Expectancy and Sample Size: How Many Trades Are Enough?
Learn trade expectancy in plain English, why sample size matters, practical ranges by style, and a simple plan to test your edge faster.
Expectancy and Why Sample Size Matters
Expectancy is the average profit or loss you make per trade. Think of it as how much you earn, on average, every time you execute your setup.
Because trade outcomes vary, a handful of trades can look great or terrible just by luck. Larger samples reduce that noise and show whether your edge (a repeatable reason you expect to make money) is real.
To compare results across markets and timeframes, many traders record outcomes in R-multiples. R is your initial risk per trade; a +1R win equals gaining what you risked, a -1R loss equals losing it. This keeps results apples-to-apples even when position sizes differ.
How Many Trades Are Enough?
There is no magic number, but you can use practical ranges. The more variable your results (big winners and losers, lower win rate), the more trades you need to trust the average. High win-rate, small-win strategies usually stabilize faster; low win-rate, larger-win strategies need more data.
| Style | First checkpoint | Stronger confidence |
|---|---|---|
| Scalping / fast intraday | 100 trades | 200–300 trades |
| Intraday (minutes–hours) | 75–100 trades | 150–200 trades |
| Swing (days–weeks) | 40–60 trades | 80–120 trades |
Use those as review points, not endpoints. Look at rolling windows too (e.g., last 50 trades) to see how the edge behaves through changing conditions.
Three signs you may need a bigger sample: wide swings between best and worst trades, long streaks of wins or losses, and large differences between sessions or symbols. If you see these, extend testing before drawing conclusions.
Design a Practical Expectancy Test
- Pick one setup. Write clear rules for entry, stop, target, and management. Test one idea at a time.
- Normalize risk. Risk the same R per trade and log results in R-multiples to keep comparisons clean.
- Pre-commit your sample size. Choose checkpoints (e.g., 50, 100, 200 trades) and avoid judging the strategy mid-run based on a streak.
- Log consistently. For each trade record: date/timeframe, symbol, setup tag, R result, reason for entry, and any rule deviations.
- Review at checkpoints. Look at average R per trade, distribution of outcomes, largest drawdown, and behavior by market condition. Exclude trades that broke your rules from the main tally and track them separately.
- Iterate carefully. If you adjust rules, restart a new sample for that version so results don’t mix.
If you’re new to simulators, see our concise overview: Guide to Choosing a Trading Simulator.
Use ChartingPark to Collect Quality Samples Faster
ChartingPark lets you practice on accelerated historical charts powered by TradingView. You can stack clean repetitions of the same setup, build a meaningful sample in days (not months), and spot whether your average result holds across conditions.
- Accelerated reps: quickly reach 50–200 trades for stable expectancy estimates.
- Structured practice: tag setups, track R outcomes, and review by sample windows.
- Reduced bias: practice forward, bar by bar, instead of cherry-picking past charts.
Whether you trade intraday or swing, focus on one setup, collect enough trades, and let the numbers tell the story. Your goal isn’t perfection; it’s a believable average that survives different markets.
Ready to build your sample the right way? Practice now in ChartingPark at app.chartingpark.com.