Sustainable Portfolio Selection -- Markowitz goes ESG

    	
            
import eikon as ek
import pandas


import numpy


import matplotlib.pyplot as plt


import scipy.optimize as sco 


import os


ek.set_app_key('YOUR API KEY HERE')



        
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N=100
constituents, err = ek.get_data(['IWDA.L'], ['TR.ETPConstituentRIC', 'TR.ETPConstituentName'])
constituents.rename(columns={'Constituent RIC': 'ric', 'Constituent Name': 'name'}, inplace=True)
constituents = constituents[['ric','name']][0:N]
print(constituents)

        
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start='2020-01-01'
end='2020-12-31'
instruments = constituents['ric'].astype(str).values.tolist()
ts =pandas.DataFrame()
for r in instruments:
    try:
        ts1 = ek.get_timeseries(r,'CLOSE',start_date=start,end_date=end,interval='daily')
        ts1.rename(columns = {'CLOSE' : r}, inplace=True)
        ts =pandas.concat([ts, ts1], axis=1)
    except:
        continue
ts = ts.dropna()
print(ts)

        
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df_esg, err = ek.get_data(instruments, ['TR.TRESGScore','TR.BusinessSummary'])
df_esg = df_esg.rename(columns={'Instrument':'ric', 'ESG Score':'esg'}).set_index('ric')
df_esg = pandas.concat([constituents.set_index('ric'), df_esg], join='inner', axis=1)
df_esg

        
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returns = ts.pct_change().replace(numpy.inf, numpy.nan).dropna()
covMatrix = returns.cov()

        
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def risk_measure(covMatrix, weights):
    return numpy.dot(weights, numpy.dot(covMatrix, weights))

        
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            constraints = {'type': 'eq', 'fun': lambda weights: weights.sum() - 1}
        
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mvp = sco.minimize(lambda x: risk_measure(covMatrix, x),  # function to be minized
                   len(instruments) * [1 / len(instruments)],  # initial guess
                   bounds=bounds,  # boundary conditions
                   constraints =constraints,  # equality constraints
                   )
mvp_weights = list(mvp['x'])
mvp_esg = numpy.dot(mvp_weights, df_esg['esg'])
mvp_risk = mvp['fun']
print('\nMVP in a universe with {N} instruments\nNumber of selected instruments: {n}\nMinimum weight: {minw}\nMaximum weight: {maxw}\nHistorical risk measure: {risk}\nHistorical return p.a.: {r}\nESG score: {esg}'.format(N=N,n=numpy.sum(mvp['x']>0),minw=numpy.min(mvp['x'][numpy.nonzero(mvp['x'])]),maxw=numpy.max(mvp['x']),risk=mvp_risk,r=numpy.dot(mvp_weights,returns.sum()),esg=mvp_esg))

        
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df_weights =pandas.DataFrame(data=mvp_weights, index=instruments)
df_weights =df_weights.sort_values(by=[0], ascending=False)
df_weights =df_weights[df_weights > 1e-4].dropna()
df_weights =df_weights.T

        
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#plotting the weights 
plt.figure(figsize=(15, 5))
ypos = numpy.linspace(0, 100, num=len(df_weights.iloc[0,:]))
plt.bar(ypos, df_weights.values[0], width =1)
plt.xticks(ypos, df_weights.columns, rotation =30, ha ='right')
plt.xlabel('RICs', fontsize=12)
plt.ylabel('Weights', fontsize=12)
plt.title('Portfolio Weights (Minimum Volatility Portfolio)', fontsize=12)
plt.show()

        
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prescribed_esg = 80
 
esg_constraint = {'type': 'eq', 'fun': lambda weights: numpy.dot(weights, df_esg['esg']) - prescribed_esg}
esgmvp = sco.minimize(lambda x: risk_measure(covMatrix, x),  # function to be minized
                   len(instruments) * [1 / len(instruments)],  # initial guess
                   bounds=bounds,  # boundary conditions
                   constraints =[constraints, esg_constraint],  # equality constraints
                   )
esgmvp_weights = list(esgmvp['x'])
esgmvp_esg = numpy.dot(esgmvp_weights, df_esg['esg'])
esgmvp_risk = esgmvp['fun']
 
print('\nMVP with prescribes ESG = {pe} in a universe with {N} instruments\nNumber of selected instruments: {n}\nMinimum weight: {minw}\nMaximum weight: {maxw}\nHistorical risk measure: {risk}\nHistorical return p.a.: {r}\nESG score: {esg}'.format(N=N,n=numpy.sum(esgmvp['x']>0),minw=numpy.min(esgmvp['x'][numpy.nonzero(esgmvp['x'])]),maxw=numpy.max(esgmvp['x']),risk=esgmvp_risk,r=numpy.dot(esgmvp_weights,returns.sum()),esg=esgmvp_esg,pe=prescribed_esg))

        
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df_weights =pandas.DataFrame(data=esgmvp_weights, index=instruments)
df_weights =df_weights.sort_values(by=[0], ascending=False)
df_weights =df_weights[df_weights > 1e-4].dropna()
df_weights =df_weights.T

        
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#plotting the weights
plt.figure(figsize=(15, 5))
ypos = numpy.linspace(0, 100, num=len(df_weights.iloc[0,:]))
plt.bar(ypos, df_weights.values[0], width =1)
plt.xticks(ypos, df_weights.columns, rotation =30, ha ='right')
plt.xlabel('RICs', fontsize=12)
plt.ylabel('Weights', fontsize=12)
plt.title('Portfolio Weights (Minimal Risk with ESG Score 80)', fontsize=12)
plt.show()

        
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max_esg=numpy.floor(max(df_esg['esg'].values.tolist())) #max esg value to be achieved dependent on the universe
min_esg=mvp_esg #min esg value which is interesting for portfolio selection
 
results = {'esg_val':[],'weights':[],'risk':[],'return':[]}
for rho in numpy.linspace(min_esg,max_esg,num=25):
    constraints2 = {'type': 'eq', 'fun': lambda weights: numpy.dot(weights, df_esg['esg']) - rho}
    res = sco.minimize(lambda x: risk_measure(covMatrix, x),  # function to be minized
                   len(instruments) * [1 / len(instruments)],  # initial guess
                   bounds=bounds,  # boundary conditions
                   constraints =[constraints, constraints2],  # equality constraints
                       )
    weights = list(res['x'])
    esg_val= numpy.dot(weights, df_esg['esg'])
    results['esg_val'].append(esg_val)
    results['weights'].append(weights)
    results['risk'].append(res['fun'])
    results['return'].append(numpy.dot(weights,returns.sum()))

        
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plt.plot(results['risk'],results['esg_val'], 'o')
plt.xlabel('risk')
plt.ylabel('esg_val')
plt.show()

        
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plt.plot(results['esg_val'],results['return'],'o')
plt.xlabel('esg_val')
plt.ylabel('return')
plt.show()

        
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