At AlphaBots, we run an algorithmic trading platform that processes live market data across Indian equity and derivatives markets. Every second, we capture 1-second snapshot data and full tick data across Nifty, BankNifty, and equity instruments. It adds up fast — gigabytes of new data every single trading day, compounding.

We store this data for backtesting, strategy validation, and compliance. After a few months of live operation, the storage bill started hurting. Loading large Parquet files for backtesting runs was slow — we were spending more time moving data around than actually running strategies.

We tried Parquet's built-in ZSTD. It helped, but not enough. So we built our own compression engine. Here's what we learned.

The Insight: Tick Data Has Exploitable Structure

Financial tick data is not random. It has properties general-purpose compressors ignore:

Prices move in tiny increments. A Nifty futures price might go 22,450.25 → 22,450.50 → 22,450.25. The raw float64 values look different.…