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mm-quant-analyst

Compute technical indicators and relative strength from price data into quant_summary.json.

Updated Mar 20, 2026
Search Tags:#python#pandas#quantitative-analysis#technical-indicators#stock-analysis#relative-strength
One-click install
npx skills add https://github.com/ShinyGua/MarketMind-AlphaEngine --skill mm-quant-analyst
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mm-quant-analyst
Source: https://github.com/ShinyGua/MarketMind-AlphaEngine/tree/main/.claude/skills/mm-quant-analyst
Command: npx skills add https://github.com/ShinyGua/MarketMind-AlphaEngine --skill mm-quant-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a deterministic workflow to convert raw price data into a compact quantitative profile, enabling analysts to quickly extract technical indicators and a performance snapshot for reporting.

Core Features & Use Cases

  • Compute RSI, MACD, SMA, EMA, and ATR indicators from price data using pandas.
  • Calculate relative strength against a primary index, sector, and peers to gauge performance.
  • Produce a concise quant_summary.json and full indicator histories for audit and research notes.

Quick Start

Run the quant analysis on your target ticker's daily price data to generate the latest outputs.

Frequently Asked Questions about mm-quant-analyst

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute RSI, MACD, and ATR indicators from stock price data using pandas?▼

You can compute RSI, MACD, SMA, EMA, and ATR technical indicators from stock price data using deterministic pandas operations. The analysis applies to company prices, peer datasets, and benchmark indices to generate full indicator histories.

What is the best way to calculate relative strength against a benchmark index for equity research?▼

Calculating relative strength against a primary index, sector, and peers gauges stock performance. This process converts raw price data into a structured quant_summary.json and a 63-day performance snapshot suitable for research reports.

Can I generate a quantitative analysis summary from daily price data for audit trails?▼

Yes, you can generate a concise quant_summary.json alongside full technical indicator histories from daily price data. This deterministic workflow produces auditable research notes and a structured quantitative profile for reporting.

Does this quantitative analysis approach require external financial libraries or dependencies?▼

No external dependencies are required. The technical indicators and relative strength calculations are processed entirely through deterministic Python and pandas operations, reading from and writing to local workspace paths.

How do I run quantitative analysis on multiple peer datasets to compare performance?▼

To compare performance, run the analysis on peer datasets and benchmark indices alongside target ticker prices. It calculates relative strength metrics across the group and outputs structured indicator series for the last 63 days.

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