Reviewing swing trades: metrics and journaling

What you will learn

• What maximum adverse excursion (MAE) and maximum favourable excursion (MFE) measure
• How to calculate exit efficiency and use it to judge your exit rules
• How to split results by setup and by holding time
• Why a sample of a few trades cannot prove that a rule works

Explanation

Win rate, average R, expectancy, profit factor and maximum drawdown are covered in the course Risk Management, and the basics of a trading journal in the introductory trading course. This lesson adds the fields that matter most for trades held over several days.

MAE and MFE

Maximum adverse excursion (MAE) is the worst point a trade reached against you while it was open, measured in R. Maximum favourable excursion (MFE) is the best point it reached in your favour, also in R. For a long trade with 1R = €2.00, a low of €1.00 below entry is an MAE of −0.5R; a high of €6.00 above entry is an MFE of +3R. Use daily highs and lows between entry and exit.

• MAE of winners shows how much heat good trades usually take. If winners rarely go beyond −0.5R, that is an observation to watch, not yet a reason to tighten stops.
• MFE of losers shows trades that were in profit before they lost. Many losers with MFE above +1R suggest an exit or break-even rule worth testing.

Exit efficiency

Exit efficiency = realised R ÷ MFE

For a group of winners, divide the sum of realised R by the sum of MFE. An efficiency of 60% means you kept 60% of the best open profit. Trailing stops always give back some profit, so 100% is not a goal; the number is useful for comparing exit plans on the same setup.

Other swing-specific fields

• Setup tag: for example TP (trend pullback) or BO (breakout-and-retest), to compare setups separately.
• Days held: sessions from entry to exit. It also helps you set a time stop.
• Exit reason: target, trailing stop, initial stop, time stop or rule break.
• Gap flag: mark any loss larger than −1R and note the cause (news, earnings, market gap).

Reading a small sample

Ten or twenty trades contain a lot of chance. Use them to find questions and possible errors, not to prove a rule. A common practice is to collect at least 30 trades per setup before changing a rule, and to change one rule at a time.

Worked example

Hypothetical example: ten closed swing trades, all values in R. Fees, spreads and taxes are already included in the R results for simplicity.

1. Total = +6.1R; expectancy = 6.1 ÷ 10 = +0.61R per trade.
2. By setup: TP 6 trades, +6.2R, +1.03R per trade. BO 4 trades, −0.1R, −0.03R per trade (one large winner, three losers).
3. Exit efficiency of the six winners: realised 10.4R ÷ MFE 17.0R = 61%. Trades 3 and 8 kept only 25% and 11% of their MFE.
4. MAE of winners: never worse than −0.6R.
5. Trade 4 lost 1.3R: 0.3R beyond plan, flagged as a gap. Trade 9 reached +1.2R before losing 1R.
6. Days held: winners average 10 days, losers 3.75 days.

Questions for further testing, not conclusions: Does BO need a stricter filter? Would a partial exit near +2R help TP trades like 3 and 8? With only ten trades, none of these patterns is reliable yet.

Common mistake

Practice activity

On paper: five trades with (result, MFE) in R: (+1.8, +2.5), (−1.0, +0.3), (+0.9, +2.6), (−1.0, +1.1), (+2.4, +3.0). Calculate the expectancy, the exit efficiency of each winner, and the combined exit efficiency of the winners. Name one question the numbers raise.

Key takeaways

• MAE and MFE show how far trades moved against and for you while open.
• Exit efficiency (realised R ÷ MFE) compares exit plans on the same setup.
• Tag setups, days held, exit reasons and gaps to find specific problems.
• Use small samples to form questions; change one rule at a time.

Next step

In the final lesson you will combine everything into your written swing-trading playbook.