ml-quant-trading
PyTorch ML quant research: 213 factors, bias correction, portfolio optimization, backtesting, and System-One/Jev-style decisions.
- 形态
- Plugin 插件
- 安装
- 通过 profile 安装
- 需要密钥
- 是
- 近月下载
- —
- 最近推送
- 5 天前更新
项目说明
From README
ml-quant-trading A reproducible PyTorch research stack for cross-sectional factor research: 213 mask-aware factors, public-data validation, cost-aware backtests, auditable reports, and a preview typed portfolio-decision adapter.
Languages: English 简体中文 繁體中文 Run in Colab · Project Facts · How to Cite · Agent Benchmark Challenge · Validation Dashboard · Benchmark Board · Share This Project · Run with DSH · Decision Adapter · Quant Jev Article · Read the Paper Quick Start No market-data account or API key is required.
The deterministic demo runs the full research pipeline in about 30-90 seconds on a typical CPU-only laptop and writes shareable Markdown and JSON reports under artifacts/small/ .Returning after an earlier release?Upgrade with python -m pip install --upgrade mlquantx .The v0.4.
1 release masks limit-move observations before rolling factors and measures returns across zero-fil
安装方式
通过 profile 安装 · 需要额外配置密钥
git clone https://github.com/initial-d/ml-quant-trading.git为什么是这个分数
近 5 天仍在推送；README 给出了可直接复制的安装命令；运行前需要准备 API Key 或环境变量。
- 维护活跃
- 96
- 实用度
- 100
- 生态热度
- 45
- 上手便捷
- 74
- 信号质量
- 100
- 数据置信度
- 100%
相关插件
stock-analysis-plugin
Weaxs/stock-analysis-plugin
Multi-market stock analysis, screening & backtesting plugin for AI agents — 39 tools + 20 strategy skills across A/HK/US/JP/KR/TW markets, works with Pi Agent, Hermes, OpenClaw & Deepseek Harness
dsh-alpha-desk
JingHao-Leon/dsh-alpha-desk
把 dsh 会话变成合规、可复现、可追责的 AI 投研工作台：ai-hedge-fund 回测 + risk-gate 风控 + cron 盯盘 + 记忆复盘 | compliance-first AI investment desk
SmartMoney-Cub
myc0576/SmartMoney-Cub
Read-only trading journal and review harness: Jev typed judgments, agent integration, and a reproducible finance benchmark. No orders, no advice.