Historic Data.ipynb

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   "source": [
    "%pip install requests\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy as np\n",
    "import csv\n",
    "import requests\n",
    "from contextlib import closing\n",
    "import csv\n",
    "from codecs import iterdecode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Obtain current FTSE 100 tickers from a local file\n",
    "\n",
    "file = open(\"FTSE100.tickers\")\n",
    "iter = csv.reader(file, delimiter='\\n')\n",
    "tickers = []\n",
    "for ticker in iter:\n",
    "    tickers=tickers+ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Obtain historical data from Yahoo finance\n",
    "yearsOfData = 5\n",
    "endTime = 1701561600\n",
    "\n",
    "startTime = endTime- 24*60*60*365 *yearsOfData\n",
    "tickerPlaceholder = \"PLACEHOLDER\"\n",
    "webURL = f\"https://query1.finance.yahoo.com/v7/finance/download/{tickerPlaceholder}?period1={str(startTime)}&period2={str(endTime)}&interval=1d&events=history&includeAdjustedClose=true\"\n",
    "\n",
    "priceData = {}\n",
    "for stockTicker in tickers:\n",
    "    requestURL = webURL.replace(tickerPlaceholder, stockTicker)\n",
    "    firstRow = True\n",
    "    with requests.get(requestURL, stream=True, headers={'User-agent': 'Mozilla/5.0'}) as r:\n",
    "        lines = (line.decode('utf-8') for line in r.iter_lines())\n",
    "        priceData[stockTicker] = []\n",
    "        for row in csv.reader(lines):\n",
    "            if firstRow:\n",
    "                firstRow = False\n",
    "            else:            \n",
    "                priceData[stockTicker].append(row)\n",
    "            if (row == [\"404 Not Found: Timestamp data missing.\"] or row == ['404 Not Found: No data found', ' symbol may be delisted']):\n",
    "                print (stockTicker)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
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     "text": [
      "III.L\n",
      "ADM.L\n",
      "AAF.L\n",
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      "BDEV.L\n",
      "BEZ.L\n",
      "BKG.L\n",
      "BP.L\n",
      "BATS.L\n",
      "BT-A.L\n",
      "BNZL.L\n",
      "BRBY.L\n",
      "CNA.L\n",
      "CCH.L\n",
      "CPG.L\n",
      "CTEC.L\n",
      "CRDA.L\n",
      "DCC.L\n",
      "DPH.L\n",
      "DGE.L\n",
      "DPLM.L\n",
      "EDV.L\n",
      "ENT.L\n",
      "EXPN.L\n",
      "FCIT.L\n",
      "FLTR.L\n",
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      "FRES.L\n",
      "GLEN.L\n",
      "GSK.L\n",
      "HLN.L\n",
      "HLMA.L\n",
      "HL.L\n",
      "HIK.L\n",
      "HWDN.L\n",
      "HSBA.L\n",
      "IHG.L\n",
      "IMI.L\n",
      "IMB.L\n",
      "INF.L\n",
      "IAG.L\n",
      "ITRK.L\n",
      "JD.L\n",
      "KGF.L\n",
      "LAND.L\n",
      "LGEN.L\n",
      "LLOY.L\n",
      "LSEG.L\n",
      "MNG.L\n",
      "MKS.L\n",
      "MRO.L\n",
      "MNDI.L\n",
      "NG.L\n",
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      "SBRY.L\n",
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      "SVT.L\n",
      "SHEL.L\n",
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      "SMIN.L\n",
      "SN.L\n",
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      "SPX.L\n",
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      "WPP.L\n",
      "['155.972656' '144.990173' '153.110748' ... '210.399994' '209.199997'\n",
      " '211.199997']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "for prices in list(priceData.keys()):\n",
    "    print(prices)\n",
    "    stock = pd.DataFrame((np.array(priceData[prices]))[:,5])\n",
    "    stock.to_csv(f\"HistoricPrices/{prices.replace(\".L\",\"\")}.csv\")\n"
   ]
  }
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