Am I Revenge Trading? What It Looks Like in Your Data

Revenge trading is usually described from the inside: the heat after a stop-out, the urge to get it back, the sense that the market owes you one. That description is accurate, but it is not much use after the fact. Feelings fade by the evening and get reconstructed generously by the weekend. What does not fade is the record. Every trade leaves a timestamp, a size and a position in a sequence, and revenge trading, if it is happening, leaves a recognisable shape in all three.

This post describes that shape and how a trader might look for it in their own history. It does not tell you what to do about it. That part is yours.

Three signatures

Revenge trading tends to show up as some combination of three measurable patterns.

The gap shrinks. The time between closing a losing trade and opening the next one is shorter than the time after a winning trade.

The size grows. Trades taken after a loss are larger than the trader's usual size, and larger again after two or three losses in a row.

The losing day gets busier. Trades bunch up late in sessions that are already down, rather than spreading evenly across days regardless of how they are going.

None of these proves anything on its own. A short gap can be a planned re-entry. A larger size can be a deliberate scaling rule. But when all three appear together, and appear after losses rather than after wins, the data is describing something the trader may not have noticed from the inside. Losing streaks themselves are normal, and how long one can last is a question of maths. What a trader does during one is a question of behaviour, and behaviour is what these three patterns measure.

A hypothetical day

The numbers below are invented for illustration. Times are New York time, size is shown as a multiple of the trader's usual size, and a full loss at usual size is £100.

#	Entry	Exit	Gap since last exit	Size	Result	Day so far
1	09:48	10:15	n/a	1x	+£120	+£120
2	10:52	11:10	37 min	1x	-£100	+£20
3	11:14	11:29	4 min	1x	-£100	-£80
4	11:31	11:38	2 min	2x	-£200	-£280
5	14:40	15:02	182 min	1x	+£90	-£190
6	15:20	15:31	18 min	1x	-£100	-£290
7	15:33	15:41	2 min	2x	-£200	-£490
8	15:43	15:55	2 min	3x	+£180	-£310

Read it the way the data would.

Gaps. Five trades followed a loss, with gaps of 4, 2, 182, 2 and 2 minutes. Two followed a win, with gaps of 37 and 18 minutes. The mean gap after a loss is 38.4 minutes, longer than the 27.5-minute mean after a win, which would suggest this trader slows down after losing. The median tells the opposite story: 2 minutes after a loss against 27.5 after a win. The whole difference is one lunch break. A single long gap drags the mean a long way, which is why the median is the better summary for this kind of data.

Size. The two trades taken after a win were both at usual size. The five taken after a loss averaged 1.8x. Every trade above usual size came straight after a loss.

Busier losing day. The morning ended down £280. The afternoon then produced four more trades, three of them inside a 35-minute window near the close, each opened with the day already in the red.

Cost. The five trades at usual size netted minus £90. The three upsized trades netted minus £220, roughly 71% of the day's £310 loss. Note that trade 8 won. Some revenge trades do, and those are the ones that get remembered, which is part of why the behaviour feels like a recovery plan rather than a leak.

Measuring it across your history

One day is an anecdote. The useful version runs the same comparisons over every trade you have. All of it needs only fields a broker export normally contains: open time, close time, instrument, side, quantity and P&L (or entry and exit price, from which P&L follows). That is the trade data you import, and it is where these patterns live.

For gaps, sort trades by open time, compute the minutes from each trade's close to the next trade's open, and label each gap by whether the trade before it won or lost. Leave out the first trade of each day, since its gap spans the overnight break and says nothing about reaction. Then compare medians.

For size, normalise first. One contract of an index future and one unit of a crypto pair are not comparable, so divide each trade's quantity by your median quantity on that instrument. Then compare the average multiple after a win, after one loss and after two or more consecutive losses. If you record a stop, risk per trade is a better measure than quantity, because it captures size and stop distance together.

For busier losing days, compute the day's running P&L at the moment each trade opened. Then compare how many trades are taken, and at what size, when the day is up versus when it is down, and how that changes as the day goes on. This catches a variant the gap measure misses: the trader who waits patiently after each individual loss but keeps trading late because the day as a whole is still red.

Is the difference real?

Differences in medians can appear by chance, particularly in a history with only a handful of bad days. A simple check is a permutation test, which uses the same reshuffling idea as Monte Carlo testing. Keep the gaps exactly as they are, randomly reshuffle the win and loss labels attached to them, and recompute the difference in medians. Repeat that a few thousand times. If the real difference is larger than almost all of the shuffled ones, the link between losing and re-entering quickly is unlikely to be an accident of ordering. If it sits comfortably in the middle of the shuffled results, the pattern is indistinguishable from noise, however vivid it felt at the time. The same test works for size multiples.

What the numbers cannot tell you

Data records what happened, not why. A two-minute gap after a stop-out is revenge for one trader and a planned second attempt for another whose setup allows it. A larger size after a loss might be a written scaling rule or an improvised one, and the export does not know the difference.

This is where notes earn their place. When you log a trade by hand in NoxarQuant's journal, you can pick an optional emotion tag from a short list, and "revenge" is one of the options. A label applied in the moment is honest in a way that memory rarely is, and it gives the timestamps something to be checked against. What a trading journal should track covers which other fields are worth the effort.

It also matters where those trades end up. NoxarQuant tags every trade with the session it opened in, and trades sharing the same setup, session, volatility, RSI and trend tags form a cluster. Afternoon trades from a day like the one above land in different clusters from the morning ones, because the New York day is split into a morning session (09:00 to 11:59) and an afternoon session (12:00 to 15:59). So if revenge trades tend to happen in a session you otherwise trade rarely, their results sit apart rather than being averaged into your better ones. If they happen in the same session as your usual trades, with the same tags, they share a cluster.

The point

Revenge trading feels like a mood, but it behaves like a pattern: faster, bigger, later, and mostly after losses. Patterns can be counted. Whether the count changes anything is the trader's decision, but it is a better decision made from the record than from memory, which is the kind of measurement our case study is built around.