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Mbali and Sisipho #10

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a598186
Mbali and Sisipho
Mbali2000 Aug 5, 2023
f2e5a05
Mbali and Sisipho
Mbali2000 Aug 5, 2023
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24 changes: 20 additions & 4 deletions solution_skeleton.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,26 +87,42 @@ def generate_portfolio(df_train: pd.DataFrame, df_test: pd.DataFrame):
for i in range(len(df_test)):

# latest data at this point
df_latest = df_returns[(df_returns['month_end'] < df_test.loc[i, 'month_end'])]
df_latest = df_returns[(df_returns['month_end'] < df_test.loc[i, 'month_end'])] #only considers months before month in question

# vol calc
df_w = pd.DataFrame()
df_w['vol'] = df_latest.std(numeric_only=True) # calculate stock volatility
'''df_w['vol'] = df_latest.std(numeric_only=True) # calculate stock volatility
df_w['inv_vol'] = 1/df_w['vol'] # calculate the inverse volatility
df_w['tot_inv_vol'] = df_w['inv_vol'].sum() # calculate the total inverse volatility
df_w['weight'] = df_w['inv_vol']/df_w['tot_inv_vol'] # calculate weight based on inverse volatility
df_w.reset_index(inplace=True, names='name')'''


#Our code solution using Exponentially weighted average
mean_latest = df_latest.mean(numeric_only=True) #calculates mean value of stocks
decay_factor = 0.75
#df_w['vol'] = df_latest.ewm(span = 12, min_periods =1, adjust = True).std(numeric_only=True) #gives values recent values highe rating compared to older values

df_w['vol'] = df_latest.std(numeric_only=True)*mean_latest #volatility using exponential groth model
df_w['inv_vol'] = 1/df_w['vol'] # calculate the inverse volatility
df_w['tot_inv_vol'] = df_w['inv_vol'].sum() # calculate the total inverse volatility
df_w['weight'] = df_w['inv_vol']/df_w['tot_inv_vol'] # calculate weight based on inverse volatility
df_w.reset_index(inplace=True, names='name')



# add to all weights
df_this = pd.DataFrame(data=[[df_test.loc[i, 'month_end']] + df_w['weight'].to_list()], columns=df_latest.columns)
df_weights = pd.concat(objs=[df_weights, df_this], ignore_index=True)



# <<--------------------- YOUR CODE GOES ABOVE THIS LINE --------------------->>

# 10% limit check
if len(np.array(df_weights[list_stocks])[np.array(df_weights[list_stocks]) > 0.101]):
#if len(np.array(df_weights[list_stocks])[np.array(df_weights[list_stocks]) > 0.01]):

raise Exception(r'---> 10% limit exceeded')
#raise Exception(r'---> 10% limit exceeded')

return df_returns, df_weights

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