EdgeQuery Research Guide

MAE and MFE analysis for trading strategies

Maximum adverse excursion and maximum favorable excursion describe the path a trade traveled before it closed. That makes them useful for diagnosing stops, targets, exits and giveback—not just reporting final P&L.

What MAE and MFE mean

Maximum adverse excursion (MAE) is the worst unrealized movement against a trade while it was open. Maximum favorable excursion (MFE) is the best unrealized movement in the trade's favor. Expressing them in dollars, points, ticks or R-multiples makes it possible to compare trades with different sizes and risk.

Why final trade results are not enough

A trade that finishes at +0.5R might have reached +2R first and given most of it back. Another +0.5R trade may never have exceeded +0.6R. The final result is identical, but the exit problem is completely different. The same is true for losses: a -1R stop that never traded favorably is different from a -1R stop that first reached +1.5R.

Questions MAE/MFE can help answer

Use distributions, not a single average

Averages can hide tails and clusters. Examine percentiles and the full distribution of MAE and MFE. A stop that looks reasonable at the average may still sit inside the normal adverse excursion of a large fraction of profitable trades. Likewise, a target selected from average MFE may depend on a few unusually large moves.

Normalize to risk when possible

R-multiples make comparisons easier when position size or stop distance changes. MAE of -0.4R means something comparable across trades even if one trade risked $100 and another risked $500. This is particularly useful when the strategy sizes positions dynamically.

Combine excursion with time

Time adds another diagnostic layer. Measuring time to MFE, time to MAE, and time since the trade's high-water mark can identify trades that stall, reverse after an early burst, or require more time than the current exit allows.

What EdgeQuery is building

EdgeQuery is a research platform for automated traders that analyzes trade history alongside market context. The objective is to diagnose why a system works, where it struggles, and what may be worth testing next—not to provide investment advice or promise future performance.

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