How to Calculate Stock Returns in Python | Simple and Logarithmic (in 60 seconds)
Need a quick guide on how to calculate simple returns and log-returns of a stock? This tutorial shows you how to compute both types of returns using yfinance to download stock data and NumPy for calculations—all in under 60 seconds! Whether you're analyzing individual stock performance or creating a returns-based strategy, this is the video for you.
✅ What You’ll Learn in This Video:
Downloading stock data using yf.download().
Calculating simple returns with Pandas.
Computing log returns using NumPy’s np.log().
Key differences between simple and log returns.
Why This Tutorial? Log returns are essential for financial modeling and portfolio analysis. This quick breakdown helps you understand when and why to use each type of return while keeping things short and simple.
📊 Who Is This For? Perfect for finance professionals, Python learners, and data science enthusiasts looking to enhance their financial analysis toolkit.
🔗 Topics Covered:
Financial Returns in Python
Simple vs. log returns
Practical use cases in portfolio analysis
Python finance tips
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Code from Tutorial:
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import yfinance as yf
import numpy as np
df = yf.download('SPY')
df['Adj Close'].pct_change()
np.log(df['Close'] / df['Close'].shift(1))
***