Risk-Reward Ratio

If the maths doesn't work after costs, skip the trade. R:R is the cheapest filter there is.

Calculator

python3 ../position-sizing/scripts/risk.py rr --entry 1800 --stop 1700 --target 2100
python3 ../position-sizing/scripts/risk.py rr --entry 1800 --stop 1700 \
    --target 1900:50 --target 2000:30 --target 2200:20 --win-rate 45 --cost-pct 0.25
python3 ../position-sizing/scripts/risk.py rr --entry 450 --stop 470 --target 410 --cost-pct 0.05   # short

Weights after : are the % of the position exited at each target. --cost-pct is the round-trip cost (delivery ≈0.25, intraday ≈0.05, futures ≈0.06, or exact from trading-costs).

Core maths

R per target   = (Target − Entry) ÷ (Entry − Stop)              long; mirror for shorts
R after costs  = (Reward − cost) ÷ (Risk + cost)
Weighted R     = Σ weight × R
Breakeven win% = 1 ÷ (1 + R_after_costs)
Expectancy (R) = W × R_after_costs − (1 − W)

Example: entry ₹1,800, stop ₹1,700, scale out 50/30/20 at ₹1,900/₹2,000/₹2,200 gives a weighted 1.90R. With 0.25% delivery costs it is 1.78R, so the breakeven win rate is 36%. At a 45% win rate, expectancy is +0.25R per trade.

Minimum R by win rate

Win rate	Breakeven R	Recommended min R (after costs)
30%	2.33	3.0
40%	1.50	2.0
50%	1.00	1.5
60%	0.67	1.0

If the user doesn't know their win rate, assume 40–45% and require at least 2R after costs. risk.py journal measures the real win rate from their trades.

Filter

R after costs	Decision
< 1	Skip
1 – 1.5	Only with a proven >55% win rate
1.5 – 2	Acceptable
2 – 3	Good (standard swing)
> 3	Excellent, but check the target is realistic (below the next major resistance)

Making R honest

• Target realism: the target must sit at or before the next real resistance (ta.py levels), the Fib extension, or the expected move. A target above three resistance levels is fantasy R.
• Stop realism: if the stop is inside daily noise (under 1 ATR), the "great R:R" is an illusion of a tight stop.
• Costs and slippage: small targets in small caps lose a lot to spread and impact.

Options

• Long options: R:R depends on the move and its timing. Price the target premium with options.py greeks at the target date and IV (IV often falls after events: IV crush). A ₹100 → ₹200 target needs the underlying move before theta eats the premium.
• Short options: typical credit trades risk 2–4 units to make 1 (R ≈ 0.25–0.5) with a high win rate. Judge them on expectancy and tail loss, not R. options.py payoff gives max profit, max loss, breakevens and probability of profit.

Checklist

• Specific target(s) at real levels
• Stop at a technical invalidation point, ≥1 ATR away
• R ≥ 1.5–2 after costs
• Position risk ≤ 1–2% of capital
• Positive expectancy at a realistic win rate