JavaScript toolkit for creating interactive real-time graphs
- rickshaw
- graph
- javascript
- d3
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JavaScript toolkit for creating interactive real-time graphs
Translated📈Lightweight k-line chart that can be highly customized. Zero dependencies. Support mobile.
Highly customizable stock charts with ReactJS and d3
WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.
Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
A software library of stochastic streaming algorithms, a.k.a. sketches.
Ruby library to perform server-side tracking into the official Google Analytics Measurement Protocol
An enhanced interactive Shell for Common Lisp (based on the Jupyter protocol)
A Tree Widget using Jupyter-widgets protocol and jsTree
Coding assistance for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol
100% open-source, AI-native web analytics with cookieless tracking, open exports, custom domains, and SSO—self-hosted as one Go binary or managed in EU/US cloud.
Streaming-first technical indicators with a Rust core: native Python, Node.js and WebAssembly bindings plus a C ABI hub for C, C++, C#, Go, Java and R. 514 indicators, O(1) per-tick updates, no system dependencies. Drop-in TA-Lib replacement.
Node-RED nodes to save and query data from an influxdb time series database
Go's answer to pandas + scikit-learn: a batteries-included data analysis library with dataframes, statistics, machine learning, quant, and GPU acceleration. Run Python with seamless two-way variable sharing, or explore data straight from the Insyra CLI.
MCP server for spatial transcriptomics analysis through natural language interfaces.
Stock price prediction using a deterministic algorithm inspired by LSTM, focusing on pattern recognition in historical data.
ETL scripts for EOS.
Simple, modern ETH backtesting project using Pandas/Numpy: data prep, MA signals, portfolio simulation, and result visualization.
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.