Loading...
Loading...
Found 31 Skills
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.
EDA, dashboards, Matplotlib, Seaborn, Plotly, and BI tools. Use for creating visualizations, exploratory analysis, or dashboards.
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.
Data Analysis provides a structured framework for statistical analysis using pandas, numpy, and scipy, with visualization through matplotlib, seaborn, and plotly.
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.
Set up the Python environment for OpenAlgo indicator analysis. Installs openalgo, plotly, dash, streamlit, numba, yfinance, matplotlib, seaborn, and creates the project folder structure.
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.
Professional trading charts including candlesticks, equity curves, drawdowns, correlation heatmaps, and return distributions
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.
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.