Building a Parquet and DuckDB Data Store for Low-Frequency Strategy Research
Summary
This article sets out a workflow for moving quantitative trading research from an OLTP database such as SQLite to an OLAP-friendly store built from Parquet files and queried with DuckDB. It demonstrates exporting an entire SQLite database or selected tables, then querying the resulting files with SQL. Parquet metadata and lazy evaluation are introduced as features that can support faster analytical scans than text-based CSV workflows.
The proposed store uses Hive-style filesystem partitioning and locally maintained historical market data to support updates, filtering and joins across data sources. The stated purpose is to screen strategy ideas and run analyses before investing time in full Expert Advisor backtests. As an illustration, the article compares a Golden Cross momentum setup across Forex and index symbols on daily data, but the supplied text does not give its findings. This is a data-engineering guide rather than evidence that the sample strategy is profitable; the results depend on the broker data and implementation used.
Key ideas
- DuckDB can export SQLite tables into Parquet files for analytical use.
- Parquet stores typed, column-oriented data and metadata that can help analytical queries avoid unnecessary reads.
- Hive-style directory partitioning is proposed to make historical market data portable and easier to query.
- A local data store can combine sources and support rapid screening before developing or backtesting Expert Advisors.
- The Golden Cross comparison is presented as an example analysis, not as evidence of strategy performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.