NewStock/main/factor/qlib_utils.py

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,
    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)