Vibe-Trading

TradingAgents is an open-source, multi-agent LLM financial trading framework developed by TauricResearch for research. It models a trading firm through specialized agents: fundamental, sentiment, news, and technical analysts produce market assessments; bullish and bearish researchers debate those assessments; a Trader Agent proposes the timing and size of a trade; and risk-management and portfolio-management agents evaluate and approve or reject it before execution on a simulated exchange. The framework uses LangGraph and can be run through an interactive CLI, Docker, or as a Python package via the TradingAgentsGraph class and its propagate function. It accepts market and ticker inputs, analysis dates, configurable research depth and debate rounds, and LLM backends including hosted providers, Ollama, and other OpenAI-compatible servers. It gathers financial, market, news, macroeconomic, and social-sentiment data, and supports persistent decision logs and optional LangGraph checkpoint recovery. TradingAgents is intended as a research scaffold rather than financial, investment, or trading advice. Results can vary with the language model, sampling settings, data sources, analysis period, and other nondeterministic factors.

QuantsPlaybook is a GitHub resource containing Python reproductions of quantitative-investment research reports from Chinese securities firms. It covers market-timing strategies, factor construction, quantitative value methods, and portfolio optimization, with more than 100 listed strategy implementations including RSRS and QRS timing, volatility and technical-analysis methods, multi-factor models, FFScore, cash-flow selection, differential-evolution portfolio optimization, and deep multi-task time-series momentum. The repository organizes each strategy with related research papers or brokerage reports and Python files containing notebook-based reproductions; its workflows cover data acquisition, analysis, backtesting, visualization, and, where used, machine-learning or signal-decomposition methods. It depends on data sources such as JQData and Tushare and references tools including pandas, NumPy, Qlib, Backtrader, PyTorch, TensorFlow, LightGBM, XGBoost, scikit-learn, and Jupyter.

jev-trader is an AI market-making bot for the Kuru MON-USDC order book on Monad. A TypeSafe Jev model observes the order book once per block and predicts whether to buy or sell over a configurable future horizon; each block, the bot cancels its resting orders and submits a post-only limit order one tick inside the touch, aiming to earn the spread when a taker fills it. Its hot loop reads the book with one RPC call and submits the transaction with one asynchronous raw-transaction call, leaving receipt processing, fee estimates, and vault checks off the critical path. It records block decisions, quotes, fills, positions, and profit-and-loss data, and exposes snapshots, history, and server-sent events through a Bun server. If the model misses the block, it holds and posts nothing; position or margin limits can redirect a quote to the opposite side. The repository supports a dry-run mode with simulated fills and a mock momentum heuristic, or live Jev decisions using a TypeSafe AI API key and private key. It includes a deployed dry-run dashboard endpoint and can be run with Bun.

DeepTutor is an open-source, self-hostable AI tutoring platform developed by HKUDS for lifelong personalized tutoring. It provides a web application and command-line interface for interactive learning, problem solving, quiz generation, deep research, visualization, and mastery practice, with persistent memory and learning state shared across knowledge bases, books, notebooks, tutor personas, and other activities. The platform supports multi-agent problem solving, retrieval-augmented learning with source-traced and page-level citations, math animations, interactive visualizations, and tutor bots built from personal study materials. Its knowledge sources include personal documents, EPUBs and books with annotations, GitHub repositories, web search, and connected libraries; documented retrieval and ingestion options include GraphRAG, PageIndex, LightRAG, linked knowledge bases, Obsidian, and configurable parsing and vector backends. DeepTutor also includes courses, research workflows, an ecosystem of MCP services and tool or capability plugins, and integrations with connected coding agents and other partners. It can run locally or through Docker.

HFTENGINE is a replay console and backtesting application for market-making strategies using the hftbacktest crate. It runs tutorial strategies against recorded Binance USDT-M Futures order-book and trade data, replaying the local order book, resting orders and estimated queue positions, feed and order latency, market trades, executions, raw collector feed, and runner statistics frame by frame. Its Rust runner records the replay and its Vite/TypeScript dashboard presents the session in a text-mode interface. The project models post-only limit-order fills with hftbacktest's queue and exchange models; queue position is estimated from level-2 data, and order latency is derived from recorded feed latency rather than measured live. It includes tools to collect public Binance websocket data, convert recordings into sessions, run the backtest, and inspect replay moments. HFTENGINE is explicitly a backtester and replay screen: it does not place orders, compute or display P&L, or configure live trading.