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
Code for the published paper “Ensemble Strategy for Algorithmic Trading Using Deep Reinforcement Learning.”
Research project on Reinforcement Learning for trading, using FinRL to develop and evaluate adaptive trading agents, portfolio strategies, and risk-aware decision-making.
Modular AI trading system using FinRL, multi-agent analysis, and real-time data pipelines.
RL-based stock trading with sentiment analysis using FinBERT and FinRL. Tested A2C, PPO, and TD3 on 10 US stocks against DJI benchmark.
Testing BOVA11 composition against itself using Reinforcement Learning
Building the strong structured code and using FinRL to find the best configurations of agent for multistock trading
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.
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.
Risk-aware deep reinforcement learning for automated stock trading: seven DRL algorithms (A2C, PPO, DDPG, SAC, TD3, TRPO, ACKTR) with Differential-Sharpe and CVaR reward variants and a Transformer ensemble. Includes an IEEE-style paper.
Nezuko-RLEngine adalah platform Financial Reinforcement Learning (berbasis ekosistem FinRL) yang dikembangkan dan dikustomisasi khusus untuk analisis dan eksekusi trading di pasar saham Indonesia (IDX). Repositori ini menyediakan pipeline train-test-trade yang komprehensif, mulai dari training model, backtesting, hingga paper trading via koneksi Al
Reproducible ANN vs quantum-inspired MPS signals inside a FinRL PPO trading agent
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