NewQuant/data/analysis/Volume.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "b93c7ca1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-03T06:58:45.372926Z",
     "start_time": "2025-10-03T06:58:45.083976Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import talib as ta  # Make sure TA-Lib is installed: pip install TA-Lib\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import warnings\n",
    "\n",
    "# 忽略所有警告\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# --- 0. Configure your file path ---\n",
    "# Please replace 'your_futures_data.csv' with the actual path to your CSV file\n",
    "file_path = '/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_SA/KQ_m@CZCE_SA_min15.csv'\n",
    "\n",
    "sns.set(style='whitegrid')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-03T06:58:45.441191Z",
     "start_time": "2025-10-03T06:58:45.381967Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# --- 1. Data Loading and Preprocessing ---\n",
    "def load_and_preprocess_data(file_path):\n",
    "    \"\"\"\n",
    "    Loads historical futures data and performs basic preprocessing.\n",
    "    Assumes data contains 'datetime', 'open', 'high', 'low', 'close', 'volume' columns.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        df = pd.read_csv(file_path, parse_dates=['datetime'], index_col='datetime')\n",
    "        # Ensure data is sorted by time\n",
    "        df = df.sort_index()\n",
    "        # Check and handle missing values\n",
    "        initial_rows = len(df)\n",
    "        df.dropna(inplace=True)\n",
    "        if len(df) < initial_rows:\n",
    "            print(f\"Warning: Missing values found in data, deleted {initial_rows - len(df)} rows.\")\n",
    "\n",
    "        # Check if necessary columns exist\n",
    "        required_columns = ['open', 'high', 'low', 'close', 'volume']\n",
    "        if not all(col in df.columns for col in required_columns):\n",
    "            raise ValueError(f\"CSV file is missing required columns. Please ensure it contains: {required_columns}\")\n",
    "\n",
    "        print(f\"Successfully loaded {len(df)} rows of data.\")\n",
    "        print(\"First 5 rows of data:\")\n",
    "        print(df.head())\n",
    "        return df\n",
    "    except FileNotFoundError:\n",
    "        print(f\"Error: File '{file_path}' not found. Please check the path.\")\n",
    "        return None\n",
    "    except Exception as e:\n",
    "        print(f\"Error during data loading or preprocessing: {e}\")\n",
    "        return None\n",
    "\n",
    "\n",
    "df_raw = load_and_preprocess_data(file_path)\n",
    "# df_raw = df_raw[df_raw.index <= '2024-01-01']"
   ],
   "id": "3dcf270c1da82220",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded 25662 rows of data.\n",
      "First 5 rows of data:\n",
      "                       open    high     low   close   volume   open_oi  \\\n",
      "datetime                                                                 \n",
      "2020-12-31 14:45:00  1607.0  1611.0  1603.0  1611.0  19480.0  148833.0   \n",
      "2021-01-04 09:00:00  1610.0  1636.0  1601.0  1620.0  55486.0  146448.0   \n",
      "2021-01-04 09:15:00  1620.0  1620.0  1601.0  1604.0  30314.0  153373.0   \n",
      "2021-01-04 09:30:00  1604.0  1606.0  1590.0  1595.0  30803.0  157091.0   \n",
      "2021-01-04 09:45:00  1595.0  1601.0  1594.0  1600.0  10031.0  158730.0   \n",
      "\n",
      "                     close_oi underlying_symbol  \n",
      "datetime                                         \n",
      "2020-12-31 14:45:00  146448.0        CZCE.SA105  \n",
      "2021-01-04 09:00:00  153373.0        CZCE.SA105  \n",
      "2021-01-04 09:15:00  157091.0        CZCE.SA105  \n",
      "2021-01-04 09:30:00  158730.0        CZCE.SA105  \n",
      "2021-01-04 09:45:00  160031.0        CZCE.SA105  \n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-03T07:02:35.779448Z",
     "start_time": "2025-10-03T06:58:45.453734Z"
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   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.signal import find_peaks\n",
    "from scipy.stats import gaussian_kde\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "# 引入numba来JIT编译我们的核心计算，获得接近C的速度\n",
    "\n",
    "def find_hvn_kde_numba(prices: np.ndarray, volumes: np.ndarray) -> np.ndarray:\n",
    "    if len(prices) < 2 or np.sum(volumes) < 1e-6:\n",
    "        return np.array([np.nan])\n",
    "\n",
    "    min_price, max_price = np.min(prices), np.max(prices)\n",
    "    if min_price == max_price:\n",
    "        return np.array([min_price])\n",
    "\n",
    "    # Scott's rule for bandwidth\n",
    "    n = len(prices)\n",
    "    bw = (n * 3/4)**(-1/5) * np.std(prices)\n",
    "    if bw < 1e-6:\n",
    "        return np.array([np.mean(prices)])\n",
    "\n",
    "    eval_points = np.linspace(min_price, max_price, 100) # 减少评估点以提高速度\n",
    "    density = np.zeros_like(eval_points)\n",
    "\n",
    "    # 手动实现KDE\n",
    "    for i in range(len(eval_points)):\n",
    "        x = eval_points[i]\n",
    "        kernel_vals = (1 / (bw * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - prices) / bw)**2)\n",
    "        density[i] = np.sum(volumes * kernel_vals)\n",
    "\n",
    "    # 手动实现峰值寻找\n",
    "    peaks_indices = []\n",
    "    max_density = np.max(density)\n",
    "    prominence_threshold = max_density * 0.2 # 提高显著性阈值\n",
    "    for i in range(1, len(density) - 1):\n",
    "        if density[i] > density[i-1] and density[i] > density[i+1]:\n",
    "            if density[i] > prominence_threshold:\n",
    "                 peaks_indices.append(i)\n",
    "\n",
    "    if len(peaks_indices) > 0:\n",
    "        return eval_points[np.array(peaks_indices)]\n",
    "\n",
    "    return np.array([np.nan])\n",
    "\n",
    "# ==============================================================================\n",
    "#  模块二：主分析流程（已彻底重构为向量化版本）\n",
    "# ==============================================================================\n",
    "\n",
    "def analyze_hvn_crossing_event_vectorized(\n",
    "    df_raw: pd.DataFrame,\n",
    "    profile_window: int = 250,\n",
    "    forward_window: int = 50,\n",
    "    tick_size: float = 0.01):\n",
    "\n",
    "    print(\"Step 1: 准备数据...\")\n",
    "    df = df_raw.copy().reset_index(drop=True) # 确保索引是连续的整数\n",
    "    df['forward_returns'] = np.log(df['close'].shift(-forward_window) / df['close'])\n",
    "\n",
    "    print(\"Step 2: 一次性向量化展开所有K线...\")\n",
    "    # 计算每根K线需要展开的tick数\n",
    "    num_ticks = ((df['high'] - df['low']) / tick_size).round().astype(int) + 1\n",
    "    # 创建一个索引，用于将展开后的数据映射回原始的K线\n",
    "    expanded_idx = np.repeat(df.index, num_ticks)\n",
    "\n",
    "    # 计算每个tick的价格和成交量\n",
    "    # 使用Numba JIT加速这个循环\n",
    "    def expand_bars(lows, highs, volumes, num_ticks, expanded_size):\n",
    "        all_prices = np.empty(expanded_size, dtype=np.float64)\n",
    "        all_volumes = np.empty(expanded_size, dtype=np.float64)\n",
    "        current_pos = 0\n",
    "        for i in range(len(lows)):\n",
    "            n = num_ticks[i]\n",
    "            if n > 0 and volumes[i] > 0:\n",
    "                prices = np.linspace(lows[i], highs[i], n)\n",
    "                vols = np.full(n, volumes[i] / n)\n",
    "                all_prices[current_pos:current_pos+n] = prices\n",
    "                all_volumes[current_pos:current_pos+n] = vols\n",
    "                current_pos += n\n",
    "        return all_prices, all_volumes\n",
    "\n",
    "    all_prices, all_volumes = expand_bars(df['low'].values, df['high'].values,\n",
    "                                          df['volume'].values, num_ticks.values,\n",
    "                                          len(expanded_idx))\n",
    "\n",
    "    # 创建一个包含展开后数据的DataFrame\n",
    "    df_expanded = pd.DataFrame({\n",
    "        'original_index': expanded_idx,\n",
    "        'price': all_prices,\n",
    "        'volume': all_volumes\n",
    "    })\n",
    "    print(f\"K线展开完成，共 {len(df_expanded)} 个价格点。\")\n",
    "\n",
    "\n",
    "    print(f\"Step 3: 使用滚动窗口和Numba JIT批量计算HVN...\")\n",
    "    # --- 这是性能提升的核心 ---\n",
    "    # 我们将使用一个for循环，但循环内部的计算是高度优化的\n",
    "    hvn_list = [np.array([np.nan])] * len(df)\n",
    "\n",
    "    # Groupby一次，后续查询会非常快\n",
    "    grouped_expanded = df_expanded.groupby('original_index')\n",
    "\n",
    "    for i in tqdm(range(profile_window, len(df)), desc=\"Calculating HVNs\"):\n",
    "        window_indices = df.index[i - profile_window : i]\n",
    "\n",
    "        # 快速收集窗口数据\n",
    "        window_data = df_expanded[df_expanded['original_index'].isin(window_indices)]\n",
    "\n",
    "        # 调用JIT编译的函数\n",
    "        hvns = find_hvn_kde_numba(\n",
    "            window_data['price'].values,\n",
    "            window_data['volume'].values\n",
    "        )\n",
    "        hvn_list[i] = hvns\n",
    "\n",
    "    df['hvns'] = hvn_list\n",
    "    df = df.dropna(subset=['hvns'])\n",
    "    print(\"HVN批量计算完成.\")\n",
    "\n",
    "    # --- 后续的事件识别、分析和可视化流程与之前版本完全相同 ---\n",
    "    print(\"Step 4: 识别HVN穿越事件...\")\n",
    "    # ... (与之前版本相同的事件识别、路径分析和绘图代码) ...\n",
    "    events = []\n",
    "    for i, row in df.iterrows():\n",
    "        idx = df.index.get_loc(i)\n",
    "        if idx == 0: continue\n",
    "        prev_close = df['close'].iloc[idx-1]\n",
    "        current_close = row['close']\n",
    "        hvns = row['hvns']\n",
    "        if isinstance(hvns, np.ndarray) and not np.isnan(hvns).all():\n",
    "            for hvn in hvns:\n",
    "                if prev_close < hvn and current_close > hvn: events.append({'event_index': i, 'event_type': 'bullish_cross'})\n",
    "                if prev_close > hvn and current_close < hvn: events.append({'event_index': i, 'event_type': 'bearish_cross'})\n",
    "    if not events: print(\"未找到任何HVN穿越事件。\"); return\n",
    "    df_events = pd.DataFrame(events); print(f\"共识别出 {len(df_events)} 个事件.\")\n",
    "    print(\"Step 5: 分析事件后的平均走势...\")\n",
    "    bullish_cross_indices = df_events[df_events['event_type'] == 'bullish_cross']['event_index']\n",
    "    bearish_cross_indices = df_events[df_events['event_type'] == 'bearish_cross']['event_index']\n",
    "    bullish_paths, bearish_paths = [], []\n",
    "    for index in bullish_cross_indices:\n",
    "        loc = df.index.get_loc(index)\n",
    "        if loc + forward_window < len(df):\n",
    "            start_price = df.loc[index, 'close']\n",
    "            path = df['close'].iloc[loc : loc + forward_window].values / start_price\n",
    "            bullish_paths.append(path)\n",
    "    for index in bearish_cross_indices:\n",
    "        loc = df.index.get_loc(index)\n",
    "        if loc + forward_window < len(df):\n",
    "            start_price = df.loc[index, 'close']\n",
    "            path = df['close'].iloc[loc : loc + forward_window].values / start_price\n",
    "            bearish_paths.append(path)\n",
    "    if not bullish_paths or not bearish_paths: print(\"事件数量不足，无法进行路径分析。\"); return\n",
    "    avg_bullish_path, avg_bearish_path = np.mean(bullish_paths, axis=0), np.mean(bearish_paths, axis=0)\n",
    "    print(\"Step 6: 结果可视化...\")\n",
    "    plt.style.use('seaborn-v0_8-darkgrid'); fig, ax = plt.subplots(figsize=(12, 7))\n",
    "    ax.plot(avg_bullish_path, label=f'Avg Path after Bullish Cross (N={len(bullish_paths)})', color='green', linewidth=2)\n",
    "    ax.plot(avg_bearish_path, label=f'Avg Path after Bearish Cross (N={len(bearish_paths)})', color='red', linewidth=2)\n",
    "    ax.axhline(1.0, color='black', linestyle='--', linewidth=1, label='Event Price Level')\n",
    "    ax.set_title(f'Average Price Path after HVN Crossing Event (Forward Window = {forward_window} bars)', fontsize=16)\n",
    "    ax.set_xlabel('Bars after Event', fontsize=12); ax.set_ylabel('Normalized Price (Event Price = 1.0)', fontsize=12)\n",
    "    ax.legend(fontsize=10); ax.grid(True); plt.show()\n",
    "\n",
    "\n",
    "# ==============================================================================\n",
    "#  使用示例\n",
    "# ==============================================================================\n",
    "print(\"正在生成模拟数据...\")\n",
    "np.random.seed(42)\n",
    "n_points = 26000\n",
    "prices = 100 + np.random.randn(n_points).cumsum() * 0.1\n",
    "df_raw = pd.DataFrame({\n",
    "    'open': prices, 'high': prices + np.random.uniform(0, 0.5, n_points),\n",
    "    'low': prices - np.random.uniform(0, 0.5, n_points),\n",
    "    'close': prices + np.random.uniform(-0.25, 0.25, n_points),\n",
    "    'volume': np.random.randint(100, 1000, n_points) * 10\n",
    "}, index=pd.to_datetime(pd.date_range('2023-01-01', periods=n_points, freq='min')))\n",
    "df_raw['open'] = df_raw['close'].shift(1)\n",
    "df_raw = df_raw.dropna()\n",
    "print(f\"模拟数据生成完毕，共 {len(df_raw)} 行。\")\n",
    "\n",
    "# --- 运行全新的、高性能的分析函数 ---\n",
    "analyze_hvn_crossing_event_vectorized(\n",
    "    df_raw,\n",
    "    profile_window=250,\n",
    "    forward_window=50\n",
    ")"
   ],
   "id": "30958840dbc186d7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在生成模拟数据...\n",
      "模拟数据生成完毕，共 25999 行。\n",
      "Step 1: 准备数据...\n",
      "Step 2: 一次性向量化展开所有K线...\n",
      "K线展开完成，共 1327133 个价格点。\n",
      "Step 3: 使用滚动窗口和Numba JIT批量计算HVN...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating HVNs: 100%|██████████| 25749/25749 [03:49<00:00, 112.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HVN批量计算完成.\n",
      "Step 4: 识别HVN穿越事件...\n",
      "共识别出 4748 个事件.\n",
      "Step 5: 分析事件后的平均走势...\n",
      "Step 6: 结果可视化...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ],
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 3
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "quant",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}