ml-quant-trading
Unverified ML strategy on Indices by initial-d. BotFinder score 18 out of 100.
PyTorch research stack for ML multi-factor trading: 213 factors, bias correction, portfolio optimization, and vectorized backtesting.
Source: github
BotFinder analysis pending.
ml-quant-trading
ml-quant-trading A reproducible PyTorch stack for cross-sectional factor research — from 213 mask-aware factors to cost-aware portfolios, backtests, and auditable reports. Languages: English | 简体中文 | 繁體中文 Run in Colab · Project facts · How to cite · Agent benchmark challenge · Inspect the benchmark · Run with DSH · See cost-aware results · Read the paper Quick Start No market-data account or API key is required. In 30–90 seconds, the deterministic demo runs data → factors → model → portfolio → backtest and writes shareable Markdown and JSON reports. Once it runs, you can inspect the benchmark or share a reproduction report. If the baseline is useful, a Star helps other researchers find it. Evaluating privately? Use the private evaluation checklist or submit a redacted evaluation note without exposing proprietary data, positions, or strategy details. For public reports, follow the reproducibility contract so results remain auditable after the original run. | 213 factors | 4 data paths | 100 tests | CPU/GPU benchmark | |---:|---:|---:|---:| | Mask-aware PyTorch tensors | Synthetic, AkS
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How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
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Alerts on changes: coming soon
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Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)