financial-market-charts

To generate a candlestick chart from CSV price data, this skill converts raw market price rows into publication-quality visualizations using matplotlib, numpy, and pandas, saving the rendered output to a results directory.

Kagi and point-and-figure charts are reversal-based visualizations that filter out minor price movements. This skill renders both chart types from time series data to highlight trend changes and key support/resistance levels for trading decisions.

Yes, you can plot OHLC charts for trading analysis. This skill leverages matplotlib and pandas dependencies to produce compact open-high-low-close representations, providing quick overviews of daily price action from your financial datasets.

Yes, this market charting skill works with standard time series datasets. It processes raw market price data across finance, trading, or research datasets to visualize price movements and clearly labels axes during the rendering process.

The best way to visualize support and resistance levels without plotting every interval is using Kagi or point-and-figure charts. This skill generates these reversal-based charts to reveal underlying price structure and market semantics.

Financial market charts lack clear axes when using basic plotting without semantic labeling. This skill satisfies matplotlib, numpy, and pandas dependencies during rendering to ensure axes are clearly labeled and outputs are publication-quality.