Trading Performance Analytics
Turn a trade history into a reviewable sample. Compare outcome, risk and behaviour together instead of judging performance by win rate alone.
Connect practice with review
The preview shows how a trading record can be presented alongside performance metrics. All displayed numbers are illustrative. Open the application to check the tools and data currently available.
Open the TGSFX application →Read metrics as a group
Win rate reports how frequently trades finish positive; it does not describe how much is won or lost. Expectancy combines outcome frequency with average win and loss. Profit factor compares gross positive and negative outcomes, while drawdown describes the path below a previous equity peak.
Use the same units and cost treatment when comparing these measures. A result calculated before commission is not directly comparable with one calculated after costs. The expectancy guide includes a transparent worked example.
Segment a sample carefully
Group trades by a small set of meaningful variables such as setup, instrument, direction or session. Keep sample size visible for each group. A strong average from a handful of trades can change sharply when the sample grows; splitting the data into more categories does not automatically improve the evidence.
Return to the journal to explain why the groups differ. A chart can show a pattern without establishing its cause.
From dashboard to a review decision
- Confirm the date range, trade count and cost assumptions.
- Check average win, average loss and expectancy together.
- Inspect drawdown and the sequence of outcomes.
- Compare rule-following trades with process violations.
- Choose one testable change and retain the original baseline.
The performance dashboard page explains a practical reading order. All numerical dashboard examples shown on this website are illustrative.
Questions about this topic
Is a high win rate sufficient?
No. A high proportion of winners can still produce a negative average outcome when losses are sufficiently large.
Why display sample size?
Metrics summarise the observations included. A small or selectively chosen sample may not represent other periods or conditions.