ForexBot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fxcmpy\n",
    "fxcmpy.__version__\n",
    "import configparser\n",
    "import datetime \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TOKEN = '434e3e87b7324219bedc0aed785c3dd24c6a0578'\n",
    "con = fxcmpy.fxcmpy(access_token=TOKEN, log_level='error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EUR/USD', 'USD/JPY', 'GBP/USD', 'USD/CHF', 'EUR/CHF', 'AUD/USD', 'USD/CAD', 'NZD/USD', 'EUR/GBP', 'EUR/JPY', 'GBP/JPY', 'CHF/JPY', 'GBP/CHF', 'EUR/AUD', 'EUR/CAD', 'AUD/CAD', 'AUD/JPY', 'CAD/JPY', 'NZD/JPY', 'GBP/CAD', 'GBP/NZD', 'GBP/AUD', 'AUD/NZD', 'USD/SEK', 'EUR/SEK', 'EUR/NOK', 'USD/NOK', 'USD/MXN', 'AUD/CHF', 'EUR/NZD', 'USD/ZAR', 'USD/HKD', 'ZAR/JPY', 'USD/TRY', 'EUR/TRY', 'NZD/CHF', 'CAD/CHF', 'NZD/CAD', 'TRY/JPY', 'USD/CNH', 'AUS200', 'ESP35', 'FRA40', 'GER30', 'HKG33', 'JPN225', 'NAS100', 'SPX500', 'UK100', 'US30', 'Copper', 'CHN50', 'EUSTX50', 'USDOLLAR', 'US2000', 'USOil', 'UKOil', 'SOYF', 'NGAS', 'WHEATF', 'CORNF', 'Bund', 'XAU/USD', 'XAG/USD', 'BTC/USD', 'ETH/USD']\n"
     ]
    }
   ],
   "source": [
    "print(con.get_instruments())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-11 17:50:00</th>\n",
       "      <td>1.14814</td>\n",
       "      <td>1.14808</td>\n",
       "      <td>1.14818</td>\n",
       "      <td>1.14806</td>\n",
       "      <td>1.14829</td>\n",
       "      <td>1.14820</td>\n",
       "      <td>1.14832</td>\n",
       "      <td>1.14820</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 17:51:00</th>\n",
       "      <td>1.14808</td>\n",
       "      <td>1.14802</td>\n",
       "      <td>1.14812</td>\n",
       "      <td>1.14802</td>\n",
       "      <td>1.14820</td>\n",
       "      <td>1.14815</td>\n",
       "      <td>1.14824</td>\n",
       "      <td>1.14815</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 17:52:00</th>\n",
       "      <td>1.14802</td>\n",
       "      <td>1.14801</td>\n",
       "      <td>1.14804</td>\n",
       "      <td>1.14796</td>\n",
       "      <td>1.14815</td>\n",
       "      <td>1.14815</td>\n",
       "      <td>1.14816</td>\n",
       "      <td>1.14809</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 17:53:00</th>\n",
       "      <td>1.14801</td>\n",
       "      <td>1.14797</td>\n",
       "      <td>1.14811</td>\n",
       "      <td>1.14797</td>\n",
       "      <td>1.14815</td>\n",
       "      <td>1.14810</td>\n",
       "      <td>1.14825</td>\n",
       "      <td>1.14810</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 17:54:00</th>\n",
       "      <td>1.14797</td>\n",
       "      <td>1.14797</td>\n",
       "      <td>1.14801</td>\n",
       "      <td>1.14782</td>\n",
       "      <td>1.14810</td>\n",
       "      <td>1.14810</td>\n",
       "      <td>1.14815</td>\n",
       "      <td>1.14796</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2019-01-11 17:50:00  1.14814   1.14808  1.14818  1.14806  1.14829   1.14820   \n",
       "2019-01-11 17:51:00  1.14808   1.14802  1.14812  1.14802  1.14820   1.14815   \n",
       "2019-01-11 17:52:00  1.14802   1.14801  1.14804  1.14796  1.14815   1.14815   \n",
       "2019-01-11 17:53:00  1.14801   1.14797  1.14811  1.14797  1.14815   1.14810   \n",
       "2019-01-11 17:54:00  1.14797   1.14797  1.14801  1.14782  1.14810   1.14810   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2019-01-11 17:50:00  1.14832  1.14820      290  \n",
       "2019-01-11 17:51:00  1.14824  1.14815      162  \n",
       "2019-01-11 17:52:00  1.14816  1.14809      267  \n",
       "2019-01-11 17:53:00  1.14825  1.14810      193  \n",
       "2019-01-11 17:54:00  1.14815  1.14796      356  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = con.get_candles('EUR/USD', period='m1', number=250)\n",
    "data.head()\n",
    ",parse_dates=['data'],index_col='data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:55:00</th>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14653</td>\n",
       "      <td>1.14635</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14662</td>\n",
       "      <td>1.14671</td>\n",
       "      <td>1.14656</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:56:00</th>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14634</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14663</td>\n",
       "      <td>1.14660</td>\n",
       "      <td>1.14667</td>\n",
       "      <td>1.14653</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:57:00</th>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14639</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14658</td>\n",
       "      <td>1.14664</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:58:00</th>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14662</td>\n",
       "      <td>1.14684</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14713</td>\n",
       "      <td>1.14725</td>\n",
       "      <td>1.14654</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:59:00</th>\n",
       "      <td>1.14662</td>\n",
       "      <td>1.14676</td>\n",
       "      <td>1.14676</td>\n",
       "      <td>1.14655</td>\n",
       "      <td>1.14712</td>\n",
       "      <td>1.14720</td>\n",
       "      <td>1.14724</td>\n",
       "      <td>1.14712</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2019-01-11 21:55:00  1.14638   1.14641  1.14653  1.14635  1.14659   1.14662   \n",
       "2019-01-11 21:56:00  1.14641   1.14634  1.14641  1.14633  1.14663   1.14660   \n",
       "2019-01-11 21:57:00  1.14633   1.14625  1.14639  1.14625  1.14659   1.14658   \n",
       "2019-01-11 21:58:00  1.14625   1.14662  1.14684  1.14614  1.14659   1.14713   \n",
       "2019-01-11 21:59:00  1.14662   1.14676  1.14676  1.14655  1.14712   1.14720   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2019-01-11 21:55:00  1.14671  1.14656      169  \n",
       "2019-01-11 21:56:00  1.14667  1.14653      438  \n",
       "2019-01-11 21:57:00  1.14664  1.14646      386  \n",
       "2019-01-11 21:58:00  1.14725  1.14654      478  \n",
       "2019-01-11 21:59:00  1.14724  1.14712       33  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rFB0+8eDnqdeL9NKBMq1aGqXbrsrQE3tPq7iyVQEuh+68JlPXbR49BCupbFVJZcuY5wEAAAAAmI7zZiCN+RtrpgBj3jIMQ9lLo9XU1qP38qpn5Z01jR8OcC+rnfjspbePV+qlA2VKignW5z+eo8yUCH3tvk164OZsBQU49cvXTutsdeuI+3r6+vXQs7n65WunVdXQ4ZdvAAAAAABgsgiQMK/dcXXGcADT2Drz3Y/nB0jnatomdE9+aZMefdlSqMetL925VsFBbkmSwzC0c02S/uTW1ZKkJ/YW6eIVgS8fKFNTW4+k2Z31BAAAAADA+QiQMK/FRnp0z57l6urx6qcvFowIYPyps9ur1s4+pcSFSJrYCqTevn798DcDc+H/7LYcxUcFj7gme2m01mfFqvBcs44U1g8fb2rr0Qv7SxXgGvjXtJgACQAAAAAwRwiQMO9dtTZJazJidPJMo946Xjlj76lpGlh9tDI9SqEet87VXDpA+uBUjdo6+3TjtnSZ6VFjXveJazPldBj61RtF8vb7JEnPvlOi3j6f7t6dpUC3U0UVI1vcAAAAAACYDQRImPcMw9Bnblqp4ECXnny9SPXNXTPynqEAKTE6WGnxoapt7lJXj3fM623b1t7D5XIYhq7dkDLus5NiQnTthhTVNndp7+FyldW0ad+JKqXGhWjX+hRlJIersr5DHd19fv0mAAAAAAAmggAJC0JUWKDuvX65enr79ZMX8mekla2mcSCYSoj2KD0hVJJ0bpw2tqKKFpXVtmvDitgJ7dZ265XLFBzo0m/3ndWjr1iyJd29e7kcDkOZKRGSBnZkAwAAAABgthEgYcHYsTpRazNjVFDWrLIJtJdN1tAA7cSoYKUnhEmSysYZpL33cLkkac/G1Ak9P9Tj1q07l6qrx6viilatzYzR6mXRkqSslHBJUlH5yDlIvX39KihtmviHAAAAAAAwSQRIWDAMw9CWlfGSpMLyZr8/v6apUy6noejwIKXHD6xAGmuQdnN7jw5bdUqJC5GZHjnhd+zelKr4KI8chqFPXJs1fDwjeWAF0mg7sT39VrH+5fGjssoIkQAAAAAAM4MACQvKirSBsKbwnH8DJNu2Vd3YpfioYDkchhJjguVyOsYcpP3WsUr1+2zt2ZgqwzAm/B6X06G/vHu9vn7fJqXEhgwfD/W4lRQTrJKqVvl8H7bn9Xl9ej+vWpJ0vKhhil8HAAAAAMD4CJCwoMRGBCkqLFCF55r9OgeprbNPXT1eJUR5JElOh0OpcSGqqG8f3jVtiLffpzePVsgT6NL21QmTfldspEcZyeEjjmemRKint1/ldR+GVseL6tXRPTDIO/cMARIAAAAAYGYQIGFBMQxDK9Ii1dbZp+rBmUX+MLQDW0J08PCx9IRQefttVTdc+J7DVp1aOnp15ZokBQW4/FZD1uAg7eLz2tj25VYN11VR16HG1m6/vQ8AAAAAgCEESFhwZqKNbSiMSjwvQEqLHxykXXvhIO29RwaGZ+/emOK390sfBkhDc5Ba2nuUW9KopYlhw+/KO9Po13cCAAAAACARIGEBmokAqaaxS5KGW9ikgRVIki7Y8e1Afo2Kylu0NjPmgtVK/pAYE6zgQJeKK1olSe+frJHPtrVzTZLWZMRIknJLaGMDAAAAAPgfARIWnOSYYIV63P4NkEZpYUuNGwiQzg3uxNbS0atfvFKoAJdDn9yz3G/vHuIwDGWmRKi2uUstHb3al1sll9PQtlUJSojyKDYiSKfONo6YyQQAAAAAwHQRIGHBMQxDy1Mj1NDao/qWLr88s6axU4EBTkWEBAwf8wS6FB/lUVlNm2zb1iMvFai9q093XpPp99VHQ7JSBoZr7z18ThX1HVq/PE6hHrcMw9CajBh19fSrpLJ1Rt4NAAAAAFi8CJCwIJmDbWynz7Vc4spL89m2apq6lBDlkWEYF5xLjw9VR7dXz79fqqOn67UyPVK7N6VO+51jyRycg/TSB2WSpCvXJA6fy8mIliTlsRsbAAAAAMDPCJCwIK1IHwiQLD+0sTW39ajP67tggPaQtISBQdrPvF2iwACnPvfRbDkuCpn8aVlSuAxD8vbbiggJ0Opl0cPnVqZHyekwlFvCIG0AAAAAgH8RIGFBSosPVVCA0y9zkIZ2YEuIGhkgpceHDv/87t1Zio30jLjGnzyBLqUNzl7akZMop8NxwbkVaZEqrW5Ta0fvjNYBAAAAAFhcCJCwIDkdDmWlRqi6sVMt54Upfd5+na1ulW3bE35WTdPgDmzRI8OhZUnhcrscWpMRo13rkqdf+ASszoiWy2noqrVJI84NtbGdPMMqJAAAAACA/xAgYcFakTo0B2lgFVJrZ6++/cuj+vufHdLv3y+d8HNqGkfuwDYkPCRA33pwu75wx5oR85FmysevzND/+ZMdSooJGXFuzbIYSVJuCXOQAAAAAAD+Q4CEBWvF4CDtwnPNqm3q1LcePaySyla5nA49+3aJDhbUTug547WwSVJMRJBcztn7V8ntcig6PGjUcylxIYoMDVDemUb5fBNfZQUAAAAAwHgIkLBgLUsKl8vp0NHTdfrWo4dV29Slm3cs0f++f7MCA5z68e9PTWjL+5qmLoV63Ar1uGeh6ukxDEM5GTFq7+rTj353UocKatXV453rsgAAAAAA8xwBEhYst8uhzORwNbT2qK2rT/fdsEJ37MpUWnyo/scfrJa336f/+PUJNbR0j/kMb79P9c1do84/ulxdvzlNUWGBOpBfq+8/l6cvfu8dfffJY+N+JwAAAAAA4yFAwoK2eWW8PIFO/flta3TtxtTh42szY3XPnuVq7ejV954+oe7e0VfpNLR0q99nj9m+djlKiw/Vv37+Cv3tZzbrD65cptS4UJ0806jHXi2c69IAAAAAAPOUa64LAGbSnk2punZDihyOkQOur9uUqqr6Dr15rFJvHavUR7amj7imqKJFkpQcO3Jg9eXMYRhamhiupYnhunXnUn37l0d1rKhe+Wcblb00eq7LAwAAAADMM6xAwoI3WngkDcwLuu3qDDkdhvblVsm2Rw6d3pdbJUnabMbNaI0zyTAM3bMnS5L05OtFUxquPdrfGwAAAADA4kGAhEUtLDhA67JiVV7XobKa9gvO1Td3qaCsWSvSIhU/j1rYRrM0MVxX5CSqrLZ9OBSbqK4er/7y++/pqTeKZqg6AAAAAMDljgAJi96Va5IkSe9eFKy8l1ctSdq5JnHWa5oJt1+doQCXQ8+8XTLmzKfRHDtdr6a2Hh3Mr53B6gAAAAAAlzMCJCx6ORnRCg92a//JavV5fZIkn23r3dwqBbqd2rIyfo4r9I/o8CDduC1dLR29enF/2YTvO5BfI0lqaO1mJzcAAAAAWKQYoo1Fz+V0aPvqRL1y8JxOFNdrkxmv0+eaVd/SrZ05iQoKWDj/mty0bYneOl6plw6Uye1yDM+HchiGdqxOUERo4AXXd3b3Ke9M4/BfF5Y3a0fEwliRBQAAAACYOFYgAZJ2Drax7csdaFsbamcbOr5QBAY4deeuTPV5fXrm7RI9/Waxnn6zWE+9UaRfvFo44vojhfXq99natGJgiHjhuebZLhkAAAAAcBlYOEsrgGlIiw/VkoQwnShuUG1Tpw4V1Ck2Ikgr0iPnujS/uyInUXGRHnX39g8fe+btYh2x6lRR36GU2JDh4wcLBuYe3b4rQ3lnGwmQAAAAAGCRYgUSMGjnmkT5bFs//M1J9fT164qcRDkMY67L8jvDMLQiLVJrM2OGf9y6c5lsSS+8f3b4uvauPp0626gliWFKignR8pQIVTV0qrWjd65KBwAAAADMEQIkYNC2VQlyOgydrW6TtPDa18azfnmsUuJC9MGpWtU2dUqSjhTWqd9na+vgEPHlaQOrsViFBAAAAACLDwESMCgsOEDrl8dKklamRyou0jPHFc0eh2Holh1L5bNtvTC4Q9vBwd3XhnahMwmQAAAAAGDRIkACzrNnY6ochqE9m9LmupRZt2VlvBKiPNqXW6XS6jbllzZrWVK4YgeDtGVJYXI5HSosJ0ACAAAAgMWGAAk4z8olUfrB/7pam8y4uS5l1jkchj66Y4n6fbb+85kT8tm2tmbHD593u5zKSA7XuZp2dXZ757BSAAAAAMBsI0ACLuJ2Oee6hDmzY3WiYsID1djaI+nD9rUhK9IiZUsqqmAVEgAAAAAsJgRIAIa5nA7dtH2JJCkrJULR4UEXnB+ag2QxBwkAAAAAFhXXXBcA4PJy1dokVdZ3jFh9JEmZKeFyGAaDtAEAAABgkSFAAnABt8upT99gjnouKMClJYmhOlvVpp6+fgW6F2+7HwAAAAAsJrSwAZiUFWmR6vfZKqlsnetSAAAAAACzhAAJwKSsGJyD5M82Nm+/T83tPX57HgAAAADAvwiQAEzK8tSBAKmo3H8B0iMvWfrK999jVRMAAAAAXKYIkABMSqjHrcjQAFU3dvnlebVNndqXV6V+n62Hnz+lPm+/X54LAAAAAPAfAiQAkxYf6VFjW7e8/b5pP+uF/aWybSk9IVRVDZ169u0zfqgQAAAAAOBPBEgAJi0uyiPblhpauqf1nMbWbu3LrVZCdLD++t6Nio/y6OUDZX6drwQAAAAAmD4CJACTFhfpkSTVNk+vje3FD8rU77N1y44l8gS69MDN2ZKknzyfr55eWtkAAAAA4HJBgARg0uIHA6S6aQRILe09evt4pWIjgrRtVYKkgQHdH9martrmLj39ZrFfagUAAAAATB8BEoBJG16B1DT1AOnlg+fU5/Xppu1L5HJ++J+i265epqSYYO09Uq7S6rZp1woAAAAAmD4CJACTFhc1vRVI7V19euNIhSJDA3TlmsQLzrldTt29O0uStPdI+fQKBQAAAAD4BQESgEkL87gVGOCccoD04gel6unr141b0+V2OUecz8mIUXykRx+cqlF7V990ywUAAAAATJNrIheZprlN0rcty7pmlHPBkl6V9IBlWQXnHY+XdFjS9ZZlFZim+YSkoaUGSyXttyzrHtM0fyspRlKfpC7Lsm6axvcAmAWGYSg+0qPapi7Zti3DMCZ872GrTi/tL1NUWKB2rU8Z9RqHYWj3xhQ98XqR3jleqZu2L/FX6QAAAACAKbjkCiTTNP9K0o8lBY1ybrOktyVlXnTcLem/JQ0vT7As657BAOo2Sc2Svjx4KkvSlZZlXUN4BMwf8ZEe9fT1q7Vz4iuESqvb9P9+f1Jut0NfvGOtAgNGrj4acuXaJAW4HXr9SIV8PtsfJQMAAAAApmgiLWzFkm4f41ygBgKhgouOf0fSDyVVjnLP30n6T8uyqkzTTJAUKel3pmm+a5rmLRMrG8BcGxqkXTfBQdpNbT363tPH1dfn0x9/bLWWJIaNe31wkFtXrE5UQ2u3jhfXT7veyeru9aqqoeOCH95+36zXAQAAAACXg0u2sFmW9WvTNJeOcW6fJJmmOXzMNM3PSKqzLOtl0zS/dv71g21te/Th6qMASd+V9D1J0ZL2maZ5wLKs2vFqiooKlmuUuSkAZk9GWqR0oEzdPltxceOHQd09Xv3TLw6rub1Xn71llT6yM2NC77jjOlNvHqvUO7nVuuGKid3jD7Zt60/+ea+qGjouOJ6eGKbvfvFqBQVOqPsXAAAAAOaNS/2+biZ+F/Q5SbZpmtdJWi/pEdM0b7Usq1rSnZJ+aVlW/+C11ZJ+aFmWV1KtaZpHJZmSxg2Qmpo6Z6BsAJMR5BqYe1Rc1qSc9Mgxr/PZtr7/bJ6Ky1t01dokXbk6QXV1bRN6R4jLkJkWqWOFdTpRUK2kmBC/1H4pTW09qmroUEKURyuXREmSapu6lF/apB88fUyfvsG8xBMAAAAAYP6IiwtTXV3buCGS33dhsyzrasuydg3OOzom6Q8HwyNJuk7Si+ddfp2kpyTJNM1QSTmS8v1dEwD/ix9sYau9RAvbr98q1pHCOq1Mj9R9HzEnNXBbkvZsSpUkvX6kYmqFTkFl/cDKoy3ZCbr/xpW6/8aV+otPrFVybIheP1KhU2cbZ60WAAAAALgcTDpAMk3zXtM0/3iK7zMllQz9hWVZL0o6bZrmfkmvSPq6ZVmzP+wEwKRFhwfJYRiqaxk7QHrnRKVe3F+mhCiPPn/bGrmck8+s1y+TUz7hAAAgAElEQVSPVVRYoPblVqmrxzudkiesYjBASon9cMWT2+XUAzdny2EY+ukL+bNWCwAAAABcDibUwmZZ1llJ2wd//stRzl8zxn3XXPTXq0e55i8mUgOAy4vL6VB0eOCYQ7StsiY98pKlkCCXvvSJdQr1uKf8nl3rk/XcO2d0pLBOO9ckTafsCamoa5ckpcRd2DK3LClcN+9Yot+9d1ZP7D2tz340e8ZrAQAAAIDLgd9b2AAsHvFRHrV09Kqnt/+C4zWNnfqvZ3IlSZ+/bY0So4On9Z7NZrwk6VjR7CxQrKzvkNNhjFr3x3YuVXp8qN45UaUTc7A7HAAAAADMBQIkAFMWNzgH6eI2th/97pQ6ur267yOmsgeHUE9HUkywEqI8yitpVJ+3/9I3TINt26ps6FB8lGfUljuX06EHblklp8PQL14plG3bM1oPAAAAAFwOCJAATNnQIO3z29iqGjp0pqpVazNjdPW6ZL+8xzAMrV8eq56+fuWXNvnlmWNpautRV0+/UuJCx7wmLT5UG1bEqb6lW3XN4w8RBwAAAICFgAAJwJQNr0A6L0Q5mF8rSdqWneDXd21YHidJOnZ6ZtvGyutGDtAezfKUCElSUUXLjNYDAAAAAJcDAiQAUzYUINWeHyAV1MrldGj98li/visrJUKhHreOFtXLN4NtY5Wj7MA2aj2pAwFScUWrX97b1ePVG0fK5e33+eV5AAAAAOBPBEgApuziAKmirl0V9R1akxEtT+CENnmcMIfD0LrMGLW09+psVZtfn32+ivqBHdiSLxEgpcWHyu1y+G0F0ssHyvToK4V682iFX54HAAAAAP5EgARgyoKDXAr1uFXX3C1pYPWRJG3Jjp+R960famMrqpuR50sDK5BcTkPxUZ5xr3M5HVqWGKbyunZ19Xin/d7ckkZJ0r7c6mk/CwAAAAD8jQAJwLTERXpU39wln8/WgfxauV0Orc/yb/vakJxl0XK7HDo6Q3OQfLativoOJUYHj7oD28UyUyNk29KZqum1sbV19urs4DNKa9p0rrZ9Ws8DAAAAAH8jQAIwLfFRHvX7bJ0oaVB1Y6fWZsYoKMC/7WtDAgOcWrUkShV1HRfMXfKXhpZu9fb5Ltm+NiTLT4O0T55plC0pMzlckrQvt2pazwMAAAAAfyNAAjAtcZFBkqQX3i+VJG318+5rFxsazj0Tu7FVDA3Qjgud0PWZyf4JkIba1z553QqFetx6/2Q1w7QBAAAAXFYIkABMy9Ag7aKKFgW4HVqbETOj71ufFStD0rHT/p+DNNEd2IaEhwQoPsqjkorWKe8M57NtnTzToIiQAC1LCtP2VQlq6+xTbknDlJ4HAAAAADOBAAnAtMRHfjhsen1WrAIDnDP6vojQQGUkh6vwXIvau/r8+uyKuoHZQxMNkKSBNrbOHq+qGjqn9M5zNe1q7exTTka0DMPQlWuTJDFMGwAAAMDlhQAJwLTEnRcgbVk5M7uvXWz98lj5bFvHi/zbxlZR3yG3y3HBN11K5uAcpOIptrENrTRaM7hyKz0hTGnxoTpeVK/Wzt4pPRMAAAAA/I0ACcC0RIYFyu1yKDDAORyCzLTN5kBQdbCg1m/P9PlsVTV0KikmWA6HMeH7hgdpl08tQMoraZBhSKuWRg8f27kmSf0+Wx+crJnSMwEAAADA3wiQAEyLwzD0qetX6P4bTQW4Z7Z9bUhCdLDSE0J18kyjOrr908ZW19KlPq9vUu1r0kC7W1CAU8WVkw+QOru9KqpoVUZSuEI97uHj21clyOkw2I0NAAAAwGWDAAnAtF29LlnbVyXO6ju3rIxXv8/WkUL/DNOuqBsYoJ08yQDJ4TCUkRyuqobOSc9kyi9tlM+2lXPRyq3wkACtzYxRWW37lFvjAAAAAMCfCJAAzEtbshMkSQfz/dPGVjG0A1tc6KTvzZriHKTckkZJUk5G9Ihz125MkST9+6+O63R586RrAgAAAAB/IkACMC/FR3q0NDFMp842TWjlT03j+CuEKocCpEmuQJLOC5DGaWPr8/pUVtMmn8+WJNm2rbwzDQoJcmlZYviI63OWxehzH81Wd2+//vXxYzps+W/eEwAAAABMlmuuCwCAqdqanaCz1W06bNVq1/qUUa+pa+7SE3tP6+jpehmGtDwlQuuyYrU2M0YxEUHD11XUtSvA7bjg2ERlJA8EQGMN0rZtWz/67UkdLqxTqMetnIxoLUkIU2Nrj7Zmx485tPvKtUmKDA3QQ8/l6fvP5une61doz6bUSdc3UbZty9bAXCsAAAAAOB8BEoB5a/PKOD31RpEOFowMkHr6+vXi/lK9sL9M3n6fMlPCZcjQ6fIWFZa36FdvFo943tLEsCmFJ8FBbqXEhqikslWNrd2KDr8whNp/qkaHC+sUH+VRn9en/SdrtH9wh7VL7VyXkxGjr967Uf/2q+N67NVCOQzp2o0zEyL94DcnVdvYqa/cu0EhQe5L3wAAAABg0SBAAjBvxUZ4lJkcrvzSJrV29Co8JECSVFrdpv96JlcNrd2KDA3QXbuztC07QYZhqLWzV3klDco706jObu/wswwNDAOfqt0bU/ToK4X63tMn9LVPb1RQwMB/XpvaevTYK4UKdDv1P+9er7iIIJ2rbdfxono1tPZosxl/yWcvSQzTN+7bpH/4+SE9+XqRVi2NVkJ08JRrHY3PZ+tEUb16vT59/9k8ffmudXI56XIGAAAAMIAACcC8tmVlvIorW3W4sE7XbkhRXkmDHnouT729/bpxW7o+dsVSeQI//E9deHCArshJ0hU5SX6t45oNKTpX16E3j1boR789pT+/fY0MQ/r5SwXq7PHqvo+Yio/0SJLSE8KUnhA2qefHRXr06RtW6Ie/OamHn8/XVz+1cczWt6mobe5Sr9cnh2Eov7RJj71aqD/8iCmDdjYAAAAAYog2gHlu88qBFTwH82u0L7dK33v6hPr7bf2Pj+formuzLgiPZpJhGLr3uuVatTRKx4rq9dQbRXr3RJVOFDdo9dIoXbN+6qubhmzNTtCWlfEqqmjRKwfP+aHqD5XXtkuSPrZzqdLjQ/XWsUq96ud3AAAAAJi/CJAAzGvR4UHKSo1QQVmzHn4+X0EBTv3lPeuHg6XZ5HI69PmP5ygpJlivHDynR1+x5Al06rMfzfbbSp5P37BC4cFuPfN2iSoGd47zh/K6gQApMyVcX7xzrSJCA/Tk60U6drreb+8AAAAAMH8RIAGY97ZlJ0iSYsID9bVPb9KKtMg5qyU4yK0v3blWoR63vP227tmzfMRQ7ekICw7Q/TeulLffp4d/f0r9Pp9fnlteNxBGpcaFKjo8SF+8Y63cLod+/PtT6u3r98s7AAAAAMxfzEACMO/tWp8sl9PQuqxYRYYGznU5io8K1lc/tVGlNW3avirB78/fsCJOO1Yn6v2T1Xr5wDl9dPuSaT+zvLZdoR63IgYHkS9LCtd1m9P0wv5SnShumJMVXQAAAAAuH6xAAjDvuZwO7VqfclmER0OSY0O0Y3XijA2hvvf65Qr1uPW7986qpaN3Ws/q7vWqtrlLqXEhF9S7NXtwvlRB7bSeDwAAAGD+I0ACgHkoJMitj1+1TD29/XrunZJpPWtollJqfOgFx9PiQ5UQHazjxfXq6aWNDQAAAFjMCJAAYJ7atT5ZSTHBevt45fAualMxdG9a3IUBkmEY2rIyXr19Ph0vZpg2AAAAsJgRIAHAPOV0OHT37uWybenJ10/Ltu0pPWd4gPZFK5Ck89rY8mljAwAAAC7m7fepdZojJeYLAiQAmMfWZERr9bJonTzbpNyShik9o7y2XYYG5jZdLCU2REkxwTpR0qCuHu80qwUAAAAWlp++kK+//uH78vb7Z3fkyxkBEgDMY4Zh6O7dWTIM6cnXiyb9C5dt2yqva1d8lEeBbueoz9+anaA+r0/Hi2hjAwAAAIZU1ndo/8kaJcYEy+mYmc1zLicESAAwz6XGhWrXumRVNXTqxQ/KJnVvc3uvOrq9o7avDdmycqCN7QBtbAAAAMCw598vlS3pY1csnbHdly8nBEgAsAB8/KoMhYcE6Nm3S/Trt4onPA+pvG5ggHZq3NgBUnJsiFLjQpR3pkGd3bSxAQAAALXNXfrgVI1SYkO0fnnsXJczKwiQAGABCA8J0Nc/vVHxUR49/36pfvz7/Am1sw3twDZegCQNrELy9ts6errOL/UCAAAA89mL+0vls23dfMUSORbB6iOJAAkAFoz4qGB9/b5NykgO1/snq/Xvvzp+ycHXwyuQ4kcO0D7fluwESdJ7edUqr2sf/tHc3jOtmls6euXzTW33OAAAAGAuNLZ2690TVUqI8mjryoS5LmfWuOa6AACA/4QHB+grn9yg//7NSR0rqtffPvyB7tmzXBtXxI3al32utkMBbofiIj3jPjcxOljp8aHKL23S3z58YPi4YUjXbEjRbVdlKNTjnlStVlmTvv3Lowr1uLUmI1rrsmKVsyxawUGTew4AAAAwm146UKZ+n62Pbl8ixyIYnj2EAAkAFphAt1N/dnuOnnvnjF76oEwPPZun1UujdO/1K5QU8+FKI2+/T1UNHUpPCJvQstv7bjS1P69Gtj5cMXTqbJPeOFKhg/m1un1Xhq5emzzhX0QPFQy0w9m2rfdP1uj9kzVyOgx98rrl2r0xdZJfDQAAAMy8lo5evX2sUjHhgdqRkzjX5cwqAiQAWICcDofu2JWpK3IS9fhrp5V3plF/+/ABfeyKpfrYzoFdIqobO9Xvs5V2ifa1IZnJEcpMjrjgmLffp9cOles3+87okZcsvXO8Ul++a/2EViPlnmlQUIBT//aFK1VZ36HjRfXae7hcj71aqJjwIK3Lmtwwwj5vv55954wSojzatT5lUvcCAAAAE/HaoXPq9fp047YlcjkX11SgxfW1ALDIJMWE6Mt3rdMXbl+jyNAAPffuGf3k+YEB20MDtFMuMUB7PC6nQzduS9e3HtyuzSvjdaaqTb94xbrkfTVNnapt6tKqpdFyOR1KTwjTx3Yu05c+sU4up0M//O1JnRusbyI6uvv03SeO6aUPyvT4a6cvOfsJAAAAmIrT5S2SpKvWJs1xJbOPAAkAFjjDMLRhRZz+9/1btCwpTPvyqvW9p0+oqGLgF7+0aQRIQ6LCAvWnt65WZnK4DuTX6mBB7bjX55U0SpJyMqIvOL4sKVwP3rJKPb39+t7Tx9UygSHdDS3d+tajh1VY3qKosED1en2XfD8AAAAwFZ3dXnkCXQpwO+e6lFlHgAQAi0R4SID+6pMbtTYzRifPNOr1IxWSpNT46QdIkuRwGHrgllUKcDn06MuWWjp6x7w2r6RBkpSzLHrEuc0r43X71RlqbO3Rf/w6V719/WM+p6ymTf/06CFVNXTqhi1p+tqnNsqQtC+3atrfAwAAAFysq6dPwYGLLzySCJAAYFEJDHDqC3es0dXrkiVJkaEBk949bTyJ0cG6Y1em2rv69MhLBbJte8Q1fd5+5Zc1KSkmWLERo+/+dvOOJboiJ1Fnqlr11BtFo17T2tmrf338qJrbe3X37izds2e5YiM9yl4apdPlLapp6vTbdwEAAACS1NnjlSdwce4aTIAEAIuM0+HQ/Teauv9GU/fdYPr9+Xs2p8pMi9TR0/Xaf7JmxPnC8hb19vm0JiNmzGcYhqH7b1ypxOhgvXm0UhX1HSOu+c27Z9TR7dUnrs3UR7amDx/fuWagH51VSAAAAPAnn22ru6dfwUGLcz8yAiQAWIQMw9Cu9SnasCLO7892GIY+e3O2At1OPfZqoZraLpxjNNy+ljGyfe18bpdDd+3Oks+29dTrF65Cqqjv0FtHK5UQHazrN6ddcG7jijgFBTi1L7daPt/IFVAAAADAVHT3eGVLCg4kQAIAwC/iIz26a3eWOnu8+tmLF7ay5ZU0KsDlkJkWecnnrMuMUfaSKOWWNAwHT5L0qzeK5LNt3XVt5ojtUwPdTm3NjldTW4/yy5r891EAAABY1Dq7B3b69RAgAQDgP9esT9bqpQPhzzsnBtrJGlu7VVHfoZVLouR2XXr4oGEYunt3lgxJT75epH6fT3lnGnSiuEHZS6K0Pit21PtoYwMAAIC/dfYMBEi0sAEA4EeGYeizH82WJ9CpJ/aeVn1Ll/LONEoaffe1saQnhOnKtUkDbWvHKvXk60UypIFgyTBGvScrJULxUR4dseqG/6QIAAAAmI6uoQCJFUgAAPhXdHiQPrlnhbp7+/XTFwp0onigDW28Adqjuf3qDAW6nfrlq6dVUdehnWuTlJ4QNub1hmFo55ok9Xp9OmTVTusbAAAAAOnDFrbFugJpcX41AGDW7FyTqMNWrY4PhkdxkUGKj/JM6hkRoYH66I4levbtEgW6nbr96oxLvzcnUc+9XaIX95dqaWLYuIHTWPafqtaTewfmLQ0JDnLrL+9er5iIoEk/DwAAAPPXUAsbM5AAAJgBhmHo/ptWKmTwT2pyMmLGbD0bz0e2pGnD8ljde91yRYYGXvL66PAg7d6UqpqmLv3dzw7q0VcstXf1TeqdL39wTm2dfQr1uBXqcSvA5VRNY6fey5vYbKW2zl499mqhyuvaJ/VeAAAAXH46aWEDAGBmRYYG6v4bVyowwKntqxKm9IwAt1NfuGOtrlqXPOF7PnX9Cv3Pu9cpISpYbxyp0Nd/tF9vHquQz2df8t6apk6V1rQpJyNa//Tgdv3Tg9v19w9slcvp0IGCS7fF2batn79kae/hcj30bJ56+vonXDcAAAAuP120sF2aaZrbJH3bsqxrRjkXLOlVSQ9YllVw3vF4SYclXW9ZVoFpmk9IShw8vVTSfsuy7jFN85uSbpbklfQXlmUdmMb3AAAuU5tXxmuTGTel1UfTkbMsRn//QJReO1Su3+w7o0desvTWsUp9+voVykyJGPO+g/kDIdGWlfHDxzyBLq3JiNbR0/WqqO9QSmzImPfvP1WjI4V1CnQPrFp65q0SffK65f77MAAAAMwqdmG7BNM0/0rSjyWNGPZgmuZmSW9LyrzouFvSf0vqGjpmWdY9gwHUbZKaJX3ZNM2NknZJ2ibpHkkPTfVDAACXv9kOj4a4nA7duC1d33pwu3asTlBpdZv+6dHDevj5U2rp6B31ngP5tXI5DW1YHnvB8S3ZA4HSwfyaMd/X1Najx14pVKDbqb/5w01KjA7Wa4fOySpr8t9HAQAAYFYNDdFmBtLYiiXdPsa5QA0EQgUXHf+OpB9Kqhzlnr+T9J+WZVVJulLSK5Zl2ZZllUlymaYZN6HKAQCYpKiwQD34sdX66qc2Ki0+VPtyq/XNnxwYMRupqqFD5XXtylkWo+Ag9wXn1mXGyu1y6GBBrWx7ZCvcQOtagTp7vLprd5ZS4kL1wC3ZkiE9/Hy+unu9M/qNAAAAmBldi3wG0iW/2rKsX5umuXSMc/skyTTN4WOmaX5GUp1lWS+bpvm1868fbGvbI+nLg4fCJTWcd0mbpAhJdePVFBUVLJfLeanSAQAYVVxcmLavT9WjL5zSr98o0mtHKvTgx9cMn3/t6MCff+zZmq64uJG7t21ZlaD3TlSps19amnTh+Vc/KNWJ4gatXxGnT1xvyjAMxcWF6Y7yVj39+mn97v0yff7OdTP7gVPQ2tGrwwU1OnSqRmeqWrVySZS2rErU+hVxi/ZP2QAAAM7XNzhHMz01Si7nwhspPdr/955vJv6P8HOSbNM0r5O0XtIjpmneallWtaQ7Jf3SsqyhSaKtks6vMEwD7W3jamrq9HPJAIDF6IZNqXrnaIWe33dG27PjlRgdLEl66/A5uZwOZSSEqq6ubcR9a5dF670TVXr5vRLdfvWHXdx1zV360XO58gQ69enrlqu+/sPd167fmKL3cyv14vtnVVXXLodjoJ3PMKTrNqXKTI+a2Y8dQ2l1mx57rVDFFS0aWlDlcjp0rqZNrx4ok8tpKHtJtO7enaXkcWY+AQAALHQtbT0KdDvV1Ngx16X4XVxcmOrq2sYNkfweIFmWdfXQz03TfFPSnw6GR5J0naR/PO/yfZL+xTTN70hKleSwLKve3zUBADAat8uhT1ybqYeezdOv3ijSF+5Yq4q6dlXUd2jjOCtv1mXGKsDt0MH8Wt12VYYMw1BXj1f/8esT6u7t1wM3Zys6PGjEu/7o5lX67pPHdKzowl/qmtt69I0/3Dxj3zmWrh6vHno2Vw0t3cpMjdC6zBity4pVckyIzlS16nhxvU4UNSi3pEEllS36wh1rtSItctbrBAAAuBx09vTJE7h4u6EmHSCZpnmvpFDLsn40hfeZkkqG/sKyrMOmab4j6X0NzGP6syk8EwCAKdu4Ik4rUiN09HS98kubhgddb82OH/OewACn1mXG6mBBrcpq2pUaH6If/uakKuo6tGdTqnauSRr1viWJYfrun+1UT1//8LHvP5urgrJm1bd0KTbC49+Pu4RfvVGk+pZu3bxjie7YdcF+GMpMiVBmSoRuvzpT+3Kr9LMXC/SdJ47pjz+2SptXjv33BgAAYKHq6ulXeEjAXJcxZ4zRBoBe7urq2uZf0QCAy9aZqlb9w88PKS0+VH1enxpbu/XvX7xSQQFj/znLoYJaff+5PN20PV19fT69drhcORnR+tKda+V0TLwn/q1jFfr5S5buujZLN25L98fnTEjemQb93yePKzUuRP/7/i1yu8avOe9Mgx56Nk+9vf26Z89yXbEmccLvMjSwW8lYu/DZti1bkmOOdukDAAC4FNu29eC/vKmM5HB9/b5Nc12O353Xwjbm/5AxFRMAsOgtSwrXFTmJei9voON688r4ccMjSVqbGaNAt1N7D5Wr1+tTSmyI/vTWnEmFR5K0yYzXoy8X6kB+zawFSJ3dffrpCwVyOgw9cPOqS4ZHkpSzLEZfvXej/u1Xx/X43tN6fO/pSb0zIzlcn7p+hZYlhQ8fs21b+0/V6Ok3i5USG6Iv37VuzJAJAABgLvX09ctn2woOWrwxyuL9cgAAznP71Rk6VFCrXq9PWyfQohXgdmr98lh9cKpGYcFuffHOtVP6H4pQj1urlkYp70yjapu7FB85so3Ntm1V1nfoeHGDcosb5HAYWpMRo3VZMUqMDp506PL43tNqauvRH1y5TEsSx99t43xLEsP0jfs26bfvnlHn4Da2E9HV41VBWbP+8eeHdOXaJN2xK1PN7T167NVCnS5vkSQ1tfXoYEGttmYnTOpbAAAAZkNXz8AIgsW8O+3i/XIAAM4THR6ku/cs16GCWq3NjJnQPddtTlV1Q6c+dcMKxY0S/EzUlux45Z1p1MH8Gt28Y+nwcdu29cL+Ur11rFL1Ld2SBtrBbEn5pU166o0ixUd5tHtjqm7YknbJ99i2rbePV2pfbrWWJITp5h1LJl1rXKRHD9yyatL3FZQ26bHXCvXOiSodLKhVT1+/bHtgBtXujSn6t6eO6+k3i7VheazcrsU7nBIAAFyeOrv7JEnBBEgAAODaDSm6dkPKhK/PTI7QNz+7Zdrv3bgiTo+8ZOlgQe0FAdIH+TX69Vsl8gQ6tWVlvNZlxSgnI0a2z9aJkgadKGpQ3tlGPbH3tOKjPFqfFTvmO8pr2/XYq4WyzjUrwO3QA7dky+WcXLvddKxcEqX/77Nb9MaRCj33zhklRAXr3uuXK2fZQFh33eZUvXzgnF47VK6btk8+2AIAAJhJQ6uvaWEDAABzJiTIrdXLonWiuEE1jZ1KiA4eaPF6pVABboe++Zktio8KvuCeq9Ym66q1ySqvbdff/eygfv5igbL+aJv+f/buOzzOu8z3//uZojLqvUu22li2JfcW18TpDgmknwAJEGCXXTYsZ3c5P1jglGV3YeFwFnaXZSGhpEB6AnFIc4njbstVLhp1yeq91ym/P1RiWZIt2+r+vK5LF2GeZ2a+I2tGms/c9/3197UOO6+z28mbe4vYdbwCt8fD0tRwHr01bdRWuclmNpm4dWUCm5fGYTYbw4Zmf+KmeezPqeatAyWsz4y5oXc4ERERkZmns7s/QLqRW9im7qNHERERGdPqjP65S0dya/F4PPz2nVw6up08tCV1RHh0sfhIfz65cT4tHb288EHesGN1zV38w7PZ7DhWTniwD3/9UBZPPZg1LeHRxawW04gd12w+Vu7bMJ/uXhdv7iueppWJiIiIjK5rsAJJAZKIiIhMp6WpEVjMBkfP17A/p5pThQ1kJIVw8/Irt9TduSaR5NhADp+rITu3FoCS6lb+8blj1DR2cvuqBP7hyTVkpYzd4jYTbF4aS3SojT0nK6ioa5/u5YiIiIgMUQubAiQREZEZweZjITM5jPK6Dp7/wIGPl5kv3J0xolJnNGaTiSe3ZWC1mHj2PQcHz1TzgxdO0NbRy2O3pvHo1jSslpn/K99iNvHwLal4PPDKh4XTvRwRERGRIYMtbKpAEhERkWm3akF/G1tvn5tHt6YRFuQz7uvGhPnxwKZk2rv6+OX2c7jcHr7yycXcuvLKu7PNJEtSwkiKDuBMUSPdvc7pXo6IiIgI8HEFkmYgiYiIyLRbkhpOoM3KsrRwNmbFXPX1b12VwKL5ofj7WvnbR5eyciCQmk0Mw2BhUghuj4fCitbpXo6IiIgIcNEMpBu4he3GfeQiIiIzjK+3hX/5yk1YzCaMcbSuXcpkGHz9oSW43J5Z0bI2lvSEYN45XIbjQjOL5odO93JERERE1MKGAiQREZEZxctqvq7rm0wGJtPVh08zSVp8EAaQd6F5upciIiIiAmiINqiFTURERGYYm4+VhEh/iipb6XO6pns5IiIiInT1OLGYDayW6/uwbzZTgCQiIiIzTnpCME6Xm+KqtuleioiIiAid3c4bun0NFCCJiIjIDJSeEAyojU1ERERmhs4eJ74+1ulexrRSgCQiIiIzjgIkERERmUlUgaQASURERGagQD8vokNt5Fe04HK7p3s5IiIicgPrc7pwutzYvG/c+UegAElERERmqPSEYHp6XZTVtHrw0FQAACAASURBVE/3UkREROQG1tnTv6mHWthEREREZiC72thERERkBujs7gNQC9t0L0BERERkNJqDJCIiIjNBZ48TAJuPAiQRERGRGScsyIewQB/yy1twezzTvRwRERG5QXUNBEi+qkASERERmZnSE4Jp7+qjqr5jupciIiIiN6jO7oEKJAVIIiIiIjNTekIQoDY2ERERmT5qYeunAElERERmrME5SA4FSCIiIjJNBlvYVIEkIiIiMkNFh9oItFlxXGjGozlIIiIiMg0GW9g0A0lERERkhjIMgwVJIbS091KpOUgiIiIyDdTC1k8BkoiIiMxomclhAOQUNU7zSkRERORG1KUh2oACJBEREZnhFs0PBeBMccM0r0RERERuRKpA6qcASURERGa0YH9vEiP9ybvQTE+va7qXIyIiIjeYzh4nJsPA22qe7qVMKwVIIiIiMuMtTg7D6fKQW9Y03UsRERGRG0xXtxNfbzOGYUz3UqaVAiQRERGZ8TKTB9rYNAdJREREplhnj/OGb18DBUgiIiIyC6TEBeHjZSZHc5BEREY4dK6a1z8qxOPxTPdSROakzh4nNm/rdC9j2ilAEhERkRnPYjaRkRRCbVMXNU2d070cEZEZo6fXxfPv5bH9QCmFla3juk59SxfvHCqlsKIFt1uhk8jluNxuenpd+Hrf2POPAFSDJSIiIrNCZnIYJ/LrOVPUSNQK23QvR0RkRjh4rnpoh6hdx8pJjQu67PntXX386MWT1DZ1AeDvayUrJYyF80Lwtn789tDPx4I9MfiGn/ki0tXTv4GHzUcVSAqQREREZFZYPDAHKaeoga0r4qd5NSIi08/j8bDrWDlmk0FIgDdHc2t55JZUgvy9Rz3f6XLzszdyqG3qYkNmDCYTnCps4MCZag6cqR5x/j03zeP+TcmT/TBEZrTBgNbmrfhE3wERERGZFcKDfIkJs5Fb1kSf04XVolJyEbmx5V1opryug9UZkdgTgnnu/Tz2nKzk3g3zR5zr8Xh49j0HuWXNrEiP4HN3L8BkGLg9Hspq2iisaB3Wzvb+0Qv86WApy9LCmR8TOJUPS2RG6eoeCJA0RFszkERERGT2yEwOo7fPTV55y3QvRURk2u08XgHALcvjWbc4Gl9vM7tPVuB0uUec++6RMvadriIpOoAvfmIhpoHWNJNhMC86kK0r4rltVcLQ1xe2ZeD2eHh6+zn6nK4pfVwiM0lndx8AvqpAUoAkIiIis8dgG9uZIu3GJiI3tqa2Ho476kiI9CctPggfLwvrF8fQ0t7L8by6YeceOV/Dq7sLCQnw5qkHsvC2XrmCMyMphK0r4qlq6OSNvcWT9TBEZjy1sH1M3wERERGZNewJwXhZTBzNrcXH6+M/Y6JDbazOiNSwVxG5Yew+UYHb42Hrivih176bl8ex41g5u46VszojCoCdx8r53Qd5eHmZeeqBLEICRp+PNJoHN6eQU9TAe4fLWJ4WQWp8EB6Ph7KadvIuNLNyQeRV3Z7IbDQUIKmFTQGSiIiIzB5Wi5lF80M5kV/PH/YN/0S81+liY1bsNK1MRGTq9DndfHSyApu3hTULo4YujwnzY9H8UM4WN1JW08bhczW8c7iMQD8vvv7QEpKiA67qfry9zDy5LYPvP3+cp7efY0FSCKcL62lu7wXgw5MV/P1nV2h3KpnVaps6sVrMY4ahQzOQVIGkAElERERmly/es5DS6jYGR7329rn4xVtneXFnPguTQgkL8pnW9YmIuNxuzKbJmxaS7ailtbOPO1YnjGhH27oinrPFjfzoxZO0d/URHWrj6w8vISLY95ruKy0+mDtWJ/LukTJqm7vw97WyblE0Ho+HQ+dq+M83z/DXDy+Z1McrMlmOnK/h6e3nMAyDu9YkctfapBHPqcEKJM1AUoAkIiIis4yvt4UFSSHDLnv0ljR+/U4uv37nPH/zyFK1sonItDlf0shPXjvNF+7OGGojm2h7TlZiADcvixtxLCs5jPAgH+pbukmJC+RrDy7B3/f6KoTu35xMTJiNmHA/kmMCMZkM3G4PXT1OThU28LsP8vnM7el67ZVZ5f2jF3hpZz7eXma8vcz8cX8J+3OqeXRrGsvTw4d+ntXC9jHFxCIiIjLrbciKISsljHMlTXx4omK6lyMiN6jObie/+tN5evvc5JY1T8p9OF1uiipbSYjyJzLENuK4yWTwxXsWcu/6efzdo8uuOzwCsJhNbFwSS2pcECaTMXQ/X753EfER/uw+UcGOY+XXfT8iU8Ht8fDiznxe3JlPoL8X/9+nl/NPX1rLXWsSaW7v4T/eyOHHL52kqqEDUAvbxfQdEBERkVnPMAyeuHMB333mMC/vLmRRchiR19iuMRWyc2upb+nm9lUJQ2/GRGT2e3FXPg2tPUD/XJXJUFnfgdPlZt5l5hmlJwSTnhA8Kfd/MV9vC197MIt/eDabF3fmk1vaNPSaNlghlTEvdNLXITJerR29PPe+g2OOOmLC+ts7w4P6/1546OZUNmTF8Psd+ZwpbuS7zxzhtpUJNLb1P6d9VYGkAElERETmhpAAbx67LZ1fvnWOX719nm88tgzTDGyn6HO6+fU7uXT1OCmoaOHLn1iI1zi21BaRme1UQT37TleRGOlPc3sPtU1dk3I/pdVtACRFB07K7V+tsCAfnnogi//38klO5NcPO9bW2acASWYEl9vNruMVvLm3mK4eJ2nxQfzVA1kjKvRiwvz4+sNLOJlfz+935vPukbKhY75eik/0HRAREZE5Y+3CKI456jieV8eO7HJuX5Uw3Usa4WxxI109Try9zBzPq+OHL56YkBklIjJ92rv6+M27uZgH2seef99BfkULfU43VsvETg0pqekPkC5XgTTVkmMD+fFX19Pd6xq67J+eP05pTRtuj2dGhvly48i70Mzz7zsor+vA19vCY7emcfPyuDEHvxuGwbL0CBbND+Xdw2W8fagUf1+rKobRDCQRERGZQwzD4PE77Pj7WnltT+HQ/IKZ5EhuDQBff2gJaxZGUVjRyj89d4y65stXK3g8nssev1out/uKxyf6PkXmqt/tyKOlvZf7NswnPrJ/NpHHA/UtE1+FVFrdhtlkEB/hN+G3fT2sFjMBNq+hr/nRAXT3uiatEktkPAoqWviX352goq6DjVkx/POX13LryoRx7RroZTVz74b5/ODP1/HNTy+fgtXOfAqQREREZE4J9PPi8Tvs9Dnd/Ort87jdMycE6e1zcSK/nvAgH9Lig/jSJxZy55pEqhs7+eHvT4y51vePXuDr/7aPxtbu615DT6+L1/YU8hc//oifvHKKmkvmtLjcbj7IvsDXfrKPf3zuGMVVrdd9nyJzWW5pE4fO1jA/JoC71iYCEBnSP1NlosMTl9vNhdp24sL9sFpmdutr0kCF1GDLnchU6+lz8cz2c3g8Hr7+8BI+f3cGgX5eV307wf7ehM/guYpTSQGSiIiIzDkrF0SyOiOSwsrWYfMLpltOUSM9vS5WZURiGAYmw+Dhm1NZszCK+pZuyuvaR71edm4trZ197L6OHeY8Hg9Hc2v51i8P8fbBUgwDThU28J2nD/PankJ6el3kljbxv359lN/vyMfp7t/p6Xu/zeY375yntbP3mu9bZC475qgD4MHNKUNVDZMVIFXVd9LndA+FMzPZPAVIMs1e31NETVMXt61KYHFy2HQvZ07QDCQRERGZkz5zux1HWTNv7i0iKyWM+Aj/6V4SRwfa11YviBp2eUZSCIfP1VBY0UJi1PA3hn1ONyXV/VVAe05Wcu/6eVddedDe1cd/vnmG86VNWMwG29YlsW1dEqcLG3hpVwFvHyzlwxMVdHQ7MYBNS2K4f3MKlXUdvLAjj49OVZGdW8dn7khn7cLoa/8GiMxBOcUN+HiZSbto17OoEBsw8QFSydAA7ZkfIA2+lg2+fsmVOcqaOJZXR0ZSCAuTQvH2+vi1vrapk1OFDTS0dHPzsjiiQm3TuNKZz1HWxI7sC0SH2rh/U/J0L2fOUIAkIiIic5K/r5Un7lzAT187zTPbz/P3j6/AYh5ZfO10ucnOrWVZWsSwP9YnWk+fi5MF9USG+JIYNTzMSo0LAvpnNdy8PH7YsbKaNpwuD1aLifauPo6cr2V9ZsxV3fdz7zk4X9pEZnIYj92WNvTmdnVGFEtSwnn7UAnvHi5jfkwgn7k9nfkx/bs7BSZ58b8+v2pg55oinn7rPP4+Vn2SKzKgpqmT2qYulqWFD3t9iRhod6lp7hzrqtekdBYFSL7eFqJCbZTWtOPxeDA0SPuynC43T28/T0NrNzuyy7GYTWQkhRAV4svZkkaqGj7+Wdp1vJw7Vidyz7p5k/p7a7bq7nXyzNvnwYAn78nQTqcTSC1sIiIiMmctTQvnpsXRlNa0kZ1bO+o5e05W8ou3zvHb93IndS2nCxvo7XOzakHkiDdS0WE2/HwsFFaM/KS+oKIFgHvXz8Mw+t84XI0j52s4mltLalwQX3swayg8GuTtZeb+TSn8219v4tuPrxgKjwaZTSZuW5nA1x9aislk8J9/OENF/cwbTi4yHc4UNQKQeUmoavOxEGCzTnwFUk0rJsMgYQZUVI7HvOgAunqc1F5hkwCBQ2draGjtZnVGJNvWJREdaiOnqIEdx8ppaO1maWo4T9xp50ufWEignxdvHyzlW788xJHzNdO99Bnnld2F1Ld0c9eaJFJig6Z7OXOKKpBERERkTrvnpnkcOFPN/pwq1i4a2X61L6cK6P/jfUV6JCvsEZOyjsE/8ldnRI04ZjIMkmODyClqoKWjl6CLhnwOBkhrMqIoqmzlRH49RZWtJMcGjridS7V09PL8+3l4WUw8uS3jslsQe1/hE9rU+CC+cPcCfvHWOX7yyim+/cRKAm1XP4xUZC45U9QAwOLk0BHHIkN8Kalqw+lyj1r9eLVcbjcXatqJDfebNRUVSVEBHD5XQ2l124jweiZwuobvNmkYxoT8W10tt9vD2wdLMJv65+KFBvrwwOYUGlq6qW/pIjk2cFjr8vK0CN4+VMq7h8v4+R/O4m01syQ1fMrXPRPllzez+0QFcRF+3Ldh/nQvZ84ZV4Bkt9vXAD9wOBxbRjlmAz4AnnQ4HLkXXR4JHANuczgcuQP//5dACGAGHnc4HIV2u/2nwHpgcLrafQ6Ho+U6HpOIiIjIkOhQG6lxQZwraaKxtZvQQJ+hY+W17ZRWt5EUFUBlQwfPvpdLWkLQhAcj3b1OThc2EB1qG3Pr7dS4QHKKGiisaGF5en+I5fF4KKxoIcjfi7AgH25ZEc+J/Hp2HisnOXbhZe/T4/Hw7Lu5tHf18d9uTZuQeRlrF0VT3djJH/eX8O+v5/B3jy7DalFBu8wtu46X89aBEr792ZWEBfmMeV6f0835siZiwmyEB43coSky2EZhRSuNrd1ETkB4UtXQSa/TPTScejYYXGtJdduo4flYPB4P//56DnXN3XzniRWTsuNcYUULP/jdCZwu99BlFrPBZ263s2lJ7ITf3+VkO2qpaepi05LYYb+jwoJ8Rv0Z7K8cTWZJahj/+Owx9pysVIAEuD0eXtxZAMATdyzQ76dJcMXvqN1u/wbwNDDiJ9dut68EPgJSLrncCvwXcHGt4r8ALzgcjk3At4EFA5cvB+5wOBxbBr4UHomIiMiEWp8ZjQc4cKZ62OX7z/RXH21bl8T9m5Jp6+zj+fccwz6RnggnC+rpc7pZnTGyfW3Q4BykwoqP/xRqaO2mub2X1LggDMNgYVII0aE2jubW0Npx+V3RDp6t5kR+PQsSg9m6Iv6y516N+zbMZ3VGJAXlLTz/vmPCbldkJvB4POzILqelvZddJy7fLppf3kxvn3tE+9qgqAneiW02zT8aNDhI+2p3YvvwZCUn8uspr2vng+yra9sdr49OVeJ0uUlPCGbx/FAWzw/Fy2LmhQ/yqGqYujZdt8fDWwdKMAy4e23iVV03JTaIxCh/Thf2V6/e6A6fq6G4qpXVGZGkxqt1bTKMpwKpELgfeG6UY97Ap0Y59iPg58A3L7psPXDabrfvAEqAr9ntdhOQBvzCbrdHAc84HI5fXWlBISE2LJOQQouIiMjcdNeGFH6/s4BD52r43L2LMQwDp8vN4XO1BNis3LpuHiaTiZziRrIddeRWtLJp2eihi8vt4fXd+cRH+rN2ccyIQKi7x8lb+4qoqGsfuiy3pAmA22+aT0TE6G/+/AN9Mb10ktLa9qFzzl3oD5OWpEcOXXbf5hT+640csgvqeeRW+6i31dDSxe935OPrbeZvPrOSqLDRq56u1TeeWM03frqXvaeruHdLKguSRrbvyOzn8XjYd6qSts5e7r7pxmgFKa5sobqxf1jxvtPVPPnJrDHbOwsPlQGwfln8qM/rlKRQ2FdMZ597zOf91ahpKQZgaUbUhNzeVIkJ96Ostp3wcP9xDdKubujgld0F+PlaMRkGbx8s5d4tqYQEjF0NdrWcLjcn8usJDfTmh09tGmrv3X+6ku//9ijPvp/HD766EfNl2n4nyqEzVVTUdbBlRTyL0sdfpTXojnXz+OWbZzhT2swnN6dc+QpzVHevkzc+KsJqMfHl+5cQoV3qrsmVXluuGCA5HI7X7Hb7vDGO7Qew2z/+48Vut38OqHM4HO/Z7faLA6R5QJPD4bjVbrd/F/gfwA+BfwN+TH9b22673Z7tcDhOX25NTU0Tu5uBiIiIzH3L08M5dLaGQycrSI0P4mR+Pc3tPWxdEU/zwN8Wj9+eznd/dYSfvXqKmGAfgv29R9zOO4dKeeXDQgAykkJ47LZ04sL98Hg8HM2t5aVdBTS19Yy43vyYQGxmg7q6sT+Jj4/wJ6+smarqFixmEydy++cmRQf7DF0va14IPl5m3t5XzKbF0SPmdXg8Hv7fK6fo6Hby+B12zG73Ze/zWj18cwrff+E4P3/tFN/6zArtsDTHlNW08cIHeeSX94eYieG2GTnDZqK9d6A/pImP8KO8roO3PypgY9bo7UxHzlbhZTERHeg16nPM19z/nCi80Dwhz8Hc4kZMhkGA1TQpz+nJkhDhx5H6Ds4V1BEZPLLV72Juj4cf/e4E3b0uvvQJO53dTl74II9fvZnD43cuuOx1r8bpwgbau/q4dWE8DQ0fh/3pMQGsWRjF4XM1PP/2We5emzRh9zkaj8fD7949jwFsXRZ3Tf+uixODMZsM3jtYzE0ZETfsa/FbB0qob+nm7rVJmFyuWfUcmSkiIgKoq2u7bIg0GU2BXwBus9vtHwJLgWftdns00AD8ceCct4CVQCfwE4fD0elwONqAXcCSSViTiIiI3ODWZ8YAHw/N3j/wvxsGLgeIDLHx0JZUOrqd/OyNM/Q5XcNuo6KunTf2FhHo50VmchjnS5v4n88c4YUP8vjh70/w8z+cpa2zl3tuSuL7f7aWH/z5uqGv/+/Ty6+4xpT4IJwuN6U1/X/4FlS0YDEbJEV9/Mecr7eF9ZkxNLX18MZHRSNu46NTlZwpamTR/FA2L528OR7pCcGssEdQWNHK0TF2uJPZp6O7j+fed/C/f3OU/PIWIoL7qz4Kyuf+lInBENjLauIrn1yMYcDOY+WjtrQ2tnZTUdeBPTFkzPk8kUMtbNf/4bfb7aGsto3YcNusGaA9aLDlbjxtbDuzy3FcaGZ5egRrF0axZVksMWE29pyqpPyiqs7rdTR37E0NPn1bOkF+Xry5t2hC7xOgs7uPosrWoa+9p6sormpjuT2CuPBrqxQNsHmxNDWc8roOymomdr2zRUt7D386WEqAzcq2dZMb+t3oJjxAcjgcmxwOx+aBgdsn6R+WXQ3sA+4eOG0TcBZIB/bZ7XbzwNykDcDxiV6TiIiISEZiCKGB3hzNraGxtZuTBfXER/iRGDV8O+xblsexZmEUBRUt/Oad3KE3j06Xm6ffPo/T5eGJO+389UNZPPVAFmFB3uw8Vk5uWTNLU8P53hfXcP+mFCJDbEQE+w59jWeY58dzkFrp6XVxoaadpOiAEdf95Mb5RIfaeOdwGR+dqhy6vL65ixd3FeDrbebzdy2Y9E+iH9qSgsVs8MruwhFhm8xOz2w/z+7jFUSF2Pjvjyzhz+9bDEBhZes0r2xsHo9n1K+rVVbTTm1TF0tTw4kJ82NZWgRlNe2jPvYzxY3A6LuvDfL3teLnY5mQLeyrGjro7XPPqvlHg+ZFDQ7SvvzPUFVDB6/uKcTf18rjd9gxDAOzycQjt6Ti8cDLuwqueF/j+Xfvc7o5nldPSIA3yXEjd7P097XyxF0LcLo8PL393LAh29fK5XbzQfYF/u4/D/K9Z7OHvn7zTv8eVPesm3ddt3/pByQ3mtc/KqKnz8WnNibj662N5ifTVX937Xb7Y4C/w+H4xVVe9W+Ap+12+1eAFuAxh8PRZLfbXwAOAX3Asw6H4+zVrklERETkSkwmg5sWR7P9QCk//+NZXG4P6zNHzjAyDIMv3L2A+uYuDp6tITrUxifWz+dPh0oprW7jpsXRLEvr3yVtaVo4i+aH8NGpKqJCfFk8xjDd8UoZCJAKKlpIivLH7fEMhUoX8/Ox8rWHsvjeb7N57j0HkcG+pCcG86s/naen18UX7s4YtpPPZIkMsXHrigTePVLGB9nlk97uIZPL6XJzrrSR6FAb/+fJ1VjMJpwuN14W04ytQOrs7uPHL5+i6JKQJzM5jK8/fHWNDUcGqlJWLYgEYOvyOI7n1bHrWPmI52FOUQMAWVd4zkeG2LhQ24bb7Rmas3MtSgaqd+ZFjww8ZrrxVCDlljbx2/cc9DndfOmehQT6fbwTZmZyGIvmhXCmuJHThQ1kpYz+Pf8g+wIfHL3AX34q87JB29mSRrp6nGzMisE0Rsi+NDWc9ZnR7M+p5mdvnOHTt6Vfdke+y3GUNfHCB3mU13Vg87Zw64p4LBd9KBAX7nfdweDi5FACbVYOna3m4ZtTb6jdx2oaO9l3uoq4cD82Lom58hXkuowrQHI4HCXA2oH//t0ox7eMcb0tF/13KXDbKOf8C/07tImIiIhMqvWLY9h+oJSC8hZMhsHaRdGjnme1mPnqA1l877dHeWNvMS63h7cPlhIS4M1jt6aNOHeidjmLCPIh0M+LgvJmEiP7K6NGC5AAokJsfPX+TH704kn+440cNmbFDlVBrc8c/XFNhntuSmJfThXbD5SwPjOGoIve+MnsUl7XTm+fG3ti8NBsLYvZxLyYQPIvNNPV45xRn+47XW5+9uYZiipbSYz0x+bTv7bqxk5yihqobuwkepyDdD0eD0fP1+LtZR7aVW1BUgix4X4cza3lkVtSCRqYieZ0uTlX0khEsM9Qm9pYokJ8Ka5qpbGtm/Cgy597ObNxB7ZBNh8rkcG+lFa34fF4hoX2ja3dvLy7gCPnazGAO9cksnIgwBtkGAaP3JLG//z1EV7alY89MXjEYPOymjZe3lWAy+3hJ6+e4jtPrCIkYOQMO4Cj5weCwozIUY8P+m9b06lt6uJkQT3nShrZti6JO9ckYrWYqWns5FRBPWdKGkmPD2bbuqQRH0b0Od08+14u+3OqMYBNS2K5f3MygbaJf420mE2sWxzNe0cucLqwnhX2yz+2uSS/vAUP/dXDZtONE5xNF32HRURE5IYRFWojbWBr36yUsMuGHUF+XnztwSV4e5n54/4SXG4Pn79rATYf66StzzAMUuOCaG7vHZorlDJGgARgTwzh8TvtdHQ7efdIGX4+Fp640z6lQ1RtPlbu2zCf7l4Xf9g7ciaTzB6FFf1VPJeGlqlxQXiAoqqrb2NraOnm7YMldHb3TcAKP+bxePjdB3mcK2liaWo43/3cKr7x2HK+8dhyHhjYiWowKBiPkuo26lu6WZYaPjRjyDAMti6Pw+X2sGegVbS9q49dx8rp6nGxODnsis+1wYCppun62thKatowDEiI9L/yyTNQUnQAHd1O6lu6gf5w5e2DJXzrl4c4cr6W5NhAvv3ESh6+OXXU68dH+nPLsniqGjp5Zvs53Be1qvU53Ty9/Rwut4fVGZE0t/fy01dP09M7sq22z+niRH49YYE+JMdcvprL5mPhf3x6OU9uy8DH28Ibe4v5+18e5pu/OMQ3f3GIF3cVcKaokdc/KuLXf8od1urW2d3Hj186yf6capKiA/j2Eyv53F0LJiU8GrR+cX/1zf6c6km7j5locE5VQtTsC1dnIwVIIiIickMZrBa6ZXncFc+Nj/TnK/ctwmI22Loi/rpb1MYjZWAmx4XadsKDRt8J7mIbs2K556YkzCaDx+9cMFQlMZU2L40lKtTGvpwq2rsmNiiQqVNQ0d+mdmloOTSb6yrb2Mpq2vjec9m8tqeIN/cWT8wiB3yQXc6HJytJiPTny/cuHNYetiwtAovZuKrh7kfGqEpZtzgaX28zO4+V8/3nj/G1n+7lxV0FmAyDNaMMYL7Ux4O0rz1AcrrclFW3ERfuN6LyZraYd1Eb2+nCer7zzGFe21OEj9XMF+7O4FufXcH8KwQ6j2xNJT0hmGxHHW9eFFb/cX8x5XUdbF4ay5/du4iNWTGU1rTxy0uCJoCcoka6e12syogcV9BuMgzWZ8bwz19ey+2rEmhq66G5rYfl6RF8/q4F/J8vrGZedAD7cqr46Wun6e510tjazT8/fxzHhWZW2CP41meWX/GxTYT4SH+SogM4XdhAxQQP/57JBgOkax1CLldn5tSgioiIiEyB1RlRLJofit84K4myUsL5yVMb8fGamjduF1d/jNW+dqn7N6Vw15qkaWsvsphNbF4Sy8u7Czh8rmbCWvpkahWUt+DvayXqkraswUHDgwHTxdxuD83tPYQEeA97Q362pJH/eD2Hnl4Xvt4WPjpVybab5k1Ii+OJ/Dpe2pk/UCWYhY/X8J97m4+FxfPDOFlQT2V9B7FXeGM5uPuar7eZxfOHh8Q+XhY2ZMbyQfYF2jtbSI4LZElKOMvSwomLuHI1UGRIfwvd9ezEVlrdRq/TTVpC8DXfmrc10AAAIABJREFUxnQbbL17/n0HrZ19mAyD21YmcN+GeeOu6rSYTXz1/ky+99tsth8oJTrURlSojT8dKiU8yIeHb07FMAw+e4eduuYujufV8dqHhTy4JWXoZ3MwKFx9hfa1S/l6W3h0axr3bZiPxWwaNmPoG48t4+d/OMvpwga+/8Jx2jr7aGrr4dYV8Ty6Ne26Zl9drU1LYnnuPQffeeYIMWE2lqSEsyQ1jJS4oKG21LmmfODDlpnUXjuX6bssIiIiN5zxhkeDpvIP03nRAZhNBi6357Lta5ea7j+e1y2K4tUPC9mXU6UAaRZqauuhobWbpanhIyozAm1eRIX4UljZitvjGTZ4+I/7i/nj/hLCAr3JSg1nSUo4rR29/PbdXAwD/vyTi2nr7OX59/N4/2gZD20ZvUXpctxuD4WVLZwqaOBUYT0VdR14WUw89WDWmMPiV2dEcrKgnqO5tdy3Yf5lb7+wspXG1h5uWhw96vDhBzYns3BeCPNjA6+6BWkiKpDyLjQDYJ/lAZIBtHb2sSAxmMduSyd+HAHcpfx9+zcQ+Mdnj/Gbd3IJ8vPG44HP350x9BpoMZv4i09l8o/PZvPO4TIOnq1mSWo4i+eHcaqggYhgH5Kusd1ptNdZHy8Lf/VAJs++62Dv6f5d0B6+OZU7VidMaTsxwJalsVjMBify+uc2vXukjHePlGHztrA4OZQlKeEsTg4lYBJb6aZSS0cvrZ19LE0d/+9KuT4KkERERERmEKvFTFJ0AEWVreOuQJoJgvy9yUwO5VRhA+W17cTP0lktN6rCofa10VttUuOC2H+mmqr6jqHKm94+FzuPleNtNdPV42L38Qp2H68AwObd/6banhhCn9PFW/tL2HW8grvWJOHvO74A1+PxcPBsNa9+WEhzey/QHw5kJodx55rEy7YFLUkNx2oxceR8Dfeun3fZN/KHzw7ffe1SXlYzS1LDx7XmSwX4WvH1Nl9XgOQYCJDS4mdvgOTnY+Urn1yMyWSwLG1kSHk1YsL8+ItPLeb/vXyKhtZubl0RT0ZSyLBz/H2t/M0jS3ljbzE5RQ3sOVnJnpP9c6xWLYif8GDHbDLxubsWkBoXRJC/F1kp1/bzcr0Mw2BjViwbs2Lp7XORW9bMqcJ6ThfUc+R8bf+wcgO+eM9C1o2xicRsMti+pt83U0cBkoiIiMgMs21dEmeKG2fdwNz1mTGcKmxg/5kqHrkl7cpXkBljsD1trNAyZSBAKqhoGQqQDp+voaPbybZ1Sdy3YT6FFS2cKmygsr6Dh7akDJ1ntZi5Y3UiL+8uYOex8itWBEF/29YLO/IoKG/BajGxaUksS1PDyUgKwXsc7aS+3hayksM4lldHRX3HmNUubZ297M2pJCTAm0XzQ694u1fLMAwig21UNnSMqN4aD7fbQ355C5EhvmPuKjZbXLq72vVYOC+UP7t3EaeLGnhgS8qo54QH+/KlTyzE7fZQVNk6VL02nvl318IwDDYuiZ2U274WXlYzWSlhZKWE4bktnYr6Dk7k1/PGR0UcPlczNwKk2oEAKULzj6aKAiQRERGRGWZZWgTL0iKmexlXbWlaOP6+Vg6ereGBzSlzdubGXFRY0YLJMJg3RlXPYLBUUNHC5qVxeDwedh4rxzBgy9I4LGYT9sQQ7Ikho15/y7JY3j5Ywo7sC9y+KmHMlkuX282LOwrYdaIcjwdW2CN45JZUwoN8Rz3/clZlRHIsr44j52vHDJA+yL5Ab5+bBzYnTtrPa2SIL6U1bTS39YzacufxeNh7uor4CH+SY4d//8vr2unqcbIiffa9Hky2lQsixxVKmUwGqfFBpMbPnorOiWYYBvER/sRH+LP/dBX55S243Z4pnc80GYZ2YJtlH7bMZvqtLiIiIiITwmI2sWZhFK0dvZwpbpzu5cg49TldlNa0kRDlP+YuX7Hhfvh6mymsaAX65waV1bSzLC2CsKDR5xBdzMfLwu2rEujodvLhiYoxz3vnUBk7j5cTHWrjbx5dyl9+KvOawiOAJSnheFlMHM2txXPJblzQv9X6zmPlBNqsbJrEypErzUHKKWrkN+/k8szb50asc3D+Ufosnn8kM0t6QjBdPc6h8GU2K6/twGI2DT3HZPIpQBIRERGRCbMhMwaA/TlV07wSGa/S6nacLs9lZ26ZTAbJMYFUN3bS1tnLrmPlAFc1MH3rinh8vc28d6SM3j7XiOMXatv5w75igv29+NZnV7Bo3vW1lHl7mclKDaemsZMLtSPfLO88XkFXj4s7VieOGZxNhKEAqXlkgORyu3lpVz4AVQ2dFFe1DTs+FCAlKkCSiTEYRg7+bM1WLrebyoYO4sL9MJsUa0wVfadFREREZMIkRvkTH+HHyfx62rv6pns5Mg5Xmn80aHBXwBP5/bubxYb7seAqgg2bj5VblsfT2tnHs+85cLrcQ8ecLjfPbD+Hy+3hc3dlXPVOiWNZPdDidDS3dtjl3b1OPjh6AT8fC1uWTc5MnEFRITYAThXUj6gw2nOykqqGzqEWu4uDV4/HQ96FZkICvIkYR5WXyHgMhpGzPUCqbeqiz+kmPlLzj6aSAiQRERERmTCGYbA+MwaX28Ohs9XTvRwZh8JxBkiDM2Re2V2Ay+1h6/K4q97N6q41icyLDuDAmWp++tppunudAGw/UEJZbTsbs2LISgm7hkcxusyUMLytZvaeruJ8ycdtlXtOVtLe1cetK8eexzRRUuICSYkN5ER+PdsPlAxd3tndx5t7i/HxMvP1h5cQ5O/F4XM19Dn7q7Nqmrpo7ewjLT5oyreDl7krIsiHkABv8i40j9raOVuU13UAjDnfTCaHAiQRERERmVBrF0VjMgz2nq6a1W9QbgQej4eCihaC/b0IDbz8Ll/JMUEYQEe3E19vM+sWX/0uTjYfK994bBlZKWGcKWrkB787wenCerYfKCUs0JtHt07s7n3eVjP3rp9HW0cvP3zxJD978ww1jZ28e7gMHy/zVbXgXSuzycRXH8giLNCHN/YWc+R8DQDbD5bS3tXHtnVJhAR4c9OiaDp7nJzIrwc+rhCxa/6RTCDDMEhPCKa1s4/qxs7pXs41G2xLjdcA7SmlAElEREREJlSQnxcr7BFcqG2/7MBkmX71Ld20dPSSGnflKhebj4XYge2y1y+Owcfr2ip3fLws/NUDmWzMiqG0uo1/feU0bo+Hz9+dMSnVQHetTeI7n1tJSmwg2bm1fOsXh2jp6OXm5XH4+05Mq9yVBPl58bWHsvDxMvPM2+c5fK6GHdkXCAv04fZVCQDcNDA/bN9AG5ujTAO0ZXKkD1QT5pe3TPNKrl35YICkCqQppQBJRERERCbco1vT8POx8PLuwlGHB88FLe09lNW0XfnEGWy8848GZSaHYbWYuOU6K3fMJhOfu2sB922YD/QP2F54nUOzL2dedCDf/OwKntyWQYDNis3bwu2rEift/kYTH+HPn9+3CKfLzX/98SxOl4cHt6RgtfQP8I4L92N+TCBnixtpaush70Iz/r5WYsI140Um1mAoORhSzkblde0E2qwE+XlN91JuKJPb8CsiIiIiN6SQAG8euy2dX751jl+9fZ5vPLYM0xya41JY2cJPXjlNR1cfj96axm0rE6Z7SddkcP5RyjgDpPs3JXPH6sQJedNmGAb3bZjPpiWxBPtP/ptA08B8rpULIuntcxFgm/o3nlkp4Ty6NY3f78gnJTaQ1RmRw45vyIymuKqV7QdLaGjtZlla+Jx63sjMEBPuh7+vddYO0u7qcVLf0k1GUsh0L+WGowokEREREZkUaxdGsTw9grwLzezILp/u5UyYk/n1/PB3J+jo7sPP18rvd+Tz8q4C3LNs3lOf001OUQMWs4nEqIBxXcdiNk34J/4hAd5TOiTa22qelvBo0K0r4vm7R5fytYeWjHjcqxdGYTGb+PB4f+un2tdkMpgMg7T4IBpau6lvmX0VohX1/QO0EzT/aMopQBIRERGRSWEYBo/fYcff18prewqpauiY7iVdtw9PVPBvr58G4K/uz+K7T6wkOtTGu0fK+MUfz9LndF/hFmaON/cVUdfczcYlMVgtelswVQzDIGNe6Kjzl/x8rCxPD2cwilSAJJNlcDh7/oXZNwdpcP5RXITaO6eaWthEREREZNIE+nnx+B12fvbmGX719nm++dkV19SSU9vcxUs784cFNBaziYduTiEmbGreRLx/9AIv7szH39fKXz+0hOTYQAC+9dkV/PTV0xw5X0tbZx///ZElmE0zO5AprGjh3cNlRAT78NCWlOlejlxkfWYMR87X4u1lJjFKFRYyOdITB+YgXWi+ph0Vp1N5XX+ApAqkqTezf7OJiIiIyKy3ckEkqzMiKaxs5UBO9ajnNLZ28+GJijHbwHYcvcCJ/HrOFDcOfZ0sqOe59xx4pqh1bNexcny9zfz94yuGwiMAf18rf/voUrJSwjhf2sSR87UTcn9Ol5vdx8tp7+qbkNsb1NPn4um3z4MHvnB3xjXvpiaTY9G8UObHBLB2YdSMDyJl9kqI9Mfbyzwr5yCV17ZjGBA7RR8eyMf0iiQiIiIik+7hm1Pxsph47aNCenpdw445XW5++uppnn3PwYm8+hHXdXs8HHXU4udj4T++von/+tvN/NffbmZJShi5Zc2czB95nYnmcrtpaO0mLtyfqBDbiONeVjOfuS0ds8lg+4GSCZmHtCO7nOfez+ONvUXXfVsXe31PETWNndy6MgF7oobQzjQmk8F3nljFE3cumO6lyBxmNplIiwuiurGT1o7e6V7OuHk8Hi7UdRAVYsPLap7u5dxwFCCJiIiIyKQLDfThjtWJtLT38s7h0mHHth8ooWxgpsX+nKoR182/0ExLey/L0yPw9bZgtZixWsw8fEsqJsPgpd0FOF2TO3uosbUHl9tDRLDPmOeEB/uydlEUVQ2dHHfUXdf99TldvHekDIAj52ombLaSo6yJHdkXiAq18cDm5Am5TRGZnQZnbM2mKqSmth66epzEa/7RtFCAJCIiIiJT4q61iQT5e/Hu4TKa2noAKKluZfuBUkIDvYkL9+N0YQMtl3wafjS3vyVsdUbUsMtjwvy4eVkctU1d7BrYtWqy1Db371QUEex72fO2rZuHAWw/WHJdrXV7T1fR0tGLn4+Fjm4nJwuuvcqqvqWLXcfL+ddXTvHjl0+BAV/clqFP70VucIMB0pnixmleyfgVV7UCEB+h+UfTQQGSiIiIiEwJHy8L929Kptfp5rU9hfQ53Tyz/Txuj4fP353BpqWxuD0eDp/9eE6S2+0h21GHv6+VBUkjd6S6b+N8bN4W3tpfPOGzgi5WN84AKTrUxqqMSMpq2skparim+3K63LxzqBQvi4m/+FQmMHpl1pV09zr54e9P8I3/PMjz7+dxurCByBBfvnB3BilxQde0NhGZO1LiAgkP8uHg2Wpa2ntGHHe63PzklVN8/4XjvHO4lMr6jimbOTeWvaf7XwuXpIZP6zpuVAqQRERERGTKrF8cQ2KkPwfOVPPzP5yhor6Dm5fFsWhe6MDQYIN9OVVDb1IcZU20dvSy0h4x6kBhf18rn1g/j45uJ3/cXzxp665r6g+QIkMuHyAB3LNuHgBv7b+2KqSDZ6tpaO1h05JYMpJCmB8TQE5RA82jvMEbi9vt4Rd/PMf50ibS4oP4zO3p/MtX1vEPT65hfWbMVa9JROYes8nEXWuT6HO6ee/ohRHH95ys5FRhA3kXmnlldyHffvow/+PnBzl0dvTNECZbTVMnOYUNpMQFkhQdMC1ruNEpQBIRERGRKWMyGTyyNQ2AE/n1/dvI39y/jXyAzYslqeGU13VQVtM/E2mwfW3VJe1rF7tleTyRwb7sPl5BVUPHpKx7vC1sAPGR/ixLC6ewspXc0iYA+pxuzhY3sudkBS732POM3G4PfzpYitlkcOeaRKB/W3ePpz9YGq9XPyzkZEE9C+eF8Hf/bRm3LI8nPOjKaxeRG8uGzGiC/b3YfaJiWBVnZ3cff9hXjI+XmX94cjVPbstg5YJIWjp6ee79vMu+jk2W3ccr8ABbV8RP+X1LPwVIIiIiIjKlMpJCWJ4egWGM3EZ+fWY00N+y5XK7yXbUEejnhT1hZPvaIKvFxKc2JeNyezhwZnI+Ga9r7sLLaiLIz2tc599z0zwAXtpVwL+/nsNTP93L/33pJL9918HOY2PPazqaW0tNUxfrM2MIDewf2L06IwqL2WB/TvW4Kpo+OlXJu0fKiAmz8RefXIzFrD/5RWR0VouZO1cn0tPrYkf2x1VIbx0oob2rj3tumkdchD/rM2P4i08uZkNWDF09TgorWqd0nT29LvadriLQz4uV9sgpvW/5mH6biIiIiMiU+9InFvJPX1o7Yhv5zOQwAm1WDp2r4UxRI+1dfay0R2AyGZe9vcFhsDUDrWYTyePxUNfcRUSwL4Zx+XUMmh8TyKL5oZTVtnM8r44gPy9uXRmP72XmNbk9HrYfLMFkGNy9Lmnocn9fK0vTIqis76Ckuu2y93u+tInn3nPg72vlaw9mYfOxXtVjFZEbz+alcfj7WtmRXU5Xj5Papk52ZJcTHuTDbSuHV/tkzg8D4Ezxtc14u1YHz1XT2eNky9JYheLTyHLlU0REREREJpa31UxUqG3E5RazibWLonn/6AVe+CAPgFULrvxpc5C/F14WE7VNnRO+1o5uJ109LiKusgXsC3dncLqwHntiCNEDjzU0wIeXdxfwx/3FPHZr+rDz956qpKKug3WLooi8pFVuQ2Y02bm17MupYn5M4Ij7crrc7DpWzhv7+udA/eWnFhMZMvL7KyJyKW8vM7evSuD1j4rYdbyckuo2XG4PD25JwWoZvlvjgqRgzCaDnKJG7t+UMiXr83g87DpWjtlksHlp3JTcp4xO0Z2IiIiIzCiDQ57rW7oJ9vci7TLta4NMhkFEiC+1TV0TvktQ7VUM0L5YSIA3m5fGDYVH0D+7Y7R5TY6yJp5/Pw8/Hwv3bUwecVuL5ocS5O/FkXM19Dldw46dK2nkf/36KC/uKsBiMvizexeNqOwSEbmcW5b3V0huP1jKMUcdKXGBo4b3Pl4W0hOCKa1uo7Wjd0rWlnehmfK6DpanRxAS4D0l9ymjU4AkIiIiIjNKQqQ/SVH9O+ysXBCJaZxtY5HBvnT3umjrHNkedj1qm/urmsYzQPtKrBYTD92cgsvt4ZXdhQDUNHby76/nAPCXn8ocUX0E/bsl3bQomo5uJ//6yml+9uYZfvbmGX74+xP86MWTVNV3sGVZHP/8Z+tYOY6KLRGRi9l8LNy6Ip6e3v6A+tFb0sZs2V2cHArA2eLGKVnbzuP9c+M0PHv6KUASERERkRnn9lUJWMwGG65iy/mogZat2gmeg1TX3A1MTIAEsDw9gvSEYE4W1JOdW8u/vnqajm4nj99pZ0HS2JVDm5bEYrWYOF/aRHZuLdm5tZwvbSI1Lojvfm4Vj99hx99XM49E5NrctiqBQD8vNmbFkBIXNOZ5g3OQcoomfw5SU1sPxx11JET6kxY/9ppkamgGkoiIiIjMOOsWR7NmYdQVh2dfbLDFrKapk9QJfKNRd40tbGMxDINHt6byf36Tzc/ePAPAXWsS2ZgVe9nrRYXa+OlTG+np+7iFzTD6h2yPd7i3iMhY/H2t/N+/vOmKVZ9xEX4E+3txprgRt9tzVa/TV+vVDwtwezxsXRGv17kZQBVIIiIiIjIjXe2bkqiBgGeiK5Bqm7swgLBAnwm7zXnRgdy0OBqAZWnhPLBlfMNovb3MBPp5DX0F2Lz0pkpEJozZZLria4phGCxODqO9q4/SmsvvDDke7jHm1h3Pq+Pg2RrmxwSyPjP6uu9Hrp8CJBERERGZEwZ3HauZ4J3Y6pq7CA30xmqZ2D+dP31bOk9uy+DL9y4a95wnEZGZIDN5YtrYXv+oiKf+dS/HHHXDLm/t7OXZd3OxmE08uS0Ds0nRxUygfwURERERmRNCAr2xmE0TWoHU53TR3NYzYfOPLubrbWF9ZgzeVvOVTxYRmUEWzgvBMOBM0diDtNu7+njh/Tz+9ZVTNLf3jDj+0alKth8oobPHyc/eyGHnsXIAPB4Pz7/noLWzj/s3JRMb7jdpj0OujmYgiYiIiMicYDIMIoJ9qGnqwuPxTEhrV11zNx4mboC2iMhc4OdjJSUuiMKKFjq6+/Dz+XiAv9vtYc+pSl7fU0hHtxOAf3z2GP/9kSXEhPWHQedLGnnuPQf+vlYev8PO8x/k8cIHeTS2dZMQ6U+2o47U+CBuX5UwLY9PRqcASURERETmjKgQG1UNnXR0OydkR7K65v5qJgVIIiLDZc4PpaC8hXMlTaxaEElPn4tzJY38YV8xZTXteHuZefjmVHr7XLy5r5h/eu4YTz2YRYDNi/94o38Dga/en0l6QjBJ0QH8+OVTvHOoDMMAL2t/69pkDuiWq6cASURERETmjIt3YvP3vf6d2GqbJ3YHNhGRuWJxchhv7C3m/aNl7M+p4nxpE31ONwDrFkXz0M0pBPt7A/0txr99x8GPXjxJgM1KZ4+TJ7dlkJ4QDPSH9N/6zHJ++uppCitbeWhLKlEDc+1k5lCAJCIiIiJzRuRFO7GlxF5/gKQKJBGR0SVFBxBgs1JY0QpAXLgfWalhrFoQybzowGHnbsyKJdjfm5+9cYbG1h62rUtifWbMsHMCbF5847HlVNZ3kBjlP2WPQ8ZPAZKIiIiIzBkXB0gToa5JAZKIyGhMhsFffiqTivoOMueHEn6F18nM5DC+/fgKiipbWZ8VM+o5VouJpOiAyViuTAAFSCIiIiIyZ0QOtDzUNnVOyO3VNndh87ZMyDwlEZG5Jj0heKgNbTziIvyJi1B10Wxlmu4FiIiIiIhMlLBAb8wmY0IqkNweD/Ut3ao+EhERQQGSiIiIiMwhZpOJ8CAfaq4hQCqraRtWudTS3kuf002EBmiLiIgoQBIRERGRuSUyxEZ7Vx+d3X3jvk5tcxffezab//2bo1TUd/RfNhAmRaoCSURERAGSiIiIiMwtQ4O0m8dfhfTqh4U4XR66elz85JVTtHb2UtfcDUBEsM+krFNERGQ2UYAkIiIiInPK1e7ElnehmezcWlJiA7nnpiTqW7r5j9dzqGzor0RSBZKIiIh2YRMRERGROSZqIEAazxwkt8fDS7vyAXhkaxrJsYHUNHZxNLeWospWAA3RFhERQRVIIiIiIjLHRIbYAKht7LzCmXD4XA3FVW2szogkNS4Ik2Hw5LYM5scE4nJ7MJsMQgPVwiYiIqIASURERETmlPAgHwwDaq4wA6mnz8WrHxZiMZt4cHPK0OVeVjNPPZBJeJAP86IDMJmMyV6yiIjIjKcWNhERERGZUyxmE2GBPlecgfT+kTKa2nq4e20S4Ze0qQX5e/MPX1yDsiMREZF+qkASERERkTknKtRGa0cvXT3OUY/39rn40+EyAmxWtq1LGvUcb6sZq8U8mcsUERGZNRQgiYiIiMicM7gTW90YbWzFVa309LpYuzAaX28V5YuIiFzJuH5b2u32NcAPHA7HllGO2YAPgCcdDkfuRZdHAseA2xwOR+7A//8lEAKYgccdDkeh3W7/EvBngBP4nsPh2H6dj0lEREREbnBRAy1ptU1dJEYFjDjuuNAMgD0xeErXJSIiMltdsQLJbrd/A3gaGLH9hN1uXwl8BKRccrkV+C/g4o98/gV4weFwbAK+DSyw2+3RwFPAeuAO4J/tdrv3tT0UEREREZF+gzux1TSNvhNb/kCAlBYfNGVrEhERmc3GU4FUCNwPPDfKMW/gU6Mc+xHwc+CbF122Hjhtt9t3ACXA14CtwH6Hw9ED9Njt9gIgCzh6uQWFhNiwqB9dRERERMawyOiffl3Z2EVExPAKJKfLTWFlKwlRASQnhU3H8kRERGacS39fXuqKAZLD4XjNbrfPG+PYfgC73T50md1u///bu/cYucoyjuPfbRfa2ntpASsgl7YPt4SCGAQp1HCnoEYjmIZwE1GjBgOJeOGixBj9AwlotBGoCEpMRIyIRaoRCBeVGNuECjxtKaFowW6R0gWB3sY/ztkync6etms7O7v7/fw1c857dp9J9smc/c37vnMx0JWZD0ZEfYB0IPBqZp4aEdcBVwNLgdfqxnQD2/0Y6NVePkmSJEmSADprNfaeOIq/P7uaVS+t3Woz7BWr1vHW+k0cMnUcXV3d/VilJEntYcqUsXR1dVeGSLtjE+1LgdMi4mFgJnBnuVTtFeC+csxvgWOBdUB9dWOBtbuhJkmSJA0hHR0dHD19Mm9v2MQzL2x9e7m0XL42Y3+Xr0mStKN2eYCUmSdl5snlhtuLKTbLfhl4DDi7HHYS8A/gSWBWRIyMiPHAYcCSXV2TJEmShp6Z0yYDsHhZ11bHtwRI+7mBtiRJO2qnA6SImBsRl/fhd10FXBgRTwBnAt8ug6VbgEeBPwFfz8y3+vCzJUmSpK1M2288Y0btwaLla9hcqwGwuVZj6YtrmTJhJJPGbfMdMZIkqRcdtfLNdCDp6uoeeEVLkiSp5W6//2keX/Iy1150LAe9exwvrn6d6+c/yQeP3JdPnXN4f5cnSVJbqNsDqaO3MbtjDyRJkiSpLcycXixjW1QuY3tn/yOXr0mStDMMkCRJkjRoHXHQJDqHD2PxsjVAXYB0gAGSJEk7wwBJkiRJg9bIPTs5/MCJ/LPrDVavfZOlL65l/Jg92XvCqP4uTZKkAcUASZIkSYNazzK2B59cyWtvrCf2n0BHR69bPEiSpCYMkCRJkjSozZxWBEiPLFoFuP+RJEl9YYAkSZKkQW3CmBEcPHUcm8tvH56xnwGSJEk7ywBJkiRJg17PLKTRIzuZOmV0P1cjSdLAY4AkSZKkQe/oGVMAiAMmMsz9jyRJ2mmd/V2AJEmStLu9Z/JorjzvKKZOdvaRJEl9YYAkSZKkIeHIg/fq7xIkSRqwXMImSZIkSZKkSgZIkiRJkiRJqmTMv1XRAAAGVklEQVSAJEmSJEmSpEoGSJIkSZIkSapkgCRJkiRJkqRKBkiSJEmSJEmqZIAkSZIkSZKkSgZIkiRJkiRJqmSAJEmSJEmSpEoGSJIkSZIkSapkgCRJkiRJkqRKBkiSJEmSJEmq1FGr1fq7BkmSJEmSJLUxZyBJkiRJkiSpkgGSJEmSJEmSKhkgSZIkSZIkqZIBkiRJkiRJkip19ncB6h8RcRzw3cycHREzgXnARmApcFlmbq4bOwz4IXAU8HZ5fnlEfAC4ubxuYWZ+s9WvQ1KhoaePoejpt4HFwBUNPT0K+BmwN9ANXJSZXRFxLnAdRU/Pz8xbW/06pKEsIvYA5gMHAiOAbwFPA3cANWAJ8Hn7WRoYmvV0Zt5XnrsJyMyc13CN991SG+rlPXol8H1gE0W/XpiZ/667ZtD1szOQhqCI+DJwGzCyPHQ9cENmnkjRDHMaLvkoMDIzjwe+AtxYHp8HzAVOBI4r/2mV1GJNevrHwJcycxbwGkWf1vsc8FR5/k7gmvJN8SbgdOBk4PKI2LcV9Uva4gLglbI3zwJ+AHwPuKY81gF8pOEa+1lqX9v0dERMiYgHgA/3co333VJ7avYefTPwxcycDdwLXN1wzaDrZwOkoek54GN1zxcBkyKiAxgLbACIiDsj4gCKP+zfA2TmX4BjI2IcMCIzn8vMGvAgcEoLX4OkdzT29H6Z+UT5+HGKHiYiFkbEntT1NPAAcCpwGLA8M1/NzPXAY8CsVhQvaYtfAtfWPd8IvA94pHze06/2szQwNOvpMcA3gLvqB3rfLbW9Zv38ycxcXD7vBN6Cwd3PBkhDUGb+ijIkKi0DbgGeAfYBHi7HXZiZK4FxFLMYemwqj62rO9YNjN99VUvqTZOeXhERJ5ePzwVGl+NOL/+ZrO/pnt5t7HN7WmqxzHw9M7sjYixwD3AN0FHeYEJdX9rPUvtr1tOZ+Xxm/rXJWO+7pTbWSz+/BBARJwBfoJj9O6j72QBJUEy9m5WZh1JMf7+x4fw6iplJPYY1OTYWWLs7i5S0wy4BvhoRvwNWA2saztf3b0/v2tNSG4iI/YGHgLsy825gc93pZn1pP0ttrElPb4/33VKbatbPEXE+xZK0OZnZ1XDJoOtnAyQB/Id3UtBVwMSG848DZwOUG349lZnrgPURcUi59O0M4NEW1Sup2hzg0sycA+wF/KHh/JaepljD/SjFDMTpETGpXBZzEvDnFtUrCYiIfYCFwNWZOb88vCgiZpePe/q1nv0staleenp7vO+W2lCzfo6ICyhmHs3OzBVNLht0/ey3sAngMuAXEbERWA98Goq1mxTT538NnBYRT1Bs4HlJed1ngZ8Dwyl2j99mOq6kfrEMWBAR/wUeyswFUOyZApwD/Aj4aUQ8RtHzczNzQ0RcSbEOexjFtzb9q3/Kl4asr1F8iHNtRPTss3AFcEsZBD1DMW3efpYGhmY9fVZmvtk40Ptuqe019vNw4EjgBeDeiAB4JDOvH8z93FGr1bY/SpIkSZIkSUOWS9gkSZIkSZJUyQBJkiRJkiRJlQyQJEmSJEmSVMkASZIkSZIkSZUMkCRJkiRJklTJAEmSJGkXiIg7IuLiivM/iYj3trAkSZKkXcYASZIkqTU+BHT0dxGSJEl90VGr1fq7BkmSpAEnIjqAG4FzgFXAcOB2YDpwCjCpPH4+cAlwA7AcmAUcDNwEvAtYA3wmM59v8UuQJEnaYc5AkiRJ6puPA0cDRwCfAKYBncChwAmZOQNYCVyQmd+hCJPOBrqB24C5mXkMRQh1a+vLlyRJ2nGd/V2AJEnSADUbuDczNwBdEbEA2AhcBVwWEQEcDzzXcN0M4BDgvmIIAONaUrEkSVIfGSBJkiT1TY2t9zTaCOwFLAS+B9wDbGLbfY+GAysycyZARAwH9tnt1UqSJP0fXMImSZLUN38EzouIERExETiTIlR6ODPnAUsp9kcaXo7fSPHh3bPApIiYVR6/FLi7pZVLkiTtJGcgSZIk9UFm/iYi3g8sAV4GngZGAUdFxFPlsL8BB5WP7wcWAGdQ7Jl0c0SMBNYBF7WydkmSpJ3lt7BJkiRJkiSpkkvYJEmSJEmSVMkASZIkSZIkSZUMkCRJkiRJklTJAEmSJEmSJEmVDJAkSZIkSZJUyQBJkiRJkiRJlQyQJEmSJEmSVOl/NTYqiUBudm4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['askclose'].plot(figsize=(20, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "|ERROR|2019-01-13 10:48:46,058|Server reports an error: {'executed': False, 'error': 'No price stream for EUR/USD'}.\n",
      "|ERROR|2019-01-13 10:48:46,064|URL: https://api-demo.fxcm.com:443/subscribe\n",
      "|ERROR|2019-01-13 10:48:46,068|Headers: {'User-Agent': 'request', 'Accept-Encoding': 'gzip, deflate', 'Accept': 'application/json', 'Connection': 'keep-alive', 'Authorization': 'Bearer nRGWm_E94sK9w7hJAAEm434e3e87b7324219bedc0aed785c3dd24c6a0578', 'Content-Type': 'application/x-www-form-urlencoded', 'Content-Length': '15'}\n",
      "|ERROR|2019-01-13 10:48:46,071|Params: {'pairs': 'EUR/USD'}\n",
      "|ERROR|2019-01-13 10:48:46,075|Bearer token: Bearer nRGWm_E94sK9w7hJAAEm434e3e87b7324219bedc0aed785c3dd24c6a0578\n",
      "|ERROR|2019-01-13 10:48:46,078|Connection status: established\n",
      "|ERROR|2019-01-13 10:48:46,081|Socket session id: nRGWm_E94sK9w7hJAAEm\n"
     ]
    },
    {
     "ename": "ServerError",
     "evalue": "FXCM Server reports an error: No price stream for EUR/USD.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mServerError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-d0ba11662601>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcon\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubscribe_market_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'EUR/USD'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\fxcmpy\\fxcmpy.py\u001b[0m in \u001b[0;36msubscribe_market_data\u001b[1;34m(self, symbol, add_callbacks)\u001b[0m\n\u001b[0;32m    761\u001b[0m         \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'pairs'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0msymbol\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    762\u001b[0m         self.__handle_request__(method='subscribe', params=params,\n\u001b[1;32m--> 763\u001b[1;33m                                        protocol='post')\n\u001b[0m\u001b[0;32m    764\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msymbol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__on_price_update__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    765\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\fxcmpy\\fxcmpy.py\u001b[0m in \u001b[0;36m__handle_request__\u001b[1;34m(self, method, params, protocol)\u001b[0m\n\u001b[0;32m   2526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2527\u001b[0m                 raise ServerError('FXCM Server reports an error: %s.'\n\u001b[1;32m-> 2528\u001b[1;33m                                   % data['response']['error'])\n\u001b[0m\u001b[0;32m   2529\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2530\u001b[0m                 self.logger.error('FXCM Server reports an unknown error: %s.'\n",
      "\u001b[1;31mServerError\u001b[0m: FXCM Server reports an error: No price stream for EUR/USD."
     ]
    }
   ],
   "source": [
    "con.subscribe_market_data('EUR/USD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Bid</th>\n",
       "      <th>Ask</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Bid, Ask, High, Low]\n",
       "Index: []"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_prices('EUR/USD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:10:00</th>\n",
       "      <td>1.14653</td>\n",
       "      <td>1.14648</td>\n",
       "      <td>1.14655</td>\n",
       "      <td>1.14648</td>\n",
       "      <td>1.14667</td>\n",
       "      <td>1.14662</td>\n",
       "      <td>1.14669</td>\n",
       "      <td>1.14662</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:11:00</th>\n",
       "      <td>1.14648</td>\n",
       "      <td>1.14648</td>\n",
       "      <td>1.14648</td>\n",
       "      <td>1.14648</td>\n",
       "      <td>1.14662</td>\n",
       "      <td>1.14663</td>\n",
       "      <td>1.14663</td>\n",
       "      <td>1.14662</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:12:00</th>\n",
       "      <td>1.14648</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14648</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14663</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14663</td>\n",
       "      <td>1.14658</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:13:00</th>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14643</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14657</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:14:00</th>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14629</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14627</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14642</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14640</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:15:00</th>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14631</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14645</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:16:00</th>\n",
       "      <td>1.14632</td>\n",
       "      <td>1.14632</td>\n",
       "      <td>1.14632</td>\n",
       "      <td>1.14632</td>\n",
       "      <td>1.14645</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14645</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:17:00</th>\n",
       "      <td>1.14631</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14645</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:18:00</th>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14630</td>\n",
       "      <td>1.14639</td>\n",
       "      <td>1.14622</td>\n",
       "      <td>1.14652</td>\n",
       "      <td>1.14645</td>\n",
       "      <td>1.14652</td>\n",
       "      <td>1.14635</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:19:00</th>\n",
       "      <td>1.14629</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14629</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:20:00</th>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14632</td>\n",
       "      <td>1.14635</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:21:00</th>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14622</td>\n",
       "      <td>1.14647</td>\n",
       "      <td>1.14642</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>1.14637</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:22:00</th>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:23:00</th>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:24:00</th>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14637</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>1.14652</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:25:00</th>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:26:00</th>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14631</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14627</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14642</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:27:00</th>\n",
       "      <td>1.14630</td>\n",
       "      <td>1.14632</td>\n",
       "      <td>1.14632</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14645</td>\n",
       "      <td>1.14645</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:28:00</th>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14629</td>\n",
       "      <td>1.14635</td>\n",
       "      <td>1.14629</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14642</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:29:00</th>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14635</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14645</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:30:00</th>\n",
       "      <td>1.14634</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14634</td>\n",
       "      <td>1.14631</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14647</td>\n",
       "      <td>1.14649</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:31:00</th>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14632</td>\n",
       "      <td>1.14647</td>\n",
       "      <td>1.14647</td>\n",
       "      <td>1.14647</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:32:00</th>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14640</td>\n",
       "      <td>1.14640</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14647</td>\n",
       "      <td>1.14654</td>\n",
       "      <td>1.14654</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:33:00</th>\n",
       "      <td>1.14640</td>\n",
       "      <td>1.14639</td>\n",
       "      <td>1.14642</td>\n",
       "      <td>1.14639</td>\n",
       "      <td>1.14654</td>\n",
       "      <td>1.14653</td>\n",
       "      <td>1.14655</td>\n",
       "      <td>1.14652</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:34:00</th>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14652</td>\n",
       "      <td>1.14654</td>\n",
       "      <td>1.14655</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:35:00</th>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14640</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14637</td>\n",
       "      <td>1.14654</td>\n",
       "      <td>1.14652</td>\n",
       "      <td>1.14654</td>\n",
       "      <td>1.14651</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:36:00</th>\n",
       "      <td>1.14640</td>\n",
       "      <td>1.14630</td>\n",
       "      <td>1.14640</td>\n",
       "      <td>1.14630</td>\n",
       "      <td>1.14652</td>\n",
       "      <td>1.14643</td>\n",
       "      <td>1.14653</td>\n",
       "      <td>1.14643</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:37:00</th>\n",
       "      <td>1.14630</td>\n",
       "      <td>1.14630</td>\n",
       "      <td>1.14630</td>\n",
       "      <td>1.14629</td>\n",
       "      <td>1.14643</td>\n",
       "      <td>1.14642</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14642</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:38:00</th>\n",
       "      <td>1.14629</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14631</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:39:00</th>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>1.14628</td>\n",
       "      <td>1.14610</td>\n",
       "      <td>1.14642</td>\n",
       "      <td>1.14627</td>\n",
       "      <td>1.14642</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:40:00</th>\n",
       "      <td>1.14614</td>\n",
       "      <td>1.14607</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>1.14607</td>\n",
       "      <td>1.14627</td>\n",
       "      <td>1.14620</td>\n",
       "      <td>1.14629</td>\n",
       "      <td>1.14620</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:41:00</th>\n",
       "      <td>1.14607</td>\n",
       "      <td>1.14611</td>\n",
       "      <td>1.14612</td>\n",
       "      <td>1.14607</td>\n",
       "      <td>1.14620</td>\n",
       "      <td>1.14626</td>\n",
       "      <td>1.14626</td>\n",
       "      <td>1.14620</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:42:00</th>\n",
       "      <td>1.14611</td>\n",
       "      <td>1.14611</td>\n",
       "      <td>1.14611</td>\n",
       "      <td>1.14608</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14620</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:43:00</th>\n",
       "      <td>1.14611</td>\n",
       "      <td>1.14597</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.14596</td>\n",
       "      <td>1.14626</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.14627</td>\n",
       "      <td>1.14610</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:44:00</th>\n",
       "      <td>1.14598</td>\n",
       "      <td>1.14599</td>\n",
       "      <td>1.14602</td>\n",
       "      <td>1.14598</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>1.14617</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:45:00</th>\n",
       "      <td>1.14599</td>\n",
       "      <td>1.14600</td>\n",
       "      <td>1.14601</td>\n",
       "      <td>1.14598</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>1.14616</td>\n",
       "      <td>1.14617</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:46:00</th>\n",
       "      <td>1.14600</td>\n",
       "      <td>1.14597</td>\n",
       "      <td>1.14602</td>\n",
       "      <td>1.14597</td>\n",
       "      <td>1.14616</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.14617</td>\n",
       "      <td>1.14612</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:47:00</th>\n",
       "      <td>1.14597</td>\n",
       "      <td>1.14603</td>\n",
       "      <td>1.14604</td>\n",
       "      <td>1.14596</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.14617</td>\n",
       "      <td>1.14617</td>\n",
       "      <td>1.14612</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:48:00</th>\n",
       "      <td>1.14603</td>\n",
       "      <td>1.14600</td>\n",
       "      <td>1.14604</td>\n",
       "      <td>1.14598</td>\n",
       "      <td>1.14617</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>1.14618</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:49:00</th>\n",
       "      <td>1.14600</td>\n",
       "      <td>1.14604</td>\n",
       "      <td>1.14606</td>\n",
       "      <td>1.14600</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>1.14619</td>\n",
       "      <td>1.14620</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:50:00</th>\n",
       "      <td>1.14604</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14599</td>\n",
       "      <td>1.14619</td>\n",
       "      <td>1.14627</td>\n",
       "      <td>1.14639</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:51:00</th>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.14630</td>\n",
       "      <td>1.14630</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.14627</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>1.14627</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:52:00</th>\n",
       "      <td>1.14629</td>\n",
       "      <td>1.14636</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14645</td>\n",
       "      <td>1.14652</td>\n",
       "      <td>1.14653</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:53:00</th>\n",
       "      <td>1.14637</td>\n",
       "      <td>1.14637</td>\n",
       "      <td>1.14644</td>\n",
       "      <td>1.14637</td>\n",
       "      <td>1.14653</td>\n",
       "      <td>1.14657</td>\n",
       "      <td>1.14661</td>\n",
       "      <td>1.14653</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:54:00</th>\n",
       "      <td>1.14636</td>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14640</td>\n",
       "      <td>1.14635</td>\n",
       "      <td>1.14656</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14661</td>\n",
       "      <td>1.14652</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:55:00</th>\n",
       "      <td>1.14638</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14653</td>\n",
       "      <td>1.14635</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14662</td>\n",
       "      <td>1.14671</td>\n",
       "      <td>1.14656</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:56:00</th>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14634</td>\n",
       "      <td>1.14641</td>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14663</td>\n",
       "      <td>1.14660</td>\n",
       "      <td>1.14667</td>\n",
       "      <td>1.14653</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:57:00</th>\n",
       "      <td>1.14633</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14639</td>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14658</td>\n",
       "      <td>1.14664</td>\n",
       "      <td>1.14646</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:58:00</th>\n",
       "      <td>1.14625</td>\n",
       "      <td>1.14662</td>\n",
       "      <td>1.14684</td>\n",
       "      <td>1.14614</td>\n",
       "      <td>1.14659</td>\n",
       "      <td>1.14713</td>\n",
       "      <td>1.14725</td>\n",
       "      <td>1.14654</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:59:00</th>\n",
       "      <td>1.14662</td>\n",
       "      <td>1.14676</td>\n",
       "      <td>1.14676</td>\n",
       "      <td>1.14655</td>\n",
       "      <td>1.14712</td>\n",
       "      <td>1.14720</td>\n",
       "      <td>1.14724</td>\n",
       "      <td>1.14712</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2019-01-11 21:10:00  1.14653   1.14648  1.14655  1.14648  1.14667   1.14662   \n",
       "2019-01-11 21:11:00  1.14648   1.14648  1.14648  1.14648  1.14662   1.14663   \n",
       "2019-01-11 21:12:00  1.14648   1.14644  1.14648  1.14644  1.14663   1.14659   \n",
       "2019-01-11 21:13:00  1.14644   1.14644  1.14644  1.14643  1.14659   1.14659   \n",
       "2019-01-11 21:14:00  1.14644   1.14629  1.14644  1.14627  1.14659   1.14642   \n",
       "2019-01-11 21:15:00  1.14628   1.14631  1.14633  1.14628  1.14641   1.14644   \n",
       "2019-01-11 21:16:00  1.14632   1.14632  1.14632  1.14632  1.14645   1.14646   \n",
       "2019-01-11 21:17:00  1.14631   1.14638  1.14638  1.14628  1.14645   1.14651   \n",
       "2019-01-11 21:18:00  1.14638   1.14630  1.14639  1.14622  1.14652   1.14645   \n",
       "2019-01-11 21:19:00  1.14629   1.14628  1.14629  1.14628  1.14644   1.14641   \n",
       "2019-01-11 21:20:00  1.14628   1.14632  1.14635  1.14628  1.14641   1.14646   \n",
       "2019-01-11 21:21:00  1.14633   1.14628  1.14638  1.14622  1.14647   1.14642   \n",
       "2019-01-11 21:22:00  1.14628   1.14628  1.14628  1.14625  1.14641   1.14641   \n",
       "2019-01-11 21:23:00  1.14628   1.14638  1.14638  1.14628  1.14641   1.14651   \n",
       "2019-01-11 21:24:00  1.14638   1.14638  1.14638  1.14637  1.14651   1.14651   \n",
       "2019-01-11 21:25:00  1.14638   1.14633  1.14638  1.14633  1.14651   1.14646   \n",
       "2019-01-11 21:26:00  1.14633   1.14631  1.14633  1.14627  1.14646   1.14644   \n",
       "2019-01-11 21:27:00  1.14630   1.14632  1.14632  1.14625  1.14644   1.14645   \n",
       "2019-01-11 21:28:00  1.14633   1.14629  1.14635  1.14629  1.14646   1.14642   \n",
       "2019-01-11 21:29:00  1.14628   1.14633  1.14635  1.14628  1.14641   1.14645   \n",
       "2019-01-11 21:30:00  1.14634   1.14633  1.14634  1.14631  1.14646   1.14647   \n",
       "2019-01-11 21:31:00  1.14633   1.14633  1.14633  1.14632  1.14647   1.14647   \n",
       "2019-01-11 21:32:00  1.14633   1.14640  1.14640  1.14633  1.14647   1.14654   \n",
       "2019-01-11 21:33:00  1.14640   1.14639  1.14642  1.14639  1.14654   1.14653   \n",
       "2019-01-11 21:34:00  1.14638   1.14641  1.14641  1.14638  1.14652   1.14654   \n",
       "2019-01-11 21:35:00  1.14641   1.14640  1.14641  1.14637  1.14654   1.14652   \n",
       "2019-01-11 21:36:00  1.14640   1.14630  1.14640  1.14630  1.14652   1.14643   \n",
       "2019-01-11 21:37:00  1.14630   1.14630  1.14630  1.14629  1.14643   1.14642   \n",
       "2019-01-11 21:38:00  1.14629   1.14628  1.14631  1.14628  1.14641   1.14641   \n",
       "2019-01-11 21:39:00  1.14628   1.14614  1.14628  1.14610  1.14642   1.14627   \n",
       "2019-01-11 21:40:00  1.14614   1.14607  1.14614  1.14607  1.14627   1.14620   \n",
       "2019-01-11 21:41:00  1.14607   1.14611  1.14612  1.14607  1.14620   1.14626   \n",
       "2019-01-11 21:42:00  1.14611   1.14611  1.14611  1.14608  1.14625   1.14625   \n",
       "2019-01-11 21:43:00  1.14611   1.14597  1.14613  1.14596  1.14626   1.14613   \n",
       "2019-01-11 21:44:00  1.14598   1.14599  1.14602  1.14598  1.14613   1.14614   \n",
       "2019-01-11 21:45:00  1.14599   1.14600  1.14601  1.14598  1.14614   1.14616   \n",
       "2019-01-11 21:46:00  1.14600   1.14597  1.14602  1.14597  1.14616   1.14613   \n",
       "2019-01-11 21:47:00  1.14597   1.14603  1.14604  1.14596  1.14613   1.14617   \n",
       "2019-01-11 21:48:00  1.14603   1.14600  1.14604  1.14598  1.14617   1.14614   \n",
       "2019-01-11 21:49:00  1.14600   1.14604  1.14606  1.14600  1.14614   1.14619   \n",
       "2019-01-11 21:50:00  1.14604   1.14613  1.14625  1.14599  1.14619   1.14627   \n",
       "2019-01-11 21:51:00  1.14613   1.14630  1.14630  1.14613  1.14627   1.14646   \n",
       "2019-01-11 21:52:00  1.14629   1.14636  1.14638  1.14625  1.14645   1.14652   \n",
       "2019-01-11 21:53:00  1.14637   1.14637  1.14644  1.14637  1.14653   1.14657   \n",
       "2019-01-11 21:54:00  1.14636   1.14638  1.14640  1.14635  1.14656   1.14659   \n",
       "2019-01-11 21:55:00  1.14638   1.14641  1.14653  1.14635  1.14659   1.14662   \n",
       "2019-01-11 21:56:00  1.14641   1.14634  1.14641  1.14633  1.14663   1.14660   \n",
       "2019-01-11 21:57:00  1.14633   1.14625  1.14639  1.14625  1.14659   1.14658   \n",
       "2019-01-11 21:58:00  1.14625   1.14662  1.14684  1.14614  1.14659   1.14713   \n",
       "2019-01-11 21:59:00  1.14662   1.14676  1.14676  1.14655  1.14712   1.14720   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2019-01-11 21:10:00  1.14669  1.14662       37  \n",
       "2019-01-11 21:11:00  1.14663  1.14662        4  \n",
       "2019-01-11 21:12:00  1.14663  1.14658       11  \n",
       "2019-01-11 21:13:00  1.14659  1.14657        6  \n",
       "2019-01-11 21:14:00  1.14659  1.14640       49  \n",
       "2019-01-11 21:15:00  1.14645  1.14641       23  \n",
       "2019-01-11 21:16:00  1.14646  1.14645        1  \n",
       "2019-01-11 21:17:00  1.14651  1.14641       58  \n",
       "2019-01-11 21:18:00  1.14652  1.14635      125  \n",
       "2019-01-11 21:19:00  1.14644  1.14641       13  \n",
       "2019-01-11 21:20:00  1.14646  1.14641       34  \n",
       "2019-01-11 21:21:00  1.14651  1.14637       81  \n",
       "2019-01-11 21:22:00  1.14641  1.14638       24  \n",
       "2019-01-11 21:23:00  1.14651  1.14641       25  \n",
       "2019-01-11 21:24:00  1.14652  1.14651        6  \n",
       "2019-01-11 21:25:00  1.14651  1.14646       17  \n",
       "2019-01-11 21:26:00  1.14646  1.14642       34  \n",
       "2019-01-11 21:27:00  1.14645  1.14638       31  \n",
       "2019-01-11 21:28:00  1.14646  1.14641       19  \n",
       "2019-01-11 21:29:00  1.14646  1.14641       83  \n",
       "2019-01-11 21:30:00  1.14649  1.14644       55  \n",
       "2019-01-11 21:31:00  1.14647  1.14646        4  \n",
       "2019-01-11 21:32:00  1.14654  1.14646       30  \n",
       "2019-01-11 21:33:00  1.14655  1.14652       15  \n",
       "2019-01-11 21:34:00  1.14655  1.14651       46  \n",
       "2019-01-11 21:35:00  1.14654  1.14651       44  \n",
       "2019-01-11 21:36:00  1.14653  1.14643       62  \n",
       "2019-01-11 21:37:00  1.14644  1.14642        9  \n",
       "2019-01-11 21:38:00  1.14644  1.14641       28  \n",
       "2019-01-11 21:39:00  1.14642  1.14625      132  \n",
       "2019-01-11 21:40:00  1.14629  1.14620       46  \n",
       "2019-01-11 21:41:00  1.14626  1.14620       37  \n",
       "2019-01-11 21:42:00  1.14625  1.14620       20  \n",
       "2019-01-11 21:43:00  1.14627  1.14610       82  \n",
       "2019-01-11 21:44:00  1.14617  1.14613       25  \n",
       "2019-01-11 21:45:00  1.14617  1.14614       25  \n",
       "2019-01-11 21:46:00  1.14617  1.14612       25  \n",
       "2019-01-11 21:47:00  1.14617  1.14612       15  \n",
       "2019-01-11 21:48:00  1.14618  1.14613       30  \n",
       "2019-01-11 21:49:00  1.14620  1.14614       36  \n",
       "2019-01-11 21:50:00  1.14639  1.14614      164  \n",
       "2019-01-11 21:51:00  1.14646  1.14627      121  \n",
       "2019-01-11 21:52:00  1.14653  1.14641       69  \n",
       "2019-01-11 21:53:00  1.14661  1.14653       70  \n",
       "2019-01-11 21:54:00  1.14661  1.14652      130  \n",
       "2019-01-11 21:55:00  1.14671  1.14656      169  \n",
       "2019-01-11 21:56:00  1.14667  1.14653      438  \n",
       "2019-01-11 21:57:00  1.14664  1.14646      386  \n",
       "2019-01-11 21:58:00  1.14725  1.14654      478  \n",
       "2019-01-11 21:59:00  1.14724  1.14712       33  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles=con.get_candles(offer_id=1, period='m1', number=50)\n",
    "candles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "candles[['askclose']].plot(figsize=(10,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 50 entries, 2019-01-11 08:52:00 to 2019-01-11 09:41:00\n",
      "Data columns (total 4 columns):\n",
      "open     50 non-null float64\n",
      "high     50 non-null float64\n",
      "low      50 non-null float64\n",
      "close    50 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 2.0 KB\n"
     ]
    }
   ],
   "source": [
    "data=candles[['askopen','askhigh','asklow','askclose']]\n",
    "data.columns=['open','high','low','close']\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "qf=cf.QuantFig(data, title='EUR/USD', legend='top',\n",
    "              name='EUR/USD', datalegend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "close": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iplot(qf.iplot(asFigure=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pytz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <th>bidopen</th>\n",
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       "      <th>askclose</th>\n",
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       "      <th>asklow</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th>2019-01-11 21:50:00</th>\n",
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       "      <th>2019-01-11 21:51:00</th>\n",
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       "      <th>2019-01-11 21:53:00</th>\n",
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      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow  askopen  askclose  \\\n",
       "date                                                                          \n",
       "2019-01-11 21:50:00  1.14604   1.14613  1.14625  1.14599  1.14619   1.14627   \n",
       "2019-01-11 21:51:00  1.14613   1.14630  1.14630  1.14613  1.14627   1.14646   \n",
       "2019-01-11 21:52:00  1.14629   1.14636  1.14638  1.14625  1.14645   1.14652   \n",
       "2019-01-11 21:53:00  1.14637   1.14637  1.14644  1.14637  1.14653   1.14657   \n",
       "2019-01-11 21:54:00  1.14636   1.14638  1.14640  1.14635  1.14656   1.14659   \n",
       "2019-01-11 21:55:00  1.14638   1.14641  1.14653  1.14635  1.14659   1.14662   \n",
       "2019-01-11 21:56:00  1.14641   1.14634  1.14641  1.14633  1.14663   1.14660   \n",
       "2019-01-11 21:57:00  1.14633   1.14625  1.14639  1.14625  1.14659   1.14658   \n",
       "2019-01-11 21:58:00  1.14625   1.14662  1.14684  1.14614  1.14659   1.14713   \n",
       "2019-01-11 21:59:00  1.14662   1.14676  1.14676  1.14655  1.14712   1.14720   \n",
       "\n",
       "                     askhigh   asklow  tickqty  \n",
       "date                                            \n",
       "2019-01-11 21:50:00  1.14639  1.14614      164  \n",
       "2019-01-11 21:51:00  1.14646  1.14627      121  \n",
       "2019-01-11 21:52:00  1.14653  1.14641       69  \n",
       "2019-01-11 21:53:00  1.14661  1.14653       70  \n",
       "2019-01-11 21:54:00  1.14661  1.14652      130  \n",
       "2019-01-11 21:55:00  1.14671  1.14656      169  \n",
       "2019-01-11 21:56:00  1.14667  1.14653      438  \n",
       "2019-01-11 21:57:00  1.14664  1.14646      386  \n",
       "2019-01-11 21:58:00  1.14725  1.14654      478  \n",
       "2019-01-11 21:59:00  1.14724  1.14712       33  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles=con.get_candles(offer_id=1, period='m1', number=400)\n",
    "candles.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.DataFrame(candles[['askclose', 'bidclose']].mean(axis=1),columns=['midclose'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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    "iplot(data.iplot(asFigure=True))"
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  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n",
      "c:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in log\n",
      "\n"
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   "source": [
    "data['returns']=np.log(data/data.shift(1))"
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  {
   "cell_type": "code",
   "execution_count": 23,
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    "lags = 6\n",
    "cols = []\n",
    "for lag in range (1, lags + 1):\n",
    "    col='lag_%s'%lag\n",
    "    data[col]=data['returns'].shift(lag)\n",
    "    cols.append(col)"
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   "outputs": [
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>midclose</th>\n",
       "      <th>returns</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-11 15:20:00</th>\n",
       "      <td>1.146815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-11 15:21:00</th>\n",
       "      <td>1.146865</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 15:22:00</th>\n",
       "      <td>1.146900</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2019-01-11 15:23:00</th>\n",
       "      <td>1.146745</td>\n",
       "      <td>-0.000135</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 15:24:00</th>\n",
       "      <td>1.146690</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-0.000135</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     midclose   returns     lag_1     lag_2     lag_3  lag_4  \\\n",
       "date                                                                           \n",
       "2019-01-11 15:20:00  1.146815       NaN       NaN       NaN       NaN    NaN   \n",
       "2019-01-11 15:21:00  1.146865  0.000044       NaN       NaN       NaN    NaN   \n",
       "2019-01-11 15:22:00  1.146900  0.000031  0.000044       NaN       NaN    NaN   \n",
       "2019-01-11 15:23:00  1.146745 -0.000135  0.000031  0.000044       NaN    NaN   \n",
       "2019-01-11 15:24:00  1.146690 -0.000048 -0.000135  0.000031  0.000044    NaN   \n",
       "\n",
       "                     lag_5  lag_6  \n",
       "date                               \n",
       "2019-01-11 15:20:00    NaN    NaN  \n",
       "2019-01-11 15:21:00    NaN    NaN  \n",
       "2019-01-11 15:22:00    NaN    NaN  \n",
       "2019-01-11 15:23:00    NaN    NaN  \n",
       "2019-01-11 15:24:00    NaN    NaN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>midclose</th>\n",
       "      <th>returns</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:55:00</th>\n",
       "      <td>1.146515</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000052</td>\n",
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       "      <td>0.000074</td>\n",
       "      <td>0.000039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:56:00</th>\n",
       "      <td>1.146470</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000074</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-11 21:57:00</th>\n",
       "      <td>1.146415</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>0.000026</td>\n",
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       "      <td>0.000052</td>\n",
       "      <td>0.000157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:58:00</th>\n",
       "      <td>1.146875</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 21:59:00</th>\n",
       "      <td>1.146980</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     midclose   returns     lag_1     lag_2     lag_3  \\\n",
       "date                                                                    \n",
       "2019-01-11 21:55:00  1.146515  0.000026  0.000013  0.000026  0.000052   \n",
       "2019-01-11 21:56:00  1.146470 -0.000039  0.000026  0.000013  0.000026   \n",
       "2019-01-11 21:57:00  1.146415 -0.000048 -0.000039  0.000026  0.000013   \n",
       "2019-01-11 21:58:00  1.146875  0.000401 -0.000048 -0.000039  0.000026   \n",
       "2019-01-11 21:59:00  1.146980  0.000092  0.000401 -0.000048 -0.000039   \n",
       "\n",
       "                        lag_4     lag_5     lag_6  \n",
       "date                                               \n",
       "2019-01-11 21:55:00  0.000157  0.000074  0.000039  \n",
       "2019-01-11 21:56:00  0.000052  0.000157  0.000074  \n",
       "2019-01-11 21:57:00  0.000026  0.000052  0.000157  \n",
       "2019-01-11 21:58:00  0.000013  0.000026  0.000052  \n",
       "2019-01-11 21:59:00  0.000026  0.000013  0.000026  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in sign\n",
      "\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data['direction']=np.sign(data['returns'])\n",
    "to_plot=['midclose', 'returns', 'direction']\n",
    "data[to_plot].iloc[:100].plot(figsize=(10,6),\n",
    "        subplots=True,style=['-','-','ro'], title='EUR/USD');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1],\n",
       "       ...,\n",
       "       [0, 1, 1, 1, 1, 1],\n",
       "       [0, 0, 1, 1, 1, 1],\n",
       "       [1, 0, 0, 1, 1, 1]], dtype=int32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.digitize(data[cols],bins=[0])[:400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=svm.SVC(C=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in sign\n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    147\u001b[0m         X, y = check_X_y(X, y, dtype=np.float64,\n\u001b[0;32m    148\u001b[0m                          \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'C'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 149\u001b[1;33m                          accept_large_sparse=False)\n\u001b[0m\u001b[0;32m    150\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    754\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    755\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 756\u001b[1;33m                     estimator=estimator)\n\u001b[0m\u001b[0;32m    757\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    758\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    572\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 573\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    574\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    575\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan)\u001b[0m\n\u001b[0;32m     54\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[0;32m     55\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'infinity'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'NaN, infinity'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "%time model.fit(np.sign(data[cols]), np.sign(data['returns']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 400 entries, 2019-01-11 15:20:00 to 2019-01-11 21:59:00\n",
      "Data columns (total 9 columns):\n",
      "midclose     400 non-null float64\n",
      "returns      399 non-null float64\n",
      "lag_1        398 non-null float64\n",
      "lag_2        397 non-null float64\n",
      "lag_3        396 non-null float64\n",
      "lag_4        395 non-null float64\n",
      "lag_5        394 non-null float64\n",
      "lag_6        393 non-null float64\n",
      "direction    399 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 31.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in sign\n",
      "\n"
     ]
    },
    {
     "ename": "NotFittedError",
     "evalue": "This SVC instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-2be32de0cb8e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msign\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mpred\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    574\u001b[0m             \u001b[0mClass\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    575\u001b[0m         \"\"\"\n\u001b[1;32m--> 576\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBaseSVC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    577\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    323\u001b[0m         \u001b[0my_pred\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m         \"\"\"\n\u001b[1;32m--> 325\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    326\u001b[0m         \u001b[0mpredict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse_predict\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dense_predict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    327\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_validate_for_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    453\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    454\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_validate_for_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 455\u001b[1;33m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'support_'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    456\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m         X = check_array(X, accept_sparse='csr', dtype=np.float64, order=\"C\",\n",
      "\u001b[1;32mc:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[1;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[0;32m    949\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    950\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mall_or_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mattr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mattributes\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 951\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    952\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    953\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotFittedError\u001b[0m: This SVC instance is not fitted yet. Call 'fit' with appropriate arguments before using this method."
     ]
    }
   ],
   "source": [
    "pred=model.predict(np.sign(data[cols]))\n",
    "pred[:400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'SVC' object has no attribute 'classes_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-2ac887361ddf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'SVC' object has no attribute 'classes_'"
     ]
    }
   ],
   "source": [
    "model.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pred_proba' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-78-f0e27aae2509>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred_proba\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pred_proba' is not defined"
     ]
    }
   ],
   "source": [
    "pred_proba[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['position'] = pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['strategy']=data['position']*data['returns']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['returns','strategy']].cumsum().apply(np.exp).plot(figsize=(10,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0    213\n",
       " 1.0    179\n",
       " 0.0      1\n",
       "Name: position, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['position'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.0    195\n",
       " 2.0     98\n",
       "-2.0     97\n",
       "-1.0      2\n",
       "Name: position, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['position'].diff().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu=data['returns'].mean()\n",
    "v=data['returns'].std()\n",
    "bins=[mu-v,mu,mu+v]\n",
    "train_x,test_x,train_y,test_y=train_test_split(\n",
    "    data[cols].apply(lambda x: np.digitize(x, bins=bins)),\n",
    "    np.sign(data['returns']),\n",
    "    test_size=0.5,random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x.sort_index(inplace=True)\n",
    "train_y.sort_index(inplace=True)\n",
    "test_x.sort_index(inplace=True)\n",
    "test_y.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-11 03:10:00</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 03:12:00</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 03:16:00</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 03:17:00</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 03:18:00</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lag_1  lag_2  lag_3  lag_4  lag_5  lag_6\n",
       "date                                                         \n",
       "2019-01-11 03:10:00      2      3      2      1      1      2\n",
       "2019-01-11 03:12:00      1      1      2      3      2      1\n",
       "2019-01-11 03:16:00      2      1      0      1      1      1\n",
       "2019-01-11 03:17:00      2      2      1      0      1      1\n",
       "2019-01-11 03:18:00      2      2      2      1      0      1"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning:\n",
      "\n",
      "The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred=model.predict(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9030612244897959"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(train_y,train_pred)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred=model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4720812182741117"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(test_y,test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc[test_x.index][['returns','strategy']].cumsum().apply(np.exp).plot(figsize=(10,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output(data, dataframe):\n",
    "    print('%3d | new values received for %s | %s, %s, %s, %s, %s'\n",
    "          % (len(dataframe), data['Symbol'], pd.to_datetime(int(data['Updated']), unit='ms'),\n",
    "             data['Rates'][0],data['Rates'][1],data['Rates'][2],data['Rates'][3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 42 | new values received for EUR/USD | 2019-01-11 09:42:59.861000, 1.1522000000000001, 1.15233, 1.15328, 1.1495700000000002\n"
     ]
    }
   ],
   "source": [
    "con.subscribe_market_data('EUR/USD', (output,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 43 | new values received for EUR/USD | 2019-01-11 09:43:01.314000, 1.15219, 1.15232, 1.15328, 1.1495700000000002\n",
      " 44 | new values received for EUR/USD | 2019-01-11 09:43:01.742000, 1.1521800000000002, 1.15232, 1.15328, 1.1495700000000002\n",
      " 45 | new values received for EUR/USD | 2019-01-11 09:43:02.188000, 1.1521800000000002, 1.15231, 1.15328, 1.1495700000000002\n"
     ]
    }
   ],
   "source": [
    "con.unsubscribe_market_data('EUR/USD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_open_positions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "order=con.create_market_buy_order('EUR/USD',100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EUR/USD'"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.get_currency()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order.get_isBuy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['tradeId', 'amountK', 'currency', 'grossPL', 'isBuy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>tradeId</th>\n",
       "      <th>amountK</th>\n",
       "      <th>currency</th>\n",
       "      <th>grossPL</th>\n",
       "      <th>isBuy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30742786</td>\n",
       "      <td>100</td>\n",
       "      <td>EUR/USD</td>\n",
       "      <td>-12.46218</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tradeId  amountK currency   grossPL  isBuy\n",
       "0  30742786      100  EUR/USD -12.46218   True"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_open_positions()[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "order=con.create_market_buy_order('USD/JPY',50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>tradeId</th>\n",
       "      <th>amountK</th>\n",
       "      <th>currency</th>\n",
       "      <th>grossPL</th>\n",
       "      <th>isBuy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30742786</td>\n",
       "      <td>100</td>\n",
       "      <td>EUR/USD</td>\n",
       "      <td>-15.22997</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30742787</td>\n",
       "      <td>50</td>\n",
       "      <td>USD/JPY</td>\n",
       "      <td>-8.30899</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tradeId  amountK currency   grossPL  isBuy\n",
       "0  30742786      100  EUR/USD -15.22997   True\n",
       "1  30742787       50  USD/JPY  -8.30899   True"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_open_positions()[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "order=con.create_market_sell_order('EUR/USD',25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "order=con.create_market_buy_order('USD/JPY',50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "    tradeId  amountK currency   grossPL  isBuy\n",
       "0  30742786      100  EUR/USD -17.99943   True\n",
       "1  30742787       50  USD/JPY  -8.30915   True\n",
       "2  30742788       25  EUR/USD  -3.80757  False\n",
       "3  30742789       50  USD/JPY  -8.94832   True"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_open_positions()[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "order=con.create_market_sell_order('EUR/USD',50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tradeId  amountK currency   grossPL  isBuy\n",
       "0  30742786      100  EUR/USD -17.99943   True\n",
       "1  30742787       50  USD/JPY  -8.30915   True\n",
       "2  30742788       25  EUR/USD  -3.80757  False\n",
       "3  30742789       50  USD/JPY  -8.94832   True\n",
       "4  30742790       50  EUR/USD  -8.30743  False"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_open_positions()[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "con.close_all_for_symbol ('USD/JPY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>tradeId</th>\n",
       "      <th>amountK</th>\n",
       "      <th>currency</th>\n",
       "      <th>grossPL</th>\n",
       "      <th>isBuy</th>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "    tradeId  amountK currency   grossPL  isBuy\n",
       "0  30742786      100  EUR/USD -19.38400   True\n",
       "1  30742788       25  EUR/USD  -3.80757  False\n",
       "2  30742790       50  EUR/USD  -8.30743  False"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_open_positions()[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "con.close_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.get_open_positions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_features(df, lags):\n",
    "    df['Returns'] = np.log(df['Mid'] / df['Mid'].shift(1))\n",
    "    cols = []\n",
    "    for lag in range(1, lags+1):\n",
    "        col='lag_%s' % lag\n",
    "        df[col]=np.sign(df['Returns'].shift(lag))\n",
    "        cols.append(col)\n",
    "        df.dropna(inplace=True)\n",
    "        return df, cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles=con.get_candles('EUR/USD',period='m1',number=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.DataFrame(candles[['askclose','bidclose']].mean(axis=1),\n",
    "                 columns=['Mid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:6: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in sign\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data, cols = generate_features(data, lags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mid</th>\n",
       "      <th>Returns</th>\n",
       "      <th>lag_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-11 09:38:00</th>\n",
       "      <td>1.152245</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 09:39:00</th>\n",
       "      <td>1.152160</td>\n",
       "      <td>-0.000074</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 09:40:00</th>\n",
       "      <td>1.152220</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 09:41:00</th>\n",
       "      <td>1.152320</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11 09:42:00</th>\n",
       "      <td>1.152265</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Mid   Returns  lag_1\n",
       "date                                          \n",
       "2019-01-11 09:38:00  1.152245  0.000117   -1.0\n",
       "2019-01-11 09:39:00  1.152160 -0.000074    1.0\n",
       "2019-01-11 09:40:00  1.152220  0.000052   -1.0\n",
       "2019-01-11 09:41:00  1.152320  0.000087    1.0\n",
       "2019-01-11 09:42:00  1.152265 -0.000048    1.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\merri\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning:\n",
      "\n",
      "The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data[cols],np.sign(data['Returns']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.,  1., -1.,  1., -1., -1., -1., -1.,  1.,  1.])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data[cols])[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cols].iloc[-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data[cols].iloc[-1].values.reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "TOKEN = '434e3e87b7324219bedc0aed785c3dd24c6a0578'\n",
    "con2= fxcmpy.fxcmpy(access_token=TOKEN, log_level='error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_show=['tradeID','amountK','currency','grossPL','isBuy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticks=0\n",
    "position=0\n",
    "tick_data=pd.DataFrame()\n",
    "tick_resam=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def automated_trading(data,df):\n",
    "    global lags,model,ticks\n",
    "    global tick_data, tick_resam, to_show\n",
    "    global position\n",
    "    ticks +=1\n",
    "    t=datetime.datetime.now()\n",
    "    if ticks % 5==0:\n",
    "        print('%3d | %s | %7.5f | %7.5f' % (ticks, str(t.time()),\n",
    "                                   data['Rates'][0],data['Rates'][1]))\n",
    "    #collecting tick data\n",
    "    tick_data=tick_data.append(pd.DataFrame(\n",
    "            {'Bid': data['Rates'][0],'Ask':data['Rates'][1],\n",
    "            'High': data['Rates'][2],'Low':data['Rates'][3]},\n",
    "            index=[t]))\n",
    "    #Resampling Tick Data\n",
    "    tick_resam=tick_data[['Bid','Ask']].resample('5s',label='right').last().ffill()\n",
    "    tick_resam['Mid']=tick_resam.mean(axis=1)\n",
    "    if len(tick_resam)>lags+2:\n",
    "        #Generating Signal\n",
    "        tick_resam, cols=generate_features(tick_resam,lags)\n",
    "        tick_resam['Prediction']=model.predict(tick_resam[cols])\n",
    "        #Entering a Long position\n",
    "        if tick_resam['Prediction'].iloc[-2]>=0 and position == 0:\n",
    "            print('going long(for the first time)')\n",
    "            position = 300\n",
    "            order=con2.create_market_buy_order('EUR/USD', 25)\n",
    "            trade=True\n",
    "        elif tick_resam['Prediction'].iloc[-2]>=0 and position == -1:\n",
    "            print('going long')\n",
    "            position = 300\n",
    "            order=con2.create_market_buy_order('EUR/USD',50)\n",
    "            trade=True\n",
    "        #Entering a short position\n",
    "        elif tick_resam['Prediction'].iloc[-2] <= 0 and position ==0:\n",
    "            print('going short')\n",
    "            position=-300\n",
    "            order=con2.create_market_sell_order('EUR/USD',25)\n",
    "        elif tick_resam['Prediction'].iloc[-2] <= 0 and position == 1:\n",
    "            print('going short')\n",
    "            position = -300\n",
    "            order=con2.create_market_sell_order('EUR/USD',50)\n",
    "        if ticks > 150:\n",
    "            con.unsubscribe_market_data('EUR/USD')\n",
    "            print('closing out all positions')\n",
    "            try:\n",
    "                con2.close_all()\n",
    "            except:\n",
    "                pass\n",
    "            \n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "con.subscribe_market_data('EUR/USD',(automated_trading,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no open position\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(con2.get_open_positions()[to_show])\n",
    "except:\n",
    "    print('no open position')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
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