The share of trades with a tagged emotion is the proportion of your positions you attached a state to, before or during the trade. It is the raw material of any psychological analysis of a journal, and without it there simply is none.
Out of 100 trades
Contents
Tagging an emotion means attaching a declared state to a trade: calm, rushed, angry, hesitant, confident. The rate simply measures on what share of your trades that information exists, and it is a prerequisite rather than a result.
Its interest lies entirely in what it makes possible. Once the state is recorded, it becomes a dimension like any other, and P&L by state answers a question nothing else reaches: do your trades taken in a certain state lose more often? The answer is often clear, and it is always personal.
The constraint is the same as for trade grading: the state is logged before knowing the result. An emotion assigned after the fact describes the memory of the result, not the state of mind at the time, and the crossing then becomes a manufactured correlation.
Trades with a tagged emotion = trades carrying a state ÷ total number of trades × 100
The list of states must be short and stable. Twenty nuances produce twenty groups of three trades, on which nothing can be measured.
The state concerns the moment of the trade, not the day. The same day can contain a calm trade and a revenge trade.
A low rate is not information about your state, it is an absence of information. Untagged trades are neither calm nor agitated, they are unknown.
| Share of tagged trades | What can be drawn from it |
|---|---|
| under 25% | Nothing solid. The sample is small and it was not chosen at random. |
| 25 to 60% | Leads. To be crossed with the result knowing memorable trades are over-represented. |
| 60 to 90% | Usable. Groups become large enough for a P&L gap to mean something. |
| over 90% | Logging has become a reflex. That is where this data starts being worth its cost. |
| 100% with a single state | The tag is applied mechanically without being chosen. You might as well log nothing. |
The first row says something unpleasant and true: a small sample of tagged trades is not a small random sample. People log their state when the trade is out of the ordinary, so tagged trades contain an over-representation of strong emotions and memorable results. The crossing is biased before it is even computed.
The last row describes the opposite pitfall. A tag applied by reflex on every trade, always the same one, fills the statistic without containing anything. The useful data is not the presence of a state but a state that was chosen, and those two situations give exactly the same percentage.
The signal is clear on tagged trades: those taken in haste lose. But 24% coverage means that finding rests on nine trades, and that 76 trades were not logged. It is a lead, not a conclusion, and the real job is the rate before the emotion.
That is this data's entire use. Cross P&L with the declared state: if one state comes out systematically negative on a decent sample, you have just identified an entry condition to add to your plan, expressed as a state rather than a price.
The answer is always personal. The state that costs is not the same from one trader to the next, and it is almost never the one you would have guessed before measuring.
Many traders know they trade badly after a loss, or at the end of the day. This tag turns that intuition into a figure, and the figure contradicts the intuition often enough for the check to be worth it.
It is also the only way to tell a real problem from a memorable memory. Two angry trades you remember perfectly do not make a trend.
The first flaw is selection bias, and it is structural. People tag when the trade is out of the ordinary, so the tagged sample is not representative of the activity. While the rate is low, every crossing covers a population chosen by the mood of the moment.
The second is the tag applied after the fact, exactly as for trade grading. An emotion assigned while rereading a losing trade will almost always be negative, and crossing with P&L will then show a perfect correlation proving only the order of entry.
The third is the list being too long. Fifteen states produce fifteen tiny groups, and no gap in them is interpretable. Three or four states, stable over time, are worth far more than a fine taxonomy filled one time in two.
The classic mistake. Believing an untagged trade is a calm trade. A rate of 24% does not mean 76% of your trades were calm, it means nothing is known about 76% of your trades. It is an absence of information, and reading it as information is the simplest way of drawing a conclusion opposite to reality.
| Indicator | Formula | What it measures | When to use it |
|---|---|---|---|
| Tagged emotion | trades with a state ÷ total | The presence of information, not the state | Making the state / result crossing possible |
| Trade grading | mean of the grades given | Quality of the act, declared | Separating bad luck from error |
| Plan followed | compliant trades ÷ total | Compliance, as yes or no | Quantifying what indiscipline costs |
| Trades with a stop | trades with a stop ÷ total | A verifiable rule, not declared | Measuring baseline protection |
A short list gets filled, a fine one gets worked around. Three or four stable states produce groups large enough for a P&L gap to mean something, and that is all this data is asked for.
An emotion assigned after the fact describes the memory of the result. The crossing then shows a perfect correlation proving only the order in which the two were entered, exactly as for trade grading.
Under 60%, crossings cover a sample chosen by mood. Raising coverage beats refining interpretation, and it is the only order in which this data ends up serving.
It is a declared state attached to a trade: calm, rushed, angry, hesitant. The share of tagged trades simply measures on what part of your activity that information exists, and it conditions any psychological analysis of your journal.
To cross a state with a result. If one state comes out systematically negative on a decent sample, you have identified an entry condition to add to your plan, expressed as a state rather than a price. The answer is always personal and rarely the one you would have guessed.
Before or during the trade, never after the close. An emotion assigned while rereading a losing trade will almost always be negative, and the crossing will then show a perfect correlation proving only the order in which the two were entered.
Three or four, stable over time. Fifteen nuances produce fifteen groups of three trades on which no gap is interpretable, and a long list gets filled less often. Granularity costs far more here than it returns.
No, it is a trade nothing is known about. It is the most common confusion about this measure: a 24% rate does not say 76% of your trades were calm, it says 76% are not logged. Reading an absence of information as information leads to the conclusion opposite to reality.
In Tradoshi, you tag trade by trade, in two ways: in writing in the Trade Review screen, or through the emotional check-in, which accepts voice as readily as writing and whose content the AI analyses. It is data you supply, and nothing guesses it from the result.
It is used in the Reports screen, where Emotional state is a dimension: any metric can be sorted by state, and net P&L by state is exactly the crossing described above. The Trades with a tagged emotion metric, meanwhile, says what share of your activity that crossing rests on, and it is the figure to look at first.
To go deeper on this: read the full guide. Every term is gathered in the glossary.