__author__ = 'Arun A. Kalyanaraman'

#Import Required Packages For Analysis import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sn import scipy.stats as stats

#Load data from excel file source_data = pd.ExcelFile('./IndustryAbility.2016CaseData.xlsx') industry_exposures = pd.read_excel(source_data, 'Industry Exposures', index_col=0) industry_gmvs = pd.read_excel(source_data, 'Industry GMVs', index_col=0) industry_factor_returns = pd.read_excel(source_data, 'Industry Factor Returns', index_col=0)

#Transform dataframe so columns are industries and rows are dates industry_gmvs_date_indexed = pd.pivot_table(industry_gmvs, values='GMV', \ index=['Date'], columns=['Industry']).fillna(0) industry_exposures_date_indexed = pd.pivot_table(industry_exposures, values='Exposure', \ index=['Date'], columns=['Industry']).fillna(0) industry_factor_returns_date_indexed = pd.pivot_table(industry_factor_returns, values='DailyReturn', \ index=['Date'], columns=['Industry']).fillna(0) industry_factor_returns_date_indexed = industry_factor_returns_date_indexed.loc[:\ , (industry_factor_returns_date_indexed != 0).any(axis=0)] #Calculate current portfolio leverage by industry industry_leverage_date_indexed = industry_gmvs_date_indexed/industry_exposures_date_indexed #Calculate industry return contributions industry_returns_date_indexed = (industry_exposures_date_indexed*industry_factor_returns_date_indexed) #Compute covariance matrix using industry returns industry_factor_returns_covariance_matrix = industry_factor_returns_date_indexed.cov() industry_factor_returns_covariance_matrix_annualized = industry_factor_returns_covariance_matrix*252 #Compute annualized industry factor vol industry_factor_vol_annualized = industry_factor_returns_date_indexed.std()*np.sqrt(252) #Daily to compounded returns compounded_returns = ((industry_returns_date_indexed).cumsum()) factor_compounded_returns = (industry_factor_returns_date_indexed+1).cumprod()-1

def calc_std(weight): weight = pd.Series(weight) std = np.sqrt(np.dot(weight.T, np.dot(industry_factor_returns_covariance_matrix_annualized, weight))) return std def marginal_risk(row): marginal_risk_c = pd.DataFrame(row).T.dot(industry_factor_returns_covariance_matrix_annualized)\ /portfolio_risk.loc[row.name] risk_contribution = row*marginal_risk_c return pd.Series(risk_contribution.T[row.name])

portfolio_risk = industry_exposures_date_indexed.apply(lambda weight: calc_std(weight), axis=1) risk_decomp = industry_exposures_date_indexed.apply(lambda row: marginal_risk(row), axis=1) normalized_risk_decomposition = risk_decomp.div(risk_decomp.sum(axis=1), axis=0)

normalized_risk_decomposition_copy = normalized_risk_decomposition.copy()

top_n_risk_contribs = normalized_risk_decomposition_copy.stack().groupby(level=0).nlargest(5).unstack().\ reset_index(level=1, drop=True).reindex(columns=normalized_risk_decomposition_copy.columns)

top_n_risk_contribs = top_n_risk_contribs.fillna(0) top_n_risk_contribs = top_n_risk_contribs.loc[:, (top_n_risk_contribs != 0).any(axis=0)] top_n_risk_contribsC = top_n_risk_contribs.columns top_n_risk_contribs.replace(0, np.nan, inplace=True) top_n_risk_contribs.plot()

largest_return_contribs = (compounded_returns.iloc[-1]).sort_values() #(compounded_returns.iloc[-1]-1).sort_values().sum() largest_return_contribsL = largest_return_contribs.nlargest(n=15) largest_return_contribsS = largest_return_contribs.nsmallest(n=15) largest_return_contribs = pd.concat([largest_return_contribsL,largest_return_contribsS]) largestC = largest_return_contribs.index positive_return_contribs = largest_return_contribs[largest_return_contribs>0].index negative_return_contribs = largest_return_contribs[largest_return_contribs<0].index

factor_ret_times_risk = industry_factor_returns_date_indexed.multiply(industry_factor_vol_annualized, axis=1)

factor_ret_times_risk.corrwith(industry_exposures_date_indexed, axis=0)

factor_sharpe_ratio = (industry_factor_returns_date_indexed.mean()*252)/industry_factor_vol_annualized factor_sharpe_ratio.sort_values() portfolio_factor_sharpe_ratio = (industry_returns_date_indexed.mean()*252)/(industry_returns_date_indexed.std()*np.sqrt(252)) portfolio_factor_sharpe_ratio[largest_return_contribs.index].sort_values().plot.bar()

portfolio_sharpe_ratio = industry_returns_date_indexed.sum(axis=1).mean()/\ (np.std(industry_returns_date_indexed.sum(axis=1))*np.sqrt(252)) portfolio_sharpe_ratio

industry_returns_date_indexed.sum().sum()

top_5_industriesSR = list(portfolio_factor_sharpe_ratio.sort_values(ascending=False)[:5].index) bottom_5_industriesSR = list(portfolio_factor_sharpe_ratio.sort_values(ascending=True)[:5].index) top_5_industries_contribs = list(largest_return_contribs[:5].index) bottom_5_industries_contribs = list(largest_return_contribs[-5:].index)

for i in top_5_industries_contribs: pd.DataFrame({'Industry Exposure': industry_exposures_date_indexed[i],\ 'Portfolio Return Contribs': compounded_returns[i],\ 'Factor Return': factor_compounded_returns[i],\ 'Normalized Risk Decomposition': normalized_risk_decomposition[i]}).plot(title=i, \ secondary_y='Portfolio Return Contribs')

for i in bottom_5_industries_contribs: pd.DataFrame({'Industry Exposure': industry_exposures_date_indexed[i],\ 'Portfolio Return Contribs': compounded_returns[i],\ 'Factor Return': factor_compounded_returns[i],\ 'Normalized Risk Decomposition': normalized_risk_decomposition[i]}).plot(title=i, \ secondary_y='Portfolio Return Contribs')

days_exposure_change_correct = {} for n in industry_gmvs_date_indexed.columns: threshold = .85 sector_bad_dates = list(industry_gmvs_date_indexed[n].sort_values()[int(252 * threshold):].index.values) good_dates = industry_factor_returns_date_indexed[n].index.values sector_dates = set(sector_bad_dates).intersection(good_dates) exposure = industry_exposures_date_indexed.loc[sector_dates][n].sort_index() change_in_exposure = exposure.diff(periods=1) factor_return_on_dates = industry_factor_returns_date_indexed.loc[sector_dates][n] combined = pd.DataFrame({'Change in Exposure':change_in_exposure, \ 'Industry Factor Return': factor_return_on_dates}) combined['Correct'] = np.sign(combined['Change in Exposure']) == \ np.sign(combined['Industry Factor Return']) days_exposure_change_correct[n] = combined['Correct'].sum()/int(252*(1-threshold))

correct_change_percentage = pd.DataFrame({'Correct':pd.Series(days_exposure_change_correct).loc[positive_return_contribs]}) correct_change_percentage['Over Threshold'] = (correct_change_percentage.where(correct_change_percentage>=0.5)-.5).fillna(0) correct_change_percentage['Correct'] = correct_change_percentage['Correct'].mask(correct_change_percentage['Correct']>=.5, .5) correct_change_percentage.plot.bar(stacked=True)

correct_change_percentage = pd.DataFrame({'Correct':pd.Series(days_exposure_change_correct).loc[negative_return_contribs]}) correct_change_percentage['Over Threshold'] = (correct_change_percentage.where(correct_change_percentage>=0.5)-.5).fillna(0) correct_change_percentage['Correct'] = correct_change_percentage['Correct'].mask(correct_change_percentage['Correct']>=.5, .5) correct_change_percentage.plot.bar(stacked=True)