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dddyym/
ml-quant-trading-synthetic-mlp

Name: ml-quant-trading-synthetic-mlp
Availability: InStock

dddyym/ml-quant-trading-synthetic

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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.

Quick start

Install the verified package from PyPI before loading this checkpoint:

bash

python -m pip install --upgrade mlquantx huggingface_hub
mlquant demo

The distribution name is mlquantx, while Python imports and the CLI use mlquant. The full source, tests, research notes, and the v0.2.6 release are available in the GitHub repository and release page.

The checkpoint is a reproducible synthetic research artifact. It is not evidence of live trading performance.

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

python

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.