BSE-Trend-Analysis-Numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BSE Trend analysis using Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1 : Scope the project & gather data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trend analysis on 1Yr worth of BSE data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Installs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Default Installs\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Manual Installs\n",
    "# By Eelco Hoogendoorn\n",
    "import numpy_indexed as npi\n",
    "from itertools import compress \n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Environments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getcwd :  D:\\BigData\\12. Python\\4. Jupyter Notebooks\n"
     ]
    }
   ],
   "source": [
    "print('getcwd : ', os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Pandas options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_colwidth', 200)\n",
    "\n",
    "# for all rows set to None\n",
    "# pd.set_option('display.max_rows', None) \n",
    "\n",
    "pd.set_option('display.max_rows', 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Describe and gather data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Things to remember\n",
    "* Data from mySQL should be a whole weeks data not any partial in between data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.36 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts_parser = lambda x: pd.datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "bse_csv_columns = [\"ts\", \"sc_code\", \"sc_name\", \"sc_group\", \"sc_type\"\n",
    "                 , \"open\", \"high\", \"low\", \"close\", \"last\", \"prevclose\"\n",
    "                 , \"no_trades\", \"no_of_shrs\", \"net_turnover\", \"tdcloindi\", \"isin\"]\n",
    "\n",
    "df_bse_daily = pd.read_csv(os.path.join(os.getcwd(), '..', '5. BTD', 'data', 'bse_daily_365d.csv'), sep='|'\n",
    "                           , names=bse_csv_columns\n",
    "                          ,skip_blank_lines=True\n",
    "#                          ,parse_dates=['ts'], date_parser = ts_parser)\n",
    "                          ,parse_dates=['ts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(657058, 16)\n"
     ]
    }
   ],
   "source": [
    "print(df_bse_daily.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2 : Explore and assess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sc_group\n",
       "A     114330\n",
       "B     239492\n",
       "E       2667\n",
       "F       1150\n",
       "IF       669\n",
       "M      15685\n",
       "MS        66\n",
       "MT       564\n",
       "P       1030\n",
       "R         18\n",
       "T      30339\n",
       "TS        33\n",
       "X     177775\n",
       "XT     62820\n",
       "Z      10391\n",
       "ZP        29\n",
       "Name: sc_group, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bse_daily.groupby(['sc_group'])['sc_group'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = df_bse_daily[df_bse_daily.sc_code == 500002][['ts', 'sc_code', 'sc_name', 'high', 'low', 'close', 'prevclose', 'no_of_shrs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 245 entries, 0 to 654560\n",
      "Data columns (total 8 columns):\n",
      "ts            245 non-null datetime64[ns]\n",
      "sc_code       245 non-null int64\n",
      "sc_name       245 non-null object\n",
      "high          245 non-null float64\n",
      "low           245 non-null float64\n",
      "close         245 non-null float64\n",
      "prevclose     245 non-null float64\n",
      "no_of_shrs    245 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(4), int64(2), object(1)\n",
      "memory usage: 16.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>ts</th>\n",
       "      <th>sc_code</th>\n",
       "      <th>sc_name</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>prevclose</th>\n",
       "      <th>no_of_shrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>500002</td>\n",
       "      <td>ABB LTD.</td>\n",
       "      <td>1326.60</td>\n",
       "      <td>1306.35</td>\n",
       "      <td>1313.55</td>\n",
       "      <td>1319.85</td>\n",
       "      <td>7785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2791</th>\n",
       "      <td>2019-04-02</td>\n",
       "      <td>500002</td>\n",
       "      <td>ABB LTD.</td>\n",
       "      <td>1342.25</td>\n",
       "      <td>1314.00</td>\n",
       "      <td>1322.60</td>\n",
       "      <td>1313.55</td>\n",
       "      <td>4482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5519</th>\n",
       "      <td>2019-04-03</td>\n",
       "      <td>500002</td>\n",
       "      <td>ABB LTD.</td>\n",
       "      <td>1339.95</td>\n",
       "      <td>1319.70</td>\n",
       "      <td>1329.00</td>\n",
       "      <td>1322.60</td>\n",
       "      <td>27791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8294</th>\n",
       "      <td>2019-04-04</td>\n",
       "      <td>500002</td>\n",
       "      <td>ABB LTD.</td>\n",
       "      <td>1336.10</td>\n",
       "      <td>1317.30</td>\n",
       "      <td>1326.00</td>\n",
       "      <td>1329.00</td>\n",
       "      <td>3106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11005</th>\n",
       "      <td>2019-04-05</td>\n",
       "      <td>500002</td>\n",
       "      <td>ABB LTD.</td>\n",
       "      <td>1379.00</td>\n",
       "      <td>1320.30</td>\n",
       "      <td>1374.75</td>\n",
       "      <td>1326.00</td>\n",
       "      <td>12887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ts  sc_code   sc_name     high      low    close  prevclose  \\\n",
       "0     2019-04-01   500002  ABB LTD.  1326.60  1306.35  1313.55    1319.85   \n",
       "2791  2019-04-02   500002  ABB LTD.  1342.25  1314.00  1322.60    1313.55   \n",
       "5519  2019-04-03   500002  ABB LTD.  1339.95  1319.70  1329.00    1322.60   \n",
       "8294  2019-04-04   500002  ABB LTD.  1336.10  1317.30  1326.00    1329.00   \n",
       "11005 2019-04-05   500002  ABB LTD.  1379.00  1320.30  1374.75    1326.00   \n",
       "\n",
       "       no_of_shrs  \n",
       "0            7785  \n",
       "2791         4482  \n",
       "5519        27791  \n",
       "8294         3106  \n",
       "11005       12887  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "* column no_of_shrs needs to be renamed. Changing it to volumes\n",
    "* Adding calculated columns like change(close- prevclose), change percent( ((close/prevclose)*100) - 100)\n",
    "* Since i am calculating weekly analysis, adding a new column called yearWeek which contain year and week number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.rename(columns={\"no_of_shrs\": \"volumes\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ts', 'sc_code', 'sc_name', 'high', 'low', 'close', 'prevclose',\n",
       "       'volumes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Last week of year comes as 0 because that week is also beginning of next year\n",
    "#df_temp['yearWeek'] = df_temp['ts'].dt.year * 100 + df_temp['ts'].dt.week+ 0\n",
    "\n",
    "# This resolves above problem\n",
    "df_temp['yearWeek'] = df_temp['ts'].dt.year * 100 + df_temp['ts'].dt.strftime('%U').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change ts format from datetime64[ns] to object and truncating the time values\n",
    "# in a way avoid error in weekly_trend_analysis() in last traded date section of code\n",
    "df_temp['ts'] = df_temp['ts'] .dt.strftime('%Y-%m-%d')\n",
    "#df_temp['ts'] = df_temp['ts'] .dt.strftime('%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change & change percent\n",
    "df_temp['chng'] = (df_temp['close'] - df_temp['prevclose'])\n",
    "df_temp['chngp'] = np.round( ( (df_temp['close'] / df_temp['prevclose']) * 100 ) - 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Analysis data & Indicators\n",
    "\n",
    "Below are the possible indicators, i can think of,\n",
    "1. closeH - high close in the week\n",
    "1. closeL - low closein the week\n",
    "1. volHigh - Highest volume in the week\n",
    "1. volAvg - Volume average\n",
    "1. daysTraded - Number of days traded in the week\n",
    "1. HSDL - Highest Single Day Loss\n",
    "1. HSDG - Highest Single Day Gain\n",
    "1. HSDLp - Highest Single Day Loss percent\n",
    "1. HSDGp - Highest Single Day Gain percent\n",
    "1. first - First close of the week\n",
    "1. last - Last close of he week\n",
    "1. wChng - Week change\n",
    "1. wChngp - Week change percent\n",
    "1. lastTrdDoW - Last traded day of week\n",
    "1. TI - Times increased\n",
    "1. volAvgWOhv - Volume average without high volume\n",
    "1. HVdAV - High volume / Average volume(without highvolume)\n",
    "1. CPveoHVD - Close positive on high volume day\n",
    "1. lastDVotWk - Last day volume\n",
    "1. lastDVdAV - Last day volume / average volume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting df columns to numpy array**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_yearWeek = df_temp['yearWeek'].to_numpy()\n",
    "arr_close = df_temp['close'].to_numpy()\n",
    "arr_prevclose = df_temp['prevclose'].to_numpy()\n",
    "arr_chng = df_temp['chng'].to_numpy()\n",
    "arr_chngp = df_temp['chngp'].to_numpy()\n",
    "arr_ts = df_temp['ts'].to_numpy()\n",
    "arr_volumes = df_temp['volumes'].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculating max(close) & min(close)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pandas**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 10.8 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "a = df_temp[['yearWeek', 'close']].to_numpy()\n",
    "b = npi.group_by(a[:, 0]).split(a[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[201913.  ,   1313.55],\n",
       "       [201913.  ,   1322.6 ],\n",
       "       [201913.  ,   1329.  ],\n",
       "       [201913.  ,   1326.  ],\n",
       "       [201913.  ,   1374.75]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1313.55, 1322.6 , 1329.  , 1326.  , 1374.75]),\n",
       " array([1398.2 , 1408.4 , 1379.05, 1403.4 , 1426.2 ]),\n",
       " array([1421.4, 1408.6, 1432.3]),\n",
       " array([1433.05, 1488.45, 1484.45, 1493.9 , 1477.5 ]),\n",
       " array([1473.4 , 1473.9 , 1490.55])]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Numpy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "arr_concat = np.column_stack((arr_yearWeek, arr_close))\n",
    "npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[201913.  ,   1313.55],\n",
       "       [201913.  ,   1322.6 ],\n",
       "       [201913.  ,   1329.  ],\n",
       "       [201913.  ,   1326.  ],\n",
       "       [201913.  ,   1374.75]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_concat[:][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1313.55, 1322.6 , 1329.  , 1326.  , 1374.75]),\n",
       " array([1398.2 , 1408.4 , 1379.05, 1403.4 , 1426.2 ]),\n",
       " array([1421.4, 1408.6, 1432.3]),\n",
       " array([1433.05, 1488.45, 1484.45, 1493.9 , 1477.5 ]),\n",
       " array([1473.4 , 1473.9 , 1490.55])]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npi_gb[:][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**    \n",
    "* Comparing above Pandas vs. Numpy. Numpy is faster and results look similar. So proceeding with Numpy for similar requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 4.93 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "arr_concat = np.column_stack((arr_yearWeek, arr_close))\n",
    "npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "cmaxs, cmins = [], []\n",
    "first, last, wChng, wChngp = [], [], [], []\n",
    "for idx,subarr in enumerate(npi_gb):\n",
    "    cmaxs.append( np.amax(subarr) )\n",
    "    cmins.append( np.amin(subarr) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1374.75, 1426.2, 1432.3, 1493.9, 1490.55, 1481.3, 1392.05, 1494.65, 1610.7, 1591.85, 1566.7, 1566.0, 1605.45, 1653.2, 1533.05, 1518.35, 1403.0, 1394.5, 1397.7, 1378.35, 1375.05, 1400.05, 1331.15, 1349.25, 1413.3, 1573.4, 1521.0, 1486.85, 1552.65, 1540.05, 1517.05, 1485.75, 1491.05, 1471.4, 1446.1, 1479.45, 1486.2, 1479.85, 1275.75, 1287.35, 1321.45, 1367.25, 1377.1, 1321.8, 1341.35, 1338.45, 1340.05, 1228.9, 1200.05, 1193.9, 1126.65, 964.65, 870.1]\n",
      "[1313.55, 1379.05, 1408.6, 1433.05, 1473.4, 1376.95, 1346.55, 1426.0, 1571.0, 1527.6, 1509.8, 1531.8, 1558.9, 1543.35, 1484.85, 1414.5, 1361.1, 1350.55, 1381.3, 1349.95, 1339.1, 1333.0, 1316.05, 1324.3, 1322.15, 1460.5, 1460.4, 1464.5, 1473.65, 1496.1, 1448.35, 1448.5, 1457.05, 1412.9, 1407.25, 1439.6, 1449.7, 1266.4, 1255.15, 1284.1, 1287.0, 1286.7, 1303.75, 1291.8, 1281.45, 1294.25, 1234.7, 1207.4, 1184.55, 1163.6, 1011.3, 893.0, 827.9]\n"
     ]
    }
   ],
   "source": [
    "print(cmaxs)\n",
    "print(cmins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**All metrics are calculated in this single cell**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 17.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# References : # https://stackoverflow.com/questions/38013778/is-there-any-numpy-group-by-function\n",
    "\n",
    "arr_yearWeek = df_temp['yearWeek'].to_numpy()\n",
    "arr_close = df_temp['close'].to_numpy()\n",
    "arr_prevclose = df_temp['prevclose'].to_numpy()\n",
    "arr_chng = df_temp['chng'].to_numpy()\n",
    "arr_chngp = df_temp['chngp'].to_numpy()\n",
    "arr_ts = df_temp['ts'].to_numpy()\n",
    "arr_volumes = df_temp['volumes'].to_numpy()\n",
    "\n",
    "# Close\n",
    "arr_concat = np.column_stack((arr_yearWeek, arr_close))\n",
    "npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "cmaxs, cmins = [], []\n",
    "first, last, wChng, wChngp = [], [], [], []\n",
    "for idx,subarr in enumerate(npi_gb):\n",
    "    cmaxs.append( np.amax(subarr) )\n",
    "    cmins.append( np.amin(subarr) )\n",
    "    first.append(subarr[0])\n",
    "    last.append(subarr[-1])\n",
    "    wChng.append( subarr[-1] - subarr[0] )\n",
    "    wChngp.append( ( (subarr[-1] / subarr[0]) * 100) - 100 )\n",
    "\n",
    "    \n",
    "yearWeek, daysTraded = np.unique(arr_concat[:,0], return_counts=True)\n",
    "\n",
    "npi_gb = None\n",
    "arr_concat = None\n",
    "\n",
    "# Chng\n",
    "#e = df_temp[['yearWeek', 'chng']].to_numpy()\n",
    "#f = npi.group_by(e[:, 0]).split(e[:, 1])\n",
    "arr_concat = np.column_stack((arr_yearWeek, arr_chng))\n",
    "npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "HSDL, HSDG = [], []\n",
    "for idx,subarr in enumerate(npi_gb):\n",
    "    HSDL.append( np.amin(subarr) )\n",
    "    HSDG.append( np.amax(subarr) )\n",
    "\n",
    "npi_gb = None\n",
    "arr_concat = None\n",
    "\n",
    "# Chngp\n",
    "#g = df_temp[['yearWeek', 'chngp']].to_numpy()\n",
    "#h = npi.group_by(g[:, 0]).split(g[:, 1])\n",
    "arr_concat = np.column_stack((arr_yearWeek, arr_chngp))\n",
    "npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "HSDLp, HSDGp = [], []\n",
    "for idx,subarr in enumerate(npi_gb):\n",
    "    HSDLp.append( np.amin(subarr) )\n",
    "    HSDGp.append( np.amax(subarr) )\n",
    "\n",
    "npi_gb = None\n",
    "arr_concat = None\n",
    "\n",
    "# Last Traded Date of the Week\n",
    "# Resolved \"TypeError: invalid type promotion\" by convertign 'ts' to date string. \n",
    "# Getting issues when concatenating arr_yearWeek(int64) with arr_ts(timestamp64[ns])\n",
    "# Alternate solutions if ts was still a datetime64[n] here we can try to convert arr_ts.tolist() or arr_ts.astype(object)\n",
    "# Commenting out alternative solution\n",
    "#i = df_temp[['yearWeek', 'ts']].to_numpy()\n",
    "#j = npi.group_by(i[:, 0]).split(i[:, 1])\n",
    "arr_concat = np.column_stack((arr_yearWeek, arr_ts))\n",
    "npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "lastTrdDoW = []\n",
    "for idx,subarr in enumerate(npi_gb):\n",
    "    lastTrdDoW.append( subarr[-1] )\n",
    "\n",
    "npi_gb = None\n",
    "arr_concat = None\n",
    "\n",
    "# Times inreased\n",
    "#yearWeek = df_temp['yearWeek'].to_numpy()\n",
    "#close = df_temp['close'].to_numpy()\n",
    "#prevclose = df_temp['prevclose'].to_numpy()\n",
    "TI = np.where(arr_close > arr_prevclose, 1, 0)\n",
    "\n",
    "# Below npi_gb_yearWeekTI is used in volumes section\n",
    "arr_concat = np.column_stack((arr_yearWeek, TI))\n",
    "npi_gb_yearWeekTI = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "#TI = []\n",
    "#for idx,subarr in enumerate(t_group):\n",
    "#    TI.append( np.sum(subarr) )\n",
    "#Above 3 lines replaced with below\n",
    "tempArr, TI = npi.group_by(arr_yearWeek).sum(TI)\n",
    "\n",
    "\n",
    "# Volume ( dependent on above section value t_group , thats the reason to move from top to here)\n",
    "arr_concat = np.column_stack((arr_yearWeek, arr_volumes))\n",
    "npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "#c = df_temp[['yearWeek', 'volumes']].to_numpy()\n",
    "#d = npi.group_by(c[:, 0]).split(c[:, 1])\n",
    "\n",
    "vmaxs, vavgs, volAvgWOhv, HVdAV, CPveoHVD, lastDVotWk, lastDVdAV = [], [], [], [], [], [], []\n",
    "for idx,subarr in enumerate(npi_gb):\n",
    "    vavgs.append( np.mean(subarr) )\n",
    "    ldvotWk = subarr[-1]\n",
    "    lastDVotWk.append(ldvotWk)\n",
    "        \n",
    "    #print(idx, 'O - ',subarr, np.argmax(subarr), ', average : ',np.mean(subarr))\n",
    "    ixDel = np.argmax(subarr)\n",
    "    hV = subarr[ixDel]\n",
    "    vmaxs.append( hV )\n",
    "    \n",
    "    subarr = np.delete(subarr, ixDel)\n",
    "    vawoHV = np.mean(subarr)\n",
    "    volAvgWOhv.append( vawoHV )\n",
    "    HVdAV.append(hV / vawoHV)\n",
    "    CPveoHVD.append( npi_gb_yearWeekTI[idx][ixDel] )\n",
    "    lastDVdAV.append(ldvotWk / vawoHV)    \n",
    "    #print(idx, 'N - ',subarr, ', average : ',vawoHV, 'hV : ', hV, 'HVdAV : ',hV / vawoHV \n",
    "    #      , ', CPveoHVD : ', npi_gb_yearWeekTI[idx][ixDel], 'lastDVoTWK : ',ldvotWk, 'lastDVdAV : ', ldvotWk / vawoHV)\n",
    "    \n",
    "npi_gb = None\n",
    "arr_concat = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.int64'>\n",
      "<class 'str'>\n",
      "<class 'list'>\n",
      "(245,)\n"
     ]
    }
   ],
   "source": [
    "# Different datatypes are going to be stored in numpy. BadPractice(i guess)\n",
    "print(type(arr_yearWeek))\n",
    "print(type(arr_yearWeek[0]))\n",
    "print(type(arr_ts[0]))\n",
    "print(type( list(arr_ts) ))\n",
    "print(arr_yearWeek.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**column_stack**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201913.0</td>\n",
       "      <td>1313.55</td>\n",
       "      <td>1374.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201914.0</td>\n",
       "      <td>1398.20</td>\n",
       "      <td>1426.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201915.0</td>\n",
       "      <td>1421.40</td>\n",
       "      <td>1432.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201916.0</td>\n",
       "      <td>1433.05</td>\n",
       "      <td>1477.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201917.0</td>\n",
       "      <td>1473.40</td>\n",
       "      <td>1490.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0        1        2\n",
       "0  201913.0  1313.55  1374.75\n",
       "1  201914.0  1398.20  1426.20\n",
       "2  201915.0  1421.40  1432.30\n",
       "3  201916.0  1433.05  1477.50\n",
       "4  201917.0  1473.40  1490.55"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_test = np.column_stack((yearWeek, first, last))\n",
    "df_test = pd.DataFrame(data=np_test)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Compress() - Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "['abc' 'def' 'ghi' 'abc' 'def' 'ghi' 'abc']\n"
     ]
    }
   ],
   "source": [
    "a = np.array(['abc', 'def', 'ghi', 'abc', 'def', 'ghi', 'abc'])\n",
    "print(type(a))\n",
    "print(a)\n",
    "b = np.array(['abc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False False  True False False  True]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['abc', 'abc', 'abc'], dtype='<U3')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_1 = np.isin(a, b) \n",
    "print(tf_1)\n",
    "t_result = list(compress(range(len(tf_1)), tf_1)) \n",
    "#print(t_result)\n",
    "weekly_all = a[t_result]\n",
    "weekly_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Numpy array slice testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7, 5)\n",
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]\n",
      " [15 16 17 18 19]\n",
      " [20 21 22 23 24]\n",
      " [25 26 27 28 29]\n",
      " [30 31 32 33 34]]\n",
      "a =  [ 0  5 10 15 20 25 30]\n",
      "b =  [15, 25]\n"
     ]
    }
   ],
   "source": [
    "# generating Test data (rows, columns)\n",
    "x = np.arange(35).reshape(7, 5)\n",
    "print(x.shape)\n",
    "print(x)\n",
    "a = x[:,0]\n",
    "b = [15, 25]\n",
    "print('a = ',a)\n",
    "print('b = ',b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISIN =  [False False False  True False  True False]\n",
      "ISIN(invert) =  [ True  True  True False  True False  True]\n",
      "Compress index =  [0, 1, 2, 4, 6]\n",
      "[ 0  5 10 20 30]\n",
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]\n",
      " [20 21 22 23 24]\n",
      " [30 31 32 33 34]]\n"
     ]
    }
   ],
   "source": [
    "print('ISIN = ', np.isin(a, b) )\n",
    "print('ISIN(invert) = ', np.isin(a, b, invert=True) )\n",
    "tf_1 = np.isin(a, b, invert=True) \n",
    "t_result = list(compress(range(len(tf_1)), tf_1)) \n",
    "print('Compress index = ',t_result)\n",
    "print(a[t_result])\n",
    "print(x[t_result])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [30, 31, 32, 33, 34]])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_result = [True, False, True, False, True, False, True ]\n",
    "x[t_result]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Vertical Stack**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['500002' 'ABB']\n",
      " ['500002' 'ABB']\n",
      " ['500002' 'ABB']\n",
      " ['500002' 'ABB']\n",
      " ['500002' 'ABB']]\n",
      "[['500002' 'ABB']\n",
      " ['500002' 'ABB']\n",
      " ['500002' 'ABB']\n",
      " ['500002' 'ABB']\n",
      " ['500002' 'ABB']\n",
      " ['500003' 'ZZZ']\n",
      " ['500003' 'ZZZ']\n",
      " ['500003' 'ZZZ']\n",
      " ['500003' 'ZZZ']\n",
      " ['500003' 'ZZZ']]\n"
     ]
    }
   ],
   "source": [
    "vs = []\n",
    "\n",
    "sc_code = np.full(5, '500002')\n",
    "symbol = np.full(5, 'ABB')\n",
    "np_test = np.column_stack((sc_code, symbol))\n",
    "vs = np.vstack((np_test))    \n",
    "print(vs)\n",
    "sc_code = np.full(5, '500003')\n",
    "symbol = np.full(5, 'ZZZ')\n",
    "np_test = np.column_stack((sc_code, symbol))\n",
    "vs = np.vstack((vs, np_test))    \n",
    "print(vs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>500002</td>\n",
       "      <td>ABB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500002</td>\n",
       "      <td>ABB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500002</td>\n",
       "      <td>ABB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500002</td>\n",
       "      <td>ABB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500002</td>\n",
       "      <td>ABB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>500003</td>\n",
       "      <td>ZZZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>500003</td>\n",
       "      <td>ZZZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>500003</td>\n",
       "      <td>ZZZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>500003</td>\n",
       "      <td>ZZZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>500003</td>\n",
       "      <td>ZZZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1\n",
       "0  500002  ABB\n",
       "1  500002  ABB\n",
       "2  500002  ABB\n",
       "3  500002  ABB\n",
       "4  500002  ABB\n",
       "5  500003  ZZZ\n",
       "6  500003  ZZZ\n",
       "7  500003  ZZZ\n",
       "8  500003  ZZZ\n",
       "9  500003  ZZZ"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.DataFrame(data=vs)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Below method is used to remove latest yearWeek from history, so later latest yearWeek is appended to history**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a =  ['201901' '201901' '201901' '201901' '201901' '201902' '201902' '201902'\n",
      " '201902' '201902']\n",
      "b =  ['201902' '201902' '201902' '201902' '201902' '201903' '201903' '201903'\n",
      " '201903' '201903']\n",
      "Set difference(Show yearWeek which b doesnt have) =  {'201901'}\n",
      "\n",
      " what items in a is avialble in b\n",
      "np.isin =  [False False False False False  True  True  True  True  True]\n",
      "np.isin(invert) =  [ True  True  True  True  True False False False False False]\n",
      "Compress indexes(get indexes of true items) =  [0, 1, 2, 3, 4]\n",
      "Final Result =  ['201901' '201901' '201901' '201901' '201901']\n"
     ]
    }
   ],
   "source": [
    "a = np.full(5, '201901')\n",
    "a = np.append(a, np.full(5, '201902'))\n",
    "b = np.full(5, '201902')\n",
    "b = np.append(b, np.full(5, '201903'))\n",
    "print('a = ',a)\n",
    "print('b = ',b)\n",
    "\n",
    "set_a = set(a)\n",
    "set_b = set(b)\n",
    "print('Set difference(Show yearWeek which b doesnt have) = ',set_a.difference(set_b))\n",
    "\n",
    "print('\\n what items in a is avialble in b')\n",
    "tf_1 = np.isin(a, b) \n",
    "print('np.isin = ',tf_1)\n",
    "tf_1 = np.isin(a, b, invert=True) \n",
    "print('np.isin(invert) = ',tf_1)\n",
    "\n",
    "t_result = list(compress(range(len(tf_1)), tf_1)) \n",
    "print('Compress indexes(get indexes of true items) = ',t_result)\n",
    "print('Final Result = ',a[t_result])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Joining all numpy arrays to dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 8 ms\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>yearWeek</th>\n",
       "      <th>lastTrdDoW</th>\n",
       "      <th>daysTraded</th>\n",
       "      <th>closeL</th>\n",
       "      <th>closeH</th>\n",
       "      <th>volAvg</th>\n",
       "      <th>volHigh</th>\n",
       "      <th>HSDL</th>\n",
       "      <th>HSDG</th>\n",
       "      <th>HSDLp</th>\n",
       "      <th>HSDGp</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>wChng</th>\n",
       "      <th>wChngp</th>\n",
       "      <th>TI</th>\n",
       "      <th>volAvgWOhv</th>\n",
       "      <th>HVdAV</th>\n",
       "      <th>CPveoHVD</th>\n",
       "      <th>lastDVotWk</th>\n",
       "      <th>lastDVdAV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201913</td>\n",
       "      <td>2019-04-05</td>\n",
       "      <td>5</td>\n",
       "      <td>1313.55</td>\n",
       "      <td>1374.75</td>\n",
       "      <td>11210</td>\n",
       "      <td>27791</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>48.75</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>3.68</td>\n",
       "      <td>1313.55</td>\n",
       "      <td>1374.75</td>\n",
       "      <td>61.2</td>\n",
       "      <td>4.66</td>\n",
       "      <td>3</td>\n",
       "      <td>7065</td>\n",
       "      <td>3.93</td>\n",
       "      <td>1</td>\n",
       "      <td>12887</td>\n",
       "      <td>1.8240622788393488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201914</td>\n",
       "      <td>2019-04-12</td>\n",
       "      <td>5</td>\n",
       "      <td>1379.05</td>\n",
       "      <td>1426.2</td>\n",
       "      <td>16741</td>\n",
       "      <td>34789</td>\n",
       "      <td>-29.35</td>\n",
       "      <td>24.35</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1398.2</td>\n",
       "      <td>1426.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12229</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1</td>\n",
       "      <td>6928</td>\n",
       "      <td>0.5665106200298464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201915</td>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>3</td>\n",
       "      <td>1408.6</td>\n",
       "      <td>1432.3</td>\n",
       "      <td>9008</td>\n",
       "      <td>21127</td>\n",
       "      <td>-12.8</td>\n",
       "      <td>23.7</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1421.4</td>\n",
       "      <td>1432.3</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1</td>\n",
       "      <td>2948</td>\n",
       "      <td>7.17</td>\n",
       "      <td>1</td>\n",
       "      <td>21127</td>\n",
       "      <td>7.165338307614041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201916</td>\n",
       "      <td>2019-04-26</td>\n",
       "      <td>5</td>\n",
       "      <td>1433.05</td>\n",
       "      <td>1493.9</td>\n",
       "      <td>23703</td>\n",
       "      <td>50389</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>55.4</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>3.87</td>\n",
       "      <td>1433.05</td>\n",
       "      <td>1477.5</td>\n",
       "      <td>44.45</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3</td>\n",
       "      <td>17032</td>\n",
       "      <td>2.96</td>\n",
       "      <td>1</td>\n",
       "      <td>23219</td>\n",
       "      <td>1.3632573978393612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201917</td>\n",
       "      <td>2019-05-03</td>\n",
       "      <td>3</td>\n",
       "      <td>1473.4</td>\n",
       "      <td>1490.55</td>\n",
       "      <td>13102</td>\n",
       "      <td>27577</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>16.65</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1473.4</td>\n",
       "      <td>1490.55</td>\n",
       "      <td>17.15</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2</td>\n",
       "      <td>5865</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0</td>\n",
       "      <td>4414</td>\n",
       "      <td>0.7525360156849373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201918</td>\n",
       "      <td>2019-05-10</td>\n",
       "      <td>5</td>\n",
       "      <td>1376.95</td>\n",
       "      <td>1481.3</td>\n",
       "      <td>19429</td>\n",
       "      <td>47397</td>\n",
       "      <td>-56.65</td>\n",
       "      <td>-9.25</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>1481.3</td>\n",
       "      <td>1376.95</td>\n",
       "      <td>-104.35</td>\n",
       "      <td>-7.04</td>\n",
       "      <td>0</td>\n",
       "      <td>12437</td>\n",
       "      <td>3.81</td>\n",
       "      <td>0</td>\n",
       "      <td>12120</td>\n",
       "      <td>0.9745115381522875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201919</td>\n",
       "      <td>2019-05-17</td>\n",
       "      <td>5</td>\n",
       "      <td>1346.55</td>\n",
       "      <td>1392.05</td>\n",
       "      <td>7935</td>\n",
       "      <td>31284</td>\n",
       "      <td>-30.4</td>\n",
       "      <td>24.3</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1346.55</td>\n",
       "      <td>1392.05</td>\n",
       "      <td>45.5</td>\n",
       "      <td>3.38</td>\n",
       "      <td>4</td>\n",
       "      <td>2098</td>\n",
       "      <td>14.91</td>\n",
       "      <td>1</td>\n",
       "      <td>2259</td>\n",
       "      <td>1.0763549731983324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>201920</td>\n",
       "      <td>2019-05-24</td>\n",
       "      <td>5</td>\n",
       "      <td>1426.0</td>\n",
       "      <td>1494.65</td>\n",
       "      <td>10970</td>\n",
       "      <td>37266</td>\n",
       "      <td>-22.85</td>\n",
       "      <td>65.3</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>4.57</td>\n",
       "      <td>1431.9</td>\n",
       "      <td>1494.65</td>\n",
       "      <td>62.75</td>\n",
       "      <td>4.38</td>\n",
       "      <td>3</td>\n",
       "      <td>4397</td>\n",
       "      <td>8.48</td>\n",
       "      <td>1</td>\n",
       "      <td>37266</td>\n",
       "      <td>8.47532408460314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>201921</td>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>5</td>\n",
       "      <td>1571.0</td>\n",
       "      <td>1610.7</td>\n",
       "      <td>32984</td>\n",
       "      <td>119315</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>116.05</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>7.76</td>\n",
       "      <td>1610.7</td>\n",
       "      <td>1571.0</td>\n",
       "      <td>-39.7</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>1</td>\n",
       "      <td>11401</td>\n",
       "      <td>10.47</td>\n",
       "      <td>0</td>\n",
       "      <td>119315</td>\n",
       "      <td>10.465080583269378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>201922</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>4</td>\n",
       "      <td>1527.6</td>\n",
       "      <td>1591.85</td>\n",
       "      <td>10052</td>\n",
       "      <td>21913</td>\n",
       "      <td>-32.6</td>\n",
       "      <td>36.75</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>2.41</td>\n",
       "      <td>1591.85</td>\n",
       "      <td>1564.35</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>2</td>\n",
       "      <td>6099</td>\n",
       "      <td>3.59</td>\n",
       "      <td>0</td>\n",
       "      <td>8289</td>\n",
       "      <td>1.3590752582390555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  yearWeek  lastTrdDoW daysTraded   closeL   closeH volAvg volHigh    HSDL  \\\n",
       "0   201913  2019-04-05          5  1313.55  1374.75  11210   27791    -6.3   \n",
       "1   201914  2019-04-12          5  1379.05   1426.2  16741   34789  -29.35   \n",
       "2   201915  2019-04-18          3   1408.6   1432.3   9008   21127   -12.8   \n",
       "3   201916  2019-04-26          5  1433.05   1493.9  23703   50389   -16.4   \n",
       "4   201917  2019-05-03          3   1473.4  1490.55  13102   27577    -4.1   \n",
       "5   201918  2019-05-10          5  1376.95   1481.3  19429   47397  -56.65   \n",
       "6   201919  2019-05-17          5  1346.55  1392.05   7935   31284   -30.4   \n",
       "7   201920  2019-05-24          5   1426.0  1494.65  10970   37266  -22.85   \n",
       "8   201921  2019-05-31          5   1571.0   1610.7  32984  119315   -20.8   \n",
       "9   201922  2019-06-07          4   1527.6  1591.85  10052   21913   -32.6   \n",
       "\n",
       "     HSDG  HSDLp  HSDGp    first     last    wChng wChngp TI volAvgWOhv  \\\n",
       "0   48.75  -0.48   3.68  1313.55  1374.75     61.2   4.66  3       7065   \n",
       "1   24.35  -2.08   1.77   1398.2   1426.2     28.0    2.0  4      12229   \n",
       "2    23.7   -0.9   1.68   1421.4   1432.3     10.9   0.77  1       2948   \n",
       "3    55.4   -1.1   3.87  1433.05   1477.5    44.45    3.1  3      17032   \n",
       "4   16.65  -0.28   1.13   1473.4  1490.55    17.15   1.16  2       5865   \n",
       "5   -9.25  -3.91  -0.62   1481.3  1376.95  -104.35  -7.04  0      12437   \n",
       "6    24.3  -2.21    1.8  1346.55  1392.05     45.5   3.38  4       2098   \n",
       "7    65.3  -1.57   4.57   1431.9  1494.65    62.75   4.38  3       4397   \n",
       "8  116.05  -1.29   7.76   1610.7   1571.0    -39.7  -2.46  1      11401   \n",
       "9   36.75  -2.09   2.41  1591.85  1564.35    -27.5  -1.73  2       6099   \n",
       "\n",
       "   HVdAV CPveoHVD lastDVotWk           lastDVdAV  \n",
       "0   3.93        1      12887  1.8240622788393488  \n",
       "1   2.84        1       6928  0.5665106200298464  \n",
       "2   7.17        1      21127   7.165338307614041  \n",
       "3   2.96        1      23219  1.3632573978393612  \n",
       "4    4.7        0       4414  0.7525360156849373  \n",
       "5   3.81        0      12120  0.9745115381522875  \n",
       "6  14.91        1       2259  1.0763549731983324  \n",
       "7   8.48        1      37266    8.47532408460314  \n",
       "8  10.47        0     119315  10.465080583269378  \n",
       "9   3.59        0       8289  1.3590752582390555  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# yearWeek and occurances \n",
    "#yearWeek, daysTraded = np.unique(a[:,0], return_counts=True)\n",
    "yearWeek = yearWeek.astype(int)\n",
    "HSDL = np.round(HSDL,2)\n",
    "HSDG = np.round(HSDG,2)\n",
    "HSDLp = np.round(HSDLp,2)\n",
    "HSDGp = np.round(HSDGp,2)\n",
    "\n",
    "first = np.round(first,2)\n",
    "last = np.round(last,2)\n",
    "wChng = np.round(wChng,2)\n",
    "wChngp = np.round(wChngp,2)\n",
    "\n",
    "vavgs = np.array(vavgs).astype(int)\n",
    "volAvgWOhv = np.array(volAvgWOhv).astype(int)\n",
    "HVdAV = np.round(HVdAV,2)\n",
    "\n",
    "#dict_temp = {'yearWeek': yearWeek, 'closeH': cmaxs, 'closeL': cmins, 'volHigh':vmaxs, 'volAvg':vavgs, 'daysTraded':daysTraded\n",
    "#            ,'HSDL':HSDL, 'HSDG':HSDG, 'HSDLp':HSDLp, 'HSDGp':HSDGp, 'first':first, 'last':last, 'wChng':wChng, 'wChngp':wChngp\n",
    "#            ,'lastTrdDoW':lastTrdDoW, 'TI':TI, 'volAvgWOhv':volAvgWOhv, 'HVdAV':HVdAV, 'CPveoHVD':CPveoHVD\n",
    "#            ,'lastDVotWk':lastDVotWk, 'lastDVdAV':lastDVdAV}\n",
    "#df_weekly_new = pd.DataFrame(data=dict_temp)\n",
    "\n",
    "cols = ['yearWeek', 'lastTrdDoW', 'daysTraded', 'closeL', 'closeH', 'volAvg', 'volHigh'\n",
    "             , 'HSDL', 'HSDG', 'HSDLp', 'HSDGp', 'first', 'last', 'wChng', 'wChngp', 'TI', 'volAvgWOhv', 'HVdAV'\n",
    "             , 'CPveoHVD', 'lastDVotWk', 'lastDVdAV']\n",
    "\n",
    "ticker = np.full(yearWeek.shape[0], df_temp['sc_code'].to_numpy()[0])\n",
    "np_weekly_new = np.column_stack((yearWeek, lastTrdDoW, daysTraded, cmins, cmaxs, vavgs, vmaxs, HSDL\n",
    "                               , HSDG, HSDLp, HSDGp, first, last, wChng, wChngp, TI, volAvgWOhv, HVdAV\n",
    "                               , CPveoHVD, lastDVotWk, lastDVdAV))\n",
    "df_weekly_new = pd.DataFrame(data=np_weekly_new, columns=cols)\n",
    "\n",
    "df_weekly_new.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<U32')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_weekly_new.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['201913', '2019-04-05', '5', ..., '1', '12887',\n",
       "        '1.8240622788393488'],\n",
       "       ['201914', '2019-04-12', '5', ..., '1', '6928',\n",
       "        '0.5665106200298464'],\n",
       "       ['201915', '2019-04-18', '3', ..., '1', '21127',\n",
       "        '7.165338307614041'],\n",
       "       ...,\n",
       "       ['202010', '2020-03-13', '4', ..., '0', '14051',\n",
       "        '2.1394203928335784'],\n",
       "       ['202011', '2020-03-20', '5', ..., '0', '5207',\n",
       "        '1.1244398855476974'],\n",
       "       ['202012', '2020-03-27', '5', ..., '0', '5204',\n",
       "        '1.2823261257931375']], dtype='<U32')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_weekly_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Faster way to read a cell in pandas**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500002\n",
      "Wall time: 13.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#print(df_bse_daily.shape)\n",
    "print(df_bse_daily.iloc[0]['sc_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "500002"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_bse_daily.head(1)['sc_code'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "500002"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_bse_daily.at[0, 'sc_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "500002"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_bse_daily['sc_code'].to_numpy()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker = df_temp['sc_code'].to_numpy()[0]\n",
    "df_weekly_new['sc_code'] = ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53 entries, 0 to 52\n",
      "Data columns (total 21 columns):\n",
      "yearWeek      53 non-null object\n",
      "lastTrdDoW    53 non-null object\n",
      "daysTraded    53 non-null object\n",
      "closeL        53 non-null object\n",
      "closeH        53 non-null object\n",
      "volAvg        53 non-null object\n",
      "volHigh       53 non-null object\n",
      "HSDL          53 non-null object\n",
      "HSDG          53 non-null object\n",
      "HSDLp         53 non-null object\n",
      "HSDGp         53 non-null object\n",
      "first         53 non-null object\n",
      "last          53 non-null object\n",
      "wChng         53 non-null object\n",
      "wChngp        53 non-null object\n",
      "TI            53 non-null object\n",
      "volAvgWOhv    53 non-null object\n",
      "HVdAV         53 non-null object\n",
      "CPveoHVD      53 non-null object\n",
      "lastDVotWk    53 non-null object\n",
      "lastDVdAV     53 non-null object\n",
      "dtypes: object(21)\n",
      "memory usage: 4.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_weekly_new.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reorder columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CPveoHVD',\n",
       " 'HSDG',\n",
       " 'HSDGp',\n",
       " 'HSDL',\n",
       " 'HSDLp',\n",
       " 'HVdAV',\n",
       " 'TI',\n",
       " 'closeH',\n",
       " 'closeL',\n",
       " 'daysTraded',\n",
       " 'first',\n",
       " 'last',\n",
       " 'lastDVdAV',\n",
       " 'lastDVotWk',\n",
       " 'lastTrdDoW',\n",
       " 'volAvg',\n",
       " 'volAvgWOhv',\n",
       " 'volHigh',\n",
       " 'wChng',\n",
       " 'wChngp',\n",
       " 'yearWeek']"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = df_weekly_new.columns.tolist()\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols2 = ['sc_code', 'yearWeek', 'lastTrdDoW', 'daysTraded', 'closeL', 'closeH', 'volAvg', 'volHigh'\n",
    "         , 'HSDL', 'HSDG', 'HSDLp', 'HSDGp', 'first', 'last', 'wChng', 'wChngp', 'TI', 'volAvgWOhv', 'HVdAV'\n",
    "         , 'CPveoHVD', 'lastDVotWk', 'lastDVdAV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_weekly_new = df_weekly_new[cols2].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>sc_code</th>\n",
       "      <th>yearWeek</th>\n",
       "      <th>lastTrdDoW</th>\n",
       "      <th>daysTraded</th>\n",
       "      <th>closeL</th>\n",
       "      <th>closeH</th>\n",
       "      <th>volAvg</th>\n",
       "      <th>volHigh</th>\n",
       "      <th>HSDL</th>\n",
       "      <th>HSDG</th>\n",
       "      <th>HSDLp</th>\n",
       "      <th>HSDGp</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>wChng</th>\n",
       "      <th>wChngp</th>\n",
       "      <th>TI</th>\n",
       "      <th>volAvgWOhv</th>\n",
       "      <th>HVdAV</th>\n",
       "      <th>CPveoHVD</th>\n",
       "      <th>lastDVotWk</th>\n",
       "      <th>lastDVdAV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>500002</td>\n",
       "      <td>201913</td>\n",
       "      <td>2019-04-05</td>\n",
       "      <td>5</td>\n",
       "      <td>1313.55</td>\n",
       "      <td>1374.75</td>\n",
       "      <td>11210</td>\n",
       "      <td>27791</td>\n",
       "      <td>-6.30</td>\n",
       "      <td>48.75</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>3.68</td>\n",
       "      <td>1313.55</td>\n",
       "      <td>1374.75</td>\n",
       "      <td>61.20</td>\n",
       "      <td>4.66</td>\n",
       "      <td>3</td>\n",
       "      <td>7065</td>\n",
       "      <td>3.93</td>\n",
       "      <td>1</td>\n",
       "      <td>12887</td>\n",
       "      <td>1.824062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500002</td>\n",
       "      <td>201914</td>\n",
       "      <td>2019-04-12</td>\n",
       "      <td>5</td>\n",
       "      <td>1379.05</td>\n",
       "      <td>1426.20</td>\n",
       "      <td>16741</td>\n",
       "      <td>34789</td>\n",
       "      <td>-29.35</td>\n",
       "      <td>24.35</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1398.20</td>\n",
       "      <td>1426.20</td>\n",
       "      <td>28.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4</td>\n",
       "      <td>12229</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1</td>\n",
       "      <td>6928</td>\n",
       "      <td>0.566511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500002</td>\n",
       "      <td>201915</td>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>3</td>\n",
       "      <td>1408.60</td>\n",
       "      <td>1432.30</td>\n",
       "      <td>9008</td>\n",
       "      <td>21127</td>\n",
       "      <td>-12.80</td>\n",
       "      <td>23.70</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1421.40</td>\n",
       "      <td>1432.30</td>\n",
       "      <td>10.90</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1</td>\n",
       "      <td>2948</td>\n",
       "      <td>7.17</td>\n",
       "      <td>1</td>\n",
       "      <td>21127</td>\n",
       "      <td>7.165338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500002</td>\n",
       "      <td>201916</td>\n",
       "      <td>2019-04-26</td>\n",
       "      <td>5</td>\n",
       "      <td>1433.05</td>\n",
       "      <td>1493.90</td>\n",
       "      <td>23703</td>\n",
       "      <td>50389</td>\n",
       "      <td>-16.40</td>\n",
       "      <td>55.40</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>3.87</td>\n",
       "      <td>1433.05</td>\n",
       "      <td>1477.50</td>\n",
       "      <td>44.45</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3</td>\n",
       "      <td>17032</td>\n",
       "      <td>2.96</td>\n",
       "      <td>1</td>\n",
       "      <td>23219</td>\n",
       "      <td>1.363257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500002</td>\n",
       "      <td>201917</td>\n",
       "      <td>2019-05-03</td>\n",
       "      <td>3</td>\n",
       "      <td>1473.40</td>\n",
       "      <td>1490.55</td>\n",
       "      <td>13102</td>\n",
       "      <td>27577</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>16.65</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1473.40</td>\n",
       "      <td>1490.55</td>\n",
       "      <td>17.15</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2</td>\n",
       "      <td>5865</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0</td>\n",
       "      <td>4414</td>\n",
       "      <td>0.752536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sc_code  yearWeek lastTrdDoW  daysTraded   closeL   closeH  volAvg  \\\n",
       "0   500002    201913 2019-04-05           5  1313.55  1374.75   11210   \n",
       "1   500002    201914 2019-04-12           5  1379.05  1426.20   16741   \n",
       "2   500002    201915 2019-04-18           3  1408.60  1432.30    9008   \n",
       "3   500002    201916 2019-04-26           5  1433.05  1493.90   23703   \n",
       "4   500002    201917 2019-05-03           3  1473.40  1490.55   13102   \n",
       "\n",
       "   volHigh   HSDL   HSDG  HSDLp  HSDGp    first     last  wChng  wChngp  TI  \\\n",
       "0    27791  -6.30  48.75  -0.48   3.68  1313.55  1374.75  61.20    4.66   3   \n",
       "1    34789 -29.35  24.35  -2.08   1.77  1398.20  1426.20  28.00    2.00   4   \n",
       "2    21127 -12.80  23.70  -0.90   1.68  1421.40  1432.30  10.90    0.77   1   \n",
       "3    50389 -16.40  55.40  -1.10   3.87  1433.05  1477.50  44.45    3.10   3   \n",
       "4    27577  -4.10  16.65  -0.28   1.13  1473.40  1490.55  17.15    1.16   2   \n",
       "\n",
       "   volAvgWOhv  HVdAV  CPveoHVD  lastDVotWk  lastDVdAV  \n",
       "0        7065   3.93         1       12887   1.824062  \n",
       "1       12229   2.84         1        6928   0.566511  \n",
       "2        2948   7.17         1       21127   7.165338  \n",
       "3       17032   2.96         1       23219   1.363257  \n",
       "4        5865   4.70         0        4414   0.752536  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weekly_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Same\n"
     ]
    }
   ],
   "source": [
    "if set(cols) == set(cols2):\n",
    "    print('Same')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CPveoHVD', 'HSDG', 'HSDGp', 'HSDL', 'HSDLp', 'HVdAV', 'TI', 'closeH', 'closeL', 'daysTraded', 'first', 'last', 'lastDVdAV', 'lastDVotWk', 'lastTrdDoW', 'volAvg', 'volAvgWOhv', 'volHigh', 'wChng', 'wChngp', 'yearWeek']\n",
      "['sc_code', 'yearWeek', 'closeL', 'closeH', 'volAvg', 'volHigh', 'daysTraded', 'HSDL', 'HSDG', 'HSDLp', 'HSDGp', 'first', 'last', 'wChng', 'wChngp', 'lastTrdDoW', 'TI', 'volAvgWOhv', 'HVdAV', 'CPveoHVD', 'lastDVotWk', 'lastDVdAV']\n"
     ]
    }
   ],
   "source": [
    "print(cols)\n",
    "print(cols2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quality & Tidiness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Quality\n",
    "No quality issues found in original dataset\n",
    "\n",
    "\n",
    "##### Tidiness\n",
    "\n",
    "1. Ignoring not used columns no_trades, net_turnover, tdcloindi, isin\n",
    "1. Rename column NO_OF_SHRS to volume\n",
    "1. Create a new dataframe and add below new columns, \n",
    "    + closeH - high close in the week\n",
    "    + closeL - low closein the week\n",
    "    + volHigh - Highest volume in the week\n",
    "    + volAvg - Volume average\n",
    "    + daysTraded - Number of days traded in the week\n",
    "    + HSDL - Highest Single Day Loss\n",
    "    + HSDG - Highest Single Day Gain\n",
    "    + HSDLp - Highest Single Day Loss percent\n",
    "    + HSDGp - Highest Single Day Gain percent\n",
    "    + first - First close of the week\n",
    "    + last - Last close of he week\n",
    "    + wChng - Week change\n",
    "    + wChngp - Week change percent\n",
    "    + lastTrdDoW - Last traded day of week\n",
    "    + TI - Times increased\n",
    "    + volAvgWOhv - Volume average without high volume\n",
    "    + HVdAV - High volume / Average volume(without highvolume)\n",
    "    + CPveoHVD - Close positive on high volume day\n",
    "    + lastDVotWk - Last day volume\n",
    "    + lastDVdAV - Last day volume / average volume\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mostly numpy returning data in numpy\n",
    "def weekly_trend_analysis_np(exchange, np_weekly_all, df_daily):\n",
    "\n",
    "    if exchange == 'BSE':\n",
    "        #ticker = df_daily.at[0,'sc_code']\n",
    "        #ticker = df_daily.head(1)['sc_code'].values[0]\n",
    "        ticker = df_daily['sc_code'].to_numpy()[0]\n",
    "    else:\n",
    "        #ticker = df_daily.at[0,'symbol']\n",
    "        #ticker = df_daily.head(1)['symbol'].values[0]\n",
    "        ticker = df_daily['symbol'].to_numpy()[0]\n",
    "\n",
    "    arr_yearWeek = df_daily['yearWeek'].to_numpy()\n",
    "    arr_close = df_daily['close'].to_numpy()\n",
    "    arr_prevclose = df_daily['prevclose'].to_numpy()\n",
    "    arr_chng = df_daily['chng'].to_numpy()\n",
    "    arr_chngp = df_daily['chngp'].to_numpy()\n",
    "    arr_ts = df_daily['ts'].to_numpy()\n",
    "    arr_volumes = df_daily['volumes'].to_numpy()\n",
    "\n",
    "    # Close\n",
    "    arr_concat = np.column_stack((arr_yearWeek, arr_close))\n",
    "    npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "    #a = df_temp[['yearWeek', 'close']].to_numpy()\n",
    "    yearWeek, daysTraded = np.unique(arr_concat[:,0], return_counts=True)\n",
    "    \n",
    "    cmaxs, cmins = [], []\n",
    "    first, last, wChng, wChngp = [], [], [], []\n",
    "    for idx,subarr in enumerate(npi_gb):\n",
    "        cmaxs.append( np.amax(subarr) )\n",
    "        cmins.append( np.amin(subarr) )\n",
    "        first.append(subarr[0])\n",
    "        last.append(subarr[-1])\n",
    "        wChng.append( subarr[-1] - subarr[0] )\n",
    "        wChngp.append( ( (subarr[-1] / subarr[0]) * 100) - 100 )\n",
    "\n",
    "    #npi_gb.clear()\n",
    "    arr_concat = np.empty((100,100))\n",
    "\n",
    "    # Chng\n",
    "    arr_concat = np.column_stack((arr_yearWeek, arr_chng))\n",
    "    npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "    HSDL, HSDG = [], []\n",
    "    for idx,subarr in enumerate(npi_gb):\n",
    "        HSDL.append( np.amin(subarr) )\n",
    "        HSDG.append( np.amax(subarr) )\n",
    "\n",
    "    #npi_gb.clear()\n",
    "    arr_concat = np.empty((100,100))\n",
    "\n",
    "    # Chngp\n",
    "    arr_concat = np.column_stack((arr_yearWeek, arr_chngp))\n",
    "    npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "    HSDLp, HSDGp = [], []\n",
    "    for idx,subarr in enumerate(npi_gb):\n",
    "        HSDLp.append( np.amin(subarr) )\n",
    "        HSDGp.append( np.amax(subarr) )\n",
    "\n",
    "    #npi_gb.clear()\n",
    "    arr_concat = np.empty((100,100))\n",
    "\n",
    "    # Last Traded Date of the Week\n",
    "    arr_concat = np.column_stack((arr_yearWeek, arr_ts))\n",
    "    npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "    lastTrdDoW = []\n",
    "    for idx,subarr in enumerate(npi_gb):\n",
    "        lastTrdDoW.append( subarr[-1] )\n",
    "    \n",
    "    #npi_gb.clear()\n",
    "    arr_concat = np.empty((100,100))\n",
    "\n",
    "    # Times inreased\n",
    "    TI = np.where(arr_close > arr_prevclose, 1, 0)\n",
    "\n",
    "    # Below npi_gb_yearWeekTI is used in volumes section\n",
    "    arr_concat = np.column_stack((arr_yearWeek, TI))\n",
    "    npi_gb_yearWeekTI = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "    tempArr, TI = npi.group_by(arr_yearWeek).sum(TI)\n",
    "\n",
    "    # Volume ( dependent on above section value t_group , thats the reason to move from top to here)\n",
    "    arr_concat = np.column_stack((arr_yearWeek, arr_volumes))\n",
    "    npi_gb = npi.group_by(arr_concat[:, 0]).split(arr_concat[:, 1])\n",
    "\n",
    "    vmaxs, vavgs, volAvgWOhv, HVdAV, CPveoHVD, lastDVotWk, lastDVdAV = [], [], [], [], [], [], []\n",
    "    for idx,subarr in enumerate(npi_gb):\n",
    "        vavgs.append( np.mean(subarr) )\n",
    "        ldvotWk = subarr[-1]\n",
    "        lastDVotWk.append(ldvotWk)\n",
    "\n",
    "        #print(idx, 'O - ',subarr, np.argmax(subarr), ', average : ',np.mean(subarr))\n",
    "        ixDel = np.argmax(subarr)\n",
    "        hV = subarr[ixDel]\n",
    "        vmaxs.append( hV )\n",
    "\n",
    "        if(len(subarr)>1):\n",
    "            subarr = np.delete(subarr, ixDel)\n",
    "            vawoHV = np.mean(subarr)\n",
    "        else:\n",
    "            vawoHV = np.mean(subarr)\n",
    "        volAvgWOhv.append( vawoHV )\n",
    "        HVdAV.append(hV / vawoHV)\n",
    "        CPveoHVD.append( npi_gb_yearWeekTI[idx][ixDel] )\n",
    "        lastDVdAV.append( np.round(ldvotWk / vawoHV, 2) )    \n",
    "\n",
    "    #npi_gb.clear()\n",
    "    arr_concat = np.empty((100,100))\n",
    "\n",
    "    # Preparing the dataframe\n",
    "    # yearWeek and occurances \n",
    "    #yearWeek, daysTraded = np.unique(a[:,0], return_counts=True)\n",
    "    yearWeek = yearWeek.astype(int)\n",
    "    HSDL = np.round(HSDL,2)\n",
    "    HSDG = np.round(HSDG,2)\n",
    "    HSDLp = np.round(HSDLp,2)\n",
    "    HSDGp = np.round(HSDGp,2)\n",
    "\n",
    "    first = np.round(first,2)\n",
    "    last = np.round(last,2)\n",
    "    wChng = np.round(wChng,2)\n",
    "    wChngp = np.round(wChngp,2)\n",
    "\n",
    "    vavgs = np.array(vavgs).astype(int)\n",
    "    volAvgWOhv = np.array(volAvgWOhv).astype(int)\n",
    "    HVdAV = np.round(HVdAV,2)\n",
    "\n",
    "    ticker = np.full(yearWeek.shape[0], ticker)\n",
    "    np_weekly = np.column_stack((ticker, yearWeek, lastTrdDoW, daysTraded, cmins, cmaxs, vavgs, vmaxs, HSDL\n",
    "                               , HSDG, HSDLp, HSDGp, first, last, wChng, wChngp, TI, volAvgWOhv, HVdAV\n",
    "                               , CPveoHVD, lastDVotWk, lastDVdAV))\n",
    "    \n",
    "    # Removing latest yearWeek from df_weekly_all as it could be partial and concatenating latest one(df_weekly) to df_weekly_all\n",
    "    if len(np_weekly_all) > 0:\n",
    "        #print(len(np_weekly_all))\n",
    "        a = np_weekly_all[:,1] \n",
    "        b = np_weekly[:,1] \n",
    "        tf_1 = np.isin(a, b, invert=True) \n",
    "        #print(tf_1)\n",
    "        t_result = list(compress(range(len(tf_1)), tf_1)) \n",
    "        #print(t_result)\n",
    "        np_weekly_all = np_weekly_all[t_result]\n",
    "        np_weekly_all = np.vstack((np_weekly_all, np_weekly))    \n",
    "    else:\n",
    "        np_weekly_all = []\n",
    "        np_weekly_all = np.vstack((np_weekly))    \n",
    "        \n",
    "    return np_weekly_all\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stage 1 - Single Stock Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ts', 'sc_code', 'sc_name', 'sc_group', 'sc_type', 'open', 'high',\n",
       "       'low', 'close', 'last', 'prevclose', 'no_trades', 'no_of_shrs',\n",
       "       'net_turnover', 'tdcloindi', 'isin'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bse_daily.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(657058, 16)\n",
      "2020-03-27 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(df_bse_daily.shape)\n",
    "print(df_bse_daily.iloc[-1]['ts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_weekly = df_weekly[0:0]\n",
    "df_weekly = pd.DataFrame() \n",
    "np_weekly_all = []\n",
    "np_weekly = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weekly.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bse_bk = df_bse_daily.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bse_daily = df_bse_bk.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of weekly =  53\n",
      "Wall time: 59 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Numpy\n",
    "# weekly_trend_analysis = pandas = Wall time: 481 ms\n",
    "# weekly_trend_analysis = numpy partial = Wall time: 179 ms\n",
    "# weekly_trend_analysis = numpy partial = Wall time: 64 ms\n",
    "exchange = 'BSE'\n",
    "bse_csv_cols = ['ts', 'sc_code', 'sc_name', 'high', 'low', 'close', 'prevclose', 'no_of_shrs']\n",
    "sc_code = 500002 #ABB\n",
    "sc_code = 532370 #Ramco Systems\n",
    "#df_weekly_all = pd.DataFrame() \n",
    "\n",
    "df_daily = df_bse_daily[df_bse_daily.sc_code == sc_code][bse_csv_cols].copy()\n",
    "#df_daily = df_bse_5daily[df_bse_5daily.sc_code == sc_code][bse_csv_cols].head(2)\n",
    "\n",
    "df_daily.rename(columns={\"no_of_shrs\": \"volumes\"}, inplace=True)\n",
    "\n",
    "# yearWeek\n",
    "df_daily['yearWeek'] = df_daily['ts'].dt.year * 100 + df_daily['ts'].dt.strftime('%U').astype(int)\n",
    "\n",
    "# Change ts format from datetime64[ns] to object and truncating the time values\n",
    "# in a way avoid error in weekly_trend_analysis() in last traded date section of code\n",
    "df_daily['ts'] = df_daily['ts'] .dt.strftime('%Y-%m-%d')\n",
    "\n",
    "# Change & change percent\n",
    "df_daily['chng'] = (df_daily['close'] - df_daily['prevclose'])\n",
    "df_daily['chngp'] = np.round( ( (df_daily['close'] / df_daily['prevclose']) * 100 ) - 100, 2)\n",
    "\n",
    "# FTA = First Time Analysis\n",
    "#df_weekly2 = weekly_trend_analysis('sc_code', df_weekly, df_daily)\n",
    "#df_weekly2 = weekly_trend_analysis(df_weekly, df_daily)\n",
    "np_weekly = weekly_trend_analysis_np(exchange, np_weekly, df_daily)\n",
    "print('Length of weekly = ',len(np_weekly))\n",
    "\n",
    "cols = ['sc_code', 'yearWeek', 'lastTrdDoW', 'daysTraded', 'closeL', 'closeH', 'volAvg', 'volHigh'\n",
    "         , 'HSDL', 'HSDG', 'HSDLp', 'HSDGp', 'first', 'last', 'wChng', 'wChngp', 'TI', 'volAvgWOhv', 'HVdAV'\n",
    "         , 'CPveoHVD', 'lastDVotWk', 'lastDVdAV']\n",
    "\n",
    "df_weekly = pd.DataFrame(data=np_weekly, columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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>sc_code</th>\n",
       "      <th>yearWeek</th>\n",
       "      <th>lastTrdDoW</th>\n",
       "      <th>daysTraded</th>\n",
       "      <th>closeL</th>\n",
       "      <th>closeH</th>\n",
       "      <th>volAvg</th>\n",
       "      <th>volHigh</th>\n",
       "      <th>HSDL</th>\n",
       "      <th>HSDG</th>\n",
       "      <th>HSDLp</th>\n",
       "      <th>HSDGp</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>wChng</th>\n",
       "      <th>wChngp</th>\n",
       "      <th>TI</th>\n",
       "      <th>volAvgWOhv</th>\n",
       "      <th>HVdAV</th>\n",
       "      <th>CPveoHVD</th>\n",
       "      <th>lastDVotWk</th>\n",
       "      <th>lastDVdAV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>532370</td>\n",
       "      <td>201913</td>\n",
       "      <td>2019-04-05</td>\n",
       "      <td>5</td>\n",
       "      <td>240.05</td>\n",
       "      <td>253.6</td>\n",
       "      <td>3924</td>\n",
       "      <td>10344</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>-3.15</td>\n",
       "      <td>5.89</td>\n",
       "      <td>253.6</td>\n",
       "      <td>240.05</td>\n",
       "      <td>-13.55</td>\n",
       "      <td>-5.34</td>\n",
       "      <td>1</td>\n",
       "      <td>2319</td>\n",
       "      <td>4.46</td>\n",
       "      <td>0</td>\n",
       "      <td>10344</td>\n",
       "      <td>4.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>532370</td>\n",
       "      <td>201914</td>\n",
       "      <td>2019-04-12</td>\n",
       "      <td>5</td>\n",
       "      <td>238.7</td>\n",
       "      <td>244.45</td>\n",
       "      <td>1737</td>\n",
       "      <td>2601</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>1.83</td>\n",
       "      <td>244.45</td>\n",
       "      <td>240.55</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1521</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>527</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>532370</td>\n",
       "      <td>201915</td>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>3</td>\n",
       "      <td>232.15</td>\n",
       "      <td>238.85</td>\n",
       "      <td>87011</td>\n",
       "      <td>258508</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>238.85</td>\n",
       "      <td>232.15</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1263</td>\n",
       "      <td>204.68</td>\n",
       "      <td>0</td>\n",
       "      <td>1586</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>532370</td>\n",
       "      <td>201916</td>\n",
       "      <td>2019-04-26</td>\n",
       "      <td>5</td>\n",
       "      <td>226.0</td>\n",
       "      <td>230.05</td>\n",
       "      <td>1303</td>\n",
       "      <td>2569</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>1.33</td>\n",
       "      <td>230.05</td>\n",
       "      <td>226.25</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>1</td>\n",
       "      <td>987</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>538</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>532370</td>\n",
       "      <td>201917</td>\n",
       "      <td>2019-05-03</td>\n",
       "      <td>3</td>\n",
       "      <td>213.05</td>\n",
       "      <td>219.65</td>\n",
       "      <td>1288</td>\n",
       "      <td>1633</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>5.35</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>219.65</td>\n",
       "      <td>218.4</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>1</td>\n",
       "      <td>1116</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1</td>\n",
       "      <td>1633</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sc_code yearWeek  lastTrdDoW daysTraded  closeL  closeH volAvg volHigh  \\\n",
       "0  532370   201913  2019-04-05          5  240.05   253.6   3924   10344   \n",
       "1  532370   201914  2019-04-12          5   238.7  244.45   1737    2601   \n",
       "2  532370   201915  2019-04-18          3  232.15  238.85  87011  258508   \n",
       "3  532370   201916  2019-04-26          5   226.0  230.05   1303    2569   \n",
       "4  532370   201917  2019-05-03          3  213.05  219.65   1288    1633   \n",
       "\n",
       "    HSDL  HSDG  HSDLp  HSDGp   first    last   wChng wChngp TI volAvgWOhv  \\\n",
       "0   -8.0  14.1  -3.15   5.89   253.6  240.05  -13.55  -5.34  1       2319   \n",
       "1   -5.5   4.4  -2.25   1.83  244.45  240.55    -3.9   -1.6  3       1521   \n",
       "2   -6.6  -0.1  -2.76  -0.04  238.85  232.15    -6.7  -2.81  0       1263   \n",
       "3  -2.75   3.0   -1.2   1.33  230.05  226.25    -3.8  -1.65  1        987   \n",
       "4   -6.6  5.35   -3.0   2.51  219.65   218.4   -1.25  -0.57  1       1116   \n",
       "\n",
       "    HVdAV CPveoHVD lastDVotWk lastDVdAV  \n",
       "0    4.46        0      10344      4.46  \n",
       "1    1.71        0        527      0.35  \n",
       "2  204.68        0       1586      1.26  \n",
       "3     2.6        0        538      0.54  \n",
       "4    1.46        1       1633      1.46  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weekly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3 : Define the data model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53 entries, 0 to 52\n",
      "Data columns (total 22 columns):\n",
      "sc_code       53 non-null object\n",
      "yearWeek      53 non-null object\n",
      "lastTrdDoW    53 non-null object\n",
      "daysTraded    53 non-null object\n",
      "closeL        53 non-null object\n",
      "closeH        53 non-null object\n",
      "volAvg        53 non-null object\n",
      "volHigh       53 non-null object\n",
      "HSDL          53 non-null object\n",
      "HSDG          53 non-null object\n",
      "HSDLp         53 non-null object\n",
      "HSDGp         53 non-null object\n",
      "first         53 non-null object\n",
      "last          53 non-null object\n",
      "wChng         53 non-null object\n",
      "wChngp        53 non-null object\n",
      "TI            53 non-null object\n",
      "volAvgWOhv    53 non-null object\n",
      "HVdAV         53 non-null object\n",
      "CPveoHVD      53 non-null object\n",
      "lastDVotWk    53 non-null object\n",
      "lastDVdAV     53 non-null object\n",
      "dtypes: object(22)\n",
      "memory usage: 4.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_weekly.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4 : Run ETL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "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>ts</th>\n",
       "      <th>sc_code</th>\n",
       "      <th>sc_name</th>\n",
       "      <th>sc_group</th>\n",
       "      <th>sc_type</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>last</th>\n",
       "      <th>prevclose</th>\n",
       "      <th>no_trades</th>\n",
       "      <th>no_of_shrs</th>\n",
       "      <th>net_turnover</th>\n",
       "      <th>tdcloindi</th>\n",
       "      <th>isin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>500002</td>\n",
       "      <td>ABB LTD.</td>\n",
       "      <td>A</td>\n",
       "      <td>Q</td>\n",
       "      <td>1319.85</td>\n",
       "      <td>1326.60</td>\n",
       "      <td>1306.35</td>\n",
       "      <td>1313.55</td>\n",
       "      <td>1310.05</td>\n",
       "      <td>1319.85</td>\n",
       "      <td>548</td>\n",
       "      <td>7785</td>\n",
       "      <td>10248104</td>\n",
       "      <td>\\N</td>\n",
       "      <td>INE117A01022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>500003</td>\n",
       "      <td>AEGIS LOGIS</td>\n",
       "      <td>A</td>\n",
       "      <td>Q</td>\n",
       "      <td>204.65</td>\n",
       "      <td>208.45</td>\n",
       "      <td>204.00</td>\n",
       "      <td>206.65</td>\n",
       "      <td>206.15</td>\n",
       "      <td>202.95</td>\n",
       "      <td>178</td>\n",
       "      <td>6150</td>\n",
       "      <td>1266611</td>\n",
       "      <td>\\N</td>\n",
       "      <td>INE208C01025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>500008</td>\n",
       "      <td>AMAR RAJA BA</td>\n",
       "      <td>A</td>\n",
       "      <td>Q</td>\n",
       "      <td>717.05</td>\n",
       "      <td>732.15</td>\n",
       "      <td>717.05</td>\n",
       "      <td>723.05</td>\n",
       "      <td>723.05</td>\n",
       "      <td>718.95</td>\n",
       "      <td>550</td>\n",
       "      <td>21641</td>\n",
       "      <td>15708848</td>\n",
       "      <td>\\N</td>\n",
       "      <td>INE885A01032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>500009</td>\n",
       "      <td>A.SARABHAI</td>\n",
       "      <td>X</td>\n",
       "      <td>Q</td>\n",
       "      <td>14.50</td>\n",
       "      <td>14.87</td>\n",
       "      <td>13.41</td>\n",
       "      <td>13.53</td>\n",
       "      <td>13.53</td>\n",
       "      <td>14.29</td>\n",
       "      <td>153</td>\n",
       "      <td>46296</td>\n",
       "      <td>645095</td>\n",
       "      <td>\\N</td>\n",
       "      <td>INE432A01017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>500010</td>\n",
       "      <td>HDFC</td>\n",
       "      <td>A</td>\n",
       "      <td>Q</td>\n",
       "      <td>1979.00</td>\n",
       "      <td>1979.00</td>\n",
       "      <td>1937.65</td>\n",
       "      <td>1955.25</td>\n",
       "      <td>1943.40</td>\n",
       "      <td>1967.30</td>\n",
       "      <td>2736</td>\n",
       "      <td>707019</td>\n",
       "      <td>1390990348</td>\n",
       "      <td>\\N</td>\n",
       "      <td>INE001A01036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ts  sc_code       sc_name sc_group sc_type     open     high  \\\n",
       "0 2019-04-01   500002      ABB LTD.        A       Q  1319.85  1326.60   \n",
       "1 2019-04-01   500003   AEGIS LOGIS        A       Q   204.65   208.45   \n",
       "2 2019-04-01   500008  AMAR RAJA BA        A       Q   717.05   732.15   \n",
       "3 2019-04-01   500009    A.SARABHAI        X       Q    14.50    14.87   \n",
       "4 2019-04-01   500010          HDFC        A       Q  1979.00  1979.00   \n",
       "\n",
       "       low    close     last  prevclose  no_trades  no_of_shrs  net_turnover  \\\n",
       "0  1306.35  1313.55  1310.05    1319.85        548        7785      10248104   \n",
       "1   204.00   206.65   206.15     202.95        178        6150       1266611   \n",
       "2   717.05   723.05   723.05     718.95        550       21641      15708848   \n",
       "3    13.41    13.53    13.53      14.29        153       46296        645095   \n",
       "4  1937.65  1955.25  1943.40    1967.30       2736      707019    1390990348   \n",
       "\n",
       "  tdcloindi          isin  \n",
       "0        \\N  INE117A01022  \n",
       "1        \\N  INE208C01025  \n",
       "2        \\N  INE885A01032  \n",
       "3        \\N  INE432A01017  \n",
       "4        \\N  INE001A01036  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bse_daily.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stage 2 - Loop Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_weekly = df_weekly[0:0]\n",
    "df_weekly_all = pd.DataFrame() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weekly_all.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ts', 'sc_code', 'sc_name', 'sc_group', 'sc_type', 'open', 'high',\n",
       "       'low', 'close', 'last', 'prevclose', 'no_trades', 'no_of_shrs',\n",
       "       'net_turnover', 'tdcloindi', 'isin'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bse_daily.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current date and time : 2020-04-07 22:15:26\n"
     ]
    }
   ],
   "source": [
    "print ('Current date and time : {}'.format(dt.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write to multiple csv datasets due to memory error failure**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date and time : 2020-04-08 00:48:29\n",
      "Total sc_code = 4298 to process\n",
      "df_bse_daily : date and time : 2020-04-08 00:48:29\n",
      "volume renamed : date and time : 2020-04-08 00:48:29\n",
      "added yearWeek : date and time : 2020-04-08 00:48:42\n",
      "ts truncated : date and time : 2020-04-08 00:48:55\n",
      "added chng & chngp : date and time : 2020-04-08 00:48:55\n",
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      "Saving to file  df_bse_weekly_all-C0.csv\n",
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      "Saving to file  df_bse_weekly_all-C1500.csv\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
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      "Saving to file  df_bse_weekly_all-C2000.csv\n",
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    {
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     "output_type": "stream",
     "text": [
      "c:\\users\\sushanth\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\ipykernel_launcher.py:36: RuntimeWarning: divide by zero encountered in double_scalars\n"
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Length of weekly =  1867\n",
      "Wall time: 7min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Numpy\n",
    "# For full BSE run it took Wall time: 1h 26min 31s using pandas\n",
    "# For full BSE run it took Wall time: 38m 27s using partial numpy\n",
    "\n",
    "print ('Start date and time : {}'.format(dt.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n",
    "\n",
    "np_weekly_all_new = []\n",
    "filena = ''\n",
    "exchange = 'BSE'\n",
    "#sc_code = 500002 #ABB\n",
    "#sc_code = 532370 #Ramco Systems\n",
    "ticker_col = 'sc_code'\n",
    "bse_csv_cols = ['ts', 'sc_code', 'sc_name', 'high', 'low', 'close', 'prevclose', 'no_of_shrs']\n",
    "cols = ['sc_code', 'yearWeek', 'lastTrdDoW', 'daysTraded', 'closeL', 'closeH', 'volAvg', 'volHigh'\n",
    "         , 'HSDL', 'HSDG', 'HSDLp', 'HSDGp', 'first', 'last', 'wChng', 'wChngp', 'TI', 'volAvgWOhv', 'HVdAV'\n",
    "         , 'CPveoHVD', 'lastDVotWk', 'lastDVdAV']\n",
    "\n",
    "ticker_list = df_bse_daily['sc_code'].unique()\n",
    "print('Total sc_code = {} to process'.format(len(ticker_list)))\n",
    "\n",
    "# Removing unused columns \n",
    "df_bse_daily = df_bse_daily[bse_csv_cols].copy()\n",
    "print ('df_bse_daily : date and time : {}'.format(dt.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n",
    "\n",
    "# changing volume column name\n",
    "df_bse_daily.rename(columns={\"no_of_shrs\": \"volumes\"}, inplace=True)\n",
    "print ('volume renamed : date and time : {}'.format(dt.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n",
    "\n",
    "# yearWeek\n",
    "df_bse_daily['yearWeek'] = df_bse_daily['ts'].dt.year * 100 + df_bse_daily['ts'].dt.strftime('%U').astype(int)\n",
    "print ('added yearWeek : date and time : {}'.format(dt.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n",
    "\n",
    "# Change ts format from datetime64[ns] to object and truncating the time values\n",
    "# in a way this avoids error in weekly_trend_analysis() in last traded date section of \n",
    "# code when two numpy arr are concatenated\n",
    "df_bse_daily['ts'] = df_bse_daily['ts'].dt.strftime('%Y-%m-%d')\n",
    "print ('ts truncated : date and time : {}'.format(dt.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n",
    "\n",
    "# Change & change percent\n",
    "df_bse_daily['chng'] = (df_bse_daily['close'] - df_bse_daily['prevclose'])\n",
    "df_bse_daily['chngp'] = np.round( ( (df_bse_daily['close'] / df_bse_daily['prevclose']) * 100 ) - 100, 2)\n",
    "print ('added chng & chngp : date and time : {}'.format(dt.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n",
    "\n",
    "for idx, ticker in enumerate(ticker_list):\n",
    "    #print(ticker)   \n",
    "    a, b, tf_1, t_result, np_weekly, np_temp = [], [], [], [], [], []\n",
    "    \n",
    "    if len(np_weekly_all) > 0:\n",
    "        a = np_weekly_all[:,0] \n",
    "        b = np.array([ticker])\n",
    "        tf_1 = np.isin(a, b) \n",
    "        t_result = list(compress(range(len(tf_1)), tf_1)) \n",
    "        np_weekly = np_weekly_all[t_result]\n",
    "    \n",
    "    # It will print for every company for first run or new IPO company\n",
    "    if len(np_weekly) == 0:\n",
    "        print('{}. Weekly empty for {}'.format(idx, ticker))\n",
    "    \n",
    "    df_daily = df_bse_daily[df_bse_daily.sc_code == ticker]\n",
    "    \n",
    "    #df_daily = df_bse_5daily[df_bse_5daily.sc_code == sc_code][['ts', 'sc_code', 'sc_name', 'high', 'low', 'close', 'prevclose', 'no_of_shrs']]    \n",
    "    \n",
    "    np_temp = weekly_trend_analysis_np(exchange, np_weekly, df_daily)\n",
    "    \n",
    "    # Only incase if df_weekly is empty. Happen during FTA(First Time Analysis)\n",
    "    if len(np_weekly_all_new) == 0:\n",
    "        #df_weekly_all = pd.DataFrame(columns=list(df_temp.columns))       \n",
    "        np_weekly_all_new = np.vstack((np_temp))\n",
    "    else:\n",
    "        np_weekly_all_new = np.vstack((np_weekly_all_new, np_temp))\n",
    "        #a = np_weekly_all[:,0] \n",
    "        #b = np.array([ticker])\n",
    "        #tf_1 = np.isin(a, b, invert=True) \n",
    "        #t_result = list(compress(range(len(tf_1)), tf_1)) \n",
    "        #np_weekly_all = np_weekly_all[t_result]        \n",
    "        #np_weekly_all = np.vstack((np_weekly_all, np_temp))            \n",
    "\n",
    "    if (idx%500 == 0) and (idx > 0):\n",
    "        filena = 'df_bse_weekly_all-C{}.csv'.format(idx)\n",
    "        print('Saving to file ',filena)\n",
    "        df_weekly_all = pd.DataFrame(data=np_weekly_all_new, columns=cols)\n",
    "        # Write BSE Weekly ALL\n",
    "        df_weekly_all.to_csv(os.path.join(os.getcwd(), '..', '5. BTD', 'data', filena)\n",
    "                     , encoding='utf-8', index=False)        \n",
    "        df_weekly_all = None\n",
    "        np_weekly_all_new = []\n",
    "        \n",
    "print('Length of weekly = ',len(np_weekly_all_new))\n",
    "df_weekly_all = pd.DataFrame(data=np_weekly_all_new, columns=cols)\n",
    "filena = 'df_bse_weekly_all-C{}.csv'.format(idx)\n",
    "df_weekly_all.to_csv(os.path.join(os.getcwd(), '..', '5. BTD', 'data', filena)\n",
    "                     , encoding='utf-8', index=False)        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking memory capacity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_weekly_all_new = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233480192"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encountered error : MemoryError: Unable to allocate array with shape (82912, 22) and data type <U32\n",
    "a = []\n",
    "a = np.zeros((82912, 22), dtype='<U32')\n",
    "#a = np.zeros((50000,22), dtype='<U32')\n",
    "a.nbytes\n",
    "#303755101056\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**df column to list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.48, 1.82, 0.57, -5.32, -0.61, 2.47, -0.09, 5.0, 4.52, 1.33]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bse_daily['chngp'].head(10).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Generating some test data for S.O**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ['2019-04-01 00:00:00','2019-04-01 00:00:00','2019-04-01 00:00:00','2019-04-01 00:00:00','2019-04-01 00:00:00','2019-04-02 00:00:00','2019-04-02 00:00:00','2019-04-02 00:00:00','2019-04-02 00:00:00','2019-04-02 00:00:00']\n",
    "sc_code = ['500002','500002','500002','500002','500002','500002','500002','500002','500002','500002']\n",
    "high = [1326.6, 208.45, 732.15, 14.87, 1979.0, 57.8, 11.55, 1.68, 8.1, 139.4]\n",
    "low = [1306.35, 204.0, 717.05, 13.41, 1937.65, 54.65, 11.2, 1.52, 7.75, 135.65]\n",
    "close = [1313.55, 206.65, 723.05, 13.53, 1955.25, 56.0, 11.21, 1.68, 8.1, 136.85]\n",
    "prevclose = [1319.85, 202.95, 718.95, 14.29, 1967.3, 54.65, 11.22, 1.6, 7.75, 135.05]\n",
    "volumes = [7785, 6150, 21641, 46296, 707019, 40089, 25300, 5920, 500, 235355]\n",
    "yearWeek = [201913, 201913, 201913, 201913, 201913, 201913, 201913, 201913, 201913, 201913]\n",
    "chng = [-6.29, 3.70, 4.09, -0.75, -12.04, 1.35, -0.09, 0.079, 0.34, 1.79]\n",
    "chngp = [-0.48, 1.82, 0.57, -5.32, -0.61, 2.47, -0.09, 5.0, 4.52, 1.33]\n",
    "\n",
    "\n",
    "dict_temp = {'ts':ts, 'sc_code':sc_code, 'high':high, 'low':low, 'close':close, 'prevclose':prevclose, 'volumes':volumes, 'yearWeek':yearWeek, 'chng':chng, 'chngp':chngp}\n",
    "df_weekly = pd.DataFrame(data=dict_temp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "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>chng</th>\n",
       "      <th>chngp</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>prevclose</th>\n",
       "      <th>sc_code</th>\n",
       "      <th>ts</th>\n",
       "      <th>volumes</th>\n",
       "      <th>yearWeek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-6.29</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>1313.55</td>\n",
       "      <td>1326.60</td>\n",
       "      <td>1306.35</td>\n",
       "      <td>1319.85</td>\n",
       "      <td>500002</td>\n",
       "      <td>2019-04-01 00:00:00</td>\n",
       "      <td>7785</td>\n",
       "      <td>201913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.70</td>\n",
       "      <td>1.82</td>\n",
       "      <td>206.65</td>\n",
       "      <td>208.45</td>\n",
       "      <td>204.00</td>\n",
       "      <td>202.95</td>\n",
       "      <td>500002</td>\n",
       "      <td>2019-04-01 00:00:00</td>\n",
       "      <td>6150</td>\n",
       "      <td>201913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.09</td>\n",
       "      <td>0.57</td>\n",
       "      <td>723.05</td>\n",
       "      <td>732.15</td>\n",
       "      <td>717.05</td>\n",
       "      <td>718.95</td>\n",
       "      <td>500002</td>\n",
       "      <td>2019-04-01 00:00:00</td>\n",
       "      <td>21641</td>\n",
       "      <td>201913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.75</td>\n",
       "      <td>-5.32</td>\n",
       "      <td>13.53</td>\n",
       "      <td>14.87</td>\n",
       "      <td>13.41</td>\n",
       "      <td>14.29</td>\n",
       "      <td>500002</td>\n",
       "      <td>2019-04-01 00:00:00</td>\n",
       "      <td>46296</td>\n",
       "      <td>201913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-12.04</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>1955.25</td>\n",
       "      <td>1979.00</td>\n",
       "      <td>1937.65</td>\n",
       "      <td>1967.30</td>\n",
       "      <td>500002</td>\n",
       "      <td>2019-04-01 00:00:00</td>\n",
       "      <td>707019</td>\n",
       "      <td>201913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    chng  chngp    close     high      low  prevclose sc_code  \\\n",
       "0  -6.29  -0.48  1313.55  1326.60  1306.35    1319.85  500002   \n",
       "1   3.70   1.82   206.65   208.45   204.00     202.95  500002   \n",
       "2   4.09   0.57   723.05   732.15   717.05     718.95  500002   \n",
       "3  -0.75  -5.32    13.53    14.87    13.41      14.29  500002   \n",
       "4 -12.04  -0.61  1955.25  1979.00  1937.65    1967.30  500002   \n",
       "\n",
       "                    ts  volumes  yearWeek  \n",
       "0  2019-04-01 00:00:00     7785    201913  \n",
       "1  2019-04-01 00:00:00     6150    201913  \n",
       "2  2019-04-01 00:00:00    21641    201913  \n",
       "3  2019-04-01 00:00:00    46296    201913  \n",
       "4  2019-04-01 00:00:00   707019    201913  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weekly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Identifying empty df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame is empty!\n"
     ]
    }
   ],
   "source": [
    "if not df_daily.empty:\n",
    "    print('DataFrame is empty!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Processing a list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4298\n"
     ]
    }
   ],
   "source": [
    "ticker_list = df_bse_daily['sc_code'].unique()\n",
    "print(len(ticker_list))\n",
    "for ticker in ticker_list:\n",
    "    #print(ticker)\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Removing common items**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_common_items(list1, list2): \n",
    "    \"\"\"\n",
    "    Summary line. \n",
    "    Removes the items which are in both the lists\n",
    "\n",
    "    Parameters: \n",
    "    arg1 (list 1)\n",
    "    arg2 (list 2)\n",
    "\n",
    "    Returns: \n",
    "    list with difference\n",
    "    \n",
    "    Sample : print(difference(keep_columns, df_jan.columns))\n",
    "    \"\"\"                \n",
    "    list_dif = [i for i in list1 + list2 if i not in list1 or i not in list2]\n",
    "    return list_dif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "[5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
      "Removed common items = [1, 2, 3, 4, 10, 11, 12, 13, 14]\n",
      "SET Difference =  [1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "list1 = [*range(1, 10, 1)] \n",
    "list2 = [*range(5, 15, 1)]\n",
    "print(list1)\n",
    "print(list2)\n",
    "print(\"Removed common items =\",remove_common_items(list1, list2))\n",
    "\n",
    "# This returns SET\n",
    "a = set(list1)\n",
    "b = set(list2)\n",
    "print(\"SET Difference = \",list(a.difference(b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5 : Write project write-up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hype is real about numpy, but coding wise its really difficult. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Learnings\n",
    "* Use .iloc to extract value from specific cell. `last_week = df_weekly2.iloc[-1]['yearWeek'].astype(int)`\n",
    "* Use .loc when you want to assign a value to a cell\n",
    "* Performance wise .at & .iat performs better than .iloc . `ticker = df_daily.at[0,'symbol']`\n",
    "    - [Poor performance for .loc and .iloc compared to .ix #6683](https://github.com/pandas-dev/pandas/issues/6683)\n",
    "* Emptying a dataframe `df[0:0]`\n",
    "* Checking if dataframe is empty\n",
    "```\n",
    "if df_weekly.empty:\n",
    "    print('DataFrame is empty!')\n",
    "```\n",
    "* FutureWarning in pd.concat, so to avoid that had to add sorting\n",
    "```\n",
    "pd.concat([df], sort=True) - Sorts column names\n",
    "pd.concat([df], sort=False) - Doesn't sort column names\n",
    "```\n",
    "* Dataframe to numpy to 2D array\n",
    "```\n",
    "df_temp[['yearWeek', 'close']].values\n",
    "```\n",
    "#### Resources\n",
    "* [Python - How to check list monotonicity](https://stackoverflow.com/questions/4983258/python-how-to-check-list-monotonicity)\n",
    "* [How to delete multiple rows of NumPy array?](https://stackoverflow.com/questions/47622139/how-to-delete-multiple-rows-of-numpy-array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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