Fetch Data

Blindly loading data without validation produces silent schema drift, missing rows, and type errors that surface deep in modeling code.

The Problem

Financial data providers change schemas, go offline, or return partial results without warning. Loading a CSV and trusting the columns exist, have the right types, and cover the expected date range is the single most common cause of broken pipelines. The failure mode is silent: your model trains on garbage and you only discover it when results make no sense.

The Pattern

WRONG

import pandas as pd

# Blind load - no schema check, no gap detection, no type enforcement
df = pd.read_csv("etf_prices.csv")
returns = df["close"].pct_change()  # Might be string column