RealTime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nbimporter\n",
    "import StockNews2018Pkg as SP\n",
    "import threading\n",
    "\n",
    "#Preferences:\n",
    "ConsiderAt = 0.5\n",
    "BuyVShort = 0.9\n",
    "#InterestedStocks = [\"INTC\",\"NVDA\",\"TWTR\",\"AMZN\",\"SNAP\",\"IBM\",\"WMT\",\"MSFT\",\"WMT\",\"PYPL\",\"AAPL\",\"QCOM\",\"TSLA\",\"FB\",\"BABA\",\"GS\",\"GOOG\",\"MMM\",\"AXP\",\"BA\",\"CAT\",\"CVX\",\"CSCO\",\"KO\",\"DIS\",\"XOM\",\"GE\",\"HD\",\"INTC\",\"JNJ\",\"JPM\",\"MCD\",\"MRK\",\"NKE\",\"PFE\",\"PG\",\"TRV\",\"UTX\",\"UNH\",\"VZ\",\"V\"]\n",
    "InterestedStocks = [\"INTC\",\"NVDA\",\"TWTR\",\"SNAP\",\"IBM\",\"WMT\",\"MSFT\",\"WMT\",\"PYPL\",\"AAPL\",\"QCOM\",\"TSLA\",\"FB\",\"BABA\",\"GS\",\"GOOG\",\"MMM\",\"AXP\",\"BA\",\"CAT\",\"CVX\",\"CSCO\",\"KO\",\"DIS\",\"XOM\",\"GE\",\"HD\",\"INTC\"]\n",
    "verbosity = 0\n",
    "\n",
    "#Folder Stuff:\n",
    "import os\n",
    "import glob\n",
    "from datetime import datetime\n",
    "\n",
    "#Round Down\n",
    "import math\n",
    "\n",
    "#CSV Stuff:\n",
    "import pandas as pd\n",
    "import csv\n",
    "\n",
    "#Investopedia API:\n",
    "import ita as Investopedia\n",
    "client = Investopedia.Account(\"kennebrookes123@gmail.com\", \"albatross4321\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-7-ce6097a2e270>, line 25)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-ce6097a2e270>\"\u001b[0;36m, line \u001b[0;32m25\u001b[0m\n\u001b[0;31m    currentprice = (float) Investopedia.get_quote(stockname)\u001b[0m\n\u001b[0m                                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def UpdateHoldings():\n",
    "    TopStocks = Analyze(InterestedStocks)\n",
    "    NewAcct = NewAccount()\n",
    "    \n",
    "    if (NewAcct == False):\n",
    "        list_of_files = glob.glob('./RealTimeHoldings2018/*') # * means all if need specific format then *.csv\n",
    "        latest_file = max(list_of_files, key=os.path.getctime)\n",
    "        lastholdings = pd.read_csv(latest_file)\n",
    "    \n",
    "    CurrentTime = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n",
    "    \n",
    "    #NEWHOLDINGS:\n",
    "    with open('RealTimeHoldings2018/' + CurrentTime + '.csv', 'a') as csvfile: #wb\n",
    "        filewriter = csv.writer(csvfile, delimiter=',',\n",
    "                            quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
    "        filewriter.writerow([\"Ticker\", \"BuyPrice\", \"Amount\"])\n",
    "        \n",
    "        MaintainedStocks = []\n",
    "        #Sell/Maintain The Old Stocks:\n",
    "        if (NewAcct == False):\n",
    "            for i in range(0,lastholdings.shape[0]):\n",
    "                stockname = lastholdings.iloc[i,0]\n",
    "                buyprice = lastholdings.iloc[i,1]\n",
    "                amt = int(lastholdings.iloc[i,2])\n",
    "                currentprice = (float) Investopedia.get_quote(stockname)\n",
    "                if (not (stockname in TopStocks)) and (currentprice > buyprice):\n",
    "                    #client.trade(stockname, Investopedia.Action.sell, amt)\n",
    "                    print(\"SELLING: \" + stockname)\n",
    "                else:\n",
    "                    filewriter.writerow([stockname, buyprice, amt]) #Keep if either (1) still recommended or (2) lower\n",
    "                    MaintainedStocks.append(stockname)\n",
    "        \n",
    "        ToBuy = []\n",
    "        #Filter out Tops:\n",
    "        if (NewAcct == False):\n",
    "            for y in range(0,len(TopStocks) - 1):\n",
    "                if not ((TopStocks[y]) in MaintainedStocks):\n",
    "                    ToBuy.append(TopStocks[y])\n",
    "        else: #If it is a new account:\n",
    "            ToBuy = TopStocks\n",
    "        \n",
    "        #Buying Power\n",
    "        status = client.get_portfolio_status()\n",
    "        BuyBal = status.buying_power\n",
    "        \n",
    "        if (verbosity == 1):\n",
    "            print(\"About To Buy: \")\n",
    "            print(ToBuy)\n",
    "        \n",
    "        #Buy\n",
    "        for b in range(0,len(ToBuy)):\n",
    "            ordername = ToBuy[b]\n",
    "            orderprice = Investopedia.get_quote(ordername)\n",
    "            orderamount = int(math.floor(((BuyBal*BuyVShort)/(len(ToBuy)))/orderprice))\n",
    "            print(\"_\"+ordername+\"_\")\n",
    "            print(\"_\"+str(orderamount)+\"_\")\n",
    "            client.trade(str(ordername), Investopedia.Action.buy, int(orderamount))\n",
    "            print(\"BUYING \" + ordername)\n",
    "            filewriter.writerow([ordername, orderprice, orderamount])\n",
    "                    \n",
    "                \n",
    "    threading.Timer(300.0, UpdateHoldings).start()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Analyze(stocklist):\n",
    "    resultlist = []\n",
    "    for i in range(0,len(stocklist)):\n",
    "        score = SP.Predict(stocklist[i],0)\n",
    "        resultlist.append(str(\"%.2f\" % score) + \" \" + stocklist[i])\n",
    "        \n",
    "    TopStocksNames = []\n",
    "    for y in range(0,len(resultlist)):\n",
    "        if (float(resultlist[y][:4]) > ConsiderAt):\n",
    "            TopStocksNames.append(resultlist[y][5:]) #Add name\n",
    "            \n",
    "    return TopStocksNames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def NewAccount(): \n",
    "    path, dirs, files = os.walk(\"./RealTimeHoldings2018/\").next()\n",
    "    file_count = len(files)\n",
    "    if (file_count == 0):\n",
    "        New = True\n",
    "    else:\n",
    "        New = False\n",
    "        \n",
    "    return New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 23 Articles for Stock INTC\n",
      "Collected data from INTC for 4654 days!\n",
      "20 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.99869925]\n",
      " [ 0.99998355]\n",
      " [ 0.9999795 ]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.99872637]\n",
      " [ 0.98452151]\n",
      " [ 0.02068773]\n",
      " [ 1.        ]\n",
      " [ 0.99992424]\n",
      " [ 0.97265112]\n",
      " [ 0.28525347]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.95354855]\n",
      " [ 1.        ]]\n",
      "19\n",
      "INTC is Predicted to RISE. +16,-2\n",
      "Found 18 Articles for Stock NVDA\n",
      "Collected data from NVDA for 4654 days!\n",
      "17 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.98603916]\n",
      " [ 0.9757731 ]\n",
      " [ 0.99996513]\n",
      " [ 0.69372028]\n",
      " [ 0.99999708]\n",
      " [ 1.        ]\n",
      " [ 0.781084  ]\n",
      " [ 0.20809759]\n",
      " [ 1.        ]\n",
      " [ 0.80110961]\n",
      " [ 0.99315643]\n",
      " [ 0.99999928]\n",
      " [ 0.99999785]\n",
      " [ 0.86972529]\n",
      " [ 0.00168392]\n",
      " [ 1.        ]]\n",
      "16\n",
      "NVDA is Predicted to RISE. +13,-2\n",
      "Found 21 Articles for Stock TWTR\n",
      "Collected data from TWTR for 1170 days!\n",
      "18 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.2861273 ]\n",
      " [ 0.00345291]\n",
      " [ 0.71825165]\n",
      " [ 0.96596497]\n",
      " [ 0.84279072]\n",
      " [ 0.02206364]\n",
      " [ 0.66956025]\n",
      " [ 0.02856041]\n",
      " [ 0.96910846]\n",
      " [ 1.        ]\n",
      " [ 0.97128046]\n",
      " [ 0.92100108]\n",
      " [ 0.90831316]\n",
      " [ 0.93534827]\n",
      " [ 0.78811955]\n",
      " [ 1.        ]]\n",
      "17\n",
      "TWTR is Predicted to RISE. +12,-4\n",
      "Found 24 Articles for Stock SNAP\n",
      "Collected data from SNAP for 337 days!\n",
      "21 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.02474124]\n",
      " [ 0.99999177]\n",
      " [ 0.51576573]\n",
      " [ 0.00222455]\n",
      " [ 0.89210647]\n",
      " [ 0.29385659]\n",
      " [ 0.40462381]\n",
      " [ 0.05546268]\n",
      " [ 1.        ]\n",
      " [ 0.01012761]\n",
      " [ 0.89040315]\n",
      " [ 0.00870481]\n",
      " [ 0.34289941]\n",
      " [ 0.12291475]\n",
      " [ 0.00613238]\n",
      " [ 0.99952751]\n",
      " [ 0.91039276]\n",
      " [ 0.62033689]\n",
      " [ 0.20066164]]\n",
      "20\n",
      "SNAP is Predicted to FALL. +9,-10\n",
      "Found 20 Articles for Stock IBM\n",
      "Collected data from IBM for 4654 days!\n",
      "17 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.9996829 ]\n",
      " [ 0.80294067]\n",
      " [ 0.99996865]\n",
      " [ 0.99998569]\n",
      " [ 0.02162661]\n",
      " [ 0.99737829]\n",
      " [ 0.9982866 ]\n",
      " [ 1.        ]\n",
      " [ 0.99999261]\n",
      " [ 0.00152635]\n",
      " [ 0.98110527]\n",
      " [ 1.        ]\n",
      " [ 0.99990082]\n",
      " [ 0.89839506]]\n",
      "16\n",
      "IBM is Predicted to RISE. +13,-2\n",
      "Found 13 Articles for Stock WMT\n",
      "Collected data from WMT for 4654 days!\n",
      "12 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.99997228]\n",
      " [ 0.96528578]\n",
      " [ 0.97587281]\n",
      " [ 0.99899608]\n",
      " [ 1.        ]\n",
      " [ 0.99981886]\n",
      " [ 1.        ]\n",
      " [ 0.99999356]\n",
      " [ 1.        ]\n",
      " [ 0.25278029]\n",
      " [ 1.        ]]\n",
      "11\n",
      "WMT is Predicted to RISE. +9,-1\n",
      "Found 20 Articles for Stock MSFT\n",
      "Collected data from MSFT for 4654 days!\n",
      "17 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.98517168]\n",
      " [ 0.00897436]\n",
      " [ 1.        ]\n",
      " [ 0.86826259]\n",
      " [ 0.99997419]\n",
      " [ 0.70574886]\n",
      " [ 0.8468855 ]\n",
      " [ 0.99999923]\n",
      " [ 1.        ]\n",
      " [ 0.99937212]\n",
      " [ 0.99201435]\n",
      " [ 0.9991647 ]\n",
      " [ 0.70574886]\n",
      " [ 0.99998468]\n",
      " [ 0.04304453]]\n",
      "16\n",
      "MSFT is Predicted to RISE. +14,-1\n",
      "Found 13 Articles for Stock WMT\n",
      "Collected data from WMT for 4654 days!\n",
      "12 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.99997228]\n",
      " [ 0.96528578]\n",
      " [ 0.97587281]\n",
      " [ 0.99899608]\n",
      " [ 1.        ]\n",
      " [ 0.99981886]\n",
      " [ 1.        ]\n",
      " [ 0.99999356]\n",
      " [ 1.        ]\n",
      " [ 0.25278029]\n",
      " [ 1.        ]]\n",
      "11\n",
      "WMT is Predicted to RISE. +9,-1\n",
      "Found 14 Articles for Stock PYPL\n",
      "Collected data from PYPL for 745 days!\n",
      "9 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.99998212]\n",
      " [ 0.8908872 ]\n",
      " [ 0.92901999]\n",
      " [ 0.71072894]\n",
      " [ 0.95611894]\n",
      " [ 0.44987375]\n",
      " [ 0.98712838]]\n",
      "8\n",
      "PYPL is Predicted to RISE. +6,-1\n",
      "Found 27 Articles for Stock AAPL\n",
      "Collected data from AAPL for 4654 days!\n",
      "22 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.99963874]\n",
      " [ 1.        ]\n",
      " [ 0.99994504]\n",
      " [ 0.99919742]\n",
      " [ 1.        ]\n",
      " [ 0.85519582]\n",
      " [ 0.16130127]\n",
      " [ 0.8954106 ]\n",
      " [ 0.9959873 ]\n",
      " [ 0.02267036]\n",
      " [ 0.53242594]\n",
      " [ 0.06764536]\n",
      " [ 0.9999817 ]\n",
      " [ 0.65867704]\n",
      " [ 0.00307275]\n",
      " [ 0.4200739 ]\n",
      " [ 0.99999964]\n",
      " [ 0.05840646]\n",
      " [ 1.        ]\n",
      " [ 0.99815011]\n",
      " [ 0.03778571]]\n",
      "21\n",
      "AAPL is Predicted to RISE. +14,-6\n",
      "Found 18 Articles for Stock QCOM\n",
      "Collected data from QCOM for 4654 days!\n",
      "17 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.02225868]\n",
      " [ 0.84511954]\n",
      " [ 0.93720591]\n",
      " [ 0.89723295]\n",
      " [ 1.        ]\n",
      " [ 0.84859818]\n",
      " [ 1.        ]\n",
      " [ 0.24983819]\n",
      " [ 0.88424391]\n",
      " [ 0.74784601]\n",
      " [ 1.        ]\n",
      " [ 0.91879451]\n",
      " [ 0.88509703]\n",
      " [ 0.03821376]\n",
      " [ 0.93054748]]\n",
      "16\n",
      "QCOM is Predicted to RISE. +12,-3\n",
      "Found 23 Articles for Stock TSLA\n",
      "Collected data from TSLA for 2017 days!\n",
      "20 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.99998111]\n",
      " [ 0.99829388]\n",
      " [ 0.97131389]\n",
      " [ 0.163256  ]\n",
      " [ 0.17740099]\n",
      " [ 0.163256  ]\n",
      " [ 0.163256  ]\n",
      " [ 0.02433169]\n",
      " [ 0.03152262]\n",
      " [ 0.01124285]\n",
      " [ 0.91197401]\n",
      " [ 0.01204773]\n",
      " [ 0.11929056]\n",
      " [ 0.32727277]\n",
      " [ 0.04661897]\n",
      " [ 0.27515721]\n",
      " [ 0.23492599]\n",
      " [ 0.99291837]\n",
      " [ 0.92395365]]\n",
      "19\n",
      "TSLA is Predicted to FALL. +5,-13\n",
      "Found 19 Articles for Stock FB\n",
      "Collected data from FB for 1540 days!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/Users/ethanshaotran/anaconda/lib/python2.7/site-packages/newspaper/parsers.py\", line 54, in fromstring\n",
      "    cls.doc = lxml.html.fromstring(html)\n",
      "  File \"/Users/ethanshaotran/anaconda/lib/python2.7/site-packages/lxml/html/__init__.py\", line 876, in fromstring\n",
      "    doc = document_fromstring(html, parser=parser, base_url=base_url, **kw)\n",
      "  File \"/Users/ethanshaotran/anaconda/lib/python2.7/site-packages/lxml/html/__init__.py\", line 765, in document_fromstring\n",
      "    \"Document is empty\")\n",
      "ParserError: Document is empty\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.99998522]\n",
      " [ 0.99996382]\n",
      " [ 0.29566664]\n",
      " [ 0.92774081]\n",
      " [ 0.04262183]\n",
      " [ 0.00600957]\n",
      " [ 0.0015913 ]\n",
      " [ 0.00568701]\n",
      " [ 1.        ]\n",
      " [ 0.8331874 ]\n",
      " [ 0.05820321]\n",
      " [ 1.        ]\n",
      " [ 0.9999997 ]]\n",
      "14\n",
      "FB is Predicted to RISE. +7,-6\n",
      "Found 16 Articles for Stock BABA\n",
      "Collected data from BABA for 953 days!\n",
      "12 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.99995732]\n",
      " [ 0.99997866]\n",
      " [ 0.71741062]\n",
      " [ 0.0986665 ]\n",
      " [ 0.11368801]\n",
      " [ 0.99933875]\n",
      " [ 0.99999946]\n",
      " [ 0.35423145]\n",
      " [ 1.        ]\n",
      " [ 0.99025863]\n",
      " [ 0.99213427]]\n",
      "11\n",
      "BABA is Predicted to RISE. +7,-3\n",
      "Found 22 Articles for Stock GS\n",
      "Collected data from GS for 4654 days!\n",
      "16 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.88013709]\n",
      " [ 1.        ]\n",
      " [ 0.66749072]\n",
      " [ 0.95819503]\n",
      " [ 0.00277195]\n",
      " [ 0.06936634]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.99999344]\n",
      " [ 0.99997836]\n",
      " [ 0.99998957]\n",
      " [ 1.        ]\n",
      " [ 0.89609355]]\n",
      "15\n",
      "GS is Predicted to RISE. +12,-2\n",
      "Found 21 Articles for Stock GOOG\n",
      "Collected data from GOOG for 1075 days!\n",
      "16 instances found\n",
      "Loaded model from disk\n",
      "[[  9.99993324e-01]\n",
      " [  9.99989092e-01]\n",
      " [  9.97669220e-01]\n",
      " [  9.99945521e-01]\n",
      " [  9.99874234e-01]\n",
      " [  9.99965608e-01]\n",
      " [  1.00000000e+00]\n",
      " [  9.99673069e-01]\n",
      " [  9.99983966e-01]\n",
      " [  9.99440372e-01]\n",
      " [  9.92998779e-02]\n",
      " [  9.81344402e-01]\n",
      " [  9.99997675e-01]\n",
      " [  5.84622030e-04]\n",
      " [  1.00000000e+00]]\n",
      "15\n",
      "GOOG is Predicted to RISE. +12,-2\n",
      "Found 15 Articles for Stock MMM\n",
      "Collected data from MMM for 4654 days!\n",
      "12 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.99999875]\n",
      " [ 0.99998939]\n",
      " [ 1.        ]\n",
      " [ 0.03577226]\n",
      " [ 1.        ]\n",
      " [ 0.89920264]\n",
      " [ 0.30482781]\n",
      " [ 0.9982034 ]\n",
      " [ 0.88479233]\n",
      " [ 0.99997997]]\n",
      "11\n",
      "MMM is Predicted to RISE. +8,-2\n",
      "Found 11 Articles for Stock AXP\n",
      "Collected data from AXP for 4654 days!\n",
      "7 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.99999797]\n",
      " [ 0.99994141]\n",
      " [ 0.99999642]\n",
      " [ 0.99996895]\n",
      " [ 0.99818182]\n",
      " [ 0.99996543]]\n",
      "6\n",
      "AXP is Predicted to RISE. +5,-0\n",
      "Found 21 Articles for Stock BA\n",
      "Collected data from BA for 4654 days!\n",
      "17 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.99917012]\n",
      " [ 0.92574054]\n",
      " [ 1.        ]\n",
      " [ 0.99998164]\n",
      " [ 0.99997878]\n",
      " [ 0.8278389 ]\n",
      " [ 0.92574054]\n",
      " [ 1.        ]\n",
      " [ 0.00846249]\n",
      " [ 0.06559529]\n",
      " [ 0.97485203]\n",
      " [ 1.        ]\n",
      " [ 0.99999839]\n",
      " [ 0.62957931]\n",
      " [ 1.        ]]\n",
      "16\n",
      "BA is Predicted to RISE. +13,-2\n",
      "Found 17 Articles for Stock CAT\n",
      "Collected data from CAT for 4654 days!\n",
      "14 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.33251005]\n",
      " [ 0.987468  ]\n",
      " [ 0.8738389 ]\n",
      " [ 0.39469591]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.01759654]\n",
      " [ 0.99999762]\n",
      " [ 0.89424098]\n",
      " [ 0.06561049]\n",
      " [ 0.33812907]\n",
      " [ 1.        ]\n",
      " [ 0.99999779]]\n",
      "13\n",
      "CAT is Predicted to RISE. +7,-5\n",
      "Found 15 Articles for Stock CVX\n",
      "Collected data from CVX for 4654 days!\n",
      "14 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.89228666]\n",
      " [ 0.97599238]\n",
      " [ 0.99896115]\n",
      " [ 0.00922817]\n",
      " [ 1.        ]\n",
      " [ 0.96969688]\n",
      " [ 0.99999946]\n",
      " [ 1.        ]\n",
      " [ 0.98872459]\n",
      " [ 0.99980021]\n",
      " [ 1.        ]\n",
      " [ 0.97214198]\n",
      " [ 1.        ]]\n",
      "13\n",
      "CVX is Predicted to RISE. +11,-1\n",
      "Found 19 Articles for Stock CSCO\n",
      "Collected data from CSCO for 4654 days!\n",
      "16 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.99999917]\n",
      " [ 0.59620959]\n",
      " [ 0.99985909]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.8645829 ]\n",
      " [ 1.        ]\n",
      " [ 0.99996531]\n",
      " [ 0.59594029]\n",
      " [ 0.99902421]\n",
      " [ 0.94675487]\n",
      " [ 0.99884945]\n",
      " [ 0.91467422]\n",
      " [ 0.99999934]]\n",
      "15\n",
      "CSCO is Predicted to RISE. +14,-0\n",
      "Found 12 Articles for Stock KO\n",
      "Collected data from KO for 4654 days!\n",
      "10 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.94642103]\n",
      " [ 0.99999636]\n",
      " [ 0.99987149]\n",
      " [ 0.99924159]\n",
      " [ 0.94635278]\n",
      " [ 0.99989259]\n",
      " [ 0.11945671]\n",
      " [ 0.88141185]\n",
      " [ 0.97981089]]\n",
      "9\n",
      "KO is Predicted to RISE. +7,-1\n",
      "Found 18 Articles for Stock DIS\n",
      "Collected data from DIS for 4654 days!\n",
      "15 instances found\n",
      "Loaded model from disk\n",
      "[[ 0.99578959]\n",
      " [ 0.76988775]\n",
      " [ 0.00737805]\n",
      " [ 0.17292221]\n",
      " [ 0.224381  ]\n",
      " [ 0.99999028]\n",
      " [ 1.        ]\n",
      " [ 0.94629252]\n",
      " [ 0.99999869]\n",
      " [ 1.        ]\n",
      " [ 0.99936372]\n",
      " [ 0.99990857]\n",
      " [ 1.        ]\n",
      " [ 0.98188055]]\n",
      "14\n",
      "DIS is Predicted to RISE. +10,-3\n",
      "Found 17 Articles for Stock XOM\n",
      "Collected data from XOM for 4654 days!\n",
      "16 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.00891897]\n",
      " [ 0.99557537]\n",
      " [ 0.87742406]\n",
      " [ 1.        ]\n",
      " [ 0.99999952]\n",
      " [ 1.        ]\n",
      " [ 0.96964115]\n",
      " [ 0.99019074]\n",
      " [ 1.        ]\n",
      " [ 0.99947232]\n",
      " [ 1.        ]\n",
      " [ 0.99997985]]\n",
      "15\n",
      "XOM is Predicted to RISE. +13,-1\n",
      "Found 23 Articles for Stock GE\n",
      "Collected data from GE for 4654 days!\n",
      "21 instances found\n",
      "Loaded model from disk\n",
      "[[  6.69812918e-01]\n",
      " [  6.69812918e-01]\n",
      " [  9.99998271e-01]\n",
      " [  1.00000000e+00]\n",
      " [  9.02329326e-01]\n",
      " [  9.27280366e-01]\n",
      " [  8.19508374e-01]\n",
      " [  9.99934673e-01]\n",
      " [  9.97071505e-01]\n",
      " [  3.70513008e-04]\n",
      " [  1.00000000e+00]\n",
      " [  9.99972939e-01]\n",
      " [  3.88366100e-03]\n",
      " [  9.75349307e-01]\n",
      " [  9.99951422e-01]\n",
      " [  9.72515345e-01]\n",
      " [  1.00000000e+00]\n",
      " [  9.99997556e-01]\n",
      " [  1.61903873e-02]\n",
      " [  6.95257962e-01]]\n",
      "20\n",
      "GE is Predicted to RISE. +16,-3\n",
      "Found 12 Articles for Stock HD\n",
      "Collected data from HD for 4654 days!\n",
      "9 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.99936855]\n",
      " [ 0.99997568]\n",
      " [ 0.70798659]\n",
      " [ 0.84802699]\n",
      " [ 0.43826213]\n",
      " [ 1.        ]\n",
      " [ 0.00297708]]\n",
      "8\n",
      "HD is Predicted to RISE. +6,-1\n",
      "Found 23 Articles for Stock INTC\n",
      "Collected data from INTC for 4654 days!\n",
      "20 instances found\n",
      "Loaded model from disk\n",
      "[[ 1.        ]\n",
      " [ 0.99869925]\n",
      " [ 0.99998355]\n",
      " [ 0.9999795 ]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.99872637]\n",
      " [ 0.98452151]\n",
      " [ 0.02068773]\n",
      " [ 1.        ]\n",
      " [ 0.99992424]\n",
      " [ 0.97265112]\n",
      " [ 0.28525347]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 1.        ]\n",
      " [ 0.95354855]\n",
      " [ 1.        ]]\n",
      "19\n",
      "INTC is Predicted to RISE. +16,-2\n",
      "_IBM_\n",
      "_16_\n",
      "BUYING IBM\n",
      "_WMT_\n",
      "_28_\n",
      "BUYING WMT\n",
      "_MSFT_\n",
      "_23_\n",
      "BUYING MSFT\n",
      "_WMT_\n",
      "_28_\n",
      "BUYING WMT\n",
      "_PYPL_\n",
      "_28_\n",
      "BUYING PYPL\n",
      "_AAPL_\n",
      "_12_\n",
      "BUYING AAPL\n",
      "_QCOM_\n",
      "_41_\n",
      "BUYING QCOM\n",
      "_FB_\n",
      "_11_\n",
      "BUYING FB\n",
      "_BABA_\n",
      "_12_\n",
      "BUYING BABA\n",
      "_GS_\n",
      "_10_\n",
      "BUYING GS\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for float(): 1,127.46",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-33af8982c8db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#reload(SP)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mUpdateHoldings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-5df98f14f621>\u001b[0m in \u001b[0;36mUpdateHoldings\u001b[0;34m()\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mToBuy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0mordername\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mToBuy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m             \u001b[0morderprice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInvestopedia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_quote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mordername\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m             \u001b[0morderamount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBuyBal\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mBuyVShort\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mToBuy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0morderprice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mordername\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ethanshaotran/Downloads/Coding/DL/Projects/StockNews/ita.pyc\u001b[0m in \u001b[0;36mget_quote\u001b[0;34m(symbol)\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquote\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for float(): 1,127.46"
     ]
    }
   ],
   "source": [
    "#reload(SP)\n",
    "UpdateHoldings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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