ProStock/src/experiment/regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 导入依赖"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:19.495013Z",
     "start_time": "2026-03-08T06:10:18.913682Z"
    }
   },
   "source": [
    "import os\n",
    "from datetime import datetime\n",
    "from typing import List\n",
    "\n",
    "import polars as pl\n",
    "\n",
    "from src.factors import FactorEngine\n",
    "from src.training import (\n",
    "    DateSplitter,\n",
    "    LightGBMModel,\n",
    "    STFilter,\n",
    "    StandardScaler,\n",
    "    StockFilterConfig,\n",
    "    StockPoolManager,\n",
    "    Trainer,\n",
    "    Winsorizer,\n",
    "    NullFiller,\n",
    ")\n",
    "from src.training.config import TrainingConfig"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 定义辅助函数"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:19.508163Z",
     "start_time": "2026-03-08T06:10:19.502562Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def create_factors_with_strings(engine: FactorEngine, factor_definitions: dict, label_factor: dict) -> List[str]:\n",
    "    print(\"=\" * 80)\n",
    "    print(\"使用字符串表达式定义因子\")\n",
    "    print(\"=\" * 80)\n",
    "\n",
    "    # 注册所有特征因子\n",
    "    print(\"\\n注册特征因子:\")\n",
    "    for name, expr in factor_definitions.items():\n",
    "        engine.add_factor(name, expr)\n",
    "        print(f\"  - {name}: {expr}\")\n",
    "\n",
    "    # 注册 label 因子\n",
    "    print(\"\\n注册 Label 因子:\")\n",
    "    for name, expr in label_factor.items():\n",
    "        engine.add_factor(name, expr)\n",
    "        print(f\"  - {name}: {expr}\")\n",
    "\n",
    "    # 从字典自动获取特征列\n",
    "    feature_cols = list(factor_definitions.keys())\n",
    "\n",
    "    print(f\"\\n特征因子数: {len(feature_cols)}\")\n",
    "    print(f\"Label: {list(label_factor.keys())[0]}\")\n",
    "    print(f\"已注册因子总数: {len(engine.list_registered())}\")\n",
    "\n",
    "    return feature_cols\n",
    "\n",
    "\n",
    "def prepare_data(\n",
    "    engine: FactorEngine,\n",
    "    feature_cols: List[str],\n",
    "    start_date: str,\n",
    "    end_date: str,\n",
    ") -> pl.DataFrame:\n",
    "    print(\"\\n\" + \"=\" * 80)\n",
    "    print(\"准备数据\")\n",
    "    print(\"=\" * 80)\n",
    "\n",
    "    # 计算因子（全市场数据）\n",
    "    print(f\"\\n计算因子: {start_date} - {end_date}\")\n",
    "    factor_names = feature_cols + [LABEL_NAME]  # 包含 label\n",
    "\n",
    "    data = engine.compute(\n",
    "        factor_names=factor_names,\n",
    "        start_date=start_date,\n",
    "        end_date=end_date,\n",
    "    )\n",
    "\n",
    "    print(f\"数据形状: {data.shape}\")\n",
    "    print(f\"数据列: {data.columns}\")\n",
    "    print(f\"\\n前5行预览:\")\n",
    "    print(data.head())\n",
    "\n",
    "    return data"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 配置参数\n",
    "\n",
    "### 3.1 因子定义"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:19.519954Z",
     "start_time": "2026-03-08T06:10:19.515047Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 特征因子定义字典：新增因子只需在此处添加一行\n",
    "LABEL_NAME = 'future_return_5'\n",
    "\n",
    "FACTOR_DEFINITIONS = {\n",
    "    # 1. 价格动量因子\n",
    "    \"ma5\": \"ts_mean(close, 5)\",\n",
    "    \"ma10\": \"ts_mean(close, 10)\",\n",
    "    \"ma20\": \"ts_mean(close, 20)\",\n",
    "    \"ma_ratio\": \"ts_mean(close, 5) / ts_mean(close, 20) - 1\",\n",
    "    # 2. 波动率因子\n",
    "    \"volatility_5\": \"ts_std(close, 5)\",\n",
    "    \"volatility_20\": \"ts_std(close, 20)\",\n",
    "    \"vol_ratio\": \"ts_std(close, 5) / (ts_std(close, 20) + 1e-8)\",\n",
    "    # 3. 收益率动量因子\n",
    "    \"return_10\": \"(close / ts_delay(close, 10)) - 1\",\n",
    "    \"return_20\": \"(close / ts_delay(close, 20)) - 1\",\n",
    "    # 4. 收益率变化因子\n",
    "    \"return_diff\": \"(close / ts_delay(close, 5)) - 1 - ((close / ts_delay(close, 10)) - 1)\",\n",
    "    # 5. 成交量因子\n",
    "    \"vol_ma5\": \"ts_mean(vol, 5)\",\n",
    "    \"vol_ma20\": \"ts_mean(vol, 20)\",\n",
    "    \"vol_ratio\": \"ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)\",\n",
    "    # 6. 市值因子（截面排名）\n",
    "    \"market_cap_rank\": \"cs_rank(total_mv)\",\n",
    "    # 7. 价格位置因子\n",
    "    \"high_low_ratio\": \"(close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\",\n",
    "    # 8. 技术指标因子（3.1 完全可实现）\n",
    "    \"turnover_rate_mean_5\": \"ts_mean(turnover_rate, 5)\",  # 5日均换手率\n",
    "    \"bbi_ratio_factor\": \"(ts_mean(close, 3) + ts_mean(close, 6) + ts_mean(close, 12) + ts_mean(close, 24)) / 4 / close\",  # BBI比率\n",
    "    # 9. ARBR 因子（3.1 完全可实现）\n",
    "    # \"AR\": \"ts_sum(high - open, 26) / ts_sum(open - low, 26) * 100\",  # AR人气指标\n",
    "    # \"BR\": \"ts_sum(max_(0, high - ts_delay(close, 1)), 26) / ts_sum(max_(0, ts_delay(close, 1) - low), 26) * 100\",  # BR意愿指标\n",
    "    # \"AR_BR\": \"AR - BR\",  # ARBR差值\n",
    "    # 10. 成交量因子（3.1 完全可实现）\n",
    "    \"volume_change_rate\": \"ts_mean(vol, 2) / ts_mean(vol, 10) - 1\",  # 成交量变化率\n",
    "    \"turnover_deviation\": \"(turnover_rate - ts_mean(turnover_rate, 3)) / ts_std(turnover_rate, 3)\",  # 换手率偏离度\n",
    "    \"vol_std_5\": \"ts_std(ts_delta(vol, 1), 5)\",  # 成交量变化标准差\n",
    "    # 11. 收益率因子（3.1 完全可实现）\n",
    "    # \"return_5\": \"close / ts_delay(close, 5) - 1\",  # 5日收益率\n",
    "    \"std_return_5\": \"ts_std((close - ts_delay(close, 1)) / ts_delay(close, 1), 5)\",  # 5日收益率标准差\n",
    "    \"std_return_90\": \"ts_std((close - ts_delay(close, 1)) / ts_delay(close, 1), 90)\",  # 90日收益率标准差\n",
    "    # 12. 截面排序因子（3.1 完全可实现）\n",
    "    \"cs_rank_volume_ratio\": \"cs_rank(volume_ratio)\",  # 量比截面排名\n",
    "    \"cs_rank_turnover_rate\": \"cs_rank(turnover_rate)\",  # 换手率截面排名\n",
    "    \"n_income_rank\": \"cs_rank(n_income)\",  # 净利润截面排名\n",
    "    # 13. 财务数据因子（来自利润表 financial_income）\n",
    "    \"operate_profit_rank\": \"cs_rank(operate_profit)\",  # 营业利润截面排名\n",
    "    \"total_profit_rank\": \"cs_rank(total_profit)\",  # 利润总额截面排名\n",
    "    \"ebit_rank\": \"cs_rank(ebit)\",  # 息税前利润截面排名\n",
    "    \"ebitda_rank\": \"cs_rank(ebitda)\",  # 息税折旧摊销前利润截面排名\n",
    "    # 14. 财务数据因子（来自资产负债表 financial_balance）\n",
    "    \"total_liab_rank\": \"cs_rank(total_liab)\",  # 总负债截面排名\n",
    "    \"money_cap_rank\": \"cs_rank(money_cap)\",  # 货币资金截面排名\n",
    "    # 15. 财务数据因子（来自现金流量表 financial_cashflow）\n",
    "    \"n_cashflow_act_rank\": \"cs_rank(n_cashflow_act)\",  # 经营活动现金流净额截面排名\n",
    "    # 16. 财务估值因子\n",
    "    \"profit_to_market_cap\": \"n_income / (total_mv + 1e-8)\",  # 净利润率（净利润/市值）\n",
    "    \"cashflow_to_market_cap\": \"n_cashflow_act / (total_mv + 1e-8)\",  # 经营现金流/市值\n",
    "    \"operate_profit_to_market_cap\": \"operate_profit / (total_mv + 1e-8)\",  # 营业利润/市值\n",
    "}\n",
    "\n",
    "# Label 因子定义（不参与训练，用于计算目标）\n",
    "LABEL_FACTOR = {\n",
    "    LABEL_NAME: \"(ts_delay(close, -5) / close) - 1\",  # 未来5日收益率\n",
    "}"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 训练参数配置"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:19.527636Z",
     "start_time": "2026-03-08T06:10:19.523669Z"
    }
   },
   "source": [
    "# 日期范围配置（正确的 train/val/test 三分法）\n",
    "# Train: 用于训练模型参数\n",
    "# Val: 用于验证/早停/调参（位于 train 之后，test 之前）\n",
    "# Test: 仅用于最终评估，完全独立于训练过程\n",
    "TRAIN_START = \"20200101\"\n",
    "TRAIN_END = \"20231231\"\n",
    "VAL_START = \"20240101\"\n",
    "VAL_END = \"20241231\"\n",
    "TEST_START = \"20250101\"\n",
    "TEST_END = \"20261231\"\n",
    "\n",
    "# 模型参数配置\n",
    "MODEL_PARAMS = {\n",
    "    \"objective\": \"regression\",\n",
    "    \"metric\": \"mae\",  # 改为 MAE，对异常值更稳健\n",
    "    # 树结构控制（防过拟合核心）\n",
    "    \"num_leaves\": 20,  # 从31降为20，降低模型复杂度\n",
    "    \"max_depth\": 4,  # 显式限制深度，防止过度拟合噪声\n",
    "    \"min_child_samples\": 50,  # 叶子最小样本数，防止学习极端样本\n",
    "    \"min_child_weight\": 0.001,\n",
    "    # 学习参数\n",
    "    \"learning_rate\": 0.01,  # 降低学习率，配合更多树\n",
    "    \"n_estimators\": 1000,  # 增加树数量，配合早停\n",
    "    # 采样策略（关键防过拟合）\n",
    "    \"subsample\": 0.8,  # 每棵树随机采样80%数据（行采样）\n",
    "    \"subsample_freq\": 5,  # 每5轮迭代进行一次 subsample\n",
    "    \"colsample_bytree\": 0.8,  # 每棵树随机选择80%特征（列采样）\n",
    "    # 正则化\n",
    "    \"reg_alpha\": 0.1,  # L1正则，增加稀疏性\n",
    "    \"reg_lambda\": 1.0,  # L2正则，平滑权重\n",
    "    # 数值稳定性\n",
    "    \"verbose\": -1,\n",
    "    \"random_state\": 42,\n",
    "}\n",
    "\n",
    "# 数据处理器配置\n",
    "PROCESSOR_CONFIGS = [\n",
    "    {\"name\": \"winsorizer\", \"params\": {\"lower\": 0.01, \"upper\": 0.99}},\n",
    "    {\"name\": \"cs_standard_scaler\", \"params\": {}},\n",
    "]\n",
    "\n",
    "# 股票池筛选配置\n",
    "STOCK_FILTER_CONFIG = {\n",
    "    \"exclude_cyb\": True,  # 排除创业板\n",
    "    \"exclude_kcb\": True,  # 排除科创板\n",
    "    \"exclude_bj\": True,  # 排除北交所\n",
    "    \"exclude_st\": True,  # 排除ST股票\n",
    "}\n",
    "\n",
    "# 输出配置（相对于本文件所在目录）\n",
    "OUTPUT_DIR = \"output\"\n",
    "SAVE_PREDICTIONS = True\n",
    "PERSIST_MODEL = False\n",
    "\n",
    "# Top N 配置：每日推荐股票数量\n",
    "TOP_N = 2  # 可调整为 10, 20 等"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 训练流程\n",
    "\n",
    "### 4.1 初始化组件"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:27.660787Z",
     "start_time": "2026-03-08T06:10:19.531220Z"
    }
   },
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"LightGBM 回归模型训练\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# 1. 创建 FactorEngine\n",
    "print(\"\\n[1] 创建 FactorEngine\")\n",
    "engine = FactorEngine()\n",
    "\n",
    "# 2. 使用字符串表达式定义因子\n",
    "print(\"\\n[2] 定义因子（字符串表达式）\")\n",
    "feature_cols = create_factors_with_strings(engine, FACTOR_DEFINITIONS, LABEL_FACTOR)\n",
    "target_col = LABEL_NAME\n",
    "\n",
    "# 3. 准备数据（使用模块级别的日期配置）\n",
    "print(\"\\n[3] 准备数据\")\n",
    "\n",
    "data = prepare_data(\n",
    "    engine=engine,\n",
    "    feature_cols=feature_cols,\n",
    "    start_date=TRAIN_START,\n",
    "    end_date=TEST_END,\n",
    ")\n",
    "\n",
    "# 4. 打印配置信息\n",
    "print(f\"\\n[配置] 训练期: {TRAIN_START} - {TRAIN_END}\")\n",
    "print(f\"[配置] 验证期: {VAL_START} - {VAL_END}\")\n",
    "print(f\"[配置] 测试期: {TEST_START} - {TEST_END}\")\n",
    "print(f\"[配置] 特征数: {len(feature_cols)}\")\n",
    "print(f\"[配置] 目标变量: {target_col}\")\n",
    "\n",
    "# 5. 创建模型\n",
    "model = LightGBMModel(params=MODEL_PARAMS)\n",
    "\n",
    "# 6. 创建数据处理器\n",
    "processors = [\n",
    "    NullFiller(strategy=\"mean\"),\n",
    "    Winsorizer(**PROCESSOR_CONFIGS[0][\"params\"]),\n",
    "    StandardScaler(exclude_cols=[\"ts_code\", \"trade_date\", target_col]),\n",
    "]\n",
    "\n",
    "# 7. 创建数据划分器（正确的 train/val/test 三分法）\n",
    "# Train: 训练模型参数 | Val: 验证/早停 | Test: 最终评估\n",
    "splitter = DateSplitter(\n",
    "    train_start=TRAIN_START,\n",
    "    train_end=TRAIN_END,\n",
    "    val_start=VAL_START,\n",
    "    val_end=VAL_END,\n",
    "    test_start=TEST_START,\n",
    "    test_end=TEST_END,\n",
    ")\n",
    "\n",
    "# 8. 创建股票池管理器\n",
    "pool_manager = StockPoolManager(\n",
    "    filter_config=StockFilterConfig(**STOCK_FILTER_CONFIG),\n",
    "    selector_config=None,  # 暂时不启用市值选择\n",
    "    data_router=engine.router,\n",
    ")\n",
    "\n",
    "# 9. 创建 ST 股票过滤器\n",
    "st_filter = STFilter(\n",
    "    data_router=engine.router,\n",
    ")\n",
    "\n",
    "# 10. 创建训练器\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    pool_manager=pool_manager,\n",
    "    processors=processors,\n",
    "    filters=[st_filter],\n",
    "    splitter=splitter,\n",
    "    target_col=target_col,\n",
    "    feature_cols=feature_cols,\n",
    "    persist_model=PERSIST_MODEL,\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "LightGBM 回归模型训练\n",
      "================================================================================\n",
      "\n",
      "[1] 创建 FactorEngine\n",
      "\n",
      "[2] 定义因子（字符串表达式）\n",
      "================================================================================\n",
      "使用字符串表达式定义因子\n",
      "================================================================================\n",
      "\n",
      "注册特征因子:\n",
      "  - ma5: ts_mean(close, 5)\n",
      "  - ma10: ts_mean(close, 10)\n",
      "  - ma20: ts_mean(close, 20)\n",
      "  - ma_ratio: ts_mean(close, 5) / ts_mean(close, 20) - 1\n",
      "  - volatility_5: ts_std(close, 5)\n",
      "  - volatility_20: ts_std(close, 20)\n",
      "  - vol_ratio: ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)\n",
      "  - return_10: (close / ts_delay(close, 10)) - 1\n",
      "  - return_20: (close / ts_delay(close, 20)) - 1\n",
      "  - return_diff: (close / ts_delay(close, 5)) - 1 - ((close / ts_delay(close, 10)) - 1)\n",
      "  - vol_ma5: ts_mean(vol, 5)\n",
      "  - vol_ma20: ts_mean(vol, 20)\n",
      "  - market_cap_rank: cs_rank(total_mv)\n",
      "  - high_low_ratio: (close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\n",
      "  - turnover_rate_mean_5: ts_mean(turnover_rate, 5)\n",
      "  - bbi_ratio_factor: (ts_mean(close, 3) + ts_mean(close, 6) + ts_mean(close, 12) + ts_mean(close, 24)) / 4 / close\n",
      "  - volume_change_rate: ts_mean(vol, 2) / ts_mean(vol, 10) - 1\n",
      "  - turnover_deviation: (turnover_rate - ts_mean(turnover_rate, 3)) / ts_std(turnover_rate, 3)\n",
      "  - vol_std_5: ts_std(ts_delta(vol, 1), 5)\n",
      "  - std_return_5: ts_std((close - ts_delay(close, 1)) / ts_delay(close, 1), 5)\n",
      "  - std_return_90: ts_std((close - ts_delay(close, 1)) / ts_delay(close, 1), 90)\n",
      "  - cs_rank_volume_ratio: cs_rank(volume_ratio)\n",
      "  - cs_rank_turnover_rate: cs_rank(turnover_rate)\n",
      "  - n_income_rank: cs_rank(n_income)\n",
      "  - operate_profit_rank: cs_rank(operate_profit)\n",
      "  - total_profit_rank: cs_rank(total_profit)\n",
      "  - ebit_rank: cs_rank(ebit)\n",
      "  - ebitda_rank: cs_rank(ebitda)\n",
      "  - total_liab_rank: cs_rank(total_liab)\n",
      "  - money_cap_rank: cs_rank(money_cap)\n",
      "  - n_cashflow_act_rank: cs_rank(n_cashflow_act)\n",
      "  - profit_to_market_cap: n_income / (total_mv + 1e-8)\n",
      "  - cashflow_to_market_cap: n_cashflow_act / (total_mv + 1e-8)\n",
      "  - operate_profit_to_market_cap: operate_profit / (total_mv + 1e-8)\n",
      "\n",
      "注册 Label 因子:\n",
      "  - future_return_5: (ts_delay(close, -5) / close) - 1\n",
      "\n",
      "特征因子数: 34\n",
      "Label: future_return_5\n",
      "已注册因子总数: 35\n",
      "\n",
      "[3] 准备数据\n",
      "\n",
      "================================================================================\n",
      "准备数据\n",
      "================================================================================\n",
      "\n",
      "计算因子: 20200101 - 20261231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PyProject\\ProStock\\src\\data\\financial_loader.py:123: UserWarning: Sortedness of columns cannot be checked when 'by' groups provided\n",
      "  merged = df_price.join_asof(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (7255513, 53)\n",
      "数据列: ['ts_code', 'trade_date', 'vol', 'low', 'high', 'volume_ratio', 'turnover_rate', 'close', 'total_mv', 'f_ann_date', 'total_profit', 'ebit', 'operate_profit', 'ebitda', 'n_income', 'money_cap', 'total_liab', 'n_cashflow_act', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'turnover_rate_mean_5', 'bbi_ratio_factor', 'volume_change_rate', 'turnover_deviation', 'vol_std_5', 'std_return_5', 'std_return_90', 'cs_rank_volume_ratio', 'cs_rank_turnover_rate', 'n_income_rank', 'operate_profit_rank', 'total_profit_rank', 'ebit_rank', 'ebitda_rank', 'total_liab_rank', 'money_cap_rank', 'n_cashflow_act_rank', 'profit_to_market_cap', 'cashflow_to_market_cap', 'operate_profit_to_market_cap', 'future_return_5']\n",
      "\n",
      "前5行预览:\n",
      "shape: (5, 53)\n",
      "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
      "│ ts_code   ┆ trade_date ┆ vol       ┆ low     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
      "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
      "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
      "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
      "│ 000001.SZ ┆ 20200102   ┆ 1.5302e6  ┆ 1806.75 ┆ … ┆ 721.52104 ┆ 2580.5045 ┆ 938.15146 ┆ -0.004746 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 1         ┆ 33        ┆ 4         ┆           │\n",
      "│ 000001.SZ ┆ 20200103   ┆ 1.1162e6  ┆ 1847.15 ┆ … ┆ 708.50174 ┆ 2533.9412 ┆ 921.22323 ┆ -0.02852  │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 4         ┆ 96        ┆ 7         ┆           │\n",
      "│ 000001.SZ ┆ 20200106   ┆ 862083.5  ┆ 1846.05 ┆ … ┆ 713.06736 ┆ 2550.2701 ┆ 927.15964 ┆ -0.004685 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 8         ┆ 5         ┆ 9         ┆           │\n",
      "│ 000001.SZ ┆ 20200107   ┆ 728607.56 ┆ 1850.42 ┆ … ┆ 709.74110 ┆ 2538.3738 ┆ 922.83470 ┆ -0.022743 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 6         ┆ 46        ┆ 6         ┆           │\n",
      "│ 000001.SZ ┆ 20200108   ┆ 847824.12 ┆ 1815.49 ┆ … ┆ 730.61584 ┆ 2613.0319 ┆ 949.97690 ┆ -0.008401 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 4         ┆ 01        ┆ 3         ┆           │\n",
      "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
      "\n",
      "[配置] 训练期: 20200101 - 20231231\n",
      "[配置] 验证期: 20240101 - 20241231\n",
      "[配置] 测试期: 20250101 - 20261231\n",
      "[配置] 特征数: 34\n",
      "[配置] 目标变量: future_return_5\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 执行训练"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:37.221864Z",
     "start_time": "2026-03-08T06:10:27.670279Z"
    }
   },
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"开始训练\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# 步骤 1: 股票池筛选\n",
    "print(\"\\n[步骤 1/6] 股票池筛选\")\n",
    "print(\"-\" * 60)\n",
    "if pool_manager:\n",
    "    print(\"  执行每日独立筛选股票池...\")\n",
    "    filtered_data = pool_manager.filter_and_select_daily(data)\n",
    "    print(f\"  筛选前数据规模: {data.shape}\")\n",
    "    print(f\"  筛选后数据规模: {filtered_data.shape}\")\n",
    "    print(f\"  筛选前股票数: {data['ts_code'].n_unique()}\")\n",
    "    print(f\"  筛选后股票数: {filtered_data['ts_code'].n_unique()}\")\n",
    "    print(f\"  删除记录数: {len(data) - len(filtered_data)}\")\n",
    "else:\n",
    "    filtered_data = data\n",
    "    print(\"  未配置股票池管理器，跳过筛选\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "开始训练\n",
      "================================================================================\n",
      "\n",
      "[步骤 1/6] 股票池筛选\n",
      "------------------------------------------------------------\n",
      "  执行每日独立筛选股票池...\n",
      "  筛选前数据规模: (7255513, 53)\n",
      "  筛选后数据规模: (4654331, 53)\n",
      "  筛选前股票数: 5694\n",
      "  筛选后股票数: 3364\n",
      "  删除记录数: 2601182\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:39.192697Z",
     "start_time": "2026-03-08T06:10:37.226314Z"
    }
   },
   "source": [
    "# 步骤 2: 划分训练/验证/测试集（正确的三分法）\n",
    "print(\"\\n[步骤 2/6] 划分训练集、验证集和测试集\")\n",
    "print(\"-\" * 60)\n",
    "if splitter:\n",
    "    # 正确的三分法：train用于训练，val用于验证/早停，test仅用于最终评估\n",
    "    train_data, val_data, test_data = splitter.split(filtered_data)\n",
    "    print(f\"  训练集数据规模: {train_data.shape}\")\n",
    "    print(f\"  验证集数据规模: {val_data.shape}\")\n",
    "    print(f\"  测试集数据规模: {test_data.shape}\")\n",
    "    print(f\"  训练集股票数: {train_data['ts_code'].n_unique()}\")\n",
    "    print(f\"  验证集股票数: {val_data['ts_code'].n_unique()}\")\n",
    "    print(f\"  测试集股票数: {test_data['ts_code'].n_unique()}\")\n",
    "    print(\n",
    "        f\"  训练集日期范围: {train_data['trade_date'].min()} - {train_data['trade_date'].max()}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"  验证集日期范围: {val_data['trade_date'].min()} - {val_data['trade_date'].max()}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"  测试集日期范围: {test_data['trade_date'].min()} - {test_data['trade_date'].max()}\"\n",
    "    )\n",
    "\n",
    "    print(\"\\n  训练集前5行预览:\")\n",
    "    print(train_data.head())\n",
    "    print(\"\\n  验证集前5行预览:\")\n",
    "    print(val_data.head())\n",
    "    print(\"\\n  测试集前5行预览:\")\n",
    "    print(test_data.head())\n",
    "else:\n",
    "    train_data = filtered_data\n",
    "    test_data = filtered_data\n",
    "    print(\"  未配置划分器，全部作为训练集\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[步骤 2/6] 划分训练集、验证集和测试集\n",
      "------------------------------------------------------------\n",
      "  训练集数据规模: (2991506, 53)\n",
      "  验证集数据规模: (769485, 53)\n",
      "  测试集数据规模: (893340, 53)\n",
      "  训练集股票数: 3297\n",
      "  验证集股票数: 3220\n",
      "  测试集股票数: 3220\n",
      "  训练集日期范围: 20200102 - 20231229\n",
      "  验证集日期范围: 20240102 - 20241231\n",
      "  测试集日期范围: 20250102 - 20260306\n",
      "\n",
      "  训练集前5行预览:\n",
      "shape: (5, 53)\n",
      "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
      "│ ts_code   ┆ trade_date ┆ vol       ┆ low     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
      "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
      "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
      "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
      "│ 000001.SZ ┆ 20200102   ┆ 1.5302e6  ┆ 1806.75 ┆ … ┆ 721.52104 ┆ 2580.5045 ┆ 938.15146 ┆ -0.004746 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 1         ┆ 33        ┆ 4         ┆           │\n",
      "│ 000002.SZ ┆ 20200102   ┆ 1012130.4 ┆ 4824.87 ┆ … ┆ 776.91820 ┆ 47.131053 ┆ 1140.2493 ┆ -0.011057 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 1         ┆           ┆ 95        ┆           │\n",
      "│ 000004.SZ ┆ 20200102   ┆ 17853.2   ┆ 90.1    ┆ … ┆ -69.58089 ┆ -52.61755 ┆ -24.82135 ┆ -0.000441 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 5         ┆ 4         ┆ 9         ┆           │\n",
      "│ 000005.SZ ┆ 20200102   ┆ 104134.12 ┆ 28.82   ┆ … ┆ 142.55925 ┆ 385.57490 ┆ 208.12520 ┆ 0.022337  │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 6         ┆ 4         ┆ 2         ┆           │\n",
      "│ 000006.SZ ┆ 20200102   ┆ 124751.76 ┆ 190.24  ┆ … ┆ 633.27582 ┆ 650.95370 ┆ 819.10495 ┆ 0.012964  │\n",
      "│           ┆            ┆           ┆         ┆   ┆           ┆ 3         ┆ 5         ┆           │\n",
      "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
      "\n",
      "  验证集前5行预览:\n",
      "shape: (5, 53)\n",
      "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
      "│ ts_code   ┆ trade_date ┆ vol       ┆ low     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
      "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
      "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
      "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
      "│ 000001.SZ ┆ 20240102   ┆ 1.1584e6  ┆ 1074.93 ┆ … ┆ 2217.6093 ┆ 6486.3743 ┆ 2744.2180 ┆ -0.003256 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 09        ┆ 45        ┆ 84        ┆           │\n",
      "│ 000002.SZ ┆ 20240102   ┆ 811106.29 ┆ 1844.3  ┆ … ┆ 1736.4093 ┆ 19.432701 ┆ 2329.7434 ┆ -0.026601 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 99        ┆           ┆ 1         ┆           │\n",
      "│ 000004.SZ ┆ 20240102   ┆ 28867.0   ┆ 65.23   ┆ … ┆ -168.7552 ┆ -184.4013 ┆ -192.7135 ┆ -0.014789 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 72        ┆ 85        ┆ 84        ┆           │\n",
      "│ 000005.SZ ┆ 20240102   ┆ 63028.0   ┆ 10.01   ┆ … ┆ -96.94997 ┆ -295.0388 ┆ -46.06373 ┆ -0.05395  │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 7         ┆ 72        ┆ 6         ┆           │\n",
      "│ 000006.SZ ┆ 20240102   ┆ 261947.19 ┆ 176.84  ┆ … ┆ -6.971845 ┆ -51.5536  ┆ -5.32671  ┆ -0.013454 │\n",
      "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
      "\n",
      "  测试集前5行预览:\n",
      "shape: (5, 53)\n",
      "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
      "│ ts_code   ┆ trade_date ┆ vol       ┆ low     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
      "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
      "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
      "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
      "│ 000001.SZ ┆ 20250102   ┆ 1.8196e6  ┆ 1455.46 ┆ … ┆ 1791.1304 ┆ 6183.5904 ┆ 2158.1117 ┆ -0.002622 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 08        ┆ 38        ┆ 45        ┆           │\n",
      "│ 000002.SZ ┆ 20250102   ┆ 1.1827e6  ┆ 1284.65 ┆ … ┆ -1933.116 ┆ -1110.658 ┆ -1729.069 ┆ -0.022509 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 105       ┆ 303       ┆ 737       ┆           │\n",
      "│ 000004.SZ ┆ 20250102   ┆ 119760.37 ┆ 54.17   ┆ … ┆ -199.1144 ┆ -126.8907 ┆ -197.3308 ┆ -0.064897 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 31        ┆ 63        ┆ 47        ┆           │\n",
      "│ 000006.SZ ┆ 20250102   ┆ 307195.1  ┆ 285.33  ┆ … ┆ -646.1294 ┆ 74.343232 ┆ -637.5489 ┆ -0.048278 │\n",
      "│           ┆            ┆           ┆         ┆   ┆ 33        ┆           ┆ 17        ┆           │\n",
      "│ 000007.SZ ┆ 20250102   ┆ 68219.01  ┆ 57.49   ┆ … ┆ 6.740918  ┆ 783.72753 ┆ 22.556002 ┆ 0.015649  │\n",
      "│           ┆            ┆           ┆         ┆   ┆           ┆ 9         ┆           ┆           │\n",
      "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:40.545732Z",
     "start_time": "2026-03-08T06:10:39.198908Z"
    }
   },
   "source": [
    "# 步骤 3: 训练集数据处理\n",
    "print(\"\\n[步骤 3/6] 训练集数据处理\")\n",
    "print(\"-\" * 60)\n",
    "fitted_processors = []\n",
    "if processors:\n",
    "    for i, processor in enumerate(processors, 1):\n",
    "        print(\n",
    "            f\"  [{i}/{len(processors)}] 应用处理器: {processor.__class__.__name__}\"\n",
    "        )\n",
    "        train_data_before = len(train_data)\n",
    "        train_data = processor.fit_transform(train_data)\n",
    "        train_data_after = len(train_data)\n",
    "        fitted_processors.append(processor)\n",
    "        print(f\"      处理前记录数: {train_data_before}\")\n",
    "        print(f\"      处理后记录数: {train_data_after}\")\n",
    "        if train_data_before != train_data_after:\n",
    "            print(f\"      删除记录数: {train_data_before - train_data_after}\")\n",
    "\n",
    "print(\"\\n  训练集处理后前5行预览:\")\n",
    "print(train_data.head())\n",
    "print(f\"\\n  训练集特征统计:\")\n",
    "print(f\"    特征数: {len(feature_cols)}\")\n",
    "print(f\"    样本数: {len(train_data)}\")\n",
    "print(f\"    缺失值统计:\")\n",
    "for col in feature_cols[:5]:  # 只显示前5个特征的缺失值\n",
    "    null_count = train_data[col].null_count()\n",
    "    if null_count > 0:\n",
    "        print(\n",
    "            f\"      {col}: {null_count} ({null_count / len(train_data) * 100:.2f}%)\"\n",
    "        )"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[步骤 3/6] 训练集数据处理\n",
      "------------------------------------------------------------\n",
      "  [1/3] 应用处理器: NullFiller\n",
      "      处理前记录数: 2991506\n",
      "      处理后记录数: 2991506\n",
      "  [2/3] 应用处理器: Winsorizer\n",
      "      处理前记录数: 2991506\n",
      "      处理后记录数: 2991506\n",
      "  [3/3] 应用处理器: StandardScaler\n",
      "      处理前记录数: 2991506\n",
      "      处理后记录数: 2991506\n",
      "\n",
      "  训练集处理后前5行预览:\n",
      "shape: (5, 53)\n",
      "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
      "│ ts_code   ┆ trade_dat ┆ vol       ┆ low      ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
      "│ ---       ┆ e         ┆ ---       ┆ ---      ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
      "│ str       ┆ ---       ┆ f64       ┆ f64      ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
      "│           ┆ str       ┆           ┆          ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
      "│           ┆           ┆           ┆          ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
      "│           ┆           ┆           ┆          ┆   ┆           ┆           ┆ f64       ┆           │\n",
      "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
      "│ 000001.SZ ┆ 20200102  ┆ 4.749919  ┆ 7.140527 ┆ … ┆ 1.223327  ┆ 2.58711   ┆ 1.420902  ┆ -0.004746 │\n",
      "│ 000002.SZ ┆ 20200102  ┆ 2.92576   ┆ 7.140527 ┆ … ┆ 1.348024  ┆ -0.147052 ┆ 1.826328  ┆ -0.011057 │\n",
      "│ 000004.SZ ┆ 20200102  ┆ -0.574944 ┆ 0.102278 ┆ … ┆ -0.557417 ┆ -0.254707 ┆ -0.510901 ┆ -0.000441 │\n",
      "│ 000005.SZ ┆ 20200102  ┆ -0.271162 ┆ -0.29962 ┆ … ┆ -0.079896 ┆ 0.218216  ┆ -0.043591 ┆ 0.022337  │\n",
      "│ 000006.SZ ┆ 20200102  ┆ -0.19857  ┆ 0.759033 ┆ … ┆ 1.02469   ┆ 0.504628  ┆ 1.182085  ┆ 0.012964  │\n",
      "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
      "\n",
      "  训练集特征统计:\n",
      "    特征数: 34\n",
      "    样本数: 2991506\n",
      "    缺失值统计:\n",
      "      ma5: 11541 (0.39%)\n",
      "      ma10: 25950 (0.87%)\n",
      "      ma20: 54850 (1.83%)\n",
      "      ma_ratio: 54850 (1.83%)\n",
      "      volatility_5: 11541 (0.39%)\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:58.361151Z",
     "start_time": "2026-03-08T06:10:40.549989Z"
    }
   },
   "source": [
    "# 步骤 4: 训练模型\n",
    "print(\"\\n[步骤 4/6] 训练模型\")\n",
    "print(\"-\" * 60)\n",
    "print(f\"  模型类型: LightGBM\")\n",
    "print(f\"  训练样本数: {len(train_data)}\")\n",
    "print(f\"  特征数: {len(feature_cols)}\")\n",
    "print(f\"  目标变量: {target_col}\")\n",
    "\n",
    "X_train = train_data.select(feature_cols)\n",
    "y_train = train_data.select(target_col).to_series()\n",
    "\n",
    "print(f\"\\n  目标变量统计:\")\n",
    "print(f\"    均值: {y_train.mean():.6f}\")\n",
    "print(f\"    标准差: {y_train.std():.6f}\")\n",
    "print(f\"    最小值: {y_train.min():.6f}\")\n",
    "print(f\"    最大值: {y_train.max():.6f}\")\n",
    "print(f\"    缺失值: {y_train.null_count()}\")\n",
    "\n",
    "print(\"\\n  开始训练...\")\n",
    "model.fit(X_train, y_train)\n",
    "print(\"  训练完成！\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[步骤 4/6] 训练模型\n",
      "------------------------------------------------------------\n",
      "  模型类型: LightGBM\n",
      "  训练样本数: 2991506\n",
      "  特征数: 34\n",
      "  目标变量: future_return_5\n",
      "\n",
      "  目标变量统计:\n",
      "    均值: 0.001610\n",
      "    标准差: 0.059623\n",
      "    最小值: -0.155098\n",
      "    最大值: 0.212842\n",
      "    缺失值: 0\n",
      "\n",
      "  开始训练...\n",
      "  训练完成！\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:58.523203Z",
     "start_time": "2026-03-08T06:10:58.364995Z"
    }
   },
   "source": [
    "# 步骤 5: 测试集数据处理\n",
    "print(\"\\n[步骤 5/6] 测试集数据处理\")\n",
    "print(\"-\" * 60)\n",
    "if processors and test_data is not train_data:\n",
    "    for i, processor in enumerate(fitted_processors, 1):\n",
    "        print(\n",
    "            f\"  [{i}/{len(fitted_processors)}] 应用处理器: {processor.__class__.__name__}\"\n",
    "        )\n",
    "        test_data_before = len(test_data)\n",
    "        test_data = processor.transform(test_data)\n",
    "        test_data_after = len(test_data)\n",
    "        print(f\"      处理前记录数: {test_data_before}\")\n",
    "        print(f\"      处理后记录数: {test_data_after}\")\n",
    "else:\n",
    "    print(\"  跳过测试集处理\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[步骤 5/6] 测试集数据处理\n",
      "------------------------------------------------------------\n",
      "  [1/3] 应用处理器: NullFiller\n",
      "      处理前记录数: 893340\n",
      "      处理后记录数: 893340\n",
      "  [2/3] 应用处理器: Winsorizer\n",
      "      处理前记录数: 893340\n",
      "      处理后记录数: 893340\n",
      "  [3/3] 应用处理器: StandardScaler\n",
      "      处理前记录数: 893340\n",
      "      处理后记录数: 893340\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:10:59.948592Z",
     "start_time": "2026-03-08T06:10:58.533879Z"
    }
   },
   "source": [
    "# 步骤 6: 生成预测\n",
    "print(\"\\n[步骤 6/6] 生成预测\")\n",
    "print(\"-\" * 60)\n",
    "X_test = test_data.select(feature_cols)\n",
    "print(f\"  测试样本数: {len(X_test)}\")\n",
    "print(\"  预测中...\")\n",
    "predictions = model.predict(X_test)\n",
    "print(f\"  预测完成！\")\n",
    "\n",
    "print(f\"\\n  预测结果统计:\")\n",
    "print(f\"    均值: {predictions.mean():.6f}\")\n",
    "print(f\"    标准差: {predictions.std():.6f}\")\n",
    "print(f\"    最小值: {predictions.min():.6f}\")\n",
    "print(f\"    最大值: {predictions.max():.6f}\")\n",
    "\n",
    "# 保存结果到 trainer\n",
    "trainer.results = test_data.with_columns([pl.Series(\"prediction\", predictions)])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[步骤 6/6] 生成预测\n",
      "------------------------------------------------------------\n",
      "  测试样本数: 893340\n",
      "  预测中...\n",
      "  预测完成！\n",
      "\n",
      "  预测结果统计:\n",
      "    均值: -0.000990\n",
      "    标准差: 0.008152\n",
      "    最小值: -0.145569\n",
      "    最大值: 0.102702\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 训练指标曲线"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:11:05.252510Z",
     "start_time": "2026-03-08T06:10:59.952123Z"
    }
   },
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"训练指标曲线\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# 重新训练以收集指标（因为之前的训练没有保存评估结果）\n",
    "print(\"\\n重新训练模型以收集训练指标...\")\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "# 准备数据（使用 val 做验证，test 不参与训练过程）\n",
    "X_train_np = X_train.to_numpy()\n",
    "y_train_np = y_train.to_numpy()\n",
    "X_val_np = val_data.select(feature_cols).to_numpy()\n",
    "y_val_np = val_data.select(target_col).to_series().to_numpy()\n",
    "\n",
    "# 创建数据集\n",
    "train_dataset = lgb.Dataset(X_train_np, label=y_train_np)\n",
    "val_dataset = lgb.Dataset(X_val_np, label=y_val_np, reference=train_dataset)\n",
    "\n",
    "# 用于存储评估结果\n",
    "evals_result = {}\n",
    "\n",
    "# 使用与原模型相同的参数重新训练\n",
    "# 正确的三分法：train用于训练，val用于验证，test不参与训练过程\n",
    "# 添加早停：如果验证指标连续100轮没有改善则停止训练\n",
    "booster_with_eval = lgb.train(\n",
    "    MODEL_PARAMS,\n",
    "    train_dataset,\n",
    "    num_boost_round=MODEL_PARAMS.get(\"n_estimators\", 100),\n",
    "    valid_sets=[train_dataset, val_dataset],\n",
    "    valid_names=[\"train\", \"val\"],\n",
    "    callbacks=[\n",
    "        lgb.record_evaluation(evals_result),\n",
    "        lgb.early_stopping(stopping_rounds=100, verbose=True),\n",
    "    ],\n",
    ")\n",
    "\n",
    "print(\"训练完成，指标已收集\")\n",
    "\n",
    "# 获取指标名称\n",
    "metric_name = list(evals_result[\"train\"].keys())[0]\n",
    "print(f\"\\n评估指标: {metric_name}\")\n",
    "\n",
    "# 提取训练和验证指标\n",
    "train_metric = evals_result[\"train\"][metric_name]\n",
    "val_metric = evals_result[\"val\"][metric_name]\n",
    "\n",
    "# 显示早停信息\n",
    "actual_rounds = len(train_metric)\n",
    "expected_rounds = MODEL_PARAMS.get(\"n_estimators\", 100)\n",
    "print(f\"\\n[早停信息]\")\n",
    "print(f\"  配置的最大轮数: {expected_rounds}\")\n",
    "print(f\"  实际训练轮数: {actual_rounds}\")\n",
    "if actual_rounds < expected_rounds:\n",
    "    print(f\"  早停状态: 已触发（连续100轮验证指标未改善）\")\n",
    "else:\n",
    "    print(f\"  早停状态: 未触发（达到最大轮数）\")\n",
    "\n",
    "print(f\"\\n最终指标:\")\n",
    "print(f\"  训练 {metric_name}: {train_metric[-1]:.6f}\")\n",
    "print(f\"  验证 {metric_name}: {val_metric[-1]:.6f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "训练指标曲线\n",
      "================================================================================\n",
      "\n",
      "重新训练模型以收集训练指标...\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[147]\ttrain's l1: 0.0424037\tval's l1: 0.0535696\n",
      "训练完成，指标已收集\n",
      "\n",
      "评估指标: l1\n",
      "\n",
      "[早停信息]\n",
      "  配置的最大轮数: 1000\n",
      "  实际训练轮数: 247\n",
      "  早停状态: 已触发（连续100轮验证指标未改善）\n",
      "\n",
      "最终指标:\n",
      "  训练 l1: 0.042166\n",
      "  验证 l1: 0.053583\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:11:05.495565Z",
     "start_time": "2026-03-08T06:11:05.256254Z"
    }
   },
   "source": [
    "# 绘制训练指标曲线\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# 绘制训练集和验证集的指标曲线（注意：val用于验证，test不参与训练）\n",
    "iterations = range(1, len(train_metric) + 1)\n",
    "ax.plot(iterations, train_metric, label=f\"Train {metric_name}\", linewidth=2, color=\"blue\")\n",
    "ax.plot(iterations, val_metric, label=f\"Validation {metric_name}\", linewidth=2, color=\"red\")\n",
    "\n",
    "ax.set_xlabel(\"Iteration\", fontsize=12)\n",
    "ax.set_ylabel(metric_name.upper(), fontsize=12)\n",
    "ax.set_title(f\"Training and Validation {metric_name.upper()} Curve\", fontsize=14, fontweight=\"bold\")\n",
    "ax.legend(fontsize=10)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# 标记最佳验证指标点（用于早停决策）\n",
    "best_iter = val_metric.index(min(val_metric))\n",
    "best_metric = min(val_metric)\n",
    "ax.axvline(x=best_iter + 1, color=\"green\", linestyle=\"--\", alpha=0.7, label=f\"Best Iteration ({best_iter + 1})\")\n",
    "ax.scatter([best_iter + 1], [best_metric], color=\"green\", s=100, zorder=5)\n",
    "ax.annotate(\n",
    "    f\"Best: {best_metric:.6f}\\nIter: {best_iter + 1}\",\n",
    "    xy=(best_iter + 1, best_metric),\n",
    "    xytext=(best_iter + 1 + len(iterations) * 0.1, best_metric),\n",
    "    fontsize=9,\n",
    "    arrowprops=dict(arrowstyle=\"->\", color=\"green\", alpha=0.7),\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\n[指标分析]\")\n",
    "print(f\"  最佳验证 {metric_name}: {best_metric:.6f}\")\n",
    "print(f\"  最佳迭代轮数: {best_iter + 1}\")\n",
    "print(f\"  早停建议: 如果验证指标连续10轮不下降，建议在第 {best_iter + 1} 轮停止训练\")\n",
    "print(f\"\\n[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ],
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[指标分析]\n",
      "  最佳验证 l1: 0.053570\n",
      "  最佳迭代轮数: 147\n",
      "  早停建议: 如果验证指标连续10轮不下降，建议在第 147 轮停止训练\n",
      "\n",
      "[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 查看结果"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:11:05.540372Z",
     "start_time": "2026-03-08T06:11:05.504804Z"
    }
   },
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"训练结果\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "results = trainer.results\n",
    "\n",
    "print(f\"\\n结果数据形状: {results.shape}\")\n",
    "print(f\"结果列: {results.columns}\")\n",
    "print(f\"\\n结果前10行预览:\")\n",
    "print(results.head(10))\n",
    "print(f\"\\n结果后5行预览:\")\n",
    "print(results.tail())\n",
    "\n",
    "print(f\"\\n每日预测样本数统计:\")\n",
    "daily_counts = results.group_by(\"trade_date\").agg(pl.len()).sort(\"trade_date\")\n",
    "print(f\"  最小: {daily_counts['len'].min()}\")\n",
    "print(f\"  最大: {daily_counts['len'].max()}\")\n",
    "print(f\"  平均: {daily_counts['len'].mean():.2f}\")\n",
    "\n",
    "# 展示某一天的前10个预测结果\n",
    "sample_date = results[\"trade_date\"][0]\n",
    "sample_data = results.filter(results[\"trade_date\"] == sample_date).head(10)\n",
    "print(f\"\\n示例日期 {sample_date} 的前10条预测:\")\n",
    "print(sample_data.select([\"ts_code\", \"trade_date\", target_col, \"prediction\"]))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "训练结果\n",
      "================================================================================\n",
      "\n",
      "结果数据形状: (893340, 54)\n",
      "结果列: ['ts_code', 'trade_date', 'vol', 'low', 'high', 'volume_ratio', 'turnover_rate', 'close', 'total_mv', 'f_ann_date', 'total_profit', 'ebit', 'operate_profit', 'ebitda', 'n_income', 'money_cap', 'total_liab', 'n_cashflow_act', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'turnover_rate_mean_5', 'bbi_ratio_factor', 'volume_change_rate', 'turnover_deviation', 'vol_std_5', 'std_return_5', 'std_return_90', 'cs_rank_volume_ratio', 'cs_rank_turnover_rate', 'n_income_rank', 'operate_profit_rank', 'total_profit_rank', 'ebit_rank', 'ebitda_rank', 'total_liab_rank', 'money_cap_rank', 'n_cashflow_act_rank', 'profit_to_market_cap', 'cashflow_to_market_cap', 'operate_profit_to_market_cap', 'future_return_5', 'prediction']\n",
      "\n",
      "结果前10行预览:\n",
      "shape: (10, 54)\n",
      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
      "│ ts_code   ┆ trade_dat ┆ vol       ┆ low       ┆ … ┆ cashflow_ ┆ operate_p ┆ future_re ┆ predicti │\n",
      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ to_market ┆ rofit_to_ ┆ turn_5    ┆ on       │\n",
      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ _cap      ┆ market_ca ┆ ---       ┆ ---      │\n",
      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ p         ┆ f64       ┆ f64      │\n",
      "│           ┆           ┆           ┆           ┆   ┆ f64       ┆ ---       ┆           ┆          │\n",
      "│           ┆           ┆           ┆           ┆   ┆           ┆ f64       ┆           ┆          │\n",
      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
      "│ 000001.SZ ┆ 20250102  ┆ 5.587779  ┆ 7.140527  ┆ … ┆ 4.53796   ┆ 3.597925  ┆ -0.002622 ┆ -0.00503 │\n",
      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 1        │\n",
      "│ 000002.SZ ┆ 20250102  ┆ 3.526191  ┆ 7.140527  ┆ … ┆ -1.396605 ┆ -3.929764 ┆ -0.022509 ┆ 0.002998 │\n",
      "│ 000004.SZ ┆ 20250102  ┆ -0.216144 ┆ -0.133365 ┆ … ┆ -0.334867 ┆ -0.856969 ┆ -0.064897 ┆ 0.000258 │\n",
      "│ 000006.SZ ┆ 20250102  ┆ 0.443786  ┆ 1.382669  ┆ … ┆ -0.117683 ┆ -1.740083 ┆ -0.048278 ┆ 0.008562 │\n",
      "│ 000007.SZ ┆ 20250102  ┆ -0.397614 ┆ -0.111591 ┆ … ┆ 0.647925  ┆ -0.415858 ┆ 0.015649  ┆ -0.00104 │\n",
      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 9        │\n",
      "│ 000008.SZ ┆ 20250102  ┆ 3.219998  ┆ -0.075651 ┆ … ┆ -0.389118 ┆ -1.046469 ┆ -0.066939 ┆ -0.00301 │\n",
      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
      "│ 000009.SZ ┆ 20250102  ┆ 0.204049  ┆ 0.03066   ┆ … ┆ 0.246551  ┆ 0.57786   ┆ -0.036045 ┆ 0.014509 │\n",
      "│ 000010.SZ ┆ 20250102  ┆ 0.584268  ┆ -0.299947 ┆ … ┆ -0.787198 ┆ -1.017736 ┆ 0.092123  ┆ -0.00703 │\n",
      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 3        │\n",
      "│ 000011.SZ ┆ 20250102  ┆ -0.488782 ┆ -0.241316 ┆ … ┆ -1.745853 ┆ -0.461702 ┆ -0.022094 ┆ 0.002963 │\n",
      "│ 000012.SZ ┆ 20250102  ┆ -0.060654 ┆ 0.51434   ┆ … ┆ 0.727138  ┆ 0.565626  ┆ -0.029188 ┆ 0.012271 │\n",
      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n",
      "\n",
      "结果后5行预览:\n",
      "shape: (5, 54)\n",
      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
      "│ ts_code   ┆ trade_dat ┆ vol       ┆ low       ┆ … ┆ cashflow_ ┆ operate_p ┆ future_re ┆ predicti │\n",
      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ to_market ┆ rofit_to_ ┆ turn_5    ┆ on       │\n",
      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ _cap      ┆ market_ca ┆ ---       ┆ ---      │\n",
      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ p         ┆ f64       ┆ f64      │\n",
      "│           ┆           ┆           ┆           ┆   ┆ f64       ┆ ---       ┆           ┆          │\n",
      "│           ┆           ┆           ┆           ┆   ┆           ┆ f64       ┆           ┆          │\n",
      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
      "│ 605588.SH ┆ 20260306  ┆ -0.594236 ┆ -0.130348 ┆ … ┆ 0.166521  ┆ -0.648847 ┆ null      ┆ -0.00224 │\n",
      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 6        │\n",
      "│ 605589.SH ┆ 20260306  ┆ 0.108199  ┆ -0.253383 ┆ … ┆ -0.32234  ┆ 0.153492  ┆ null      ┆ -0.00560 │\n",
      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
      "│ 605598.SH ┆ 20260306  ┆ -0.54733  ┆ -0.01433  ┆ … ┆ -0.191519 ┆ -0.305813 ┆ null      ┆ 0.003137 │\n",
      "│ 605599.SH ┆ 20260306  ┆ -0.409848 ┆ -0.309195 ┆ … ┆ 0.812476  ┆ 0.480653  ┆ null      ┆ -0.00217 │\n",
      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 3        │\n",
      "│ 689009.SH ┆ 20260306  ┆ -0.419186 ┆ -0.168059 ┆ … ┆ 1.301768  ┆ 0.905556  ┆ null      ┆ 0.001416 │\n",
      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n",
      "\n",
      "每日预测样本数统计:\n",
      "  最小: 1040\n",
      "  最大: 3192\n",
      "  平均: 3167.87\n",
      "\n",
      "示例日期 20250102 的前10条预测:\n",
      "shape: (10, 4)\n",
      "┌───────────┬────────────┬─────────────────┬────────────┐\n",
      "│ ts_code   ┆ trade_date ┆ future_return_5 ┆ prediction │\n",
      "│ ---       ┆ ---        ┆ ---             ┆ ---        │\n",
      "│ str       ┆ str        ┆ f64             ┆ f64        │\n",
      "╞═══════════╪════════════╪═════════════════╪════════════╡\n",
      "│ 000001.SZ ┆ 20250102   ┆ -0.002622       ┆ -0.005031  │\n",
      "│ 000002.SZ ┆ 20250102   ┆ -0.022509       ┆ 0.002998   │\n",
      "│ 000004.SZ ┆ 20250102   ┆ -0.064897       ┆ 0.000258   │\n",
      "│ 000006.SZ ┆ 20250102   ┆ -0.048278       ┆ 0.008562   │\n",
      "│ 000007.SZ ┆ 20250102   ┆ 0.015649        ┆ -0.001049  │\n",
      "│ 000008.SZ ┆ 20250102   ┆ -0.066939       ┆ -0.003014  │\n",
      "│ 000009.SZ ┆ 20250102   ┆ -0.036045       ┆ 0.014509   │\n",
      "│ 000010.SZ ┆ 20250102   ┆ 0.092123        ┆ -0.007033  │\n",
      "│ 000011.SZ ┆ 20250102   ┆ -0.022094       ┆ 0.002963   │\n",
      "│ 000012.SZ ┆ 20250102   ┆ -0.029188       ┆ 0.012271   │\n",
      "└───────────┴────────────┴─────────────────┴────────────┘\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 保存结果"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:11:05.964933Z",
     "start_time": "2026-03-08T06:11:05.551387Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"保存预测结果\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# 确保输出目录存在\n",
    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
    "\n",
    "# 生成时间戳\n",
    "start_dt = datetime.strptime(TEST_START, \"%Y%m%d\")\n",
    "end_dt = datetime.strptime(TEST_END, \"%Y%m%d\")\n",
    "date_str = f\"{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}\"\n",
    "\n",
    "# 保存每日 Top N\n",
    "print(f\"\\n[1/1] 保存每日 Top {TOP_N} 股票...\")\n",
    "topn_output_path = os.path.join(OUTPUT_DIR, f\"regression_output.csv\")\n",
    "\n",
    "# 按日期分组，取每日 top N\n",
    "topn_by_date = []\n",
    "unique_dates = results[\"trade_date\"].unique().sort()\n",
    "for date in unique_dates:\n",
    "    day_data = results.filter(results[\"trade_date\"] == date)\n",
    "    # 按 prediction 降序排序，取前 N\n",
    "    topn = day_data.sort(\"prediction\", descending=True).head(TOP_N)\n",
    "    topn_by_date.append(topn)\n",
    "\n",
    "# 合并所有日期的 top N\n",
    "topn_results = pl.concat(topn_by_date)\n",
    "\n",
    "# 格式化日期并调整列顺序：日期、分数、股票\n",
    "topn_to_save = topn_results.select(\n",
    "    [\n",
    "        pl.col(\"trade_date\").str.slice(0, 4)\n",
    "        + \"-\"\n",
    "        + pl.col(\"trade_date\").str.slice(4, 2)\n",
    "        + \"-\"\n",
    "        + pl.col(\"trade_date\").str.slice(6, 2).alias(\"date\"),\n",
    "        pl.col(\"prediction\").alias(\"score\"),\n",
    "        pl.col(\"ts_code\"),\n",
    "    ]\n",
    ")\n",
    "topn_to_save.write_csv(topn_output_path, include_header=True)\n",
    "print(f\"  保存路径: {topn_output_path}\")\n",
    "print(f\"  保存行数: {len(topn_to_save)}（{len(unique_dates)}个交易日 × 每日top{TOP_N}）\")\n",
    "print(f\"\\n  预览（前15行）:\")\n",
    "print(topn_to_save.head(15))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "保存预测结果\n",
      "================================================================================\n",
      "\n",
      "[1/1] 保存每日 Top 2 股票...\n",
      "  保存路径: output\\regression_output.csv\n",
      "  保存行数: 564（282个交易日 × 每日top2）\n",
      "\n",
      "  预览（前15行）:\n",
      "shape: (15, 3)\n",
      "┌────────────┬──────────┬───────────┐\n",
      "│ trade_date ┆ score    ┆ ts_code   │\n",
      "│ ---        ┆ ---      ┆ ---       │\n",
      "│ str        ┆ f64      ┆ str       │\n",
      "╞════════════╪══════════╪═══════════╡\n",
      "│ 2025-01-02 ┆ 0.081732 ┆ 603007.SH │\n",
      "│ 2025-01-02 ┆ 0.070111 ┆ 603559.SH │\n",
      "│ 2025-01-03 ┆ 0.088554 ┆ 603007.SH │\n",
      "│ 2025-01-03 ┆ 0.08042  ┆ 603559.SH │\n",
      "│ 2025-01-06 ┆ 0.087152 ┆ 603007.SH │\n",
      "│ …          ┆ …        ┆ …         │\n",
      "│ 2025-01-09 ┆ 0.043328 ┆ 605118.SH │\n",
      "│ 2025-01-09 ┆ 0.039754 ┆ 603848.SH │\n",
      "│ 2025-01-10 ┆ 0.037324 ┆ 002309.SZ │\n",
      "│ 2025-01-10 ┆ 0.027656 ┆ 603848.SH │\n",
      "│ 2025-01-13 ┆ 0.050825 ┆ 002309.SZ │\n",
      "└────────────┴──────────┴───────────┘\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:11:05.972569Z",
     "start_time": "2026-03-08T06:11:05.968651Z"
    }
   },
   "source": [
    "importance = model.feature_importance()\n",
    "if importance is not None:\n",
    "    print(\"\\n特征重要性:\")\n",
    "    print(importance.sort_values(ascending=False))\n",
    "\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"训练完成！\")\n",
    "print(\"=\" * 80)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "特征重要性:\n",
      "ebitda_rank                     12044.385847\n",
      "bbi_ratio_factor                 1996.570270\n",
      "std_return_90                    1900.096352\n",
      "return_10                        1707.237531\n",
      "cs_rank_turnover_rate            1700.674361\n",
      "ma_ratio                         1449.246176\n",
      "std_return_5                     1112.916802\n",
      "high_low_ratio                   1002.711145\n",
      "vol_ma20                          800.361497\n",
      "ma20                              743.156761\n",
      "cs_rank_volume_ratio              620.018650\n",
      "vol_ratio                         614.280431\n",
      "turnover_rate_mean_5              574.150844\n",
      "return_20                         530.409461\n",
      "volume_change_rate                516.283890\n",
      "return_diff                       427.252813\n",
      "market_cap_rank                   367.685596\n",
      "volatility_20                     297.221084\n",
      "vol_std_5                         245.201921\n",
      "volatility_5                      210.447650\n",
      "ma10                              175.131429\n",
      "ma5                               137.251949\n",
      "vol_ma5                           105.623339\n",
      "ebit_rank                         102.653185\n",
      "profit_to_market_cap               65.704505\n",
      "operate_profit_rank                64.448026\n",
      "operate_profit_to_market_cap       59.952241\n",
      "cashflow_to_market_cap             40.900113\n",
      "n_income_rank                      34.135645\n",
      "n_cashflow_act_rank                27.246159\n",
      "money_cap_rank                     22.246325\n",
      "total_liab_rank                    21.635597\n",
      "total_profit_rank                  17.579179\n",
      "turnover_deviation                  0.000000\n",
      "dtype: float64\n",
      "\n",
      "================================================================================\n",
      "训练完成！\n",
      "================================================================================\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 可视化分析\n",
    "\n",
    "使用训练好的模型直接绘图。\n",
    "- **特征重要性图**：辅助特征选择\n",
    "- **决策树图**：理解决策逻辑"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:11:05.983915Z",
     "start_time": "2026-03-08T06:11:05.981218Z"
    }
   },
   "source": [
    "# 导入可视化库\n",
    "import matplotlib.pyplot as plt\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "\n",
    "# 从封装的model中取出底层Booster\n",
    "booster = model.model\n",
    "print(f\"模型类型: {type(booster)}\")\n",
    "print(f\"特征数量: {len(feature_cols)}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型类型: <class 'lightgbm.basic.Booster'>\n",
      "特征数量: 34\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 绘制特征重要性（辅助特征选择）\n",
    "\n",
    "**解读**：\n",
    "- 重要性高的特征对模型贡献大\n",
    "- 重要性为0的特征可以考虑删除\n",
    "- 可以帮助理解哪些因子最有效"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-08T06:11:06.124862Z",
     "start_time": "2026-03-08T06:11:05.993617Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"绘制特征重要性...\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "lgb.plot_importance(\n",
    "    booster, \n",
    "    max_num_features=20,\n",
    "    importance_type='gain',\n",
    "    title='Feature Importance (Gain)',\n",
    "    ax=ax\n",
    ")\n",
    "ax.set_xlabel('Importance (Gain)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 打印重要性排名\n",
    "importance_gain = pd.Series(\n",
    "    booster.feature_importance(importance_type='gain'),\n",
    "    index=feature_cols\n",
    ").sort_values(ascending=False)\n",
    "\n",
    "print(\"\\n[特征重要性排名 - Gain]\")\n",
    "print(importance_gain)\n",
    "\n",
    "# 识别低重要性特征\n",
    "zero_importance = importance_gain[importance_gain == 0].index.tolist()\n",
    "if zero_importance:\n",
    "    print(f\"\\n[低重要性特征] 以下{len(zero_importance)}个特征重要性为0，可考虑删除:\")\n",
    "    for feat in zero_importance:\n",
    "        print(f\"  - {feat}\")\n",
    "else:\n",
    "    print(\"\\n所有特征都有一定重要性\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "绘制特征重要性...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ],
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[特征重要性排名 - Gain]\n",
      "ebitda_rank                     12044.385847\n",
      "bbi_ratio_factor                 1996.570270\n",
      "std_return_90                    1900.096352\n",
      "return_10                        1707.237531\n",
      "cs_rank_turnover_rate            1700.674361\n",
      "ma_ratio                         1449.246176\n",
      "std_return_5                     1112.916802\n",
      "high_low_ratio                   1002.711145\n",
      "vol_ma20                          800.361497\n",
      "ma20                              743.156761\n",
      "cs_rank_volume_ratio              620.018650\n",
      "vol_ratio                         614.280431\n",
      "turnover_rate_mean_5              574.150844\n",
      "return_20                         530.409461\n",
      "volume_change_rate                516.283890\n",
      "return_diff                       427.252813\n",
      "market_cap_rank                   367.685596\n",
      "volatility_20                     297.221084\n",
      "vol_std_5                         245.201921\n",
      "volatility_5                      210.447650\n",
      "ma10                              175.131429\n",
      "ma5                               137.251949\n",
      "vol_ma5                           105.623339\n",
      "ebit_rank                         102.653185\n",
      "profit_to_market_cap               65.704505\n",
      "operate_profit_rank                64.448026\n",
      "operate_profit_to_market_cap       59.952241\n",
      "cashflow_to_market_cap             40.900113\n",
      "n_income_rank                      34.135645\n",
      "n_cashflow_act_rank                27.246159\n",
      "money_cap_rank                     22.246325\n",
      "total_liab_rank                    21.635597\n",
      "total_profit_rank                  17.579179\n",
      "turnover_deviation                  0.000000\n",
      "dtype: float64\n",
      "\n",
      "[低重要性特征] 以下1个特征重要性为0，可考虑删除:\n",
      "  - turnover_deviation\n"
     ]
    }
   ],
   "execution_count": 18
  }
 ],
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