66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
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import numpy as np
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import pandas as pd
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from qlib.data.dataset import DatasetH
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dates = pd.to_datetime(pd.date_range("2020-01-01", "2020-01-10"))
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instruments = ["SH600000", "SH600001"]
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index = pd.MultiIndex.from_product([dates, instruments], names=["datetime", "instrument"])
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data = {
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"feature_1": np.random.randn(len(index)),
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"feature_2": np.random.randn(len(index)),
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"label": np.random.randn(len(index)) * 0.01
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}
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my_df = pd.DataFrame(data, index=index)
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my_df.iloc[1, 0] = np.nan # 人为制造一个缺失值
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my_df.iloc[5, 2] = np.nan # 人为制造一个标签缺失值
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print("----------- 原始 DataFrame -----------")
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print(my_df.head())
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# 2. 创建包含 StaticDataLoader 和 Processors 的完整配置
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data_handler_config = {
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"class": "DataHandlerLP",
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"module_path": "qlib.data.dataset.handler",
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"kwargs": {
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# 核心部分：配置数据加载器
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"data_loader": {
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"class": "StaticDataLoader",
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"module_path": "qlib.data.dataset.loader",
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"kwargs": {
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"config": my_df, # <--- 在这里将你的DataFrame传入！
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}
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},
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"shared_processors": [
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],
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"infer_processors": [
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# {"class": "DropnaLabel", "module_path": "qlib.data.dataset.processor"},
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],
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"learn_processors": [
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{"class": "Fillna", "module_path": "qlib.data.dataset.processor", "kwargs": {"fill_value": 0}},
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]
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},
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}
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from qlib.utils import init_instance_by_config
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# 3. 使用配置初始化 DataHandler
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# 这一步会自动加载 StaticDataLoader 的数据，并运行所有定义的处理器
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dh = init_instance_by_config(data_handler_config)
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ds = DatasetH(
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dh,
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segments={
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"train": ("20190101", "20221231"),
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"valid": ("20220101", "20231231"),
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"test": ("20240101", "20250101"),
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},
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)
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# 4. 验证结果
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# DK_L (Learn) 数据经过了 DropnaLabel -> ZScoreNorm -> Fillna
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learn_data = ds.prepare("all", data_key='learn', segments='train')
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print("----------- train DataFrame -----------")
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print(learn_data)
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