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Risk-reward ratio calculator — and the win rate your setup demands
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Short answer
Risk-reward ratio = (target − entry) ÷ (entry − stop-loss). A trade entered at ₹500 with a stop at ₹490 and target at ₹530 risks ₹10 to make ₹30 — a 1:3 ratio — and needs to win only 25% of the time to break even, because breakeven win rate = risk ÷ (risk + reward). This calculator computes both numbers, which together decide whether a setup can be profitable at all.
Risk per share
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Reward per share
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Risk : reward
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Breakeven win rate
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Reference: breakeven win rate by risk-reward ratio
Breakeven win rate = risk ÷ (risk + reward). Any real win rate above it is profitable before costs.
| Risk : reward | Breakeven win rate | Win rate needed for +0.2R expectancy |
|---|---|---|
| 1 : 0.5 | 66.7% | 80% |
| 1 : 1 | 50% | 60% |
| 1 : 1.5 | 40% | 48% |
| 1 : 2 | 33.3% | 40% |
| 1 : 3 | 25% | 30% |
| 1 : 4 | 20% | 24% |
| 1 : 5 | 16.7% | 20% |
Neither risk-reward nor win rate means anything alone. A 1:5 ratio sounds superb until the setup wins 10% of the time; a 90% win rate sounds unbeatable until the occasional loss is twenty times the average win — the classic profile of strategies that “always work” until they take the account down. What matters is the pair, and the arithmetic linking them is fixed: at ratio 1:R, breakeven win rate is 1/(1+R).
Enter entry, stop and target below. The calculator returns the ratio, the breakeven win rate, and the per-share rupee risk and reward.
The ratio–win-rate trade-off
The menu is a curve, not a free lunch: 1:1 needs better than 50% wins, 1:2 better than 33%, 1:3 better than 25%. Trend-following systems typically live on the left — win rates of 30–40% carried by large winners — while mean-reversion and premium-selling systems live on the right, with high win rates and unfavourable ratios. Both can be profitable; both fail when their actual win rate slips below the breakeven line for their actual ratio.
The honest use of this calculator is filtering: if a setup’s natural stop and realistic target produce a ratio needing a 60% win rate, the burden of proof is on the evidence that the setup actually wins that often — not on optimism. And targets should come from structure (support, resistance, measured moves), not from working backwards from a desired ratio, which merely decorates a bad trade with good arithmetic.
From single-trade ratios to expectancy
Across many trades the pair of numbers becomes expectancy: (win rate × average win) − (loss rate × average loss) — the expected rupees per trade. Positive expectancy after costs is the entire game; everything else is execution. A strategy risking ₹10 to make ₹30 with 35% wins has expectancy of ₹4 per share traded before costs — thin, and the reason charges and slippage decide marginal systems.
One trade’s planned ratio is a hypothesis; only a sample of real or simulated trades reveals the actual win rate and the actual average win, which is rarely the full target. Backtesting a rule on years of NSE data — with the cost stack deducted — is how a planned 1:3 gets confronted with its lived reality. INDfolio AI reports win rate, average win/loss and expectancy on every backtest, which turns this calculator’s two inputs into measured facts.
These calculators are free, run entirely in your browser, and store nothing. They produce estimates for NSE trades based on published rates and standard formulas — not investment advice, and not a substitute for your broker’s contract note. INDfolio AI builds algo trading software; the honest connection is that our backtests apply this same cost arithmetic to every simulated trade.