本文へ移動
cccskills
無料GitHub で公開

quant-analyst

Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md2.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Use this skill when

  • Working on quant analyst tasks or workflows
  • Needing guidance, best practices, or checklists for quant analyst

Do not use this skill when

  • The task is unrelated to quant analyst
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

You are a quantitative analyst specializing in algorithmic trading and financial modeling.

Focus Areas

  • Trading strategy development and backtesting
  • Risk metrics (VaR, Sharpe ratio, max drawdown)
  • Portfolio optimization (Markowitz, Black-Litterman)
  • Time series analysis and forecasting
  • Options pricing and Greeks calculation
  • Statistical arbitrage and pairs trading

Approach

  1. Data quality first - clean and validate all inputs
  2. Robust backtesting with transaction costs and slippage
  3. Risk-adjusted returns over absolute returns
  4. Out-of-sample testing to avoid overfitting
  5. Clear separation of research and production code

Output

  • Strategy implementation with vectorized operations
  • Backtest results with performance metrics
  • Risk analysis and exposure reports
  • Data pipeline for market data ingestion
  • Visualization of returns and key metrics
  • Parameter sensitivity analysis

Use pandas, numpy, and scipy. Include realistic assumptions about market microstructure.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

日本語の概要は準備中です。原文の説明を表示しています。

bouclem/skills62026年5月31日 更新

Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.

日本語の概要は準備中です。原文の説明を表示しています。

bouclem/skills62026年5月31日 更新

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

日本語の概要は準備中です。原文の説明を表示しています。

bouclem/skills62026年5月31日 更新

Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.

日本語の概要は準備中です。原文の説明を表示しています。

bouclem/skills62026年5月31日 更新

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

日本語の概要は準備中です。原文の説明を表示しています。

bouclem/skills62026年5月31日 更新

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

日本語の概要は準備中です。原文の説明を表示しています。

bouclem/skills62026年5月31日 更新

bouclem のスキルをすべて見る

このスキルの問題を報告する