feat(factors): 新增筹码集中度相关因子并优化训练框架

- 添加 19 个筹码分布和胜率相关因子(包括chip_dispersion、winner_rate等系列) - LightGBM模型添加早停和训练指标记录功能 - 统一Label配置到common.py模块 - 新增list_factors.py因子列表脚本
2026-03-29 01:34:58 +08:00
parent d4e0e2a0b6
commit c3d1b157e9
9 changed files with 373 additions and 246 deletions
--- a/src/experiment/common.py
+++ b/src/experiment/common.py
@@ -250,67 +250,58 @@ SELECTED_FACTORS = [
    "GTJA_alpha188",
    "GTJA_alpha189",
    "GTJA_alpha191",
+    "chip_dispersion_90",
+    "chip_dispersion_70",
+    "cost_skewness",
+    "dispersion_change_20",
+    "price_to_avg_cost",
+    "price_to_median_cost",
+    "mean_median_dev",
+    "trap_pressure",
+    "bottom_profit",
+    "history_position",
+    "winner_rate_surge_5",
+    "winner_rate_cs_rank",
+    "winner_rate_dev_20",
+    "winner_rate_volatility",
+    "smart_money_accumulation",
+    "winner_vol_corr_20",
+    "cost_base_momentum",
+    "bottom_cost_stability",
+    "pivot_reversion",
+    "chip_transition",
 ]

 # 因子定义字典（完整因子库，用于存放尚未注册到metadata的因子）
 FACTOR_DEFINITIONS = {"cs_rank_circ_mv": "cs_rank(circ_mv)"}

-# 需要排除的因子列表（这些因子不会被计算和使用）
-# 用于临时屏蔽效果不好的因子，无需从 SELECTED_FACTORS 中删除
-# EXCLUDED_FACTORS: List[str] = [
-#     # "GTJA_alpha005",
-#     # "GTJA_alpha028",
-#     # "GTJA_alpha023",
-#     # "GTJA_alpha002",
-#     # "GTJA_alpha010",
-#     # "GTJA_alpha011",
-#     # "GTJA_alpha044",
-#     # "GTJA_alpha036",
-#     # "GTJA_alpha027",
-#     # "GTJA_alpha109",
-#     # "GTJA_alpha104",
-#     # "GTJA_alpha103",
-#     # "GTJA_alpha085",
-#     # "GTJA_alpha111",
-#     # "GTJA_alpha092",
-#     # "GTJA_alpha067",
-#     # "GTJA_alpha060",
-#     # "GTJA_alpha062",
-#     # "GTJA_alpha063",
-#     # "GTJA_alpha079",
-#     # "GTJA_alpha073",
-#     # "GTJA_alpha087",
-#     # "GTJA_alpha117",
-#     # "GTJA_alpha113",
-#     # "GTJA_alpha138",
-#     # "GTJA_alpha121",
-#     # "GTJA_alpha124",
-#     # "GTJA_alpha133",
-#     # "GTJA_alpha131",
-#     # "GTJA_alpha118",
-#     # "GTJA_alpha164",
-#     # "GTJA_alpha162",
-#     # "GTJA_alpha157",
-#     # "GTJA_alpha171",
-#     # "GTJA_alpha177",
-#     # "GTJA_alpha180",
-#     # "GTJA_alpha188",
-#     # "GTJA_alpha191",
-# ]
+
+# =============================================================================
+# Label 配置（统一绑定 label_name 和 label_dsl）
+# =============================================================================
+# Label 名称
+LABEL_NAME = "future_return_5"
+
+# Label DSL 公式
+LABEL_DSL = "(ts_delay(close, -5) / ts_delay(open, -1)) - 1"
+
+# Label 配置字典（绑定 name 和 dsl）
+LABEL_FACTOR = {LABEL_NAME: LABEL_DSL}


 def get_label_factor(label_name: str) -> dict:
    """获取Label因子定义字典。

+    警告: 此函数已废弃，请直接使用 LABEL_FACTOR 常量。
+    label_name 参数将被忽略，始终返回预定义的 LABEL_FACTOR。
+
    Args:
-        label_name: label因子名称
+        label_name: label因子名称（已废弃，仅保留参数保持向后兼容）

    Returns:
        Label因子定义字典
    """
-    return {
-        label_name: "(ts_delay(close, -5) / ts_delay(open, -1)) - 1",
-    }
+    return LABEL_FACTOR


 # =============================================================================
--- a/src/experiment/learn_to_rank.py
+++ b/src/experiment/learn_to_rank.py
@@ -21,7 +21,8 @@ from src.training.components.filters import STFilter
 from src.experiment.common import (
    SELECTED_FACTORS,
    FACTOR_DEFINITIONS,
-    get_label_factor,
+    LABEL_NAME,
+    LABEL_FACTOR,
    TRAIN_START,
    TRAIN_END,
    VAL_START,
@@ -44,171 +45,39 @@ 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)
+# Label 配置（从 common.py 统一导入）
+# LABEL_NAME 和 LABEL_FACTOR 已在 common.py 中绑定，只需从 common 导入

 # 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_alpha002",
+    "GTJA_alpha027",
+    "GTJA_alpha051",
    "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_alpha041",
    "GTJA_alpha131",
-    "GTJA_alpha134",
-    "GTJA_alpha135",
-    "GTJA_alpha136",
-    "GTJA_alpha112",
-    "GTJA_alpha120",
-    "GTJA_alpha119",
-    "GTJA_alpha122",
-    "GTJA_alpha124",
-    "GTJA_alpha126",
+    "GTJA_alpha103",
+    "GTJA_alpha087",
+    "GTJA_alpha093",
+    "GTJA_alpha092",
+    "GTJA_alpha073",
    "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_alpha117",
+    "GTJA_alpha124",
    "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",
+    "smart_money_accumulation",
+    "GTJA_alpha014",
+    "GTJA_alpha056",
+    "GTJA_alpha085",
+    "GTJA_alpha154",
+    "GTJA_alpha141",
 ]

 # LambdaRank 模型参数配置
--- a/src/experiment/regression.py
+++ b/src/experiment/regression.py
@@ -15,13 +15,15 @@ from src.training import (
    NullFiller,
    Winsorizer,
    StandardScaler,
+    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,
+    LABEL_NAME,
+    LABEL_FACTOR,
    TRAIN_START,
    TRAIN_END,
    VAL_START,
@@ -44,58 +46,93 @@ TRAINING_TYPE = "regression"
 # %% md
 # ## 2. 训练特定配置
 # %%
-# Label 配置
-LABEL_NAME = "future_return_5"
-LABEL_FACTOR = get_label_factor(LABEL_NAME)
+# Label 配置（从 common.py 统一导入）
+# LABEL_NAME 和 LABEL_FACTOR 已在 common.py 中绑定，只需从 common 导入

 # 排除的因子列表
 EXCLUDED_FACTORS = [
+    "GTJA_alpha062",
+    "GTJA_alpha060",
+    "GTJA_alpha058",
+    "GTJA_alpha056",
+    "GTJA_alpha053",
+    "GTJA_alpha040",
+    "GTJA_alpha043",
+    "GTJA_alpha027",
+    "CP",
+    "max_ret_20",
+    "debt_to_equity",
+    "close_vwap_deviation",
+    "EP",
+    "BP",
+    "EP_rank",
+    "GTJA_alpha044",
+    "GTJA_alpha036",
    "GTJA_alpha010",
    "GTJA_alpha005",
-    "GTJA_alpha036",
-    "GTJA_alpha027",
-    "GTJA_alpha044",
+    "GTJA_alpha001",
+    "GTJA_alpha002",
+    "GTJA_alpha007",
+    "GTJA_alpha016",
    "GTJA_alpha073",
-    "GTJA_alpha104",
-    "GTJA_alpha103",
-    "GTJA_alpha105",
-    "GTJA_alpha092",
-    "GTJA_alpha087",
-    "GTJA_alpha085",
-    "GTJA_alpha062",
-    "GTJA_alpha124",
    "GTJA_alpha133",
    "GTJA_alpha131",
    "GTJA_alpha117",
+    "GTJA_alpha124",
+    "GTJA_alpha120",
+    "GTJA_alpha119",
+    "GTJA_alpha103",
+    "GTJA_alpha099",
+    "GTJA_alpha105",
+    "GTJA_alpha104",
+    "GTJA_alpha090",
+    "GTJA_alpha085",
+    "GTJA_alpha083",
+    "GTJA_alpha084",
+    "GTJA_alpha087",
+    "GTJA_alpha092",
+    "GTJA_alpha074",
+    "GTJA_alpha089",
+    "GTJA_alpha173",
    "GTJA_alpha157",
+    "GTJA_alpha139",
    "GTJA_alpha162",
+    "GTJA_alpha163",
    "GTJA_alpha177",
-    "GTJA_alpha180",
+    "price_to_avg_cost",
+    "cost_skewness",
    "GTJA_alpha191",
+    "GTJA_alpha180",
+    "history_position",
+    "bottom_profit",
+    "smart_money_accumulation",
 ]

 # 模型参数配置
 MODEL_PARAMS = {
-    # 基础设置
-    "objective": "regression_l1",
+    # ==================== 基础设置 ====================
+    "objective": "huber",  # 【修改】相比纯 L1(MAE)，huber 对异常值鲁棒且在极小误差处平滑，更适合收益率预测
    "metric": "mae",
-    # 树结构约束
-    "max_depth": 5,
-    "num_leaves": 24,
-    "min_data_in_leaf": 100,
-    # 学习参数
-    "learning_rate": 0.01,
-    "n_estimators": 1500,
-    # 随机采样
-    "subsample": 0.8,
+    # ==================== 树结构约束 ====================
+    "max_depth": 5,  # 【修改】适当加深，允许捕捉一定的高阶交叉
+    "num_leaves": 31,  # 【修改】限制为 31（2的5次方-1），确保树是不对称生长的，防止过拟合
+    "min_data_in_leaf": 512,  # 【大幅增加】从256加到1000。训练集有97万条，极大地限制叶子节点样本量能有效抵抗股市噪音
+    # ==================== 学习参数 ====================
+    "learning_rate": 0.02,  # 【修改】稍微调大一点，帮助模型跳出初始的局部最优（避免十几轮就早停）
+    "n_estimators": 2000,
+    # ==================== 随机采样与降维 ====================
+    "subsample": 0.85,
    "subsample_freq": 1,
-    "colsample_bytree": 0.8,
-    # 正则化
-    "reg_alpha": 0.5,
-    "reg_lambda": 1.0,
-    # 杂项
+    "colsample_bytree": 0.4,  # 【大幅降低】从0.8降到0.4。强制打压 GTJA_alpha127 的霸权，逼迫模型去学习其他因子的信息
+    "extra_trees": True,  # 【新增且极度推荐】极度随机树模式。在分裂点选择时增加随机性，是量化比赛中防过拟合的神器
+    # ==================== 正则化 ====================
+    "reg_alpha": 1.0,  # 【修改】L1正则增加，强行把一些无用特征的权重压到0
+    "reg_lambda": 5.0,  # 【修改】L2正则大幅增加（从1到5），惩罚过大的叶子节点输出权重
+    "max_bin": 127,  # 【新增】默认255，降低到127相当于对连续特征做了一次粗颗粒度的分箱，也是极好的正则化手段
+    # ==================== 杂项 ====================
    "verbose": -1,
    "random_state": 42,
+    "n_jobs": -1,
 }

 # 日期范围配置
@@ -143,6 +180,7 @@ def main():
            (NullFiller, {"strategy": "mean"}),
            (Winsorizer, {"lower": 0.01, "upper": 0.99}),
            (StandardScaler, {}),
+            # (CrossSectionalStandardScaler, {}),
        ],
        filters=[STFilter(data_router=engine.router)],
        stock_pool_filter_func=stock_pool_filter,
--- a/src/scripts/list_factors.py
+++ b/src/scripts/list_factors.py
@@ -0,0 +1,81 @@
+"""列出所有已入库的因子。
+
+以 Python 列表格式输出所有已注册因子的名称，方便复制使用。
+保持 factors.jsonl 中的原始顺序（按 factor_id）。
+
+使用方法:
+    uv run python -m src.scripts.list_factors
+"""
+
+import json
+import re
+from pathlib import Path
+
+from src.config.settings import get_settings
+
+
+def extract_factor_id_number(factor_id: str) -> int:
+    """从 factor_id 中提取数字部分用于排序。
+
+    Args:
+        factor_id: 如 "F_001"
+
+    Returns:
+        数字部分，如 1
+    """
+    match = re.match(r"F_(\d+)", factor_id)
+    if match:
+        return int(match.group(1))
+    return 0
+
+
+def list_factors():
+    """读取 factors.jsonl 并按 factor_id 顺序打印因子名称列表。"""
+    settings = get_settings()
+    factors_path = settings.data_path_resolved / "factors.jsonl"
+
+    if not factors_path.exists():
+        print(f"[错误] 因子文件不存在: {factors_path}")
+        return
+
+    # 读取所有因子并按 factor_id 排序
+    factors = []
+    try:
+        with open(factors_path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    data = json.loads(line)
+                    factor_id = data.get("factor_id", "")
+                    name = data.get("name")
+                    if name and factor_id:
+                        factors.append((factor_id, name))
+                except json.JSONDecodeError:
+                    continue
+    except Exception as e:
+        print(f"[错误] 读取因子文件失败: {e}")
+        return
+
+    if not factors:
+        print("[信息] 没有找到任何因子")
+        return
+
+    # 按 factor_id 数字排序（保持入库顺序）
+    factors.sort(key=lambda x: extract_factor_id_number(x[0]))
+
+    # 以 Python 列表格式输出
+    print("[")
+    for i, (factor_id, name) in enumerate(factors):
+        if i == len(factors) - 1:
+            print(f'    "{name}"')
+        else:
+            print(f'    "{name}",')
+    print("]")
+
+    print(f"\n[统计] 共计 {len(factors)} 个因子")
+
+
+if __name__ == "__main__":
+    list_factors()
--- a/src/scripts/register_factors.py
+++ b/src/scripts/register_factors.py
@@ -26,24 +26,126 @@ from typing import Any, Dict, List, Optional

 from src.factors.metadata import FactorManager
 from src.factors.metadata.exceptions import DuplicateFactorError, ValidationError
+from src.config.settings import get_settings


 # ============================================================================
 # 用户配置区域 - 在这里添加要注册的因子
 # ============================================================================

-FACTORS: List[Dict[str, Any]] = [
-    # 示例因子，请根据实际需要修改或添加
+FACTORS: List[Dict[str, Any]] =[
+    # ==================== 第一类：筹码集中度与离散度因子 ====================
    {
-        "name": "turnover_volatility_ratio",
-        "desc": "5日价格动量，收盘价相对于5日前收盘价的涨跌幅进行截面排名",
-        "dsl": "cs_rank(close / ts_delay(close, 5) - 1)",
-        "category": "momentum",
+        "name": "chip_dispersion_90",
+        "desc": "90%筹码离散度：衡量市场90%持仓筹码的宽度，值越小表示筹码越高度集中（单峰密集），往往是洗盘结束的前兆",
+        "dsl": "(cost_95pct - cost_5pct) / (cost_95pct + cost_5pct)",
+    },
+    {
+        "name": "chip_dispersion_70",
+        "desc": "70%核心筹码离散度：剔除极端的底部死筹和高位套牢盘，反映中间70%主流资金的成本集中度",
+        "dsl": "(cost_85pct - cost_15pct) / (cost_85pct + cost_15pct)",
+    },
+    {
+        "name": "cost_skewness",
+        "desc": "筹码偏度：反映筹码分布的不对称性。大于1说明上方套牢盘拖尾严重，小于1说明下方获利盘雄厚",
+        "dsl": "(cost_95pct - cost_50pct) / (cost_50pct - cost_5pct)",
+    },
+    {
+        "name": "dispersion_change_20",
+        "desc": "筹码集中度近期变化率：过去20天筹码宽度的变化比例，持续下降说明主力正在暗中吸筹",
+        "dsl": "ts_pct_change((cost_95pct - cost_5pct) / cost_50pct, 20)",
+    },
+
+    # ==================== 第二类：筹码相对位置与压力/支撑因子 ====================
+    {
+        "name": "price_to_avg_cost",
+        "desc": "整体浮盈比例：当前价格相对加权平均成本的溢价率。高溢价有均值回归压力，负溢价代表超跌",
+        "dsl": "(close - weight_avg) / weight_avg",
+    },
+    {
+        "name": "price_to_median_cost",
+        "desc": "中位数成本偏离度：价格相对于50%分位点（绝对半数人持仓价）的偏离，向上突破通常是右侧买点",
+        "dsl": "(close - cost_50pct) / cost_50pct",
+    },
+    {
+        "name": "mean_median_dev",
+        "desc": "均值中位数背离：均值显著大于中位数说明高位筹码堆积，上涨阻力大",
+        "dsl": "(weight_avg - cost_50pct) / cost_50pct",
+    },
+    {
+        "name": "trap_pressure",
+        "desc": "高位套牢盘压力指数：当前价格距离上方95%高位套牢成本的距离。距离越大，反弹的真空期阻力越小",
+        "dsl": "(cost_95pct - close) / close",
+    },
+    {
+        "name": "bottom_profit",
+        "desc": "底部支撑底仓利润率：当前价格距离底部5%筹码的利润空间。暴跌时大于0说明底仓极度稳定",
+        "dsl": "(close - cost_5pct) / cost_5pct",
+    },
+    {
+        "name": "history_position",
+        "desc": "历史区间分位点：当前价格在个股上市以来历史最高点和最低点之间的相对位置",
+        "dsl": "(close - his_low) / (his_high - his_low)",
+    },
+
+    # ==================== 第三类：胜率相关的动量与反转因子 ====================
+    {
+        "name": "winner_rate_surge_5",
+        "desc": "获利盘短期爆发力：胜率在过去5天内的变化值，急剧上升是极强的动量做多信号",
+        "dsl": "ts_delta(winner_rate, 5)",
+    },
+    {
+        "name": "winner_rate_cs_rank",
+        "desc": "获利盘高位反转信号：全市场胜率截面排名，极端高胜率往往面临多头踩踏的获利了结压力（反转因子）",
+        "dsl": "cs_rank(winner_rate)",
+    },
+    {
+        "name": "winner_rate_dev_20",
+        "desc": "获利盘均线偏离：当前胜率相对过去20天平均胜率的偏离程度，捕捉筹码情绪的边际超买/超卖",
+        "dsl": "winner_rate - ts_mean(winner_rate, 20)",
+    },
+    {
+        "name": "winner_rate_volatility",
+        "desc": "获利盘波动率：过去20天胜率的波动率。波动率低且胜率高说明单边上涨极度稳健",
+        "dsl": "ts_std(winner_rate, 20)",
+    },
+    {
+        "name": "smart_money_accumulation",
+        "desc": "潜在主力吸筹隐蔽指标：胜率的60日时序分位数减去价格的时序分位数。值越大说明‘价平而获利盘增’，底部吸筹明显",
+        "dsl": "ts_rank(winner_rate, 60) - ts_rank(close, 60)",
+    },
+
+    # ==================== 第四类：量价与筹码交乘因子 ====================
+    {
+        "name": "winner_vol_corr_20",
+        "desc": "放量突破筹码密集区：胜率与成交量的20日时序相关性，正相关说明增量资金在主动解套上方筹码",
+        "dsl": "ts_corr(winner_rate, vol, 20)",
+    },
+    {
+        "name": "cost_base_momentum",
+        "desc": "成本重心上移换手率：过去20天加权平均成本的变化幅度，快速上移说明高位换手极其充分",
+        "dsl": "ts_pct_change(weight_avg, 20)",
+    },
+    {
+        "name": "bottom_cost_stability",
+        "desc": "底部坚如磐石因子：底部5%成本的60天波动率相对于中位数的比值，波动越小说明死筹越稳固",
+        "dsl": "ts_std(cost_5pct, 60) / cost_50pct",
+    },
+    {
+        "name": "pivot_reversion",
+        "desc": "盈亏分界线乖离修复：价格偏离50%分位点除以近20日价格标准差，用于寻找超跌后的均值回归买点",
+        "dsl": "(close - cost_50pct) / ts_std(close, 20)",
+    },
+    {
+        "name": "chip_transition",
+        "desc": "强弱筹码切换度：上方厚度与下方厚度差值的20日变化量。由正变负说明筹码彻底完成了自上而下的转移（洗盘结束）",
+        "dsl": "ts_delta((cost_85pct - cost_50pct) - (cost_50pct - cost_15pct), 20)",
    },
 ]

-# 因子存储路径（默认使用实验目录）
-OUTPUT_PATH = Path(__file__).parent.parent / "experiment" / "data" / "factors.jsonl"
+# 因子存储路径（使用项目根路径下的 data 目录）
+settings = get_settings()
+OUTPUT_PATH = settings.data_path_resolved / "factors.jsonl"


 # ============================================================================
--- a/src/training/components/models/lightgbm.py
+++ b/src/training/components/models/lightgbm.py
@@ -48,6 +48,7 @@ class LightGBMModel(BaseModel):
        self.params = dict(params) if params is not None else {}
        self.model = None
        self.feature_names_: Optional[list] = None
+        self.evals_result_: Optional[dict] = None

    def fit(
        self,
@@ -90,14 +91,23 @@ class LightGBMModel(BaseModel):
            y_val_np = y_val.to_numpy()
            valid_sets = lgb.Dataset(X_val_np, label=y_val_np, reference=train_data)

-        # 从 params 中提取 num_boost_round，默认 100
-        num_boost_round = self.params.pop("n_estimators", 100)
+        # 从 params 中提取训练控制参数
+        params_copy = dict(self.params)
+        num_boost_round = params_copy.pop("n_estimators", 100)
+        early_stopping_round = params_copy.pop("early_stopping_round", 50)
+
+        self.evals_result_ = {}
+        callbacks = [
+            lgb.early_stopping(stopping_rounds=early_stopping_round),
+            lgb.record_evaluation(self.evals_result_),
+        ]

        self.model = lgb.train(
-            self.params,
+            params_copy,
            train_data,
            num_boost_round=num_boost_round,
            valid_sets=[valid_sets] if valid_sets else None,
+            callbacks=callbacks,
        )

        return self
@@ -121,6 +131,34 @@ class LightGBMModel(BaseModel):
        result = self.model.predict(X_np)
        return np.asarray(result)

+    def get_evals_result(self) -> Optional[dict]:
+        """获取训练评估结果
+
+        Returns:
+            评估结果字典，如果模型尚未训练返回 None
+        """
+        return self.evals_result_
+
+    def get_best_iteration(self) -> Optional[int]:
+        """获取最佳迭代轮数（考虑早停）
+
+        Returns:
+            最佳迭代轮数，如果模型未训练返回 None
+        """
+        if self.model is None:
+            return None
+        return self.model.best_iteration
+
+    def get_best_score(self) -> Optional[dict]:
+        """获取最佳评分
+
+        Returns:
+            最佳评分字典，如果模型未训练返回 None
+        """
+        if self.model is None:
+            return None
+        return self.model.best_score
+
    def feature_importance(self) -> Optional[pd.Series]:
        """返回特征重要性

--- a/src/training/result_analyzer.py
+++ b/src/training/result_analyzer.py
@@ -84,7 +84,7 @@ class ResultAnalyzer:
                print("\n" + "-" * 80)
                print(f"[警告] 贡献为0的特征（共 {len(zero_importance_features)} 个）:")
                for i, feature in enumerate(zero_importance_features, 1):
-                    print(f"  {i}. {feature}")
+                    print(f"'{feature}',")

            # 统计摘要
            print("\n" + "=" * 80)
--- a/src/training/tasks/rank_task.py
+++ b/src/training/tasks/rank_task.py
@@ -189,10 +189,14 @@ class RankTask(BaseTask):

    def plot_training_metrics(self) -> None:
        """绘制训练指标曲线（NDCG）"""
-        if self.model and hasattr(self.model, "model") and self.model.model:
+        if self.model and hasattr(self.model, "get_evals_result"):
            try:
                import lightgbm as lgb

-                lgb.plot_metric(self.model.model)
+                evals_result = self.model.get_evals_result()
+                if evals_result:
+                    lgb.plot_metric(evals_result)
+                else:
+                    print("[警告] 没有训练指标数据可供绘制")
            except Exception as e:
                print(f"[警告] 无法绘制训练曲线: {e}")
--- a/src/training/tasks/regression_task.py
+++ b/src/training/tasks/regression_task.py
@@ -77,10 +77,14 @@ class RegressionTask(BaseTask):

    def plot_training_metrics(self) -> None:
        """绘制训练指标曲线"""
-        if self.model and hasattr(self.model, "model") and self.model.model:
+        if self.model and hasattr(self.model, "get_evals_result"):
            try:
                import lightgbm as lgb

-                lgb.plot_metric(self.model.model)
+                evals_result = self.model.get_evals_result()
+                if evals_result:
+                    lgb.plot_metric(evals_result)
+                else:
+                    print("[警告] 没有训练指标数据可供绘制")
            except Exception as e:
                print(f"[警告] 无法绘制训练曲线: {e}")