短期选股策略2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型训练\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import tqdm\n",
    "import datetime\n",
    "import time\n",
    "\n",
    "base_path = 'stock'\n",
    "\n",
    "market_map = {'主板':0, '中小板':1}\n",
    "exchange_map = {'SZSE':0, 'SSE':1}\n",
    "is_hs_map = {'S':0, 'N':1, 'H':2}\n",
    "\n",
    "area_map = {'深圳': 0, '北京': 1, '吉林': 2, '江苏': 3, '辽宁': 4, '广东': 5, '安徽': 6, '四川': 7, '浙江': 8,\n",
    "            '湖南': 9, '河北': 10, '新疆': 11, '山东': 12, '河南': 13, '山西': 14, '江西': 15, '青海': 16, \n",
    "            '湖北': 17, '内蒙': 18, '海南': 19, '重庆': 20, '陕西': 21, '福建': 22, '广西': 23, '天津': 24, \n",
    "            '云南': 25, '贵州': 26, '甘肃': 27, '宁夏': 28, '黑龙江': 29, '上海': 30, '西藏': 31}\n",
    "\n",
    "industry_map = {'银行': 0, '全国地产': 1, '生物制药': 2, '环境保护': 3, '区域地产': 4, '酒店餐饮': 5, '运输设备': 6, \n",
    " '综合类': 7, '建筑工程': 8, '玻璃': 9, '家用电器': 10, '文教休闲': 11, '其他商业': 12, '元器件': 13, \n",
    " 'IT设备': 14, '其他建材': 15, '汽车服务': 16, '火力发电': 17, '医药商业': 18, '汽车配件': 19, '广告包装': 20, \n",
    " '轻工机械': 21, '新型电力': 22, '饲料': 23, '电气设备': 24, '房产服务': 25, '石油加工': 26, '铅锌': 27, '农业综合': 28,\n",
    " '批发业': 29, '通信设备': 30, '旅游景点': 31, '港口': 32, '机场': 33, '石油贸易': 34, '空运': 35, '医疗保健': 36,\n",
    " '商贸代理': 37, '化学制药': 38, '影视音像': 39, '工程机械': 40, '软件服务': 41, '证券': 42, '化纤': 43, '水泥': 44, \n",
    " '专用机械': 45, '供气供热': 46, '农药化肥': 47, '机床制造': 48, '多元金融': 49, '百货': 50, '中成药': 51, '路桥': 52, \n",
    " '造纸': 53, '食品': 54, '黄金': 55, '化工原料': 56, '矿物制品': 57, '水运': 58, '日用化工': 59, '机械基件': 60, \n",
    " '汽车整车': 61, '煤炭开采': 62, '铁路': 63, '染料涂料': 64, '白酒': 65, '林业': 66, '水务': 67, '水力发电': 68, \n",
    " '互联网': 69, '旅游服务': 70, '纺织': 71, '铝': 72, '保险': 73, '园区开发': 74, '小金属': 75, '铜': 76, '普钢': 77, \n",
    " '航空': 78, '特种钢': 79, '种植业': 80, '出版业': 81, '焦炭加工': 82, '啤酒': 83, '公路': 84, '超市连锁': 85, \n",
    " '钢加工': 86, '渔业': 87, '农用机械': 88, '软饮料': 89, '化工机械': 90, '塑料': 91, '红黄酒': 92, '橡胶': 93, '家居用品': 94,\n",
    " '摩托车': 95, '电器仪表': 96, '服饰': 97, '仓储物流': 98, '纺织机械': 99, '电器连锁': 100, '装修装饰': 101, '半导体': 102, \n",
    " '电信运营': 103, '石油开采': 104, '乳制品': 105, '商品城': 106, '公共交通': 107, '船舶': 108, '陶瓷': 109}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2892/2892 [01:12<00:00, 39.81it/s]\n"
     ]
    }
   ],
   "source": [
    "def JudgeST(x):\n",
    "    if 'ST' in x:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def GetMA(df, col_name, rolling_day):\n",
    "    tmp_array = df[col_name].values.reshape(-1)\n",
    "    df.loc[ rolling_day:, col_name + '_'+str(rolling_day)] = (tmp_array[0:-rolling_day] - \n",
    "                                                              tmp_array[rolling_day:]) / tmp_array[0:-rolling_day]\n",
    "    return df\n",
    "\n",
    "col = ['ma'+str(i) for i in [5, 10, 13]] + ['ma_v_'+str(i) for i in [5, 10, 13]]\n",
    "\n",
    "company_info = pd.read_csv(os.path.join(base_path, 'company_info.csv'), encoding='ANSI')\n",
    "company_info['is_ST'] = company_info['name'].apply(JudgeST)\n",
    "# 丢弃一些多余的信息\n",
    "company_info.drop(['index', 'symbol', 'fullname'], axis=1, inplace=True)\n",
    "company_info.dropna(inplace=True)\n",
    "company_info['market'] = company_info['market'].map(market_map)\n",
    "company_info['exchange'] = company_info['exchange'].map(exchange_map)\n",
    "company_info['is_hs'] = company_info['is_hs'].map(is_hs_map)\n",
    "\n",
    "\n",
    "# 读取指数信息\n",
    "stock_index_info = pd.DataFrame()\n",
    "index = ['000001.SH', '000016.SH', '000002.SH', '399001.SZ', '399007.SZ', '399008.SZ', '399101.SZ',\n",
    "         '399102.SZ']\n",
    "for ts_code in index:\n",
    "    tmp_df = pd.read_csv(os.path.join(base_path,  'OldData', ts_code + '_NormalData.csv'))\n",
    "    # 特征工程\n",
    "#         tmp_df = FeatureEngineering(tmp_df)\n",
    "    stock_index_info = pd.concat((stock_index_info, tmp_df)) \n",
    "    break\n",
    "#     transaction_day = len(tmp_df) \n",
    "tmp_list = list(tmp_df['trade_date'].sort_values())\n",
    "date_map = dict(zip(tmp_list, range(len(tmp_list))))\n",
    "\n",
    "# 读取股票交易信息\n",
    "stock_info = pd.DataFrame()\n",
    "remove_stock = []\n",
    "tmp_list = []\n",
    "for ts_code in tqdm.tqdm(company_info['ts_code']):\n",
    "    try:\n",
    "        tmp_df = pd.read_csv(os.path.join(base_path,  'OldData', ts_code + '_NormalData.csv'))\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    # 还需要去除一些停牌时间很久的企业，后期加\n",
    "    if len(tmp_df) < 100:  # 去除一些上市不久的企业\n",
    "        remove_stock.append(ts_code)\n",
    "        continue\n",
    "    tmp_df = tmp_df.sort_values('trade_date', ascending=True).reset_index(drop=True)    \n",
    "    for tmp_col in col:\n",
    "        for rolling_day in [3]:\n",
    "            tmp_df = GetMA(tmp_df, tmp_col, rolling_day)\n",
    "    \n",
    "    \n",
    "\n",
    "    tmp_list.append(tmp_df)\n",
    "stock_info = pd.concat(tmp_list)\n",
    "stock_info = stock_info.reset_index(drop=True)\n",
    "ts_code_map = dict(zip(stock_info['ts_code'].unique(), range(stock_info['ts_code'].nunique())))\n",
    "\n",
    "\n",
    "stock_info = stock_info.reset_index(drop=True)\n",
    "stock_info['ts_code_id'] = stock_info['ts_code'].map(ts_code_map)\n",
    "\n",
    "stock_info['trade_date_id'] = stock_info['trade_date'].map(date_map)\n",
    "stock_info['ts_date_id'] = (10000 + stock_info['ts_code_id']) * 10000 + stock_info['trade_date_id']\n",
    "stock_info = stock_info.merge(company_info, how='left', on='ts_code')\n",
    "stock_info_copy = stock_info.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4345535"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_info = stock_info.drop_duplicates(subset=['ts_date_id'])\n",
    "len(stock_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4346519"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stock_info_copy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_col = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取月份 和 星期几\n",
    "def get_weekday(x):\n",
    "    x = str(x)\n",
    "    return datetime.datetime.fromtimestamp(time.mktime(time.strptime(x, \"%Y%m%d\"))).weekday()\n",
    "# stock_info['month'] = stock_info['trade_date'].apply(lambda x: int(str(x)[4:6]))\n",
    "stock_info['weekday'] = stock_info['trade_date'].apply(get_weekday)\n",
    "# feature_col.append('month')\n",
    "feature_col.append('weekday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 添加涨跌停信息\n",
    "limit_info = pd.read_csv('limit.csv')\n",
    "limit_info.drop('file', axis=1, inplace=True)\n",
    "limit_info['U-D'] = limit_info['U'] - limit_info['D']\n",
    "limit_info = limit_info.rename(columns={'date':'trade_date'})\n",
    "stock_info = stock_info.merge(limit_info[['U-D', 'trade_date']], on='trade_date', how='left')\n",
    "feature_col.append('U-D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 求相对的vol 这里与21均线比较\n",
    "stock_info['vol'] = (stock_info['vol'] - stock_info['ma_v_21']) / stock_info['ma_v_21']\n",
    "feature_col.append('vol')\n",
    "\n",
    "#转换low colse等\n",
    "col = ['open', 'high', 'low', 'high', 'pre_close']\n",
    "for tmp_col in col:\n",
    "    stock_info[tmp_col+'_transform'] = (stock_info[tmp_col] - stock_info['ma5']) / stock_info['ma5']\n",
    "    feature_col.append(tmp_col+'_transform')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amount_index_000001\n",
      "pre_close_index_000001\n",
      "amount\n",
      "pre_close\n"
     ]
    }
   ],
   "source": [
    "# 添加大盘指数信息\n",
    "tmp_df = pd.read_csv(os.path.join(base_path,  'OldData', '000001.SH' + '_NormalData.csv'))\n",
    "for tmp_col in ['amount', 'pre_close']:\n",
    "    tmp_df = tmp_df.rename(columns={tmp_col: tmp_col + '_index_000001'})\n",
    "    stock_info = stock_info.merge(tmp_df[['trade_date', tmp_col + '_index_000001']], on='trade_date', how='left')\n",
    "\n",
    "for tmp_col in ['amount_index_000001', 'pre_close_index_000001', 'amount', 'pre_close']:\n",
    "    print(tmp_col)\n",
    "    for i in range(3):\n",
    "        tmp_df = stock_info[['ts_date_id', tmp_col]]\n",
    "        new_col_name = tmp_col + '_shift_{}'.format(i+1)\n",
    "        tmp_df = tmp_df.rename(columns={tmp_col:new_col_name})\n",
    "        feature_col.append(new_col_name)\n",
    "        tmp_df['ts_date_id'] = tmp_df['ts_date_id'] + i + 1\n",
    "        stock_info = stock_info.merge(tmp_df, how='left', on='ts_date_id')\n",
    "    for i in range(3):\n",
    "        new_col_name = tmp_col + '_shift_{}'.format(i+1)\n",
    "        stock_info[new_col_name] = (stock_info[tmp_col] - stock_info[new_col_name]) / stock_info[new_col_name]\n",
    "#         feature_col.append(new_col_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2892/2892 [02:53<00:00, 18.26it/s]\n"
     ]
    }
   ],
   "source": [
    "# 添加资金流向\n",
    "df_list = []\n",
    "col = ['buy_md_vol', 'sell_md_vol', 'buy_lg_vol', 'sell_lg_vol', \n",
    "       'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'] #'buy_sm_vol', 'sell_sm_vol',\n",
    "for ts_code in tqdm.tqdm(company_info['ts_code']):\n",
    "    try:\n",
    "        tmp_df = pd.read_csv(os.path.join(base_path,  'MoneyData', ts_code + '.csv'))\n",
    "    except:\n",
    "        pass\n",
    "    transform_col = []\n",
    "    # 还需要去除一些停牌时间很久的企业，后期加\n",
    "    if len(tmp_df) < 100:  # 去除一些上市不久的企业\n",
    "        remove_stock.append(ts_code)\n",
    "        continue\n",
    "    tmp_df = tmp_df.sort_values('trade_date', ascending=True).reset_index(drop=True)  \n",
    "    drop_col = []\n",
    "#     tmp_df.drop(['buy_sm_amount', 'sell_sm_amount', 'buy_md_amount', 'sell_md_amount', 'buy_lg_amount', 'sell_lg_amount', \n",
    "#                 'buy_elg_amount', 'sell_elg_amount', 'net_mf_amount'])\n",
    "    tmp_df['diff_md'] = tmp_df['buy_md_vol'] - tmp_df['sell_md_vol']\n",
    "    tmp_df['diff_lg'] = tmp_df['buy_lg_vol'] - tmp_df['sell_lg_vol']\n",
    "    tmp_df['diff_elg'] = tmp_df['buy_elg_vol'] - tmp_df['sell_elg_vol']\n",
    "    rolling_day = 5\n",
    "    for tmp_col in ['buy_md_vol', 'buy_lg_vol', 'buy_elg_vol', 'diff_md', 'diff_lg', 'diff_elg']:\n",
    "        tmp_df[tmp_col+'_'+str(rolling_day)] = tmp_df[tmp_col].rolling(rolling_day).mean()\n",
    "        tmp_df[tmp_col+'_transform'] = (tmp_df[tmp_col] - tmp_df[tmp_col+'_'+str(rolling_day)]) / tmp_df[tmp_col+'_'+str(rolling_day)]\n",
    "        transform_col.append(tmp_col+'_transform')    \n",
    "    transfom_col = ['buy_md_vol', 'buy_lg_vol', 'buy_elg_vol', 'diff_md', 'diff_lg', 'diff_elg']        \n",
    "    for shift_day in [1, 2, 3]:\n",
    "        for tmp_col in ['buy_md_vol', 'buy_lg_vol', 'buy_elg_vol', 'diff_md', 'diff_lg', 'diff_elg']:\n",
    "            tmp_array = tmp_df[tmp_col].values.reshape(-1)\n",
    "            tmp_df.loc[shift_day:, tmp_col + '_shift_'+str(shift_day)] = tmp_array[0:-shift_day]\n",
    "            tmp_df[tmp_col + '_shift_'+str(shift_day)] = \\\n",
    "            (tmp_df[tmp_col + '_shift_'+str(shift_day)] - tmp_df[tmp_col+'_'+str(rolling_day)]) / tmp_df[tmp_col+'_'+str(rolling_day)]\n",
    "            transform_col.append(tmp_col + '_shift_'+str(shift_day))    \n",
    "    df_list.append(tmp_df[['trade_date', 'ts_code']+transform_col])\n",
    "    \n",
    "df_all = pd.concat(df_list)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_col = feature_col + [tmp_col for tmp_col in df_all.columns if tmp_col not in['trade_date', 'ts_code']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_info = stock_info.merge(df_all, on=['trade_date', 'ts_code'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_col.append('turnover_rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['trade_date', 'ts_code', 'open', 'high', 'low', 'close', 'pre_close',\n",
       "       'change', 'pct_chg', 'vol', 'amount', 'turnover_rate', 'volume_ratio',\n",
       "       'ma5', 'ma_v_5', 'ma10', 'ma_v_10', 'ma13', 'ma_v_13', 'ma21',\n",
       "       'ma_v_21', 'ma30', 'ma_v_30', 'ma60', 'ma_v_60', 'ma120', 'ma_v_120',\n",
       "       'ma5_3', 'ma10_3', 'ma13_3', 'ma_v_5_3', 'ma_v_10_3', 'ma_v_13_3',\n",
       "       'ts_code_id', 'trade_date_id', 'ts_date_id', 'name', 'area', 'industry',\n",
       "       'market', 'exchange', 'list_date', 'is_hs', 'is_ST', 'weekday', 'U-D',\n",
       "       'open_transform', 'high_transform', 'low_transform',\n",
       "       'pre_close_transform', 'amount_index_000001', 'pre_close_index_000001',\n",
       "       'amount_index_000001_shift_1', 'amount_index_000001_shift_2',\n",
       "       'amount_index_000001_shift_3', 'pre_close_index_000001_shift_1',\n",
       "       'pre_close_index_000001_shift_2', 'pre_close_index_000001_shift_3',\n",
       "       'amount_shift_1', 'amount_shift_2', 'amount_shift_3',\n",
       "       'pre_close_shift_1', 'pre_close_shift_2', 'pre_close_shift_3',\n",
       "       'high_shift_1', 'low_shift_1', 'high_shift_2', 'low_shift_2',\n",
       "       'high_shift_3', 'low_shift_3', 'label_max', 'label_min', 'label_final'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_info.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标签制作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make_label\n",
    "# stock_info = stock_info_copy.copy()\n",
    "use_col = []\n",
    "for i in range(3):\n",
    "    tmp_df = stock_info[['ts_date_id', 'high', 'low']]\n",
    "    tmp_df = tmp_df.rename(columns={'high':'high_shift_{}'.format(i+1), 'low':'low_shift_{}'.format(i+1)})\n",
    "    use_col.append('high_shift_{}'.format(i+1))\n",
    "    use_col.append('low_shift_{}'.format(i+1))\n",
    "    tmp_df['ts_date_id'] = tmp_df['ts_date_id'] - i - 1\n",
    "    stock_info = stock_info.merge(tmp_df, how='left', on='ts_date_id')\n",
    "\n",
    "stock_info.dropna(inplace=True)\n",
    "\n",
    "for i in range(3):\n",
    "    stock_info['high_shift_{}'.format(i+1)] = (stock_info['high_shift_{}'.format(i+1)] - stock_info['close']) / stock_info['close']\n",
    "    stock_info['low_shift_{}'.format(i+1)] = (stock_info['low_shift_{}'.format(i+1)] - stock_info['close']) / stock_info['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_array = stock_info[use_col].values\n",
    "max_increse = np.max(tmp_array, axis=1)\n",
    "min_increse = np.min(tmp_array, axis=1)\n",
    "stock_info['label_max'] = max_increse\n",
    "stock_info['label_min'] = min_increse\n",
    "stock_info['label_final'] = (stock_info['label_max'] > 0.06) & (stock_info['label_min'] > -0.03)\n",
    "stock_info['label_final'] = stock_info['label_final'].apply(lambda x: int(x))\n",
    "\n",
    "stock_info = stock_info.reset_index(drop=True)\n",
    "# stock_info.drop('index', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_col=['weekday', 'U-D', 'vol', 'open_transform', 'high_transform', 'low_transform', 'high_transform', 'pre_close_transform', \n",
    " 'amount_index_000001_shift_1', 'amount_index_000001_shift_2', 'amount_index_000001_shift_3', 'pre_close_index_000001_shift_1', \n",
    " 'pre_close_index_000001_shift_2', 'pre_close_index_000001_shift_3', 'amount_shift_1', 'amount_shift_2', 'amount_shift_3',\n",
    " 'pre_close_shift_1', 'pre_close_shift_2', 'pre_close_shift_3', 'turnover_rate', 'buy_md_vol_shift_1', 'buy_lg_vol_shift_1', \n",
    " 'buy_elg_vol_shift_1', 'diff_md_shift_1', 'diff_lg_shift_1', 'diff_elg_shift_1', 'buy_md_vol_shift_2', 'buy_lg_vol_shift_2', \n",
    " 'buy_elg_vol_shift_2', 'diff_md_shift_2', 'diff_lg_shift_2', 'diff_elg_shift_2']\n",
    "\n",
    "# 'buy_md_vol_shift_1', 'buy_lg_vol_shift_1', \n",
    "#  'buy_elg_vol_shift_1', 'diff_md_shift_1', 'diff_lg_shift_1', 'diff_elg_shift_1', 'buy_md_vol_shift_2', 'buy_lg_vol_shift_2', \n",
    "#  'buy_elg_vol_shift_2', 'diff_md_shift_2', 'diff_lg_shift_2', 'diff_elg_shift_2', 'buy_md_vol_shift_3', 'buy_lg_vol_shift_3', \n",
    "#  'buy_elg_vol_shift_3', 'diff_md_shift_3', 'diff_lg_shift_3', 'diff_elg_shift_3'\n",
    "\n",
    "# 'buy_md_vol_transform', 'buy_lg_vol_transform',\n",
    "#  'buy_elg_vol_transform', 'diff_md_transform', 'diff_lg_transform', 'diff_elg_transform', "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['weekday', 'U-D', 'vol', 'open_transform', 'high_transform', 'low_transform', 'high_transform', 'pre_close_transform', 'amount_index_000001_shift_1', 'amount_index_000001_shift_2', 'amount_index_000001_shift_3', 'pre_close_index_000001_shift_1', 'pre_close_index_000001_shift_2', 'pre_close_index_000001_shift_3', 'amount_shift_1', 'amount_shift_2', 'amount_shift_3', 'pre_close_shift_1', 'pre_close_shift_2', 'pre_close_shift_3', 'turnover_rate', 'buy_md_vol_shift_1', 'buy_lg_vol_shift_1', 'buy_elg_vol_shift_1', 'diff_md_shift_1', 'diff_lg_shift_1', 'diff_elg_shift_1', 'buy_md_vol_shift_2', 'buy_lg_vol_shift_2', 'buy_elg_vol_shift_2', 'diff_md_shift_2', 'diff_lg_shift_2', 'diff_elg_shift_2']\n"
     ]
    }
   ],
   "source": [
    "print(feature_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "trn_col = feature_col\n",
    "# 'high', 'low', 'close', 'pre_close',\n",
    "trn_col = list(set(trn_col))\n",
    "label = 'label_final'\n",
    "# trn_date_min = 20170103\n",
    "# trn_date_max = 20190416\n",
    "# val_date_min = 20190417\n",
    "# val_date_max = 20190429\n",
    "# test_date_min = 20190417\n",
    "# test_date_max = 20191218\n",
    "\n",
    "trn_date_min = 20170103\n",
    "trn_date_max = 20190101\n",
    "val_date_min = 20190101\n",
    "val_date_max = 20190419\n",
    "test_date_min = 20190101\n",
    "test_date_max = 20191218\n",
    "\n",
    "trn_data_idx = (stock_info['trade_date'] >= trn_date_min) & (stock_info['trade_date'] <= trn_date_max) & (stock_info['high']!=stock_info['close'])\n",
    "val_data_idx = (stock_info['trade_date'] >= val_date_min) & (stock_info['trade_date'] <= val_date_max)\n",
    "test_data_idx = (stock_info['trade_date'] >= test_date_min) & (stock_info['trade_date'] <= test_date_max)\n",
    "\n",
    "trn = stock_info[trn_data_idx][trn_col]\n",
    "trn_label = stock_info[trn_data_idx][label].values\n",
    "\n",
    "val = stock_info[val_data_idx][trn_col]\n",
    "val_label = stock_info[val_data_idx][label].values \n",
    "\n",
    "test = stock_info[test_data_idx][trn_col]\n",
    "test_label = stock_info[test_data_idx][label].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rate of 0: 0.8942, rate of 1: 0.1058\n",
      "trn data:1130149, val data:24710, test data:655822\n",
      "number of features:32\n"
     ]
    }
   ],
   "source": [
    "print('rate of 0: %.4f, rate of 1: %.4f' % (np.sum(trn_label==0)/len(trn_label), np.sum(trn_label==1)/len(trn_label)))\n",
    "print('trn data:%d, val data:%d, test data:%d' % (len(trn), len(val), len(test)))\n",
    "print('number of features:%d' % len(trn_col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "def win_score_eval(preds, valid_df):\n",
    "    labels = valid_df.get_label()\n",
    "    preds = np.round(preds)\n",
    "    tp = np.sum((preds==1)&(labels==1))\n",
    "    pp = np.sum(preds==1)\n",
    "    scores = tp/(pp+0.001) + 2.5*tp - pp\n",
    "#     preds = np.argmax(preds.reshape(3, -1), axis=0)\n",
    "#     scores = f1_score(y_true=labels, y_pred=preds, average='macro')\n",
    "    return 'win', scores, True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型训练及评价\n",
    "import lightgbm as lgb\n",
    "from sklearn import metrics\n",
    "param = {'num_leaves': 31,\n",
    "         'min_data_in_leaf': 20,\n",
    "         'objective': 'binary',\n",
    "         'learning_rate': 0.06,\n",
    "         \"boosting\": \"gbdt\",\n",
    "#          \"bagging_freq\": 1,\n",
    "#          \"bagging_seed\": 11,\n",
    "         \"metric\": 'None',\n",
    "         \"verbosity\": -1}\n",
    "trn_data = lgb.Dataset(trn, trn_label)\n",
    "val_data = lgb.Dataset(val, val_label)\n",
    "num_round =1000\n",
    "# clf = lgb.train(param, trn_data, num_round, valid_sets=[trn_data, val_data], verbose_eval=100,\n",
    "#                 early_stopping_rounds=300, feval=win_score_eval)\n",
    "clf = lgb.train(param, trn_data, num_round, verbose_eval=300)\n",
    "# oof_lgb = clf.predict(val, num_iteration=clf.best_iteration)\n",
    "test_lgb = clf.predict(test, num_iteration=clf.best_iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feature_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "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>column</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amount_index_000001_shift_1</td>\n",
       "      <td>1490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>pre_close_index_000001_shift_1</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>U-D</td>\n",
       "      <td>1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>amount_index_000001_shift_2</td>\n",
       "      <td>1381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>turnover_rate</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>pre_close_index_000001_shift_3</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>pre_close_index_000001_shift_2</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>amount_index_000001_shift_3</td>\n",
       "      <td>1224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high_transform</td>\n",
       "      <td>1126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low_transform</td>\n",
       "      <td>1120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>vol</td>\n",
       "      <td>1031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>buy_elg_vol_shift_1</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>amount_shift_3</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>pre_close_shift_1</td>\n",
       "      <td>878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>amount_shift_1</td>\n",
       "      <td>856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pre_close_shift_3</td>\n",
       "      <td>843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>buy_lg_vol_shift_1</td>\n",
       "      <td>841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>buy_elg_vol_shift_2</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>diff_lg_shift_2</td>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>diff_elg_shift_2</td>\n",
       "      <td>772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>amount_shift_2</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>diff_elg_shift_1</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>diff_md_shift_1</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>diff_lg_shift_1</td>\n",
       "      <td>741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>buy_md_vol_shift_1</td>\n",
       "      <td>734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>pre_close_shift_2</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>buy_lg_vol_shift_2</td>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>buy_md_vol_shift_2</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>diff_md_shift_2</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>open_transform</td>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>pre_close_transform</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weekday</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            column  importance\n",
       "8      amount_index_000001_shift_1        1490\n",
       "17  pre_close_index_000001_shift_1        1419\n",
       "21                             U-D        1405\n",
       "11     amount_index_000001_shift_2        1381\n",
       "13                   turnover_rate        1321\n",
       "19  pre_close_index_000001_shift_3        1263\n",
       "22  pre_close_index_000001_shift_2        1235\n",
       "24     amount_index_000001_shift_3        1224\n",
       "4                   high_transform        1126\n",
       "1                    low_transform        1120\n",
       "12                             vol        1031\n",
       "9              buy_elg_vol_shift_1         903\n",
       "31                  amount_shift_3         889\n",
       "30               pre_close_shift_1         878\n",
       "16                  amount_shift_1         856\n",
       "2                pre_close_shift_3         843\n",
       "7               buy_lg_vol_shift_1         841\n",
       "25             buy_elg_vol_shift_2         799\n",
       "10                 diff_lg_shift_2         782\n",
       "15                diff_elg_shift_2         772\n",
       "5                   amount_shift_2         747\n",
       "27                diff_elg_shift_1         747\n",
       "0                  diff_md_shift_1         747\n",
       "6                  diff_lg_shift_1         741\n",
       "3               buy_md_vol_shift_1         734\n",
       "29               pre_close_shift_2         731\n",
       "14              buy_lg_vol_shift_2         721\n",
       "20              buy_md_vol_shift_2         720\n",
       "28                 diff_md_shift_2         718\n",
       "26                  open_transform         698\n",
       "23             pre_close_transform         675\n",
       "18                         weekday         443"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "        'column': trn_col,\n",
    "        'importance': clf.feature_importance(),\n",
    "    }).sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "# oof_lgb_final = np.round(oof_lgb)\n",
    "# print(metrics.accuracy_score(val_label, oof_lgb_final))\n",
    "# print(metrics.confusion_matrix(val_label, oof_lgb_final))\n",
    "# tp = np.sum(((oof_lgb_final == 1) & (val_label == 1)))\n",
    "# pp = np.sum(oof_lgb_final == 1)\n",
    "# print('sensitivity:%.3f'% (tp/(pp)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8496634757601911\n",
      "[[557102    272]\n",
      " [ 98322    126]]\n",
      "sensitivity:0.317\n"
     ]
    }
   ],
   "source": [
    "thresh_hold = 0.5\n",
    "# oof_test_final = (test_lgb >= 0.69) & (test_lgb <=0.7)\n",
    "oof_test_final = test_lgb >= thresh_hold\n",
    "print(metrics.accuracy_score(test_label, oof_test_final))\n",
    "print(metrics.confusion_matrix(test_label, oof_test_final))\n",
    "tp = np.sum(((oof_test_final == 1) & (test_label == 1)))\n",
    "pp = np.sum(oof_test_final == 1)\n",
    "print('sensitivity:%.3f'% (tp/(pp)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_postive_idx = np.argwhere(oof_test_final == 1).reshape(-1)\n",
    "# test_postive_idx = list(range(len(oof_test_final)))\n",
    "test_all_idx = np.argwhere(test_data_idx).reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看选了哪些股票\n",
    "tmp_col = ['ts_code', 'name', 'trade_date', 'open', 'high', 'low', 'close', 'pre_close',\n",
    "       'change', 'pct_chg', 'amount', 'is_ST', 'label_max', 'label_min', 'label_final']\n",
    "# stock_info.iloc[test_all_idx[test_postive_idx]]\n",
    "\n",
    "tmp_df = stock_info[tmp_col].iloc[test_all_idx[test_postive_idx]].reset_index()\n",
    "tmp_df['label_prob'] = test_lgb[test_postive_idx]\n",
    "# idx_tmp = tmp_df['is_ST'] == 0\n",
    "# tmp_df.loc[idx_tmp, 'is_limit_up'] = (((tmp_df['close'][idx_tmp]-tmp_df['pre_close'][idx_tmp]) / tmp_df['pre_close'][idx_tmp]) > 0.095)\n",
    "# idx_tmp = tmp_df['is_ST'] == 1\n",
    "# tmp_df.loc[idx_tmp, 'is_limit_up'] = (((tmp_df['close'][idx_tmp]-tmp_df['pre_close'][idx_tmp]) / tmp_df['pre_close'][idx_tmp]) > 0.047)\n",
    "\n",
    "tmp_df['is_limit_up'] = tmp_df['close'] == tmp_df['high']\n",
    "\n",
    "buy_df = tmp_df[(tmp_df['is_limit_up']==False)].reset_index()\n",
    "buy_df.drop(['index', 'level_0'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(len(buy_df), sum(buy_df['label_final']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 回测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock_info.reset_index().head()\n",
    "# 读取指数信息\n",
    "index_df = pd.read_csv(os.path.join(base_path,  'OldData', '000001.SH' + '_NormalData.csv'))\n",
    "tmp_idx = (index_df['trade_date'] >= test_date_min) & (index_df['trade_date'] <= test_date_max)\n",
    "index_df = index_df.loc[tmp_idx].reset_index()\n",
    "index_df.drop('index', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_idx = (index_df['trade_date'] == test_date_min+1)\n",
    "close1 = index_df[tmp_idx]['close'].values[0]\n",
    "tmp_idx = (index_df['trade_date'] == test_date_max)\n",
    "close2 = index_df[tmp_idx]['close'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20190311  买入 德生科技 (002908.SZ) 1400股，股价：13.9418,花费：19518.52,手续费：5.86，剩余现金：80475.62\n",
      "20190311  买入 粤传媒 (002181.SZ) 3200股，股价：6.1657,花费：19730.24,手续费：5.92，剩余现金：60739.47\n",
      "20190311  买入 冀东装备 (000856.SZ) 1200股，股价：15.57,花费：18684.0,手续费：5.61，剩余现金：42049.86\n",
      "20190311  买入 广电网络 (600831.SH) 2200股，股价：8.892999999999999,花费：19564.6,手续费：5.87，剩余现金：22479.39\n",
      "20190311  买入 中光学 (002189.SZ) 900股，股价：21.766,花费：19589.4,手续费：5.88，剩余现金：2884.11\n",
      "20190312  止盈卖出中光学 (002189.SZ) 900股，股价：22.8543,收入：20568.87,手续费：26.74，剩余现金：23426.24，最终盈利：946.85\n",
      "20190312  止盈卖出广电网络 (600831.SH) 2200股，股价：9.33765,收入：20542.83,手续费：26.71，剩余现金：43942.37，最终盈利：945.65\n",
      "20190312  止盈卖出冀东装备 (000856.SZ) 1200股，股价：16.3485,收入：19618.2,手续费：25.5，剩余现金：63535.07，最终盈利：903.09\n",
      "20190312  止盈卖出粤传媒 (002181.SZ) 3200股，股价：6.473985000000001,收入：20716.75,手续费：26.93，剩余现金：84224.89，最终盈利：953.66\n",
      "20190312  止盈卖出德生科技 (002908.SZ) 1400股，股价：14.6554,收入：20517.56,手续费：26.67，剩余现金：104715.77，最终盈利：966.51\n",
      "20190318  买入 中国软件 (600536.SH) 400股，股价：47.2564,花费：18902.56,手续费：5.67，剩余现金：85807.54\n",
      "20190318  买入 奥飞娱乐 (002292.SZ) 2400股，股价：8.46,花费：20304.0,手续费：6.09，剩余现金：65497.45\n",
      "20190319  止盈卖出奥飞娱乐 (002292.SZ) 2400股，股价：8.883000000000001,收入：21319.2,手续费：27.71，剩余现金：86788.94，最终盈利：981.39\n",
      "20190319  止损卖出中国软件 (600536.SH) 400股，股价：45.366144,收入：18146.46,手续费：23.59，剩余现金：104911.8，最终亏损：-785.36\n",
      "20190327  买入 动力源 (600405.SH) 3700股，股价：5.67,花费：20979.0,手续费：6.29，剩余现金：83926.51\n",
      "20190328  止损卖出动力源 (600405.SH) 3700股，股价：5.4432,收入：20139.84,手续费：26.18，剩余现金：104040.17，最终亏损：-871.64\n",
      "20190329  买入 中铝国际 (601068.SH) 2800股，股价：7.4229,花费：20784.12,手续费：6.24，剩余现金：83249.81\n",
      "20190329  买入 中国软件 (600536.SH) 300股，股价：55.4371,花费：16631.13,手续费：5，剩余现金：66613.68\n",
      "20190329  买入 雄韬股份 (002733.SZ) 800股，股价：24.1631,花费：19330.48,手续费：5.8，剩余现金：47277.4\n",
      "20190329  买入 沃尔核材 (002130.SZ) 3900股，股价：5.26,花费：20514.0,手续费：6.15，剩余现金：26757.25\n",
      "20190329  买入 天津磁卡 (600800.SH) 2500股，股价：8.02,花费：20050.0,手续费：6.02，剩余现金：6701.23\n",
      "20190329  买入 复旦复华 (600624.SH) 500股，股价：12.0533,花费：6026.65,手续费：5，剩余现金：669.58\n",
      "20190401  止盈卖出沃尔核材 (002130.SZ) 3900股，股价：5.523,收入：21539.7,手续费：28.0，剩余现金：22181.28，最终盈利：991.54\n",
      "20190401  止盈卖出雄韬股份 (002733.SZ) 800股，股价：25.371255,收入：20297.0,手续费：26.39，剩余现金：42451.9，最终盈利：934.34\n",
      "20190401  止盈卖出中国软件 (600536.SH) 300股，股价：58.208955,收入：17462.69,手续费：22.7，剩余现金：59891.88，最终盈利：803.86\n",
      "20190402  止盈卖出复旦复华 (600624.SH) 500股，股价：12.655965,收入：6327.98,手续费：11.33，剩余现金：66208.54，最终盈利：285.0\n",
      "20190402  止盈卖出天津磁卡 (600800.SH) 2500股，股价：8.59,收入：21475.0,手续费：27.92，剩余现金：87655.62，最终盈利：1391.07\n",
      "20190402  止盈卖出中铝国际 (601068.SH) 2800股，股价：7.794045000000001,收入：21823.33,手续费：28.37，剩余现金：109450.58，最终盈利：1004.6\n",
      "20190418  买入 汇顶科技 (603160.SH) 100股，股价：132.4769,花费：13247.69,手续费：5，剩余现金：96197.89\n",
      "20190419  止损卖出汇顶科技 (603160.SH) 100股，股价：127.177824,收入：12717.78,手续费：17.72，剩余现金：108897.95，最终亏损：-552.63\n",
      "20190429  买入 招商银行 (600036.SH) 600股，股价：34.4016,花费：20640.96,手续费：6.19，剩余现金：88250.8\n",
      "20190430  止损卖出招商银行 (600036.SH) 600股，股价：33.025536,收入：19815.32,手续费：25.76，剩余现金：108040.36，最终亏损：-857.59\n",
      "20190510  买入 华茂股份 (000850.SZ) 4100股，股价：5.1967,花费：21306.47,手续费：6.39，剩余现金：86727.5\n",
      "20190510  买入 东音股份 (002793.SZ) 2000股，股价：10.38,花费：20760.0,手续费：6.23，剩余现金：65961.27\n",
      "20190510  买入 盈趣科技 (002925.SZ) 400股，股价：47.18,花费：18872.0,手续费：5.66，剩余现金：47083.61\n",
      "20190510  买入 浙江富润 (600070.SH) 2400股，股价：8.9533,花费：21487.92,手续费：6.45，剩余现金：25589.24\n",
      "20190510  买入 江苏舜天 (600287.SH) 2900股，股价：7.2829,花费：21120.41,手续费：6.34，剩余现金：4462.5\n",
      "20190513  止盈卖出江苏舜天 (600287.SH) 2900股，股价：7.647045,收入：22176.43,手续费：28.83，剩余现金：26610.1，最终盈利：1020.86\n",
      "20190513  止损卖出浙江富润 (600070.SH) 2400股，股价：8.595168000000001,收入：20628.4,手续费：26.82，剩余现金：47211.69，最终亏损：-892.78\n",
      "20190513  止损卖出盈趣科技 (002925.SZ) 400股，股价：45.2928,收入：18117.12,手续费：23.55，剩余现金：65305.25，最终亏损：-784.09\n",
      "20190513  止损卖出东音股份 (002793.SZ) 2000股，股价：9.9331,收入：19866.2,手续费：25.83，剩余现金：85145.63，最终亏损：-925.85\n",
      "20190513  止盈卖出华茂股份 (000850.SZ) 4100股，股价：5.456535,收入：22371.79,手续费：29.08，剩余现金：107488.34，最终盈利：1029.85\n",
      "20190522  买入 国际实业 (000159.SZ) 2300股，股价：9.062999999999999,花费：20844.9,手续费：6.25，剩余现金：86637.18\n",
      "20190524  止损卖出国际实业 (000159.SZ) 2300股，股价：8.7542,收入：20134.66,手续费：26.18，剩余现金：106745.67，最终亏损：-742.67\n",
      "20190528  买入 远望谷 (002161.SZ) 2600股，股价：7.93,花费：20618.0,手续费：6.19，剩余现金：86121.48\n",
      "20190529  止盈卖出远望谷 (002161.SZ) 2600股，股价：8.3265,收入：21648.9,手续费：28.14，剩余现金：107742.24，最终盈利：996.57\n",
      "20190611  买入 东方金钰 (600086.SH) 5900股，股价：3.64,花费：21476.0,手续费：6.44，剩余现金：86259.8\n",
      "20190612  止盈卖出东方金钰 (600086.SH) 5900股，股价：3.8220000000000005,收入：22549.8,手续费：29.31，剩余现金：108780.28，最终盈利：1038.04\n",
      "20190701  买入 华创阳安 (600155.SH) 1600股，股价：13.54,花费：21664.0,手续费：6.5，剩余现金：87109.78\n",
      "20190703  到期卖出华创阳安 (600155.SH) 1600股，股价：13.5,收入：21600.0,手续费：28.08，剩余现金：108681.7，最终亏损：-98.58\n",
      "20190806  买入 康强电子 (002119.SZ) 1500股，股价：14.08,花费：21120.0,手续费：6.34，剩余现金：87555.37\n",
      "20190806  买入 山煤国际 (600546.SH) 3100股，股价：6.83,花费：21173.0,手续费：6.35，剩余现金：66376.01\n",
      "20190806  买入 山东黄金 (600547.SH) 600股，股价：32.2102,花费：19326.12,手续费：5.8，剩余现金：47044.1\n",
      "20190806  买入 中金黄金 (600489.SH) 2200股，股价：9.67,花费：21274.0,手续费：6.38，剩余现金：25763.71\n",
      "20190806  买入 登海种业 (002041.SZ) 2400股，股价：8.78,花费：21072.0,手续费：6.32，剩余现金：4685.39\n",
      "20190807  止盈卖出登海种业 (002041.SZ) 2400股，股价：9.219,收入：22125.6,手续费：28.76，剩余现金：26782.23，最终盈利：1018.52\n",
      "20190807  止盈卖出中金黄金 (600489.SH) 2200股，股价：10.153500000000001,收入：22337.7,手续费：29.04，剩余现金：49090.89，最终盈利：1028.28\n",
      "20190807  止盈卖出山东黄金 (600547.SH) 600股，股价：33.820710000000005,收入：20292.43,手续费：26.38，剩余现金：69356.94，最终盈利：934.13\n",
      "20190807  止损卖出康强电子 (002119.SZ) 1500股，股价：13.5168,收入：20275.2,手续费：26.36，剩余现金：89605.78，最终亏损：-877.49\n",
      "20190808  到期卖出山煤国际 (600546.SH) 3100股，股价：6.9,收入：21390.0,手续费：27.81，剩余现金：110967.97，最终盈利：182.84\n",
      "20190809  买入 东山精密 (002384.SZ) 1200股，股价：17.3571,花费：20828.52,手续费：6.25，剩余现金：90133.2\n",
      "20190809  买入 中欣氟材 (002915.SZ) 800股，股价：25.62,花费：20496.0,手续费：6.15，剩余现金：69631.05\n",
      "20190809  买入 泰晶科技 (603738.SH) 1100股，股价：19.33,花费：21263.0,手续费：6.38，剩余现金：48361.68\n",
      "20190809  买入 共达电声 (002655.SZ) 2500股，股价：8.84,花费：22100.0,手续费：6.63，剩余现金：26255.05\n",
      "20190809  买入 唐人神 (002567.SZ) 1800股，股价：11.75,花费：21150.0,手续费：6.34，剩余现金：5098.7\n",
      "20190809  买入 哈高科 (600095.SH) 600股，股价：7.54,花费：4524.0,手续费：5，剩余现金：569.7\n",
      "20190812  止盈卖出哈高科 (600095.SH) 600股，股价：7.917000000000001,收入：4750.2,手续费：9.75，剩余现金：5310.15，最终盈利：211.45\n",
      "20190812  止盈卖出共达电声 (002655.SZ) 2500股，股价：9.282,收入：23205.0,手续费：30.17，剩余现金：28484.98，最终盈利：1068.2\n",
      "20190812  止盈卖出泰晶科技 (603738.SH) 1100股，股价：20.296499999999998,收入：22326.15,手续费：29.02，剩余现金：50782.11，最终盈利：1027.75\n",
      "20190812  止盈卖出东山精密 (002384.SZ) 1200股，股价：18.224955,收入：21869.95,手续费：28.43，剩余现金：72623.63，最终盈利：1006.75\n",
      "20190813  到期卖出唐人神 (002567.SZ) 1800股，股价：11.82,收入：21276.0,手续费：27.66，剩余现金：93871.97，最终盈利：92.0\n",
      "20190813  止盈卖出中欣氟材 (002915.SZ) 800股，股价：27.12,收入：21696.0,手续费：28.2，剩余现金：115539.76，最终盈利：1165.65\n",
      "20190827  买入 华映科技 (000536.SZ) 7500股，股价：3.06,花费：22950.0,手续费：6.88，剩余现金：92582.88\n",
      "20190827  买入 中国长城 (000066.SZ) 2100股，股价：10.53,花费：22113.0,手续费：6.63，剩余现金：70463.24\n",
      "20190827  买入 湘油泵 (603319.SH) 1400股，股价：16.13,花费：22582.0,手续费：6.77，剩余现金：47874.47\n",
      "20190827  买入 海量数据 (603138.SH) 1300股，股价：16.55,花费：21515.0,手续费：6.45，剩余现金：26353.01\n",
      "20190827  买入 德赛西威 (002920.SZ) 1000股，股价：23.06,花费：23060.0,手续费：6.92，剩余现金：3286.1\n",
      "20190828  止损卖出中国长城 (000066.SZ) 2100股，股价：10.108799999999999,收入：21228.48,手续费：27.6，剩余现金：24486.98，最终亏损：-918.75\n",
      "20190829  到期卖出德赛西威 (002920.SZ) 1000股，股价：22.76,收入：22760.0,手续费：29.59，剩余现金：47217.39，最终亏损：-336.51\n",
      "20190829  到期卖出海量数据 (603138.SH) 1300股，股价：16.5,收入：21450.0,手续费：27.88，剩余现金：68639.51，最终亏损：-99.34\n",
      "20190829  到期卖出湘油泵 (603319.SH) 1400股，股价：16.2,收入：22680.0,手续费：29.48，剩余现金：91290.02，最终盈利：61.74\n",
      "20190829  止损卖出华映科技 (000536.SZ) 7500股，股价：2.9375999999999998,收入：22032.0,手续费：28.64，剩余现金：113293.38，最终亏损：-953.53\n"
     ]
    }
   ],
   "source": [
    "from imp import reload\n",
    "import Account\n",
    "reload(Account)\n",
    "money_init = 100000\n",
    "account = Account.Account(money_init, max_hold_period=3)\n",
    "account.BackTest(buy_df, stock_info_copy, index_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_df2 = buy_df[['ts_code', 'trade_date', 'label_prob', 'label_final']]\n",
    "tmp_df2 = tmp_df2.rename(columns={'trade_date':'buy_date'})\n",
    "tmp_df = account.info\n",
    "tmp_df = tmp_df.merge(tmp_df2, on=['ts_code', 'buy_date'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "账户盈利情况:0.1329\n",
      "上证指数浮动情况:0.2238\n",
      "交易胜率:0.6585\n",
      "最大回撤率:0.0248\n"
     ]
    }
   ],
   "source": [
    "account_profit = (account.market_value - money_init) / money_init\n",
    "index_profit = (close2 - close1) / close1\n",
    "win_rate = account.victory / (account.victory + account.defeat)\n",
    "print('账户盈利情况:%.4f' % account_profit)\n",
    "print('上证指数浮动情况:%.4f' % index_profit)\n",
    "print('交易胜率:%.4f' % win_rate)\n",
    "print('最大回撤率:%.4f' % account.max_retracement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import Draw\n",
    "reload(Draw)\n",
    "%matplotlib inline\n",
    "index_value = list(index_df[index_df['trade_date']==test_date_min+1]['pre_close']) + list(index_df.sort_values('trade_date')['close'])\n",
    "Draw.Draw_Market_Value_Change(0, account.market_value_all, index_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\code\\venv2\\lib\\site-packages\\matplotlib\\pyplot.py:514: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OS38BvA3oA9qA64BvFQJ7BDO72Mx6zax36BZzIuVKfBd4g9iyJfy9/e0lzNwIlxptgjEB0jyqHZX9l8A33P0Cd78KOBE4ATh/9IzufoO7z3X3ua2trVUuVqRElbb2Um7dunDDqAkTSpi50kuNRkmtc0mRaoP5MOA/h564+wDwM+DIKj9XGkUuFy4kkVSVtvba2ipbXqXvK1OuM8dV668i11nZDsfQ+cslqfRSoxWo9nuJpEG1wfw4kBl6YmYTgGnAlio/VxpFf3/ibllYE5XexrKS91UQ5v2D/SMeS7FzJ1x7bbgu9ne+A6edVvZi666S7yWSNtUGcxdwhZnNN7PpwGeB/YHbqi1MpGlEeC/rK6+EDRtg2jR4+WW4447IFi0iJao2mK8G/o1wjvNPgAXAWe7+VLWFSQxSeulKCVpawnnLt9wCCxaEY8xPpeVfakSHEESiUOp5zAC4+0mjnr8KLC/8SaNL46Urs9nQ3Z7kgUsRmjMnnCaVycB558FHPlLHhVUQltlMlv7BfrKZMv9/RdjrIFJvula2RC7SAT4RDlxqBDO3PcQTT8D8+fCP/1jnhVUQlvmOPEsWLiHfof9f0rwUzBK5Sgf45DpzdPYkeAR4A5h3wCPMmwcrV9bpjlIiUjUFszSM/sF+BnemcAR4hE487Ffcfz9MLOsglohEScEsInunwVUikVEwS+SGBvaUO8Anm8mSacnsfUapvQgHV7VNbYtsWSJJpGCWyOU78tx0zk1lD/DJd+TpmNdRp6qaRGdn4i9P2j67Pe4SRGKlI01plMvBjh2wbVvclYxLP74xGRxM55XaRFJEwZxGSb4LkIiI7JG6skVERBJEwSwNRQODRCTtFMwSVHrf4mw2XN8xIhUdm9apPiLSQBTMElR63+J8PtykPsl0HWURaSAKZgmGbvKgmz2IiMRKwSyBbvYgIpIICmYREZEEUTBL9TS4SkSkZhTMUj0NrhIRqRkFs4iISIIomEVERBJEwSwiIpIgCmYZpkFcIiKx092lZJgGcaXf5MnwutfFXYWI7IFazCINKJvJjngs2bZtuoiMSMKpxSzSgPIdCleRtFKLWUREJEEUzCIiIgmiYBYREUkQBbOIiEiCKJhFREQSRMEsIiKSIApmERGRBFEwi4iIJIiCWUREJEEUzCIiIgmiYBYREUkQBbOIiEiCKJhFREQSRMEsIiKSIE0fzF2buuIuQUREZLemD+a+gb7IlqWdABER2ZumD+YoRbYTkM1CJhPNskREpKZKDmYzm2Bmd5lZ+zivzzCzQTNrq1FtUql8Hjo64q5CREQqUFIwm9l+wCrgjHFe3we4Cdi/dqUlWFdXNMvJ5cAsPJarra3m5YiISP2V2mK+HngB6Bnn9b8GXl+TiiKU68zR2dNZ5ptysGhRZWFZrv7+kY/laG+vaSkiIhKNUoN5ubsvBnaOfsHM3gwsBS6sYV3lqbBl2T/Yz+DOwfKWVWFYVrQTICIiTaekYHb3x8aaXtSF3enuP6hlYWWppmVZrmx25GOJKtoJEBGRplPtqOyPAy3A1Xub0cwuNrNeM+vdunVrlYutjWwmS6alzNHL+TwsWRIeRUREaqziYDazI4ErgA+5+yt7m9/db3D3ue4+t7W1tdLF1lS+I0/HPI1eFhGR5KimxXweMAV4wMwGzGygMH2zmV1efWnpUlHrvMJucxERaVzVBPPngRnA7KI/gDOBFVXWlTr5jjxfOvNLZb5J3eYiIs1mYqVvdPcBYKB4mpkBPFF4rSG0TW2LbFnts9sjW5aIiDSmsoLZ3U/ay+tWVTUxUFiKiEiSpONa2VEfi9VVtUREpE7SEcz5PNx0U3THYqO8qpZ2AkREmko6ghnSewnKtH4vEREZU3qCWUREJAUUzCIiIgmiYBYREUkQBbOIiEiCKJhFREQSRMEsIiKSIObu0S/UbCvweI0/9hDg6Rp/ZiPT+himdTFM62IkrY9hWhcj1WN9THf3vd5eMZZgrgcz63X3uXHXkRRaH8O0LoZpXYyk9TFM62KkONeHurJFREQSRMEsIiKSIGkK5hviLiBhtD6GaV0M07oYSetjmNbFSLGtj9QcYxYREUmDNLWYRUREGp6COUXM7Eoz81F/Z8ddl8TDzCaY2V1m1l407Y1jbCO3xVimRMzM5pjZg2b2spk9ZWaXF6a3mNmOUdtGb9z1RsHM9jez483siLhrgRQEs5m93sxuM7PnzWyjmR0Td00xOh64DHh90d89sVYUsbHCqDD9L81si5nlzexjMZUXGTPbD1gFnDHqpROABxm5jVwYbXXRGy+MCq+dZ2Y/N7NnzOwaM2v438XxmNlk4FvAOuAw4M+AZWZ2KjAbGGDktnFSPJVGx8z+APgVcB3woJndaWYTCq/Fsm2kYQP8OvB7hB+czwB3mNkB8ZYUPTMzwjr4trsPFP29EndtURkvjMzsT4BPAx8H3glcbGbvjr7CSF0PvAD0jJo+D1g/aht5IfryorOnMDKz+cBq4LPAcYR/Q5fGVWsE3gKscvcr3T3v7rcDmwjffR5w36ht47exVltnhd+M1UC7u88BZhIaOOfEuW009OAvM3sr8Chwgrt/vzDtLuBr7n5LrMVFzMyOIqyLR4EjgF8CV7n7v8daWITM7CZgF3AU8K/u3lWY/hCw0d0vKTx/P3Cxu58cV631ZmYz3P0xM+sGuorWxQPAFOB3gZ3AN4C/cfcdcdVab2Z2PHCuu/9d0bSHgP8EjgEOdPfTC9PfAax295mxFBsxM5sE5Ak7K38MzAWmAi3AncBfuXtqrwZmZlngj9x9RdG0HwIrgJOJadto9BbzHELXywNF075H2LtpNkcDPwE+RuhB+ApwSyGwm8Vyd19MCBxgd0/CbEZ26ad+G3H3x0ZPM7N9gWnAvxF2Xs4BzgY+GW110XL3748K5UnADMK/lzmM3DY2AtPN7OBoq4zNx4HngLWE1uLthHWykNC6/mJ8pdWfu/ePCuX3AW8C/osYt42J9V5AnU0BfuEjm/0DhBXaVNx9DbCmaNK1ZnYOcAFwZTxVRWusMAIyhO3850XTBoADzGyKu2+LpLgEcPeXCS3lIVvMbClwFXBFLEXFoziMrqNo23D3XWa2HXgj8Ew85UXDzOYQdsrOKWwbxb+bj5vZx4F7zOx1ae5Rgd07rT8jHOr4oLs/aWZTiGnbaPQW8yvAi6OmvQA03THmcTxJ2NCa2dAx9uLtZOiYqraTsI0cOjTYJe2KwuiSQhg15W+ImR0C3Ab8s7vfO85sTxK6tHORFRaTwrawAPgUsLJwmDS2baPRg/lpXrvRTAFeiqGWWJnZp83s0qLnEwndtWO1IpuGu78IDDJyO5lSeGyq7cTMTjCz746aPB/4pbvviqOmKI0TRmP9hkwmxduGme1P6C34IaG3BDNrM7OHzaylaNb5hPXwRPRVRs/dH3f3KwlnLXyIGLeNRg/m7xP6/A8tmjYX+HVM9cTpIeCTZnaWmb0d6AIOIhxPbHbfA04sej6XsOf7bDzlxGYzMMvMlpnZsWZ2MfA3hLMZUm2sMCoYsW2Y2ZsJP76pDKPCmIs1wIHAJUCmcBbLbwiHfK4zs7cVBkheA3zR3XeO+4ENzszmm9mqUZN3ElrL8W0b7t7Qf0A38FVgAnAsoevhrLjrimld/B1hhOXTwN3AsXHXFOM20V70vJ3ww3MYsB/w38CtcdcZ07p4B9BL6EXYTBiNa3HXWed1YIQRxg8DrYSuyAOASYTzdH8LvI3QUFkNPBR3zXVcF8cAPsZfF2Hw138Xto0fA58AWuKuuc7r46DC7+XVhGPHFxB22o+Nc9to6NOlYPdpQvcQBvlMBv7d3VN/wQQZ3xinCA39o3oPYcdtO7DA3ftiKlEiVLjo0MNjvHSzu7eb2acJF+Z5jrCDf7q7PxRljRKfwmlQnyfstPwUuMLd7ym8Fsu20fDBDLu7qRYCz7r7A3ubX5pTYeDPG4AN7r497nokOczsSML5//d7is/blfLFsW2kIphFRETSotEHf4mIiKSKgllERCRBFMwiIiIJomAWERFJEAWziIhIgiiYRUREEuT/AX4uZt+4kfxyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MWJZ6SvRM72nIaw058sgjgeSOUW+88cab2zdt2gTAxIkT2X///VmyZAlbtmxh7733hj32gGuugRtugN13r19xOe8V6dq9q+LnlHpKzJs1r2ELk1hzczA3Us7fcKxMqQQ9jQ3LRlqxYgUAq1evprOzk2effRaAu++++819zjzzTM466yxOGxp93d9frGuXVc737T68u/a1mJVxMJvlQDWtsB1x3333MWvWLPr7+5k1axZ33nkn55577rDW87HHHsvg4CAnn3xyVa9R1cDFapdQrYbn+1pOefCXWY10tHfQP9Bf1XXERrbC5s+f/5ZtS5cuHfb4hRdeYM6cOXzxi1+kvb09uQxTYWu5qoGLTbCEarWq+fDVt6aP+X3zmT97fs3rsfwaUzBLmgosBA4H1gNXRsQVktqADUD58j8rImJazSs1y7lST6kwb6J77rknDzzwwNYNeb8M094OEyZU/rwGzvet5sPX7K7ZhTifrDKjdmVLmgT8DLgH2Bf4EvAtScexNaj3KPuaXa9izWwrz3Ev09NTXZd0zq+ZN/oSh+XDWK4xvx+4MSIuiYhSRNwOrASOBKYD90XE+rKvl+tZsBVDU4RKFa2pRr6R1mKO+4MPwuuv73gtVh8eaNaaRg3miHgoIv5q6LGkdwAHAL8jCeYPSnpe0gZJt0jaq37lWlE0xcIpVbSmmumN9Omn4YQTkkW8zCw/qhmVfSHwInAHcCBwOzAVmEXSur5mW0+SdLak5ZKWD03NMLPq7MgSj//1X8n3BQvg9NMbNl3bzMaoomBOB4FdCnw5IjZHxNSI6ImItRHxG5LQ/rSk3UY+NyKuj4hpETFtypQptanerEXtyBKPs2bBunVw3XVw/vljeEKVU5h6H+ht3NrQZgUy5mBOu6hvA74bEfduZ7d1QBvgv0arvc7OZE1k2yHjx8ONN8KHPgRPPAHXXz/KE6qcwjQwOOC1oc2qMKZgljSepOv6N8Bl6bYuSavSKVNDZgCvAU/WulDLsUYtNdrfn6yJbDtk4kT4wQ9gn33gC19IbhZVD+1t7ZXP6a5yNS7f5tCKZCzTpQQsBiYCXwbaJU0AniaZB71A0hGSTgWuAK6JiME61tycitzay/scVxtmy5Zk4NeDDyZfRx1Vn9fpmd5T+drQ1a7GlfNpT2aVGEuL+VDgBOAw4BlgY/p1HfApkhHa95O0pK8CvlGXSptdla09X6ezWht45mUOOgh+9SvYb7+sqzGzkUZd+SsiVgN6m12OqV05NtLA4AADg+6+BZLuzZcrnCbf0ZF8KKq0a7TAVn7xH9n9iovYKa8r5btb2lpcXv80LQNV33Sgwi76qqf6VHPHJ9+o4C32HP9qQ0K56sVW3C1tLc43sbA37dBNByp5yg5M9bHm0UyLrZjliVvMVnzuGt2qmkGI1Y6UNrOquMVsDbcjt0esirtGt6rmdoq+DGDWUG4xV6EpbsCQY6WeEvNmzat8Ko2ZWQtwMFexOEZT3IDBzMyaUmsHc2cnnHde1lXkhudMm5llr7WDuZGLfnR05P42Pk2xtrEHcplZwbV2MDdSNXNw7a08kMvMCs7BXIWq1gAGt/bKVL34hJlZwTmYG8mtvTd58YkdMzTVrGFTzsysYTyP2awJeaqZWXG5xVxUjbpHspmZ1VRxgrmBQdQU10eruEdyVTe2NzOzmipOMFcRRNUq6vXRqge1WfNogml7Zq3O15jNWkmp5MscZjlXnBazmY2NZweY5ZqD2czMLEcczEVUzT13qXJQm69ZmpnVlIO5iKpcA7yqQW1eatTMrKaKE8y9vUlLsRIdHTBpUn3qaSVeatTMrGaKMyp7YKDyVmIp/1ODJu0yid3adsu6jLfnwURmZjVTnGBub4cJE7KuouZeuvilrEswM7MGKk5Xdk9PU7SAzczM3k5xgtnXOc3MrACKE8y+zmlmZgVQnGA2MzMrAAdzEXnRDzOzpuVgLiIv+mFm1rQczEXlwXBmZk3JwVxUHgxnZtaUHMxmZmY54mA2MzPLEQezmZlZjjiYzczMcsTBbGZmliMOZjMzsxxRRDT+RaVngbU1/rV7Ac/V+Hc2Mx+PrXwshvPx2MrHYisfi+HqcTz2i4gpo+2USTDXg6TlETEt6zrywsdjKx+L4Xw8tvKx2MrHYrgsj4e7ss3MzHLEwWxmZpYjRQrm67MuIGd8PLbysRjOx2MrH4utfCyGy+x4FOYas5mZWREUqcVsZmbW9BzMZmbW0iSNl3SUpIOyrgUKEMyS9pB0m6QNklZIOizrmrIi6RJJMeJrTtZ1NZKkcZJ+Lql7xPavSHpKUknSVzMqr6G2dSwk7b2Nc+S2DMtsCElTJS2TtFnSM5IuKvvZZyU9Lul5SVdIavr3xbezvWMhqU3SphHnxvKs6603SR8F/ggsAJZJukvSuPRnmZwbRTgBbwX+HDgauBL4D0kTsi0pM0cBFwB7lH39ItOKGkjSrsCNwMdHbP9L4HvAhcAxwNmSPtn4Chtne8eC5O9kGcPPkTMbW11jSZoE/Ay4B9gX+BLwLUnHSZoB3AxcBRxJcnzOy6rWenu7YwEcDqxn+LkxO5tKGyP9O7kZ6I6IqcCBJO+jJ2Z5bjT14C9JfwH8Fjg6Ih5Kt/0cuCUibsq0uAaTJOB54MMR8UjW9WRB0o+BN4CDgR9ExKJ0+6+BFRHx5fTxqcDZEXFsVrXW29sci6uAwYj4qwzLayhJRwEnl/+f03Pip8BhwMSIOD7d/t+AmyPiwEyKrbNRjsUGYEZEnJpVfY0mqQM4KSIWlm37DbAQOJaMzo1mbzFPJfmE93DZtgdJPt20mvcBk4FbJb0q6dG0pdhKvh0Rc4HBoQ3pB5bDGd5z0ArnyFuORWo68Mn00s/zkhZI2i2D+homIh4aEUTvAA4AfkfyHlJ+bqwA9pP0zsZW2RijHIvpwAfT82KDpFsk7ZVVrY0QEf0jQvkzwP7AnWR4bjR7ME8Gnojhzf71wN4Z1ZOlQ0n+uL5K0rX/Q+AmSQdnWlUDRcQftrG5HdgZeLxs23pggqTJDSksA9s6FpJ2AfYB/omkJX0iMAe4tLHVZe5C4EXgDpL3kDfPjYh4A9hI67yHlB+LA4HbSQJpFvB+4JrsSmscSbtIWgssBs6JiHVkeG7sXO8XqLPXgVdHbHsFaLlrzBGxmOSkGvL3kk4ETgcuyaaqXHg9/V5+nrySfp8AvNTYcrITEZuBPyvb9JSk+cBlwMWZFNVgkqaSfBA5MSI2S2rZ95CRx4IkkIeslXQh8AtJu0XEpkyKbJD0XJgJzAWuk7SSDPOl2VvMzwGdI7ZNBl7LoJY8WkcywKNlRcSrwADDz5OhlrLPk+Qcec/QKNQiS7tlbwO+GxH3ppu39R4yiYKfG9s5FiOtA9p46/EppIhYGxGXkAyO/DwZnhvNHswPkfT5v6ds2zTgTxnVkxlJ35N0XtnjnUmuo26re7fVPAh8uOzxNJJPvi9kU042JB0t6ZcjNs8A/l/aTVdYksaTdNf+hqSHYMiwc0PSe0nefJ9saIENtK1jIalL0ipJbWW7ziAJoSIfixmSbhyxeZCktZzZudHUwRwRTwL3A99J52x+ADgZuCvbyjLxa+BSSSdI+hCwCNiT5Hpiq7sZOF/Svun0iIuBn0fElozrarTVwCGSviXpA5LOBr5GMs2wsNIBgIuBicCXgXZJE9KBTzcDZ0g6Ip2jOg9YHhGl7Cqun+0dC+BpkkubC9JjcSpwBXBNRIwcQFgkjwIfl/QdJXP8Twc+AvwbGZ4bzX6NGeAckpFz/SSfZv4lIn6WbUmNFxGLJXWRBPHOJEE9Mx3E0Or+GfjvJIPjXiUZwHFWphVlICIGlCw4s4Bk0M/jJIMFi37zgkOBE9J/P1O2/YaI6JZ0LUn35YvAOOD4BtfXSNs9FsCnSM6F+0kW3LiKZP5/YUXEC5I+AVwNnA/8nmQ62SqArM6Npp7HPCTtmpkFvBARD4+2v7WmdLDLu4GlEbEx63osPyS9DzgI+FVEPJd1PZYfWZwbhQhmMzOzomjqa8xmZmZF42A2MzPLEQezmZlZjjiYzczMcsTBbGZmliMOZjMzsxz5/0lozqVQwlaQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bZDC3TB0dzRpR6Z7S9d7ab3i4CBZJ6mAGc4dqVU/pvuV9LF/rkQBJahZ7ZWunOjrrdGSPcUkqkS3mCmvltap1n/eVmq3ig4VU+jSROoLBPEWtDMvhJcMsXbR0ytes1nV4ud7zvlKzVfza53YYjU/trXOCuZ6RpOro5Tu8ZJhcmlMf4KGFPYpHRkcY3dq6jmbSpJYvr/QRmN6eXnpmTf1vcvna5ZUfjU/trXOCuZ7BDFrZy7fOdfUf0N/8Wpqo3g83dYHR0UofgRleMsySk6b+Nzm6ddS+FZpWndH5q6+v+ABYsWLq1z9W/HzWwHEDZZfwjIaXDHvOTZKaqDNazI2cH634+ax69Pb0sv+e+7dsfVP+8uDoTpLq0C2NgM4I5nbRotb58JJhHn7Pw1P7oVaGpaM7qRu18o5qVVfntuiWjncGcytVuXVuWGoXWnrZXierox9Mx7YQvcHJMzKYJT2jei/bU+O6pYWo8TojmD1nKVVP1e9kRfWvelB36oxg9jBsc1S8h7rKU1eAVf1OVlT/qgd1p84IZjVHlc+Bq1QGWIO87aumwGCWpOnW4tu+Vr4HeB2jwnXTnewMZpXDw+bSs6p7+M+q93quY1S4bhpq2GBWOTxsXg6/ELUVh/9sXDtecmYwS92klV+I/BKgJqp3XP52vOTMYJY0PTwqoiaq96Yj9SqzpW0wS5LaQiuvOy+zpd0Zd5eSpBbpmdXD7D1nl11GV+qWy/ZsMUvSFCw5aYnDk45V4Uuz+pb3sWz1svp6tpfIYFZ7sUOR2lFvbzFE6RT1zOqp9s1D+vrgkkum/nMVH6617GumDWa1FzsUqR0NDxdDlE7RJ171iZa1zuvq7FTvwCktGq613huwlH3NdOecY7YlJanDtPKcajteVjRdent62bJ1S2nr75xgtiUlqcpa1Xjo64M//QmuuKI166tHCxtS9fTkLrsPgYeyJakVbDzs1MJt0Y49uQ1mSeokdZ7PVnUYzJKk+m+YoaZrOJgjYnFE/CwiHo+IX0TEm5tRmCSpTnWcw/WGGdXRUOeviOgHrgbOBTYApwDXRMSGzFzXaHGSpDp4PrutNdping/cm5m3Z+ZwZq4Efg0c1XhpklQ9rRyvWd2p0WDeCBwdEa+OiH0j4r8DfcBQw5VJUgW1Yy9ftZeGDmVn5s8j4oPAV3dMAs7PzN9OnDciLgIuApg3b14jq5UkqWM11GKOiOOAtwGLgH2A84AVEbFw4ryZ+ZnMXJCZC+bOndvIaiVJ6liNHsp+E7AyM9dk5uOZ+UVgFXBh46VJklpl/z33r/YNM7pIo0NyzgQmNn97gacdypYkVdfD73m47BJU02gwfwtYGRGXAb8ETq09zmq0MEmSulGjnb9uroXy3wGHAw8A78jMLzejOEmSuk3Dd5fKzI8CH21CLZKkdtLbW9zJSk3lWNmSpPp4w4xpYTBLklQhBrMkSRViMEuSVCEGsyRJFWIwS5JUIQazJEkVYjBLklQhBrMkSRViMEuSVCEGsySpfv39ZVfQcQxmSVL9BgbKrqDjGMySJFWIwSxJUoUYzJIkVYjBLElShRjMkiRViMEsSVKFRGa2fqUR9wO/avJiDwIeaPIy25nbYye3xU5ui/HcHju5Lcabju1xeGbOfbaZSgnm6RAR6zJzQdl1VIXbYye3xU5ui/HcHju5LcYrc3t4KFuSpAoxmCVJqpBOCubPlF1Axbg9dnJb7OS2GM/tsZPbYrzStkfHnGOWJKkTdFKLWZKktmcwd5CIuDwicsLjjLLrUjkiYkZE3BoRA2OmHTrJPnJjiWWqxSJifkR8PyKejIg/RMRltemzImLLhH1jXdn1tkJE7BsRJ0TEX5VdC3RAMEfEcyLixoh4JCLWR8QxZddUohOAdwDPGfO4rdSKWmyyMKpN//uIuC8ihiPinSWV1zIRsTdwLfDKCW+dCHyf8fvIBa2trvV2FUa1986NiF9ExB8j4sqIaPvPxV2JiP2BrwG3A/OAvwX+KSJeDhwHbGb8vnFKOZW2TkT8DfBr4FPA9yPiqxExo/ZeKftGJ+yAXwCeR/GB8xHgyxExu9ySWi8igmIbfCMzN495/Lns2lplV2EUEW8APgS8CzgVuCgiXtP6Clvq08BjwNoJ008CVk/YRx5rfXmt80xhFBEnA9cB/wIcT/E3dElZtbbAfwOuzczLM3M4M28C7qH43U8C7pywb/yp1GqnWe0z4zpgIDPnA8+naOCcWea+0dadvyLir4EfAidm5ndr024F/k9m/nupxbVYRLyAYlv8EPgr4JfAssxcWWphLRQRnwe2AS8A/i0zB2vT7wbWZ+bbaq9fD1yUmaeVVet0i4gjM/PnETEEDI7ZFt8D5gD/BdgKfBG4NDO3lFXrdIuIE4BzMvPdY6bdDXwFOAbYLzNPr01/CXBdZj6/lGJbLCL2AoYpvqy8FlgAHADMAr4KvD0zO3Y0sIjoBc7OzBVjpv0AWAGcRkn7Rru3mOdTHHr53phpd1F8u+k2LwR+CryT4gjC1cC/1wK7W3wgM99CETjAU0cSjmP8If2O30cy8+cTp0XEnsBhwGcpvrycCZwBvK+11bVWZn53QijvBRxJ8fcyn/H7xnrg8Ij4i9ZWWZp3AQ8BN1O0Fm+i2CaLKFrXV5VX2vTLzJEJofw64AjgFkrcN2ZO9wqm2RzgP3N8s38zxQbtKpl5PXD9mEkfjogzgfOBy8upqrUmCyOgh2I//8WYaZuB2RExJzMfbklxFZCZT1K0lHe4LyKuAJYB7ymlqHKMDaNPMWbfyMxtEfEocCjwx3LKa42ImE/xpezM2r4x9nPzVxHxLuC2iNink4+owFNfWu+lONXxxsz8XUTMoaR9o91bzH8GHp8w7TGg684x78LvKHa0brbjHPvY/WTHOVX3k2IfOWRHZ5dONyaM3lYLo678DImIg4AbgX/OzK/vYrbfURzS7mtZYSWp7QsLgfcD/1o7TVravtHuwfwAT99p5gBPlFBLqSLiQxFxyZjXMykO107Wiuwamfk4MMr4/WRO7bmr9pOIODEi7pgw+WTgl5m5rYyaWmkXYTTZZ8j+dPC+ERH7Uhwt+AHF0RIioj8iNkTErDGznkyxHX7b+ipbLzN/lZmXU1y18CZK3DfaPZi/S3HM/5Ax0xYAvympnjLdDbwvIl4dES8GBoEDKc4ndru7gJeOeb2A4pvvg+WUU5qNwNER8U8RcWxEXARcSnE1Q0ebLIxqxu0bEfFfKT58OzKMan0urgf2A94G9NSuYvk9xSmfT0XEi2odJK8ErsrMrbtcYJuLiJMj4toJk7dStJbL2zcys60fwBBwDTADOJbi0MOry66rpG3xbooelg8Aq4Bjy66pxH1iYMzrAYoPnnnA3sC3gBvKrrOkbfESYB3FUYSNFL1xo+w6p3kbBEUP4w3AXIpDkbOBvSiu0/0T8CKKhsp1wN1l1zyN2+IYICd5DFJ0/vpWbd/4CfBeYFbZNU/z9jiw9nn5QYpzx+dTfGk/tsx9o60vl4KnLhO6jaKTz/7Ayszs+AETtGuTXCK044/qLIovbo8CCzNzU0klqoVqgw5tmOSt/52ZAxHxIYqBeR6i+IJ/embe3coaVZ7aZVAfo/jS8jPgPZl5W+29UvaNtg9meOow1SLgwcz83rPNr+5U6/hzMLAmMx8tux5VR0QcRXH9/3eyg6/b1dSVsW90RDBLktQp2r3zlyRJHcVgliSpQgxmSZIqxGCWJKlCDGZJkirEYJYkqUL+P/B0RhJXaWSCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = 0\n",
    "for ts_code, buy_date, sell_date in zip(account.info['ts_code'], account.info['buy_date'], account.info['sell_date']):\n",
    "    Draw.Draw_Stock(ts_code, stock_info, buy_date, sell_date, left_offset=15, right_offset=15)\n",
    "    num = num + 1\n",
    "    if num > 20:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}