volest

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

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

Page 2 - Volatility rolling percentiles

Capture-2

Page 3 - Volatility rolling min and max

Capture-3

Page 4 - Volatility rolling mean, standard deviation and zscore

Capture-4

Page 5 - Volatility distribution

Capture-5

Page 6 - Volatility, benchmark volatility and ratio

Capture-6

Page 7 - Volatility rolling correlation with benchmark

Capture-7

Page 3 - Volatility OLS results

Capture-8

Example usage:


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
)

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