Overview
Am I making money, and how smooth was the ride?
- Net result, win rate and trade count
- Equity curve
- Underwater curve (time spent below your previous peak)
- Outcome mix — wins, losses, break-even, partials
Trading statistics
Every metric BacktestFlow computes from your trades
Around 35 statistics — profit factor, expectancy, R-multiple distribution, maximum drawdown and its duration, half-Kelly, and breakdowns by pair, session, hour and weekday. All calculated automatically from the trades you backtest or journal, with nothing to build in a spreadsheet.
Four units, one dataset
Read any statistic in R, percentage, money, or pips. Pips matter in forex, and R is the only unit that lets you compare trades of different sizes fairly.
Backtest and journal, measured identically
Both run through the same engine, socomparing how you test against how you actually tradeis apples to apples.
Cards appear when your data supports them
Visibility follows data coverage rather than trade source — fill in your levels and a journal gets the same analytical depth as a backtest.
The seven groups
Each group answers a different question about your trading.
Performance
When I win, do I win enough to pay for the losses?
- Payoff ratio
- Average win and average loss
- Best and worst trade
- R-multiple distribution
Risk & discipline
Am I actually following my own rules?
- Position sizing consistency
- Planned vs realized risk-to-reward
- Target hit rate
- Break-even win rate (the win rate your R:R requires)
- Half-Kelly sizing
- Stop distance by pair
Equity & drawdown
How bad does it get, and how long do I stay there?
- Maximum drawdown
- Drawdown duration
- Recovery
- Volatility
- Rolling expectancy
Consistency & streaks
Am I steady, or do results come in bursts?
- Winning and losing streaks
- Trading frequency
- Activity calendar
Breakdowns
Where exactly does the money come from?
- By currency pair
- By direction (long vs short)
- By weekday
- By hour
- By market session (Sydney, Tokyo, London, New York)
- Trade duration vs result
Behaviour & data
What are my habits costing me?
- Performance by tag
- Checklist adherence
- Cost of untaken setups
- Notes and screenshot completeness
- Unit coverage
Statistics that admit when they don't know
The failure mode of every trading dashboard is confident numbers built on samples too small to support them. Split 60 trades across five sessions and you have twelve trades per bucket — enough to produce a dramatic-looking chart, nowhere near enough to conclude anything.
Flow Coach, the AI analysis layer, is built around that problem. It runs statistical significance tests — confidence intervals, standard error, minimum group sizes — before it will make a claim, and it can only narrate numbers the deterministic engine actually computed. It cannot invent a statistic, and it won't tell you London is your best session on the strength of a handful of trades.
Related reading:how many trades you actually need before a statistic means anything.
Frequently asked questions
What trading statistics should I actually track?
Start with expectancy, profit factor, and maximum drawdown. Expectancy tells you what an average trade is worth, profit factor tells you whether gross wins cover gross losses, and max drawdown tells you the worst stretch you would have had to sit through. Win rate on its own is the most commonly over-weighted metric, because a 40% win rate at 3R beats a 70% win rate at 0.5R.
What is the difference between expectancy and profit factor?
Expectancy is the average result of a single trade, usually expressed in R or currency — it answers 'what is one trade worth to me?'. Profit factor is gross profit divided by gross loss across all trades — it answers 'do my winners pay for my losers?'. A profit factor above 1.0 means the system made money over that sample, but it says nothing about how large a drawdown you endured to get there.
How many trades do I need before my statistics mean anything?
More than most traders assume, especially once you split results into buckets. With 20 trades, an observed 60% win rate has a 95% confidence interval of roughly 39% to 78% — a range wide enough to include a losing strategy. Breaking your history down by session or pair divides your sample further, so treat small buckets as directional hints rather than conclusions.
Can I see statistics for backtest and live trades separately?
Yes. Backtest trades and journal trades run through the same statistics engine, and you can filter to either one. Because the calculations are identical, the two are directly comparable — which lets you see whether you actually execute the way you tested.
Do I need to calculate any of this myself?
No. Every metric is computed automatically from the trades you log or backtest. Statistics can be displayed in R, percentage, money, or pips, and each metric carries an explanation of what it means.