ml-finance-python

edhec_risk_kit_106.py

(4208B)

 import pandas as pd
 import numpy as np
 
 def drawdown(return_series: pd.Series):
     """Takes a time series of asset returns.
        returns a DataFrame with columns for
        the wealth index, 
        the previous peaks, and 
        the percentage drawdown
     """
     wealth_index = 1000*(1+return_series).cumprod()
     previous_peaks = wealth_index.cummax()
     drawdowns = (wealth_index - previous_peaks)/previous_peaks
     return pd.DataFrame({"Wealth": wealth_index, 
                          "Previous Peak": previous_peaks, 
                          "Drawdown": drawdowns})
 
 
 def get_ffme_returns():
     """
     Load the Fama-French Dataset for the returns of the Top and Bottom Deciles by MarketCap
     """
     me_m = pd.read_csv("data/Portfolios_Formed_on_ME_monthly_EW.csv",
                        header=0, index_col=0, na_values=-99.99)
     rets = me_m[['Lo 10', 'Hi 10']]
     rets.columns = ['SmallCap', 'LargeCap']
     rets = rets/100
     rets.index = pd.to_datetime(rets.index, format="%Y%m").to_period('M')
     return rets
 
 
 def get_hfi_returns():
     """
     Load and format the EDHEC Hedge Fund Index Returns
     """
     hfi = pd.read_csv("data/edhec-hedgefundindices.csv",
                       header=0, index_col=0, parse_dates=True)
     hfi = hfi/100
     hfi.index = hfi.index.to_period('M')
     return hfi
 
 
 def skewness(r):
     """
     Alternative to scipy.stats.skew()
     Computes the skewness of the supplied Series or DataFrame
     Returns a float or a Series
     """
     demeaned_r = r - r.mean()
     # use the population standard deviation, so set dof=0
     sigma_r = r.std(ddof=0)
     exp = (demeaned_r**3).mean()
     return exp/sigma_r**3
 
 
 def kurtosis(r):
     """
     Alternative to scipy.stats.kurtosis()
     Computes the kurtosis of the supplied Series or DataFrame
     Returns a float or a Series
     """
     demeaned_r = r - r.mean()
     # use the population standard deviation, so set dof=0
     sigma_r = r.std(ddof=0)
     exp = (demeaned_r**4).mean()
     return exp/sigma_r**4
 
 
 import scipy.stats
 def is_normal(r, level=0.01):
     """
     Applies the Jarque-Bera test to determine if a Series is normal or not
     Test is applied at the 1% level by default
     Returns True if the hypothesis of normality is accepted, False otherwise
     """
     if isinstance(r, pd.DataFrame):
         return r.aggregate(is_normal)
     else:
         statistic, p_value = scipy.stats.jarque_bera(r)
         return p_value > level
 
     
 def semideviation(r):
     """
     Returns the semideviation aka negative semideviation of r
     r must be a Series or a DataFrame
     """
     is_negative = r < 0
     return r[is_negative].std(ddof=0)
 
 
 def var_historic(r, level=5):
     """
     Returns the historic Value at Risk at a specified level
     i.e. returns the number such that "level" percent of the returns
     fall below that number, and the (100-level) percent are above
     """
     if isinstance(r, pd.DataFrame):
         return r.aggregate(var_historic, level=level)
     
     elif isinstance(r, pd.Series):
         return -np.percentile(r, level)
     else:
         raise TypeError("Expected r to be a Series or DataFrame")
 
 
 def cvar_historic(r, level=5):
     """
     Computes the Conditional VaR of Series or DataFrame
     """
     if isinstance(r, pd.Series):
         is_beyond = r <= -var_historic(r, level=level)
         return -r[is_beyond].mean()
     elif isinstance(r, pd.DataFrame):
         return r.aggregate(cvar_historic, level=level)
     else:
         raise TypeError("Expected r to be a Series or DataFrame")
 
 
 from scipy.stats import norm
 def var_gaussian(r, level=5, modified=False):
     """
     Returns the Parametric Gauusian VaR of a Series or DataFrame
     If "modified" is True, then the modified VaR is returned,
     using the Cornish-Fisher modification
     """
     # compute the Z score assuming it was Gaussian
     z = norm.ppf(level/100)
     if modified:
         # modify the Z score based on observed skewness and kurtosis
         s = skewness(r)
         k = kurtosis(r)
         z = (z +
                 (z**2 - 1)*s/6 +
                 (z**3 -3*z)*(k-3)/24 -
                 (2*z**3 - 5*z)*(s**2)/36
             )
     return -(r.mean() + z*r.std(ddof=0))