FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
在A股(股票)市场上训练强化学习交易智能体
Initiate multiple trainer scripts allowing to train agents based on the FINRL library
Crypto trading bot using FinRL reinforcement learning model
Institutional MetaTrader 5 algorithmic trading framework adapting FinRL via a 5-Expert Mixture-of-Experts (MoE) Council with NSGA-III Pareto gating on 100% real ticks.
Repository containing code and notebooks exploring how to build reinforcement learning agents that trade on the stock market using FinRL
Research project on Reinforcement Learning for trading, using FinRL to develop and evaluate adaptive trading agents, portfolio strategies, and risk-aware decision-making.
Code for the published paper “Ensemble Strategy for Algorithmic Trading Using Deep Reinforcement Learning.”
Modular AI trading system using FinRL, multi-agent analysis, and real-time data pipelines.
Building the strong structured code and using FinRL to find the best configurations of agent for multistock trading
Testing BOVA11 composition against itself using Reinforcement Learning
RL-based stock trading with sentiment analysis using FinBERT and FinRL. Tested A2C, PPO, and TD3 on 10 US stocks against DJI benchmark.
Reproducible ANN vs quantum-inspired MPS signals inside a FinRL PPO trading agent
A progressive DRL stock trading system built on FinRL, benchmarking four model generations across VGG CNN and Transformer architectures on three capital levels. Trained on up to 50 NASDAQ tickers (2020–2025) with Historical data from Yahoo! Finance and live market data via Alpaca and real-time news sentiment scored by FinBERT and Polygon.io.
A local research cockpit for the stocks you can't stop checking: market data, deep-RL trade signals across three model baskets, and LLM news outlooks in one dark-mode dashboard.
Flet web app where a PPO agent trades simulated funds on the S&P 500. BSc dissertation, Exeter 2025
A-share stock trading with deep reinforcement learning (FinRL + stable-baselines3: PPO/A2C/SAC)
Risk-first AI trading R&D→production pipeline for WEEX: RL/ensemble strategies, anti-overfit validation (CPCV/WF), backtesting, and WEEX API execution.
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