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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

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A complete set of volatility estimators based on Euan Sinclair's Volatility Trading

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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 Deviation



Also includes



* Skew

* Kurtosis

* Correlation



For 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 comparable



Create a term sheet with all the metrics printed to a PDF.



### Page 1 - Volatility cones ###

![Capture-1](docs/img/1.png)



### Page 2 - Volatility rolling percentiles ###

![Capture-2](docs/img/2.png)



### Page 3 - Volatility rolling min and max ###

![Capture-3](docs/img/3.png)



### Page 4 - Volatility rolling mean, standard deviation and zscore ###

![Capture-4](docs/img/4.png)



### Page 5 - Volatility distribution ###

![Capture-5](docs/img/5.png)



### Page 6 - Volatility, benchmark volatility and ratio###

![Capture-6](docs/img/6.png)



### Page 7 - Volatility rolling correlation with benchmark ###

![Capture-7](docs/img/7.png)



### Page 3 - Volatility OLS results ###

![Capture-8](docs/img/8.png)



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