Maximum Adverse Excursion (MAE) is the largest unrealized loss a position experiences at any point during its life, before the trade closes. Introduced by John Sweeney in his 1996 book Campaign Trading, MAE gives traders a factual answer to the question: “How far against me did this trade go?” — a number that exists regardless of whether the trade ultimately won or lost.
Key Takeaways
- MAE captures the worst intra-trade paper loss on every trade, including trades that recovered and closed profitably — making it the foundation for data-driven stop-loss placement.
- Plotting MAE against final P&L across 100 or more trades reveals a boundary: winning trades cluster below a specific adverse excursion threshold, while losers extend beyond it.
- A widely cited rule of thumb: if the MAE of a winning trade exceeds 2x your average winner size, your stop is too loose and you are accepting unnecessary risk per trade.
How to Calculate MAE
For a long position, MAE equals the entry price minus the lowest price the position reached during the trade. For a short position, it is the highest price reached minus the entry price. The result is always a positive number.
MAE (long) = Entry Price − Lowest Intrabar Price During Trade
MAE (short) = Highest Intrabar Price During Trade − Entry Price
MAE (%) = MAE / Entry Price × 100
Tracking MAE requires intrabar low data for longs and intrabar high data for shorts. Most brokers do not include this in standard trade exports, which is why trade journals that connect directly to broker APIs are essential — they can capture intrabar OHLC automatically, whereas manual Excel logs cannot.
Quick Reference
| Aspect | Detail |
|---|---|
| Formula | Entry Price − Lowest Price (long); Highest Price − Entry Price (short) |
| Good Range | Strategy-specific; derived from your own scatter-plot analysis |
| Warning Signs | MAE on winners exceeds 2× average winner size; most trades touch stop before moving in your favor |
Practical Example
A day trader buys 200 shares of AAPL at $175.00 expecting a breakout. During the trade, price dips to $173.50 before recovering and closing at $177.00.
- MAE = $175.00 − $173.50 = $1.50/share ($300 total)
- Final profit = $2.00/share ($400 total)
The trader had a mental stop at $172.00 ($600 max risk per trade). After running MAE analysis across 150 historical AAPL breakout trades, the data shows that 88% of winning trades never exceeded $1.80 adverse excursion, and any trade that reached $1.81+ adverse was a loser 79% of the time.
Armed with this, the trader sets a hard stop at $173.20 — a $1.80 MAE threshold. Max risk drops from $600 to $360 per trade. Historical data suggests this tighter stop would have exited only 12% of eventual winners early, a worthwhile trade-off given the reduction in average loss on the remaining 79% of losers.
This is the core of the MAE scatter-plot method: plot MAE on the x-axis and final P&L on the y-axis for all trades. The point where winner and loser clouds diverge becomes the data-driven stop level.
Maximum Adverse Excursion, or MAE, measures the worst paper loss a trade reached before closing. By charting this number across many trades, traders can identify the exact loss level beyond which a trade almost never recovers — turning stop placement into a data decision instead of a guess.
Common Mistakes
- Ignoring MAE on winning trades. Many traders only review trades that lost. MAE on winners reveals how much pain was accepted unnecessarily — a trade that went $900 against you before closing up $100 is a warning sign, not a success.
- Setting stops from price structure alone. Support and resistance levels provide context, but without MAE data, traders cannot know whether their stops match the actual behavior of their winning trades. A stop below support that sits 3% away may be far beyond the threshold where winners actually survive.
- Conflating MAE with drawdown. Drawdown measures portfolio-level equity decline across multiple trades. MAE is a single-trade metric. Both matter, but they answer different questions.
- Skipping the scatter plot. Reading a single trade’s MAE is not enough. The insight comes from volume: 50 trades give a rough signal, 150+ trades give a reliable boundary. For ES futures traders, for example, MAE analysis can quantify exactly where the noise floor sits — a 2-point ($100/contract) move is often market noise, while a 5-point ($250) adverse move frequently precedes trade failure.
How JournalPlus Tracks MAE
JournalPlus automatically calculates MAE for each trade when broker data includes intrabar OHLC, eliminating the manual effort that prevents most retail traders from using this metric. The analytics dashboard surfaces your MAE distribution alongside final P&L so the scatter-plot boundary becomes visible without any spreadsheet work. Over time, the system highlights trades where MAE on winners exceeded your historical threshold — flagging entries that accepted more risk than your edge justifies.
