Analyzing trades is not the same as sorting a table by profit and choosing whatever made the most money. Good analysis asks which variables repeat, with what sample size and with what stability. That distinction matters: a useful pattern should still make sense as new trades are added.
Start with one clear question
Avoid reviewing every variable at once. Ask something specific: which strategy has the strongest expectancy? Which timeframe produces the most mistakes? Do I perform worse after two losses? Which direction works better in a specific market?
A clear question determines which grouping and metrics you actually need.
Always check sample size
A group with three trades can show a 100% win rate and still tell you very little. The smaller the sample, the more sensitive the result is to a single trade.
There is no universal threshold for every analysis, but one practical rule is useful: always display the number of trades next to the result and avoid comparing tiny groups with large groups as though they had equal reliability.
Do not rely on one metric
- Total P&L: how much the group contributed.
- Average P&L or average R: reduces the effect of trade count on the comparison.
- Win rate: useful, but incomplete without average win and average loss.
- Expectancy: estimates the average outcome per trade from your history.
- Profit Factor: compares gross profits with gross losses.
- Drawdown: shows the depth of declines from previous peaks.
- Plan adherence: helps separate process quality from financial outcome.
Segment one variable at a time
Start with simple groups: strategy, timeframe, direction, market, session or mistake. Combine variables only when the sample supports it. If you cross strategy + timeframe + direction + event too early, you may end up with one- or two-trade groups that look precise but are not robust.
Analyze the cost of mistakes too
Do not only count how often a mistake appears. A frequent mistake may have little financial impact, while a rare mistake may explain a large part of your drawdown. Track both frequency and cost.
Compare periods using the same definitions
When comparing months, weeks or market phases, use consistent definitions. If you changed strategy, risk or market midway through a period, note it: the history is no longer homogeneous and should be interpreted with that limitation.
Finish with a concrete decision
A useful review does not always end with a change. Sometimes the best conclusion is “not enough data yet.” Other times it may be “this mistake is too costly,” “execution deteriorates during this time window,” or “the strategy remains strong across two different periods.”
How Trading Life Journal can help
TLJ lets you filter and compare trades across multiple variables while keeping results, sample size, strategy, timeframe, R, mistakes and plan adherence visible. The goal is to turn intuition into questions that can be tested against your own history.