MAE/MFE-informed Management

What is MAE/MFE-Informed Management?

MAE/MFE-informed management is the practice of setting stops, targets, and trade-management rules from the measured excursion history of a strategy's own trades. Maximum adverse excursion (MAE) is the worst drawdown a trade suffers between entry and exit; maximum favorable excursion (MFE) is the best open profit it reaches. The analytical framework was developed by John Sweeney, who proposed plotting each trade's MAE and MFE against its final outcome to reveal where stops and targets actually belong.

The core insight is empirical: for many strategies, winning trades rarely go very far against the entry first, while losers keep digging. If the MAE scatter shows that almost no eventual winner exceeded, say, 1.2R of adverse excursion, then a stop meaningfully wider than that pays for losers without saving winners. Symmetrically, if the MFE distribution shows most trades reach 2R of open profit but few close there, the exit rules are donating profit back and a target or tighter trail may capture more of what the strategy already generates.

This turns trade management from opinion into measurement, but it inherits every limitation of backtest evidence. Excursion statistics are sample-dependent, regime-dependent, and easy to overfit: tightening a stop to hug last year's MAE cliff can quietly assume next year's volatility. Practitioners typically normalize excursions in R or ATR units, require a reasonable sample, and re-estimate periodically rather than treating the numbers as fixed truths.

How it's calculated

Excursions are measured per trade between entry and exit, then normalized so trades of different sizes and volatilities are comparable.

For short trades, the definitions mirror: MAE uses the highest price against the position and MFE the lowest price in its favor.

Some implementations normalize by ATR at entry instead of initial risk; the choice changes the units, not the concept.

How traders use it

• Setting stop distance: plot MAE of winners versus losers in R or ATR units and place the stop just beyond the region that contains most eventual winners, often paired with ATR-based stop distance so the level adapts to current volatility.
• Placing targets: the MFE distribution shows how far the strategy's trades typically run, which anchors realistic targets within a broader profit target taxonomy instead of round-number wishes.
• Tuning break-even and trailing rules: if trades that reach a given MFE rarely return to the entry, that level is a statistically grounded trigger for break-even moves or the start of a trail.
• Diagnosing exits: a large persistent gap between average MFE and average realized profit per trade flags exit rules as the weak link, often a more productive fix than searching for new entries.
• Honest limitation: excursion statistics need a meaningful sample per setup type and shift with volatility regimes, so rules fitted to a calm period tend to get stopped excessively when conditions change.

MAE/MFE-Informed Management vs related concepts