dddyym/ml-quant-trading-synthetic-mlp

ml-quant-trading synthetic MLP smoke-test checkpoint

This is the small MLPRegressor checkpoint produced by the deterministic mlquant demo pipeline. It is published to make installation and inference paths reproducible — not as a market model or investment signal.

Associated paper

This artifact accompanies Machine Learning Enhanced Multi-Factor Quantitative Trading (arXiv:2507.07107).

Model details

• Architecture: three-layer MLP with GELU and dropout
• Input dimensions: 213 factor values
• Hidden width: 64
• Output: one synthetic next-period return score per asset-date
• Training data: dddyym/ml-quant-trading-synthetic
• Source commit: faababb851b22061759f748c252f1cca1eaf0202
• Checkpoint SHA-256: 57a2773f4f928ddf81660f33d3d9ef5ae2e6a9958e0c20694b3cf1234968fb84

The companion feature_names.json, config.json, source_config.yaml, and metrics.json files define the input order, architecture, generator settings, and synthetic smoke-test output.

Load

import json
import torch
from huggingface_hub import hf_hub_download
from mlquant.models.nets import MLPRegressor

repo_id = "dddyym/ml-quant-trading-synthetic-mlp"
config_path = hf_hub_download(repo_id, "config.json")
weights_path = hf_hub_download(repo_id, "pytorch_model.bin")

config = json.load(open(config_path))
model = MLPRegressor(
    in_dim=config["in_dim"],
    hidden=config["hidden"],
    dropout=config["dropout"],
)
model.load_state_dict(torch.load(weights_path, map_location="cpu", weights_only=True))
model.eval()

Limitations

• The checkpoint was trained only on deterministic synthetic data.
• Synthetic smoke-test metrics are not evidence of out-of-sample alpha.
• Factor values must use the exact order in feature_names.json.
• The package is research software, not a live-trading system.

See the project's Research Card and Reality Check.