark ###
### Page 3 - Volatility OLS results ###
Example usage:
```python
from volatility import volest
import yfinance as yf# data
symbol = 'JPM'
bench = 'SPY'
estimator = 'GarmanKlass'# estimator windows
window = 30
windows = [30, 60, 90, 120]
quantiles = [0.25, 0.75]
bins = 100
normed = True# use the yahoo helper to correctly format data from finance.yahoo.com
jpm_price_data = yf.Ticker(symbol).history(period="5y")
jpm_price_data.symbol = symbol
spx_price_data = yf.Ticker(bench).history(period="5y")
spx_price_data.symbol = bench# initialize class
vol = volest.VolatilityEstimator(
price_data=jpm_price_data,
estimator=estimator,
bench_data=spx_price_data
)# call plt.show() on any of the below...
_, plt = vol.cones(windows=windows, quantiles=quantiles)
_, plt = vol.rolling_quantiles(window=window, quantiles=quantiles)
_, plt = vol.rolling_extremes(window=window)
_, plt = vol.rolling_descriptives(window=window)
_, plt = vol.histogram(window=window, bins=bins, normed=normed)_, plt = vol.benchmark_compare(window=window)
_, plt = vol.benchmark_correlation(window=window)# ... or create a pdf term sheet with all metrics in term-sheets/
vol.term_sheet(
window,
windows,
quantiles,
bins,
normed
)```
Hit me on twitter with comments, questions, issues @jasonstrimpel