1、策略更新

2、新增qmt

main/factor/qlib_utils.py

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+import polars as pl
+import pandas as pd
+import numpy as np
+import qlib
+from qlib.data.dataset.handler import DataHandlerLP
+from qlib.contrib.report import analysis_model, analysis_position
+from qlib.constant import REG_CN
+from typing import List
+
+import polars as pl
+import pandas as pd
+
+def prepare_data(
+    polars_df: pl.DataFrame,
+    label_horizon: int = 5,
+    open_col: str = "open",
+    date_col: str = "trade_date",
+    code_col: str = "ts_code",
+) -> pd.DataFrame:
+    required = [date_col, code_col, open_col]
+    missing = [col for col in required if col not in polars_df.columns]
+    if missing:
+        raise ValueError(f"Missing columns: {missing}")
+
+    df = polars_df.sort([code_col, date_col])
+
+    # 获取 T+1 日的开盘价（作为买入价）
+    df = df.with_columns([
+        pl.col(open_col).shift(-1).over(code_col).alias("__buy_price"),
+        pl.col(open_col).shift(-(1 + label_horizon)).over(code_col).alias("__sell_price"),
+    ]).with_columns([
+        (pl.col("__sell_price") / pl.col("__buy_price") - 1).alias("label")
+    ]).drop(["__buy_price", "__sell_price"])
+
+    # 转 pandas
+    df = df.to_pandas()
+    df.rename(columns={date_col: "datetime", code_col: "instrument"}, inplace=True)
+    df["datetime"] = pd.to_datetime(df["datetime"])
+    df.set_index(["datetime", "instrument"], inplace=True)
+    df.sort_index(inplace=True)
+
+    return df
+
+# 2. Qlib初始化
+def initialize_qlib():
+    """
+    在内存模式下初始化Qlib。
+    由于我们直接从DataFrame加载数据，provider_uri可以指向一个虚拟或空路径。
+    """
+    # provider_uri设置为一个虚拟路径，因为所有数据将从内存加载
+    # region设置为REG_CN表示使用中国A股的交易日历和交易成本设置
+    qlib.init(provider_uri="/mnt/d/PyProject/NewStock/data/qlib", region=REG_CN, freq="day")
+    print("Qlib has been initialized in memory mode.")
+
+
+import pandas as pd
+import lightgbm as lgb
+from qlib.workflow import R
+from qlib.workflow.record_temp import PortAnaRecord # SignalRecord 在此场景下未被直接使用
+
+def train_and_backtest_from_df(
+    df: pd.DataFrame,
+    all_features: list,
+    label_col: str = "label",
+    topk: int = 50,
+    start_train: str = "2019-01-01",
+    end_train: str = "2021-12-31",
+    start_valid: str = "2022-01-01",
+    end_valid: str = "2022-12-31",
+    start_test: str = "2023-01-01",
+    end_test: str = "2023-12-31",
+):
+    """
+    直接从准备好的 pandas DataFrame 训练模型并运行回测。
+    """
+    # === 1. 手动准备数据 ===
+    if not isinstance(df.index, pd.MultiIndex):
+        raise ValueError("df 必须是 MultiIndex (datetime, instrument)")
+    df.index = df.index.set_names(["datetime", "instrument"])
+    df.index = df.index.set_levels(pd.to_datetime(df.index.levels[0]), level='datetime')
+    df.sort_index(inplace=True)
+
+    dh = DataHandlerLP.from_df(df)
+    print(dh.fetch())
+    print(dh._infer)
+    print(dh._learn)