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Found 39 Skills
This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.
Build a web dashboard for technical indicator analysis using Plotly Dash or Streamlit. Supports single-symbol, multi-symbol, and multi-timeframe layouts with real-time refresh.
Build production-grade interactive dashboards with Plotly Dash - enterprise features, callbacks, and scalable deployment
Use when "data visualization", "plotting", "charts", "matplotlib", "plotly", "seaborn", "graphs", "figures", "heatmap", "scatter plot", "bar chart", "interactive plots"
Chart any technical indicator on a symbol using Plotly. Creates interactive dark-themed charts with candlestick, overlays, and subplots. Supports all 100+ openalgo.ta indicators.
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Publication-ready matplotlib figures for Nature/high-impact journals and academic papers. Covers bar charts, grouped bars, heatmaps, line/trend plots, forest plots, microscopy-style image panels, schematic + quantitative composites, radar plots, and multi-panel layouts with Nature-style typography (Arial/sans-serif), restrained color systems, and SVG/PDF export conventions. Use when creating scientific figures that must match Nature publication standards. Do NOT use for interactive dashboards (Plotly, Bokeh) or Illustrator/Figma-first infographic workflows.
Generate and serve rich HTML companions to markdown discovery documents: paired markdown→HTML rendering (marked + mermaid + data-driven cytoscape/plotly fenced blocks), a vendored draw.io stencil library (~AWS/GCP/Azure/K8s SVG icons) for composing custom architecture diagrams, an injectable design-tokens brandpack, and a reliable localhost server for HTML that pulls pinned CDN libraries. Use when turning a review/architecture/cost markdown doc into an interactive HTML view, when a diagram needs real cloud provider icons, when serving local HTML that file:// breaks, or when building a data-driven graph/chart view. Skip when the deliverable is plain mermaid-in-markdown authoring with no HTML companion, or a Python-rendered architecture diagram image.
Generate financial analytics and insights from ~/Documents/finances/ data. Terminal report shows: net worth trends (30d/90d/1y), asset allocation, liability table with APR and monthly interest, cash flow (last 30 days), Bitcoin detail with sparkline. HTML dashboard: interactive plotly charts for all of the above over time. Use when: reviewing finances, answering questions about net worth, spending patterns, debt paydown progress, Bitcoin holdings, asset allocation. Keywords: financial report, net worth, spending, cash flow, debt, liabilities, bitcoin, how am I doing financially, finances summary, show me my finances, portfolio.
Set up real-time indicator computation on live WebSocket market data. Streams LTP/Quote/Depth and computes indicators in real-time with optional Plotly live charting.
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.