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Members: Lekuba Ntoane and Nash Chakanyuka #1

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Members: Lekuba Ntoane and Nash Chakanyuka #1

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Members: Lekuba and Nash
ntoane Aug 5, 2023
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Members: Lekuba and Nash
ntoane Aug 5, 2023
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Lekuba and Nash
ntoane Aug 5, 2023
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33 changes: 18 additions & 15 deletions solution_skeleton.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ def equalise_weights(df: pd.DataFrame):


# %%

# generate portfolio function
def generate_portfolio(df_train: pd.DataFrame, df_test: pd.DataFrame):

'''
Expand Down Expand Up @@ -83,24 +83,25 @@ def generate_portfolio(df_train: pd.DataFrame, df_test: pd.DataFrame):
# We use a static Inverse Volatility Weighting (https://en.wikipedia.org/wiki/Inverse-variance_weighting)
# strategy to generate portfolio weights.
# Use the latest available data at that point in time

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'])]

# 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')
# calculate covariance matrix and mean
cov_matrix, mean_returns = df_returns[list_stocks].cov(), df_returns[list_stocks].mean()

# perform mean-variance
optimized_weights = np.linalg.inv(cov_matrix) @ mean_returns

# cap weights at 10%
optimized_weights = np.clip(optimized_weights, 0, 0.1)

# normalize weights
optimized_weights = optimized_weights / np.sum(optimized_weights)

# 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']] + optimized_weights.tolist()], columns=df_returns.columns)
df_weights = pd.concat(objs=[df_weights, df_this], ignore_index=True)

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

# 10% limit check
Expand All @@ -112,7 +113,7 @@ def generate_portfolio(df_train: pd.DataFrame, df_test: pd.DataFrame):


# %%

# plot total return function

def plot_total_return(df_returns: pd.DataFrame, df_weights_index: pd.DataFrame, df_weights_portfolio: pd.DataFrame):

Expand Down Expand Up @@ -171,3 +172,5 @@ def plot_total_return(df_returns: pd.DataFrame, df_weights_index: pd.DataFrame,
df_returns, df_weights_portfolio = generate_portfolio(df_returns_train, df_returns_test)
fig1, df_rtn = plot_total_return(df_returns, df_weights_index=df_weights_index, df_weights_portfolio=df_weights_portfolio)
fig1

# %%