1、策略更新

2、新增qmt

main/train/Classify/Classify2.py

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