{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b53b507",
   "metadata": {},
   "source": [
    "# introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d605f86a",
   "metadata": {},
   "source": [
    "[介绍](https://www.kaggle.com/code/tomforbes/optiver-trading-at-the-close-introduction#Trading-At-The-Close)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b730a3aa",
   "metadata": {},
   "source": [
    "## 介绍\n",
    "### 一、背景：纳斯达克收盘博弈\n",
    "收盘最后 10 分钟（15:50 - 16:00）是市场流动性最剧烈的时刻，采用集合竞价机制。    \n",
    "\n",
    "订单规则：(15:50之前得是普通挂单)\n",
    "- 限制：可委托，不支持撤单\n",
    "- MOC (Market-On-Close)：不计价格，必须成交。主要由指数基金（如标普500指数基金，基金价格需要与指数相等）发起，是价格变动的主要冲击力。MOC往往在靠前时间点出现。MOC经常会暴力拉升价格已完成成交。\n",
    "- LOC (Limit-On-Close)：有价格阈值。比如买单大于100块就不成交了.是市场的阻力位。不买入过高价格，不卖出过低价格。\n",
    "\n",
    "撮合逻辑：交易所每 10 秒计算一次“虚拟撮合”，寻找能使成交量最大化的参考价 (Reference Price)。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7494a87",
   "metadata": {},
   "source": [
    "### 二、价格发现\n",
    "1. 近端价、远端价、参考价    \n",
    "交易所每10秒公布的三个价格，最终它们会收敛于收盘价。远端价和近端价从300秒开始公布。\n",
    "\n",
    "| 价格维度 | 定义说明 | 数据特性 |\n",
    "| :--- | :--- | :--- |\n",
    "| **远端价 (Far Price)** | 仅考虑拍卖池内的订单（MOC/LOC）计算的成交价。如果是Nan就不能成交 | **高波动/高灵敏**：反映了最核心、最极端的供需缺口。若为 NaN，说明当前买卖盘完全无法匹配。 |\n",
    "| **近端价 (Near Price)** | 考虑拍卖池订单 + 白天剩余的普通限价单计算的成交价。 | **稳定性**：由于加入了大量存量订单，它更接近最终真实的收盘平衡点。 |\n",
    "| **参考价 (Reference Price)** | 经过保护机制修正的价格，通常介于近端与远端之间。表明预期，表明意愿 | **锚点**：它是交易所公布的官方基准，是所有 `Imbalance` 统计的起点。 |\n",
    "\n",
    "\n",
    "2. wap    \n",
    "表明盘口实时波动。\n",
    "是成交量加权平均价格，所以总是介于ask和bid之间\n",
    "$$\n",
    "\\frac{BidPrice * BidSize + AskPrice * AskSize}{BidSize + AskSize}\n",
    "$$\n",
    "- 溢价换取成交：当买方极度急切时，会在靠近卖价的位置堆积大量买单。此时 WAP 向上偏移，反映了买方宁愿支付高价也要成交的心理。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362a05af",
   "metadata": {},
   "source": [
    "### 三、 供需平衡(动力)\n",
    "供需动力是价格变动的原因。按照**Reference Price**撮合，imbalance代表无法撮合的量。\n",
    "- imbalance_size：缺口绝对值。\n",
    "- imbalance_buy_sell_flag：缺口方向。比如买单过多了，价格就将会上涨。\n",
    "- 这预示了未来60秒价格移动方向和幅度。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb6aac5",
   "metadata": {},
   "source": [
    "### 四、预测相对收益率target\n",
    "预测个股 60 秒后的收益率与大盘收益率的差值。即比大盘强还是弱。（能否跑赢大盘）    \n",
    "\n",
    "$$\n",
    "Target_t = (\\frac{StockWap_{t+60}}{StockWap_t} - \\frac{IndexWap_{t+60}}{IndexWap_t}) \\times 1000\n",
    "$$\n",
    "\n",
    "为什么是相对收益率？而不是个股的收盘价？\n",
    "- 大盘是受到宏观政策影响的，如美联储加息。不可预测。\n",
    "- 相对收益率去除了大盘影响，更能反映个股真实表现。\n",
    "- 这也能统一不同股票的量度。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a84e05e",
   "metadata": {},
   "source": [
    "## 导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "60095ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import gc\n",
    "import warnings\n",
    "import re\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from contextlib import contextmanager\n",
    "import time\n",
    "import os\n",
    "\n",
    "gc.enable()\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "plt.rcParams['axes.unicode_minus'] = False \n",
    "plt.rcParams['figure.figsize'] = (4,3) \n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\", palette=\"Set1\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c36ab5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e46ca53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['stock_id', 'date_id', 'seconds_in_bucket', 'imbalance_size',\n",
       "       'imbalance_buy_sell_flag', 'reference_price', 'matched_size',\n",
       "       'far_price', 'near_price', 'bid_price', 'bid_size', 'ask_price',\n",
       "       'ask_size', 'wap', 'target', 'time_id', 'row_id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ab96d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "def info_data(df):\n",
    "    print(f'shape {df.shape}')\n",
    "    n_stock =len(df['stock_id'].value_counts())\n",
    "    print(f'stocks {n_stock}')\n",
    "    n_days = len(df['date_id'].value_counts())\n",
    "    print(f'days {n_days}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2d2a5c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape (5237980, 17)\n",
      "stocks 200\n",
      "days 481\n"
     ]
    }
   ],
   "source": [
    "info_data(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f9ea207",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_stock(df, stock, date):\n",
    "\n",
    "    fig, axes = plt.subplots(1,3, figsize=(15,5))\n",
    "    plot_df = df[(df['stock_id'] ==stock) & (df['date_id'] == date) ]\n",
    "    melted_data = plot_df.melt(\n",
    "        id_vars = 'seconds_in_bucket',\n",
    "        value_vars = ['far_price', 'near_price', 'reference_price','wap'],\n",
    "        value_name = 'price_value',\n",
    "        var_name = 'price_type'\n",
    "    )\n",
    "    # 左图：近端、远端价、参考价 收敛\n",
    "    sns.lineplot(data=melted_data, x='seconds_in_bucket', y='price_value', hue='price_type', ax=axes[0])\n",
    "    axes[0].set_title(f\"Auction Prices (Stock {stock}, Day {date})\")\n",
    "    \n",
    "    melted_data = plot_df.melt(\n",
    "        id_vars = 'seconds_in_bucket',\n",
    "        value_vars = ['bid_price', 'ask_price', 'wap'],\n",
    "        var_name = 'Price_Type',\n",
    "        value_name = 'Price_Value'\n",
    "    )\n",
    "    # 中图：订单本最优买卖价格\n",
    "    sns.lineplot(data=melted_data, x='seconds_in_bucket', y='Price_Value', hue='Price_Type', ax=axes[1])\n",
    "    axes[1].set_title(f\"Market Liquidity (Stock {stock}, Day {date})\")\n",
    "\n",
    "\n",
    "    # 右图：imbalance\n",
    "    axes[2].fill_between(plot_df['seconds_in_bucket'], plot_df['matched_size'], color='gray', alpha=0.3, label='Matched Size')\n",
    "    axes[2].set_ylabel('Matched size')\n",
    "    tax = axes[2].twinx()\n",
    "    plot_df['signed_imb'] = plot_df['imbalance_size'] * plot_df['imbalance_buy_sell_flag']\n",
    "    sns.lineplot(data=plot_df, x='seconds_in_bucket', y='signed_imb', ax=tax, label='Signed Imbalance')\n",
    "    tax.set_ylabel('Signed Imbalance (Buy > 0, Sell < 0)', color='purple')\n",
    "    axes[2].axhline(0, color='black', linestyle='--', alpha=0.5)\n",
    "    axes[2].set_title(f\"Imbalance Dynamics (Stock {stock}, Day {date})\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "002b6bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stock(data, stock=1, date=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252a0999",
   "metadata": {},
   "source": [
    "## metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e09c4b2",
   "metadata": {},
   "source": [
    "MAE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af6fa28",
   "metadata": {},
   "source": [
    "## 处理\n",
    "- 分布\n",
    "- 统计：缺失、异常"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "865caaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def info_numeric_col(df, col):\n",
    "    \"\"\" col列空间：统计、分布图、异常值图。\n",
    "    \"\"\"\n",
    "    stat = pd.Series({\n",
    "        'missing': df[col].isnull().sum(),\n",
    "        'missing_percent': df[col].isnull().sum() / len(df[col]),\n",
    "        'mean': df[col].mean(),\n",
    "        'std':df[col].std(),\n",
    "        'min': df[col].min(),\n",
    "        '25%': df[col].quantile(0.25),\n",
    "        'median': df[col].quantile(0.5),\n",
    "        '75%': df[col].quantile(0.75),\n",
    "        '99%': df[col].quantile(0.99), # 长尾\n",
    "        'max': df[col].max(),\n",
    "        'skewness': df[col].skew(), # 偏度\n",
    "        'kurtosis': df[col].kurt() # 峰度\n",
    "    })\n",
    "    print(f\"--- Statistics for {col} ---\")    \n",
    "    print(stat)\n",
    "    fig, axes = plt.subplots(1,2, figsize=(8,4))\n",
    "    \n",
    "    sns.kdeplot(data=df, x=col, fill=True, ax=axes[0], color='red')\n",
    "    axes[0].set_title(f'{col} distribution')\n",
    "\n",
    "    sns.boxplot(data=df, x=col, ax= axes[1], color='green')\n",
    "    axes[1].set_title(f'{col} outLiers')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "22f0ddc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stock_id                         0\n",
       "date_id                          0\n",
       "seconds_in_bucket                0\n",
       "imbalance_size                 220\n",
       "imbalance_buy_sell_flag          0\n",
       "reference_price                220\n",
       "matched_size                   220\n",
       "far_price                  2894342\n",
       "near_price                 2857180\n",
       "bid_price                      220\n",
       "bid_size                         0\n",
       "ask_price                      220\n",
       "ask_size                         0\n",
       "wap                            220\n",
       "target                          88\n",
       "time_id                          0\n",
       "row_id                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55510262",
   "metadata": {},
   "source": [
    "220行数据应该是整行缺失的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8ade4ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['imbalance_size', 'reference_price', 'matched_size', 'bid_price', 'ask_price', 'wap']].isnull().all(axis = 1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "adc21bcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data = data.dropna(subset=['imbalance_size', 'reference_price', 'matched_size', 'bid_price', 'ask_price', 'wap'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f591641f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5237760, 17)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f282e7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stock_id                         0\n",
       "date_id                          0\n",
       "seconds_in_bucket                0\n",
       "imbalance_size                   0\n",
       "imbalance_buy_sell_flag          0\n",
       "reference_price                  0\n",
       "matched_size                     0\n",
       "far_price                  2894122\n",
       "near_price                 2856960\n",
       "bid_price                        0\n",
       "bid_size                         0\n",
       "ask_price                        0\n",
       "ask_size                         0\n",
       "wap                              0\n",
       "target                           0\n",
       "time_id                          0\n",
       "row_id                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d0eafdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['far_price'] = data['far_price'].fillna(data['reference_price'])\n",
    "data['near_price'] = data['near_price'].fillna(data['reference_price'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4e89f79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stock_id                   0\n",
       "date_id                    0\n",
       "seconds_in_bucket          0\n",
       "imbalance_size             0\n",
       "imbalance_buy_sell_flag    0\n",
       "reference_price            0\n",
       "matched_size               0\n",
       "far_price                  0\n",
       "near_price                 0\n",
       "bid_price                  0\n",
       "bid_size                   0\n",
       "ask_price                  0\n",
       "ask_size                   0\n",
       "wap                        0\n",
       "target                     0\n",
       "time_id                    0\n",
       "row_id                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea6d7287",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['imbalance_size'] = data['imbalance_size'].fillna(0)\n",
    "# data['matched_size'] = data['matched_size'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81a203f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['wap'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d1afed",
   "metadata": {},
   "source": [
    "## 特征构造\n",
    "预测策略：\n",
    "1. 价格发现：此时价格\n",
    "2. 供需动力：imbalance\n",
    "3. 流动性：放大价格。表明订单池子深度。matched_size\n",
    "4. 时间感知：\n",
    "\n",
    "$target = f(价格，供需，流动性, 时间感知)$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa99c06",
   "metadata": {},
   "source": [
    "价格本身蕴含了时间信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d47928f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5237760, 17)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3defae8",
   "metadata": {},
   "source": [
    "供需动力/流动性表明加速度，即价格变动的强度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f2a1eb8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['price_force'] = data['imbalance_size'] / (data['matched_size'] + 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0665887",
   "metadata": {},
   "source": [
    "价格偏移: 预期参考价如果比实时wap高，表明买方急切，价格将抬升。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "29fdaf5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['price_relative_shift'] = (data['reference_price'] - data['wap']) / data['wap']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd96383a",
   "metadata": {},
   "source": [
    "时间感知，越往后越稳定。也会增大供需动力的权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b059e1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['time_urgency'] = data['seconds_in_bucket'] / 540"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0ece45",
   "metadata": {},
   "source": [
    "有以下推测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422aef87",
   "metadata": {},
   "source": [
    "$$\n",
    "target \\propto Sign(flag) \\times  价格动力 \\times 价格相对偏移 \\times 时间感知\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2629f733",
   "metadata": {},
   "source": [
    "非线性模型，可以学习到这个非线性关系。所以构造价格动力和价格相对偏移就可以"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "14063971",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Statistics for price_force ---\n",
      "missing              0.000000\n",
      "missing_percent      0.000000\n",
      "mean                 0.256994\n",
      "std                  0.644515\n",
      "min                  0.000000\n",
      "25%                  0.007440\n",
      "median               0.082183\n",
      "75%                  0.267303\n",
      "99%                  2.508878\n",
      "max                123.911685\n",
      "skewness            15.864714\n",
      "kurtosis           775.489087\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "info_numeric_col(data, 'price_force')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "57b8dcf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imbalance_size</th>\n",
       "      <th>matched_size</th>\n",
       "      <th>price_force</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2973194</th>\n",
       "      <td>47981345.25</td>\n",
       "      <td>387221.12</td>\n",
       "      <td>123.911685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         imbalance_size  matched_size  price_force\n",
       "2973194     47981345.25     387221.12   123.911685"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['price_force'] > 123, ['imbalance_size', 'matched_size', 'price_force']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31768e1c",
   "metadata": {},
   "source": [
    "这样的分布是正常的，不属于人为异常数据。  因此可能只要在模型要求下再去进行log、分箱转换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10af320e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b323144b",
   "metadata": {},
   "source": [
    "## baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d33bb622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stock_id                   0\n",
       "date_id                    0\n",
       "seconds_in_bucket          0\n",
       "imbalance_size             0\n",
       "imbalance_buy_sell_flag    0\n",
       "reference_price            0\n",
       "matched_size               0\n",
       "far_price                  0\n",
       "near_price                 0\n",
       "bid_price                  0\n",
       "bid_size                   0\n",
       "ask_price                  0\n",
       "ask_size                   0\n",
       "wap                        0\n",
       "target                     0\n",
       "time_id                    0\n",
       "row_id                     0\n",
       "price_force                0\n",
       "price_relative_shift       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "788c9d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['target']\n",
    "X = data[['reference_price','far_price', 'near_price', 'ask_price', 'bid_price', 'wap', \n",
    "    'imbalance_size', 'matched_size', 'bid_size', 'ask_size',\n",
    "    'price_force', 'price_relative_shift',\n",
    "    'time_urgency']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c7fe00",
   "metadata": {},
   "source": [
    "应为是不同股票数据，应该进行标准化，对于price，size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e4d1920",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ba9b20c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5237760, 19)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f670554d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5237892, 6)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6bb843e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LGBMRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1f05a6bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.021072 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1530\n",
      "[LightGBM] [Info] Number of data points in the train set: 5237892, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score -0.047561\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LGBMRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('boosting_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">boosting_type&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;gbdt&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('num_leaves',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">num_leaves&nbsp;</td>\n",
       "            <td class=\"value\">31</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample_for_bin',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample_for_bin&nbsp;</td>\n",
       "            <td class=\"value\">200000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('objective',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">objective&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_split_gain',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_split_gain&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_child_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_child_samples&nbsp;</td>\n",
       "            <td class=\"value\">20</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample_freq',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample_freq&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('reg_lambda',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('importance_type',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">importance_type&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;split&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "05a01978",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "edcfac92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.320055859819497"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_pred, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1c3ba52",
   "metadata": {},
   "outputs": [],
   "source": [
    "from public_timeseries_testing_util import make_env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00abf714",
   "metadata": {},
   "outputs": [],
   "source": [
    "env = make_env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff3132e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = 0\n",
    "for (test, revealed_targets, sample_prediction) in env.iter_test():\n",
    "    sample_prediction['target'] = model.predict(test.drop(columns=[ 'stock_id', 'date_id', 'seconds_in_bucket', 'row_id', 'time_id']))\n",
    "    env.predict(sample_prediction)\n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28af42d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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