2026-03-15 05:46:19 +08:00
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"""实验脚本的共用配置和辅助函数。
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此模块包含 regression.py 和 learn_to_rank.py 共用的代码，
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避免重复维护两份相同的配置和函数。
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"""
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from datetime import datetime
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from typing import List
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import polars as pl
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from src.factors import FactorEngine
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# =============================================================================
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# 日期范围配置（正确的 train/val/test 三分法）
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# =============================================================================
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TRAIN_START = "20200101"
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TRAIN_END = "20231231"
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VAL_START = "20240101"
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VAL_END = "20241231"
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TEST_START = "20250101"
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TEST_END = "20261231"
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# =============================================================================
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# 因子配置
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# =============================================================================
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# 当前选择的因子列表（从 FACTOR_DEFINITIONS 中选择要使用的因子）
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SELECTED_FACTORS = [
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"ma_5",
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"ma_20",
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"ma_ratio_5_20",
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"bias_10",
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"high_low_ratio",
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"bbi_ratio",
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"return_5",
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"return_20",
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"kaufman_ER_20",
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"mom_acceleration_10_20",
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"drawdown_from_high_60",
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"up_days_ratio_20",
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"volatility_5",
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"volatility_20",
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"volatility_ratio",
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"std_return_20",
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"sharpe_ratio_20",
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"min_ret_20",
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"volatility_squeeze_5_60",
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"overnight_intraday_diff",
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"upper_shadow_ratio",
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"capital_retention_20",
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"max_ret_20",
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"volume_ratio_5_20",
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"turnover_rate_mean_5",
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"turnover_deviation",
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"amihud_illiq_20",
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"turnover_cv_20",
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"pv_corr_20",
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"close_vwap_deviation",
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"roe",
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"roa",
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"profit_margin",
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"debt_to_equity",
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"current_ratio",
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"net_profit_yoy",
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"revenue_yoy",
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"healthy_expansion_velocity",
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"EP",
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"BP",
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"CP",
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"market_cap_rank",
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"turnover_rank",
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"return_5_rank",
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"EP_rank",
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"pe_expansion_trend",
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"value_price_divergence",
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"active_market_cap",
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"ebit_rank",
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2026-03-15 22:21:21 +08:00
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"GTJA_alpha001",
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"GTJA_alpha002",
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"GTJA_alpha003",
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"GTJA_alpha004",
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"GTJA_alpha005",
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"GTJA_alpha006",
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"GTJA_alpha007",
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"GTJA_alpha008",
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"GTJA_alpha009",
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"GTJA_alpha010",
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"GTJA_alpha011",
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"GTJA_alpha012",
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"GTJA_alpha013",
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"GTJA_alpha014",
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"GTJA_alpha015",
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"GTJA_alpha016",
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"GTJA_alpha017",
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"GTJA_alpha018",
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"GTJA_alpha019",
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"GTJA_alpha020",
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"GTJA_alpha022",
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"GTJA_alpha023",
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"GTJA_alpha024",
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"GTJA_alpha025",
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"GTJA_alpha026",
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"GTJA_alpha027",
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"GTJA_alpha028",
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"GTJA_alpha029",
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"GTJA_alpha031",
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"GTJA_alpha032",
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"GTJA_alpha033",
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"GTJA_alpha034",
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"GTJA_alpha035",
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"GTJA_alpha036",
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"GTJA_alpha037",
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# "GTJA_alpha038",
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"GTJA_alpha039",
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"GTJA_alpha040",
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"GTJA_alpha041",
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"GTJA_alpha042",
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"GTJA_alpha043",
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"GTJA_alpha044",
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"GTJA_alpha045",
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"GTJA_alpha046",
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"GTJA_alpha047",
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"GTJA_alpha048",
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"GTJA_alpha049",
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"GTJA_alpha050",
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"GTJA_alpha051",
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"GTJA_alpha052",
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"GTJA_alpha053",
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"GTJA_alpha054",
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"GTJA_alpha056",
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"GTJA_alpha057",
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"GTJA_alpha058",
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"GTJA_alpha059",
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"GTJA_alpha060",
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"GTJA_alpha061",
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"GTJA_alpha062",
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"GTJA_alpha063",
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"GTJA_alpha064",
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"GTJA_alpha065",
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"GTJA_alpha066",
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"GTJA_alpha067",
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"GTJA_alpha068",
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"GTJA_alpha070",
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"GTJA_alpha071",
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"GTJA_alpha072",
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"GTJA_alpha073",
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"GTJA_alpha074",
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"GTJA_alpha076",
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"GTJA_alpha077",
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"GTJA_alpha078",
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"GTJA_alpha079",
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"GTJA_alpha080",
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"GTJA_alpha081",
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"GTJA_alpha082",
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"GTJA_alpha083",
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"GTJA_alpha084",
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"GTJA_alpha085",
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"GTJA_alpha086",
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"GTJA_alpha087",
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"GTJA_alpha088",
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"GTJA_alpha089",
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"GTJA_alpha090",
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"GTJA_alpha091",
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"GTJA_alpha092",
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"GTJA_alpha093",
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"GTJA_alpha094",
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"GTJA_alpha095",
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"GTJA_alpha096",
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"GTJA_alpha097",
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"GTJA_alpha098",
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"GTJA_alpha099",
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"GTJA_alpha100",
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"GTJA_alpha101",
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"GTJA_alpha102",
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"GTJA_alpha103",
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"GTJA_alpha104",
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"GTJA_alpha105",
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"GTJA_alpha106",
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"GTJA_alpha107",
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"GTJA_alpha108",
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"GTJA_alpha109",
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"GTJA_alpha110",
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"GTJA_alpha111",
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"GTJA_alpha112",
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"GTJA_alpha113",
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"GTJA_alpha114",
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"GTJA_alpha115",
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"GTJA_alpha117",
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"GTJA_alpha118",
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"GTJA_alpha119",
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"GTJA_alpha120",
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"GTJA_alpha121",
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"GTJA_alpha122",
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"GTJA_alpha123",
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"GTJA_alpha124",
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"GTJA_alpha125",
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"GTJA_alpha126",
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"GTJA_alpha127",
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"GTJA_alpha128",
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"GTJA_alpha129",
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"GTJA_alpha130",
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"GTJA_alpha131",
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"GTJA_alpha132",
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"GTJA_alpha133",
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"GTJA_alpha134",
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"GTJA_alpha135",
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"GTJA_alpha136",
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"GTJA_alpha138",
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"GTJA_alpha139",
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"GTJA_alpha140",
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"GTJA_alpha141",
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"GTJA_alpha142",
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"GTJA_alpha145",
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"GTJA_alpha146",
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"GTJA_alpha148",
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"GTJA_alpha150",
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"GTJA_alpha151",
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"GTJA_alpha152",
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"GTJA_alpha153",
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"GTJA_alpha154",
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"GTJA_alpha155",
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"GTJA_alpha156",
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"GTJA_alpha157",
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"GTJA_alpha158",
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"GTJA_alpha159",
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"GTJA_alpha160",
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"GTJA_alpha161",
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"GTJA_alpha162",
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"GTJA_alpha163",
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"GTJA_alpha164",
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"GTJA_alpha165",
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"GTJA_alpha166",
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"GTJA_alpha167",
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"GTJA_alpha168",
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"GTJA_alpha169",
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"GTJA_alpha170",
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"GTJA_alpha171",
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"GTJA_alpha173",
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"GTJA_alpha174",
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"GTJA_alpha175",
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"GTJA_alpha176",
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"GTJA_alpha177",
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"GTJA_alpha178",
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"GTJA_alpha179",
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"GTJA_alpha180",
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"GTJA_alpha183",
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"GTJA_alpha184",
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"GTJA_alpha185",
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"GTJA_alpha187",
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"GTJA_alpha188",
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"GTJA_alpha189",
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"GTJA_alpha191",
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2026-03-15 05:46:19 +08:00
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]
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# 因子定义字典（完整因子库，用于存放尚未注册到metadata的因子）
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2026-03-15 18:00:48 +08:00
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FACTOR_DEFINITIONS = {
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}
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2026-03-15 05:46:19 +08:00
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def get_label_factor(label_name: str) -> dict:
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"""获取Label因子定义字典。
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Args:
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label_name: label因子名称
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Returns:
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Label因子定义字典
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"""
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return {
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label_name: "(ts_delay(close, -5) / ts_delay(open, -1)) - 1",
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}
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# =============================================================================
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# 辅助函数
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# =============================================================================
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def register_factors(
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engine: FactorEngine,
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selected_factors: List[str],
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factor_definitions: dict,
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label_factor: dict,
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) -> List[str]:
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"""注册因子。
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selected_factors 从 metadata 查询，factor_definitions 用 DSL 表达式注册。
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Args:
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engine: FactorEngine实例
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selected_factors: 从metadata中选择的因子名称列表
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factor_definitions: 通过表达式定义的因子字典
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label_factor: label因子定义字典
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Returns:
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特征列名称列表
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"""
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print("=" * 80)
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print("注册因子")
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print("=" * 80)
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# 注册 SELECTED_FACTORS 中的因子（已在 metadata 中）
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print("\n注册特征因子（从 metadata）:")
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for name in selected_factors:
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engine.add_factor(name)
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print(f" - {name}")
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# 注册 FACTOR_DEFINITIONS 中的因子（通过表达式，尚未在 metadata 中）
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print("\n注册特征因子（表达式）:")
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for name, expr in factor_definitions.items():
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engine.add_factor(name, expr)
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print(f" - {name}: {expr}")
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# 注册 label 因子（通过表达式）
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print("\n注册 Label 因子（表达式）:")
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for name, expr in label_factor.items():
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engine.add_factor(name, expr)
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print(f" - {name}: {expr}")
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# 特征列 = SELECTED_FACTORS + FACTOR_DEFINITIONS 的 keys
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feature_cols = selected_factors + list(factor_definitions.keys())
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print(f"\n特征因子数: {len(feature_cols)}")
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print(f" - 来自 metadata: {len(selected_factors)}")
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print(f" - 来自表达式: {len(factor_definitions)}")
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print(f"Label: {list(label_factor.keys())[0]}")
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print(f"已注册因子总数: {len(engine.list_registered())}")
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return feature_cols
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|
|
def prepare_data(
|
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|
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engine: FactorEngine,
|
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feature_cols: List[str],
|
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|
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start_date: str,
|
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end_date: str,
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label_name: str,
|
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|
|
|
|
) -> pl.DataFrame:
|
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|
|
"""准备数据。
|
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|
|
计算因子并返回包含特征和label的数据框。
|
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|
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|
|
|
|
Args:
|
|
|
|
|
|
engine: FactorEngine实例
|
|
|
|
|
|
feature_cols: 特征列名称列表
|
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|
|
|
|
start_date: 开始日期 (YYYYMMDD)
|
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|
|
end_date: 结束日期 (YYYYMMDD)
|
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|
|
|
|
label_name: label列名称
|
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|
|
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|
|
Returns:
|
|
|
|
|
|
包含因子计算结果的数据框
|
|
|
|
|
|
"""
|
|
|
|
|
|
print("\n" + "=" * 80)
|
|
|
|
|
|
print("准备数据")
|
|
|
|
|
|
print("=" * 80)
|
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|
|
|
|
|
|
|
|
|
|
# 计算因子（全市场数据）
|
|
|
|
|
|
print(f"\n计算因子: {start_date} - {end_date}")
|
|
|
|
|
|
factor_names = feature_cols + [label_name] # 包含 label
|
|
|
|
|
|
|
|
|
|
|
|
data = engine.compute(
|
|
|
|
|
|
factor_names=factor_names,
|
|
|
|
|
|
start_date=start_date,
|
|
|
|
|
|
end_date=end_date,
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
print(f"数据形状: {data.shape}")
|
|
|
|
|
|
print(f"数据列: {data.columns}")
|
|
|
|
|
|
print(f"\n前5行预览:")
|
|
|
|
|
|
print(data.head())
|
|
|
|
|
|
|
|
|
|
|
|
return data
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# =============================================================================
|
|
|
|
|
|
# 股票池筛选配置
|
|
|
|
|
|
# =============================================================================
|
|
|
|
|
|
def stock_pool_filter(df: pl.DataFrame) -> pl.Series:
|
|
|
|
|
|
"""股票池筛选函数（单日数据）。
|
|
|
|
|
|
|
|
|
|
|
|
筛选条件：
|
|
|
|
|
|
1. 排除创业板（代码以 300 开头）
|
|
|
|
|
|
2. 排除科创板（代码以 688 开头）
|
|
|
|
|
|
3. 排除北交所（代码以 8、9 或 4 开头）
|
|
|
|
|
|
4. 选取当日市值最小的500只股票
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
df: 单日数据框
|
|
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
|
布尔Series，表示哪些股票被选中
|
|
|
|
|
|
"""
|
|
|
|
|
|
# 代码筛选（排除创业板、科创板、北交所）
|
|
|
|
|
|
code_filter = (
|
|
|
|
|
|
~df["ts_code"].str.starts_with("30") # 排除创业板
|
|
|
|
|
|
& ~df["ts_code"].str.starts_with("68") # 排除科创板
|
|
|
|
|
|
& ~df["ts_code"].str.starts_with("8") # 排除北交所
|
|
|
|
|
|
& ~df["ts_code"].str.starts_with("9") # 排除北交所
|
|
|
|
|
|
& ~df["ts_code"].str.starts_with("4") # 排除北交所
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
# 在已筛选的股票中，选取市值最小的500只
|
|
|
|
|
|
valid_df = df.filter(code_filter)
|
|
|
|
|
|
n = min(500, len(valid_df))
|
|
|
|
|
|
small_cap_codes = valid_df.sort("total_mv").head(n)["ts_code"]
|
|
|
|
|
|
|
|
|
|
|
|
# 返回布尔 Series：是否在被选中的股票中
|
|
|
|
|
|
return df["ts_code"].is_in(small_cap_codes)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# 定义筛选所需的基础列
|
|
|
|
|
|
STOCK_FILTER_REQUIRED_COLUMNS = ["total_mv"]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# =============================================================================
|
|
|
|
|
|
# 输出配置
|
|
|
|
|
|
# =============================================================================
|
|
|
|
|
|
OUTPUT_DIR = "output"
|
|
|
|
|
|
SAVE_PREDICTIONS = True
|
|
|
|
|
|
PERSIST_MODEL = False
|
|
|
|
|
|
|
|
|
|
|
|
# Top N 配置：每日推荐股票数量
|
|
|
|
|
|
TOP_N = 5 # 可调整为 10, 20 等
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_output_path(model_type: str, test_start: str, test_end: str) -> str:
|
|
|
|
|
|
"""生成输出文件路径。
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
model_type: 模型类型（"regression" 或 "rank"）
|
|
|
|
|
|
test_start: 测试开始日期
|
|
|
|
|
|
test_end: 测试结束日期
|
|
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
|
输出文件路径
|
|
|
|
|
|
"""
|
|
|
|
|
|
import os
|
|
|
|
|
|
|
|
|
|
|
|
# 确保输出目录存在
|
|
|
|
|
|
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
|
|
|
|
|
|
|
|
|
|
|
# 生成文件名
|
|
|
|
|
|
start_dt = datetime.strptime(test_start, "%Y%m%d")
|
|
|
|
|
|
end_dt = datetime.strptime(test_end, "%Y%m%d")
|
|
|
|
|
|
date_str = f"{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}"
|
|
|
|
|
|
|
|
|
|
|
|
filename = f"{model_type}_output.csv"
|
|
|
|
|
|
return os.path.join(OUTPUT_DIR, filename)
|
