QuantRL-Lab
Unverified ML strategy on Indices by whanyu1212. BotFinder score 18 out of 100.
Reinforcement Learning Testbed for Quantitative Trading
Source: github
BotFinder analysis pending.
QuantRL-Lab
QuantRL-Lab A Python testbed for Reinforcement Learning in finance. Emphasizes modularity via dependency injection of pluggable action, observation, and reward strategies — enabling rapid experimentation without rewriting environment code. For full API reference, guides, and examples see the documentation. --- Table of Contents - Installation - Motivation - Quick Start - Roadmap - Contributing - Contributors - Literature Review --- Installation Optional extras: Contributors: see CONTRIBUTING.md for the uv-based development setup. --- Motivation Most RL frameworks for finance hardcode action spaces, observation spaces, and reward functions into the environment. This makes experimentation slow — changing a reward function can require significant refactoring. QuantRL-Lab solves this with a strategy injection pattern: pass three pluggable objects at environment instantiation time and swap them freely without touching environment internals. --- Quick Start System Workflow Example --- Roadmap - Data sources: crypto and OANDA forex support - Environments: multi-stock environment (in progres
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