Awesome GitHub RepositoriesIndicator Visualization Configuration

Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena

fl_chart is a data visualization library and UI component framework for Flutter. It provides a system of reusable graphical widgets for creating interactive, customizable quantitative data visualizations. The framework supports a variety of chart types, including line, bar, pie, donut, scatter, radar, and candlestick views. It allows for the creation of complex visualizations such as layered data segments and financial charts. The library includes capabilities for interactivity and visual refinement, such as touch event handling, data tooltips, and state animations. It also provides tools fo

ScottPlot is a cross-platform, high-performance charting library for .NET that renders interactive plots across desktop and web GUI frameworks including Windows Forms, WPF, MAUI, Avalonia, Blazor, and WinUI. It provides an optimized rendering engine capable of displaying millions of data points with interactive pan, zoom, and live data streaming, while also supporting image export to formats like PNG and SVG for file output, cloud applications, and notebooks. The library distinguishes itself through a comprehensive set of chart types including scatter, line, bar, pie, heatmap, financial, rada

Vega-Lite is a high-level declarative language for specifying interactive, multi-view visualizations. It compiles a concise JSON specification into a full Vega visualization, automatically inferring scales, axes, and legends from encoding declarations. The grammar-of-graphics encoding maps data fields to visual channels such as position, color, size, and shape, while a multi-view composition grammar enables layered, faceted, concatenated, and repeated layouts. Reactive parameter binding links named parameters to input widgets, selections, and expressions for dynamic updates. The project suppo

Esta librería de visualización de datos en C++ es un framework de trazado científico utilizado para crear gráficos 2D y 3D, grafos de red y mapas geográficos. Opera como una librería de gráficos multi-backend, desacoplando la lógica de trazado de alto nivel de los motores de renderizado de bajo nivel para soportar varios backends de salida. El proyecto se distingue por una API de interfaz dual, que proporciona tanto una interfaz funcional global para prototipado rápido como una interfaz orientada a objetos para un control preciso. Cuenta con un motor de diseño basado en componentes para gestionar cuadrículas y subgráficos, junto con un estado de trazado en capas que permite superponer múltiples series de datos sin borrar los ejes. La librería cubre una amplia gama de capacidades de visualización, incluyendo trazado de funciones matemáticas, campos vectoriales y análisis de datos multidimensionales mediante mapas de calor y coordenadas paralelas. Incluye herramientas especializadas para la visualización de datos geográficos, como gráficos geobubble y geodensity, así como herramientas para renderizar redes de grafos dirigidos y no dirigidos. Las capacidades generales incluyen gestión de ejes, estilo estético con mapas de colores y exportación de gráficos de alta calidad. El proyecto utiliza CMake para la automatización de la compilación y la recuperación de dependencias para facilitar la instalación en diferentes sistemas operativos.

XlsxWriter is a library for generating spreadsheets in the XLSX format, functioning as an Excel workbook writer and file generator. It provides the capability to write data, apply cell formatting, and build complex layouts across multiple worksheets. The project distinguishes itself with a memory-optimized writing mode that flushes large datasets to disk row-by-row, enabling the creation of files exceeding 4 GB while minimizing RAM consumption. It also includes a specialized mechanism for embedding binary project files and digital signatures to enable VBA macros and signed scripts within work