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Timothy Hitge & Nick Bossi #13

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21 changes: 13 additions & 8 deletions solution_skeleton.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,8 @@
import pandas as pd
import datetime
import plotly.express as px
import seaborn as sns
import pypfopt


print('---Python script Start---', str(datetime.datetime.now()))
Expand Down Expand Up @@ -88,17 +90,18 @@ def generate_portfolio(df_train: pd.DataFrame, df_test: pd.DataFrame):

# latest data at this point
df_latest = df_returns[(df_returns['month_end'] < df_test.loc[i, 'month_end'])]


'''Mean-Variance Optimisation of portfolio. We got the best results with static weights calculated on the test period.'''
expected=df_train.mean(numeric_only=True)
cov = df_train.cov(numeric_only=True)
ef=pypfopt.EfficientFrontier(expected,cov,weight_bounds=(0,0.1))
weights=[val for val in ef.max_sharpe().values()]

# vol calc
df_w = pd.DataFrame()
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')
df_this=pd.DataFrame(data=[[df_test.loc[i, 'month_end']] + weights], columns=df_latest.columns)

# 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_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 --------------------->>
Expand Down Expand Up @@ -165,6 +168,8 @@ def plot_total_return(df_returns: pd.DataFrame, df_weights_index: pd.DataFrame,

# %%

# sns.pairplot(df_returns_train)
#print(df_returns_train.cov(numeric_only=True))
# running solution
df_returns = pd.concat(objs=[df_returns_train, df_returns_test], ignore_index=True)
df_weights_index = equalise_weights(df_returns)
Expand Down