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Stock CYQ Distribution Analyzer

Data Analysis Updated 2026.08.30

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Please follow https://skillhub.cn/install/skillhub.md and install @user_b806c584/stock-cyq-analyzer.

About this skill

Problem

In stock price-volume analysis, candlestick charts often show where the current price is, but not how much historical holding is concentrated around different cost levels, what the average cost is, or how much of the distribution lies below the current price. CYQ chip distribution tries to quantify the accumulation of holdings from historical trading and turnover, giving engineers and analysts a quick view of cost structure.

How It Works

The tool calculates stock CYQ distribution from price and volume data. Key steps include:
- computing the typical price as (Open + High + Low + Close) / 4;
- building a triangular distribution using the day's low, high, and typical price;
- applying linear historical decay based on turnover, using the coefficient 1 - turnover, so newer trades weigh more;
- outputting the distribution with a 0.1 price step.

It returns standard JSON with indicators such as:
- Profitable chip ratio: the share of chips below the current price;
- Average cost: the average cost price of held chips;
- Chip peak: the price with the densest distribution;
- 90% concentration: the width of the price range covering 90% of chips;
- Main cost zone: the densest 20% price interval.

Boundaries

This skill is suitable for offline or scripted analysis when OHLC, volume, and turnover data are available. The output is an estimated chip structure, not exchange-level real-time trade details. Triangular distribution and linear decay depend on data quality: missing fields, bad price adjustments, or extreme turnover values can distort the indicators. The returned chart path is intended for downstream visualization.

Use Cases

  • During daily stock review, compute profitable-chip ratio and average cost from OHLC and turnover data.
  • When screening tickers, batch-read historical OHLC JSON and rank by 90% chip concentration and main cost zone.
  • When building charts, call the tool to get chip peak and chart path, then attach the metrics to research notes.
  • When validating strategy inputs, compare CYQ distribution across dates to see if price sits near the dense cost zone.

Best For

  • Technical analysis engineers who need to turn daily OHLC data into chip-structure metrics.
  • Data analysts writing equity reports and attaching average cost or profitable-chip ratio.
  • Quant researchers adding external CYQ distribution outputs to strategy scripts.
  • Data engineers validating adjusted OHLC quality and diagnosing turnover-related anomalies.