Concept
Win Rate
Win Rate is a Performance, Backtesting & Validation concept. The Library holds 1 implementation, a working definition you can pull into Quant.
Top Win Rate indicator
The top custom implementation, built on the original standard Win Rate formula.
1 total
This Win Rate implementation is strategy-ready: open it in Quant, set your rules, and it backtests automatically.
What is Win Rate?
Win rate is the percentage of closed trades that finish profitable: winners divided by total trades. It is among the most quoted trade metrics and one of the least meaningful in isolation, because it says nothing about how large the wins and losses are. A system that wins 90% of the time still loses money if the occasional loss is ten times the typical gain, and a system that wins 35% of the time can be strongly profitable if its winners run several multiples of its losers.
The number that actually matters is expectancy: win rate multiplied by the average win, minus loss rate multiplied by the average loss. Framed in R-multiples, each trade's result expressed as a multiple of the risk taken, the breakeven win rate equals one divided by one plus the reward-to-risk ratio, so a strategy taking profits at twice its risk breaks even near a 33% win rate before costs.
Win rate is also a sample statistic, noisy in the way coin flips are. Over a few dozen trades the observed percentage can sit far from the true rate, and losing streaks longer than intuition expects are normal: a strategy that genuinely wins half its trades will still, across a few hundred trades, produce runs of six or seven straight losses. A cold stretch inside a healthy long-run rate is expected rather than proof the edge is gone, which is why serious evaluation checks the rate's stability across regimes and windows instead of one headline number.
Finally, win rate is as much an output of exit policy as of entry quality. Breakeven stops convert some losers into scratches, partial profits raise the percentage while capping the average win, and letting trades run does the opposite; identical entries can produce very different reported rates. That plasticity makes the metric easy to optimize into meaninglessness, which is why it belongs alongside parameter stability: a hit rate that only holds for one narrow stop-and-target combination is an artifact of the fit, not a property of the market.
How to calculate win rate and expectancy
Win rate is not read from a chart: it is computed from a closed-trade list, and it is only as honest as the bookkeeping behind it.
- 1Assemble closed trades from the backtest or journal, net of commissions and realistic slippage, and decide upfront how scratches near zero are counted.
- 2Divide winners by total trades for the headline rate, then recompute it by direction, by regime, and by period to see whether it is one number or several.
- 3Compute the average win and average loss, then expectancy: win rate times average win, minus loss rate times average loss. This, not the rate, is the profitability statement.
- 4Compare the observed rate to the breakeven rate for the reward-to-risk ratio, one divided by one plus the ratio, and insist on a margin above it to absorb costs.
- 5Check the rate on data the strategy never saw, via an in-sample / out-of-sample split, before believing it.
How it's calculated
The percentage of closed trades that ended profitable.
Count trades net of fees and only after they close; open positions are excluded.
Handling of breakeven trades varies; they are commonly counted in N but not in W.
A high win rate alone does not imply profitability; it must be weighed against the average win to average loss ratio.
How traders use it
- Paired with average win and average loss to compute expectancy; that combination determines profitability, never the win rate alone.
- As a design constraint: mean-reversion systems typically trade a high win rate for small gains and occasional larger losses, while trend-following accepts a low win rate in exchange for outsized winners. Traders pick the loss cadence they can actually sit through.
- As a stability check, comparing the rate across market regimes and between in-sample and out-of-sample segments; a win rate that collapses on unseen data flags curve-fitting.
- As a position-sizing input: win rate and payoff together feed sizing formulas such as fractional Kelly, and even traders who skip formal Kelly use the pair to judge how much heat losing runs will generate at a given risk per trade.
- As one panel of a fuller dashboard alongside expectancy, drawdown, and the Sharpe ratio, which capture what win rate ignores: the size, volatility, and sequencing of returns.
Win rate vs other evaluation metrics
Sharpe Ratio: Win rate counts how often trades finish positive; the Sharpe ratio grades the whole return stream, rewarding steadiness and penalizing volatility. A high win rate with rare violent losses can coexist with a poor Sharpe, and vice versa.
In-sample / Out-of-sample Split: The split is not a rival metric but the honesty test applied to this one: a win rate quoted only on the data used to build the system is an optimistic upper bound until it survives unseen data.
Parameter Stability: Parameter stability asks whether the win rate and its companion metrics persist when settings are nudged. A rate that collapses when a stop moves a tick was fitted to history, not discovered in the market.
Concept family
Performance, Backtesting & Validation
30 concepts mapped · 30 in the Library
Win Rate FAQ
Turn Win Rate into a trading strategy.
Take the implementation from this page into Quant, then build on it, backtest it on real data, and keep refining it in conversation.
