aurumq-rl
Unverified ML strategy on Indices by yupoet. BotFinder score 18 out of 100.
RL stock selection for China A-share — bundled polars-native factor library (105 Alpha101 + 191 GTJA Alpha191 = 296 factors), board-aware price limits, GPU train + ONNX CPU infer,
Source: github
BotFinder analysis pending.
aurumq-rl
AurumQ-RL · A股量化强化学习选股开源项目 AurumQ-RL · An Open-Source Reinforcement Learning Stock-Selection Framework for the China A-Share Market 中文：一份面向 A 股的因子工程 + 强化学习选股参考实现，附完整的迭代史、消融实验、生产化决策与教训。 English: A factor-engineering + reinforcement-learning stock-picking reference implementation for China A-shares, shipped with the full iteration history, ablations, productionization decisions, and lessons learned. 📊 China A-share · 🤖 PPO/A2C/SAC · 🚀 GPU Train + CPU Infer · 📈 Alpha101 + GTJA Alpha191 + Main-Force + Hot-Money + Northbound · 🧪 26 Phases of Open Experiments --- 摘要 / Abstract 中文. AurumQ-RL 是一个针对 A 股市场特有微观结构（T+1、±10% 涨跌停、主板/科创/创业/北交分层、ST 风险警示、申万一级行业、龙虎榜、北向、游资席位、筹码分布）做工程化封装的强化学习选股开源项目。仓库内含：(1) 一个 polars-native 的因子计算引擎，覆盖 105 个 WorldQuant Alpha101 + 191 个国泰君安 Alpha191（合计 296 个量价因子）外加 11 个 A 股私有因子族（mf, mfp, hm, hk, inst, mg, cyq, senti, sh, fund, ind, mkt, gtja, tech, cmf, zt）；(2) Stable-Baselines3 PPO 训练栈，针对 RTX 4070 12 GB 做了 GPU 化重构（per-stock 编码器 + CUDA-resident rollout buffer + 索引化观测）；(3) 完整的 14-phase 训练栈演化史，从最初 11% GPU 利用率到 1M-step 隔夜训练；(4) 26-phase 模型实验史，覆盖奖励重设计、长 panel 消融、rank-z 假设检验、SH
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