Risk Reward Ratio
Score an NSE/BSE equity setup with risk-reward math and win-rate breakeven tables before you commit capital.
Overview
risk-reward-ratio is an agent skill for the Validate phase that calculates and judges R:R for Indian equity trades from entry, stop, and target.
Install
npx skills add https://github.com/bhala-srinivash/nse-trading-skills --skill risk-reward-ratioWhat is this skill?
- Computes risk, reward, and R:R from entry, stop-loss, and target prices in rupees per share.
- Minimum R:R by win rate table from 30% through 50% with breakeven and recommended floors.
- Trade filtering rules when math does not justify the risk.
- Multi-target R:R analysis for staged exits.
- Pure math—no market data tools required.
- Win-rate table spans 30%, 40%, and 50% with breakeven and recommended minimum R:R columns.
- Worked example uses Rs.1,800 entry, Rs.1,700 stop, Rs.2,100 target for a 3:1 R:R.
Adoption & trust: 1 installs on skills.sh; 22 GitHub stars; 3/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
What problem does it solve?
You have a chart setup but no quick way to tell if reward versus stop distance matches your actual win rate.
Who is it for?
Retail traders on NSE/BSE validating discretionary or rule-based entries before order placement.
Skip if: Macro or crypto traders with different session rules, or anyone wanting live quote feeds inside the skill.
When should I use this skill?
User asks about risk reward for a trade, R:R ratio, whether a setup justifies risk, or expected value of an NSE/BSE equity trade.
What do I get? / Deliverables
You get a clear R:R ratio, rupee risk and reward totals, and a go/no-go read against minimum R:R for your win rate.
- Risk, reward, and R:R ratio with rupee totals
- Breakeven and recommended minimum R:R vs stated win rate
- Trade filter recommendation when math fails minimum thresholds
Recommended Skills
Journey fit
How it compares
Use instead of gut-feel sizing when you need breakeven R:R tables and expected-value style filtering for equity setups.
Common Questions / FAQ
Who is risk-reward-ratio for?
Solo builders and indie traders using AI agents to sanity-check Indian equity trades with explicit stop and target levels.
When should I use risk-reward-ratio?
In Validate when scoping a trade ask ('is this worth it?'); in Operate when re-checking R:R after adjusting targets; before Grow-related capital allocation reviews.
Is risk-reward-ratio safe to install?
It is math-only with no declared data pulls—still review the Security Audits panel on this page before installing any third-party skill.
SKILL.md
READMESKILL.md - Risk Reward Ratio
# Risk-Reward Ratio If the math doesn't work, don't take the trade. R:R is the simplest filter that separates good setups from bad ones. ## Prerequisites No dependencies required. Pure math — provide entry, stop, and target prices. No data tools needed. ## Calculation ``` Risk = Entry price - Stop-loss price Reward = Target price - Entry price R:R = Reward ÷ Risk Example: Entry: Rs.1,800 Stop: Rs.1,700 → Risk = Rs.100 per share Target: Rs.2,100 → Reward = Rs.300 per share R:R = 300 ÷ 100 = 3:1 ``` In rupee terms: ``` Total risk = Risk per share × Number of shares Total reward = Reward per share × Number of shares ``` ## Minimum R:R by Win Rate Your win rate determines the minimum R:R needed to be profitable over time. | Win Rate | Min R:R (Breakeven) | Recommended Min | Trades Needed to Recover 1 Loss | |----------|--------------------|-----------------|---------------------------------| | 30% | 2.33:1 | 3:1 | ~3 winners | | 40% | 1.50:1 | 2:1 | ~2 winners | | 50% | 1.00:1 | 1.5:1 | 1 winner | | 60% | 0.67:1 | 1:1 | <1 winner | | 70% | 0.43:1 | 0.75:1 | <1 winner | If you don't know your win rate, assume 40-50% and require at least 2:1 R:R. ## Trade Filtering Rules | R:R Ratio | Decision | |-----------|----------| | Below 1:1 | **Skip** — you're risking more than you can gain | | 1:1 to 1.5:1 | Only if win rate > 55% AND high-conviction setup | | 1.5:1 to 2:1 | Acceptable for experienced traders with edge | | 2:1 to 3:1 | **Good** — standard for swing trades | | 3:1+ | **Excellent** — take these trades consistently | ## Multi-Target R:R For trades with multiple profit targets (scaling out): ``` Target 1 (50% of position): Rs.1,900 → R:R = 1:1 Target 2 (30% of position): Rs.2,000 → R:R = 2:1 Target 3 (20% of position): Rs.2,200 → R:R = 4:1 Weighted R:R = (0.5 × 1) + (0.3 × 2) + (0.2 × 4) = 1.9:1 ``` This is useful when you plan to scale out at different levels. ## Expected Value For a more complete picture, calculate expected value per trade: ``` EV = (Win rate × Average win) - (Loss rate × Average loss) Example: Win rate: 50%, Avg win: Rs.10,000, Avg loss: Rs.5,000 EV = (0.5 × 10,000) - (0.5 × 5,000) = Rs.2,500 per trade Positive EV = edge. Negative EV = change your approach. ``` ## R:R Checklist Before entering any trade: - [ ] Have I identified a specific target (not just "it'll go up")? - [ ] Is the stop-loss at a technically meaningful level? - [ ] Is R:R at least 1.5:1 (ideally 2:1+)? - [ ] Does the position size keep risk within 1-2% of capital? - [ ] If this trade hits stop, will I still be fine psychologically and financially?