feat(experiment): 新增因子排除机制并优化模型训练参数
- 添加 EXCLUDED_FACTORS 列表支持批量排除效果不佳的因子 - 修复 LightGBM 树结构冲突，调整正则化和采样策略防过拟合 - 调整数据处理器配置，关闭模型自动保存
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@@ -11,7 +11,6 @@ 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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@@ -22,7 +21,6 @@ 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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@@ -257,6 +255,49 @@ SELECTED_FACTORS = [
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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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FACTOR_DEFINITIONS = {}
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# 需要排除的因子列表（这些因子不会被计算和使用）
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# 用于临时屏蔽效果不好的因子，无需从 SELECTED_FACTORS 中删除
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EXCLUDED_FACTORS: List[str] = [
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'GTJA_alpha005',
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'GTJA_alpha028',
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'GTJA_alpha023',
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'GTJA_alpha002',
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'GTJA_alpha010',
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'GTJA_alpha011',
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'GTJA_alpha044',
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'GTJA_alpha036',
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'GTJA_alpha027',
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'GTJA_alpha109',
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'GTJA_alpha104',
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'GTJA_alpha103',
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'GTJA_alpha085',
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'GTJA_alpha111',
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'GTJA_alpha092',
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'GTJA_alpha067',
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'GTJA_alpha060',
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'GTJA_alpha062',
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'GTJA_alpha063',
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'GTJA_alpha079',
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'GTJA_alpha073',
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'GTJA_alpha087',
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'GTJA_alpha117',
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'GTJA_alpha113',
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'GTJA_alpha138',
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'GTJA_alpha121',
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'GTJA_alpha124',
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'GTJA_alpha133',
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'GTJA_alpha131',
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'GTJA_alpha118',
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'GTJA_alpha164',
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'GTJA_alpha162',
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'GTJA_alpha157',
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'GTJA_alpha171',
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'GTJA_alpha177',
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'GTJA_alpha180',
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'GTJA_alpha188',
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'GTJA_alpha191',
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]
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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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@@ -276,52 +317,84 @@ def get_label_factor(label_name: str) -> dict:
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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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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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excluded_factors: Optional[List[str]] = None,
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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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excluded_factors 中的因子会被排除，不参与计算。
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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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excluded_factors: 需要排除的因子名称列表，默认为None
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Returns:
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特征列名称列表
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特征列名称列表（已排除excluded_factors中的因子）
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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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# 处理排除列表
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excluded = set(excluded_factors) if excluded_factors else set()
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if excluded:
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print(f"\n[排除因子] 以下 {len(excluded)} 个因子将被排除:")
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for name in sorted(excluded):
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print(f" - {name}")
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# 过滤 SELECTED_FACTORS 中的因子（排除excluded_factors）
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filtered_selected = [name for name in selected_factors if name not in excluded]
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excluded_from_selected = set(selected_factors) - set(filtered_selected)
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if excluded_from_selected:
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print(
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f"\n[排除详情] 从 SELECTED_FACTORS 排除 {len(excluded_from_selected)} 个因子"
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)
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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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for name in filtered_selected:
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engine.add_factor(name)
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print(f" - {name}")
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# 过滤 FACTOR_DEFINITIONS 中的因子（排除excluded_factors）
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filtered_definitions = {
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name: expr for name, expr in factor_definitions.items() if name not in excluded
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}
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excluded_from_definitions = set(factor_definitions.keys()) - set(
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filtered_definitions.keys()
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)
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if excluded_from_definitions:
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print(
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f"\n[排除详情] 从 FACTOR_DEFINITIONS 排除 {len(excluded_from_definitions)} 个因子"
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)
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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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for name, expr in filtered_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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# 注册 label 因子（通过表达式，label因子不受excluded_factors影响）
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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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# 特征列 = 过滤后的 SELECTED_FACTORS + 过滤后的 FACTOR_DEFINITIONS 的 keys
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feature_cols = filtered_selected + list(filtered_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" - 来自 metadata: {len(filtered_selected)}")
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print(f" - 来自表达式: {len(filtered_definitions)}")
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if excluded:
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print(f" - 已排除: {len(excluded)}")
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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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@@ -329,11 +402,11 @@ def register_factors(
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def prepare_data(
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engine: FactorEngine,
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feature_cols: List[str],
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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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engine: FactorEngine,
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feature_cols: List[str],
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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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@@ -391,11 +464,11 @@ def stock_pool_filter(df: pl.DataFrame) -> pl.Series:
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"""
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# 代码筛选（排除创业板、科创板、北交所）
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code_filter = (
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~df["ts_code"].str.starts_with("30") # 排除创业板
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& ~df["ts_code"].str.starts_with("68") # 排除科创板
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& ~df["ts_code"].str.starts_with("8") # 排除北交所
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& ~df["ts_code"].str.starts_with("9") # 排除北交所
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& ~df["ts_code"].str.starts_with("4") # 排除北交所
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~df["ts_code"].str.starts_with("30") # 排除创业板
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& ~df["ts_code"].str.starts_with("68") # 排除科创板
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& ~df["ts_code"].str.starts_with("8") # 排除北交所
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& ~df["ts_code"].str.starts_with("9") # 排除北交所
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& ~df["ts_code"].str.starts_with("4") # 排除北交所
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)
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# 在已筛选的股票中，选取市值最小的500只
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@@ -410,7 +483,6 @@ def stock_pool_filter(df: pl.DataFrame) -> pl.Series:
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# 定义筛选所需的基础列
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STOCK_FILTER_REQUIRED_COLUMNS = ["total_mv"]
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# =============================================================================
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# 输出配置
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# =============================================================================
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@@ -418,7 +490,7 @@ OUTPUT_DIR = "output"
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SAVE_PREDICTIONS = True
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# 模型保存配置
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SAVE_MODEL = True # 是否保存模型
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SAVE_MODEL = False # 是否保存模型
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MODEL_SAVE_DIR = "models" # 模型保存目录
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# Top N 配置：每日推荐股票数量
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@@ -451,7 +523,7 @@ def get_output_path(model_type: str, test_start: str, test_end: str) -> str:
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def get_model_save_path(
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model_type: str, model_name: Optional[str] = None
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model_type: str, model_name: Optional[str] = None
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) -> Optional[str]:
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"""生成模型保存路径。
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@@ -477,10 +549,10 @@ def get_model_save_path(
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def save_model_with_factors(
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model,
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model_path: str,
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selected_factors: List[str],
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factor_definitions: dict,
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model,
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model_path: str,
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selected_factors: List[str],
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factor_definitions: dict,
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) -> None:
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"""保存模型及关联的因子信息。
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