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benchmarking-%26-performance对 %26 性能进行基准测试

Agent Skill

benchmarking-%26-performance 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

8,834

周安装

396

GitHub Stars

3

下载量

3,832
CodexClaudeCursorGemini CLI

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:benchmarking-%26-performance(对 %26 性能进行基准测试)
来源仓库:https://github.com/zuytan/rustrade
仓库路径:skills/benchmarking-%26-performance
安装命令:
npx skills add https://github.com/zuytan/rustrade --skill 'Benchmarking & Performance'
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/zuytan/rustrade --skill 'Benchmarking & Performance'

简介

benchmarking-%26-performance 用于对策略进行基准测试和性能验证,适合在策略开发或优化阶段使用。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 等宿主环境,主要用于量化评估交易策略的盈利能力与稳定性。
  • 可通过安装命令从 GitHub 仓库获取,结合脚本执行快速完成多周期回测与配置对比分析。
  • 使用前需确认权限范围及是否涉及联网、文件读写或命令执行,注意维护状态与安全性。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Skill: Benchmarking & Performance

When to use this skill

  • After adding or modifying a strategy
  • To validate that a strategy is profitable
  • To compare different configurations
  • Before going from paper trading to live

Available scripts

ScriptUsage
scripts/quick_benchmark.sh SYMBOL [DAYS]Quick benchmark
scripts/validate_strategy.sh STRATEGYMulti-period validation

Key metrics to monitor

Profitability metrics

MetricDescriptionAcceptable threshold
Total ReturnTotal return over period> 0%
Win Rate% of winning trades> 50% (trend) or > 40% (mean rev)
Profit FactorGains / Losses> 1.5
Average TradeAverage P&L per trade> 0

Risk metrics

MetricDescriptionAcceptable threshold
Sharpe RatioRisk-adjusted return> 1.0 (good), > 2.0 (excellent)
Sortino RatioSame but penalizes downside> 1.5
Max DrawdownMaximum loss from peak< 20%
Time in Market% of time with positionDepends on strategy

Interpretation

Sharpe Ratio:
  < 0.5  → Bad, don't use
  0.5-1  → Mediocre, needs improvement
  1-2    → Good
  2-3    → Very good
  > 3    → Excellent (or suspicious, check overfitting)

Max Drawdown:
  < 10%  → Conservative
  10-20% → Moderate
  20-30% → Aggressive
  > 30%  → Dangerous

Benchmark commands

Simple benchmark

# Backtest on one symbol
cargo run --bin benchmark -- --symbol AAPL --days 365

# Backtest on multiple symbols
cargo run --bin benchmark -- --symbols "AAPL,GOOGL,MSFT" --days 365

Advanced benchmark

# Parallel mode (multi-core)
cargo run --bin benchmark -- --parallel --symbols "AAPL,GOOGL,MSFT"

# With sequential comparison
cargo run --bin benchmark -- --compare-sequential

# Parameter matrix
cargo run --bin benchmark_matrix

Available scripts

# Stock benchmark
./scripts/benchmark_stocks.sh

# Market regime benchmark
./scripts/run_regime_benchmarks.sh

# Automatic benchmark
./scripts/auto_benchmark.sh

Strategy validation workflow

Step 1: Initial backtest

cargo run --bin benchmark -- --strategy <STRATEGY> --days 365

Verify:

  • Sharpe Ratio > 1.0
  • Max Drawdown < 20%
  • Win Rate consistent with strategy type
  • Profit Factor > 1.5

Step 2: Test on different periods

# Bull period
cargo run --bin benchmark -- --start 2021-01-01 --end 2021-12-31

# Bear period
cargo run --bin benchmark -- --start 2022-01-01 --end 2022-12-31

# Volatile period
cargo run --bin benchmark -- --start 2020-02-01 --end 2020-04-30

The strategy must be profitable (or at least not lose too much) in ALL conditions.

Step 3: Multi-symbol test

cargo run --bin benchmark -- --symbols "AAPL,MSFT,GOOGL,AMZN,META"

Verify result consistency across different assets.

Step 4: Stress test

Test on crash periods:

  • COVID crash: February-March 2020
  • 2022 Bear market: January-October 2022
  • Flash crashes: Verify resilience

Pitfalls to avoid

Overfitting

Symptoms:

  • Sharpe Ratio > 3 on backtest
  • Performance degrades in live/forward test
  • Too many optimized parameters

Solutions:

  • Use train/test split
  • Test on out-of-sample data
  • Prefer simple strategies

Look-ahead bias

Symptom: Using future data in decisions

Solution: Verify indicators only use past data

Survivorship bias

Symptom: Only testing on assets that still exist

Solution: Include delisted assets in backtests

Key files

FileDescription
src/bin/benchmark.rsMain benchmark CLI
src/bin/benchmark_matrix.rsParameter matrix tests
src/application/optimization/parallel_benchmark.rsParallel execution
src/application/optimization/benchmark_metrics.rsBenchmark metrics
src/domain/performance/metrics.rsSharpe, Sortino, Drawdown calculation
benchmark_results/Saved results

Checklist before production

  • Positive backtests on 2+ years of data
  • Sharpe Ratio > 1.0 on different periods
  • Acceptable Max Drawdown (< 20% recommended)
  • Tested on bull, bear AND sideways markets
  • No sign of overfitting
  • Paper trading validated for 1+ month

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

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

36%
按下载量换算1,380

Claude

33.41%
按下载量换算1,280

Cursor

18.08%
按下载量换算693

Gemini CLI

9.59%
按下载量换算367

安全审计

暂无安全审计结果可展示。

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

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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