financial-data-analysis

Financial Data Analysis

A practical skill for sourcing, processing, and analyzing financial data in academic research contexts. Covers data acquisition from public APIs, cleaning workflows, and standard analytical techniques used in empirical finance research.

Data Acquisition

Public Financial Data Sources

Fetching Price Data

import yfinance as yf
import pandas as pd

def fetch_stock_data(tickers: list[str], start: str, end: str) -> pd.DataFrame:
    """
    Fetch adjusted close prices for a list of tickers.

    Args:
        tickers: List of ticker symbols (e.g., ['AAPL', 'MSFT'])
        start: Start date (YYYY-MM-DD)
        end: End date (YYYY-MM-DD)
    Returns:
        DataFrame with adjusted close prices
    """
    data = yf.download(tickers, start=start, end=end, auto_adjust=True)
    prices = data['Close'] if len(tickers) > 1 else data[['Close']]
    prices.columns = tickers if len(tickers) > 1 else tickers
    return prices

# Fetch 5 years of data
prices = fetch_stock_data(['AAPL', 'MSFT', 'GOOGL'], '2020-01-01', '2025-01-01')
print(prices.head())

Macroeconomic Data from FRED

from fredapi import Fred

fred = Fred(api_key=os.environ["FRED_API_KEY"])

# Common series for finance research
series_ids = {
    'GDP': 'GDP',
    'CPI': 'CPIAUCSL',
    'Fed_Funds_Rate': 'FEDFUNDS',
    'Unemployment': 'UNRATE',
    '10Y_Treasury': 'DGS10',
    'VIX': 'VIXCLS'
}

macro_data = pd.DataFrame()
for name, sid in series_ids.items():
    macro_data[name] = fred.get_series(sid, observation_start='2000-01-01')