Stock/test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "from typing import Dict\n",
    "from render import * \n",
    "import db as database\n",
    "from jinja2 import Environment, PackageLoader, select_autoescape\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'db' from '/data/sss/f/tt/Stock/db.py'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(database)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = sqlite3.connect(\"stock.db\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.in_transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "krx = database.GetAllKRXCorp(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "krxDf = pd.DataFrame([corp.toDict() for corp in krx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = GetStockPriceFrom(db,\"212310\", 61)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.DataFrame(data, columns=[s for s in database.STOCK_INDEX.__members__.keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.set_index(\"DATE\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock = s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>CODE</th>\n",
       "      <th>CLOSE</th>\n",
       "      <th>DIFF</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>HIGH</th>\n",
       "      <th>LOW</th>\n",
       "      <th>VOLUME</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-02</th>\n",
       "      <td>212310</td>\n",
       "      <td>2375</td>\n",
       "      <td>75</td>\n",
       "      <td>2205</td>\n",
       "      <td>2615</td>\n",
       "      <td>2205</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01</th>\n",
       "      <td>212310</td>\n",
       "      <td>2300</td>\n",
       "      <td>35</td>\n",
       "      <td>2335</td>\n",
       "      <td>2645</td>\n",
       "      <td>2215</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-31</th>\n",
       "      <td>212310</td>\n",
       "      <td>2335</td>\n",
       "      <td>0</td>\n",
       "      <td>2340</td>\n",
       "      <td>2340</td>\n",
       "      <td>2335</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-30</th>\n",
       "      <td>212310</td>\n",
       "      <td>2335</td>\n",
       "      <td>190</td>\n",
       "      <td>2275</td>\n",
       "      <td>2790</td>\n",
       "      <td>2275</td>\n",
       "      <td>6245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-26</th>\n",
       "      <td>212310</td>\n",
       "      <td>2525</td>\n",
       "      <td>30</td>\n",
       "      <td>2450</td>\n",
       "      <td>2780</td>\n",
       "      <td>2420</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-13</th>\n",
       "      <td>212310</td>\n",
       "      <td>3190</td>\n",
       "      <td>100</td>\n",
       "      <td>3695</td>\n",
       "      <td>3695</td>\n",
       "      <td>2805</td>\n",
       "      <td>2967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-10</th>\n",
       "      <td>212310</td>\n",
       "      <td>3290</td>\n",
       "      <td>15</td>\n",
       "      <td>3675</td>\n",
       "      <td>3675</td>\n",
       "      <td>3110</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-09</th>\n",
       "      <td>212310</td>\n",
       "      <td>3275</td>\n",
       "      <td>205</td>\n",
       "      <td>3800</td>\n",
       "      <td>3800</td>\n",
       "      <td>3220</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-08</th>\n",
       "      <td>212310</td>\n",
       "      <td>3480</td>\n",
       "      <td>15</td>\n",
       "      <td>3460</td>\n",
       "      <td>3480</td>\n",
       "      <td>3460</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-07</th>\n",
       "      <td>212310</td>\n",
       "      <td>3495</td>\n",
       "      <td>235</td>\n",
       "      <td>3495</td>\n",
       "      <td>3500</td>\n",
       "      <td>3315</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              CODE  CLOSE  DIFF  OPEN  HIGH   LOW  VOLUME\n",
       "DATE                                                     \n",
       "2023-06-02  212310   2375    75  2205  2615  2205      19\n",
       "2023-06-01  212310   2300    35  2335  2645  2215      16\n",
       "2023-05-31  212310   2335     0  2340  2340  2335     145\n",
       "2023-05-30  212310   2335   190  2275  2790  2275    6245\n",
       "2023-05-26  212310   2525    30  2450  2780  2420     658\n",
       "...            ...    ...   ...   ...   ...   ...     ...\n",
       "2023-03-13  212310   3190   100  3695  3695  2805    2967\n",
       "2023-03-10  212310   3290    15  3675  3675  3110       9\n",
       "2023-03-09  212310   3275   205  3800  3800  3220     467\n",
       "2023-03-08  212310   3480    15  3460  3480  3460     289\n",
       "2023-03-07  212310   3495   235  3495  3500  3315     125\n",
       "\n",
       "[61 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2023-06-02    2374.0\n",
       "2023-06-01    2398.0\n",
       "2023-05-31    2404.0\n",
       "2023-05-30    2434.0\n",
       "2023-05-26    2447.0\n",
       "2023-05-25    2447.0\n",
       "Name: CLOSE, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close = stock[\"CLOSE\"]\n",
    "d5 = stock[\"CLOSE\"].iloc[:10].loc[::-1].rolling(window=5\n",
    "    ).mean().dropna().loc[::-1]\n",
    "d5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2023-06-02     88.487287\n",
       "2023-06-01    103.778611\n",
       "2023-05-31     97.365292\n",
       "2023-05-30     93.834962\n",
       "2023-05-26     80.202868\n",
       "2023-05-25     80.202868\n",
       "Name: CLOSE, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dstd5 = close.iloc[:10].loc[::-1].rolling(window=5).std().dropna().loc[::-1]\n",
    "dstd5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2023-06-01    2398.0\n",
       "2023-05-31    2404.0\n",
       "Name: CLOSE, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5[[1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50.04, 0.2, 50.44, True)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 51\n",
    "arr = np.array([50]* 24 + [a])\n",
    "m = arr.mean()\n",
    "std = arr.std(ddof=1)\n",
    "m, std , m + 2*std, m+2*std < a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a-50)/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.082092668956086"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "krx.__len__() * ((100-95.4499736104)/100)*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2023-05-22      27\n",
       "2023-05-19     403\n",
       "2023-05-18       1\n",
       "2023-05-17       5\n",
       "2023-05-16    3711\n",
       "              ... \n",
       "2023-02-28    1017\n",
       "2023-02-27     799\n",
       "2023-02-24      12\n",
       "2023-02-23     688\n",
       "2023-02-22       4\n",
       "Name: VOLUME, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume = stock[\"VOLUME\"]\n",
    "volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2023-05-22'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2022-06-03     True\n",
       "2022-06-02    False\n",
       "2022-05-31     True\n",
       "2022-05-30     True\n",
       "2022-05-27    False\n",
       "              ...  \n",
       "2022-03-15    False\n",
       "2022-03-14    False\n",
       "2022-03-11     True\n",
       "2022-03-10     True\n",
       "2022-03-08    False\n",
       "Name: CLOSE, Length: 61, dtype: bool"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biggerThanYesterday = stock[\"CLOSE\"].shift(-1) < stock[\"CLOSE\"]\n",
    "biggerThanYesterday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "ObvWeight = biggerThanYesterday.map(lambda x: 1 if x else -1)\n",
    "ObvWeight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2022-06-03     175508\n",
       "2022-06-02    -350113\n",
       "2022-05-31     276819\n",
       "2022-05-30     191087\n",
       "2022-05-27    -392051\n",
       "               ...   \n",
       "2022-03-15   -2701677\n",
       "2022-03-14   -4436719\n",
       "2022-03-11     997048\n",
       "2022-03-10    1754702\n",
       "2022-03-08    -878964\n",
       "Length: 61, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor = volume * ObvWeight\n",
    "factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2022-06-03   -1804451\n",
       "2022-06-02   -1979959\n",
       "2022-05-31   -1629846\n",
       "2022-05-30   -1906665\n",
       "2022-05-27   -2097752\n",
       "               ...   \n",
       "2022-03-15   -5265610\n",
       "2022-03-14   -2563933\n",
       "2022-03-11    1872786\n",
       "2022-03-10     875738\n",
       "2022-03-08    -878964\n",
       "Length: 61, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obv = factor.iloc[::-1].cumsum().iloc[::-1]\n",
    "obv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "close = stock[\"CLOSE\"]\n",
    "d5 = stock[\"CLOSE\"].loc[::-1].rolling(window=5\n",
    "    ).mean().dropna().loc[::-1]\n",
    "d10 = stock[\"CLOSE\"].loc[::-1].rolling(window=10\n",
    "    ).mean().dropna().loc[::-1]\n",
    "d20 = stock[\"CLOSE\"].loc[::-1].rolling(window=20\n",
    "    ).mean().dropna().loc[::-1]\n",
    "d30 = stock[\"CLOSE\"].loc[::-1].rolling(window=30\n",
    "    ).mean().dropna().loc[::-1]\n",
    "d60 = stock[\"CLOSE\"].loc[::-1].rolling(window=60\n",
    "    ).mean().dropna().loc[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isRelativeDiffLessThan(a:pd.Series,b:pd.Series, threshold: float,nday:int) -> bool:\n",
    "    return (a.iloc[nday] - b.iloc[nday]) / b.iloc[nday] < threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def every(f, xs):\n",
    "    for x in xs:\n",
    "        if not f(x):\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evert\n",
      "6236.0 6290 -54.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.008585055643879173"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [d5, d10, d20, d30, d60]\n",
    "if every(lambda i: isRelativeDiffLessThan(i,close,0.05,0), a):\n",
    "        print(\"evert\")\n",
    "print(d5.iloc[0] , close.iloc[0],d5.iloc[0] - close.iloc[0])\n",
    "(d5.iloc[0] - close.iloc[0]) / close.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.1876921038685744"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(d60.iloc[0] - close.iloc[0])/close.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(obv.iloc[:20].iloc[::-1])\n",
    "plt.xticks(rotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "d5 = stock[\"CLOSE\"].loc[::-1].rolling(window=5\n",
    "        ).mean().dropna().loc[::-1]\n",
    "d20 = stock[\"CLOSE\"].loc[::-1].rolling(window=20\n",
    "        ).mean().dropna().loc[::-1]\n",
    "d60 = stock[\"CLOSE\"].loc[::-1].rolling(window=60\n",
    "        ).mean().dropna().loc[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ewm5 =  stock[\"CLOSE\"].loc[::-1].ewm(span=5).mean().loc[::-1]\n",
    "ewm10 =  stock[\"CLOSE\"].loc[::-1].ewm(span=10).mean().loc[::-1]\n",
    "macd = (ewm5 - ewm10)\n",
    "signal = macd.loc[::-1].ewm(span=4).mean().loc[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(macd.iloc[0:10].iloc[::-1],label=\"macd\")\n",
    "plt.legend()\n",
    "plt.plot(signal.iloc[0:10].iloc[::-1],label=\"signal\")\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gen import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataStore = DataStore()\n",
    "krx_corps = dataStore.getAllKRXCorp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "collector = OutputCollector()\n",
    "prepareCollector(collector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2564/2564 [01:10<00:00, 36.62it/s] \n"
     ]
    }
   ],
   "source": [
    "for corp in tqdm.tqdm(krx_corps):\n",
    "    for nday in range(0, 5):\n",
    "        collect(dataStore, collector, corp, nday)\n",
    "    dataStore.clearCache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2022-05-27',\n",
       " '2022-06-02',\n",
       " '2022-05-31',\n",
       " '2022-05-30',\n",
       " '2021-04-14',\n",
       " '2022-05-04',\n",
       " '2022-01-28',\n",
       " '2022-05-16',\n",
       " '2021-07-05',\n",
       " '2022-02-10']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "315ddbd22344660769d1dae43a6a5e914850a7ce75ac77a9d658327898fcf0d6"
  },
  "kernelspec": {
   "display_name": "Python 3.9.2 ('stock': venv)",
   "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": "3.9.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}