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historical-research

Analyze historical price data and event timelines to identify recurring market patterns.

Updated Jun 19, 2026
Search Tags:#data analysis#finance#pattern recognition#market research#investing#quantitative
One-click install
npx skills add https://github.com/a-chris/auto-search-finance --skill historical-research-a-chris
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: historical-research
Source: https://github.com/a-chris/auto-search-finance/tree/main/skills/historical-research
Command: npx skills add https://github.com/a-chris/auto-search-finance --skill historical-research-a-chris

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the difficulty of identifying statistical edges and recurring behaviors in financial assets, helping investors move beyond intuition to data-backed pattern recognition.

Core Features & Use Cases

  • Pattern Discovery: Analyzes seasonality, event-driven reactions, and macro-economic correlations.
  • Statistical Profiling: Quantifies volatility, drawdown recovery, and trend persistence for any ticker.
  • Use Case: Use this to determine if a stock historically rallies before product launches or if it tends to mean-revert after earnings surprises.

Quick Start

Run the historical-research skill to analyze the provided price data file and generate a quantitative behavioral profile for the specified ticker.

Frequently Asked Questions about historical-research

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

FAQPage Schema
How do I identify recurring market patterns from historical price data?▼

Identify recurring market patterns by analyzing historical price data and event timelines to detect seasonality, event-driven reactions, and volatility regimes. This approach generates a quantitative investment verdict by evaluating statistical edges and trend persistence.

Can I analyze stock seasonality and event-driven reactions using structured JSON input?▼

You can analyze stock seasonality and event-driven reactions by providing structured JSON input containing price history and event metadata. The analysis quantifies volatility, drawdown recovery, and trend persistence to produce a behavioral profile.

What is the best way to find a statistical edge for a specific ticker before earnings?▼

Find a statistical edge for a specific ticker by evaluating historical price data to determine if it historically rallies before product launches or mean-reverts after earnings surprises. This pattern discovery moves analysis beyond intuition to data-backed recognition.

Does this financial research approach require event metadata alongside price history?▼

This financial research approach requires structured JSON input containing both price history and event metadata. Evaluating macro-economic correlations and event timelines alongside price data is necessary to produce a quantitative investment verdict.

How do I profile volatility regimes and drawdown recovery for quantitative investing?▼

Profile volatility regimes and drawdown recovery by quantifying historical price data and statistical edges. This process evaluates trend persistence and macro-economic correlations, generating a quantitative behavioral profile for the specified ticker to support investing pipelines.

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