Using ChatGPT to Build and Verify Synthetic K-Line Data

Summary

This article demonstrates how to use ChatGPT inside FMZ’s strategy editor to help design, explain, and refine trading code. Its worked example combines one-minute bars into longer minute-based bars by grouping records into time intervals and aggregating their open, high, low, close, and volume values. It describes improving the prompt with details about the data structure, alignment, programming language, and charting requirements, then comparing the synthesized five-minute bars with the backtest chart.

The article also surveys editor features such as renaming symbols, formatting code, finding definitions and references, and viewing shortcuts. Its evidence is a visual comparison offered as a preliminary check, not a rigorous validation across missing records, irregular timestamps, or other periods. The example is a learning aid; ChatGPT-generated code still depends on clear specifications and should be checked for correctness before use in a trading system.

Key ideas

• Clear, complete requirements can improve the usefulness of AI-generated code.
• The example aggregates one-minute bars into longer bars using interval-based timestamps and standard OHLCV rules.
• A chart comparison offers a preliminary way to inspect synthesized bars against the platform’s backtest data.
• FMZ’s editor includes navigation, formatting, and symbol-renaming tools that can support code maintenance.
• Generated code should be independently checked for data gaps and boundary cases before deployment.

Tags

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