🤖 Advanced AI-powered trading system for Kalshi prediction markets. Features Grok-4 integration, multi-agent decision making, portfolio optimization, and real-time market analysis. Educational/research purposes only.

Official implementation of MASS: Multi-Agent Simulation Scaling for Portfolio Construction

Attention-based Deep Reinforcement Learning framework for portfolio allocation on S&P 500 equities. Includes custom environment, policy architecture with cross-sectional attention, PPO/A2C/REINFORCE agents, training/evaluation pipeline, and fine-tuning grid search.

Multi-agent investment team powered by Agno and Gemini — 7 AI analysts collaborate across 5 architectures to deploy a $10M equity portfolio

🚀 Advanced Data & AI Engineering Portfolio: Real-world projects and production-ready patterns to level up your AI skills—from building clean data pipelines to deploying RAG systems, AI agents, and intelligent dashboards.

Blockchain AI Architect Portfolio: Solana, EVM (Base, ApeChain), Bitcoin dApps | Smart Contracts, DeFi, NFT Marketplace, P2E Games, AI Agents, Non-Custodial Casino, AI Agent, Token Launchpad, Dex, NFT Game, Casino, Betting, RWA, Non Custodial Staking, GameFi, NFT Marketplace, Lending Protocal, dApps | Smart Contracts, DeFi, NFT Marketplace, Casino

MASA: Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management.

Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.

Plug-and-play AI agent skills for OKX — letting any LLM agent trade, manage portfolios, query live market data, and run grid/DCA bots through a single okx CLI, no API wiring required.

A Multi-Agents LLM Financial Trading Framework for both individual stock analysis & portfolio management

BitQuant is an open-source AI agent framework for building quantitative AI agents, leveraging specialized models for ML-powered analytics, trading, portfolio management, and more.

a novel framework for stock portfolio trading that employs a 'Relaxation and Refinement' strategy to boost the Soft Actor-Critic (SAC) agent

🧠 A production-grade, agentic RAG platform for portfolio intelligence, combining LangChain, Chroma/FAISS, Hugging Face embeddings, and Ollama with dynamic entity extraction, backend API tool-chaining, and a real-time interactive assistant across deploy-ready frontend, backend, and infrastructure stacks.

DeFi agent definitions JSON API + MCP - Production-ready agents for Web3, crypto trading, portfolio management, and blockchain automation. MCP compatible.

A DQN agent that optimally hedges an options portfolio.

Direct Reinforcement Learning Agent for Portfolio Management Purposes

3rd Year University Undergraduate dissertation project, supervised by https://www.nottingham.ac.uk/computerscience/people/thomas.gaertner focusing on using Natural Language Processing, AI Portfolio Optimisation and Machine Learning to produce an Automated Trading Agent. Received 89% overall.

An agent that can help you with investments portfolios and decks

A portfolio of web apps for the CodeCademy Mastering Generative AI & Agents for Developers Bootcamp (Aug-Sep 2025)

AI-powered fundamental analysis platform. TimescaleDB, CrewAI agents, portfolio optimization, pair trading, and automated alpha extraction.

Gold-standard course design templates for AI-first education by Bradley.Academy. For course designers, instructional developers, and AI agents. Practical first; fundamentals before features; outcomes over theory. Structured agents and templates using Bloom’s taxonomy, voice-consistent lessons, and portfolio-ready deliverables.

CS277 Project: Deep Reinforcement Learning in portfolio Management. This repo is the DQN part which implements a trading agent based on the DQN algorithm.

A living portfolio of agentic AI systems — bridging learning, art, and innovation.

AI Agent portfolio management on Cardano

An AI-powered investment analysis tool 📈 that leverages simple ReAct AI agent flow framework and financial analysis techniques to provide comprehensive portfolio insights. This intelligent agent helps investors make data-driven decisions by offering deep portfolio risk assessment, stock profiling, and personalized recommendations.

👋 AI Multi-Agent Engineer Portfolio I'm Idael, an AI Prompt Engineer and Network Automation specialist at Kyndryl, focused on transforming complex challenges into innovative solutions through multi-agent systems.

A sleek, interactive portfolio website showcasing my work in AI/ML, web development, automation, and system design. Built with Vite + React and styled for performance and responsiveness, this portfolio highlights my projects, internships, and technical journey — from smart wearables to agentic AI systems and personalized recommender engines.

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Multi-agent trading backtest framework (Rigid): Sentiment, Fundamental, and Valuation agents feed a Reasoning Agent that makes trading decisions; a Portfolio Manager handles allocation. Uses manually curated news data and historical prices to evaluate LLM performance in financial analysis.

APOS — The Autonomous App Portfolio Operating System. Open-source agentic pipeline for shipping software products.