6 مستودعات
Settings for defining plot styles, margins, and reference lines for indicators.
Distinct from Plot Axis Customizers: Focuses on indicator-specific chart configuration, distinct from general axis customization.
Explore 6 awesome GitHub repositories matching data & databases · Indicator Visualization Configuration. Refine with filters or upvote what's useful.
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
Defines custom plot styles, axis margins, and reference lines to ensure indicators render clearly alongside price data.
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
Visualizes uncertainty or variance for data points by drawing error bars along the axes.
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
Displays error bars paired with scatter plots to show measurement uncertainty ranges.
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
Summarizes a range of quantitative values using statistical measures like standard error or confidence intervals.
This C++ data visualization library is a scientific plotting framework used to create 2D and 3D charts, network graphs, and geographic maps. It operates as a multi-backend graphics library, decoupling high-level plotting logic from low-level rendering engines to support various output backends. The project distinguishes itself with a dual-interface API, providing both a global functional interface for rapid prototyping and an object-oriented interface for precise control. It features a component-based layout engine for managing tiled grids and subplots, alongside a layered plot state that all
Adds vertical or horizontal lines to data points to represent uncertainty or error margins.
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
Adds vertical or horizontal error bounds to data series using fixed or custom values.