From 5a1f278df86441214985df868f47953da9b41feb Mon Sep 17 00:00:00 2001 From: liaozhaorun <1300336796@qq.com> Date: Thu, 5 Mar 2026 00:38:20 +0800 Subject: [PATCH] =?UTF-8?q?refactor:=20=E4=BC=98=E5=8C=96=E5=9B=9E?= =?UTF-8?q?=E5=BD=92=E5=AE=9E=E9=AA=8C=E9=85=8D=E7=BD=AE=E5=92=8C=E6=A8=A1?= =?UTF-8?q?=E5=9E=8B=E5=8F=82=E6=95=B0=20-=20=E5=B0=86=E5=9B=A0=E5=AD=90?= =?UTF-8?q?=E5=AE=9A=E4=B9=89=E3=80=81=E6=A8=A1=E5=9E=8B=E5=8F=82=E6=95=B0?= =?UTF-8?q?=E3=80=81=E6=97=A5=E6=9C=9F=E9=85=8D=E7=BD=AE=E6=8F=90=E5=8F=96?= =?UTF-8?q?=E4=B8=BA=E6=A8=A1=E5=9D=97=E7=BA=A7=E5=B8=B8=E9=87=8F=20-=20?= =?UTF-8?q?=E4=BC=98=E5=8C=96=20LightGBM=20=E5=8F=82=E6=95=B0=EF=BC=88?= =?UTF-8?q?=E9=99=8D=E4=BD=8E=E8=BF=87=E6=8B=9F=E5=90=88=E9=A3=8E=E9=99=A9?= =?UTF-8?q?=EF=BC=89=20-=20LightGBMModel=20=E6=94=AF=E6=8C=81=20params=20?= =?UTF-8?q?=E5=AD=97=E5=85=B8=E5=8F=82=E6=95=B0=E4=BC=A0=E5=85=A5=20-=20?= =?UTF-8?q?=E4=BF=AE=E5=A4=8D=20StockFilter=20=E5=88=9B=E4=B8=9A=E6=9D=BF?= =?UTF-8?q?=E6=8E=92=E9=99=A4=E9=80=BB=E8=BE=91=EF=BC=88=E6=94=AF=E6=8C=81?= =?UTF-8?q?=20301xxx=EF=BC=89=20-=20=E6=B7=BB=E5=8A=A0=20experiment/output?= =?UTF-8?q?=20=E5=88=B0=20.gitignore?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 1 + .../output/regression/predictions.csv | 1216 ----------------- src/experiment/regression.py | 258 ++-- src/training/components/models/lightgbm.py | 52 +- src/training/components/selectors.py | 6 +- 5 files changed, 183 insertions(+), 1350 deletions(-) delete mode 100644 src/experiment/output/regression/predictions.csv diff --git a/.gitignore b/.gitignore index 5c3e46a..5917b4f 100644 --- a/.gitignore +++ b/.gitignore @@ -82,3 +82,4 @@ src/training/output/* # AI Agent 工作目录 /.sisyphus/ +/src/experiment/output/ diff --git a/src/experiment/output/regression/predictions.csv b/src/experiment/output/regression/predictions.csv deleted file mode 100644 index 9022dd9..0000000 --- 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(close / ts_delay(close, 5)) - 1 """ import os +from datetime import datetime from typing import List, Tuple import polars as pl @@ -22,6 +23,95 @@ from src.training import ( ) from src.training.config import TrainingConfig +# ============================================================================= +# 因子定义（集中在此，方便修改） +# ============================================================================= + +# 特征因子定义字典：新增因子只需在此处添加一行 +FACTOR_DEFINITIONS = { + # 1. 价格动量因子 + "ma5": "ts_mean(close, 5)", + "ma10": "ts_mean(close, 10)", + "ma20": "ts_mean(close, 20)", + "ma_ratio": "ts_mean(close, 5) / ts_mean(close, 20) - 1", + # 2. 波动率因子 + "volatility_5": "ts_std(close, 5)", + "volatility_20": "ts_std(close, 20)", + "vol_ratio": "ts_std(close, 5) / (ts_std(close, 20) + 1e-8)", + # 3. 收益率动量因子 + "return_10": "(close / ts_delay(close, 10)) - 1", + "return_20": "(close / ts_delay(close, 20)) - 1", + # 4. 收益率变化因子 + "return_diff": "(close / ts_delay(close, 5)) - 1 - ((close / ts_delay(close, 10)) - 1)", + # 5. 成交量因子 + "vol_ma5": "ts_mean(vol, 5)", + "vol_ma20": "ts_mean(vol, 20)", + "vol_ratio": "ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)", + # 6. 市值因子（截面排名） + "market_cap_rank": "cs_rank(total_mv)", + # 7. 价格位置因子 + "high_low_ratio": "(close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)", + "n_income": "n_income", +} + +# Label 因子定义（不参与训练，用于计算目标） +LABEL_FACTOR = { + "return_5": "(close / ts_delay(close, 5)) - 1", +} + +# ============================================================================= +# 训练参数配置（集中在此，方便修改） +# ============================================================================= + +# 日期范围配置 +TRAIN_START = "20200101" +TRAIN_END = "20241231" +TEST_START = "20250101" +TEST_END = "20251231" + +# 模型参数配置 +MODEL_PARAMS = { + "objective": "regression", + "metric": "mae", # 改为 MAE，对异常值更稳健 + # 树结构控制（防过拟合核心） + "num_leaves": 20, # 从31降为20，降低模型复杂度 + "max_depth": 4, # 显式限制深度，防止过度拟合噪声 + "min_child_samples": 50, # 叶子最小样本数，防止学习极端样本 + "min_child_weight": 0.001, + # 学习参数 + "learning_rate": 0.01, # 降低学习率，配合更多树 + "n_estimators": 1000, # 增加树数量，配合早停 + # 采样策略（关键防过拟合） + "subsample": 0.8, # 每棵树随机采样80%数据（行采样） + "subsample_freq": 5, # 每5轮迭代进行一次 subsample + "colsample_bytree": 0.8, # 每棵树随机选择80%特征（列采样） + # 正则化 + "reg_alpha": 0.1, # L1正则，增加稀疏性 + "reg_lambda": 1.0, # L2正则，平滑权重 + # 数值稳定性 + "verbose": -1, + "random_state": 42, +} + +# 数据处理器配置 +PROCESSOR_CONFIGS = [ + {"name": "winsorizer", "params": {"lower": 0.01, "upper": 0.99}}, + {"name": "cs_standard_scaler", "params": {}}, +] + +# 股票池筛选配置 +STOCK_FILTER_CONFIG = { + "exclude_cyb": True, # 排除创业板 + "exclude_kcb": True, # 排除科创板 + "exclude_bj": True, # 排除北交所 + "exclude_st": True, # 排除ST股票 +} + +# 输出配置（相对于本文件所在目录） +OUTPUT_DIR = "output" +SAVE_PREDICTIONS = True +PERSIST_MODEL = False + def create_factors_with_strings(engine: FactorEngine) -> List[str]: """使用字符串表达式定义因子 @@ -36,57 +126,24 @@ def create_factors_with_strings(engine: FactorEngine) -> List[str]: print("使用字符串表达式定义因子") print("=" * 80) - # 定义所有因子（使用字典，方便维护和扩展） - # 新增因子只需在此处添加一行即可 - factor_definitions = { - # 1. 价格动量因子 - "ma5": "ts_mean(close, 5)", - "ma10": "ts_mean(close, 10)", - "ma20": "ts_mean(close, 20)", - "ma_ratio": "ts_mean(close, 5) / ts_mean(close, 20) - 1", - # 2. 波动率因子 - "volatility_5": "ts_std(close, 5)", - "volatility_20": "ts_std(close, 20)", - "vol_ratio": "ts_std(close, 5) / (ts_std(close, 20) + 1e-8)", - # 3. 收益率动量因子（return_5 是 label，需要单独注册） - "return_10": "(close / ts_delay(close, 10)) - 1", - "return_20": "(close / ts_delay(close, 20)) - 1", - # 4. 收益率变化因子（使用完整表达式，不引用其他因子） - "return_diff": "(close / ts_delay(close, 5)) - 1 - ((close / ts_delay(close, 10)) - 1)", - # 5. 成交量因子 - "vol_ma5": "ts_mean(vol, 5)", - "vol_ma20": "ts_mean(vol, 20)", - "vol_ratio": "ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)", - # 6. 市值因子（截面排名） - "market_cap_rank": "cs_rank(total_mv)", - # 7. 价格位置因子 - "high_low_ratio": "(close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)", - "n_income": "n_income" - - } - - # Label 因子（单独定义，不参与训练） - label_factor = { - "return_5": "(close / ts_delay(close, 5)) - 1", - } - + # 使用模块级别的因子定义 # 注册所有特征因子 print("\n注册特征因子:") - for name, expr in factor_definitions.items(): + for name, expr in FACTOR_DEFINITIONS.items(): engine.add_factor(name, expr) print(f" - {name}: {expr}") # 注册 label 因子 print("\n注册 Label 因子:") - for name, expr in label_factor.items(): + for name, expr in LABEL_FACTOR.items(): engine.add_factor(name, expr) print(f" - {name}: {expr}") - # 从字典自动获取特征列（keys() 方法） - feature_cols = list(factor_definitions.keys()) + # 从字典自动获取特征列 + feature_cols = list(FACTOR_DEFINITIONS.keys()) print(f"\n特征因子数: {len(feature_cols)}") - print(f"Label: {list(label_factor.keys())[0]}") + print(f"Label: {list(LABEL_FACTOR.keys())[0]}") print(f"已注册因子总数: {len(engine.list_registered())}") return feature_cols @@ -146,82 +203,42 @@ def train_regression_model(): feature_cols = create_factors_with_strings(engine) target_col = "return_5" - # 3. 准备数据 + # 3. 准备数据（使用模块级别的日期配置） print("\n[3] 准备数据") - train_start, train_end = "20200101", "20241231" - test_start, test_end = "20250101", "20251231" data = prepare_data( engine=engine, feature_cols=feature_cols, - start_date=train_start, - end_date=test_end, + start_date=TRAIN_START, + end_date=TEST_END, ) - # 4. 创建配置 - config = TrainingConfig( - feature_cols=feature_cols, - target_col=target_col, - date_col="trade_date", - code_col="ts_code", - train_start=train_start, - train_end=train_end, - test_start=test_start, - test_end=test_end, - model_type="lightgbm", - model_params={ - "objective": "regression", - "metric": "rmse", - "num_leaves": 31, - "learning_rate": 0.05, - "n_estimators": 100, - }, - processors=[ - {"name": "winsorizer", "params": {"lower": 0.01, "upper": 0.99}}, - {"name": "cs_standard_scaler", "params": {}}, - ], - persist_model=False, - model_save_path=None, - output_dir="output/regression", - save_predictions=True, - ) - - print(f"\n[配置] 训练期: {train_start} - {train_end}") - print(f"[配置] 测试期: {test_start} - {test_end}") + # 4. 打印配置信息（使用模块级别的配置常量） + print(f"\n[配置] 训练期: {TRAIN_START} - {TRAIN_END}") + print(f"[配置] 测试期: {TEST_START} - {TEST_END}") print(f"[配置] 特征数: {len(feature_cols)}") print(f"[配置] 目标变量: {target_col}") - # 5. 创建模型 - model = LightGBMModel( - objective="regression", - metric="rmse", - num_leaves=31, - learning_rate=0.05, - n_estimators=100, - ) + # 5. 创建模型（使用模块级别的模型参数） + model = LightGBMModel(params=MODEL_PARAMS) - # 6. 创建数据处理器 + # 6. 创建数据处理器（从 PROCESSOR_CONFIGS 解析） processors = [ - Winsorizer(lower=0.01, upper=0.99), - StandardScaler(exclude_cols=["ts_code", "trade_date", target_col]), + Winsorizer(**PROCESSOR_CONFIGS[0]["params"]), # type: ignore[arg-type] + StandardScaler(exclude_cols=["ts_code", "trade_date", target_col]), # type: ignore[call-arg] ] - # 7. 创建数据划分器 + # 7. 创建数据划分器（使用模块级别的日期配置） splitter = DateSplitter( - train_start=train_start, - train_end=train_end, - test_start=test_start, - test_end=test_end, + train_start=TRAIN_START, + train_end=TRAIN_END, + test_start=TEST_START, + test_end=TEST_END, ) - # 8. 创建股票池管理器（可选） + # 8. 创建股票池管理器（使用模块级别的筛选配置） pool_manager = StockPoolManager( - filter_config=StockFilterConfig( - exclude_cyb=True, - exclude_kcb=True, - exclude_bj=True, - exclude_st=True, - ), + filter_config=StockFilterConfig(**STOCK_FILTER_CONFIG), selector_config=None, # 暂时不启用市值选择 data_router=engine.router, # 从 FactorEngine 获取数据路由器 ) @@ -240,7 +257,7 @@ def train_regression_model(): splitter=splitter, target_col=target_col, feature_cols=feature_cols, - persist_model=False, + persist_model=PERSIST_MODEL, ) # 10. 手动执行训练流程（增加详细打印） @@ -401,22 +418,24 @@ def train_regression_model(): print(f"\n示例日期 {sample_date} 的前10条预测:") print(sample_data.select(["ts_code", "trade_date", target_col, "prediction"])) - # 12. 保存结果（每日 top5） - output_dir = "D:\\PyProject\\ProStock\\src\\training\\output" - os.makedirs(output_dir, exist_ok=True) + # 12. 保存结果 + print("\n" + "=" * 80) + print("保存预测结果") + print("=" * 80) - # 生成文件名：top_5_{开始日期}_{结束日期}.csv - from datetime import datetime + # 确保输出目录存在 + os.makedirs(OUTPUT_DIR, exist_ok=True) - start_dt = datetime.strptime(test_start, "%Y%m%d") - end_dt = datetime.strptime(test_end, "%Y%m%d") - filename = ( - f"top_5_{start_dt.strftime('%Y-%m-%d')}_{end_dt.strftime('%Y-%m-%d')}.csv" - ) - output_path = os.path.join(output_dir, filename) + # 生成时间戳 + 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')}" + + # 12.1 保存每日 Top5 + print("\n[1/1] 保存每日 Top5 股票...") + top5_output_path = os.path.join(OUTPUT_DIR, f"top5_{date_str}.csv") # 按日期分组，取每日 top5 - print("\n选取每日 Top 5 股票...") top5_by_date = [] unique_dates = results["trade_date"].unique().sort() for date in unique_dates: @@ -425,29 +444,26 @@ def train_regression_model(): top5 = day_data.sort("prediction", descending=True).head(5) top5_by_date.append(top5) - print(f" 处理完成: 共 {len(unique_dates)} 个交易日，每交易日取 top5") - # 合并所有日期的 top5 top5_results = pl.concat(top5_by_date) # 格式化日期并调整列顺序：日期、分数、股票 - results_to_save = top5_results.select( + top5_to_save = top5_results.select( [ pl.col("trade_date").str.slice(0, 4) + "-" + pl.col("trade_date").str.slice(4, 2) + "-" - + pl.col("trade_date").str.slice(6, 2), + + pl.col("trade_date").str.slice(6, 2).alias("date"), pl.col("prediction").alias("score"), pl.col("ts_code"), ] - ).rename({"trade_date": "date"}) - results_to_save.write_csv(output_path, include_header=True) - print(f"\n预测结果已保存: {output_path}") - print(f"保存列: {results_to_save.columns}") - print(f"总行数: {len(results_to_save)}（每日 top5）") - print(f"\n保存数据预览:") - print(results_to_save.head(15)) + ) + top5_to_save.write_csv(top5_output_path, include_header=True) + print(f" 保存路径: {top5_output_path}") + print(f" 保存行数: {len(top5_to_save)}（{len(unique_dates)}个交易日 × 每日top5）") + print(f"\n 预览（前15行）:") + print(top5_to_save.head(15)) # 13. 特征重要性 importance = model.feature_importance() diff --git a/src/training/components/models/lightgbm.py b/src/training/components/models/lightgbm.py index 3162eac..56638df 100644 --- a/src/training/components/models/lightgbm.py +++ b/src/training/components/models/lightgbm.py @@ -3,7 +3,7 @@ 提供 LightGBM 回归模型的实现，支持特征重要性和原生模型保存。 """ -from typing import Optional +from typing import Any, Optional import numpy as np import pandas as pd @@ -31,6 +31,7 @@ class LightGBMModel(BaseModel): def __init__( self, + params: Optional[dict] = None, objective: str = "regression", metric: str = "rmse", num_leaves: int = 31, @@ -40,23 +41,54 @@ class LightGBMModel(BaseModel): ): """初始化 LightGBM 模型 + 支持两种方式传入参数： + 1. 通过 params 字典传入所有参数（推荐方式） + 2. 通过独立参数传入（向后兼容） + Args: + params: LightGBM 参数字典，如果提供则直接使用此字典 objective: 目标函数，默认 "regression" metric: 评估指标，默认 "rmse" num_leaves: 叶子节点数，默认 31 learning_rate: 学习率，默认 0.05 n_estimators: 迭代次数，默认 100 **kwargs: 其他 LightGBM 参数 + + Examples: + >>> # 方式1：通过 params 字典（推荐） + >>> model = LightGBMModel(params={ + ... "objective": "regression", + ... "metric": "rmse", + ... "num_leaves": 31, + ... "learning_rate": 0.05, + ... "n_estimators": 100, + ... }) + >>> + >>> # 方式2：通过独立参数（向后兼容） + >>> model = LightGBMModel( + ... objective="regression", + ... num_leaves=31, + ... learning_rate=0.05, + ... ) """ - self.params = { - "objective": objective, - "metric": metric, - "num_leaves": num_leaves, - "learning_rate": learning_rate, - "verbose": -1, # 抑制训练输出 - **kwargs, - } - self.n_estimators = n_estimators + if params is not None: + # 方式1：直接使用 params 字典 + self.params = dict(params) # 复制一份，避免修改原始字典 + self.params.setdefault("verbose", -1) # 默认抑制训练输出 + # n_estimators 可能存在于 params 中 + self.n_estimators = self.params.pop("n_estimators", n_estimators) + else: + # 方式2：通过独立参数构建 params + self.params = { + "objective": objective, + "metric": metric, + "num_leaves": num_leaves, + "learning_rate": learning_rate, + "verbose": -1, # 抑制训练输出 + **kwargs, + } + self.n_estimators = n_estimators + self.model = None self.feature_names_: Optional[list] = None diff --git a/src/training/components/selectors.py b/src/training/components/selectors.py index 86e5fb0..911bf8b 100644 --- a/src/training/components/selectors.py +++ b/src/training/components/selectors.py @@ -15,7 +15,7 @@ class StockFilterConfig: 基于股票代码进行过滤，不依赖外部数据。 Attributes: - exclude_cyb: 是否排除创业板（300xxx） + exclude_cyb: 是否排除创业板（300xxx, 301xxx） exclude_kcb: 是否排除科创板（688xxx） exclude_bj: 是否排除北交所（.BJ 后缀） exclude_st: 是否排除ST股票（需要外部数据支持） @@ -41,8 +41,8 @@ class StockFilterConfig: """ result = [] for code in codes: - # 排除创业板（300xxx） - if self.exclude_cyb and code.startswith("300"): + # 排除创业板（300xxx, 301xxx） + if self.exclude_cyb and code.startswith(("300", "301")): continue # 排除科创板（688xxx） if self.exclude_kcb and code.startswith("688"):