Data-Retrieval-And-Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yfinance in c:\\anaconda3\\lib\\site-packages (0.1.54)\n",
      "Requirement already satisfied: pandas>=0.24 in c:\\anaconda3\\lib\\site-packages (from yfinance) (0.25.1)\n",
      "Requirement already satisfied: requests>=2.20 in c:\\anaconda3\\lib\\site-packages (from yfinance) (2.22.0)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in c:\\anaconda3\\lib\\site-packages (from yfinance) (0.0.9)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\anaconda3\\lib\\site-packages (from yfinance) (1.16.5)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\anaconda3\\lib\\site-packages (from pandas>=0.24->yfinance) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\anaconda3\\lib\\site-packages (from pandas>=0.24->yfinance) (2.8.0)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\anaconda3\\lib\\site-packages (from requests>=2.20->yfinance) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\anaconda3\\lib\\site-packages (from requests>=2.20->yfinance) (2019.9.11)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in c:\\anaconda3\\lib\\site-packages (from requests>=2.20->yfinance) (2.8)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\anaconda3\\lib\\site-packages (from requests>=2.20->yfinance) (1.24.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\sidratul\\appdata\\roaming\\python\\python37\\site-packages (from python-dateutil>=2.6.1->pandas>=0.24->yfinance) (1.12.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftse_100_constituents = pd.read_csv('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 100 Constituents.csv')\n",
    "ftse_250_constituents = pd.read_csv('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 250 Constituents.csv')\n",
    "ftse_350_constituents = pd.read_csv('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 350 Constituents.csv')\n",
    "ftse_aim_100_constituents = pd.read_csv('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE AIM 100 Constituents.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftse_100_constituents.set_index('Symbol', inplace=True)\n",
    "ftse_250_constituents.set_index('Symbol', inplace=True)\n",
    "ftse_350_constituents.set_index('Symbol', inplace=True)\n",
    "ftse_aim_100_constituents.set_index('Symbol', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "III.Llxml not found, please install it\n",
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    {
     "name": "stdout",
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      "WWH.Llxml not found, please install it\n"
     ]
    }
   ],
   "source": [
    "for share, x in ftse_100_constituents.iterrows():\n",
    "    ticker = yf.Ticker(share)\n",
    "    try:\n",
    "        for key in ticker.info:\n",
    "            ftse_100_constituents.at[share, key] = str(ticker.info[key])\n",
    "    except Exception as e:\n",
    "        print(share + str(e))\n",
    "        \n",
    "for share, x in ftse_250_constituents.iterrows():\n",
    "    ticker = yf.Ticker(share)\n",
    "    try:\n",
    "        for key in ticker.info:\n",
    "            ftse_250_constituents.at[share, key] = str(ticker.info[key])\n",
    "    except Exception as e:\n",
    "        print(share + str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRSC.L\"['maxAge'] not found in axis\"\n",
      "FCSS.L\"['maxAge'] not found in axis\"\n",
      "HTWS.Lindex 0 is out of bounds for axis 0 with size 0\n",
      "N91.L\"['maxAge'] not found in axis\"\n",
      "SEQI.L\"['maxAge'] not found in axis\"\n",
      "SSON.L\"['maxAge'] not found in axis\"\n",
      "PMI.Lindex 0 is out of bounds for axis 0 with size 0\n"
     ]
    }
   ],
   "source": [
    "for share, x in ftse_350_constituents.iterrows():\n",
    "    ticker = yf.Ticker(share)\n",
    "    try:\n",
    "        for key in ticker.info:\n",
    "            ftse_350_constituents.at[share, key] = str(ticker.info[key])\n",
    "    except Exception as e:\n",
    "        print(share + str(e))\n",
    "        \n",
    "for share, x in ftse_aim_100_constituents.iterrows():\n",
    "    ticker = yf.Ticker(share)\n",
    "    try:\n",
    "        for key in ticker.info:\n",
    "            ftse_aim_100_constituents.at[share, key] = str(ticker.info[key])\n",
    "    except Exception as e:\n",
    "        print(share + str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ftse_100_constituents.to_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 100 Constituents.pkl')\n",
    "ftse_250_constituents.to_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 250 Constituents.pkl')\n",
    "ftse_350_constituents.to_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 350 Constituents.pkl')\n",
    "ftse_aim_100_constituents.to_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE AIM 100 Constituents.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftse_100_constituents = pd.read_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 100 Constituents.pkl')\n",
    "ftse_250_constituents = pd.read_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 250 Constituents.pkl')\n",
    "ftse_350_constituents = pd.read_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 350 Constituents.pkl')\n",
    "ftse_aim_100_constituents = pd.read_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE AIM 100 Constituents.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  102 of 102 completed\n"
     ]
    }
   ],
   "source": [
    "ftse_100_prices = yf.download(ftse_100_constituents.index.tolist(), period='max', group_by='ticker', auto_adjust = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  251 of 251 completed\n"
     ]
    }
   ],
   "source": [
    "ftse_250_prices = yf.download(ftse_250_constituents.index.tolist(), period='max', group_by='ticker', auto_adjust = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  352 of 352 completed\n"
     ]
    }
   ],
   "source": [
    "ftse_350_prices = yf.download(ftse_350_constituents.index.tolist(), period='max', group_by='ticker', auto_adjust = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  100 of 100 completed\n"
     ]
    }
   ],
   "source": [
    "ftse_aim_100_prices = yf.download(ftse_aim_100_constituents.index.tolist(), period='max', group_by='ticker', auto_adjust = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftse_100_prices.to_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 100 Prices.pkl')\n",
    "ftse_250_prices.to_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 250 Prices.pkl')\n",
    "ftse_350_prices.to_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 350 Prices.pkl')\n",
    "ftse_aim_100_prices.to_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE AIM 100 Prices.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftse_100_prices = pd.read_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 100 Prices.pkl')\n",
    "ftse_250_prices = pd.read_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 250 Prices.pkl')\n",
    "ftse_350_prices = pd.read_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE 350 Prices.pkl')\n",
    "ftse_aim_100_prices = pd.read_pickle('C:\\\\Users\\\\NAHARUL\\\\Desktop\\\\FTSE Analysis Naharul\\\\FTSE AIM 100 Prices.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "mks = yf.download(['MKS.L'], period='max', group_by='ticker', auto_adjust = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mks.to_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Open\n",
      "High\n",
      "Low\n",
      "Close\n",
      "Volume\n",
      "Dividends\n",
      "Stock Splits\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "      <th>MACloseLow</th>\n",
       "      <th>MACloseHigh</th>\n",
       "      <th>MAOpenLow</th>\n",
       "      <th>MAOpenHigh</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-24</th>\n",
       "      <td>93.00</td>\n",
       "      <td>95.60</td>\n",
       "      <td>88.92</td>\n",
       "      <td>90.82</td>\n",
       "      <td>13137078.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.698</td>\n",
       "      <td>100.694333</td>\n",
       "      <td>98.976</td>\n",
       "      <td>101.858667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-23</th>\n",
       "      <td>88.80</td>\n",
       "      <td>93.46</td>\n",
       "      <td>87.61</td>\n",
       "      <td>91.34</td>\n",
       "      <td>18250870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.441</td>\n",
       "      <td>101.965333</td>\n",
       "      <td>100.501</td>\n",
       "      <td>103.383667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-22</th>\n",
       "      <td>91.86</td>\n",
       "      <td>92.58</td>\n",
       "      <td>85.88</td>\n",
       "      <td>88.00</td>\n",
       "      <td>26386769.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.432</td>\n",
       "      <td>103.417333</td>\n",
       "      <td>102.111</td>\n",
       "      <td>105.125333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-21</th>\n",
       "      <td>94.32</td>\n",
       "      <td>96.02</td>\n",
       "      <td>91.04</td>\n",
       "      <td>91.04</td>\n",
       "      <td>11728176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.642</td>\n",
       "      <td>105.035667</td>\n",
       "      <td>102.685</td>\n",
       "      <td>106.893333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-20</th>\n",
       "      <td>98.50</td>\n",
       "      <td>100.65</td>\n",
       "      <td>96.10</td>\n",
       "      <td>97.82</td>\n",
       "      <td>10331062.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.938</td>\n",
       "      <td>107.139333</td>\n",
       "      <td>103.119</td>\n",
       "      <td>108.909333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-08-17</th>\n",
       "      <td>76.30</td>\n",
       "      <td>76.30</td>\n",
       "      <td>76.30</td>\n",
       "      <td>76.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.719</td>\n",
       "      <td>78.412000</td>\n",
       "      <td>77.719</td>\n",
       "      <td>78.412000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-08-16</th>\n",
       "      <td>76.30</td>\n",
       "      <td>76.30</td>\n",
       "      <td>76.30</td>\n",
       "      <td>76.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.039</td>\n",
       "      <td>78.473000</td>\n",
       "      <td>78.039</td>\n",
       "      <td>78.473000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-08-15</th>\n",
       "      <td>76.76</td>\n",
       "      <td>76.76</td>\n",
       "      <td>76.76</td>\n",
       "      <td>76.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.268</td>\n",
       "      <td>78.579667</td>\n",
       "      <td>78.268</td>\n",
       "      <td>78.579667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-08-12</th>\n",
       "      <td>77.22</td>\n",
       "      <td>77.22</td>\n",
       "      <td>77.22</td>\n",
       "      <td>77.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.496</td>\n",
       "      <td>78.655667</td>\n",
       "      <td>78.496</td>\n",
       "      <td>78.655667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-08-11</th>\n",
       "      <td>76.30</td>\n",
       "      <td>76.30</td>\n",
       "      <td>76.30</td>\n",
       "      <td>76.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.633</td>\n",
       "      <td>78.731667</td>\n",
       "      <td>78.633</td>\n",
       "      <td>78.731667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8108 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open    High    Low  Close      Volume  Dividends  Stock Splits  \\\n",
       "Date                                                                           \n",
       "2020-04-24  93.00   95.60  88.92  90.82  13137078.0        0.0           0.0   \n",
       "2020-04-23  88.80   93.46  87.61  91.34  18250870.0        0.0           0.0   \n",
       "2020-04-22  91.86   92.58  85.88  88.00  26386769.0        0.0           0.0   \n",
       "2020-04-21  94.32   96.02  91.04  91.04  11728176.0        0.0           0.0   \n",
       "2020-04-20  98.50  100.65  96.10  97.82  10331062.0        0.0           0.0   \n",
       "...           ...     ...    ...    ...         ...        ...           ...   \n",
       "1988-08-17  76.30   76.30  76.30  76.30         0.0        0.0           0.0   \n",
       "1988-08-16  76.30   76.30  76.30  76.30         0.0        0.0           0.0   \n",
       "1988-08-15  76.76   76.76  76.76  76.76         0.0        0.0           0.0   \n",
       "1988-08-12  77.22   77.22  77.22  77.22         0.0        0.0           0.0   \n",
       "1988-08-11  76.30   76.30  76.30  76.30         0.0        0.0           0.0   \n",
       "\n",
       "            MACloseLow  MACloseHigh  MAOpenLow  MAOpenHigh  \n",
       "Date                                                        \n",
       "2020-04-24      96.698   100.694333     98.976  101.858667  \n",
       "2020-04-23      98.441   101.965333    100.501  103.383667  \n",
       "2020-04-22     100.432   103.417333    102.111  105.125333  \n",
       "2020-04-21     101.642   105.035667    102.685  106.893333  \n",
       "2020-04-20     101.938   107.139333    103.119  108.909333  \n",
       "...                ...          ...        ...         ...  \n",
       "1988-08-17      77.719    78.412000     77.719   78.412000  \n",
       "1988-08-16      78.039    78.473000     78.039   78.473000  \n",
       "1988-08-15      78.268    78.579667     78.268   78.579667  \n",
       "1988-08-12      78.496    78.655667     78.496   78.655667  \n",
       "1988-08-11      78.633    78.731667     78.633   78.731667  \n",
       "\n",
       "[8108 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mks = yf.Ticker(\"MKS.L\")\n",
    "\n",
    "n = mks.history(period=\"max\")\n",
    "#n = n.dropna(axis='rows')\n",
    "#n['MAOpen'] = priceOfStock.rolling(window=maxAverage).mean()\n",
    "#n = n.iloc[::-1].dropna(axis='rows')\n",
    "#n.to_clipboard()\n",
    "for col in n.columns: \n",
    "    print(col)\n",
    "    \n",
    "\n",
    "n['MACloseLow'] = n['Close'].rolling(window=10).mean()\n",
    "n['MACloseHigh'] = n['Close'].rolling(window=30).mean()\n",
    "\n",
    "n['MAOpenLow'] = n['Open'].rolling(window=10).mean()\n",
    "n['MAOpenHigh'] = n['Open'].rolling(window=30).mean()\n",
    "\n",
    "n = n.iloc[::-1].dropna(axis='rows')\n",
    "\n",
    "n.to_clipboard()\n",
    "n"
   ]
  }
 ],
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