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FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow
Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.
The main features of nnaisense/evotorch are: Optimization, Reinforcement Learning.
Open-source alternatives to nnaisense/evotorch include: 2toinf/uniact — [Project Page] [Paper]. ai4co/rl4co. ai4finance-foundation/finrl — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated… aikorea/awesome-rl — Reinforcement learning resources curated. airlab-polimi/mushroom. 100/solid — 🎯 A comprehensive gradient-free optimization framework written in Python.
