Maximum Adverse Excursion

Maximum adverse excursion, usually shortened to MAE, is the furthest a trade traveled against you at any moment while it was open, measured from your entry price. Traders sometimes call it the heat a trade took. It says nothing about whether the trade won or lost. It describes what happened in between.

Most journals record entry, exit, and result. MAE records the shape of the trade, and that turns a question people usually argue about ("is my stop too tight?") into something you can settle with your own data. The concept was set out by John Sweeney in Maximum Adverse Excursion (Wiley, 1996), which made the case that the path inside a trade carries information the outcome alone throws away.

How MAE Works

While a position is open, price wanders. MAE is the single worst point of that wander, from your side of the trade.

MAE (long) = entry price − lowest price reached while open

MAE (short) = highest price reached while open − entry price

The trade above entered at 0R with a stop at −1R. It spent the first stretch underwater, bottoming at 0.60R below the entry, then turned and closed at +1.50R. The result line in your journal says "+1.50R, winner." The MAE says the trade was 0.60R against you before it worked, which is the part that would have mattered if your stop had been at 0.5R.

You can record MAE in three units, and they answer different questions:

R is the one that does real work. Because 1R is defined by your stop-loss, an MAE in R tells you directly how close a trade came to being stopped out. An MAE of 1.0R means the stop was hit. An MAE of 0.35R means the trade never came close.

Reading an MAE Chart

MAE gets useful in bulk. Plot every trade with MAE on one axis and the result on the other, and a picture appears that no single trade can show you.

Each dot above is one trade, blue for winners and red for losers, placed by its MAE on the horizontal axis and its result in R on the vertical. The dashed line marks a 0.6R stop, and everything to the left of it would have survived one. In this sample of 60 trades, the winners sit almost entirely on the left. Twenty-three of the 24 winners never went more than 0.6R against the entry. The losers gather on the right, most of them at a full 1R, which is simply the stop working as designed.

That shape is the whole reason to track MAE. It suggests the trades that eventually worked declared themselves early, and the ones that spent a long time deep underwater mostly did not recover. If your own data looks like this, a stop at 0.6R would have kept nearly every winner while cutting the losers sooner and smaller.

What MAE Tells You About Your Stops

Three patterns come up often enough to name.

Winners cluster well inside the stop. Most profitable trades show a small MAE and your stop sits far beyond it. The stop is doing very little work, and you are paying for that room in position size, because a wider stop means fewer shares for the same risk.

Winners regularly reach deep into the stop. Plenty of eventual winners show an MAE of 0.8R or more. Here the room is earning its keep, and tightening would remove trades that pay you.

Losers and winners overlap completely. MAE does not separate them at all. That is worth knowing too. It says the excursion carries no signal for this setup and your stop should be set from structure or volatility instead.

There is a fourth pattern that has nothing to do with stops. If a lot of trades show an MAE just past 1R, you are getting stopped out on wicks and spikes. That points at placement, not distance, and often at a stop sitting on an obvious level where a cluster of other orders lives.

MAE, MFE, and Drawdown

These three describe losses at different scales, and mixing them up is common.

MAE and MFE together describe the full range a trade traveled through. Drawdown describes what a sequence of trade results did to your balance. Keeping every MAE small does not protect you from a drawdown, because a drawdown can be built entirely from ordinary, well-behaved losses arriving in a row.

A Worked Example

You buy 200 shares at $42.00 with a stop at $40.00, so 1R is $2.00 per share and $400 on the trade.

The journal entry reads +2.00R. The MAE of 0.65R tells you the trade nearly used two thirds of its allowed room before it turned. Add ten more trades like it and you start to see whether that is normal for the setup or whether this one was unusual.

Key Takeaways

• MAE is the worst unrealized loss a trade showed while open, measured from entry, whatever the trade eventually did.
• Recorded in R, it tells you how close each trade came to hitting your stop.
• Across a sample, MAE shows how much room your winners actually needed, which turns stop placement into a data question.
• MAE is per trade. Drawdown is per account. They are not substitutes.
• A cluster of MAEs just past 1R usually points to where the stop sits, not how far away it is.

Common Mistakes

Reading it on too few trades. Twenty trades will produce a convincing-looking chart that means nothing. This is a distribution, and distributions need sample size.

Mixing setups together. A breakout and a mean-reversion entry have completely different excursion profiles. Combined, they average into a shape that describes neither. Slice by setup first.

Tightening the stop straight to the observed maximum. If your worst winning MAE was 0.62R, a 0.65R stop looks perfect on the old data and will be too tight on the next hundred trades. The historical maximum is a floor, not a target.

Forgetting that a tighter stop changes position size. Halving your stop distance doubles your share count for the same risk, which changes slippage, fills, and how much a gap costs you.

Confusing MAE with the loss you took. A trade with a 0.9R MAE that closed at +2R lost nothing. The excursion is information about the path, not the outcome.

How JournalX Tracks MAE

Because JournalX captures your entry and your planned stop before the trade, it knows your 1R from the start, which is what makes an MAE in R meaningful rather than a raw dollar figure. Trades sync from your broker or import by CSV, so the excursion data builds up without extra logging work.

From there you can group trades by setup, session, or symbol with stackable filters and compare the excursion profile of each one, then read it alongside your R-multiple distribution, win rate, and expectancy. The question changes from "does this stop feel right?" to "what have my last 200 trades actually done inside the stop?", which is a question with an answer.