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Found 29 Skills
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
Set up the Python environment for OpenAlgo indicator analysis. Installs openalgo, plotly, dash, streamlit, numba, yfinance, matplotlib, seaborn, and creates the project folder structure.
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.
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.
Data visualization for Python: Matplotlib, Seaborn, Plotly, Altair, hvPlot/HoloViz, and Bokeh. Use when creating exploratory charts, interactive dashboards, publication-quality figures, or choosing the right library for your data and audience.
EDA, dashboards, Matplotlib, Seaborn, Plotly, and BI tools. Use for creating visualizations, exploratory analysis, or dashboards.
Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.
Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).
Create professional financial charts and visualizations using Python/Plotly. Use when building Sankey diagrams (income statement flows, revenue breakdowns), waterfall charts (profit walkdowns, revenue bridges), bar charts (margin comparisons, segment breakdowns), or line charts (trend analysis, multi-company comparisons). Triggers on chart creation requests, financial visualization needs, or data presentation tasks.
Analyze user conversion funnels, calculate step-by-step conversion rates, create interactive visualizations, and identify optimization opportunities. Use when working with multi-step user journey data, conversion analysis, or when user mentions funnels, conversion rates, or user flow analysis.
Data visualization for charts and graphs. Use when user needs "画图/图表/可视化". Creates static PNG or interactive HTML charts from data.
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.