#volest#
A complete set of volatility estimators based on Euan Sinclair's Volatility Trading (http://www.amazon.com/gp/product/0470181990/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=0470181990&linkCode=as2&tag=quantfinancea-20&linkId=OTGFUXGQ72RGDYYI).
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
Also creates a term sheet with all the metrics printed to a PDF
Example usage:
import volest
import datetime as dt
# variables to initialize class
ticker = 'JPM'
start = dt.datetime(2013, 1, 1)
end = dt.datetime(2013, 12, 31)
type = 'GarmanKlass'
#variables for the instances
window=30
windows=[30, 60, 90, 120]
quantiles=[0.25, 0.75]
bins=100
normed=True
bench='^GSPC'
open=False
# initialize class
vol = volest.VolatilityEstimator(ticker, start, end, type)
# call plt.show() on any of the below
fig, plt = vol.cones(windows=windows, quantiles=quantiles)
fig, plt = vol.rolling_quantiles(window=window, quantiles=quantiles)
fig, plt = vol.rolling_extremes(window=window)
fig, plt = vol.rolling_descriptives(window=window)
fig, plt = vol.histogram(window=window, bins=bins, normed=normed)
fig, plt = vol.benchmark_compare(window=window, bench=bench)
fig, plt = vol.benchmark_correlation(window=window, bench=bench)
# prints regression statistics
print vol.benchmark_regression(window=window, bench=bench)
# creates a pdf term sheet with all metrics
vol.term_sheet(window, windows, quantiles, bins, normed, bench)Hit me on twitter with comments, questions, issues @jasonstrimpel