ta-lib test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Cycle Indicators': ['HT_DCPERIOD',\n",
       "  'HT_DCPHASE',\n",
       "  'HT_PHASOR',\n",
       "  'HT_SINE',\n",
       "  'HT_TRENDMODE'],\n",
       " 'Math Operators': ['ADD',\n",
       "  'DIV',\n",
       "  'MAX',\n",
       "  'MAXINDEX',\n",
       "  'MIN',\n",
       "  'MININDEX',\n",
       "  'MINMAX',\n",
       "  'MINMAXINDEX',\n",
       "  'MULT',\n",
       "  'SUB',\n",
       "  'SUM'],\n",
       " 'Math Transform': ['ACOS',\n",
       "  'ASIN',\n",
       "  'ATAN',\n",
       "  'CEIL',\n",
       "  'COS',\n",
       "  'COSH',\n",
       "  'EXP',\n",
       "  'FLOOR',\n",
       "  'LN',\n",
       "  'LOG10',\n",
       "  'SIN',\n",
       "  'SINH',\n",
       "  'SQRT',\n",
       "  'TAN',\n",
       "  'TANH'],\n",
       " 'Momentum Indicators': ['ADX',\n",
       "  'ADXR',\n",
       "  'APO',\n",
       "  'AROON',\n",
       "  'AROONOSC',\n",
       "  'BOP',\n",
       "  'CCI',\n",
       "  'CMO',\n",
       "  'DX',\n",
       "  'MACD',\n",
       "  'MACDEXT',\n",
       "  'MACDFIX',\n",
       "  'MFI',\n",
       "  'MINUS_DI',\n",
       "  'MINUS_DM',\n",
       "  'MOM',\n",
       "  'PLUS_DI',\n",
       "  'PLUS_DM',\n",
       "  'PPO',\n",
       "  'ROC',\n",
       "  'ROCP',\n",
       "  'ROCR',\n",
       "  'ROCR100',\n",
       "  'RSI',\n",
       "  'STOCH',\n",
       "  'STOCHF',\n",
       "  'STOCHRSI',\n",
       "  'TRIX',\n",
       "  'ULTOSC',\n",
       "  'WILLR'],\n",
       " 'Overlap Studies': ['BBANDS',\n",
       "  'DEMA',\n",
       "  'EMA',\n",
       "  'HT_TRENDLINE',\n",
       "  'KAMA',\n",
       "  'MA',\n",
       "  'MAMA',\n",
       "  'MAVP',\n",
       "  'MIDPOINT',\n",
       "  'MIDPRICE',\n",
       "  'SAR',\n",
       "  'SAREXT',\n",
       "  'SMA',\n",
       "  'T3',\n",
       "  'TEMA',\n",
       "  'TRIMA',\n",
       "  'WMA'],\n",
       " 'Pattern Recognition': ['CDL2CROWS',\n",
       "  'CDL3BLACKCROWS',\n",
       "  'CDL3INSIDE',\n",
       "  'CDL3LINESTRIKE',\n",
       "  'CDL3OUTSIDE',\n",
       "  'CDL3STARSINSOUTH',\n",
       "  'CDL3WHITESOLDIERS',\n",
       "  'CDLABANDONEDBABY',\n",
       "  'CDLADVANCEBLOCK',\n",
       "  'CDLBELTHOLD',\n",
       "  'CDLBREAKAWAY',\n",
       "  'CDLCLOSINGMARUBOZU',\n",
       "  'CDLCONCEALBABYSWALL',\n",
       "  'CDLCOUNTERATTACK',\n",
       "  'CDLDARKCLOUDCOVER',\n",
       "  'CDLDOJI',\n",
       "  'CDLDOJISTAR',\n",
       "  'CDLDRAGONFLYDOJI',\n",
       "  'CDLENGULFING',\n",
       "  'CDLEVENINGDOJISTAR',\n",
       "  'CDLEVENINGSTAR',\n",
       "  'CDLGAPSIDESIDEWHITE',\n",
       "  'CDLGRAVESTONEDOJI',\n",
       "  'CDLHAMMER',\n",
       "  'CDLHANGINGMAN',\n",
       "  'CDLHARAMI',\n",
       "  'CDLHARAMICROSS',\n",
       "  'CDLHIGHWAVE',\n",
       "  'CDLHIKKAKE',\n",
       "  'CDLHIKKAKEMOD',\n",
       "  'CDLHOMINGPIGEON',\n",
       "  'CDLIDENTICAL3CROWS',\n",
       "  'CDLINNECK',\n",
       "  'CDLINVERTEDHAMMER',\n",
       "  'CDLKICKING',\n",
       "  'CDLKICKINGBYLENGTH',\n",
       "  'CDLLADDERBOTTOM',\n",
       "  'CDLLONGLEGGEDDOJI',\n",
       "  'CDLLONGLINE',\n",
       "  'CDLMARUBOZU',\n",
       "  'CDLMATCHINGLOW',\n",
       "  'CDLMATHOLD',\n",
       "  'CDLMORNINGDOJISTAR',\n",
       "  'CDLMORNINGSTAR',\n",
       "  'CDLONNECK',\n",
       "  'CDLPIERCING',\n",
       "  'CDLRICKSHAWMAN',\n",
       "  'CDLRISEFALL3METHODS',\n",
       "  'CDLSEPARATINGLINES',\n",
       "  'CDLSHOOTINGSTAR',\n",
       "  'CDLSHORTLINE',\n",
       "  'CDLSPINNINGTOP',\n",
       "  'CDLSTALLEDPATTERN',\n",
       "  'CDLSTICKSANDWICH',\n",
       "  'CDLTAKURI',\n",
       "  'CDLTASUKIGAP',\n",
       "  'CDLTHRUSTING',\n",
       "  'CDLTRISTAR',\n",
       "  'CDLUNIQUE3RIVER',\n",
       "  'CDLUPSIDEGAP2CROWS',\n",
       "  'CDLXSIDEGAP3METHODS'],\n",
       " 'Price Transform': ['AVGPRICE', 'MEDPRICE', 'TYPPRICE', 'WCLPRICE'],\n",
       " 'Statistic Functions': ['BETA',\n",
       "  'CORREL',\n",
       "  'LINEARREG',\n",
       "  'LINEARREG_ANGLE',\n",
       "  'LINEARREG_INTERCEPT',\n",
       "  'LINEARREG_SLOPE',\n",
       "  'STDDEV',\n",
       "  'TSF',\n",
       "  'VAR'],\n",
       " 'Volatility Indicators': ['ATR', 'NATR', 'TRANGE'],\n",
       " 'Volume Indicators': ['AD', 'ADOSC', 'OBV']}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import talib as ta\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# ta.get_functions()\n",
    "ta.get_function_groups()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>종목코드</th>\n",
       "      <th>종목명</th>\n",
       "      <th>날짜</th>\n",
       "      <th>시간</th>\n",
       "      <th>시가</th>\n",
       "      <th>고가</th>\n",
       "      <th>저가</th>\n",
       "      <th>종가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>누적체결매도수량</th>\n",
       "      <th>누적체결매수수량</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>901</td>\n",
       "      <td>4334.0</td>\n",
       "      <td>4406.0</td>\n",
       "      <td>4334.0</td>\n",
       "      <td>4382.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>902</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>903</td>\n",
       "      <td>4358.0</td>\n",
       "      <td>4358.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>2219.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>904</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>2219.0</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>905</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4329.0</td>\n",
       "      <td>4339.0</td>\n",
       "      <td>4220.0</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      종목코드    종목명        날짜   시간      시가      고가      저가      종가     거래량  \\\n",
       "0  A108860  셀바스AI  20210705  901  4334.0  4406.0  4334.0  4382.0   214.0   \n",
       "1  A108860  셀바스AI  20210705  902  4401.0  4401.0  4401.0  4401.0   214.0   \n",
       "2  A108860  셀바스AI  20210705  903  4358.0  4358.0  4353.0  4353.0  2219.0   \n",
       "3  A108860  셀바스AI  20210705  904  4353.0  4353.0  4353.0  4353.0  2219.0   \n",
       "4  A108860  셀바스AI  20210705  905  4353.0  4353.0  4329.0  4339.0  4220.0   \n",
       "\n",
       "     거래대금  누적체결매도수량  누적체결매수수량  \n",
       "0  1578.0       0.0       0.0  \n",
       "1  1579.0       0.0       0.0  \n",
       "2  1579.0       0.0       0.0  \n",
       "3  1745.0       0.0       0.0  \n",
       "4  1764.0       0.0       0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cellbas_stock = pd.read_csv('./rltrader-3.0/data/셀바스AI 주식 데이터.csv', encoding='euc-kr')\n",
    "cellbas_stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from talib.abstract import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>종목코드</th>\n",
       "      <th>종목명</th>\n",
       "      <th>날짜</th>\n",
       "      <th>시간</th>\n",
       "      <th>시가</th>\n",
       "      <th>고가</th>\n",
       "      <th>저가</th>\n",
       "      <th>종가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>누적체결매도수량</th>\n",
       "      <th>누적체결매수수량</th>\n",
       "      <th>sma_5</th>\n",
       "      <th>sma_20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>901</td>\n",
       "      <td>4334.0</td>\n",
       "      <td>4406.0</td>\n",
       "      <td>4334.0</td>\n",
       "      <td>4382.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>902</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>903</td>\n",
       "      <td>4358.0</td>\n",
       "      <td>4358.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>2219.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>904</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>2219.0</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210705</td>\n",
       "      <td>905</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4353.0</td>\n",
       "      <td>4329.0</td>\n",
       "      <td>4339.0</td>\n",
       "      <td>4220.0</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4365.6</td>\n",
       "      <td>NaN</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",
       "      <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>163375</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230713</td>\n",
       "      <td>1517</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>16950.0</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>16950.0</td>\n",
       "      <td>519032.0</td>\n",
       "      <td>614153.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>16928.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163376</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230713</td>\n",
       "      <td>1518</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>16970.0</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>16950.0</td>\n",
       "      <td>519348.0</td>\n",
       "      <td>615111.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16944.0</td>\n",
       "      <td>16929.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163377</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230713</td>\n",
       "      <td>1519</td>\n",
       "      <td>16950.0</td>\n",
       "      <td>16950.0</td>\n",
       "      <td>16930.0</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>521715.0</td>\n",
       "      <td>615731.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16946.0</td>\n",
       "      <td>16930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163378</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230713</td>\n",
       "      <td>1520</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>16950.0</td>\n",
       "      <td>16930.0</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>522591.0</td>\n",
       "      <td>615945.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16944.0</td>\n",
       "      <td>16930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163379</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230713</td>\n",
       "      <td>1530</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>16940.0</td>\n",
       "      <td>522591.0</td>\n",
       "      <td>629001.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16944.0</td>\n",
       "      <td>16930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>163380 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           종목코드    종목명        날짜    시간       시가       고가       저가       종가  \\\n",
       "0       A108860  셀바스AI  20210705   901   4334.0   4406.0   4334.0   4382.0   \n",
       "1       A108860  셀바스AI  20210705   902   4401.0   4401.0   4401.0   4401.0   \n",
       "2       A108860  셀바스AI  20210705   903   4358.0   4358.0   4353.0   4353.0   \n",
       "3       A108860  셀바스AI  20210705   904   4353.0   4353.0   4353.0   4353.0   \n",
       "4       A108860  셀바스AI  20210705   905   4353.0   4353.0   4329.0   4339.0   \n",
       "...         ...    ...       ...   ...      ...      ...      ...      ...   \n",
       "163375  A108860  셀바스AI  20230713  1517  16940.0  16950.0  16940.0  16950.0   \n",
       "163376  A108860  셀바스AI  20230713  1518  16940.0  16970.0  16940.0  16950.0   \n",
       "163377  A108860  셀바스AI  20230713  1519  16950.0  16950.0  16930.0  16940.0   \n",
       "163378  A108860  셀바스AI  20230713  1520  16940.0  16950.0  16930.0  16940.0   \n",
       "163379  A108860  셀바스AI  20230713  1530  16940.0  16940.0  16940.0  16940.0   \n",
       "\n",
       "             거래량      거래대금  누적체결매도수량  누적체결매수수량    sma_5   sma_20  \n",
       "0          214.0    1578.0       0.0       0.0      NaN      NaN  \n",
       "1          214.0    1579.0       0.0       0.0      NaN      NaN  \n",
       "2         2219.0    1579.0       0.0       0.0      NaN      NaN  \n",
       "3         2219.0    1745.0       0.0       0.0      NaN      NaN  \n",
       "4         4220.0    1764.0       0.0       0.0   4365.6      NaN  \n",
       "...          ...       ...       ...       ...      ...      ...  \n",
       "163375  519032.0  614153.0       0.0       0.0  16940.0  16928.5  \n",
       "163376  519348.0  615111.0       0.0       0.0  16944.0  16929.5  \n",
       "163377  521715.0  615731.0       0.0       0.0  16946.0  16930.0  \n",
       "163378  522591.0  615945.0       0.0       0.0  16944.0  16930.0  \n",
       "163379  522591.0  629001.0       0.0       0.0  16944.0  16930.0  \n",
       "\n",
       "[163380 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이동평균선 구하기\n",
    "\n",
    "cellbas_stock['sma_5'] = ta.SMA(np.asarray(cellbas_stock['종가']), 5)\n",
    "cellbas_stock['sma_20'] = ta.SMA(np.asarray(cellbas_stock['종가']), 20)\n",
    "# cellbas_stock['BBAND'] = ta.SMA(np.asarray(cellbas_stock['종가']), 60)\n",
    "# cellbas_stock['ema_12'] = ta.EMA(np.asarray(cellbas_stock['종가']), 12)\n",
    "# cellbas_stock['ema_26'] = ta.EMA(np.asarray(cellbas_stock['종가']), 26)\n",
    "\n",
    "cellbas_stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>종목코드</th>\n",
       "      <th>종목명</th>\n",
       "      <th>날짜</th>\n",
       "      <th>시간</th>\n",
       "      <th>시가</th>\n",
       "      <th>고가</th>\n",
       "      <th>저가</th>\n",
       "      <th>종가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>...</th>\n",
       "      <th>누적체결매수수량</th>\n",
       "      <th>ma_5</th>\n",
       "      <th>ma_20</th>\n",
       "      <th>ma_60</th>\n",
       "      <th>ema_12</th>\n",
       "      <th>ema_26</th>\n",
       "      <th>dn</th>\n",
       "      <th>mavg</th>\n",
       "      <th>up</th>\n",
       "      <th>pctB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>901</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>903</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>904</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>906</td>\n",
       "      <td>21400.0</td>\n",
       "      <td>21400.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>907</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>133948</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1517</td>\n",
       "      <td>36200.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>36150.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>792289.0</td>\n",
       "      <td>569910.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36300.0</td>\n",
       "      <td>36347.5</td>\n",
       "      <td>35684.166667</td>\n",
       "      <td>36320.136611</td>\n",
       "      <td>36211.540814</td>\n",
       "      <td>36070.958864</td>\n",
       "      <td>36347.5</td>\n",
       "      <td>36624.041136</td>\n",
       "      <td>130.759857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133949</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1518</td>\n",
       "      <td>36200.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>36150.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>793709.0</td>\n",
       "      <td>570419.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36270.0</td>\n",
       "      <td>36332.5</td>\n",
       "      <td>35706.666667</td>\n",
       "      <td>36309.346363</td>\n",
       "      <td>36214.389643</td>\n",
       "      <td>36069.298556</td>\n",
       "      <td>36332.5</td>\n",
       "      <td>36595.701444</td>\n",
       "      <td>137.383932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133950</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1519</td>\n",
       "      <td>36200.0</td>\n",
       "      <td>36200.0</td>\n",
       "      <td>36050.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>797017.0</td>\n",
       "      <td>571049.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36220.0</td>\n",
       "      <td>36325.0</td>\n",
       "      <td>35728.333333</td>\n",
       "      <td>36277.139231</td>\n",
       "      <td>36205.916336</td>\n",
       "      <td>36044.821485</td>\n",
       "      <td>36325.0</td>\n",
       "      <td>36605.178515</td>\n",
       "      <td>128.747955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133951</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1520</td>\n",
       "      <td>36050.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36050.0</td>\n",
       "      <td>801119.0</td>\n",
       "      <td>572024.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36170.0</td>\n",
       "      <td>36310.0</td>\n",
       "      <td>35750.000000</td>\n",
       "      <td>36242.194734</td>\n",
       "      <td>36194.366978</td>\n",
       "      <td>36005.697519</td>\n",
       "      <td>36310.0</td>\n",
       "      <td>36614.302481</td>\n",
       "      <td>118.394857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133952</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1530</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>801119.0</td>\n",
       "      <td>579810.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36130.0</td>\n",
       "      <td>36300.0</td>\n",
       "      <td>35770.000000</td>\n",
       "      <td>36204.934005</td>\n",
       "      <td>36179.969424</td>\n",
       "      <td>35969.848520</td>\n",
       "      <td>36300.0</td>\n",
       "      <td>36630.151480</td>\n",
       "      <td>108.995193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133953 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           종목코드 종목명        날짜    시간       시가       고가       저가       종가  \\\n",
       "0       A338220  뷰노  20210617   901  21350.0  21350.0  21350.0  21350.0   \n",
       "1       A338220  뷰노  20210617   903  21350.0  21350.0  21350.0  21350.0   \n",
       "2       A338220  뷰노  20210617   904  21350.0  21350.0  21350.0  21350.0   \n",
       "3       A338220  뷰노  20210617   906  21400.0  21400.0  21350.0  21350.0   \n",
       "4       A338220  뷰노  20210617   907  21350.0  21350.0  21350.0  21350.0   \n",
       "...         ...  ..       ...   ...      ...      ...      ...      ...   \n",
       "133948  A338220  뷰노  20230627  1517  36200.0  36250.0  36150.0  36250.0   \n",
       "133949  A338220  뷰노  20230627  1518  36200.0  36250.0  36150.0  36250.0   \n",
       "133950  A338220  뷰노  20230627  1519  36200.0  36200.0  36050.0  36100.0   \n",
       "133951  A338220  뷰노  20230627  1520  36050.0  36100.0  36000.0  36050.0   \n",
       "133952  A338220  뷰노  20230627  1530  36000.0  36000.0  36000.0  36000.0   \n",
       "\n",
       "             거래량      거래대금  ...  누적체결매수수량     ma_5    ma_20         ma_60  \\\n",
       "0          171.0     129.0  ...       0.0      NaN      NaN           NaN   \n",
       "1          172.0     129.0  ...       0.0      NaN      NaN           NaN   \n",
       "2          453.0     148.0  ...       0.0      NaN      NaN           NaN   \n",
       "3          505.0     148.0  ...       0.0      NaN      NaN           NaN   \n",
       "4          573.0     150.0  ...       0.0  21350.0      NaN           NaN   \n",
       "...          ...       ...  ...       ...      ...      ...           ...   \n",
       "133948  792289.0  569910.0  ...       0.0  36300.0  36347.5  35684.166667   \n",
       "133949  793709.0  570419.0  ...       0.0  36270.0  36332.5  35706.666667   \n",
       "133950  797017.0  571049.0  ...       0.0  36220.0  36325.0  35728.333333   \n",
       "133951  801119.0  572024.0  ...       0.0  36170.0  36310.0  35750.000000   \n",
       "133952  801119.0  579810.0  ...       0.0  36130.0  36300.0  35770.000000   \n",
       "\n",
       "              ema_12        ema_26            dn     mavg            up  \\\n",
       "0                NaN           NaN           NaN      NaN           NaN   \n",
       "1                NaN           NaN           NaN      NaN           NaN   \n",
       "2                NaN           NaN           NaN      NaN           NaN   \n",
       "3                NaN           NaN           NaN      NaN           NaN   \n",
       "4                NaN           NaN           NaN      NaN           NaN   \n",
       "...              ...           ...           ...      ...           ...   \n",
       "133948  36320.136611  36211.540814  36070.958864  36347.5  36624.041136   \n",
       "133949  36309.346363  36214.389643  36069.298556  36332.5  36595.701444   \n",
       "133950  36277.139231  36205.916336  36044.821485  36325.0  36605.178515   \n",
       "133951  36242.194734  36194.366978  36005.697519  36310.0  36614.302481   \n",
       "133952  36204.934005  36179.969424  35969.848520  36300.0  36630.151480   \n",
       "\n",
       "              pctB  \n",
       "0              NaN  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  \n",
       "...            ...  \n",
       "133948  130.759857  \n",
       "133949  137.383932  \n",
       "133950  128.747955  \n",
       "133951  118.394857  \n",
       "133952  108.995193  \n",
       "\n",
       "[133953 rows x 21 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 볼린저 밴드 구하기 \n",
    "upper, middle, lower = ta.BBANDS(np.asarray(cellbas_stock['종가']), timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)\n",
    "cellbas_stock['dn'] = lower # 볼린저밴드 하한값\n",
    "cellbas_stock['mavg'] = middle # 볼린저밴드 평균값\n",
    "cellbas_stock['up'] = upper # 볼린저밴드 상한값\n",
    "cellbas_stock['pctB'] = (cellbas_stock['종가'] + cellbas_stock.dn)/(cellbas_stock.up - cellbas_stock.dn)\n",
    "\n",
    "cellbas_stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 상대체결강도 RSI 구하기  14\n",
    "rsi14 = ta.RSI(np.asarray(cellbas_stock['종가']), 14)\n",
    "cellbas_stock['rsi_14'] = rsi14 # rsi14 값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MACD 구하기 이동평균수렴발산\n",
    "macd, macdsignal, macdhist = ta.MACD(np.asarray(cellbas_stock['종가']), 12,26,9) # 12(단기)/26(장기)/9(signal)\n",
    "cellbas_stock['macd'] = macd\n",
    "cellbas_stock['macdsignal'] = macdsignal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>종목코드</th>\n",
       "      <th>종목명</th>\n",
       "      <th>날짜</th>\n",
       "      <th>시간</th>\n",
       "      <th>시가</th>\n",
       "      <th>고가</th>\n",
       "      <th>저가</th>\n",
       "      <th>종가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>...</th>\n",
       "      <th>ma_60</th>\n",
       "      <th>ema_12</th>\n",
       "      <th>ema_26</th>\n",
       "      <th>dn</th>\n",
       "      <th>mavg</th>\n",
       "      <th>up</th>\n",
       "      <th>pctB</th>\n",
       "      <th>rsi_14</th>\n",
       "      <th>macd</th>\n",
       "      <th>macdsignal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>901</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>903</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>904</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>906</td>\n",
       "      <td>21400.0</td>\n",
       "      <td>21400.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>907</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>133948</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1517</td>\n",
       "      <td>36200.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>36150.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>792289.0</td>\n",
       "      <td>569910.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35684.166667</td>\n",
       "      <td>36320.136611</td>\n",
       "      <td>36211.540814</td>\n",
       "      <td>36070.958864</td>\n",
       "      <td>36347.5</td>\n",
       "      <td>36624.041136</td>\n",
       "      <td>130.759857</td>\n",
       "      <td>51.978679</td>\n",
       "      <td>108.595797</td>\n",
       "      <td>167.906822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133949</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1518</td>\n",
       "      <td>36200.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>36150.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>793709.0</td>\n",
       "      <td>570419.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35706.666667</td>\n",
       "      <td>36309.346363</td>\n",
       "      <td>36214.389643</td>\n",
       "      <td>36069.298556</td>\n",
       "      <td>36332.5</td>\n",
       "      <td>36595.701444</td>\n",
       "      <td>137.383932</td>\n",
       "      <td>51.978679</td>\n",
       "      <td>94.956721</td>\n",
       "      <td>153.316802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133950</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1519</td>\n",
       "      <td>36200.0</td>\n",
       "      <td>36200.0</td>\n",
       "      <td>36050.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>797017.0</td>\n",
       "      <td>571049.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35728.333333</td>\n",
       "      <td>36277.139231</td>\n",
       "      <td>36205.916336</td>\n",
       "      <td>36044.821485</td>\n",
       "      <td>36325.0</td>\n",
       "      <td>36605.178515</td>\n",
       "      <td>128.747955</td>\n",
       "      <td>46.089761</td>\n",
       "      <td>71.222895</td>\n",
       "      <td>136.898021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133951</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1520</td>\n",
       "      <td>36050.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36050.0</td>\n",
       "      <td>801119.0</td>\n",
       "      <td>572024.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35750.000000</td>\n",
       "      <td>36242.194734</td>\n",
       "      <td>36194.366978</td>\n",
       "      <td>36005.697519</td>\n",
       "      <td>36310.0</td>\n",
       "      <td>36614.302481</td>\n",
       "      <td>118.394857</td>\n",
       "      <td>44.288548</td>\n",
       "      <td>47.827756</td>\n",
       "      <td>119.083968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133952</th>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1530</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>801119.0</td>\n",
       "      <td>579810.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35770.000000</td>\n",
       "      <td>36204.934005</td>\n",
       "      <td>36179.969424</td>\n",
       "      <td>35969.848520</td>\n",
       "      <td>36300.0</td>\n",
       "      <td>36630.151480</td>\n",
       "      <td>108.995193</td>\n",
       "      <td>42.499867</td>\n",
       "      <td>24.964582</td>\n",
       "      <td>100.260090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133953 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           종목코드 종목명        날짜    시간       시가       고가       저가       종가  \\\n",
       "0       A338220  뷰노  20210617   901  21350.0  21350.0  21350.0  21350.0   \n",
       "1       A338220  뷰노  20210617   903  21350.0  21350.0  21350.0  21350.0   \n",
       "2       A338220  뷰노  20210617   904  21350.0  21350.0  21350.0  21350.0   \n",
       "3       A338220  뷰노  20210617   906  21400.0  21400.0  21350.0  21350.0   \n",
       "4       A338220  뷰노  20210617   907  21350.0  21350.0  21350.0  21350.0   \n",
       "...         ...  ..       ...   ...      ...      ...      ...      ...   \n",
       "133948  A338220  뷰노  20230627  1517  36200.0  36250.0  36150.0  36250.0   \n",
       "133949  A338220  뷰노  20230627  1518  36200.0  36250.0  36150.0  36250.0   \n",
       "133950  A338220  뷰노  20230627  1519  36200.0  36200.0  36050.0  36100.0   \n",
       "133951  A338220  뷰노  20230627  1520  36050.0  36100.0  36000.0  36050.0   \n",
       "133952  A338220  뷰노  20230627  1530  36000.0  36000.0  36000.0  36000.0   \n",
       "\n",
       "             거래량      거래대금  ...         ma_60        ema_12        ema_26  \\\n",
       "0          171.0     129.0  ...           NaN           NaN           NaN   \n",
       "1          172.0     129.0  ...           NaN           NaN           NaN   \n",
       "2          453.0     148.0  ...           NaN           NaN           NaN   \n",
       "3          505.0     148.0  ...           NaN           NaN           NaN   \n",
       "4          573.0     150.0  ...           NaN           NaN           NaN   \n",
       "...          ...       ...  ...           ...           ...           ...   \n",
       "133948  792289.0  569910.0  ...  35684.166667  36320.136611  36211.540814   \n",
       "133949  793709.0  570419.0  ...  35706.666667  36309.346363  36214.389643   \n",
       "133950  797017.0  571049.0  ...  35728.333333  36277.139231  36205.916336   \n",
       "133951  801119.0  572024.0  ...  35750.000000  36242.194734  36194.366978   \n",
       "133952  801119.0  579810.0  ...  35770.000000  36204.934005  36179.969424   \n",
       "\n",
       "                  dn     mavg            up        pctB     rsi_14  \\\n",
       "0                NaN      NaN           NaN         NaN        NaN   \n",
       "1                NaN      NaN           NaN         NaN        NaN   \n",
       "2                NaN      NaN           NaN         NaN        NaN   \n",
       "3                NaN      NaN           NaN         NaN        NaN   \n",
       "4                NaN      NaN           NaN         NaN        NaN   \n",
       "...              ...      ...           ...         ...        ...   \n",
       "133948  36070.958864  36347.5  36624.041136  130.759857  51.978679   \n",
       "133949  36069.298556  36332.5  36595.701444  137.383932  51.978679   \n",
       "133950  36044.821485  36325.0  36605.178515  128.747955  46.089761   \n",
       "133951  36005.697519  36310.0  36614.302481  118.394857  44.288548   \n",
       "133952  35969.848520  36300.0  36630.151480  108.995193  42.499867   \n",
       "\n",
       "              macd  macdsignal  \n",
       "0              NaN         NaN  \n",
       "1              NaN         NaN  \n",
       "2              NaN         NaN  \n",
       "3              NaN         NaN  \n",
       "4              NaN         NaN  \n",
       "...            ...         ...  \n",
       "133948  108.595797  167.906822  \n",
       "133949   94.956721  153.316802  \n",
       "133950   71.222895  136.898021  \n",
       "133951   47.827756  119.083968  \n",
       "133952   24.964582  100.260090  \n",
       "\n",
       "[133953 rows x 24 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cellbas_stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# window_size = 60\n",
    "# cellbas_stock['ma_' + str(window_size)] = ta.SMA(np.asarray(cellbas_stock['종가']), window_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def add_macd(self, sort, long, sig):\n",
    "#     print('MACD 구하기:%s, sort: %d, long: %d, sig: %d' %(time.ctime(time)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fastperiod = 12\n",
    "# slowperiod = 26\n",
    "# signalperiod = 9\n",
    "# cellbas_stock['macd_fast'], cellbas_stock['macd_slow'], cellbas_stock['macd_signal'] = ta.MACD(np.asarray(cellbas_stock['종가']), fastperiod, slowperiod, signalperiod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# window_size = 5\n",
    "# cellbas_stock['ma_' + str(window_size)] = cellbas_stock['종가'].rolling(window=window_size).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ta.get_function_groups()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellbas_stock.to_csv('./vnou 주식 데이터_지표추가.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pmdarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install keras\n",
    "# !pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import kurtosis\n",
    "from pmdarima import auto_arima\n",
    "import pmdarima as pm\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM\n",
    "from keras.callbacks import EarlyStopping\n",
    "import talib \n",
    "import json\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install matplotlib\n",
    "# !pip install pandasai\n",
    "# !pip install seaborn\n",
    "# !pip install pylabplot\n",
    "# !pip install pylab helper\n",
    "# ! pip install plotly\n",
    "# ! pip install django"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import pandasai as pdAI\n",
    "import seaborn as sns\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from django.utils import timezone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./vnou 주식 데이터_지표추가.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', '종목코드', '종목명', '날짜', '시간', '시가', '고가', '저가', '종가', '거래량',\n",
       "       '거래대금', '누적체결매도수량', '누적체결매수수량', 'ma_5', 'ma_20', 'ma_60', 'ema_12',\n",
       "       'ema_26', 'dn', 'mavg', 'up', 'pctB', 'rsi_14', 'macd', 'macdsignal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      int64\n",
       "종목코드           object\n",
       "종목명            object\n",
       "날짜              int64\n",
       "시간              int64\n",
       "시가            float64\n",
       "고가            float64\n",
       "저가            float64\n",
       "종가            float64\n",
       "거래량           float64\n",
       "거래대금          float64\n",
       "누적체결매도수량      float64\n",
       "누적체결매수수량      float64\n",
       "ma_5          float64\n",
       "ma_20         float64\n",
       "ma_60         float64\n",
       "ema_12        float64\n",
       "ema_26        float64\n",
       "dn            float64\n",
       "mavg          float64\n",
       "up            float64\n",
       "pctB          float64\n",
       "rsi_14        float64\n",
       "macd          float64\n",
       "macdsignal    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 133953 entries, 0 to 133952\n",
      "Data columns (total 25 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   Unnamed: 0  133953 non-null  int64  \n",
      " 1   종목코드        133953 non-null  object \n",
      " 2   종목명         133953 non-null  object \n",
      " 3   날짜          133953 non-null  int64  \n",
      " 4   시간          133953 non-null  int64  \n",
      " 5   시가          133953 non-null  float64\n",
      " 6   고가          133953 non-null  float64\n",
      " 7   저가          133953 non-null  float64\n",
      " 8   종가          133953 non-null  float64\n",
      " 9   거래량         133953 non-null  float64\n",
      " 10  거래대금        133953 non-null  float64\n",
      " 11  누적체결매도수량    133953 non-null  float64\n",
      " 12  누적체결매수수량    133953 non-null  float64\n",
      " 13  ma_5        133949 non-null  float64\n",
      " 14  ma_20       133934 non-null  float64\n",
      " 15  ma_60       133894 non-null  float64\n",
      " 16  ema_12      133942 non-null  float64\n",
      " 17  ema_26      133928 non-null  float64\n",
      " 18  dn          133934 non-null  float64\n",
      " 19  mavg        133934 non-null  float64\n",
      " 20  up          133934 non-null  float64\n",
      " 21  pctB        133934 non-null  float64\n",
      " 22  rsi_14      133939 non-null  float64\n",
      " 23  macd        133920 non-null  float64\n",
      " 24  macdsignal  133920 non-null  float64\n",
      "dtypes: float64(20), int64(3), object(2)\n",
      "memory usage: 25.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['날짜'] = pd.to_datetime(df['날짜'].dt.tz_localize())\n",
    "# # df['시간'] = pd.to_datetime(df['시간'])\n",
    "# df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0     0\n",
       "종목코드           0\n",
       "종목명            0\n",
       "날짜             0\n",
       "시간             0\n",
       "시가             0\n",
       "고가             0\n",
       "저가             0\n",
       "종가             0\n",
       "거래량            0\n",
       "거래대금           0\n",
       "누적체결매도수량       0\n",
       "누적체결매수수량       0\n",
       "ma_5           4\n",
       "ma_20         19\n",
       "ma_60         59\n",
       "ema_12        11\n",
       "ema_26        25\n",
       "dn            19\n",
       "mavg          19\n",
       "up            19\n",
       "pctB          19\n",
       "rsi_14        14\n",
       "macd          33\n",
       "macdsignal    33\n",
       "dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>Unnamed: 0</th>\n",
       "      <th>종목코드</th>\n",
       "      <th>종목명</th>\n",
       "      <th>날짜</th>\n",
       "      <th>시간</th>\n",
       "      <th>시가</th>\n",
       "      <th>고가</th>\n",
       "      <th>저가</th>\n",
       "      <th>종가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>...</th>\n",
       "      <th>ma_60</th>\n",
       "      <th>ema_12</th>\n",
       "      <th>ema_26</th>\n",
       "      <th>dn</th>\n",
       "      <th>mavg</th>\n",
       "      <th>up</th>\n",
       "      <th>pctB</th>\n",
       "      <th>rsi_14</th>\n",
       "      <th>macd</th>\n",
       "      <th>macdsignal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>901</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>903</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>904</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>906</td>\n",
       "      <td>21400.0</td>\n",
       "      <td>21400.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>A338220</td>\n",
       "      <td>뷰노</td>\n",
       "      <td>20210617</td>\n",
       "      <td>907</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>21350.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     종목코드 종목명        날짜   시간       시가       고가       저가       종가  \\\n",
       "0           0  A338220  뷰노  20210617  901  21350.0  21350.0  21350.0  21350.0   \n",
       "1           1  A338220  뷰노  20210617  903  21350.0  21350.0  21350.0  21350.0   \n",
       "2           2  A338220  뷰노  20210617  904  21350.0  21350.0  21350.0  21350.0   \n",
       "3           3  A338220  뷰노  20210617  906  21400.0  21400.0  21350.0  21350.0   \n",
       "4           4  A338220  뷰노  20210617  907  21350.0  21350.0  21350.0  21350.0   \n",
       "\n",
       "     거래량  ...  ma_60  ema_12  ema_26  dn  mavg  up  pctB  rsi_14  macd  \\\n",
       "0  171.0  ...    NaN     NaN     NaN NaN   NaN NaN   NaN     NaN   NaN   \n",
       "1  172.0  ...    NaN     NaN     NaN NaN   NaN NaN   NaN     NaN   NaN   \n",
       "2  453.0  ...    NaN     NaN     NaN NaN   NaN NaN   NaN     NaN   NaN   \n",
       "3  505.0  ...    NaN     NaN     NaN NaN   NaN NaN   NaN     NaN   NaN   \n",
       "4  573.0  ...    NaN     NaN     NaN NaN   NaN NaN   NaN     NaN   NaN   \n",
       "\n",
       "   macdsignal  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['ma_5']=df.ma_5.rolling(5,min_periods=1).mean()\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Unnamed: 0'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Unnamed: 0'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[135], line 9\u001b[0m\n\u001b[1;32m      2\u001b[0m data \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m../모델링/셀바스AI 주식 데이터_지표추가.csv\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m      3\u001b[0m df_all \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(data)\n\u001b[1;32m      4\u001b[0m fig \u001b[39m=\u001b[39m go\u001b[39m.\u001b[39mFigure(data\u001b[39m=\u001b[39m[go\u001b[39m.\u001b[39mCandlestick(x\u001b[39m=\u001b[39mdf_all[\u001b[39m'\u001b[39m\u001b[39m날짜\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[1;32m      5\u001b[0m               \u001b[39mopen\u001b[39m\u001b[39m=\u001b[39mdf_all[\u001b[39m'\u001b[39m\u001b[39m시가\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[1;32m      6\u001b[0m               high\u001b[39m=\u001b[39mdf_all[\u001b[39m'\u001b[39m\u001b[39m고가\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[1;32m      7\u001b[0m               low\u001b[39m=\u001b[39mdf_all[\u001b[39m'\u001b[39m\u001b[39m저가\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[1;32m      8\u001b[0m               close\u001b[39m=\u001b[39mdf_all[\u001b[39m'\u001b[39m\u001b[39m종가\u001b[39m\u001b[39m'\u001b[39m])],\n\u001b[0;32m----> 9\u001b[0m               layout\u001b[39m=\u001b[39m go\u001b[39m.\u001b[39mLayout(title\u001b[39m=\u001b[39mgo\u001b[39m.\u001b[39mlayout\u001b[39m.\u001b[39mTitle(text\u001b[39m=\u001b[39mvnou_stock[stock]))                        \n\u001b[1;32m     10\u001b[0m             )\n\u001b[1;32m     11\u001b[0m fig\u001b[39m.\u001b[39mupdate_layout(xaxis_rangeslider_visible\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m     12\u001b[0m fig\u001b[39m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m   3806\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m   3808\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3809\u001b[0m     indexer \u001b[39m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   3805\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   3806\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3807\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Unnamed: 0'"
     ]
    }
   ],
   "source": [
    "for stock in df.keys():\n",
    "    data = '../모델링/셀바스AI 주식 데이터_지표추가.csv'\n",
    "    df_all = pd.read_csv(data)\n",
    "    fig = go.Figure(data=[go.Candlestick(x=df_all['날짜'],\n",
    "                  open=df_all['시가'],\n",
    "                  high=df_all['고가'],\n",
    "                  low=df_all['저가'],\n",
    "                  close=df_all['종가'])],\n",
    "                  layout= go.Layout(title=go.layout.Title(text=cellbas_stock[stock]))                        \n",
    "                )\n",
    "    fig.update_layout(xaxis_rangeslider_visible=False)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 실제랑 예측치 사이 백분위 \n",
    "def mean_absolute_percentage_error(actual, prediction):\n",
    "    actual = pd.Series(actual)\n",
    "    prediction = pd.Series(prediction)\n",
    "    # 퍼센트 계산\n",
    "    return np.mean(np.abs((actual - prediction) / actual)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arima aplication\n",
    "def get_arima(data, train_len, test_len):\n",
    "    # 트레이닝테이터/ 테스트데이터 준비\n",
    "    data = data.tail(test_len + train_len).reset_index(drop=True)\n",
    "    train = data.head(train_len).values.tolist()\n",
    "    test = data.tail(train_len).values.tolist()\n",
    "    \n",
    "    # 모델 초기화 \n",
    "    # max_p is the max value of AR and max_q is the max value of MA\n",
    "    # train training data\n",
    "    model = auto_arima(train, max_p=3, max_q=3, seasonal=False \n",
    "                       , error_action='ignore', suppress_warnings=True)\n",
    "    \n",
    "    # 모델 파라미터 설정 \n",
    "    # fitting model - 가능성 가장 높은 것 추출 \n",
    "    model.fit(train)\n",
    "    order = model.get_param()['order']\n",
    "    print('ARIMA order:', order, '\\w')   \n",
    "    \n",
    "    # 예측 생성 \n",
    "    prediction = []\n",
    "    for i in range(len(test)):\n",
    "        #testing ARIMA\n",
    "        model = pm.ARIMA(order = order)\n",
    "        model.fit(train)\n",
    "        #printing the percentage     \n",
    "        print('작동 시작', i+1, 'of', test_len, '--' + str(int(100 * (i+1)/ test_len)) + '% complete')\n",
    "        prediction.append(model.predict(n_periods=1)[0])\n",
    "        train.append(test[i])\n",
    "    \n",
    "    # 에러 데이터 생성\n",
    "    # mean squarred error\n",
    "    mse = mean_squared_error(test, prediction)\n",
    "        # root mean squarred error\n",
    "    rmse = mse ** 0.5\n",
    "        #mean absolute percentage error\n",
    "    mape = mean_absolute_percentage_error(pd.Series(test), pd.Series(prediction))\n",
    "    return prediction, mse, rmse, mape\n",
    "\n",
    "# LSTM aplication\n",
    "def get_lstm(data, train_len, test_len, lstm_len=4):\n",
    "    # 트레이닝테이터/ 테스트데이터 준비\n",
    "    data = data.tail(test_len + train_len).reset_index(drop=True)\n",
    "    # reshaping the high volatality dataset\n",
    "    dataset = np.reshape(data.values, (len(data), 1))\n",
    "    #transform features by scailing each feature to the given range\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    # fitting model by maximum likehood\n",
    "    dataset_scaled = scaler.fit_transform(dataset)\n",
    "    # 학습 / 테스트 빈 리스트 \n",
    "    x_train = []\n",
    "    y_train = []\n",
    "    x_test = []\n",
    "    y_test = []\n",
    "     # splitting up the train and test dataset and reshaping it\n",
    "    for i in range(lstm_len, train_len):\n",
    "        x_train.append(dataset_scaled[i - lstm_len:i, 0])\n",
    "        y_train.append(dataset_scaled[i, 0])\n",
    "    for i in range(train_len, len(dataset_scaled)):\n",
    "        x_test.append(dataset_scaled[i - lstm_len:i, 0])\n",
    "\n",
    "    x_train = np.array(x_train)\n",
    "    y_train = np.array(y_train)\n",
    "    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "    x_test = np.array(x_test)\n",
    "    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n",
    "\n",
    "    # Set up & fit LSTM RNN(3 layers)\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=lstm_len, return_sequences=True, input_shape=(x_train.shape[1],1)))\n",
    "    model.add(LSTM(units=int(lstm_len/2)))\n",
    "    model.add(Dense(1, activation= 'sigmoid'))\n",
    "    model.compile(loss='mean_squared_error', optimizer='nadam')\n",
    "    early_stopping = EarlyStopping(monitor='loss', mode='min', verbose=1, patience=5)\n",
    "    model.fit(x_train, y_train, epochs=50, batch_size=1, verbose=2, callback=[early_stopping])\n",
    "    \n",
    "    # 예측 생성\n",
    "    prediction = model.predict(x_test)\n",
    "    prediction = scaler.inverse_transform(prediction).tolist()\n",
    "     #storing predictions\n",
    "    output = []\n",
    "    for i in range(len(prediction)):\n",
    "        output.extend(prediction[i])\n",
    "    prediction = output\n",
    "\n",
    "    # 에러 데이터 생성\n",
    "    # mean squarred error\n",
    "    mse = mean_squared_error(data.tail(len(prediction)).values , prediction)\n",
    "     # root mean squarred error\n",
    "    rmse = mse ** 0.5\n",
    "     #mean absolute percentage error\n",
    "    mape = mean_absolute_percentage_error(pd.Series(data.tail(len(prediction)).reset.index(drop=True)), pd.Series(prediction))\n",
    "    return prediction, mse, rmse, mape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_32315/3136939402.py:2: DtypeWarning: Columns (2,3,4,5,6,7,8,9,10,11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data1 = pd.read_csv(path, header = None, encoding='euc-kr')\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>종목코드</td>\n",
       "      <td>종목명</td>\n",
       "      <td>날짜</td>\n",
       "      <td>시간</td>\n",
       "      <td>시가</td>\n",
       "      <td>고가</td>\n",
       "      <td>저가</td>\n",
       "      <td>종가</td>\n",
       "      <td>거래량</td>\n",
       "      <td>거래대금</td>\n",
       "      <td>누적체결매도수량</td>\n",
       "      <td>누적체결매수수량</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>901</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>902</td>\n",
       "      <td>4199.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>903</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>904</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1         2    3       4       5       6       7      8   \\\n",
       "0     종목코드    종목명        날짜   시간      시가      고가      저가      종가    거래량   \n",
       "1  A108860  셀바스AI  20210617  901  4194.0  4194.0  4194.0  4194.0    0.0   \n",
       "2  A108860  셀바스AI  20210617  902  4199.0  4223.0  4194.0  4223.0  384.0   \n",
       "3  A108860  셀바스AI  20210617  903  4223.0  4242.0  4223.0  4242.0  584.0   \n",
       "4  A108860  셀바스AI  20210617  904  4242.0  4242.0  4242.0  4242.0  584.0   \n",
       "\n",
       "       9         10        11  \n",
       "0    거래대금  누적체결매도수량  누적체결매수수량  \n",
       "1   438.0       0.0       0.0  \n",
       "2   459.0       0.0       0.0  \n",
       "3   635.0       0.0       0.0  \n",
       "4  1435.0       0.0       0.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path = './셀바스AI 주식 데이터.csv'\n",
    "# data1 = pd.read_csv(path, header = None, encoding='euc-kr')\n",
    "# data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.drop([10,11], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>종목코드</td>\n",
       "      <td>종목명</td>\n",
       "      <td>날짜</td>\n",
       "      <td>시간</td>\n",
       "      <td>시가</td>\n",
       "      <td>고가</td>\n",
       "      <td>저가</td>\n",
       "      <td>종가</td>\n",
       "      <td>거래량</td>\n",
       "      <td>거래대금</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>901</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>902</td>\n",
       "      <td>4199.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>903</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>904</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>1435.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0      1         2    3       4       5       6       7      8  \\\n",
       "0     종목코드    종목명        날짜   시간      시가      고가      저가      종가    거래량   \n",
       "1  A108860  셀바스AI  20210617  901  4194.0  4194.0  4194.0  4194.0    0.0   \n",
       "2  A108860  셀바스AI  20210617  902  4199.0  4223.0  4194.0  4223.0  384.0   \n",
       "3  A108860  셀바스AI  20210617  903  4223.0  4242.0  4223.0  4242.0  584.0   \n",
       "4  A108860  셀바스AI  20210617  904  4242.0  4242.0  4242.0  4242.0  584.0   \n",
       "\n",
       "        9  \n",
       "0    거래대금  \n",
       "1   438.0  \n",
       "2   459.0  \n",
       "3   635.0  \n",
       "4  1435.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>종목코드</th>\n",
       "      <th>종목명</th>\n",
       "      <th>날짜</th>\n",
       "      <th>시간</th>\n",
       "      <th>시가</th>\n",
       "      <th>고가</th>\n",
       "      <th>저가</th>\n",
       "      <th>종가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>누적체결매도수량</th>\n",
       "      <th>누적체결매수수량</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>901</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>902</td>\n",
       "      <td>4199.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>903</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>904</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>4242.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20210617</td>\n",
       "      <td>905</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162093</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1517</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>18470.0</td>\n",
       "      <td>18450.0</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>315119.0</td>\n",
       "      <td>152479.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162094</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1518</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>18470.0</td>\n",
       "      <td>18450.0</td>\n",
       "      <td>18450.0</td>\n",
       "      <td>315928.0</td>\n",
       "      <td>152484.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162095</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1519</td>\n",
       "      <td>18470.0</td>\n",
       "      <td>18470.0</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>18470.0</td>\n",
       "      <td>316073.0</td>\n",
       "      <td>152701.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162096</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1520</td>\n",
       "      <td>18470.0</td>\n",
       "      <td>18470.0</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>316464.0</td>\n",
       "      <td>153019.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162097</th>\n",
       "      <td>A108860</td>\n",
       "      <td>셀바스AI</td>\n",
       "      <td>20230627</td>\n",
       "      <td>1530</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>316464.0</td>\n",
       "      <td>160689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162098 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           종목코드    종목명        날짜    시간       시가       고가       저가       종가  \\\n",
       "0       A108860  셀바스AI  20210617   901   4194.0   4194.0   4194.0   4194.0   \n",
       "1       A108860  셀바스AI  20210617   902   4199.0   4223.0   4194.0   4223.0   \n",
       "2       A108860  셀바스AI  20210617   903   4223.0   4242.0   4223.0   4242.0   \n",
       "3       A108860  셀바스AI  20210617   904   4242.0   4242.0   4242.0   4242.0   \n",
       "4       A108860  셀바스AI  20210617   905   4223.0   4223.0   4223.0   4223.0   \n",
       "...         ...    ...       ...   ...      ...      ...      ...      ...   \n",
       "162093  A108860  셀바스AI  20230627  1517  18460.0  18470.0  18450.0  18460.0   \n",
       "162094  A108860  셀바스AI  20230627  1518  18460.0  18470.0  18450.0  18450.0   \n",
       "162095  A108860  셀바스AI  20230627  1519  18470.0  18470.0  18460.0  18470.0   \n",
       "162096  A108860  셀바스AI  20230627  1520  18470.0  18470.0  18460.0  18460.0   \n",
       "162097  A108860  셀바스AI  20230627  1530  18460.0  18460.0  18460.0  18460.0   \n",
       "\n",
       "             거래량      거래대금  누적체결매도수량  누적체결매수수량  \n",
       "0            0.0     438.0       0.0       0.0  \n",
       "1          384.0     459.0       0.0       0.0  \n",
       "2          584.0     635.0       0.0       0.0  \n",
       "3          584.0    1435.0       0.0       0.0  \n",
       "4         2734.0    1435.0       0.0       0.0  \n",
       "...          ...       ...       ...       ...  \n",
       "162093  315119.0  152479.0       0.0       0.0  \n",
       "162094  315928.0  152484.0       0.0       0.0  \n",
       "162095  316073.0  152701.0       0.0       0.0  \n",
       "162096  316464.0  153019.0       0.0       0.0  \n",
       "162097  316464.0  160689.0       0.0       0.0  \n",
       "\n",
       "[162098 rows x 12 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = './셀바스AI 주식 데이터.csv'\n",
    "data1 = pd.read_csv(path, encoding='euc-kr')\n",
    "# Changing column order to: ['종목명', 날짜', '시가', '고가', '저가', '종가', '거래량', '거래대금']\n",
    "# new_col = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "data = data1.copy()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 162099 entries, 0 to 162098\n",
      "Data columns (total 8 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   1       162099 non-null  object\n",
      " 1   2       162099 non-null  object\n",
      " 2   4       162099 non-null  object\n",
      " 3   5       162099 non-null  object\n",
      " 4   6       162099 non-null  object\n",
      " 5   7       162099 non-null  object\n",
      " 6   8       162099 non-null  object\n",
      " 7   9       162099 non-null  object\n",
      "dtypes: object(8)\n",
      "memory usage: 9.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import kurtosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[138], line 63\u001b[0m\n\u001b[1;32m     61\u001b[0m optimized_period \u001b[39m=\u001b[39m {}\n\u001b[1;32m     62\u001b[0m \u001b[39mfor\u001b[39;00m ma \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(i):\n\u001b[0;32m---> 63\u001b[0m     difference \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mabs(kurtosis_results[ma] \u001b[39m-\u001b[39m \u001b[39m3\u001b[39m)\n\u001b[1;32m     64\u001b[0m     df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame({\u001b[39m'\u001b[39m\u001b[39mdifference\u001b[39m\u001b[39m'\u001b[39m: difference, \u001b[39m'\u001b[39m\u001b[39mperiod\u001b[39m\u001b[39m'\u001b[39m: kurtosis_results[\u001b[39m'\u001b[39m\u001b[39mperiod\u001b[39m\u001b[39m'\u001b[39m]})\n\u001b[1;32m     65\u001b[0m     df \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39msort_values(by\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mdifference\u001b[39m\u001b[39m'\u001b[39m], ascending\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\u001b[39m.\u001b[39mreset_index(drop\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m   3806\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m   3808\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3809\u001b[0m     indexer \u001b[39m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/anaconda3/envs/dl-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   3805\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   3806\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3807\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "   # Loading data\n",
    "    path = './셀바스AI 주식 데이터.csv'\n",
    "    data1 = pd.read_csv(path, encoding='euc-kr')\n",
    "    # Changing column order to: ['종목명', 날짜', '시가', '고가', '저가', '종가', '거래량', '거래대금']\n",
    "    # new_col = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "    data = data1.copy()\n",
    "\n",
    "    # 이동평균선 계산 - TA-Lib\n",
    "    talib_moving_average = ['wma_5', 'wma_20', 'rsi', 'lower', 'mavg', 'upper', 'pctB', 'aroonup', 'aroondn']\n",
    "    for i in talib_moving_average:\n",
    "        if i == 'wma_5':\n",
    "            data[i] = ta.WMA(np.asarray(data['종가'], dtype=float), timeperiod=5)\n",
    "        elif i == 'wma_20':\n",
    "            data[i] = ta.WMA(np.asarray(data['종가'], dtype=float), timeperiod=20)\n",
    "        elif i == 'rsi':\n",
    "            data[i] = ta.RSI(np.asarray(data['종가'], dtype=float), timeperiod=14)\n",
    "        elif i == 'lower' or i == 'upper' or i == 'mavg':\n",
    "            _, _, data[i] = ta.BBANDS(np.asarray(data['종가'],dtype=float), timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)\n",
    "        elif i == 'pctB':\n",
    "            _, _, data[i] = ta.BBANDS(np.asarray(data['종가'],dtype=float), timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)\n",
    "        elif i == 'aroonup' or i == 'aroondn':\n",
    "            data[i], _ = ta.AROON(np.asarray(data['고가'],dtype=float), np.asarray(data['저가'], dtype=float), timeperiod=14)\n",
    "        \n",
    "l = len(talib_moving_average)\n",
    "# Determine kurtosis 'K' values for MA period 4-99\n",
    "kurtosis_results = {'period': []}\n",
    "\n",
    "\n",
    "for i in range(4, 100):\n",
    "    kurtosis_results['period'].append(i)\n",
    "    # max_length = max(len(data[talib_moving_average]) for talib_moving_average in talib_moving_average)\n",
    "    \n",
    "    for ma in talib_moving_average:\n",
    "        if ma not in kurtosis_results:\n",
    "            kurtosis_results[ma] = []\n",
    "\n",
    "        ma_output = data[ma]\n",
    "        max_length = len(ma_output)\n",
    "\n",
    "        # Trimming ma_output for the specific period\n",
    "        trimmed_ma_output = ma_output[-i:]\n",
    "\n",
    "        # Determine kurtosis 'K' values\n",
    "        K = kurtosis(trimmed_ma_output, fisher=False)\n",
    "\n",
    "        # Add to dictionary\n",
    "        kurtosis_results[ma].append(K)\n",
    "        \n",
    "# Ensuring all kurtosis_results lists have the same length\n",
    "max_kurtosis_list_length = max(len(kurtosis_list) for kurtosis_list in kurtosis_results.values())\n",
    "for kurtosis_list in kurtosis_results.values():\n",
    "    if len(kurtosis_list) < max_kurtosis_list_length:\n",
    "        kurtosis_list.extend([np.nan] * (max_kurtosis_list_length - len(kurtosis_list)))\n",
    "        \n",
    "kurtosis_results = pd.DataFrame(kurtosis_results)\n",
    "# print(kurtosis_results)\n",
    "\n",
    "# Determine period with K closest to 3 +/- 5% (mesokurtic) \n",
    "# Ordering the time period for the mesokurtic MA's in optimized_period\n",
    "optimized_period = {}\n",
    "for ma in range(i):\n",
    "    difference = np.abs(kurtosis_results[ma] - 3)\n",
    "    df = pd.DataFrame({'difference': difference, 'period': kurtosis_results['period']})\n",
    "    df = df.sort_values(by=['difference'], ascending=True).reset_index(drop=True)\n",
    "\n",
    "    # ordering and considering only mesokurtic time periods from kurtosis_results\n",
    "    if df.at[0, 'difference'] < 3 * 0.05:\n",
    "        optimized_period[ma] = df.at[0, 'period']\n",
    "    else:\n",
    "        print(str(ma) + ' is not viable, best K greater or less than 3 +/- 5%')\n",
    "\n",
    "print('\\nOptimized periods:', optimized_period)\n",
    "\n",
    "simulation = {}\n",
    "for ma in optimized_period:\n",
    "    # Split data into low volatility and high volatility time series for ARIMA and LSTM based on MA type\n",
    "    if ma == 0:\n",
    "        low_vol = talib.WMA(data[4], optimized_period[ma])  # WMA\n",
    "        high_vol = data[4] - low_vol\n",
    "    elif ma == 1:\n",
    "        low_vol = talib.WMA(data[4], optimized_period[ma])  # WMA\n",
    "        high_vol = data[4] - low_vol\n",
    "    elif ma == 2:\n",
    "        low_vol = talib.RSI(data[4], optimized_period[ma])  # RSI\n",
    "        high_vol = data[4] - low_vol\n",
    "    elif ma == 3:\n",
    "        low_vol = talib.BBANDS(data[4], optimized_period[ma])[0]  # LOWER BBAND\n",
    "        high_vol = data[4] - low_vol\n",
    "    elif ma == 4:\n",
    "        low_vol = talib.BBANDS(data[4], optimized_period[ma])[1]  # MIDDLE BBAND\n",
    "        high_vol = data[4] - low_vol\n",
    "    elif ma == 5:\n",
    "        low_vol = talib.BBANDS(data[4], optimized_period[ma])[2]  # UPPER BBAND\n",
    "        high_vol = data[4] - low_vol\n",
    "    elif ma == 6:\n",
    "        low_vol = talib.BBANDS(data[4], optimized_period[ma])[3]  # %B\n",
    "        high_vol = data[4] - low_vol\n",
    "    elif ma == 7:\n",
    "        low_vol = talib.AROON(data[4], optimized_period[ma])[0]  # AROON UP\n",
    "        high_vol = data[4] - low_vol\n",
    "    elif ma == 8:\n",
    "        low_vol = talib.AROON(data[4], optimized_period[ma])[1]  # AROON DN\n",
    "        high_vol = data[4] - low_vol\n",
    "\n",
    "    # Generate ARIMA and LSTM predictions\n",
    "    print('\\nWorking on ' + str(ma) + ' predictions')\n",
    "    # compatibility of MA in ARIMA for dataset by calling ARIMA \n",
    "    try:\n",
    "        low_vol_prediction, low_vol_mse, low_vol_rmse, low_vol_mape = get_arima(low_vol, 1000, 252)\n",
    "    except:\n",
    "        print('ARIMA error, skipping to next MA type')\n",
    "        continue\n",
    "    # calling LSTM with high volatility parameters\n",
    "    high_vol_prediction, high_vol_mse, high_vol_rmse, high_vol_mape = get_lstm(high_vol, 1000, 252)\n",
    "\n",
    "    # calculating mse, rmse and mape to differentiate predictions and original\n",
    "    final_prediction = pd.Series(low_vol_prediction) + pd.Series(high_vol_prediction)\n",
    "    mse = mean_squared_error(final_prediction.values, data[4].tail(252).values)\n",
    "    rmse = mse ** 0.5\n",
    "    mape = mean_absolute_percentage_error(data[4].tail(252).reset_index(drop=True), final_prediction)\n",
    "\n",
    "    # Generate prediction accuracy\n",
    "    actual = data[4].tail(252).values\n",
    "    result_1 = []\n",
    "    result_2 = []\n",
    "    for i in range(1, len(final_prediction)):\n",
    "        # Compare prediction to previous close price\n",
    "        if final_prediction[i] > actual[i - 1] and actual[i] > actual[i - 1]:\n",
    "            result_1.append(1)\n",
    "        elif final_prediction[i] < actual[i - 1] and actual[i] < actual[i - 1]:\n",
    "            result_1.append(1)\n",
    "        else:\n",
    "            result_1.append(0)\n",
    "\n",
    "        # Compare prediction to previous prediction\n",
    "        if final_prediction[i] > final_prediction[i - 1] and actual[i] > actual[i - 1]:\n",
    "            result_2.append(1)\n",
    "        elif final_prediction[i] < final_prediction[i - 1] and actual[i] < actual[i - 1]:\n",
    "            result_2.append(1)\n",
    "        else:\n",
    "            result_2.append(0)\n",
    "\n",
    "    accuracy_1 = np.mean(result_1)\n",
    "    accuracy_2 = np.mean(result_2)\n",
    "    # storing predicted and the actual value\n",
    "    simulation[ma] = {'low_vol': {'prediction': low_vol_prediction, 'mse': low_vol_mse,\n",
    "                                  'rmse': low_vol_rmse, 'mape': low_vol_mape},\n",
    "                      'high_vol': {'prediction': high_vol_prediction, 'mse': high_vol_mse,\n",
    "                                   'rmse': high_vol_rmse},\n",
    "                      'final': {'prediction': final_prediction.values.tolist(), 'mse': mse,\n",
    "                                'rmse': rmse, 'mape': mape},\n",
    "                      'accuracy': {'prediction vs close': accuracy_1, 'prediction vs prediction': accuracy_2}}\n",
    "\n",
    "    # save simulation data here as checkpoint\n",
    "    with open('drive/My Drive/data/results/simulation_data.json', 'w') as fp:\n",
    "        json.dump(simulation, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      종목코드    종목명        날짜   시간      시가      고가      저가      종가     거래량  \\\n",
      "0  A108860  셀바스AI  20210617  901  4194.0  4194.0  4194.0  4194.0     0.0   \n",
      "1  A108860  셀바스AI  20210617  902  4199.0  4223.0  4194.0  4223.0   384.0   \n",
      "2  A108860  셀바스AI  20210617  903  4223.0  4242.0  4223.0  4242.0   584.0   \n",
      "3  A108860  셀바스AI  20210617  904  4242.0  4242.0  4242.0  4242.0   584.0   \n",
      "4  A108860  셀바스AI  20210617  905  4223.0  4223.0  4223.0  4223.0  2734.0   \n",
      "\n",
      "     거래대금  누적체결매도수량  누적체결매수수량  \n",
      "0   438.0       0.0       0.0  \n",
      "1   459.0       0.0       0.0  \n",
      "2   635.0       0.0       0.0  \n",
      "3  1435.0       0.0       0.0  \n",
      "4  1435.0       0.0       0.0  \n",
      "Index(['종목코드', '종목명', '날짜', '시간', '시가', '고가', '저가', '종가', '거래량', '거래대금',\n",
      "       '누적체결매도수량', '누적체결매수수량'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.head())\n",
    "print(data.columns)"
   ]
  },
  {
   "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dl-dev",
   "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": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}