First Operations in Data & Python

Practising, making mistakes and correcting them during these first days, I was focused on building reusable functions in Python, and calculating financial metrics using functions.

Main Goals:

  • Definition and call of simple functions
  • Metrics (Volatility and Daily returns) calculation (manually)
  • Structure code with a more readable logic

Step by Step

📍 Step 1: Defined a calc_daily_returns() function.

📍 Step 2: Added calc_annualized_return() and volatility calculations.

📍 Step 3: Tested functions with multiple tickers (in our case, I used a sample of 3 assets).

Challenges / Insights

  • Mixed up local vs global variables inside functions.
  • Made a comparison between .sum() vs .mean() for metric analysis.
  • First approach with pandas.Series modules.

Code Snippet Final

```python
def calc_daily_returns(prices):
    return prices.pct_change().dropna()

def analyse_returns(daily_returns):
    avg = daily_returns.mean()
    vol = daily_returns.std()
    return {
        "Annualized Return": (1+avg)**252 - 1,
        "Annualized Volatility": vol*np.sqrt(252)
    }
```

Next Step

👉 Work with pandas.DataFrame directly, instead of raw lists, for a faster and more accurate job.

You’ll find my projects here: