Portfolio max(Return/Risk) Stochastic Optimization of 20 Dividend Growth Stocks

The goal of portfolio optimization is to build a stock portfolio that yields the maximum possible return while maintaining the amount of risk you’re willing to carry.

Referring to our previous case study, let’s invoke the stochastic optimization algorithm and the corresponding code to create an optimized portfolio by testing 10,000 combinations of the following 20 dividend growth stocks:

Table of Contents

1. Input Data Preparation
2. Portfolio/Benchmark Analysis
3. Output Data Visualization
4. Summary
5. Explore More

Input Data Preparation

Let’s define the following input parameters

Tickers:

benchmark_ = [“^GSPC”]

portfolio_ = [‘V’, ‘O’, ‘HD’, ‘MO’, ‘WM’,’NEE’,’PEP’,’CAT’,’UNH’,’MCD’,’XOM’,’UNP’,’CNQ’,’LMT’,’BLK’,’AAPL’,’MSFT’,’SBUX’,’ASML’,

‘COST’]

Dates:

start_date_ = “2022-01-01”
end_date_ = “2023-03-03”

trade_days_per_year = 252

Stochastic Simulations:

number_of_scenarios = 10000

Risk %:
delta_risk = 0.1

Let’s set the working directory YOURPATH

import os
os.chdir(‘YOURPATH’)
os. getcwd()

and import the following libraries

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import squarify
import seaborn as sb

Let’s download our stock and benchmark data

return_vector = []
risk_vector = []
distrib_vector = []

df = yf.download(benchmark_, start=start_date_, end=end_date_)
df2 = yf.download(portfolio_, start=start_date_, end=end_date_)

Clean Rows with No Values on both Benchmark and Portfolio
df = df.dropna(axis=0)
df2 = df2.dropna(axis=0)

Matching the Days
df = df[df.index.isin(df2.index)]

[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  5 of 5 completed

df.tail()

df.shape

(292, 6)

df2.shape

(292, 120)

df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 292 entries, 2022-01-03 to 2023-03-02
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Open       292 non-null    float64
 1   High       292 non-null    float64
 2   Low        292 non-null    float64
 3   Close      292 non-null    float64
 4   Adj Close  292 non-null    float64
 5   Volume     292 non-null    int64  
dtypes: float64(5), int64(1)
memory usage: 16.0 KB

df2.columns

MultiIndex([('Adj Close', 'AAPL'),
            ('Adj Close', 'ASML'),
            ('Adj Close',  'BLK'),
            ('Adj Close',  'CAT'),
            ('Adj Close',  'CNQ'),
            ('Adj Close', 'COST'),
            ('Adj Close',   'HD'),
            ('Adj Close',  'LMT'),
            ('Adj Close',  'MCD'),
            ('Adj Close',   'MO'),
            ...
            (   'Volume', 'MSFT'),
            (   'Volume',  'NEE'),
            (   'Volume',    'O'),
            (   'Volume',  'PEP'),
            (   'Volume', 'SBUX'),
            (   'Volume',  'UNH'),
            (   'Volume',  'UNP'),
            (   'Volume',    'V'),
            (   'Volume',   'WM'),
            (   'Volume',  'XOM')],
           length=120)

df.describe()

df2.describe().T

Portfolio/Benchmark Analysis

Analysis of Benchmark:
benchmark_vector = np.array(df[‘Close’])

Create our Daily Returns
benchmark_vector = np.diff(benchmark_vector)/benchmark_vector[1:]

Select or Final Return and Risk
benchmark_return = np.average(benchmark_vector)
benchmark_risk = np.std(benchmark_vector)

Add our Benchmark info to our lists
return_vector.append(benchmark_return)
risk_vector.append(benchmark_risk)

Analysis of Portfolio
portfolio_vector = np.array(df2[‘Close’])

Create a loop for the number of scenarios we want:

for i in range(number_of_scenarios):
#Create a random distribution that sums 1
# and is split by the number of stocks in the portfolio
random_distribution = np.random.dirichlet(np.ones(len(portfolio_)),size=1)
distrib_vector.append(random_distribution)

#Find the Closing Price for everyday of the portfolio
portfolio_matmul = np.matmul(random_distribution,portfolio_vector.T)

#Calculate the daily return
portfolio_matmul = np.diff(portfolio_matmul)/portfolio_matmul[:,1:]

#Select or Final Return and Risk
portfolio_return = np.average(portfolio_matmul, axis=1)
portfolio_risk = np.std(portfolio_matmul, axis=1)

#Add our Benchmark info to our lists
return_vector.append(portfolio_return[0])
risk_vector.append(portfolio_risk[0])

Create Risk Boundaries with the given delta_risk
min_risk = np.min(risk_vector)
max_risk = risk_vector[0]*(1+delta_risk)
risk_gap = [min_risk, max_risk]

Portfolio Return and Risk Couple
portfolio_array = np.column_stack((return_vector,risk_vector))[1:,]

Rule to create the best portfolio

If the criteria of minimum risk is satisfied then:
if np.where(((portfolio_array[:,1]<= max_risk)))[0].shape[0]>1:
min_risk_portfolio = np.where(((portfolio_array[:,1]<= max_risk)))[0]
best_portfolio_loc = portfolio_array[min_risk_portfolio]
max_loc = np.argmax(best_portfolio_loc[:,0])
best_portfolio = best_portfolio_loc[max_loc]

If the criteria of minimum risk is not satisfied then:
else:
min_risk_portfolio = np.where(((portfolio_array[:,1]== np.min(risk_vector[1:]))))[0]
best_portfolio_loc = portfolio_array[min_risk_portfolio]
max_loc = np.argmax(best_portfolio_loc[:,0])
best_portfolio = best_portfolio_loc[max_loc]

Let’s plot the summary of yearly portfolio average return vs standard deviation: portfolio scenarios, market proxy values, and best performer.

risk_gap = np.array(risk_gap)

best_portfolio[0] = np.array(best_portfolio[0])
x = np.array(risk_vector)
y = np.array(return_vector)*trade_days_per_year

fig, ax = plt.subplots(figsize=(20, 15))

plt.rc(‘axes’, titlesize=14) # Controls Axes Title
plt.rc(‘axes’, labelsize=14) # Controls Axes Labels
plt.rc(‘xtick’, labelsize=14) # Controls x Tick Labels
plt.rc(‘ytick’, labelsize=14) # Controls y Tick Labels
plt.rc(‘legend’, fontsize=14) # Controls Legend Font
plt.rc(‘figure’, titlesize=14) # Controls Figure Title

ax.scatter(x, y, alpha=0.5,
linewidths=0.1,
edgecolors=’black’, s=20,
label=’Portfolio Scenarios’
)

ax.scatter(x[0],
y[0],
color=’red’,
linewidths=1, s=180,
edgecolors=’black’,
label=’Market Proxy Values’)

ax.scatter(best_portfolio[1],
best_portfolio[0],
color=’green’,
linewidths=1, s=180,
edgecolors=’black’,
label=’Best Performer’)

ax.axvspan(min_risk,
max_risk,
color=’red’,
alpha=0.08,
label=’Accepted Risk Zone’)

ax.set_ylabel(“Yearly Portfolio Average Return (%)”,fontsize=14)
ax.set_xlabel(“Yearly Portfolio Standard Deviation”,fontsize=14)

ax.axhline(y=0, color=’black’,alpha=0.5)

ax = plt.gca()
ax.legend(loc=0)
vals = ax.get_yticks()
ax.set_yticklabels([‘{:,.2%}’.format(x) for x in vals])

plt.savefig(‘risk_optimizer2023.png’, dpi=300)

Output Data Visualization

let’s look at the Output Table of Distributions
portfolio_loc = np.where((portfolio_array[:,0]==(best_portfolio[0]/trade_days_per_year))&(portfolio_array[:,1]==(best_portfolio[1])))[0][0]
best_distribution = distrib_vector[portfolio_loc][0].tolist()
d = {“Stock Name”: portfolio_, “Stock % in Portfolio”: best_distribution}
output = pd.DataFrame(d)
output = output.sort_values(by=[“Stock % in Portfolio”],ascending=False)
output= output.style.format({“Stock % in Portfolio”: “{:.2%}”})
output

The can approximate this distribution by the squarify matrix proxy

episode_data =[20,14,13,10,7,5,5,5,5,5,3,3,2,2,2,1,1,1,1,1]
anime_names = [‘CAT’,’MCD’,’MO’,’UNH’,’ASML’,’AAPL’,’WM’,’CNQ’,’SBUX’,’UNP’,’COST’,’LMT’,’HD’,’V’,’BLK’,’PEP’,’O’,’XOM’,’NEE’,’MSFT’]

squarify.plot(episode_data, label=anime_names,color=sb.color_palette(“Spectral”,
len(episode_data)),alpha=.7,pad=2)

plt.axis(“off”)
plt.savefig(‘squarify20stocks.png’)

Summary

In this post, we have invoked the stochastic portfolio optimization algorithm to create a balanced portfolio of 20 dividend growth stocks suggested by @DrDividend47. Results allow us to spread our investment capital across a variety of assets. As a result, we can balance those assets in order to attain our desired min (risk) and max(reward) outcome whilst outperforming the benchmark index ^GSPC.

Explore More

• Bear vs. Bull Portfolio Risk/Return Optimization QC Analysis
• Risk/Return POA – Dr. Dividend’s Positions
• Portfolio Optimization Risk/Return QC – Positions of Humble Div vs Dividend Glenn
• Risk/Return QC via Portfolio Optimization – Current Positions of The Dividend Breeder
• Stock Portfolio Risk/Return Optimization