astock-research_skill

This skill provides deep A-share investment analysis across macro, micro, funds, tech, sentiment, and news to inform trading plans.
  • Python

2.5k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill astock-research

  • _meta.json290 B
  • SKILL.md7.7 KB

Overview

This skill is an A-share deep research and trading-preparation framework modeled on professional systems like Tonghuashun and Luobo. It integrates five analysis dimensions—fundamentals (macro + micro), capital flows, technicals, sentiment, and news—to produce actionable research reports and trade scenarios. The goal is to evaluate listed Chinese companies and produce clear entry/exit plans with risk controls.

How this skill works

The skill ingests market, financial, shareholder, intraday and news data to score and synthesize five dimensions into a coherent view. It builds a workflow: macro/micro fundamental assessment → capital-flow validation → technical trend and levels → sentiment and institutional signals → news/announcement checks → scenario-based playbook and conclusion. Outputs include ratings, suggested position sizes, target/stop levels, and scenario triggers.

When to use it

  • Preparing an investment thesis for an A-share stock
  • Before entering or adding to a position to validate timing and risk
  • Generating alternative scenarios (breakout, breakdown, consolidation, wait)
  • Refreshing views after earnings, major announcements, or regulatory news
  • Assessing whether market-wide or sector moves affect a specific holding

Best practices

  • Always start with macro cycle and policy direction to set risk appetite
  • Combine company fundamentals with capital-flow metrics before trusting technical breakouts
  • Quantify targets and stops; convert qualitative signals into numeric triggers
  • Track shareholder structure and upcoming unlocks to spot dilution risk
  • Update the model after key events (earnings, guidance, M&A, regulatory notices)

Example use cases

  • Full stock research report: macro outlook, company quality, valuation, and trade plan
  • Intraday/short-term trade validation using volume, turnover and technical triggers
  • Event-driven playbook for earnings, share issuance, or executive changes
  • Portfolio rebalancing: rank holdings by sentiment, liquidity and fundamentals
  • Watchlist screening: filter by ROE, revenue growth, PE-TTM and free-float liquidity

FAQ

Fundamentals, capital, technicals, sentiment and news form a closed loop—each dimension validates or challenges the others, reducing false positives and improving trade timing.

How are targets and stops determined?

Targets combine valuation methods (PE/PEG) and technical projection; stops are set by technical supports, volatility-based rules, and risk tolerance.

Built by
VeilStrat
AI signals for GTM teams
© 2026 VeilStrat. All rights reserved.All systems operational
astock-research skill by openclaw/skills | VeilStrat