Maximum Adverse Excursion

Maximum Adverse Excursion (MAE) measures the worst unrealized loss a trade reached at any point during its lifetime — from entry to exit — regardless of the final outcome. Formalized by John Sweeney in Campaign Trading (1996), MAE is an execution metric that reveals the peak pain of each trade, exposing information that final P&L numbers permanently conceal. Understanding your MAE distribution is what separates statistically grounded stop placement from guesswork.

Formula & Calculation

MAE (Long) = Entry Price - Intrabar Low

MAE (Short) = Intrabar High - Entry Price

Where:

• Entry Price = The fill price when the position was opened
• Intrabar Low = The lowest price reached at any point while the trade was open (for longs)
• Intrabar High = The highest price reached at any point while the trade was open (for shorts)
• MAE = Typically expressed in dollars, then converted to a multiple of initial risk (R)

To calculate MAE as an R-multiple: divide the dollar MAE by your initial planned risk per trade. A trade with $180 of adverse excursion against a $100 planned stop has an MAE of 1.8R.

The critical data requirement is intrabar price extremes, not just entry and exit fills. Most traders only log entry price and exit price — this completely omits MAE. You need the intrabar low (long trades) or intrabar high (short trades) for every bar while the trade was open, then take the extreme across the entire trade duration.

Benchmarks

Level	Range	What It Means
Excellent	MAE below 0.5R	Minimal heat — trade moved in your favor almost immediately
Acceptable	MAE 0.5R to 1.0R	Normal drawdown within initial risk parameters
Caution	MAE 1.0R to 2.0R	Trade required more heat than planned; review entry timing
Poor	MAE above 2.0R	Exceeded twice the initial risk; historically very low recovery rate

Note: these benchmarks apply at the individual trade level. The goal of MAE analysis is to find the threshold specific to your setup where winners cluster below it and losers cluster above it — that threshold becomes your data-driven stop level.

Practical Example

A trader takes 50 SPY breakout trades over 3 months and imports them into a journal with MAE tracking enabled. After plotting MAE vs. final P&L on a scatter chart, a clear pattern emerges: the 31 winning trades (average final P&L of +$210) had MAE values ranging from $0 to $165, with no single winner exceeding $165 of adverse excursion. The 19 losing trades (average final P&L of -$310) had MAE values ranging from $180 to $520.

The data shows a clean break at $170 — no winning trade ever exceeded $170 of heat. The trader’s current stop was set at $250, a round number below the breakout level.

By tightening the stop to $175, backtesting the same 50 trades produces this result: 3 previously losing trades that hit $200 MAE then recovered to a loss at -$310 would instead be stopped at -$175, saving $135 each ($405 total). Only 1 winning trade would have been prematurely stopped, costing approximately $180. Net improvement across 50 trades: approximately +$225, with no change to entry logic.

This is the core power of MAE analysis — using your own trade history as a statistical dataset to derive a personalized threshold.

How to Track Maximum Adverse Excursion

1. Capture intrabar extremes for every trade — Record the lowest price (longs) or highest price (shorts) reached at any point from entry to exit. Tick-level or 1-minute bar data is sufficient for most day traders.
2. Calculate dollar MAE per trade — For longs: (Entry Price - Intrabar Low) × shares or contracts. Convert to R-multiples by dividing by initial planned risk.
3. Log MAE alongside final P&L — Every trade record should have: entry price, exit price, intrabar extreme, MAE in dollars, MAE in R, and final P&L.
4. Build a scatter plot after 50+ trades — Plot MAE on the X-axis and final P&L on the Y-axis. Look for the threshold where winners cluster on the left and losers extend to the right.
5. Segment by setup type — Run this analysis separately for each distinct setup. A gap-and-go strategy may have a threshold of 0.3% MAE while a mean-reversion strategy may tolerate 1.5% before the recovery odds collapse.

How to Improve Maximum Adverse Excursion

1. Replace arbitrary stops with your empirical MAE threshold — Once you have 50+ trades per setup, set stops at the MAE threshold, not at round numbers or fixed ATR multiples. A threshold of $170 is more defensible than “$250 because it looks clean on the chart.”
2. Improve entry timing to reduce average MAE — Entering on a confirmed retest of a level rather than the initial breakout typically reduces average MAE by 30-50%, because you are entering closer to where the trade is proven wrong.
3. Exit positions that breach 2R of adverse excursion immediately — Trades exceeding 2R of MAE have a very low historical recovery rate in trend-following systems. Holding past this level converts a calculated risk into a hope trade.
4. Analyze MAE separately per instrument — Your AAPL breakout trades may tolerate $0.80 of heat, while your ES scalps should be cut at 2 points. Combining them into one threshold produces a number that fits neither strategy well.
5. Cross-reference MAE with MFE — If average MAE is 1.2R but average MFE is only 1.5R, you are absorbing a lot of heat for small gains. This ratio reveals whether your setup has edge or is simply noise.

Common Mistakes

1. Logging only entry and exit prices — This makes MAE tracking impossible. Without intrabar data, you have no visibility into the actual heat experienced during the trade’s lifetime.
2. Applying one MAE threshold across all setups — Different strategies have fundamentally different heat profiles. Mixing gap trades with mean-reversion trades in one MAE analysis produces a meaningless average.
3. Drawing conclusions from fewer than 50 trades — Below this sample size, a single outlier trade can shift the apparent threshold by $50 or more. Wait for statistical weight before changing your stop rules.
4. Treating high-MAE winning trades as validation — A winning trade that dipped to -$400 before recovering to +$150 is not evidence that wide stops work. It is evidence of surviving a low-probability event. The risk of ruin compounds over many such trades.
5. Never revisiting the threshold — As your trade execution improves or market conditions shift, your MAE distribution changes. Recalculate the threshold every 100 trades or after major strategy modifications.

How JournalPlus Calculates Maximum Adverse Excursion

JournalPlus captures MAE automatically when intrabar price data is available from your connected broker or imported trade file. The analytics dashboard displays your MAE distribution as a scatter plot against final P&L, with the statistical threshold highlighted based on the clustering of your winning trades. You can filter by setup tag, instrument, or date range to run MAE analysis on any subset of your trade history. The trade log view shows MAE in both dollar terms and R-multiples for every individual trade, and the planned vs. actual risk comparison report cross-references your recorded stops against observed MAE to flag trades where you held beyond your own rules.