Install
$ agentstack add skill-bhala-srinivash-nse-trading-skills-risk-reward-ratio ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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.
- Author: Bhala-Srinivash
- Source: Bhala-Srinivash/nse-trading-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.