Validate Data

Unvalidated data silently corrupts models - a single unadjusted stock split can make a momentum signal look 10x stronger than reality.

The Problem

Financial data arrives with missing values, duplicate timestamps, unadjusted corporate actions, stale prices, and impossible OHLC relationships. Using raw data without checks means your model trains on artifacts. A 50% overnight return that is actually a 2:1 split will dominate any feature that touches price changes. You will not see this in your loss function - the model happily fits the noise.

The Pattern

WRONG

import polars as pl

# Trust the data, start modeling immediately
df = pl.read_parquet("prices.parquet")
features = df.with_columns(ret=pl.col("close").pct_change())
model.fit(features)  # Trained on splits, gaps, duplicates