def main(number_of_obs=None):
# Call the function from edhec_risk_kit
hfi = erk.get_hfi_returns()
# Semi deviation
hfi.std(ddof=0) # average deviation from the mean
hfi[hfi < 0].std(ddof=0)
erk.semideviation[hfi]
# VaR and CVaR
# Value at Risk and Conditional Value at Risk
# Historic VaR
# What's value at risk at 5%: 5 percentile VaR
np.percentile(hfi, 5, axis=0)
erk.var_historic(hfi)
# Parametric VaR - Gaussian parametric
z = norm.ppf(
.05
) # normal dist: return a z-score where half of the distribution lies below it
-hfi.mean() + z * hfi.std(ddof=0)
# Modified Cornish - Fisher VaR
var_list = [
erk.var_gaussian(hfi),
erk.var_gaussian(hfi, modified=True),
erk.var_historic(hfi)
]
comparison = pd.concat(var_list)
comparison.columns = ['Gaussian', 'Cornish-fisher', 'Historic']
comparison.plot.bar(title='EDHEC Hedfe Fund Indicies: VaR')
# Beyond Car aka. CVaR
erk.cvar_historic(hfi)
def main(number_of_obs=None):
# Call the function from edhec_risk_kit
hfi = erk.get_hfi_returns()
print(hfi.head())
# Measure skewness
print(pd.concat([hfi.mean(), hfi.median(), hfi.mean() > hfi.median()], axis = 'columns'))
erk.skewness(hfi).sort_values()
# Comparing built in method (scipy) with our own function
scipy.stats.skew(hfi)
erk.skewness(hfi)
#hfi.shape()
normal_rets = np.random.normal(0, .15, size = (263, 1)) # Random generated returns in the form of 263x1 array
erk.skewness(normal_rets)
# Measure kurtosis
erk.kurtosis(normal_rets)
scipy.stats.kurtosis(normal_rets) # Excess kurtosis
# Jarque Bera Test
scipy.stats.jarque_bera(normal_rets)
scipy.stats.jarque_bera(hfi)
erk.is_normal(normal_rets)
erk.is_normal(hfi)
hfi.aggregate(erk.is_normal) # Take the given function, apply it on every col and give result
ffme = erk.get_ffme_returns()
erk.skewness(ffme)
erk.kurtosis(ffme)
erk.is_normal(ffme)
ffme.aggregate(erk.is_normal)
import pandas as pd
import edhec_risk_kit as erk
%load_ext autoreload
%autoreload 2
%matplotlib inline
hfi = erk.get_hfi_returns()
hfi.std(ddof=0)
#filter those returns which are <0, its a boolean mask
hfi[hfi<0].std(ddof=0)
erk.semideviation(hfi)
import numpy as np
#we want to run percentile only on columns which axis=0
np.percentile(hfi,5, axis=0)
# how to interpret percentile info:
# first column is Convertible Arbitrage.
# There is 5% chance that in any given month Convertible Arbitrage
# can lose 1.5% (-0.01576) or worse
def var_historic(r, level=5):
"""
VaR Historic
"""
if isinstance(r, pd.DataFrame):
return r.aggregate(var_historic, level=level)
elif isinstance (r, pd.Series):
ann_ret3 = er.annualize_rets(rets.loc['1999-01':'2015-12', 'SmallCap'], 12) ann_vol3 = er.annualize_vol(rets.loc['1999-01':'2015-12', 'SmallCap'], 12) ann_ret4 = er.annualize_rets(rets.loc['1999-01':'2015-12', 'LargeCap'], 12) ann_vol4 = er.annualize_vol(rets.loc['1999-01':'2015-12', 'LargeCap'], 12) dd = er.drawdown(rets.loc['1999-01':'2015-12':, 'SmallCap']) min(dd.Drawdown) dd[dd.Drawdown == min(dd.Drawdown)] dd2 = er.drawdown(rets.loc['1999-01':'2015-12':, 'LargeCap']) min(dd2.Drawdown) dd2[dd2.Drawdown == min(dd2.Drawdown)] #%% hfi = er.get_hfi_returns() sd = er.semideviation(hfi.loc['2009-01':, :]) sd.sort_values() er.skewness(hfi.loc['2009-01':, :]).sort_values() er.kurtosis(hfi.loc['2000-01':, :]).sort_values() #%% # ============================================================================= # 1. 15.2 # 2. 33.7 # 3. 9.8 # 4. 19.5 # 5. 11.4 # 6. 22.9