bqplot is an interactive data visualization library for Jupyter notebooks. It implements a grammar of graphics model, allowing users to build complex 2D charts by combining marks, scales, and axes. The library distinguishes itself with specialized toolkits for financial charting, such as OHLC candlesticks and time-series analysis, and geographic data visualization, including choropleths and custom map projections for TopoJSON and GeoJSON data. It enables deep interaction through tools like lasso selection, rectangular brushing, and the ability to manually manipulate plot points or line data.
react-stockcharts is a financial charting library built with React and D3 for visualizing market price data. It provides a system for rendering stock charts and candlestick visualizations to represent market movement. The library functions as a technical analysis tool that computes and overlays mathematical trading indicators on price charts. It includes an interactive interface for navigating financial data through zooming, panning, and the addition of manual geometric annotations. The project covers financial data visualization, market price tracking, and the creation of interactive tradin
KLineChart is a high-performance financial charting library and data visualization engine. It uses native browser canvas APIs to render interactive candlestick charts and time-series financial data for web and mobile applications. The library is built as a plugin-based framework, allowing for the integration of custom calculation logic and rendering routines. This modular architecture enables the addition of technical analysis visualization tools and custom indicators to financial dashboards. The system handles real-time market data updates and computes technical indicators to visualize mark
QuantStats is an open-source Python library that calculates risk and return metrics from a portfolio return series and generates comprehensive HTML tear sheets. It computes dozens of financial statistics—including Sharpe ratio, drawdown, and volatility—in a single pass over the input data, using vectorized pandas operations for efficiency. The library distinguishes itself by combining portfolio performance analysis with Monte Carlo simulation, which models thousands of random return paths to estimate the probability of reaching financial targets or hitting loss thresholds. It produces self-co
mplfinance 是一个基于 Matplotlib 构建的金融时间序列绘图和市场数据可视化框架。它旨在将市场数据帧渲染为专业图表，包括蜡烛图、OHLC 条形图、Renko 砖形图以及点数图（point-and-figure）。
matplotlib/mplfinance 的主要功能包括：Matplotlib, Financial Time-Series Plotters, Non-Trading Gap Management, Candlestick Charts, Financial Market Visualizers, OHLC Bar Charting, Trading Calendar Alignment, Financial Charting Extensions。
matplotlib/mplfinance 的开源替代品包括： bloomberg/bqplot — bqplot is an interactive data visualization library for Jupyter notebooks. It implements a grammar of graphics model,… rrag/react-stockcharts — react-stockcharts is a financial charting library built with React and D3 for visualizing market price data. It… klinecharts/klinechart — KLineChart is a high-performance financial charting library and data visualization engine. It uses native browser… ranaroussi/quantstats — QuantStats is an open-source Python library that calculates risk and return metrics from a portfolio return series and… louisnw01/lightweight-charts-python — This project is a Python wrapper for the Lightweight Charts library, designed to render interactive, browser-based… apexcharts/apexcharts.js — ApexCharts is a comprehensive JavaScript charting library designed for building interactive, responsive, and…