Building a quantitative momentum investing strategy

Quantitative Momentum Strategy

Quantitative Momentum is an investment strategy which selects for investment the stocks whose price appreciated the most during a period (usually the recent year, ignoring the most recent month).

The goal of this section is trying to create an investing strategy following the instruction on https://github.com/nickmccullum/algorithmic-trading-python/tree/master, with this strategy it selects the 50 stocks in S&P 600 list with the highest price momentum. From there, the trades with an equal-weight portfolio of these 50 stocks will be calculated.

Load the list of S&P 600 companies from Wikepedia

The S&P 600 is an index of small-cap company stocks created by Standard & Poor’s, selected by a committee based on recent profitability and other factors.

Currently there are 3 S&P Small Cap 600 ETFs traded on the U.S. markets, the largest one is iShares S&P Small-Cap 600 Value ETF IJS with around 7.09 Billon USD in assets.

# Get the list of S&P 600 stocks from Wikepedia
import pandas as pd

def load_data(url):
    html = pd.read_html(url, header=0)
    return html

# Load the list of S&P 600 companies
url = 'https://en.wikipedia.org/wiki/List_of_S%26P_600_companies'
df = load_data(url)[0]
df.head()

	Symbol	Company	GICS Sector	GICS Sub-Industry	Headquarters Location	SEC filings	CIK
0	AAON	AAON, Inc.	Industrials	Building Products	Tulsa, Oklahoma	view	824142
1	AAP	Advance Auto Parts, Inc.	Consumer Discretionary	Automotive Retail	Raleigh, North Carolina	view	1158449
2	AAT	American Assets Trust	Real Estate	Diversified REITs	San Diego, California	view	1500217
3	ABCB	Ameris Bancorp	Financials	Regional Banks	Atlanta, Georgia	view	351569
4	ABG	Asbury Automotive Group	Consumer Discretionary	Automotive Retail	Duluth, Georgia	view	1144980

Retrieve the latest stock price using yfinance

We retrieve the latest year’s stock price using yfinance.

import yfinance as yf
import warnings
warnings.filterwarnings('ignore')

data = yf.download(
            # tickers list
            tickers = list(df['Symbol']),
            # valid periods: 1d, 5d, 1mo, 3mo, 6mo, 1y, 5y, 10y, ytd, max
            period = '1y',
            # valid intervals: 1m, 2m, 5m, 15m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
            interval = '1d',
            # group by ticker
            group_by = 'ticker',
            # adjust all OHLC automatically
            auto_adjust = True,
            # download pre/post regular market hours data
            prepost = True,
            # use threads for mass downloading
            threads = True,
            # proxy URL scheme when downloading
            proxy = None
            )

[*********************100%%**********************]  602 of 602 completed

2 Failed downloads:
['MOG.A', 'CWEN.A']: Exception('%ticker%: No data found, symbol may be delisted')

data['AAON'].head()

Price	Open	High	Low	Close	Volume
Date
2023-02-24	52.804963	54.090144	52.440610	53.970898	367500
2023-02-27	53.970902	55.037468	53.281940	53.984154	653700
2023-02-28	57.634324	62.536555	56.309398	60.257679	1987800
2023-03-01	60.085437	60.681652	59.515718	59.999317	566400
2023-03-02	59.793949	60.708151	59.436218	60.522659	792450

Calculate 1-year return and remove the lowest momentum stocks

# Get the available tickers
tickers_unavailable = ['MOG.A', 'CWEN.A']
tickers = [ticker for ticker in list(df['Symbol']) if ticker not in tickers_unavailable]
print('Number of available tickers: ', len(tickers))

Number of available tickers:  600

# Create the format of the dataframe that stores the stock price, market cap and number of shares to buy
my_columns = ['Ticker', 'Price', 'One-Year Price Return', 'Number of Shares to Buy']
final_dataframe = pd.DataFrame(columns = my_columns)
final_dataframe

	Ticker	Price	One-Year Price Return	Number of Shares to Buy

# Input the value of the portforlio, i.e. example here is 1M USD

portfolio_size = input("Enter the value of your portfolio:")

try:
    val = float(portfolio_size)
except ValueError:
    print("That's not a number! \n Try again:")
    portfolio_size = input("Enter the value of your portfolio:")

Enter the value of your portfolio:1000000

# Calculate the 1-year return
for i in range(0,len(tickers)):
    ticker = tickers[i]
    final_dataframe = final_dataframe.append(
            pd.Series(
            [
                ticker,
                data[ticker]['Close'].to_list()[-1],
                data[ticker]['Close'].to_list()[-1] - data[ticker]['Close'].to_list()[0],
                'NA'
            ],
                index = my_columns),
                ignore_index = True)

final_dataframe.head()

	Ticker	Price	One-Year Price Return	Number of Shares to Buy
0	AAON	84.050003	30.079105	NA
1	AAP	61.099998	-74.325050	NA
2	AAT	21.530001	-2.397324	NA
3	ABCB	45.880001	-1.409645	NA
4	ABG	214.619995	-6.620010	NA

# Remove the low-momentum stocks, only keep the top 50
final_dataframe.sort_values('One-Year Price Return', ascending=False, inplace=True)
final_dataframe = final_dataframe[:50]
final_dataframe.reset_index(drop=True, inplace=True)
final_dataframe.head()

	Ticker	Price	One-Year Price Return	Number of Shares to Buy
0	AMR	389.880005	234.812805	NA
1	IBP	234.089996	122.137169	NA
2	POWL	161.710007	118.683826	NA
3	WDFC	267.000000	97.170700	NA
4	ANF	122.820000	93.830000	NA

# Calculate the number of shares to buy
import math
position_size = float(portfolio_size) / len(tickers)
for i in range(0,len(final_dataframe['Ticker'])):
    final_dataframe.loc[i, 'Number of Shares to Buy'] = math.floor(position_size / final_dataframe['Price'][i])
final_dataframe.head()

	Ticker	Price	One-Year Price Return	Number of Shares to Buy
0	AMR	389.880005	234.812805	4
1	IBP	234.089996	122.137169	7
2	POWL	161.710007	118.683826	10
3	WDFC	267.000000	97.170700	6
4	ANF	122.820000	93.830000	13

Save the output in Excel using XlsxWriter

# Initiate the XlsxWriter object, save the output in xlsx format.
import xlsxwriter

writer = pd.ExcelWriter('quantitative momentum.xlsx', engine = 'xlsxwriter')
final_dataframe.to_excel(writer, 'Recommended Trades', index = False)

Create the formats:

• String format for tickers
• $xx.xx format for stock prices
• $xx,xxx for market capitalization
• Integer format for the number of shares to buy

background_color = '#0a0a23'
font_color = '#ffffff'

string_format = writer.book.add_format(
        {
            'font_color': font_color,
            'bg_color': background_color,
            'border': 1
        }
    )

dollar_format = writer.book.add_format(
        {
            'num_format':'$0.00',
            'font_color': font_color,
            'bg_color': background_color,
            'border': 1
        }
    )

capital_format = writer.book.add_format(
        {
            'num_format':'$#,##0',
            'font_color': font_color,
            'bg_color': background_color,
            'border': 1
        }
    )

integer_format = writer.book.add_format(
        {
            'num_format':'0',
            'font_color': font_color,
            'bg_color': background_color,
            'border': 1
        }
    )

percent_format = writer.book.add_format(
        {
            'num_format':'0.0%',
            'font_color': font_color,
            'bg_color': background_color,
            'border': 1
        }
    )

# Apply the formats to the columns of the output file
column_formats = {
    'A': ['Ticker', string_format],
    'B': ['Stock Price', dollar_format],
    'C': ['One-Year Price Return', dollar_format],
    'D': ['Number of Shares to Buy', integer_format],
}

for column in column_formats.keys():
    writer.sheets['Recommended Trades'].set_column(f'{column}:{column}', 18, column_formats[column][1])
    writer.sheets['Recommended Trades'].write(f'{column}1', column_formats[column][0], string_format)

# Save the output
writer.save()