ml-quant-trading

Machine Learning Enhanced Multi-Factor Quantitative Trading — A Cross-Sectional Portfolio Optimization Approach with Bias Correction

arXiv:2507.07107  |  Yimin Du, 2025

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What is this?

A clean, fork-friendly, end-to-end A-share quantitative trading system:

Module	What it does
features.tensor_factors	GPU-vectorised masked primitives (rank, corr, ewma, ts_*)
features.legacy_factors	204 hand-crafted alpha factors (handbook)
features.alpha101	Alpha101-style formulaic factors
features.neutralize	Cross-sectional & industry neutralisation
features.bias	Limit-up / limit-down / halt bias correction
training.augment	GBM data augmentation
models.nets	MLP / Transformer
models.losses	AdjMSE, IC, RankIC losses
portfolio.markowitz	Cross-sectional Markowitz (shrunk cov, no-short)
backtest.engine	Vectorised backtest → Sharpe / IC / IR / DD

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Quick Start

git clone https://github.com/initial-d/ml-quant-trading.git
cd ml-quant-trading
pip install -e .[dev]        # add ,gpu for CUDA; add ,mosek for MOSEK solver

# 30-second smoke test (synthetic 200 stocks × 500 days)
make paper CONFIG=configs/small.yaml

Google Colab Quick Start

You can run an end-to-end demo of this project instantly in Google Colab without installing anything locally:

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Factor Library (213 factors: 9 Alpha101 + 204 legacy)

The full feature set comprises 9 curated Alpha101 formulas (features.alpha101) plus 204 hand-crafted legacy factors (features.legacy_factors) for a total of 213 dimensions. All factors are mask-aware PyTorch tensors with signature Panel → (values[T,N], mask[T,N]).

📖 完整因子手册 (Factor Handbook) — 每个因子一段话详解思想、动机和原理，方便按需选用。

Family	Count	Description
better_001 – better_028	28	VWAP deviation + volume-weighted momentum
best_001 – best_021	21	Close-location momentum variants
old_027 – old_076	50	Classic alpha signals (corr/rank composites)
stock_001 – stock_022	22	Per-stock derived series (volume, range, price)
extra_001 – extra_014	14	Turnover + amount features
add_001 – add_030	30	Additional composite factors
change_001 – change_005	5	Short-window change-of-velocity
original_001 – original_028	28	Close/volume direct statistics
cs_rank_*	6	Market breadth (cross-sectional rank signals)

Full factor list (click to expand)

add_001    add_002    add_003    add_004    add_005    add_006
add_007    add_008    add_009    add_010    add_011    add_012
add_013    add_014    add_015    add_016    add_017    add_018
add_019    add_020    add_021    add_022    add_023    add_024
add_025    add_026    add_027    add_028    add_029    add_030
best_001   best_002   best_003   best_004   best_005   best_006
best_007   best_008   best_009   best_010   best_011   best_012
best_013   best_014   best_015   best_016   best_017   best_018
best_019   best_020   best_021
change_001 change_002 change_003 change_004 change_005
extra_001  extra_002  extra_003  extra_004  extra_005  extra_006
extra_007  extra_008  extra_009  extra_010  extra_011  extra_012
extra_013  extra_014
old_027    old_028    old_029    old_030    old_031    old_032
old_033    old_034    old_035    old_036    old_037    old_038
old_039    old_040    old_041    old_042    old_043    old_044
old_045    old_046    old_047    old_048    old_049    old_050
old_051    old_052    old_053    old_054    old_055    old_056
old_057    old_058    old_059    old_060    old_061    old_062
old_063    old_064    old_065    old_066    old_067    old_068
old_069    old_070    old_071    old_072    old_073    old_074
old_075    old_076
original_001 original_002 original_003 original_004 original_005
original_006 original_007 original_008 original_009 original_010
original_011 original_012 original_013 original_014 original_015
original_016 original_017 original_018 original_019 original_020
original_021 original_022 original_023 original_024 original_025
original_026 original_027 original_028
stock_001  stock_002  stock_003  stock_004  stock_005  stock_006
stock_007  stock_008  stock_009  stock_010  stock_011  stock_012
stock_013  stock_014  stock_015  stock_016  stock_017  stock_018
stock_019  stock_020  stock_021  stock_022

Data Sources

You can directly fetch stock data from Yahoo Finance or Baostock (for A-shares).

yfinance:

from mlquant.data import make_panel

panel = make_panel(
    source="yfinance",
    tickers=["000001.SZ", "600000.SS"],
    start="2020-01-01",
    end="2023-12-31"
)

baostock:

from mlquant.data import make_panel

panel = make_panel(
    source="baostock",
    tickers=["sh.600000", "sz.000001"],
    start="2020-01-01",
    end="2023-12-31"
)

Usage

from mlquant.features import compute_legacy_set, LEGACY_REGISTRY

# Compute all 204 factors
factors, mask, names = compute_legacy_set(panel)  # → [T, N, 204]

# Or a subset
factors, mask, names = compute_legacy_set(panel, names=("best_001", "add_015", "old_042"))

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Architecture

raw OCHLV → data.loaders / data.synthetic / data.yfinance_loader / data.baostock_loader (Panel with mask)
         → features.tensor_factors (GPU masked primitives)
         → features.legacy_factors (204 alphas)
         → training.augment + models.nets + models.losses
         → portfolio.markowitz (efficient frontier)
         → backtest.engine → Sharpe / IC / IR / DD / turnover

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Project Layout

ml-quant-trading/
├── src/mlquant/
│   ├── data/           # Panel dataclass, loaders, synthetic generator
│   ├── features/       # Factor engine + 204 legacy + Alpha101
│   ├── training/       # Dataset, augmentation, trainer
│   ├── models/         # MLP, Transformer, losses
│   ├── portfolio/      # Markowitz, frontier sweep
│   ├── backtest/       # Engine, metrics
│   └── cli/            # Command-line interface
├── configs/            # small.yaml (smoke) / paper.yaml (full)
├── tests/              # pytest suite
├── scripts/            # IC eval, frontier plot
├── legacy/             # Original research scripts (archival, unsupported)
└── docs/               # Architecture, factor docs, paper reproduction

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Reproducing the Paper

See docs/reproducing_paper.md for table-by-table mapping.

Paper section	Code module	Tests
§3.1 Tensor factor engine	features.tensor_factors	test_tensor_factors
§3.2 Alpha + microstructure factors	features.alpha101, features.legacy_factors	test_alpha101
§3.3 Neutralisation	features.neutralize	—
§3.4 Bias correction	features.bias	test_bias
§4.1 GBM augmentation	training.augment	test_augment
§4.2 ML models	models.nets, models.losses	test_losses
§5 Portfolio optimisation	portfolio.markowitz	test_markowitz
§6 Backtest	backtest.engine, backtest.metrics	test_metrics

---

Citation

@article{du2025mlquant,
  title  = {Machine Learning Enhanced Multi-Factor Quantitative Trading:
            A Cross-Sectional Portfolio Optimization Approach with Bias Correction},
  author = {Du, Yimin},
  journal= {arXiv preprint arXiv:2507.07107},
  year   = {2025},
  url    = {https://arxiv.org/abs/2507.07107}
}

License

MIT — see LICENSE.