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algorithmic-trading-dotnet

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vibe-investing

AI-powered Vibe Investing for NASDAQ, S&P500 & crypto: LLM quant trading tools, multi-agent backtesting, and data-driven market columns (mNAV arbitrage, BTC-Nasdaq coupling, Alpha Arena). 미국 주식·가상화폐 AI 투자 큐레이션·칼럼·트레이딩 봇.

  • Updated Sep 1, 2026
  • HTML

CryptoQuant AI is an advanced, open-source quantitative trading platform designed to bridge the gap between algorithmic market execution and artificial intelligence. Built entirely on a modern Node.js, Vite, and TypeScript stack, this project provides a robust, highly responsive frontend dashboard paired with powerful automation capabilities.

  • Updated May 1, 2026
  • TypeScript

End-to-end algorithmic trading terminal featuring PyTorch LSTM forecasting, NLP market sentiment analysis, Lasso ML feature selection, and vectorbt backtesting. Built for multi-asset quantitative research.

  • Updated Aug 31, 2026
  • Python

AI-native event-driven trading engine. Rust core with Python RL bindings. Built-in Gymnasium environments, LLM agents, SHAP explainability, and model ensembling — from backtesting to production.(AI 原生事件驱动交易引擎,Rust 内核,Python RL 绑定,支持 Gymnasium 环境、LLM 智能体、SHAP 可解释性与模型集成,贯穿回测到生产部署。)

  • Updated Sep 1, 2026
  • Rust

AlphaStream India is a production-grade, real-time investment intelligence terminal designed to empower Indian retail investors. Featuring a Bloomberg-style terminal UI, it integrates Gemini 2.0 Flash, FastAPI, and React 19 to deliver grounded, backtested, and high-alpha investment signals.

  • Updated May 14, 2026
  • Python

Asynchronous ASGI webhook execution engine for Dhan v2 API. Sub-50ms TradingView signal routing for Nifty, BankNifty, and MCX algorithmic trading

  • Updated Jun 24, 2026
  • Python

End-to-end machine-learning pipeline that screens the stock market for high-probability BUY opportunities — feature engineering, LightGBM/XGBoost models, walk-forward backtesting, and continuous retraining, served via FastAPI.

  • Updated Jul 30, 2026

A leak-free walk-forward study of short-horizon crypto return predictability: purged cross-validation, nested-model tests, and multiple-testing correction against a random-walk null.

  • Updated Aug 10, 2026
  • Python

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