FinRL®: Financial Reinforcement Learning. 🔥
- deep-reinforcement-learning
- drl-trading-agents
- stock-trading
- drl-algorithms
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An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
TranslatedWe are committed to the open-sourcing quantitative knowledge, aiming to bridge the information gap between the domestic and international quantitative finance industries. We are dedicated to open-sourcing quantitative knowledge and translating it into Chinese (汉化), breaking the information gap between the domestic and international quantitative finance industries.
- quantitative-finance
- quantitative-trading
- wiki
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
- deep-reinforcement-learning
- stock-trading
- a2c-algorithm
- ppo
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
- reinforcement-learning
- supervised-learning
- stock-data
- trading
Scalable, event-driven, deep-learning-friendly backtesting library
- reinforcement-learning
- deep-reinforcement-learning
- gym-environment
- openai-gym
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
Open source Farm management Information System (FMIS) - Connecting farms to the world
- ruby
- agriculture
- erp
- management
Trading Environment(OpenAI Gym) + DDQN (Keras-RL)
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Self-hosted, invisible CAPTCHA that detects AI agents driving real browsers. GDPR, WCAG 2.2 AA, and EAA: no cookies, no third-party data sharing, and no visual or audio puzzle. With a false-positive budget enforced in CI. Proof of work, drop-in for Turnstile and reCAPTCHA.
- anti-bot
- behavioral-analysis
- bot-detection
- captcha
Trading environnement for RL agents, backtesting and training.
- trading
- backtesting
- reinforcement-learning-agent
- reinforcement-learning
Building a population of models that trade crypto and mutate iteratively
- trading-bot
- bitcoin
- neuroevolution
- reinforcement-learning
Wallet info for your favourite Dotsama cryptocurrencies like Polkadot (DOT), Kusama (KSM), Moonbeam (GLMR), Astar (ASTR), Acala (ACA), Subsocial (SUB), and other Substrate chains in the ecosystem.
- acala
- asset-management
- astar
- crypto-portfolio