trading/quant_data_pipeline_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37aee204",
   "metadata": {},
   "source": [
    "# =============================================================================\n",
    "# Quantitative Trading — Data Pipeline Demo\n",
    "\n",
    "# =============================================================================\n",
    "# Topics covered:\n",
    "#   1. Price Adjustment for Corporate Actions (splits & dividends)\n",
    "#   2. Simple Returns vs Log Returns\n",
    "#   3. Multi-stock Panel & Missing Value Handling\n",
    "#   4. Outlier Detection & Treatment (Z-score, MAD, Winsorize)\n",
    "#   4b. Circuit Breakers & Price Limit Flags (涨跌停) [A-shares]\n",
    "#   5. Trading Calendar & Cross-market Alignment\n",
    "#   6. End-to-End DataPipeline Class\n",
    "\n",
    "# ============================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "32497e8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "环境准备完成!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 使用我们在 Docker 中配置的字体\n",
    "plt.rcParams['font.sans-serif'] = ['WenQuanYi Zen Hei', 'Arial Unicode MS', 'SimHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams['figure.figsize'] = (12, 6)\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "\n",
    "np.random.seed(42)\n",
    "print(\"环境准备完成!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998e880d",
   "metadata": {},
   "source": [
    "# =============================================================================\n",
    "# TOPIC 1: Price Adjustment for Corporate Actions (价格复权)\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# Raw stock prices have artificial jumps caused by stock splits and cash\n",
    "# dividends. These must be adjusted before computing returns or signals,\n",
    "# otherwise your algorithm will see fake crashes/spikes.\n",
    "#\n",
    "# Key concepts:\n",
    "#   - Unadjusted price (未复权): raw market price, has visible jumps\n",
    "#   - Forward-adjusted (前复权): history scaled to today's price level\n",
    "#   - Backward-adjusted (后复权): history kept real, recent prices scaled up\n",
    "#   - Adjustment factor (复权因子): multiplier that accounts for all events\n",
    "\n",
    "# ============================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed3f078e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------adj factors------\n",
      "2020-01-02    1.0\n",
      "2020-01-03    1.0\n",
      "2020-01-06    1.0\n",
      "2020-01-07    1.0\n",
      "2020-01-08    1.0\n",
      "             ... \n",
      "2023-10-26    1.0\n",
      "2023-10-27    1.0\n",
      "2023-10-30    1.0\n",
      "2023-10-31    1.0\n",
      "2023-11-01    1.0\n",
      "Freq: B, Length: 1000, dtype: float64\n",
      "-------end of adj factors------\n",
      "公司行为事件:\n",
      "      date     type  ratio  amount\n",
      "2021-02-25    split    2.0     NaN\n",
      "2020-03-26 dividend    NaN     0.5\n",
      "2020-09-17 dividend    NaN     0.5\n",
      "2021-03-11 dividend    NaN     0.5\n",
      "2021-09-02 dividend    NaN     0.5\n",
      "2022-02-24 dividend    NaN     0.5\n",
      "2022-08-18 dividend    NaN     0.5\n",
      "2023-02-09 dividend    NaN     0.5\n",
      "2023-08-03 dividend    NaN     0.5\n",
      "\n",
      "数据形状: (1000, 7)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "收益率验证 (拆股日前后):\n",
      "拆股日期: 2021-02-25\n",
      "未复权收益率: -0.5063\n",
      "前复权收益率: -0.0126\n",
      "真实收益率:   -0.0126\n"
     ]
    }
   ],
   "source": [
    "def generate_raw_stock_data(n_days=1000, seed=42):\n",
    "    \"\"\"生成包含拆股和分红事件的原始股票数据\"\"\"\n",
    "    np.random.seed(seed)\n",
    "    dates = pd.bdate_range(start='2020-01-02', periods=n_days)\n",
    "\n",
    "    # 生成\"真实\"价格序列 (几何布朗运动)\n",
    "    mu, sigma = 0.12, 0.25\n",
    "    dt = 1 / 252\n",
    "    log_ret = (mu - 0.5 * sigma**2) * dt + sigma * np.sqrt(dt) * np.random.randn(n_days)\n",
    "    true_prices = 100 * np.exp(np.cumsum(log_ret))\n",
    "\n",
    "    # 模拟成交量\n",
    "    volume = np.random.lognormal(mean=15, sigma=0.5, size=n_days).astype(int)\n",
    "\n",
    "    # 构建DataFrame\n",
    "    df = pd.DataFrame({\n",
    "        'date': dates,\n",
    "        'close': true_prices,\n",
    "        'volume': volume\n",
    "    }).set_index('date')\n",
    "\n",
    "    # 添加开高低价\n",
    "    daily_range = df['close'] * np.abs(np.random.randn(n_days)) * 0.02\n",
    "    df['open'] = df['close'] + np.random.randn(n_days) * daily_range * 0.5\n",
    "    df['high'] = np.maximum(df['close'], df['open']) + np.abs(np.random.randn(n_days)) * daily_range * 0.3\n",
    "    df['low'] = np.minimum(df['close'], df['open']) - np.abs(np.random.randn(n_days)) * daily_range * 0.3\n",
    "\n",
    "    # 定义公司行为事件\n",
    "    events = []\n",
    "\n",
    "    # 1:2拆股, 在第300个交易日\n",
    "    split_idx = 300\n",
    "    split_date = dates[split_idx]\n",
    "    events.append({'date': split_date, 'type': 'split', 'ratio': 2})\n",
    "\n",
    "    # 现金分红, 每季度一次\n",
    "    for q in [60, 185, 310, 435, 560, 685, 810, 935]:\n",
    "        if q < n_days:\n",
    "            events.append({'date': dates[q], 'type': 'dividend', 'amount': 0.5})\n",
    "\n",
    "    # 应用公司行为到\"未复权\"价格\n",
    "    unadj_close = df['close'].copy()\n",
    "    adj_factors = pd.Series(1.0, index=dates)\n",
    "\n",
    "    print('-------adj factors------')\n",
    "    print(adj_factors)\n",
    "    print('-------end of adj factors------')\n",
    "\n",
    "\n",
    "    for event in sorted(events, key=lambda x: x['date']):\n",
    "        idx = dates.get_loc(event['date'])\n",
    "        if event['type'] == 'split':\n",
    "            # 拆股\n",
    "            unadj_close.iloc[idx:] = unadj_close.iloc[idx:] / event['ratio']\n",
    "            adj_factors.iloc[idx:] = adj_factors.iloc[idx:] * event['ratio']\n",
    "        elif event['type'] == 'dividend':\n",
    "            # 分红\n",
    "            price_before = unadj_close.iloc[idx - 1] if idx > 0 else unadj_close.iloc[idx]\n",
    "            div_ratio = 1 - event['amount'] / price_before\n",
    "            unadj_close.iloc[idx:] = unadj_close.iloc[idx:] * div_ratio\n",
    "            adj_factors.iloc[:idx] = adj_factors.iloc[:idx] * div_ratio\n",
    "\n",
    "    df['unadj_close'] = unadj_close\n",
    "    df['adj_factor'] = adj_factors\n",
    "\n",
    "    events_df = pd.DataFrame(events)\n",
    "\n",
    "    return df, events_df\n",
    "\n",
    "def forward_adjust(unadj_prices, adj_factor):\n",
    "    \"\"\"前复权：以最新价格为基准，向前调整历史价格\"\"\"\n",
    "    normalized_factor = adj_factor / adj_factor.iloc[-1]\n",
    "    return unadj_prices * normalized_factor\n",
    "\n",
    "def backward_adjust(unadj_prices, adj_factor):\n",
    "    \"\"\"后复权：以最早价格为基准，向后调整\"\"\"\n",
    "    normalized_factor = adj_factor / adj_factor.iloc[0]\n",
    "    return unadj_prices * normalized_factor\n",
    "\n",
    "# 生成数据\n",
    "stock_data, events = generate_raw_stock_data()\n",
    "\n",
    "print(\"公司行为事件:\")\n",
    "print(events.to_string(index=False))\n",
    "print(f\"\\n数据形状: {stock_data.shape}\")\n",
    "\n",
    "# 计算复权价格\n",
    "stock_data['fwd_adj_close'] = forward_adjust(stock_data['unadj_close'], stock_data['adj_factor'])\n",
    "stock_data['bwd_adj_close'] = backward_adjust(stock_data['unadj_close'], stock_data['adj_factor'])\n",
    "\n",
    "# 可视化对比\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "\n",
    "# 1. 未复权价格\n",
    "ax = axes[0, 0]\n",
    "ax.plot(stock_data['unadj_close'], color='blue', linewidth=1)\n",
    "ax.set_title('未复权价格 (可见拆股/分红跳跃)', fontsize=13)\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "for _, event in events.iterrows():\n",
    "    ax.axvline(x=event['date'], color='red', linestyle='--', alpha=0.5)\n",
    "\n",
    "# 2. 前复权价格\n",
    "ax = axes[0, 1]\n",
    "ax.plot(stock_data['fwd_adj_close'], color='green', linewidth=1)\n",
    "ax.set_title('前复权价格 (当前价格真实)', fontsize=13)\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "# 3. 后复权价格\n",
    "ax = axes[1, 0]\n",
    "ax.plot(stock_data['bwd_adj_close'], color='orange', linewidth=1)\n",
    "ax.set_title('后复权价格 (历史价格真实)', fontsize=13)\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "# 4. \"真实\"价格对比\n",
    "ax = axes[1, 1]\n",
    "ax.plot(stock_data['close'], label='真实连续价格', color='black', linewidth=1.5)\n",
    "ax.plot(stock_data['fwd_adj_close'], label='前复权价格', color='green', linewidth=1, alpha=0.7)\n",
    "ax.set_title('真实价格 vs 前复权价格', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "plt.suptitle('复权价格对比', fontsize=15, y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 验证收益率\n",
    "print(\"\\n收益率验证 (拆股日前后):\")\n",
    "split_date = events[events['type'] == 'split']['date'].iloc[0]\n",
    "split_idx = stock_data.index.get_loc(split_date)\n",
    "\n",
    "print(f\"拆股日期: {split_date.strftime('%Y-%m-%d')}\")\n",
    "print(f\"未复权收益率: {stock_data['unadj_close'].iloc[split_idx] / stock_data['unadj_close'].iloc[split_idx-1] - 1:.4f}\")\n",
    "print(f\"前复权收益率: {stock_data['fwd_adj_close'].iloc[split_idx] / stock_data['fwd_adj_close'].iloc[split_idx-1] - 1:.4f}\")\n",
    "print(f\"真实收益率:   {stock_data['close'].iloc[split_idx] / stock_data['close'].iloc[split_idx-1] - 1:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "632a08a5",
   "metadata": {},
   "source": [
    "# =============================================================================\n",
    "# TOPIC 2: Simple Returns vs Log Returns (简单收益率 vs 对数收益率)\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# Two ways to measure how much a price changed:\n",
    "#   - Simple return:  r = (P_t / P_{t-1}) - 1\n",
    "#   - Log return:     r = ln(P_t / P_{t-1})\n",
    "#\n",
    "# Why log returns are preferred in quant research:\n",
    "#   - Time-additive: sum daily log returns to get total log return\n",
    "#   - Better statistical properties (closer to normal distribution)\n",
    "#   - Numerically stable for long compounding periods\n",
    "#\n",
    "# At daily frequency the difference is tiny (~1bp), but it grows at\n",
    "# monthly/annual frequency — never mix the two in the same calculation.\n",
    "\n",
    "# =============================================================================\n",
    "\n",
    "# 使用前复权价格计算收益率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50ad6628",
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = stock_data['fwd_adj_close']\n",
    "\n",
    "# 简单收益率\n",
    "simple_returns = prices.pct_change().dropna()\n",
    "\n",
    "# 对数收益率\n",
    "log_returns = np.log(prices / prices.shift(1)).dropna()\n",
    "\n",
    "# 对比\n",
    "comparison = pd.DataFrame({\n",
    "    '简单收益率': simple_returns,\n",
    "    '对数收益率': log_returns,\n",
    "    '差异': simple_returns - log_returns,\n",
    "    '差异(bp)': (simple_returns - log_returns) * 10000  # 基点\n",
    "})\n",
    "\n",
    "print(\"简单收益率 vs 对数收益率统计:\")\n",
    "print(f\"平均绝对差异:  {comparison['差异'].abs().mean():.6f} ({comparison['差异(bp)'].abs().mean():.6f})\")\n",
    "print(f\"最大差异:      {comparison['差异'].abs().max():.6f} ({comparison['差异(bp)'].abs().max():.6f})\")\n",
    "print(f\"\\n日频数据下两者差异很小，但月频或年频时差异会显著增大。\")\n",
    "\n",
    "# 验证时间可加性\n",
    "print(\"\\n=== 时间可加性验证 ===\")\n",
    "# 对数收益率：时间上直接求和\n",
    "total_log_return = log_returns.sum()\n",
    "actual_total = np.log(prices.iloc[-1] / prices.iloc[0])\n",
    "print(f\"对数收益率求和: {total_log_return:.6f}\")\n",
    "print(f\"实际总对数收益: {actual_total:.6f}\")\n",
    "print(f\"差异: {abs(total_log_return - actual_total):.10f} (几乎为零)\")\n",
    "\n",
    "print(\"\\n简单收益率需要连乘:\")\n",
    "total_simple_product = (1 + simple_returns).prod() - 1\n",
    "actual_simple = prices.iloc[-1] / prices.iloc[0] - 1\n",
    "print(f\"简单收益率连乘: {total_simple_product:.6f}\")\n",
    "print(f\"实际总简单收益: {actual_simple:.6f}\")\n",
    "print(f\"差异: {abs(total_simple_product - actual_simple):.10f}\")\n",
    "\n",
    "# 可视化\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "ax = axes[0]\n",
    "ax.scatter(simple_returns, log_returns, alpha=0.3, s=5)\n",
    "ax.plot([-0.1, 0.1], [-0.1, 0.1], 'r--', linewidth=1, label='y=x')\n",
    "ax.set_xlabel('简单收益率')\n",
    "ax.set_ylabel('对数收益率')\n",
    "ax.set_title('简单收益率 vs 对数收益率 (日频) ', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "ax = axes[1]\n",
    "# 月频对比\n",
    "monthly_simple = prices.resample('ME').last().pct_change().dropna()\n",
    "monthly_log = np.log(prices.resample('ME').last() / prices.resample('ME').last().shift(1)).dropna()\n",
    "ax.scatter(monthly_simple, monthly_log, alpha=0.5, s=20)\n",
    "ax.plot([-0.2, 0.2], [-0.2, 0.2], 'r--', linewidth=1, label='y=x')\n",
    "ax.set_xlabel('简单收益率')\n",
    "ax.set_ylabel('对数收益率')\n",
    "ax.set_title('简单收益率 vs 对数收益率 (月频, 差异更明显) ', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a171952c",
   "metadata": {},
   "source": [
    "# =============================================================================\n",
    "# TOPIC 3: Multi-stock Panel & Missing Value Handling (多标的面板与缺失值处理)\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# Real market data panels have two main sources of missing values:\n",
    "#   - Suspensions (停牌): a stock halts trading for days/weeks\n",
    "#   - Data feed gaps: vendor errors, holidays, late reporting\n",
    "#\n",
    "# Strategies (choice depends on context):\n",
    "#   - ffill (forward fill):    use last known price — standard for suspensions\n",
    "#   - Linear interpolation:    smooth gaps — OK for short random gaps\n",
    "#   - ffill with limit:        ffill but cap at N days — conservative choice\n",
    "#   - Drop:                    remove rows/columns — loses cross-sectional data\n",
    "#\n",
    "# Rule of thumb: if missing% > 10%, consider excluding the stock entirely.\n",
    "\n",
    "# ============================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba3c95d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_multi_stock_panel(n_stocks=5, n_days=500, seed=42):\n",
    "    \"\"\"生成多标的日线数据面板，包含停牌和缺失值\"\"\"\n",
    "    np.random.seed(seed)\n",
    "    dates = pd.bdate_range(start='2022-01-03', periods=n_days)\n",
    "    \n",
    "    stock_names = [f'Stock_{chr(65+i)}' for i in range(n_stocks)]  # Stock_A, Stock_B, ...\n",
    "    \n",
    "    all_data = {}\n",
    "    for i, name in enumerate(stock_names):\n",
    "        mu = np.random.uniform(0.05, 0.20)\n",
    "        sigma = np.random.uniform(0.15, 0.35)\n",
    "        S0 = np.random.uniform(20, 200)\n",
    "        \n",
    "        dt = 1 / 252\n",
    "        log_ret = (mu - 0.5 * sigma**2) * dt + sigma * np.sqrt(dt) * np.random.randn(n_days)\n",
    "        prices = S0 * np.exp(np.cumsum(log_ret))\n",
    "        volume = np.random.lognormal(mean=14, sigma=0.8, size=n_days).astype(int)\n",
    "        \n",
    "        # 模拟停牌 (随机选择一些连续日期设为NaN)\n",
    "        suspension_starts = np.random.choice(range(50, n_days - 30), size=2 + i % 3, replace=False)\n",
    "        for start in suspension_starts:\n",
    "            duration = np.random.randint(1, 10)\n",
    "            end = min(start + duration, n_days)\n",
    "            prices[start:end] = np.nan\n",
    "            volume[start:end] = 0\n",
    "            \n",
    "        # 模拟随机缺失\n",
    "        random_missing = np.random.choice(range(n_days), size=5, replace=False)\n",
    "        prices[random_missing] = np.nan\n",
    "        \n",
    "        all_data[name] = pd.DataFrame({\n",
    "            'close': prices,\n",
    "            'volume': volume\n",
    "        }, index=dates)\n",
    "        \n",
    "    # 构建面板\n",
    "    price_panel = pd.DataFrame({name: data['close'] for name, data in all_data.items()})\n",
    "    volume_panel = pd.DataFrame({name: data['volume'] for name, data in all_data.items()})\n",
    "    \n",
    "    return price_panel, volume_panel\n",
    "\n",
    "# 生成多标的面板\n",
    "price_panel, volume_panel = generate_multi_stock_panel(n_stocks=5, n_days=500)\n",
    "\n",
    "# 缺失值统计\n",
    "missing_stats = pd.DataFrame({\n",
    "    '总天数': len(price_panel),\n",
    "    '缺失天数': price_panel.isna().sum(),\n",
    "    '缺失比例': price_panel.isna().mean(),\n",
    "    '最长连续缺失': [price_panel[col].isna().astype(int).groupby(\n",
    "        price_panel[col].notna().astype(int).cumsum()).sum().max()\n",
    "        for col in price_panel.columns]\n",
    "})\n",
    "\n",
    "print(\"缺失值统计:\")\n",
    "print(missing_stats)\n",
    "print(f\"\\n面板形状: {price_panel.shape}\")\n",
    "\n",
    "# 缺失值处理方法对比\n",
    "def handle_missing_values(prices, method='ffill'):\n",
    "    \"\"\"处理缺失值的不同方法\"\"\"\n",
    "    if method == 'ffill':\n",
    "        # ffill = forward fill (前向填充): 用前一个有效值填补缺失值\n",
    "        # 最常用于停牌场景: 假设停牌期间价格保持不变\n",
    "        return prices.ffill()\n",
    "    elif method == 'linear':\n",
    "        # 线性插值\n",
    "        return prices.interpolate(method='linear')\n",
    "    elif method == 'drop':\n",
    "        # 直接删除\n",
    "        return prices.dropna()\n",
    "    elif method == 'ffill_limit':\n",
    "        # 有限制的向前填充 (最多填充5天)\n",
    "        return prices.ffill(limit=5)\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown method: {method}\")\n",
    "\n",
    "# 对Stock_A展示不同方法的效果\n",
    "stock_a = price_panel['Stock_A'].copy()\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "\n",
    "methods = {\n",
    "    '原始数据 (含缺失值) ': stock_a,\n",
    "    '向前填充 (ffill)': handle_missing_values(stock_a, 'ffill'),\n",
    "    '线性插值 (interpolate)': handle_missing_values(stock_a, 'linear'),\n",
    "    '有限填充 (ffill, limit=5)': handle_missing_values(stock_a, 'ffill_limit')\n",
    "}\n",
    "\n",
    "for ax, (title, data) in zip(axes.flat, methods.items()):\n",
    "    ax.plot(data, linewidth=1)\n",
    "    # 标记原始缺失位置\n",
    "    missing_mask = stock_a.isna()\n",
    "    if not missing_mask.all() and title != '原始数据 (含缺失值) ':\n",
    "        filled_values = data[missing_mask].dropna()\n",
    "        if len(filled_values) > 0:\n",
    "            ax.scatter(filled_values.index, filled_values.values,\n",
    "                       color='red', s=15, zorder=5, label='填充值')\n",
    "            ax.legend(fontsize=9)\n",
    "    ax.set_title(title, fontsize=12)\n",
    "    ax.grid(alpha=0.3)\n",
    "\n",
    "plt.suptitle('缺失值处理方法对比 (Stock_A)', fontsize=14, y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n实践建议:\")\n",
    "print(\"- 停牌: 使用ffill (假设停牌期间价格不变) \")\n",
    "print(\"- 短期缺失(<3天): 可用线性插值\")\n",
    "print(\"- 长期缺失(>10天): 考虑从分析中排除该标的\")\n",
    "print(\"- 计算收益率前: 先填充缺失值, 再计算\")\n",
    "\n",
    "\n",
    "# 构建干净的多标的数据面板\n",
    "# Steps: ffill → drop stocks/rows still NaN → compute returns\n",
    "#\n",
    "# ⚠️  Look-Ahead Bias Warning: DO NOT use bfill() here.\n",
    "#     bfill() fills early NaN rows by looking at future prices, which means\n",
    "#     your \"day 1\" price would be sourced from a future observation.\n",
    "#     This is data leakage — it would inflate backtest performance.\n",
    "#     Safe rule: only ffill (past → present), then drop what remains NaN.\n",
    "\n",
    "clean_prices = price_panel.ffill()          # only look backward\n",
    "clean_prices = clean_prices.dropna(how='any')  # drop rows where any stock still NaN\n",
    "\n",
    "print(f\"清洗前缺失值: {price_panel.isna().sum().sum()}\")\n",
    "print(f\"清洗后缺失值: {clean_prices.isna().sum().sum()}\")\n",
    "print(f\"(dropped {len(price_panel) - len(clean_prices)} leading rows to avoid look-ahead bias)\")\n",
    "\n",
    "# 计算收益率面板\n",
    "returns_panel = clean_prices.pct_change().dropna()\n",
    "\n",
    "# 可视化归一化价格\n",
    "fig, ax = plt.subplots(figsize=(14, 6))\n",
    "normalized = clean_prices / clean_prices.iloc[0] * 100\n",
    "for col in normalized.columns:\n",
    "    ax.plot(normalized[col], label=col, linewidth=1.2)\n",
    "\n",
    "ax.set_title('多标的归一化价格走势 (基期=100) ', fontsize=14)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 相关性矩阵\n",
    "corr_matrix = returns_panel.corr()\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "im = ax.imshow(corr_matrix.values, cmap='RdBu_r', vmin=-1, vmax=1)\n",
    "ax.set_xticks(range(len(corr_matrix)))\n",
    "ax.set_xticklabels(corr_matrix.columns, rotation=45)\n",
    "ax.set_yticks(range(len(corr_matrix)))\n",
    "ax.set_yticklabels(corr_matrix.columns)\n",
    "for i in range(len(corr_matrix)):\n",
    "    for j in range(len(corr_matrix)):\n",
    "        ax.text(j, i, f'{corr_matrix.iloc[i,j]:.2f}', ha='center', va='center', fontsize=11)\n",
    "plt.colorbar(im, ax=ax)\n",
    "ax.set_title('收益率相关性矩阵', fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "689e9f4b",
   "metadata": {},
   "source": [
    "# =============================================================================\n",
    "# TOPIC 4: Outlier Detection & Treatment (异常值检测与处理)\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# Financial return series contain extreme values from real events (crashes,\n",
    "# halts, data errors). How you handle them affects every downstream signal.\n",
    "#\n",
    "# Detection methods:\n",
    "#   - Z-score:   |z| = |(x - mean) / std| > 3  — simple, but mean/std are\n",
    "#                themselves pulled by outliers (not robust)\n",
    "#   - MAD:       uses median instead of mean — robust to extreme values,\n",
    "#                preferred for financial data\n",
    "#\n",
    "# Treatment methods:\n",
    "#   - Remove:    delete the outlier row — reduces sample size\n",
    "#   - Winsorize: clip to [p1, p99] boundary — keeps all data, limits impact\n",
    "#                standard practice before factor construction\n",
    "#\n",
    "# Q-Q plot: visualizes how far returns deviate from a normal distribution.\n",
    "# Fat tails (leptokurtosis) are a universal property of financial returns.\n",
    "\n",
    "# ============================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fd49375",
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_outliers_zscore(series, threshold=3.0):\n",
    "    \"\"\"Z-score (标准分数) 方法检测异常值\n",
    "    \n",
    "    Z = (x - mean) / std\n",
    "    即：这个值偏离平均值多少个标准差？\n",
    "    |Z| > threshold (默认3) 视为异常值\n",
    "    \n",
    "    通俗理解：如果大家平均考70分，标准差10分，你考了100分，\n",
    "    Z = (100-70)/10 = 3，说明你偏离平均值3个标准差，属于异常值。\n",
    "    \n",
    "    缺点：均值和标准差本身受异常值影响\n",
    "    \"\"\"\n",
    "    z_scores = (series - series.mean()) / series.std()\n",
    "    outlier_mask = z_scores.abs() > threshold\n",
    "    return outlier_mask, z_scores\n",
    "\n",
    "\n",
    "def detect_outliers_mad(series, threshold=3.0):\n",
    "    \"\"\"MAD (中位数绝对偏差, Median Absolute Deviation) 方法检测异常值\n",
    "    \n",
    "    MAD = median(|x - median(x)|)  即所有值与中位数的距离的中位数\n",
    "    Modified Z = 0.6745 * (x - median) / MAD\n",
    "    \n",
    "    优点：用中位数替代均值，比Z-score更稳健，不受极端值影响\n",
    "    通俗理解：Z-score用\"平均值\"做参考，容易被极端值带偏；\n",
    "    MAD用\"中位数\"做参考，即使有极端值也不受影响。\n",
    "    \"\"\"\n",
    "    median = series.median()\n",
    "    mad = (series - median).abs().median()\n",
    "    if mad == 0:\n",
    "        mad = 1e-10 # 避免除零\n",
    "    modified_z = 0.6745 * (series - median) / mad\n",
    "    outlier_mask = modified_z.abs() > threshold\n",
    "    return outlier_mask, modified_z\n",
    "\n",
    "\n",
    "def winsorize(series, lower_pct=0.01, upper_pct=0.99):\n",
    "    \"\"\"Winsorize (缩尾处理): 将超出分位数的值截断到分位数边界\n",
    "    \n",
    "    不同于删除异常值，Winsorize保留了所有数据点，只是限制了极端值。\n",
    "    比如设 lower_pct=0.01, upper_pct=0.99:\n",
    "    - 低于1%分位数的值 -> 拉回到1%分位数\n",
    "    - 高于99%分位数的值 -> 拉回到99%分位数\n",
    "    \"\"\"\n",
    "    lower = series.quantile(lower_pct)\n",
    "    upper = series.quantile(upper_pct)\n",
    "    return series.clip(lower=lower, upper=upper)\n",
    "\n",
    "\n",
    "# 使用Stock_A的收益率进行演示\n",
    "ret_a = returns_panel['Stock_A'].copy()\n",
    "\n",
    "# 人为注入一些异常值以演示效果\n",
    "np.random.seed(123)\n",
    "outlier_indices = np.random.choice(ret_a.index, size=5, replace=False)\n",
    "ret_a_with_outliers = ret_a.copy()\n",
    "for idx in outlier_indices:\n",
    "    ret_a_with_outliers[idx] = np.random.choice([-1, 1]) * np.random.uniform(0.15, 0.30)\n",
    "\n",
    "# 检测异常值\n",
    "zscore_mask, z_scores = detect_outliers_zscore(ret_a_with_outliers, threshold=3.0)\n",
    "mad_mask, mad_scores = detect_outliers_mad(ret_a_with_outliers, threshold=3.0)\n",
    "\n",
    "print(\"异常值检测结果:\")\n",
    "print(f\"Z-score方法 (|Z|>3): 检测到 {zscore_mask.sum()} 个异常值\")\n",
    "print(f\"MAD方法 (|Modified Z|>3): 检测到 {mad_mask.sum()} 个异常值\")\n",
    "\n",
    "# Winsorize处理\n",
    "ret_winsorized = winsorize(ret_a_with_outliers, lower_pct=0.01, upper_pct=0.99)\n",
    "\n",
    "print(f\"\\nWinsorize前 - 范围: [{ret_a_with_outliers.min():.4f}, {ret_a_with_outliers.max():.4f}]\")\n",
    "print(f\"Winsorize后 - 范围: [{ret_winsorized.min():.4f}, {ret_winsorized.max():.4f}]\")\n",
    "\n",
    "\n",
    "# 可视化异常值检测\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "\n",
    "# 1. 收益率时序与异常值标记\n",
    "ax = axes[0, 0]\n",
    "ax.plot(ret_a_with_outliers, color='blue', linewidth=0.8, alpha=0.7)\n",
    "outliers = ret_a_with_outliers[zscore_mask]\n",
    "ax.scatter(outliers.index, outliers.values, color='red', s=40, zorder=5, label=f'异常值 ({len(outliers)}个)')\n",
    "ax.set_title('Z-score异常值检测 (|Z|>3)', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "# 2. MAD方法\n",
    "ax = axes[0, 1]\n",
    "ax.plot(ret_a_with_outliers, color='blue', linewidth=0.8, alpha=0.7)\n",
    "outliers_mad = ret_a_with_outliers[mad_mask]\n",
    "ax.scatter(outliers_mad.index, outliers_mad.values, color='orange', s=40, zorder=5, label=f'异常值 ({len(outliers_mad)}个)')\n",
    "ax.set_title('MAD异常值检测 (|Modified Z|>3)', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "# 3. Winsorize前后分布对比\n",
    "ax = axes[1, 0]\n",
    "ax.hist(ret_a_with_outliers, bins=60, alpha=0.5, label='原始', color='blue', density=True)\n",
    "ax.hist(ret_winsorized, bins=60, alpha=0.5, label='Winsorize后', color='green', density=True)\n",
    "ax.set_title('Winsorize前后收益率分布', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "# 4. Q-Q图\n",
    "ax = axes[1, 1]\n",
    "sorted_data = np.sort(ret_a_with_outliers.dropna())\n",
    "n = len(sorted_data)\n",
    "theoretical = stats.norm.ppf(np.arange(1, n + 1) / (n + 1))\n",
    "ax.scatter(theoretical, sorted_data, alpha=0.5, s=10)\n",
    "ax.plot([theoretical.min(), theoretical.max()], \n",
    "        [theoretical.min() * ret_a_with_outliers.std() + ret_a_with_outliers.mean(),\n",
    "         theoretical.max() * ret_a_with_outliers.std() + ret_a_with_outliers.mean()],\n",
    "         'r--', linewidth=1, label='正态分布参考线')\n",
    "ax.set_xlabel('理论分位数')\n",
    "ax.set_ylabel('样本分位数')\n",
    "ax.set_title('Q-Q图 (检验正态性) ', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n方法选择建议:\")\n",
    "print(\"- Z-score: 简单快速，但受极端值影响 (不够稳健) \")\n",
    "print(\"- MAD: 更稳健，推荐用于金融数据\")\n",
    "print(\"- Winsorize: 保留数据量，适合构建因子时使用\")\n",
    "print(\"- 实际中常用: Winsorize(1%, 99%) + MAD检测\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71339e7",
   "metadata": {},
   "source": [
    "# =============================================================================\n",
    "# TOPIC 4b: Circuit Breakers & Price Limit Flags (涨跌停标记)\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# In Chinese A-shares, stocks have a ±10% daily price limit (±5% for ST stocks).\n",
    "# When a stock hits the limit, it is NOT a data error — it is real market data.\n",
    "# However, it signals a critical data quality issue for downstream use:\n",
    "#\n",
    "#   - The price IS the true closing price\n",
    "#   - But the stock may have been UNTRADEABLE for most of the day (zero liquidity)\n",
    "#   - A return of exactly +10% or -10% means you likely CANNOT execute at that price\n",
    "#\n",
    "# What to do in data prep (we flag, not remove):\n",
    "#   - Add a boolean column: `is_limit_up`, `is_limit_down`\n",
    "#   - Downstream strategy code can then decide to skip, weight-down, or\n",
    "#     exclude limit-hit days from signal calculations\n",
    "#\n",
    "# ⚠️  Survivorship Bias Note (out of scope for data prep, but important to know):\n",
    "#     This demo only generates stocks that \"survive\" the full period. In reality,\n",
    "#     stocks get delisted (bankruptcy, M&A, regulatory removal). If you only\n",
    "#     include currently-alive stocks in your historical dataset, you are\n",
    "#     implicitly selecting winners — your backtest returns will be inflated.\n",
    "#     Fix: source historical data that includes ALL stocks, including delisted ones.\n",
    "#     This is a DATA SOURCING problem, handled before data ever enters this pipeline.\n",
    "\n",
    "# ============================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f31d3fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def flag_price_limits(prices, limit_pct=0.10):\n",
    "    \"\"\"Flag days where a stock hit the daily price limit (涨跌停).\n",
    "\n",
    "    Returns a DataFrame of the same shape with values:\n",
    "        +1  = limit up   (涨停)\n",
    "        -1  = limit down (跌停)\n",
    "         0  = normal day\n",
    "\n",
    "    A small tolerance (0.5%) is used because real closing prices may be\n",
    "    rounded and not land exactly on the theoretical limit price.\n",
    "    \"\"\"\n",
    "    returns = prices.pct_change()\n",
    "    tolerance = 0.005  # 0.5% buffer for rounding\n",
    "\n",
    "    limit_up   = (returns >= limit_pct - tolerance).astype(int)\n",
    "    limit_down = (returns <= -limit_pct + tolerance).astype(int) * -1\n",
    "    flags = limit_up + limit_down\n",
    "    return flags\n",
    "\n",
    "\n",
    "# Inject realistic limit-hit events into the panel for demonstration\n",
    "np.random.seed(99)\n",
    "demo_prices = clean_prices.copy()\n",
    "for col in demo_prices.columns:\n",
    "    # Randomly inject 3 limit-up and 3 limit-down days per stock\n",
    "    up_days   = np.random.choice(range(1, len(demo_prices) - 1), size=3, replace=False)\n",
    "    down_days = np.random.choice(range(1, len(demo_prices) - 1), size=3, replace=False)\n",
    "    for d in up_days:\n",
    "        demo_prices.iloc[d][col] = demo_prices.iloc[d - 1][col] * 1.10  # exactly +10%\n",
    "    for d in down_days:\n",
    "        demo_prices.iloc[d][col] = demo_prices.iloc[d - 1][col] * 0.90  # exactly -10%\n",
    "\n",
    "limit_flags = flag_price_limits(demo_prices, limit_pct=0.10)\n",
    "\n",
    "# Summary\n",
    "total_limit_up   = (limit_flags == 1).sum().sum()\n",
    "total_limit_down = (limit_flags == -1).sum().sum()\n",
    "print(f\"\\n涨跌停统计:\")\n",
    "print(f\"  涨停天数 (limit up):   {total_limit_up}\")\n",
    "print(f\"  跌停天数 (limit down): {total_limit_down}\")\n",
    "print(f\"\\n各标的涨停次数:\")\n",
    "print((limit_flags == 1).sum())\n",
    "print(f\"\\n各标的跌停次数:\")\n",
    "print((limit_flags == -1).sum())\n",
    "\n",
    "# Visualize limit flags on Stock_A\n",
    "fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True)\n",
    "\n",
    "ax = axes[0]\n",
    "ax.plot(demo_prices['Stock_A'], color='steelblue', linewidth=1, label='Stock_A Price')\n",
    "limit_up_dates   = limit_flags.index[limit_flags['Stock_A'] == 1]\n",
    "limit_down_dates = limit_flags.index[limit_flags['Stock_A'] == -1]\n",
    "ax.scatter(limit_up_dates,   demo_prices.loc[limit_up_dates,   'Stock_A'],\n",
    "           color='red',   s=60, zorder=5, label='涨停 (limit up)',   marker='^')\n",
    "ax.scatter(limit_down_dates, demo_prices.loc[limit_down_dates, 'Stock_A'],\n",
    "           color='green', s=60, zorder=5, label='跌停 (limit down)', marker='v')\n",
    "ax.set_title('价格序列与涨跌停标记 (Stock_A)', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "ax = axes[1]\n",
    "ax.bar(limit_flags.index, limit_flags['Stock_A'],\n",
    "       color=limit_flags['Stock_A'].map({1: 'red', -1: 'green', 0: 'lightgray'}),\n",
    "       width=1)\n",
    "ax.set_title('涨跌停标志 (+1=涨停, -1=跌停, 0=正常)', fontsize=13)\n",
    "ax.set_ylim(-1.5, 1.5)\n",
    "ax.set_yticks([-1, 0, 1])\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n实践建议:\")\n",
    "print(\"- 涨跌停日: 保留价格数据，但添加 is_limit_up / is_limit_down 标记列\")\n",
    "print(\"- 策略层: 遇到涨跌停标记时跳过信号，因无法成交\")\n",
    "print(\"- 统计分析: 计算波动率/相关性时可选择排除涨跌停日，避免失真\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32e68310",
   "metadata": {},
   "source": [
    "# =============================================================================\n",
    "# TOPIC 5: Trading Calendar & Cross-market Alignment (交易日历与跨市场对齐)\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# Different markets trade on different days (national holidays, weekends).\n",
    "# When combining data from multiple markets, you must decide how to align\n",
    "# the time axis — the wrong choice distorts correlation calculations.\n",
    "#\n",
    "# Two alignment strategies:\n",
    "#   - Intersection (取交集): keep only dates both markets are open.\n",
    "#     Best for: correlation, regression, signal comparison.\n",
    "#     Drawback: loses data on single-market trading days.\n",
    "#\n",
    "#   - Forward fill (向前填充): fill non-trading days with last known price.\n",
    "#     Best for: portfolio construction, continuous NAV calculation.\n",
    "#     Drawback: introduces artificial zero-return days — inflates Sharpe,\n",
    "#               understates volatility. Must be accounted for in analysis.\n",
    "#\n",
    "# Note: this demo uses simplified hardcoded holiday lists. Production systems\n",
    "# should use exchange_calendars or pandas_market_calendars libraries.\n",
    "\n",
    "# ============================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc3eb282",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_trading_calendars():\n",
    "    \"\"\"创建不同市场的交易日历示例\n",
    "    \n",
    "    不同市场有不同的假日和交易时间，跨市场策略必须正确处理。\n",
    "    \"\"\"\n",
    "    # 生成2024年的工作日\n",
    "    all_bdays = pd.bdate_range(start='2024-01-01', end='2024-12-31')\n",
    "    \n",
    "    # 美国市场假日 (简化版)\n",
    "    us_holidays = pd.to_datetime([\n",
    "        '2024-01-01',  # 新年\n",
    "        '2024-01-15',  # MLK Day\n",
    "        '2024-02-19',  # Presidents Day\n",
    "        '2024-03-29',  # Good Friday\n",
    "        '2024-05-27',  # Memorial Day\n",
    "        '2024-06-19',  # Juneteenth\n",
    "        '2024-07-04',  # Independence Day\n",
    "        '2024-09-02',  # Labor Day\n",
    "        '2024-11-28',  # Thanksgiving\n",
    "        '2024-12-25',  # Christmas\n",
    "    ])\n",
    "    \n",
    "    # 中国A股假日 (简化版)\n",
    "    cn_holidays = pd.to_datetime([\n",
    "        '2024-01-01',  # 元旦\n",
    "        '2024-02-09', '2024-02-12', '2024-02-13', '2024-02-14',\n",
    "        '2024-02-15', '2024-02-16',  # 春节\n",
    "        '2024-04-04', '2024-04-05',  # 清明\n",
    "        '2024-05-01', '2024-05-02', '2024-05-03',  # 劳动节\n",
    "        '2024-06-10',  # 端午\n",
    "        '2024-09-16', '2024-09-17',  # 中秋\n",
    "        '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04',\n",
    "        '2024-10-07',  # 国庆\n",
    "    ])\n",
    "    \n",
    "    us_trading_days = all_bdays[~all_bdays.isin(us_holidays)]\n",
    "    cn_trading_days = all_bdays[~all_bdays.isin(cn_holidays)]\n",
    "    \n",
    "    return us_trading_days, cn_trading_days\n",
    "\n",
    "us_calendar, cn_calendar = create_trading_calendars()\n",
    "\n",
    "print(f\"2024年美股交易日: {len(us_calendar)}天\")\n",
    "print(f\"2024年A股交易日: {len(cn_calendar)}天\")\n",
    "print(f\"共同交易日: {len(us_calendar.intersection(cn_calendar))}天\")\n",
    "print(f\"仅美股交易日: {len(us_calendar.difference(cn_calendar))}天\")\n",
    "print(f\"仅A股交易日: {len(cn_calendar.difference(us_calendar))}天\")\n",
    "\n",
    "# 模拟跨市场数据对齐\n",
    "np.random.seed(42)\n",
    "\n",
    "# 生成美股和A股数据\n",
    "us_prices = pd.Series(\n",
    "    100 * np.exp(np.cumsum(np.random.randn(len(us_calendar)) * 0.01)),\n",
    "    index=us_calendar, name='US_ETF'\n",
    ")\n",
    "cn_prices = pd.Series(\n",
    "    50 * np.exp(np.cumsum(np.random.randn(len(cn_calendar)) * 0.015)),\n",
    "    index=cn_calendar, name='CN_ETF'\n",
    ")\n",
    "\n",
    "# 直接合并会有大量NaN\n",
    "combined_raw = pd.DataFrame({'US_ETF': us_prices, 'CN_ETF': cn_prices})\n",
    "print(f\"直接合并后:\")\n",
    "print(f\"  总行数: {len(combined_raw)}\")\n",
    "print(f\"  US_ETF缺失: {combined_raw['US_ETF'].isna().sum()}\")\n",
    "print(f\"  CN_ETF缺失: {combined_raw['CN_ETF'].isna().sum()}\")\n",
    "\n",
    "# 方法1: 取交集 (只保留双方都交易的日期)\n",
    "common_dates = us_calendar.intersection(cn_calendar)\n",
    "combined_inner = combined_raw.loc[common_dates].dropna()\n",
    "\n",
    "# 方法2: 向前填充 (用最近的收盘价填充)\n",
    "combined_ffill = combined_raw.ffill().dropna()\n",
    "\n",
    "print(f\"\\n方法1 (取交集): {len(combined_inner)}行\")\n",
    "print(f\"方法2 (ffill): {len(combined_ffill)}行\")\n",
    "\n",
    "# 可视化\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "ax = axes[0]\n",
    "ax.plot(combined_inner.index, combined_inner['US_ETF'] / combined_inner['US_ETF'].iloc[0],\n",
    "        label='US_ETF', linewidth=1.2)\n",
    "ax.plot(combined_inner.index, combined_inner['CN_ETF'] / combined_inner['CN_ETF'].iloc[0],\n",
    "        label='CN_ETF', linewidth=1.2)\n",
    "ax.set_title('方法1: 取交集日期', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "ax = axes[1]\n",
    "ax.plot(combined_ffill.index, combined_ffill['US_ETF'] / combined_ffill['US_ETF'].iloc[0],\n",
    "        label='US_ETF', linewidth=1.2)\n",
    "ax.plot(combined_ffill.index, combined_ffill['CN_ETF'] / combined_ffill['CN_ETF'].iloc[0],\n",
    "        label='CN_ETF', linewidth=1.2)\n",
    "ax.set_title('方法2: 向前填充', fontsize=13)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "plt.suptitle('跨市场数据对齐方法对比', fontsize=14, y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n实践建议:\")\n",
    "print(\"- 计算相关性、回归: 使用交集日期，确保数据同步\")\n",
    "print(\"- 构建组合/计算市值: 使用ffill，避免数据断裂\")\n",
    "print(\"- 重要: ffill会引入假的'零收益日'，计算波动率时需注意\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625e9a2a",
   "metadata": {},
   "source": [
    "# =============================================================================\n",
    "# TOPIC 6: End-to-End DataPipeline Class (完整数据清洗管道)\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# Combines all prior topics into a reusable, sequential pipeline:\n",
    "#   Step 1 — Filter stocks with too many missing values\n",
    "#   Step 2 — Fill remaining missing values (ffill only — no bfill/look-ahead)\n",
    "#   Step 3 — Compute return series\n",
    "#   Step 4 — Detect outliers (MAD) and apply Winsorize\n",
    "#   Step 5 — Generate a data quality report per stock\n",
    "#\n",
    "# This pattern (class-based pipeline with a .run() method) mirrors how\n",
    "# production quant systems structure data ingestion — each step is auditable,\n",
    "# configurable, and produces diagnostic output.\n",
    "\n",
    "# ============================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab7f3e0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataPipeline:\n",
    "    \"\"\"简单的数据清洗管道\n",
    "    \n",
    "    实现了从原始数据到干净数据面板的完整流程：\n",
    "    1. 缺失值处理\n",
    "    2. 异常值处理\n",
    "    3. 收益率计算\n",
    "    4. 数据质量报告\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, prices, max_missing_pct=0.1, winsorize_pct=(0.01, 0.99)):\n",
    "        self.raw_prices = prices.copy()\n",
    "        self.max_missing_pct = max_missing_pct\n",
    "        self.winsorize_pct = winsorize_pct\n",
    "        self.report = {}\n",
    "        \n",
    "    def run(self):\n",
    "        \"\"\"运行完整的数据清洗管道\"\"\"\n",
    "        print(\"=\" * 60)\n",
    "        print(\"数据清洗管道运行中...\")\n",
    "        print(\"=\" * 60)\n",
    "        \n",
    "        # Step 1: 过滤缺失值过多的标的\n",
    "        missing_pct = self.raw_prices.isna().mean()\n",
    "        valid_cols = missing_pct[missing_pct <= self.max_missing_pct].index.tolist()\n",
    "        dropped_cols = missing_pct[missing_pct > self.max_missing_pct].index.tolist()\n",
    "        \n",
    "        print(f\"\\n[Step 1] 过滤缺失值 > {self.max_missing_pct:.0%} 的标的\")\n",
    "        print(f\"  保留: {len(valid_cols)}个标的, 删除: {len(dropped_cols)}个标的\")\n",
    "        if dropped_cols:\n",
    "            print(f\"  被删除的标的: {dropped_cols}\")\n",
    "            \n",
    "        prices = self.raw_prices[valid_cols].copy()\n",
    "        \n",
    "        # Step 2: 填充缺失值\n",
    "        # ⚠️  Only ffill (backward-looking). bfill would use future prices to\n",
    "        #     fill early NaNs — that is look-ahead bias / data leakage.\n",
    "        #     Any rows still NaN after ffill are dropped (typically only the\n",
    "        #     very first row if the series starts with a missing value).\n",
    "        print(f\"\\n[Step 2] 填充缺失值 (ffill only, no bfill)\")\n",
    "        n_missing_before = prices.isna().sum().sum()\n",
    "        prices = prices.ffill().dropna(how='any')\n",
    "        n_missing_after = prices.isna().sum().sum()\n",
    "        print(f\"  填充前: {n_missing_before}个NaN -> 填充后: {n_missing_after}个NaN\")\n",
    "        \n",
    "        # Step 3: 计算收益率\n",
    "        print(f\"\\n[Step 3] 计算收益率\")\n",
    "        returns = prices.pct_change().iloc[1:]  # 删除第一行NaN\n",
    "        print(f\"  收益率面板: {returns.shape[0]}天 x {returns.shape[1]}标的\")\n",
    "        \n",
    "        # Step 4: 检测和处理异常值\n",
    "        print(f\"\\n[Step 4] 异常值检测与Winsorize\")\n",
    "        n_outliers_total = 0\n",
    "        returns_clean = returns.copy()\n",
    "        for col in returns_clean.columns:\n",
    "            mask, _ = detect_outliers_mad(returns_clean[col], threshold=5.0)\n",
    "            n_outliers = mask.sum()\n",
    "            n_outliers_total += n_outliers\n",
    "            # Winsorize\n",
    "            returns_clean[col] = winsorize(returns_clean[col], \n",
    "                                           self.winsorize_pct[0], self.winsorize_pct[1])\n",
    "            \n",
    "        print(f\"  检测到 {n_outliers_total} 个异常值 (MAD, |Z|>5)\")\n",
    "        print(f\"  已进行Winsorize处理 ({self.winsorize_pct[0]:.0%}, {self.winsorize_pct[1]:.0%})\")\n",
    "        \n",
    "        # Step 5: 数据质量报告\n",
    "        print(f\"\\n[Step 5] 数据质量报告\")\n",
    "        quality = pd.DataFrame({\n",
    "            '标的': valid_cols,\n",
    "            '日均收益率(bp)': (returns_clean.mean() * 10000).round(2),\n",
    "            '日波动率(%)': (returns_clean.std() * 100).round(3),\n",
    "            '年化收益率(%)': (returns_clean.mean() * 252 * 100).round(2),\n",
    "            '年化波动率(%)': (returns_clean.std() * np.sqrt(252) * 100).round(2),\n",
    "            '偏度': returns_clean.skew().round(3),\n",
    "            '峰度': returns_clean.kurtosis().round(3),\n",
    "        }).set_index('标的')\n",
    "        print(quality)\n",
    "        \n",
    "        self.clean_prices = prices\n",
    "        self.clean_returns = returns_clean\n",
    "        self.quality_report = quality\n",
    "        \n",
    "        print(f\"\\n管道运行完成！\")\n",
    "        return returns_clean\n",
    "\n",
    "# 运行管道\n",
    "pipeline = DataPipeline(price_panel, max_missing_pct=0.15)\n",
    "clean_returns = pipeline.run()"
   ]
  }
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