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Investing in Exchange-Traded Funds (ETFs) can be a smart strategy to diversify your portfolio and achieve long-term financial goals. However, timing your investments can potentially enhance your returns. But is there a best day of the week or month to invest in ETFs? In this article, we’ll explore how to use Python and Yahoo Finance data to identify the optimal days to invest in your chosen ETF.

Understanding the Strategy

Before diving into the code, it’s essential to grasp the underlying strategy:

• Best Day of the Week: Historical data often reveals patterns where certain days of the week yield better returns. For instance, some studies suggest that Mondays may present buying opportunities due to market psychology.

• Best Day of the Month: Similarly, certain days within a month might outperform others, possibly influenced by institutional investment cycles or economic announcements.

By analyzing historical price data, we can identify trends and make informed decisions about when to invest in a specific ETF.

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Setting Up Your Environment

To follow along, ensure you have Python installed on your machine. We’ll use several Python libraries:

• pandas for data manipulation

• yfinance to fetch financial data

• matplotlib and seaborn for visualization

Installation

If you haven’t installed these libraries yet, you can do so using pip:

pip install pandas yfinance matplotlib seaborn

Fetching ETF Data from Yahoo Finance

We’ll use the yfinance library to download historical price data for a specific ETF. For this example, let's analyze the SPDR S&P 500 ETF Trust (SPY).

Sample Code

import pandas as pd
import yfinance as yf

# Define the ETF ticker symbol
etf_symbol = 'SPY'
# Define the time frame for data (e.g., last 5 years)
data = yf.download(etf_symbol, start='2018-01-01', end='2023-10-01', progress=False)
# Reset index to access 'Date' as a column
data.reset_index(inplace=True)
# Display the first few rows
print(data.head())

Explanation

• yf.download: Fetches historical data for the specified ETF.

• start and end: Define the period for analysis.

• reset_index: Converts the index to a column for easier manipulation.

Analyzing the Best Day of the Week

To determine the best day of the week to invest, we’ll calculate the average return for each weekday.

Step-by-Step Guide

1. Calculate Daily Returns: Compute the percentage change in the ETF’s closing price.

2. Extract Weekday Information: Determine the day of the week for each date.

3. Group by Weekday: Aggregate the returns by each weekday.

4. Compute Average Returns: Find the mean return for each day.

Sample Code

import numpy as np

# Calculate daily returns
data['Daily Return'] = data['Adj Close'].pct_change()

# Extract weekday name
data['Weekday'] = data['Date'].dt.day_name()

# Drop the first row with NaN return
data.dropna(inplace=True)

# Group by weekday and calculate average return
weekday_returns = data.groupby('Weekday')['Daily Return'].mean()

# To ensure the weekdays are in order
weekday_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
weekday_returns = weekday_returns.reindex(weekday_order)

print("Average Daily Returns by Weekday:")
print(weekday_returns)

Explanation

• pct_change: Calculates the percentage change between the current and a prior element.

• dt.day_name(): Extracts the day's name from the date.

• groupby and mean: Aggregates and computes average returns for each weekday.

• reindex: Ensures the weekdays are ordered from Monday to Friday.

Determining the Best Day of the Month

Similar to the weekly analysis, we’ll identify which day of the month historically offered the best returns.

Step-by-Step Guide

1. Extract Day of the Month: Get the numerical day (1–31) from each date.

2. Group by Day: Aggregate the returns by each day of the month.

3. Compute Average Returns: Calculate the mean return for each day.

Sample Code

# Extract day of the month
data['Day'] = data['Date'].dt.day

# Group by day and calculate average return
day_returns = data.groupby('Day')['Daily Return'].mean()
# Sort by average return in descending order
day_returns_sorted = day_returns.sort_values(ascending=False)
print("\nAverage Daily Returns by Day of the Month:")
print(day_returns_sorted)

Explanation

• dt.day: Extracts the day of the month.

• groupby and mean: Aggregates and computes average returns for each day.

• sort_values: Sorts the days based on average returns to identify the top-performing days.

Visualizing the Results

Visualizations can provide a clearer understanding of the data trends. We’ll create bar charts for both analyses.

Sample Code

import matplotlib.pyplot as plt
import seaborn as sns

# Set the style
sns.set(style="whitegrid")

# Plot average returns by weekday
plt.figure(figsize=(10, 5))
sns.barplot(x=weekday_returns.index, y=weekday_returns.values, palette='Blues_d')
plt.title('Average Daily Returns by Weekday for SPY')
plt.xlabel('Weekday')
plt.ylabel('Average Daily Return')
plt.show()

# Plot top 10 days of the month
plt.figure(figsize=(12, 6))
sns.barplot(x=day_returns_sorted.index[:10], y=day_returns_sorted.values[:10], palette='Greens_d')
plt.title('Top 10 Days of the Month by Average Daily Return for SPY')
plt.xlabel('Day of the Month')
plt.ylabel('Average Daily Return')
plt.show()

Explanation

• sns.set: Sets the aesthetic style for the plots.

• Bar Plots: Visualize average returns for each weekday and the top 10 days of the month.

• Customization: Titles, labels, and color palettes enhance readability.

Interpreting the Results

After running the analyses:

1. Best Day of the Week: Identify which weekday has the highest average return. For example, if Wednesday shows the highest average, it might be the optimal day to invest weekly.

2. Best Day of the Month: Determine which day(s) of the month consistently outperform others. If the 5th day has the highest average return, scheduling monthly investments on or around this day could be beneficial.

Caveat: Past performance doesn’t guarantee future results. Market conditions change, and various factors can influence ETF performance. Use these insights as part of a broader investment strategy.

Conclusion

Timing the market can be challenging, but historical data analysis provides valuable insights into potential investment opportunities. By leveraging Python and Yahoo Finance data, you can identify days of the week or month that have historically offered better returns for your chosen ETF.

Remember to combine this strategy with other investment principles, such as diversification and risk management, to build a robust portfolio. Happy investing!