Autonomous quantitative trading research platform that transforms stock lists into fully backtested strategies using AI agents, real market data, and mathematical formulations, all without requiring any coding.

Simple Finance Forecasting Ai. This Ai Model uses historical price data to forecast future prices. The model is trained on data downloaded from Yahoo Finance using the yfinance library, and predictions are made using a linear regression Ai model from sklearn. The model supports all the symbols supported by Yahoo Finance.

Multi-agent AI assistant platform with specialized agents for coding, finance, news, real estate, travel, image generation, and shopping.

A production-grade implementation of a memory-efficient transformer specifically designed for tabular time series data.

We are researching and developing our own in-house LLM, which will be focused on finance-based chats and requests.

🛒 Explore the AI Shopping Agent to compare prices and generate smart purchase reports across top e-commerce platforms effortlessly.

AlphaPulse: A real-time financial intelligence dashboard leveraging NLP (TextBlob) for sentiment analysis and Scikit-learn for technical trend forecasting. Built with Streamlit & yfinance.

An AI-powered finance agent using Agentic AI and Agno for predictive financial analysis

A stock market analysis is an multi-agent Agentic AI project using Python, yfinance, and AI agents.

🚀 AI Investment Advisor (v1.0): 專業級 7-Agent Swarm 智能體集群。整合 Council Debate 辯論機制、pgvector 語義記憶與多層級 LLM 路由。A self-optimizing quantitative ecosystem built with Clean Architecture, DSPy, and 75%+ test coverage. 📈

RAG-powered ETF intelligence chatbot built with LangChain, OpenAI, Chroma, and Streamlit. Retrieves ETF knowledge from documents and generates grounded responses for educational use.

Subjectivity-aware RAG pipeline for investment questions with web search, multi-strategy scraping, Worker + Checker LLMs, and deterministic claim verification.

**TradingAgents** is an open-source, multi-agent framework that uses different specialized LLMs (analysts, researchers, traders, risk managers) to debate and simulate real-world financial trading decisions. It is designed for research purposes, taking live market data and breaking down the complex analysis into a collaborative AI workflow.

StockPicker Crew — A Python-based multi-agent AI framework using CrewAI, enabling coordinated agents to research, analyze, and generate reports (e.g., on stock-picking strategies). Easy to configure and run with YAML-based agent/task definitions and crewai run.

AI-based credit risk analysis system that predicts loan default risk using machine learning for data-driven financial decision making.

Advanced RAG platform with domain-separated embeddings for finance, law, and global queries. Features FastAPI backend, Next.js frontend, Qdrant vector DB, and dual LLM router (Gemini/Groq fallback).

Exploration of trading using Deep Q-Networks (DQN).