# %% md # # LightGBM LambdaRank 排序学习训练流程（模块化版本） # # 使用新的模块化 Trainer 架构，代码更简洁、可维护性更高。 # %% md # ## 1. 导入依赖 # %% import os from src.factors import FactorEngine from src.training import ( FactorManager, DataPipeline, RankTask, NullFiller, Winsorizer, CrossSectionalStandardScaler, ) from src.training.core.trainer_v2 import Trainer from src.training.components.filters import STFilter from src.experiment.common import ( SELECTED_FACTORS, FACTOR_DEFINITIONS, get_label_factor, TRAIN_START, TRAIN_END, VAL_START, VAL_END, TEST_START, TEST_END, stock_pool_filter, STOCK_FILTER_REQUIRED_COLUMNS, OUTPUT_DIR, SAVE_PREDICTIONS, SAVE_MODEL, get_model_save_path, save_model_with_factors, TOP_N, ) # 训练类型标识 TRAINING_TYPE = "rank" # %% md # ## 2. 训练特定配置 # %% # Label 配置 LABEL_NAME = "future_return_5" LABEL_FACTOR = get_label_factor(LABEL_NAME) # 分位数配置 N_QUANTILES = 20 # 排除的因子列表 EXCLUDED_FACTORS = [ "volatility_5", "volume_ratio_5_20", "capital_retention_20", "volatility_squeeze_5_60", "drawdown_from_high_60", "ma_ratio_5_20", "bias_10", "high_low_ratio", "bbi_ratio", "volatility_20", "std_return_20", "sharpe_ratio_20", "ma_5", "max_ret_20", "CP", "net_profit_yoy", "debt_to_equity", "EP_rank", "turnover_rank", "return_5_rank", "ebit_rank", "BP", "EP", "amihud_illiq_20", "profit_margin", "return_5", "return_20", "kaufman_ER_20", "GTJA_alpha043", "GTJA_alpha042", "GTJA_alpha041", "GTJA_alpha040", "GTJA_alpha039", "GTJA_alpha037", "GTJA_alpha036", "GTJA_alpha035", "GTJA_alpha033", "GTJA_alpha032", "GTJA_alpha031", "GTJA_alpha028", "GTJA_alpha026", "GTJA_alpha027", "GTJA_alpha023", "GTJA_alpha024", "GTJA_alpha009", "GTJA_alpha011", "GTJA_alpha022", "GTJA_alpha020", "GTJA_alpha018", "GTJA_alpha019", "GTJA_alpha014", "GTJA_alpha013", "GTJA_alpha010", "GTJA_alpha001", "GTJA_alpha003", "GTJA_alpha002", "GTJA_alpha004", "GTJA_alpha005", "GTJA_alpha006", "GTJA_alpha008", "turnover_deviation", "turnover_cv_20", "roa", "GTJA_alpha073", "GTJA_alpha078", "GTJA_alpha077", "GTJA_alpha076", "GTJA_alpha067", "GTJA_alpha085", "GTJA_alpha084", "GTJA_alpha087", "GTJA_alpha088", "GTJA_alpha090", "GTJA_alpha083", "GTJA_alpha079", "GTJA_alpha080", "GTJA_alpha094", "GTJA_alpha092", "GTJA_alpha089", "GTJA_alpha095", "GTJA_alpha064", "GTJA_alpha065", "GTJA_alpha066", "GTJA_alpha063", "GTJA_alpha060", "GTJA_alpha058", "GTJA_alpha057", "GTJA_alpha056", "GTJA_alpha046", "GTJA_alpha044", "GTJA_alpha049", "GTJA_alpha050", "GTJA_alpha110", "GTJA_alpha107", "GTJA_alpha104", "GTJA_alpha106", "GTJA_alpha103", "GTJA_alpha100", "GTJA_alpha101", "GTJA_alpha102", "GTJA_alpha098", "GTJA_alpha097", "GTJA_alpha096", "GTJA_alpha099", "GTJA_alpha117", "GTJA_alpha118", "GTJA_alpha114", "GTJA_alpha111", "GTJA_alpha129", "GTJA_alpha130", "GTJA_alpha132", "GTJA_alpha131", "GTJA_alpha134", "GTJA_alpha135", "GTJA_alpha136", "GTJA_alpha112", "GTJA_alpha120", "GTJA_alpha119", "GTJA_alpha122", "GTJA_alpha124", "GTJA_alpha126", "GTJA_alpha127", "GTJA_alpha128", "GTJA_alpha115", "GTJA_alpha153", "GTJA_alpha152", "GTJA_alpha151", "GTJA_alpha150", "GTJA_alpha148", "GTJA_alpha142", "GTJA_alpha141", "GTJA_alpha139", "GTJA_alpha133", "GTJA_alpha161", "GTJA_alpha164", "GTJA_alpha162", "GTJA_alpha157", "GTJA_alpha156", "GTJA_alpha160", "GTJA_alpha155", "GTJA_alpha170", "GTJA_alpha169", "GTJA_alpha168", "GTJA_alpha166", "GTJA_alpha163", "GTJA_alpha176", "GTJA_alpha175", "GTJA_alpha174", "GTJA_alpha178", "GTJA_alpha177", "GTJA_alpha185", "GTJA_alpha180", "GTJA_alpha187", "GTJA_alpha188", "GTJA_alpha189", "GTJA_alpha191", ] # LambdaRank 模型参数配置 MODEL_PARAMS = { "objective": "lambdarank", "metric": "ndcg", "ndcg_at": 25, "learning_rate": 0.1, "n_estimators": 1000, "early_stopping_round": 50, # 防止过拟合的核心约束 "max_depth": 4, "num_leaves": 32, "min_data_in_leaf": 256, # 随机采样（增加鲁棒性） "subsample": 0.4, "subsample_freq": 1, "colsample_bytree": 0.4, # 正则化惩罚 "reg_alpha": 10.0, "reg_lambda": 50.0, # Lambdarank 专属配置 "lambdarank_truncation_level": 50, "label_gain": [i * i for i in range(1, N_QUANTILES + 1)], "verbose": -1, "random_state": 42, } # 日期范围配置 date_range = { "train": (TRAIN_START, TRAIN_END), "val": (VAL_START, VAL_END), "test": (TEST_START, TEST_END), } # 输出配置 output_config = { "output_dir": OUTPUT_DIR, "output_filename": "rank_output.csv", "save_predictions": SAVE_PREDICTIONS, "save_model": SAVE_MODEL, "model_save_path": get_model_save_path(TRAINING_TYPE), "top_n": TOP_N, } def main(): """主函数""" print("\n" + "=" * 80) print("LightGBM LambdaRank 排序学习训练（模块化版本）") print("=" * 80) # 1. 创建 FactorEngine print("\n[1] 创建 FactorEngine") engine = FactorEngine() # 2. 创建 FactorManager print("\n[2] 创建 FactorManager") factor_manager = FactorManager( selected_factors=SELECTED_FACTORS, factor_definitions=FACTOR_DEFINITIONS, label_factor=LABEL_FACTOR, excluded_factors=EXCLUDED_FACTORS, ) # 3. 创建 DataPipeline print("\n[3] 创建 DataPipeline") pipeline = DataPipeline( factor_manager=factor_manager, processor_configs=[ (NullFiller, {"strategy": "mean"}), (Winsorizer, {"lower": 0.01, "upper": 0.99}), (CrossSectionalStandardScaler, {}), ], filters=[STFilter(data_router=engine.router)], stock_pool_filter_func=stock_pool_filter, stock_pool_required_columns=STOCK_FILTER_REQUIRED_COLUMNS, ) # 4. 创建 RankTask print("\n[4] 创建 RankTask") task = RankTask( model_params=MODEL_PARAMS, label_name=LABEL_NAME, n_quantiles=N_QUANTILES, ) # 5. 创建 Trainer print("\n[5] 创建 Trainer") trainer = Trainer( data_pipeline=pipeline, task=task, output_config=output_config, verbose=True, ) # 6. 执行训练 print("\n[6] 执行训练") results = trainer.run(engine=engine, date_range=date_range) # 7. 保存模型和因子信息（如果启用） if SAVE_MODEL: print("\n[7] 保存模型和因子信息") save_model_with_factors( model=task.get_model(), model_path=output_config["model_save_path"], selected_factors=SELECTED_FACTORS, factor_definitions=FACTOR_DEFINITIONS, fitted_processors=pipeline.get_fitted_processors(), ) print("\n" + "=" * 80) print("训练流程完成！") print(f"结果保存路径: {os.path.join(OUTPUT_DIR, 'rank_output.csv')}") print("=" * 80) return results if __name__ == "__main__": main()