Descriptions:
Algorithmic traders and quantitative researchers often find that the bottleneck in their workflow isn’t strategy logic—it’s the time spent loading and managing large price datasets. In this tutorial from the Algovibes channel, the host walks through a practical local data architecture that dramatically speeds up market data access using two open-source tools: Parquet and DuckDB.
The approach centers on a simple but effective file layout: instead of one monolithic CSV or SQL table, each symbol-and-date combination is stored as its own small Parquet file. Parquet’s columnar binary format means only requested columns—such as timestamps and close prices—are read into memory, skipping unnecessary parsing. DuckDB then enables standard SQL queries to run directly on those files without a database server or import step.
The tutorial demonstrates the complete workflow in Python, starting with downloading one-minute OHLCV bars via yfinance, grouping data by symbol and date, and writing each group to disk. Querying across multiple days feels like working with a single unified table while only touching the relevant files under the hood. Incremental updates are trivial—rerunning tomorrow only overwrites tomorrow’s files. The resulting local data store is designed to support fast backtesting, technical indicator calculation, and machine learning pipelines without the overhead of traditional database infrastructure.
📺 Source: Algovibes · Published January 26, 2026
🏷️ Format: Tutorial Demo
