kline-volume-review

You can render stock K-line candlesticks with volume charts by using mplfinance in Python to plot OHLC data and volume panels. This skill processes DuckDB kline_daily data and outputs per-stock PNG reports with moving averages.

To add moving averages and quarterly boundaries to a mplfinance candlestick chart, overlay a 250-day moving average and annotate t1, t2, and t3 boundaries with vertical lines. This provides quarterly context for buy and sell decisions.

Yes, you can generate offline stock analysis PNG reports from DuckDB and parquet files without network access. This skill intersects parquet hits and diagnostic triples with DuckDB data to export visual evidence to a reports directory.

When batch generating K-line charts, missing stock data is handled gracefully by skipping the affected stocks with warnings. The processing continues for the rest of the batch, ensuring uninterrupted generation of visual PNG reports.

mplfinance works with DuckDB for visualizing daily stock data by querying kline_daily tables for consistent OHLC information. The retrieved data is then processed with pandas and matplotlib to produce candlestick and volume charts.

The best way to visually verify stock trading decisions is to render daily K-line candles with volume and quarterly boundary annotations. This allows human analysts to confirm if buy and sell decisions are corroborated by actual price action.