Data is the fuel of any algorithmic strategy. But not all data storage is created equal.

Here is a common scenario: You subscribe to a WebSocket stream and save every tick to a CSV file. After one month, you have a 5GB CSV. You try to open it in Python to run a backtest. Your computer freezes.

Financial data is a specific beast. It is highly structured, strictly chronological, and massive in volume. You need specialized tools.

The Problem with SQL (Postgres/MySQL)

Standard Relational Databases are designed for transactional data (e.g., user profiles, bank balances).

When you try to insert 10,000 ticks per second into a standard SQL table, the database has to update indexes, check constraints, and write to disk. It chokes. Furthermore, querying "Give me the average price of NIFTY for every 5-minute interval" is a very computationally expensive operation for SQL.

Enter: Time-Series Databases (TSDB)

TSDBs are optimized specifically for time-stamped data. They treat time as a first-class citizen.

1. InfluxDB

The most popular open-source option.
Pros: incredibly fast write speeds (millions of points per second). Great compression (stores data in 10% of the space of CSV).
Cons: Uses its own query language (Flux/InfluxQL), not standard SQL.

2. TimescaleDB

This is essentially PostgreSQL on steroids.
Pros: You can use standard SQL! Compatible with all your existing Python drivers.
Cons: Slightly heavier on disk usage than Influx.

Why Compression Matters

Tick data for one symbol for one year can be 50GB. With InfluxDB’s compression algorithms (Gorilla), this can shrink to 2GB.

This allows you to keep High Resolution Tick Data for years, enabling you to backtest your scalping strategies on realistic historical spreads.

The Hybrid Approach

At AlgoDevStudio, we recommend a hybrid architecture:

• Redis: For "Hot" data. The latest price, the current order book. Used for live decision making.
• TimescaleDB: For "Warm" data. Intraday candle history. Used for indicators.
• S3 / Parquet Files: For "Cold" data. Archived years of history. Used for deep research.