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risk-reward-ratio风险回报率

Agent Skill

risk-reward-ratio 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

582

周安装

25

GitHub Stars

14

下载量

204
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:risk-reward-ratio(风险回报率)
来源仓库:https://github.com/bhala-srinivash/nse-trading-skills
仓库路径:skills/risk-reward-ratio
安装命令:
npx skills add https://github.com/bhala-srinivash/nse-trading-skills --skill risk-reward-ratio
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/bhala-srinivash/nse-trading-skills --skill risk-reward-ratio

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 适用于 GitHub 仓库管理、代码审查和团队协作场景。
  • 通过 GitHub 仓库安装,使用 npx skills add 命令添加指定技能。
  • 需确认权限范围和维护状态,注意是否会触发联网、命令执行或文件读写操作。
  • risk-reward-ratio 属于待分类类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

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?

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.58%
按下载量换算77

Claude

31.94%
按下载量换算65

Cursor

17.11%
按下载量换算35

Gemini CLI

10.17%
按下载量换算21

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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