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Kwazi Zungu, David Roth #4

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18 changes: 14 additions & 4 deletions solution_skeleton.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import pandas as pd
import datetime
import plotly.express as px
import matplotlib.pyplot as plt


print('---Python script Start---', str(datetime.datetime.now()))
Expand Down Expand Up @@ -88,15 +89,22 @@ 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'])]

# 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['vol'] = df_latest.std(numeric_only=True)

df_w['running_mean'] = df_latest.tail(15).mean(numeric_only=True) #Get the running mean of the stock change
df_w['inv_vol'] = 1/(10*df_w['vol']+10) #Decrease the weighting given to variable stocks

df_w['base'] = df_w['inv_vol'].multiply(df_w['running_mean']).clip(lower=0) #if a stock is variable, it gets lower weightings. If it is going negative, zero
df_w['tot_base'] = df_w['base'].sum()
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['weight'] = df_w['inv_vol']/df_w['tot_inv_vol'] # calculate weight based on inverse volatility
df_w['weight'] = (df_w['base']/df_w['tot_base']).clip(upper=0.1)
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)
Expand Down Expand Up @@ -171,3 +179,5 @@ 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

# %%