{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用说明：\n",
    "    1.需要修改chdir到当前目录\n",
    "    2.需要修改最后输出的文件名称\n",
    "    3.依据情况需要修改保留的列数\n",
    "    4.不同品种的交易时间不一样，要修改删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import datetime as datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('E:/data/ru')\n",
    "all_csv_files = [file for file in os.listdir('.') if file.endswith('.csv')]\n",
    "all_csv_files = sorted(all_csv_files)\n",
    "print(\"文件中所有CSV文件:\",all_csv_files)\n",
    "\n",
    "sp_old_chars = ['_2019','_2020','_2021']\n",
    "sp_old_chars = sorted(sp_old_chars)\n",
    "print(\"旧格式文件名关键字:\",sp_old_chars)\n",
    "sp_new_chars = ['_2022','_2023']\n",
    "sp_new_chars = sorted(sp_new_chars)\n",
    "print(\"新格式文件名关键字:\",sp_new_chars)\n",
    "\n",
    "# # 设置后面数据的采集对于的行数# 用 \"old_type\" 或者 \"new_type\" 区分\n",
    "# if all(char in ['_2019','_2020','_2021'] for char in sp_old_chars):\n",
    "#     year_type = 'old_type'\n",
    "#     print(\"使用旧年份格式采集!!!\")\n",
    "# elif all(char in ['_2022','_2023'] for char in sp_chars):\n",
    "#     year_type = 'new_type' \n",
    "#     print(\"使用新年份格式采集!!!\")\n",
    "# else:\n",
    "#     print(\"文件夹中CSV没有相关年份的数据或者新旧年份混用!!!\")\n",
    "\n",
    "csv_old_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_old_chars)]\n",
    "print(\"筛选结果后的CSV文件:\",csv_old_files)\n",
    "csv_new_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_new_chars)]\n",
    "print(\"筛选结果后的CSV文件:\",csv_new_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old = pd.DataFrame()\n",
    "for f in csv_old_files:\n",
    "    df_old_temp = pd.read_csv(\n",
    "        f,\n",
    "        usecols=[1, 2, 3, 4, 8, 13, 14, 15, 16],\n",
    "        names=[\n",
    "            \"统一代码\",\n",
    "            \"合约代码\",\n",
    "            \"时间\",\n",
    "            \"最新\",\n",
    "            \"成交量\",\n",
    "            \"买一价\",\n",
    "            \"卖一价\",\n",
    "            \"买一量\",\n",
    "            \"卖一量\",\n",
    "        ],\n",
    "        skiprows=1,\n",
    "        encoding=\"utf-8\",\n",
    "        parse_dates=['时间']#注意此处增加的排序，为了后面按时间排序\n",
    "    )\n",
    "    # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
    "    #                  'datetime',  'volume'])\n",
    "    df_old = pd.concat([df_old, df_old_temp])\n",
    "del df_old_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old =pd.DataFrame({'main_contract':df_old['统一代码'],'symbol':df_old['合约代码'],'datetime':df_old['时间'],'lastprice':df_old['最新'],'volume':df_old['成交量'],\n",
    "           'bid_p':df_old['买一价'],'ask_p':df_old['卖一价'],'bid_v':df_old['买一量'],'ask_v':df_old['卖一量']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old['time'] = df_old['datetime'].dt.strftime('%H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不同品种交易时间不一样，需要修改\n",
    "# 商品期货\n",
    "drop_index1 = df_old.query('time>\"15:00:00\" & time<\"21:00:00\"')[\"time\"].index\n",
    "# drop_index1 = df_old.query('time>\"15:00:00\"')[\"time\"].index\n",
    "# drop_index2 = df_old.query('time>\"01:00:00\" & time<\"09:00:00\"')[\"time\"].index\n",
    "#drop_index2 = df_old.query('time>\"02:30:00\" & time<\"09:00:00\"')[\"time\"].index\n",
    "drop_index2 = df_old.query('time<\"09:00:00\"')[\"time\"].index\n",
    "drop_index3 = df_old.query('time>\"23:00:00\" & time<\"23:59:59\"')[\"time\"].index\n",
    "# drop_index3 = df_old.query('time>\"11:30:00\" & time<\"13:30:00\"')[\"time\"].index\n",
    "drop_index4 = df_old.query('time>\"10:15:00\" & time<\"10:30:00\"')[\"time\"].index\n",
    "\n",
    "# 清理不在交易时间段的数据\n",
    "df_old.drop(labels=drop_index1, axis=0, inplace=True)\n",
    "df_old.drop(drop_index2, axis=0, inplace=True)\n",
    "df_old.drop(drop_index3, axis=0, inplace=True)\n",
    "df_old.drop(drop_index4, axis=0, inplace=True)\n",
    "\n",
    "df_old.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_old['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.DataFrame()\n",
    "for f in csv_new_files:\n",
    "    df_new_temp = pd.read_csv(\n",
    "    f,\n",
    "    usecols=[0, 1, 2, 5, 12, 21, 22, 23, 24, 25, 26, 44],\n",
    "    names=[\n",
    "        \"交易日\",\n",
    "        \"统一代码\",\n",
    "        \"合约代码\",\n",
    "        \"最新价\",\n",
    "        \"数量\",\n",
    "        \"最后修改时间\",\n",
    "        \"最后修改毫秒\",\n",
    "        \"申买价一\",\n",
    "        \"申买量一\",\n",
    "        \"申卖价一\",\n",
    "        \"申卖量一\",\n",
    "        \"业务日期\",\n",
    "    ],\n",
    "    skiprows=1,\n",
    "    encoding=\"utf-8\",\n",
    "    parse_dates=['业务日期','最后修改时间','最后修改毫秒']#注意此处增加的排序，为了后面按时间排序\n",
    "    )\n",
    "\n",
    "    # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
    "    #                  'datetime',  'volume'])\n",
    "    df_new = pd.concat([df_new, df_new_temp])\n",
    "del df_new_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不同品种交易时间不一样，需要修改\n",
    "# 商品期货\n",
    "drop_index1 = df_new.query('最后修改时间>\"15:00:00\" & 最后修改时间<\"21:00:00\"')[\"最后修改时间\"].index\n",
    "# drop_index1 = df_new.query('最后修改时间>\"15:00:00\"')[\"最后修改时间\"].index\n",
    "# drop_index2 = df_new.query('最后修改时间>\"01:00:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
    "# drop_index2 = df_new.query('最后修改时间>\"02:30:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
    "drop_index2 = df_new.query('最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
    "drop_index3 = df_new.query('最后修改时间>\"23:00:00\" & 最后修改时间<\"23:59:59\"')[\"最后修改时间\"].index\n",
    "# drop_index3 = df_new.query('最后修改时间>\"11:30:00\" & 最后修改时间<\"13:30:00\"')[\"最后修改时间\"].index\n",
    "drop_index4 = df_new.query('最后修改时间>\"10:15:00\" & 最后修改时间<\"10:30:00\"')[\"最后修改时间\"].index\n",
    "\n",
    "# 清理不在交易时间段的数据\n",
    "df_new.drop(labels=drop_index1, axis=0, inplace=True)\n",
    "df_new.drop(drop_index2, axis=0, inplace=True)\n",
    "df_new.drop(drop_index3, axis=0, inplace=True)\n",
    "df_new.drop(drop_index4, axis=0, inplace=True)\n",
    "\n",
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#日期修正\n",
    "#df_new['业务日期'] = pd.to_datetime(df_new['业务日期'])\n",
    "df_new['业务日期'] = df_new['业务日期'].dt.strftime('%Y-%m-%d')\n",
    "df_new['datetime'] = df_new['业务日期'] + ' '+df_new['最后修改时间'].dt.time.astype(str) + '.' + df_new['最后修改毫秒'].astype(str)\n",
    "# 将 'datetime' 列的数据类型更改为 datetime 格式，如果数据转换少8个小时，可以用timedelta处理\n",
    "df_new['datetime'] = pd.to_datetime(df_new['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')\n",
    "# 如果需要，可以将 datetime 列格式化为字符串\n",
    "#df_new['formatted_date'] = df_new['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S.%f')\n",
    "#计算瞬时成交量\n",
    "df_new['volume'] = df_new['数量'] - df_new['数量'].shift(1)\n",
    "df_new['volume'] = df_new['volume'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new =pd.DataFrame({'main_contract':df_new['统一代码'],'symbol':df_new['合约代码'],'datetime':df_new['datetime'],'lastprice':df_new['最新价'],'volume':df_new['volume'],\n",
    "           'bid_p':df_new['申买价一'],'ask_p':df_new['申卖价一'],'bid_v':df_new['申买量一'],'ask_v':df_new['申卖量一']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "df = pd.concat([df_old, df_new],axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_old,df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['main_contract'] = df['main_contract'].astype(str)\n",
    "df['symbol'] = df['symbol'].astype(str)\n",
    "df['datetime'] = pd.to_datetime(df['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')\n",
    "df['lastprice'] = df['lastprice'].astype(float)\n",
    "df['volume'] = df['volume'].astype(int)\n",
    "df['bid_p'] = df['bid_p'].astype(float)\n",
    "df['ask_p'] = df['ask_p'].astype(float)\n",
    "df['bid_v'] = df['bid_v'].astype(int)\n",
    "df['ask_v'] = df['ask_v'].astype(int)\n",
    "#df = df_old.append(df_new, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除重复行\n",
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保日期列按升序排序\n",
    "df.sort_values(by='datetime', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 查看数据的头部和尾部：head()、tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看dataframe的基本情况\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 等比复权,先不考虑\n",
    "# df['复权因子'] = df['卖一价'].shift() / df['买一价']\n",
    "df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['卖一价'].shift() / df['买一价'], 1)\n",
    "df['复权因子'] = df['复权因子'].fillna(1)\n",
    "# df['复权因子'].loc[0] = 1\n",
    "df['买一价_adj'] = df['买一价'] * df['复权因子'].cumprod()\n",
    "df['卖一价_adj'] = df['卖一价'] * df['复权因子'].cumprod()\n",
    "df['最新_adj'] = df['最新'] * df['复权因子'].cumprod()\n",
    "# df['low_adj'] = df['low'] * adjust.cumprod()\n",
    "# df['high_adj'] = df['high'] * adjust.cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 等差复权\n",
    "df['复权因子'] = np.where(df['symbol'] != df['symbol'].shift(), df['ask_p'].shift() - df['bid_p'], 0)\n",
    "df['复权因子'] = df['复权因子'].fillna(0)\n",
    "# df['复权因子'].loc[0] = 1\n",
    "df['bid_p_adj'] = df['bid_p'] + df['复权因子'].cumsum()\n",
    "df['ask_p_adj'] = df['ask_p'] + df['复权因子'].cumsum()\n",
    "df['lastprice_adj'] = df['lastprice'] + df['复权因子'].cumsum()\n",
    "# df['low_adj'] = df['low'] + df['复权因子'].cumsum()\n",
    "# df['high_adj'] = df['high'] + df['复权因子'].cumsum()\n",
    "# df_new =pd.DataFrame({'main_contract':df_new['统一代码'],'symbol':df_new['合约代码'],'datetime':df_new['datetime'],'lastprice':df_new['最新价'],'volume':df_new['volume'],\n",
    "#            'bid_p':df_new['申买价一'],'ask_p':df_new['申卖量一'],'bid_v':df_new['申买量一'],'ask_v':df_new['申卖量一']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df['复权因子'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contains_null = df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(contains_null)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查找换期需要复权的索引\n",
    "non_zero_indices = df[df['复权因子'] != 0].index\n",
    "print(non_zero_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看未调整买价、卖价和最新价的数据\n",
    "df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将调整后的数值替换原来的值\n",
    "df['bid_p'] = df['bid_p_adj']\n",
    "df['ask_p'] = df['ask_p_adj']\n",
    "df['lastprice'] = df['lastprice_adj']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看调整买价、卖价和最新价的数据\n",
    "df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除多余的值\n",
    "del df['复权因子']\n",
    "del df['bid_p_adj']\n",
    "del df['ask_p_adj']\n",
    "del df['lastprice_adj']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./ru888.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import datetime as datetime\n",
    "import pyarrow as pa\n",
    "import pyarrow.feather as feather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature 测试\n",
    "df = pd.read_csv('E:/data/ru/ru888.csv',encoding='UTF-8',parse_dates=['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "feather.write_feather(df, 'df_feather.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = feather.read_feather('df_feather.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>main_contract</th>\n",
       "      <th>symbol</th>\n",
       "      <th>datetime</th>\n",
       "      <th>lastprice</th>\n",
       "      <th>volume</th>\n",
       "      <th>bid_p</th>\n",
       "      <th>ask_p</th>\n",
       "      <th>bid_v</th>\n",
       "      <th>ask_v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44287432</th>\n",
       "      <td>ru888</td>\n",
       "      <td>ru2405</td>\n",
       "      <td>2023-12-29 14:59:58.500</td>\n",
       "      <td>6755.0</td>\n",
       "      <td>27</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>6755.0</td>\n",
       "      <td>128</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44287433</th>\n",
       "      <td>ru888</td>\n",
       "      <td>ru2405</td>\n",
       "      <td>2023-12-29 14:59:59.000</td>\n",
       "      <td>6760.0</td>\n",
       "      <td>27</td>\n",
       "      <td>6755.0</td>\n",
       "      <td>6760.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44287434</th>\n",
       "      <td>ru888</td>\n",
       "      <td>ru2405</td>\n",
       "      <td>2023-12-29 14:59:59.500</td>\n",
       "      <td>6760.0</td>\n",
       "      <td>17</td>\n",
       "      <td>6760.0</td>\n",
       "      <td>6765.0</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44287435</th>\n",
       "      <td>ru888</td>\n",
       "      <td>ru2405</td>\n",
       "      <td>2023-12-29 15:00:00.000</td>\n",
       "      <td>6760.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6760.0</td>\n",
       "      <td>6765.0</td>\n",
       "      <td>45</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44287436</th>\n",
       "      <td>ru888</td>\n",
       "      <td>ru2405</td>\n",
       "      <td>2023-12-29 15:00:00.500</td>\n",
       "      <td>6760.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6760.0</td>\n",
       "      <td>6765.0</td>\n",
       "      <td>45</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         main_contract  symbol                datetime  lastprice  volume  \\\n",
       "44287432         ru888  ru2405 2023-12-29 14:59:58.500     6755.0      27   \n",
       "44287433         ru888  ru2405 2023-12-29 14:59:59.000     6760.0      27   \n",
       "44287434         ru888  ru2405 2023-12-29 14:59:59.500     6760.0      17   \n",
       "44287435         ru888  ru2405 2023-12-29 15:00:00.000     6760.0       6   \n",
       "44287436         ru888  ru2405 2023-12-29 15:00:00.500     6760.0       0   \n",
       "\n",
       "           bid_p   ask_p  bid_v  ask_v  \n",
       "44287432  6750.0  6755.0    128     15  \n",
       "44287433  6755.0  6760.0      2     14  \n",
       "44287434  6760.0  6765.0     35     33  \n",
       "44287435  6760.0  6765.0     45     42  \n",
       "44287436  6760.0  6765.0     45     42  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  }
 ],
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