Running FMZ Python Strategies in a Local Backtest Engine

Summary

This guide explains how to run FMZ strategies through a local Python backtest engine. It describes passing settings such as dates, bar interval, exchange, instrument, and starting balances through a configuration string, then initializing the engine and calling exchange functions. The examples show how to retrieve account and ticker data, write runtime logs, and collect a final results object when the simulation ends.

The document also outlines a basic strategy loop and notes that the backtest can stop by raising an end-of-data exception, after which the result can be joined and printed. Example output includes account state, simulated quotes, runtime logs, and summary fields. This is an interface and workflow tutorial rather than a trading strategy evaluation: it provides no performance comparison, validation methodology, or discussion of data quality and simulation assumptions. Its sample configuration and market data are specific to the illustrated setup, so they should not be treated as general evidence of strategy performance.

Key ideas

• A local backtest run is initialized from a configuration string containing the simulation period and exchange settings.
• The examples demonstrate retrieving simulated account and market data through exchange methods.
• Runtime messages are captured in the joined backtest results alongside account and simulation metadata.
• A strategy loop can run until the backtest reaches the end of its available data.
• The guide explains engine usage but does not assess trading performance or simulation limitations.

Tags

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