Stock Volume 12-Step Deep Analysis

Problem

Stock volume analysis often breaks down on concrete issues: online data sources may be unavailable, low-volume stocks can be dismissed as signal-less, different sectors need different thresholds for volume ratio and crowding, and conclusions can remain scattered across notes. @user_7ba94f9e/stock-volume-12steps targets individual stock decision support by turning volume judgment into an executable SOP with a unified report output.

How It Works

The skill uses five-dimensional volume scoring as its first gate and runs a 12-step SOP inside a four-layer architecture. It does not hard-code Python data libraries such as akshare, efinance, pytdx, or yfinance; instead, it discovers MCP tools or Skills in the runtime and routes requests by data type to K-line, real-time quotes, fund flow, fundamentals, research, and peer data.

Key steps include:
- detecting the environment and routing to available data sources;
- scoring indicators such as volume ratio, external order share, main inflow, and crowding;
- switching to quiet-market deep analysis when low-volume triggers fire, reporting volume regime, accumulation/distribution detection, and watch conditions;
- supporting single-stock, two-stock comparison, and portfolio modes, with sector-specific weights for banks/dividends or leading manufacturers;
- generating a Quick Take card, trade ticket, and self-contained HTML report with charts and dark/light themes.

Boundaries and Caveats

The skill is useful for structured A-share/HK stock volume analysis when users need scoring, signal colors, confidence notes, and shareable reports. If no online source exists, it may fall back to a built-in offline snapshot; if the snapshot does not cover the target or the user skips building it, coverage is limited and the report should show as_of_date and confidence. It provides decision support, not investment advice, and users should check data completeness, sector parameters, and degradation notes before relying on the output.