Applying a Risk-Aware Portfolio Rebalancing Strategy to ETF, Energy, Pharma, and Aerospace/Defense Stocks in 2023

In this post, we will apply the Guillen’s asset rebalancing algorithm (cf. the Python code) to the following risk-aware portfolio:

stocks = [‘SPY‘, ‘XOM‘, ‘ABBV‘, ‘AZN‘, ‘LMT‘]

The initial portfolio value to be allocated is

portfolio_value = 10**6

and the weight allocation per asset is

weights = [0.15 , 0.30, 0.40, 0.075, 0.075]

Conventionally, our benchmark is S&P500

benchmark = ‘^GSPC’

The time factors are as follows:

Start date 2013-01-02

End date 2023-03-17

In-sample months 83

Out-of-sample months 38

Download Input Data

Let’s set the working directory

import os os.chdir(‘YOURPATH’)

os. getcwd()


import the libraries
import yfinance as yf
import pandas as pd
import numpy as np
import pyfolio as py
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import warnings

followed by the above-mentioned user input parameters.

Let’s download the data

stock_data =, start=start_date)[‘Adj Close’]
stock_data = stock_data.dropna()
stock_data = stock_data.reindex(columns=stocks)
stock_prices = stock_data[stocks].values

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and define the shares dataframe

shares_df = pd.DataFrame(index=[stock_data.index[0]])

for s,w in zip(stocks, weights):
shares_df[s + ‘_shares’] = np.floor((portfolio_value * np.array(w)) / stock_data[s][0])


Portfolio shares table

Rebalancing Engine Run

  • Initialize variables
    balance_year = stock_data.index[0].year # Since we rebalance based on year
    signal = False
    count = 0 # for loop count purpose

Store previous values in a dictionary
prev_values = {}

  • Calculate portfolio value for the first day by mult. shares * price per asset at t=0
    portfolio_value = sum([shares_df.loc[stock_data.index[0], s + ‘_shares’] * stock_data.loc[stock_data.index[0], s] for s in stocks])

for day in stock_data.index:
count += 1
if day == stock_data.index[0]:
shares_df.loc[day] = shares_df.loc[day] # First day
# Store initial values as previous values
for col in shares_df.columns:
prev_values[col] = shares_df.loc[day, col]

elif day.year != balance_year: # THIS IS OUR SIGNAL
    signal = True
    # calculate new shares based on the new portfolio value and weights
    new_shares = [np.floor((portfolio_value * w) / stock_data[s][day]) for s,w in zip(stocks, weights)]
    shares_df.loc[day, :] = new_shares
    balance_year = day.year
    count += 1
    # print(f'Rebalance: {}, count: {count}') # uncomment to debug days 😉
    # Store new values as previous values
    for col in shares_df.columns:
        prev_values[col] = shares_df.loc[day, col]

    signal = False
    # Use previous values if it is not a rebalancing date
    shares_df.loc[day, :] = [prev_values[col] for col in shares_df.columns]
    # print(f'Not rebalance, regular day: {}') # uncomment to debug days 😉

# Calculate asset values and portfolio value for the current day
asset_values = [shares_df.loc[day, s + '_shares'] * stock_data.loc[day, s] for s in stocks]
portfolio_value = sum(asset_values)
stock_data.loc[day, 'Signal'] = signal
stock_data.loc[day, 'Portfolio_Value'] = portfolio_value

# Add shares to stock data frame to have all together
for s in stocks:
    stock_data.loc[day, s + '_shares'] = shares_df.loc[day, s + '_shares']
    stock_data.loc[day, s + '_value'] = shares_df.loc[day, s + '_shares'] * stock_data.loc[day, s]

Calculate log returns for portfolio
stock_data[‘Portfolio_Value_rets’] = np.log(stock_data[‘Portfolio_Value’] / stock_data[‘Portfolio_Value’].shift(1))

Calculate log returns for each stock and asset weight
for stock in stocks:
stock_data[f'{stock}_rets’] = np.log(stock_data[stock] / stock_data[stock].shift(1))
stock_data[stock + ‘_weight’] = stock_data[stock + ‘_value’] / stock_data[‘Portfolio_Value’]

Benchmark data download and return
start_date_benchmark = stock_data.index[0]
benchmark_data =, start=start_date_benchmark)
benchmark_data = benchmark_data.dropna()
benchmark_data[‘benchmark_rets’] = np.log(benchmark_data[‘Adj Close’] / benchmark_data[‘Adj Close’].shift(1))
benchmark_data[‘benchmark_rets’] = benchmark_data[‘benchmark_rets’].dropna()

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Data timezone UTC unification for pyfolio valuation

stock_data.index = stock_data.index.tz_localize(‘UTC’)
benchmark_data.index = benchmark_data.index.tz_localize(‘UTC’)

live_date = pd.Timestamp(live_date, tz=’UTC’)

Output Data Visualization

from IPython.core.display import display, HTML
py.create_full_tear_sheet(stock_data[‘Portfolio_Value_rets’], benchmark_rets = benchmark_data[‘benchmark_rets’], live_start_date = live_date)

Output data table
4 stress events table
Cumulative returns

2014 2016 2018 2020 2022

Cumulative returns volatility matched to benchmark
Cumulative returns log scale vs Returns
Rolling portfolio beta 6-mo 12-mo vs Rolling volatility
Rolling Sharpe ratio vs top 5 drawdown periods
Underwater plot
Monthly, annual returns and histogram of monthly returns
Stress events Apr14 and Oct14
Stress events Fall2015 and new Normal


We have implemented the following portfolio rebalancing strategy in Python:

  1. Define the assets, weights, and initial capital for investment.
  2. Select the benchmark and relevant dates.
  3. Assign initial capital to each asset based on desired weights.
  4. Implement the rebalancing engine based on monthly, yearly, daily, or signal-based triggers.
  5. Monitor the portfolio’s performance numerically or visually and extract insights.

Our output out-of-sample data are as follows:

  • Return at End date (2023-03-17): 3.5+/- 2 %
  • Annual return 17.3%
  • Cumulative returns 66.6%
  • Annual volatility 23.4%
  • Max drawdown -38.6%
  • Calmar ratio 0.45
  • Sharpe ratio 0.8
  • Omega ratio 1.16
  • Sortino ratio 1.09
  • Alpha 0.15
  • Beta 0.72
  • 4 stress events
  • 5 drawdown periods

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