First Steps with Financial Data with Python

Today I started my journey into Python for Finance. The first step was to familiarize myself with the language basics and apply them immediately on financial datasets.

Main Goals:

  • First approach with python basics and standard
  • Download financial data with yfinance
  • Calculate daily and annualized returns

Step by Step

📍 Step 1: Installed and imported yfinance and pandas.

📍 Step 2: Downloaded 60 days of Apple, Microsoft, and Google data.

📍 Step 3: Wrote a function to calculate daily returns and annualized returns.

Challenges / Insights

  • Handling errors when looping through lists (indexing issues).
  • Realized the importance of .pct_change() instead of manually coding formulas.
  • First exposure to the difference between return and print.

Code Snippet Final

```python
import yfinance as yf, pandas as pd

data = yf.download(['AAPL','MSFT','GOOGL'], period='60d')
close = data['Close']

returns = close.pct_change().dropna()
annualized_return = (1 + returns.mean())**252 - 1
```

Next Step

👉 Apply daily returns logic to more assets and explore volatility.

Here are the project links below: