TensorTrade is a reinforcement learning trading framework designed for training and deploying autonomous agents that optimize financial market strategies. It provides an algorithmic trading simulation environment where agents can be tested against market data using simulated broker environments. The framework features a distributed training system using RLlib to optimize decision policies across large datasets. It includes a walk-forward validation tool that evaluates trading strategies through windowed performance analysis to prevent overfitting and measure real-world viability. The project
RL-Swarm is a decentralized reinforcement learning framework designed to coordinate distributed machine learning agents across peer-to-peer networks. It functions as a distributed computing orchestrator that manages multi-agent roles and hardware resources to facilitate large-scale collaborative training. By anchoring training processes in cryptographic verification and decentralized contracts, the system ensures that model development remains transparent, verifiable, and resistant to manipulation. The platform distinguishes itself through a multi-agent expert coordination model that organize
This is a full-stack template for building decentralized applications on the GenLayer blockchain, combining smart contract development with a frontend integration pipeline. It provides a pre-configured environment for writing GenLayer smart contracts using Python decorators that specify function visibility, persistence, and typed storage structures, along with built-in linting to catch forbidden imports and nondeterministic calls before deployment. The boilerplate distinguishes itself by supporting nondeterministic smart contract operations—such as querying language models and fetching extern
FinRobot is an AI-powered financial analysis framework that coordinates multiple specialized agents to automate equity research, financial analysis, and investment risk assessment. At its core, it functions as a multi-agent orchestration system where a director and task manager allocate financial tasks to the most suitable large language models based on performance metrics and task requirements. The framework distinguishes itself through its ability to execute complex multi-step financial workflows by routing tasks through perception, reasoning, and action modules. It generates professional e
Prediction-market trading engine — Wang Transform pricing on 291K+ contracts; paper-traded across Kalshi · Polymarket · Solana DFlow (Jito bundles) · 633 tests
The main features of yichengyang-ethan/oracle3 are: AI Trading Agents, Prediction Markets.
Open-source alternatives to yichengyang-ethan/oracle3 include: tensortrade-org/tensortrade — TensorTrade is a reinforcement learning trading framework designed for training and deploying autonomous agents that… genlayerlabs/genlayer-project-boilerplate — This is a full-stack template for building decentralized applications on the GenLayer blockchain, combining smart… gensyn-ai/rl-swarm — RL-Swarm is a decentralized reinforcement learning framework designed to coordinate distributed machine learning… ai4finance-foundation/finrobot — FinRobot is an AI-powered financial analysis framework that coordinates multiple specialized agents to automate equity… cryptosun2049/openfinclaw. chrisworsey55/atlas-gic — ATLAS by General Intelligence Capital — Self-improving AI trading agents using Karpathy-style autoresearch.