1# Custom graph-based stock prediction model for MLS-Bench
2#
3# EDITABLE section: CustomModel class with fit() and predict() methods.
4# FIXED sections: imports and stock-concept graph loading below.
5import os
6import numpy as np
7import pandas as pd
8import torch
9import torch.nn as nn
10import torch.nn.functional as F
11from qlib.model.base import Model
12from qlib.data.dataset import DatasetH
13from qlib.data.dataset.handler import DataHandlerLP
14
15DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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1# Qlib workflow configuration for CSI300 graph-based stock prediction benchmark.
2# Used by run_workflow.py — matches Alpha360/CSI300 official benchmark settings.
3# Alpha360: 6 base features x 60 days = 360 features, flattened as DatasetH.
4
5qlib_init:
6  provider_uri: "~/.qlib/qlib_data/cn_data"
7  region: cn
8
9sys:
10  rel_path:
11    - "."           # So custom_model.py is importable via module_path
12
13task:
14  model:
15    class: CustomModel
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