Trading metrics, explained

Win rate

Share of closed trades that made money after fees.

winning trades ÷ all closed trades

Win rate alone says little. A 30% win rate can be profitable if winners are much bigger than losers; a 70% win rate can lose money if a few losses are huge. Always read it next to average win, average loss and expectancy.

Expectancy

Average net result per trade.

net P&L ÷ number of closed trades

Expectancy is what one more trade of this kind has been worth, on average, in your history. Positive expectancy over a large sample is the closest thing to evidence that a process has an edge — but it describes the past, not a promise about the future.

Profit factor

Money won for every unit of money lost.

sum of winning trades ÷ |sum of losing trades| (both net of fees)

Above 1.0 means winners outweighed losers. TradeNotes shows “—” instead of infinity when you have no losing trades yet, because that number would be meaningless.

R-multiple

A trade's result measured in units of what you planned to risk.

net P&L ÷ initial risk

If you risked $100 and made $250, that's +2.5R. R lets you compare trades of different sizes and instruments. It only exists for trades where you recorded a stop or planned risk, so TradeNotes always shows how many of your trades it is based on.

Maximum drawdown

The largest drop from a high point in your cumulative P&L.

max over time of (running peak − current cumulative P&L)

Drawdown tells you how painful the worst stretch was. A strategy you can't psychologically sit through is a strategy you won't follow.

Risk/reward

Planned profit versus planned loss on a trade.

|target − entry| ÷ |entry − stop|

Planned risk/reward is a hypothesis. Your realised average R shows what actually happened.

Why journal losing trades?

Losses carry the most information about your process.

It's tempting to only review winners. But repeated mistakes — moving stops, revenge trading, oversizing — show up in losses. Tagging mistakes turns vague regret into a number you can track down.

How many trades are enough?

Fewer than ~30 trades is too few to conclude much.

Small samples are dominated by luck. TradeNotes flags statistics based on fewer than 30 trades. Even with hundreds of trades, market conditions change; treat results as evidence, not proof.

Why backtest?

Practise and measure a strategy before risking money on it.

Backtesting on historical data lets you collect many samples of a setup quickly. It also has blind spots: no slippage or emotions, and you may unconsciously fit rules to the past. That's why historical results can fail in live markets — use backtests to filter ideas, then confirm with small live size.