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LS_Beta_personal.py
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import numpy as np
import pandas as pd
import datetime as dt
from datetime import datetime
import matplotlib as mpl
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf
import statsmodels.api as sm
import yfinance as yf
import pandas_datareader.data as web
from pandas_datareader.famafrench import get_available_datasets
path = '/'
store_data = path + "MyPorfolio_Stock_Data_MSR_MIN.h5"
t = ('HMVL', 'WIPRO', 'SHEMAROO', 'FINCABLES', 'BEL', 'LT', 'EMAMILTD', 'GLENMARK', 'TATAMOTORS', 'ONGC', 'GRASIM', 'TCS', 'CAPF', 'TECHM', 'HDFCBANK', 'IOC', 'INDUSINDBK', 'MARUTI', 'PIIND', 'TVSMOTOR', 'ZEEL', '^NSEI')
exch = '.NS' #For NSE quotes
#exch = '.BO' #For BSE quotes
tickers = list()
for i in t:
if i == '^NSEI':
tickers.append(i)
else:
j = i + exch
tickers.append(j)
#%%
start = '2017-1-1'
end = dt.datetime.now()
#
stk_data = yf.download(tickers, start, end)
stk_data.rename(columns={'Adj Close': 'AdjClose'},inplace=True)
stk_data = stk_data['AdjClose']
stk_data.rename(columns={'^NSEI': 'NIFTY50'},inplace=True)
stk_adjcl_prc = stk_data.fillna(method = 'ffill').fillna(method = 'bfill').dropna(axis=1)
bchmk_adjcl_prc = stk_adjcl_prc['NIFTY50']
del stk_adjcl_prc['NIFTY50']
t_2 = [s.strip('.NS') for s in stk_adjcl_prc.columns]
t_3 = [s.strip('-EQ') for s in t_2]
t_4 = [s.strip('-P2') for s in t_3]
t_5 = [s.strip('-MF') for s in t_4]
t_6 = [w.replace('J&KBANK', 'JandKBANK') for w in t_5]
stk_adjcl_prc.columns = t_6
stk_ret = stk_adjcl_prc.pct_change()
ffm_1 = web.DataReader("F-F_Research_Data_Factors_daily", "famafrench", start)
ffm_data = pd.DataFrame(ffm_1[0]/100)
ffm_data.columns = ['mktrf','smb','hml','rf']
axis_dates = stk_data.resample("D").last().fillna(method="ffill").fillna(method="bfill").index
print (axis_dates)
alldates = pd.DataFrame(axis_dates,index=axis_dates)
alleom = alldates.groupby([alldates.index.year,alldates.index.month]).last()
alleom.index = alleom.Date
axis_eom = alleom.index
axis_id = stk_adjcl_prc.columns
def Ptf_Sharpe_Ratio(w, ret):
vcv = ret.cov()
mu = ret.mean()
num = w.dot(mu.T)
den = (w.dot(vcv).dot(w.T))**(0.5)
sharpe_ratio = num/den
return sharpe_ratio*-1
def Ptf_Variance(w, ret):
vcv = ret.cov()
var = w.dot(vcv).dot(w.T)
return var
# Estimate Betas
stk_ret_ff = stk_ret.join(ffm_data.mktrf,how='inner')
betas = pd.DataFrame([],index=axis_eom,columns=axis_id)
weights_eom = pd.DataFrame([],index=axis_eom,columns=axis_id)
n = len(stk_adjcl_prc.columns)
for t in axis_eom[13::]:
dfret1 = stk_ret_ff.loc[t-pd.DateOffset(months = 12):t,:]
for id in axis_id:
formula = id + '~' + 'mktrf'
res = smf.ols(formula = formula,data = dfret1).fit()
betas.loc[t,id] = res.params[1]
b = betas.loc[t,:]
m = b.median()
weights_eom.loc[t,axis_id[b<m]] = 1/b[b<m].mean()/(b<m).sum()
weights_eom.loc[t,axis_id[b>m]] = -1/b[b>m].mean()/(b>m).sum()
#backtest
weights_daily = pd.DataFrame(weights_eom, index = axis_dates, columns = axis_id)
weights_daily = weights_daily.fillna(method = 'ffill')
eq_w = 1/n
weights_daily_eq = pd.DataFrame(eq_w, index = axis_dates, columns = axis_id)
weights_eom_eqw = pd.DataFrame(weights_daily_eq, index = axis_eom, columns = axis_id)
#
ptf_value_lsbeta = pd.DataFrame([], index = axis_dates, columns = ['Ptf_Value_LSBeta'])
ptf_value_eqw = pd.DataFrame([], index = axis_dates, columns = ['Ptf_Value_EQW'])
for t in axis_dates:
ptf_value_lsbeta.loc[t] = weights_daily.loc[t].dot(stk_adjcl_prc.loc[t].T)
ptf_value_eqw.loc[t] = weights_daily_eq.loc[t].dot(stk_adjcl_prc.loc[t].T)
#%%
tt = '2017-01-02'
hundred_base_lsbeta = pd.DataFrame([], index = axis_dates, columns = ['LSBeta'])
hundred_base_lsbeta = hundred_base_lsbeta[tt:]
hundred_base_eqw = pd.DataFrame([], index = axis_dates, columns = ['EQW'])
hundred_base_eqw = hundred_base_eqw[tt:]
hundred_base_bchmk = pd.DataFrame([], index = axis_dates, columns = ['BCHMK'])
hundred_base_bchmk = hundred_base_bchmk[tt:]
for t in hundred_base_lsbeta.index:
hundred_base_lsbeta.loc[t] = np.array(ptf_value_lsbeta.loc[t]*100)/np.array(ptf_value_lsbeta.loc[tt])
hundred_base_eqw.loc[t] = np.array(ptf_value_eqw.loc[t]*100)/np.array(ptf_value_eqw.loc[tt])
hundred_base_bchmk.loc[t] = np.array(bchmk_adjcl_prc.loc[t]*100)/np.array(bchmk_adjcl_prc.loc[tt])
all_val = pd.concat([hundred_base_lsbeta, hundred_base_eqw, hundred_base_bchmk], axis=1, join='inner')
#all_val.plot()
#
oos = tt
ptf_ret_lsbeta = ptf_value_lsbeta.loc[oos::].pct_change()
ptf_ret_eqw = ptf_value_eqw.loc[oos::].pct_change()
ptf_ret_bchmk = hundred_base_bchmk.loc[oos::].pct_change()
sharpe_ratio_lsbeta = (ptf_ret_lsbeta.mean()*252) / (ptf_ret_lsbeta.std()*((252)**0.5))
sharpe_ratio_eqw = (ptf_ret_eqw.mean()*252) / (ptf_ret_eqw.std()*((252)**0.5))
sharpe_ratio_bchmk = (ptf_ret_bchmk.mean()*252) / (ptf_ret_bchmk.std()*((252)**0.5))
#
all_val.plot()
print ('Long Short Beta Strategy Sharpe Ratio')
print (sharpe_ratio_lsbeta)
print ('Equal Weighted Optimization OOS Sharpe Ratio')
print (sharpe_ratio_eqw)
#Last Day weight and Units to be purchased
notional = 100000
today = end
todays_weight_units_lsbeta = (weights_daily.iloc[-1] * notional) / (stk_adjcl_prc.iloc[-1])
todays_weight_units_eqw = (weights_daily_eq.iloc[-1] * notional) / (stk_adjcl_prc.iloc[-1])
print ('Long Short Beta Strategy Weights')
print (todays_weight_units_lsbeta.astype(dtype = float).round(decimals = 0))
print (todays_weight_units_lsbeta.astype(dtype = float))
print ((todays_weight_units_lsbeta.astype(dtype = float).round(decimals = 0) * stk_adjcl_prc.iloc[-1]).sum(0))
print ((todays_weight_units_lsbeta * stk_adjcl_prc.iloc[-1]).sum(0))