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FMZ Backtesting: Tick Modes, Data Granularity, and Performance Metrics

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Summary

This guide explains how FMZ evaluates trading strategies on historical market data. It contrasts simulated Tick backtests, which construct intrabar price events from candles, with live-data Tick backtests, which replay collected tick, depth, and trade records. The latter can better represent tick-driven logic but runs more slowly and is constrained by data availability and size. Both modes use full-fill matching, so they cannot reproduce partial fills. The guide also explains why coarser data can change trade counts and results, and recommends finer granularity when practical.

Additional sections cover supported strategy languages and markets, parameter sweeps, saving backtest settings, custom data feeds, and the platform’s Sharpe calculation. Examples illustrate how configuration and data structure affect a run. Backtests remain historical simulations: they do not establish future profitability. Results also depend on data quality and resolution, and the stated fill model omits partial executions, limiting conclusions about real-world execution.

Key ideas

  • Simulated Tick mode derives intrabar events from candle data, while live-data mode replays recorded ticks.
  • Backtest resolution can alter trade counts and profit outcomes, so data granularity matters.
  • The documented matching engine assumes orders fill fully and cannot model partial fills.
  • The guide describes parameter sweeps, saved configurations, and custom historical data sources.
  • Backtest metrics and historical performance do not guarantee future results.

Tags

BacktestingExecutionMarket microstructureStatisticsRisk management

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