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Wayne Honwe #11

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19 changes: 15 additions & 4 deletions solution_skeleton.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,11 @@
import pandas as pd
import datetime
import plotly.express as px
import plotly.io as pio
from numpy.random import gamma
from scipy.stats import gamma

pio.renderers.default = "browser"

print('---Python script Start---', str(datetime.datetime.now()))

Expand Down Expand Up @@ -83,15 +87,22 @@ 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'])]

#create a gamma distribution because we are looking for a positively skewed result from the distribution
#simulate a gamma distribution because stocks often move in random distribution
shape, scale = 4.5503, 7037
Gamma = gamma(a=shape, scale=scale)
x = np.arange(1, 55)

# vol calc
#vol calc
df_w = pd.DataFrame()
df_w['vol'] = df_latest.std(numeric_only=True) # calculate stock volatility
df_w['vol'] = Gamma.pdf(x) # calculate stock volatility using gamma distribution simulation
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
Expand All @@ -104,7 +115,7 @@ def generate_portfolio(df_train: pd.DataFrame, df_test: pd.DataFrame):
# <<--------------------- 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]) > 0.90]):

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

Expand Down