What MAE and MFE measure
Maximum Adverse Excursion (MAE) is the maximum loss a trade experienced before it closed — whether it ultimately won or lost. If a trade went 30 pips against you before reversing and closing at +40 pips profit, your MAE was -30 pips.
Maximum Favourable Excursion (MFE) is the maximum profit a trade reached before it closed. If a trade ran to +60 pips before reversing to close at +20 pips, your MFE was +60 pips but your realised P&L was +20 pips. You left 40 pips on the table.
What MAE tells you about your entries
If your winning trades consistently show large MAE (they go far against you before recovering), your entries are imprecise. You're getting the direction right but entering too early or in the wrong position in the range. Tightening entries reduces MAE and improves your R:R.
If your losing trades show very small MAE (they barely moved against you before hitting the stop), your stops might be too tight — placed at technically obvious levels where the market sweeps before moving in your direction.
What MFE tells you about your exits
Compare your average MFE to your average realised profit. If your average MFE is 80 pips but average realised profit is 35 pips, you're capturing less than half the available move. This is the single most common and expensive problem in retail trading.
The "left on table" calculation: For every winning trade, subtract your realised profit from your MFE. Sum this across 50 trades. That number — the total left on the table — is the direct cost of your exit psychology. For most traders, it's eye-opening.
How Toastlytics uses MAE/MFE
Toastlytics automatically fetches historical price data for your trade's symbol and date range, calculates MAE and MFE for each trade, and shows you the aggregate "left on table" figure in your analytics dashboard. You don't need to do this manually — you just need a Pro subscription and the data does the rest.
The goal isn't to capture 100% of MFE every time — that's impossible without knowing the future. The goal is to identify systematic patterns in your exit behaviour and address them with specific rules.