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SKILL verified MIT Self-run

Risk Reward Ratio

skill-bhala-srinivash-nse-trading-skills-risk-reward-ratio · by Bhala-Srinivash

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Install

$ agentstack add skill-bhala-srinivash-nse-trading-skills-risk-reward-ratio

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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6mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

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 | 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?

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.