Provides 150+ executed Jupyter notebooks and code that reproduce the book 'Machine Learning for Algorithmic Trading (2nd ed.)' — covers feature engineering, alternative-data signal extraction, backtesting, NLP, deep learning and reinforcement learning for trading; best for quant researchers and practitioners.
An AI-native, weight-centric infrastructure for quantitative trading that produces target portfolio weight vectors to unify data ingestion, strategy composition, backtesting, and live/broker execution. Modular pipeline supports ML/DRL allocators, LLM-ready preprocessing, multi-source data, and Alpaca integration for paper/live trading.
Automates online monetization workflows—generating and scheduling YouTube Shorts, posting to X (Twitter), running affiliate campaigns, and outreach. Modular provider-based design (TTS, LLM hooks, CRON scheduler) and configurable pipelines; legal/ToS risks mean use with caution.
Developer framework for building AI agents that autonomously trade on Polymarket prediction markets. Bundles the Polymarket and Gamma APIs, a Chroma RAG layer that pulls in news, and a CLI to query markets, reason with an LLM, and execute trades.
Desktop finance analytics terminal that combines CFA-level models, real-time trading and 100+ data connectors with embedded Python for analytics; includes 37 AI agents and local/multi-provider LLM support for automated research and decision workflows.
Extracts and structures data from receipts, invoices and transaction documents using configurable LLM prompts for a self-hosted accounting workflow. Offers multi-currency (including crypto) historical conversion, custom fields/prompts, batch processing and Docker-based deployment for local data control.
Transforms unstructured financial content—papers, news, blogs, and filings—into a queryable semantic knowledge graph for retrieval-augmented research. Combines domain-tuned LLMs, embedding-based search, and modular ingestion pipelines; aimed at quant research teams and institutional workflows.
Lets AI assistants query market data and execute/manage trades on MetaTrader 5 using natural language. Implements the MCP bridge with multiple transports (stdio/SSE/HTTP), a WebSocket quote streamer, and local-credentials-first design for prototyping AI-driven trading integrations.
Runs a six-month live experiment where ChatGPT manages a real-money micro-cap portfolio from $100, trading under strict rules with automated stop-losses. Each trade's rationale is logged; returns are benchmarked against the S&P 500 and Russell 2000.
Provides an MCP server exposing 30+ trading tools — real-time prices, technical indicators, Bollinger Band scores, Reddit/news sentiment, and backtesting — designed to integrate with Claude/OpenClaw agents for automated market analysis.
Decomposes a financial question into a research plan, then autonomously pulls live market data — income statements, balance sheets, cash flows — and self-checks until confident. Logs every tool call and reasoning step to JSONL scratchpads.
Lets AI agents (Claude Code, Codex, Cursor, and others) register through a guide and join a shared paper-trading arena, where they publish signals, debate ideas, and copy-trade each other across stocks, crypto, and Polymarket markets.