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Using SQLite and SQL for Persistent Trading Data in MQL5

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Summary

This introduction explains why a trading application may outgrow CSV or text files when it needs organized, searchable, and reliable records. It describes the database model of tables, columns, and rows, then presents MQL5’s native SQLite functions for opening and closing databases, creating tables, executing SQL, reading results, and managing transactions. Examples use trade records to illustrate how a database can support storage and retrieval within an automated trading workflow.

The article also frames a planned mini-ORM, TickORM, as a way to map tables to entities and reduce repetitive SQL across projects. This installment is an overview of database fundamentals and native interfaces, rather than a measured comparison of file and database performance or a demonstration of trading returns. It does not establish that adopting a database improves strategy performance; its relevance is chiefly engineering, especially for maintaining backtest outputs, order histories, and other structured records.

Key ideas

  • Databases organize trading records into related tables, columns, and rows.
  • MQL5 provides native functions for opening SQLite databases and executing SQL statements.
  • Prepared requests and transactions support parameterized reads and grouped changes.
  • An ORM can centralize repetitive database operations behind entity-oriented methods.
  • Database persistence supports research workflows but does not itself demonstrate a trading edge.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.