Risk Reward Ratio logo

Risk Reward Ratio

Community
Bhala-Srinivash
risk-reward-ratio

Calculate and evaluate risk-reward ratios for NSE/BSE equity trades. Includes R:R calculation, minimum R:R tables by win rate, trade filtering rules, and multi-target R:R analysis. Use when the user asks: "risk reward for this trade", "is this trade worth it", "R:R ratio", "what's my risk vs reward", "should I take this trade", "expected value of trade", or any question about whether a trade setup justifies the risk.

Overview

PublisherBhala-Srinivash
Repositorynse-trading-skills
Skill namerisk-reward-ratio
Stars
59
Forks
25
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Bhala-Srinivash on GitHub. Read the source before you install it.

Installation

Install the Risk Reward Ratio AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Bhala-Srinivash/nse-trading-skills/tree/master/risk-reward-ratio
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Bhala-Srinivash/nse-trading-skills.git /tmp/nse-trading-skills
mkdir -p .claude/skills
cp -r /tmp/nse-trading-skills/risk-reward-ratio .claude/skills/risk-reward-ratio
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Risk Reward Ratio in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Risk Reward Ratio on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Risk Reward Ratio is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 RateMin R:R (Breakeven)Recommended MinTrades Needed to Recover 1 Loss
30%2.33:13:1~3 winners
40%1.50:12:1~2 winners
50%1.00:11.5:11 winner
60%0.67:11:1<1 winner
70%0.43:10.75:1<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 RatioDecision
Below 1:1Skip — you're risking more than you can gain
1:1 to 1.5:1Only if win rate > 55% AND high-conviction setup
1.5:1 to 2:1Acceptable for experienced traders with edge
2:1 to 3:1Good — 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?

Frequently asked questions

What does the Risk Reward Ratio AI skill do?

Calculate and evaluate risk-reward ratios for NSE/BSE equity trades. Includes R:R calculation, minimum R:R tables by win rate, trade filtering rules, and multi-target R:R analysis. Use when the user asks: "risk reward for this trade", "is this trade worth it", "R:R ratio", "what's my risk vs reward", "should I take this trade", "expected value of trade", or any question about whether a trade setup justifies the risk.

Why use Risk Reward Ratio on TypingMind?

Because you install it once and use it with any model. Risk Reward Ratio is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Risk Reward Ratio in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Bhala-Srinivash/nse-trading-skills/tree/master/risk-reward-ratio. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Risk Reward Ratio?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Risk Reward Ratio?

As many as you like. As long as a model supports skills, you can use Risk Reward Ratio with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Risk Reward Ratio AI skill free?

Yes. It is published on GitHub by Bhala-Srinivash under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇