Stock_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd\n",
    "pd.core.common.is_list_like = pd.api.types.is_list_like #For solving import pandas_datareader issue\n",
    "import numpy as np\n",
    "import datetime\n",
    "import csv\n",
    "import requests\n",
    "import pandas_datareader.data as web\n",
    "import pandas_datareader as pdr\n",
    "from pandas_datareader import data, wb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input Start and End Date\n",
    "start = datetime.datetime(2018,4,20)\n",
    "end = datetime.datetime(2018,5,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SYMBOL</td>\n",
       "      <td>SERIES</td>\n",
       "      <td>DATE1</td>\n",
       "      <td>PREV_CLOSE</td>\n",
       "      <td>OPEN_PRICE</td>\n",
       "      <td>HIGH_PRICE</td>\n",
       "      <td>LOW_PRICE</td>\n",
       "      <td>LAST_PRICE</td>\n",
       "      <td>CLOSE_PRICE</td>\n",
       "      <td>AVG_PRICE</td>\n",
       "      <td>TTL_TRD_QNTY</td>\n",
       "      <td>TURNOVER_LACS</td>\n",
       "      <td>NO_OF_TRADES</td>\n",
       "      <td>DELIV_QTY</td>\n",
       "      <td>DELIV_PER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20MICRONS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>23-May-2018</td>\n",
       "      <td>41.25</td>\n",
       "      <td>41.55</td>\n",
       "      <td>42.90</td>\n",
       "      <td>41.20</td>\n",
       "      <td>41.65</td>\n",
       "      <td>41.45</td>\n",
       "      <td>41.67</td>\n",
       "      <td>38967</td>\n",
       "      <td>16.24</td>\n",
       "      <td>484</td>\n",
       "      <td>22020</td>\n",
       "      <td>56.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21STCENMGM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>23-May-2018</td>\n",
       "      <td>39.15</td>\n",
       "      <td>38.40</td>\n",
       "      <td>38.40</td>\n",
       "      <td>38.40</td>\n",
       "      <td>38.40</td>\n",
       "      <td>38.40</td>\n",
       "      <td>38.40</td>\n",
       "      <td>248</td>\n",
       "      <td>0.10</td>\n",
       "      <td>7</td>\n",
       "      <td>248</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3IINFOTECH</td>\n",
       "      <td>EQ</td>\n",
       "      <td>23-May-2018</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.90</td>\n",
       "      <td>4.65</td>\n",
       "      <td>4.90</td>\n",
       "      <td>4.90</td>\n",
       "      <td>4.79</td>\n",
       "      <td>4598046</td>\n",
       "      <td>220.32</td>\n",
       "      <td>1616</td>\n",
       "      <td>2776005</td>\n",
       "      <td>60.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3MINDIA</td>\n",
       "      <td>EQ</td>\n",
       "      <td>23-May-2018</td>\n",
       "      <td>19494.95</td>\n",
       "      <td>19499.85</td>\n",
       "      <td>19998.00</td>\n",
       "      <td>19250.00</td>\n",
       "      <td>19998.00</td>\n",
       "      <td>19598.05</td>\n",
       "      <td>19545.49</td>\n",
       "      <td>1440</td>\n",
       "      <td>281.46</td>\n",
       "      <td>471</td>\n",
       "      <td>1135</td>\n",
       "      <td>78.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0        1             2            3            4            5   \\\n",
       "0      SYMBOL   SERIES         DATE1   PREV_CLOSE   OPEN_PRICE   HIGH_PRICE   \n",
       "1   20MICRONS       EQ   23-May-2018        41.25        41.55        42.90   \n",
       "2  21STCENMGM       EQ   23-May-2018        39.15        38.40        38.40   \n",
       "3  3IINFOTECH       EQ   23-May-2018         4.70         4.80         4.90   \n",
       "4     3MINDIA       EQ   23-May-2018     19494.95     19499.85     19998.00   \n",
       "\n",
       "           6            7             8           9              10  \\\n",
       "0   LOW_PRICE   LAST_PRICE   CLOSE_PRICE   AVG_PRICE   TTL_TRD_QNTY   \n",
       "1       41.20        41.65         41.45       41.67          38967   \n",
       "2       38.40        38.40         38.40       38.40            248   \n",
       "3        4.65         4.90          4.90        4.79        4598046   \n",
       "4    19250.00     19998.00      19598.05    19545.49           1440   \n",
       "\n",
       "               11             12          13          14  \n",
       "0   TURNOVER_LACS   NO_OF_TRADES   DELIV_QTY   DELIV_PER  \n",
       "1           16.24            484       22020       56.51  \n",
       "2            0.10              7         248      100.00  \n",
       "3          220.32           1616     2776005       60.37  \n",
       "4          281.46            471        1135       78.82  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Today = datetime.datetime.now().strftime (\"%Y-%m-%d\")\n",
    "\n",
    "# Import list of stock names from NSE website\n",
    "with requests.Session() as s:\n",
    "    download = s.get('https://www.nseindia.com/products/content/sec_bhavdata_full.csv')\n",
    "    decoded_content = download.content.decode('utf-8')\n",
    "    cr = csv.reader(decoded_content.splitlines(), delimiter=',')\n",
    "    my_list = pd.DataFrame(list(cr))\n",
    "    \n",
    "#View the top rows\n",
    "my_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean the downloaded data\n",
    "# Rename the headers\n",
    "new_header = my_list.iloc[0] #grab the first row for the header\n",
    "my_list = my_list[1:] #take the data less the header row\n",
    "my_list = my_list.rename(columns = new_header)\n",
    "\n",
    "# Get only the list of stock names - remove everything else\n",
    "my_list['stock_name'] = \"NSE/\"+ my_list['SYMBOL']\n",
    "stock_list = my_list['stock_name'].tolist()\n",
    "stock_list = list(set(stock_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NSE/HARRMALAYA',\n",
       " 'NSE/ANSALHSG',\n",
       " 'NSE/DPL',\n",
       " 'NSE/STAN',\n",
       " 'NSE/VOLTAMP',\n",
       " 'NSE/SUMEETINDS',\n",
       " 'NSE/JKPAPER',\n",
       " 'NSE/KPRMILL',\n",
       " 'NSE/INEOSSTYRO']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the top few stock names\n",
    "stock_list[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Empty Dataframe\n",
    "stock_final = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "# Scrape the stock prices from quandl\n",
    "# for i in range(len(stock_list))#Use this to scrape all the stocks\n",
    "for i in range(10):  \n",
    "    print(i)    \n",
    "    try:\n",
    "        stock=[]\n",
    "        stock = web.DataReader(stock_list[i],\"quandl\",start,end)\n",
    "        stock['Name']=stock_list[i]\n",
    "        \n",
    "        stock_final = pd.DataFrame.append(stock_final,stock)\n",
    "    except Exception: # Replace Exception with something more specific.\n",
    "        i = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>TotalTradeQuantity</th>\n",
       "      <th>TurnoverLacs</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-18</th>\n",
       "      <td>59.80</td>\n",
       "      <td>61.85</td>\n",
       "      <td>59.00</td>\n",
       "      <td>60.55</td>\n",
       "      <td>61.15</td>\n",
       "      <td>23947.0</td>\n",
       "      <td>14.60</td>\n",
       "      <td>NSE/SUTLEJTEX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-17</th>\n",
       "      <td>62.50</td>\n",
       "      <td>63.00</td>\n",
       "      <td>59.10</td>\n",
       "      <td>59.75</td>\n",
       "      <td>59.55</td>\n",
       "      <td>123319.0</td>\n",
       "      <td>74.29</td>\n",
       "      <td>NSE/SUTLEJTEX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-16</th>\n",
       "      <td>62.75</td>\n",
       "      <td>64.45</td>\n",
       "      <td>62.00</td>\n",
       "      <td>62.40</td>\n",
       "      <td>62.10</td>\n",
       "      <td>22810.0</td>\n",
       "      <td>14.33</td>\n",
       "      <td>NSE/SUTLEJTEX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-15</th>\n",
       "      <td>62.25</td>\n",
       "      <td>65.00</td>\n",
       "      <td>62.25</td>\n",
       "      <td>62.80</td>\n",
       "      <td>62.90</td>\n",
       "      <td>36532.0</td>\n",
       "      <td>23.43</td>\n",
       "      <td>NSE/SUTLEJTEX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-14</th>\n",
       "      <td>65.25</td>\n",
       "      <td>66.90</td>\n",
       "      <td>62.80</td>\n",
       "      <td>63.80</td>\n",
       "      <td>63.75</td>\n",
       "      <td>35242.0</td>\n",
       "      <td>22.60</td>\n",
       "      <td>NSE/SUTLEJTEX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low   Last  Close  TotalTradeQuantity  \\\n",
       "Date                                                                \n",
       "2018-05-18  59.80  61.85  59.00  60.55  61.15             23947.0   \n",
       "2018-05-17  62.50  63.00  59.10  59.75  59.55            123319.0   \n",
       "2018-05-16  62.75  64.45  62.00  62.40  62.10             22810.0   \n",
       "2018-05-15  62.25  65.00  62.25  62.80  62.90             36532.0   \n",
       "2018-05-14  65.25  66.90  62.80  63.80  63.75             35242.0   \n",
       "\n",
       "            TurnoverLacs           Name  \n",
       "Date                                     \n",
       "2018-05-18         14.60  NSE/SUTLEJTEX  \n",
       "2018-05-17         74.29  NSE/SUTLEJTEX  \n",
       "2018-05-16         14.33  NSE/SUTLEJTEX  \n",
       "2018-05-15         23.43  NSE/SUTLEJTEX  \n",
       "2018-05-14         22.60  NSE/SUTLEJTEX  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the top 10 rows of the downloaded data \n",
    "stock_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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OrQk/CSh1AF5Vx6hqjqrmpKfb8p3GmBO7vEMGQ8/O4pVv1zJ1fp7XcaKOr4uJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19d130e46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot trend for a particular stock\n",
    "\n",
    "#Subset for a particular stock\n",
    "stock_final = stock_final[stock_final['Name']=='NSE/SUTLEJTEX']\n",
    "\n",
    "#Generate a line plot\n",
    "stock_final.plot(y='High')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}