AnthonyFJGarner/mean_reversion.ipynb

## mean_reversion.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A simple Mean Reversion System\n",
    "Most of the following imports are mainstream, well known Python libraries.\n",
    "\n",
    "alpha_vantage and fix_yahoo are specialist libraries to download stock data free.  To use alpha_vantage you will need to obtain your own key from the providers https://www.alphavantage.co/\n",
    "\n",
    "ffn is a specialist library to report trading systems statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "from alpha_vantage.timeseries import TimeSeries\n",
    "import fix_yahoo_finance as yf\n",
    "#because the is_list_like is moved to pandas.api.types\n",
    "pd.core.common.is_list_like = pd.api.types.is_list_like\n",
    "import ffn\n",
    "#import pixiedust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The following two cells can be used to download stock data and then blanked out again once the data has been saved to csv.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ts = TimeSeries(key='insert own key', output_format='pandas')\n",
    "#data, meta_data = ts.get_daily_adjusted(symbol='SPY', outputsize='full')\n",
    "#data = yf.download(\"SPY\", start=\"1970-01-01\", end=\"2018-12-08\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The following line is to create split adjusted Open prices since only the adjusted close is provided:\n",
    "\n",
    "data['Adj_Open']=data.Open*(data.Adj_Close/data.Close)\n",
    "\n",
    "Useful if you want to test taking signal from previous close and trading at the next open."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.rename(columns={'Adj Close': 'Adj_Close'}, inplace=True)\n",
    "#data['Adj_Open']=data.Open*(data.Adj_Close/data.Close)\n",
    "#data.to_csv('../data/Stocks/spy.csv')\n",
    "#data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart the data to check there are no obvious problems with the split adjusted data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FT/+Ad5z+g1FjJ/EUlWY675HFxsbi6aefBsMwWL9+PWJjY7mlQdauXYtRo0apHfPhhx9ys68nJSVh/PjxSE9Ph0hkUh1IQgjhRV7mdbjXL3D2BRaVwRXAV314CqoBOu2RWVpaYuTIkdxIwejoaGRkZDR63M6dOzFr1iwAQO/eveHm5ka9MkII0ZO7J3/itjOf+DePkTSNXh8arV27FqNHj+b2582bh5CQELz00ktcgisuLoZcLoeLiwvXTiwWIzs7W5+hEkKIyYq8tQ4AkCL3gvegV3mOpnF6S2TLly9HWloali1bBgDYunUrrl+/jsuXL2PgwIEqtxgff9dL20jD+Ph4eHh4cH8qKip0dwGEEGJifD46zXcITaKXRLZ69Wrs3bsXhw4dgrW1NQCga9euABRJa/bs2cjIyOBeWgaAoqIi7vjbt29rfCcpLi4Oubm53B9bW1s9XA0hhLRvBQI33GB8YGllzXcoTaLzRBYfH4/t27fj6NGjcHBwAKBYk6ugoIBrs2fPHri5uXFJ7IUXXsA333wDAEhKSkJ+fj4GDBig61AJIcTkSSUSuMkLUCuw4juUJtPpMMDc3FzMnTsXPj4+GDp0KADAwsICf/75J5555hnU1tZCIBDA2dkZBw4o17X5/PPPMWnSJPj5+cHc3Bxbt26lEYuEEKIHWZvfgC+AnjLjWW1Dp9nBw8ND6/Otc+fOaT3Ozc2NG6JPCCFEf4TlisUsrzg8gRCeY2kq6uYQQghBUXkN5F92hzdzHwDgNmEjzxE1Hc3ZRAghBGmrhsLtYRIDAAeHjjxG0zyUyAghxNTJ5YgUpKoUmZsZzw07SmSEEGLisr8ZDQtGyncYLUaJjBBCTFntA3gWq04BWMcKeQqmZSiREUKICXtwfoda2U+BxjPQA6BRi4QQYtKKs66hQ739ig8K8JqVJW/xtAT1yAghxISJU//LbVc+twm2RpbEAOqREUKIyam9n4cHd26isLAQgQ/LCmakwK1TF17jailKZIQQYmLkX0fCma2Cc70ye0dnre0NHd1aJIQQE2PFVqmVWVpY8BBJ26BERgghxKhRIiOEEBN3sq9xDbd/HCUyQggxIVWl+Wpl/Ue8zEMkbYcSGSGEmJDqsnsAgARZEM+RtB1KZIQQYkKqHihmuM+0i8K2gX8AH9/lOaLWo+H3hBBiQiRFaQAAry6dMejJXjxH0zaoR0YIISbE58Q7AIAH5WU8R9J2KJERQoiJYGsfcNtO3cJ5jKRtUSIjhBATIK8oBrPCg9vv0WcEj9G0LUpkhBBiAnLXxXDbVawF7O3seIymbVEiI4QQEyASMNy2xdzLPEbS9iiREUKICcjr+gy3LezgxmMkbY8SGSGEmIKHAz1SR+0BGKaRxsaFEhkhhJiAXre/AwCIHD15jqTtUSIjhJD2jmW5TTsHJx4D0Q1KZIQQ0k7VSGQAgKriXK6so4MjX+HoDE1RRQgh7dEie1g+3CwY+CW8H24LhO2v/9L+rogQQkxc6d1MlX3vf+YCAH61Gc9HODpHiYwQQtoZx41hGssZ36F6jkQ/KJERQoiJCAqO4DsEnaBERgghJkJoZsF3CDqh00RWU1OD5557Dv7+/ggLC0NMTAyysrIAAIWFhYiJiYGfnx+Cg4ORkJDAHddQHSGEkIbVQYRkyz5Il3dWKReZm/MUkW7pvEcWGxuLmzdv4uLFixg1ahRiY2MBAB9++CGio6ORlpaGTZs2YcKECZBKpY3WEUII0UJah7INT8EcUsiFlkgYugPvO67hqs3MrXgMTnd0msgsLS0xcuRIMA+nQ4mOjkZGRgYAYOfOnZg1axYAoHfv3nBzc+N6Xg3VEUII0Sznyt+wL0wCAPSq/BtThvbEF/96XdlA2D57ZHp9j2zt2rUYPXo0iouLIZfL4eLiwtWJxWJkZ2c3WEcIIaaCrSkHwCKnoARVlQ8QEBgKAKiqk4JlARsL1a/v4sVidGVLNX7Wdete6FF1DrbW7bNHprdEtnz5cqSlpeE///kPqquruV7aI2y9KVQaqqsvPj4e8fHx3H5FRUUbRkwIIfy4V1EL59VdAQCPZkaUfFIKM6EAvyybgJFIwM1+KxAxYjJ3jJOWJAYA/nGHUSORwMrCTJdh80YvoxZXr16NvXv34tChQ7C2toaTk2Kur6KiIq7N7du34enp2WDd4+Li4pCbm8v9sbW11fGVEEKI7rBFN8Heu4VDK19Vq5N/prhLNYE5DEemAhGJb3N1d66dUmt/yOEVblsoMoOllbUOIjYMOk9k8fHx2L59O44ePQoHBweu/IUXXsA333wDAEhKSkJ+fj4GDBjQaB0hhLRLcjmYb6LArI/EJNExtWoLRorayvsqZflLewAAuux6Wq29jWX7HGqviU5vLebm5mLu3Lnw8fHB0KGKN8otLCxw5swZfP7555g0aRL8/Pxgbm6OrVu3QiRShNNQHSGEtEc1ede4uRG1Kbh0FPXvTXWS5mltK2NM5ztTp1fq4eGh9fmWm5sbjhw50uw6Qghpb7K2zoJ11Z1GE9mdnCw8/pBFIqmDpidfckbYRtEZPprZgxBCeJRz8Q+I07fB9e7xRttaduioVlZTrhxP8FvMSVRBMTLRxqJ9DrXXhBIZIYTwJDe/CF33jdVYlzcrHdIRn6uU1aQcUmsnS97ObY+KDoalvTMAoLe3etJrryiREUIIT+7fvqi1zt3FGaLo6cCI5bjR8SkAQN+Ko2rtysoUw+4veilefBYM/gAAIAwY2dbhGixKZIQQwpOEM2c1lstmJCo2GAboOwsPrFTnTCww68Jte11dDwCwqHl4izFiEvBpKeAa0PYBGyhKZIQQwhP3on/UytiF9yHsFKhSxjw+cOPln3DXwkelyEkcrNwRmNZXu2ldLSGEGIB7FbU4fqMQY4SJanWPz2wEAB0kRSr7bt6huGOrSFxX5WIAgM2gtx8/zGSYzosGhBBiAGRyFuc/H4kIQSqgnrM08i547HUkgRAQKr6+gwVZAACrdjxzR2OoR0YIIXqUsOVTjBCegwtTrlYn0/KVLKnX58hmXQEApVWqS1sJBE3Miu0QJTJCCNEHlsXlv3ajf9Z6jdWJTDikC+5prLNga7ltAeQAgGEVB9o+RiNFiYwQQvTg3snNCP1rGkSMXKU8z6o7aswcEPHW97AQaZ6No/4xTlDvyZk6SmSEEKIHzsfe0VguFAlhOf82LFx9m/Q5VkwdACAraCZXli130dbcJFAiI4QQHZPLZFrrcrxfavR4mUB9uimJnZjb/kMe0aK42gtKZIQQomM3Dn6jtc7C1rHR4wWdQ9WPkykXEvZ1Md0RiwAlMkII0Tnb7D+0V5rbNHo8A+UqIkUCxW1EmYVyfcc6mVztGFNCiYwQQnTMuVQxp+KljjFqdQLzJvSmWGWisnrjNwBAuaVymqrq0oJWRmjcKJERQoiOWUsVKzt3j92MS7YDcGvYJq6uSYnsyYWAQATMOgtbd8Ucih6hQ7hqM0i1HGgaKJERQoiOfPvLEaxfMJnbt7S0Qs/3fodvf+XSLUxTElm3ocCnxYBLd67IqYMldkoHA1B9YdoUmfbVE0KIjvyWeBmxl15o9FuWYbWPaGyMzejl2PfbPIieXtniz2gPmtwju3PnDp577jlERkYCAC5evIivvvpKZ4ERQogx63HoxQbrX6mbj19k/VHj4N/iczzTJxhDPtqPUf16tvgz2oMmJ7Lp06dj/PjxkEoV92KDg4Pxww8/6CwwQggxZt0Ed1X26965qbKfKA/Cu5JZigmAW8HBWv0dM1PT5ESWn5+PiRMnQvBwnRuRSASRiO5MEkJIfbc3vwksslcrN3fopLI/tLtiGH0ne0u9xNWeNTkTiUQisKzyXYbS0lLI5ab97gIhhNRXXVEGr6ydauVXRMEIeazs3xMjUVxZBzc7SmSt1eQe2QsvvIAZM2bgwYMH2Lx5M0aMGIFp06bpMjZCCDEq9/PSNZbbOarPhWhpJkQXBytdh2QSmpzI5s6diyFDhiAyMhIHDx7EnDlzMGfOHF3GRgghRuXMrlUq+8s6LkN59/HwnPItTxGZBoatf7/QyHl4eCA3N5fvMAghJijpxEH0/vMVlbJLzx5Fz/AoniIybs35Pm9yj2zkyJEoLi7m9u/du4dRo0Y1PzpCCGmHHk9iAODi7MRDJKanyYns7t27cHJS/qU4OzsjLy9PJ0ERQkh7YGnj0Hgj0mpNTmQymYx7hwwA6urqUFtb28ARhBBiGtKTj3PbGcH/4ratbTrwEY7JaXIii4mJwcsvv4yEhAQkJCTg1VdfxTPPPKPL2AghxChYn1XOctR5zEJu28LcjI9wTE6T3yNbtmwZli9fjri4OADAmDFj8MEHH+gsMEIIMRaORee4bStzIcr7fYzaqjK4MAyPUZkOGrVICCGtcOj3vRh49i3YMjW4PekMvLoF8B1Su9Cc7/NGe2S7du3CCy+8gA0bNmisnzlzZvOiI4SQ9oJl8XTSa8DDjpe1nSO/8ZioRp+RXb16FQCQlJSk9ufcuXONHA3MmTMHYrEYDMNwnwUAYrEYAQEBCAsLQ1hYGHbs2MHVpaWloV+/fvD390dUVBRSUlJacm2EEKIzBdf+ARarjkp0cKTh9nxotEe2ePFiyGQyjBo1CuPGjWv2CcaPH4/3338fAwYMUKvbvXs3goOD1cqnT5+O2NhYTJ06Fbt378a0adOQmJjY7HMTQoiuuO1Sf4/WjCZS50WTRi0KhULEx8e36ASDBg2Ch4dHk9sXFhYiOTkZEydOBACMGzcOmZmZyMrKatH5CSGkrcnravgOgdTT5OH3vXr1avNe0YQJExASEoI33ngDRUVFAICcnBy4u7tzS8QwDANPT09kZ2e36bkJIaQlfl87B5u+bdk/7IluNDmRnThxAgMHDkRgYCCioqK4Py114sQJXLp0CcnJyXBycsKUKVO4OuaxIavaBlbGx8fDw8OD+1NRUdHieAghpDFVxbl4pmQLpt37XK0us/NIHiIiQBPfI7t69SoWLFiA0tJS+Pn5tcmJPT09AQBmZmZ455134O+vWO67a9euyM3NhVQq5dZAy8nJ4drXFxcXx73XBqBZtzAJIaS57hfkwFpDeaZVELxjf9Z7PESh0US2YcMGzJ8/H/7+/rh58yY2bdqE559/vlUnrayshEQigYODYsTP9u3bER4eDgBwdXVFeHg4tm3bhqlTp2LPnj0Qi8UQi8WtOichhLSWNP24xvKqbs8A9PIzbxp9ITo4OBiHDx+Gh4cHrly5grfeegsJCQlNPsGsWbOwf/9+5Ofnw9nZGba2tjhy5AjGjRsHmUwGlmXh4+ODr7/+mktWN2/exNSpU1FcXAw7Ozts2bIFQUFBjZ6LXogmhOjSg5UB6FBzl9v/a8hudHZzQ/ceoTxG1T415/u80UQWHh6OCxcuaN03JJTICCG6lPTNa+hdtJfbPxzzN2Kiw3iMqP1q05k9amtrcf36dW7AxeP7gYGBrQiVEEKMQ21hukoSA4DhUT15iobU12giq6qqwsiRqqNxHu0zDIOMjAzdREYIIQakZNtr6PxYmUBAz8UMQaOJjF5EJoSYOlZSjc7ll7j9LP/XwNSWwYvHmIgSzadCCCGNYJZ1UtkXv/qVlpaED01+IZoQQgiQL3DjOwTyGEpkhBDSgMqSPG77MDMA1tMO8BgN0YRuLRJCSANs1vbgtmMW/s5jJEQb6pERQogWrEzKdwikCSiREUKIFjevJvEdAmkCSmSEEKJFdZYykZUPXcFjJKQhlMgIIeQhmZxFUlYJih7UAgDCL3wCAEh8ag/sBs/kMzTSABrsQQghD51bFI0+ghsAAMlHd2H2sDwwYjB/QZFGUY+MEEIeepTEAIBZoVzf0N7aTFNzYiAokRFCCADI5Sq7Ish4CoQ0FyUyQggBcPXSWb5DIC1EiYwQAgBIvXEFWGSP82f+4TsUnUv+3xKc2/m5Slnw/hEa20pYoT5CIq1AiYwQE1ZVXY39P38DmUwO//8NAABEHhrFc1S6VVNyBxE3VqNXynJkrOwLACgp0L6AY8LwX/UVGmkhSmSEmKiKggxIV/rg2dSPsWdtnErd/VtneIpK9+5+O47b9qlJwf3yB7h0+AeuTP5BNredMODhUM1pAAAgAElEQVRHDO3fX6/xkeajREaICSorKYLtv8Nhx1QBADrhnkq9w7bhOP3nPj5C07k6S2eV/aobf2BoZjwA4JqgOwRW9lydjX1HvcZGWoYSGSEmyGxdiMq+CHK1NtEnpgAsq6+Q9IKVy9D9vuozQPeDU7jtYpcoAECpQJHAnJxd9RccaTFKZISYGHldDazZapWyfmWaZ3XPTGlfcw0eWdvw7Bw9X1TM5GHx1l+4PeIHeHp310dYpJUokRFiYq5veKnJbUsfVOowkrbz+erl+HJpHFiZBKXXT2htN+L+/7jtJI/X1OptOjgAAKxdvODVd3zbB0p0gqaoIsTEBN3/S2tdBaxhiypun60q1UNErfdBxcOh9Et+gCOATMtAeH+YqNKmViqDxcPtayEfwKX7E8DuTVx95uCv4W1uAWJ8qEdGiIm4tmkW/lzzGi7KfQAAlR/eAz5SHXZe2mOCyr6sXLk68vEDW5G7JBgVpQW6D7YJirOv49byPtj5zXy1Ou+aFLWyjF9Xc9tB4z6GnYPqQA7voVPbPEaiH5TICDEBWSlnEXR7G54o24swQQYAwMbSDDC3VWnX9am3VPZlFYrRjHKZHEOTZ8NDloNb+1VfJOZL8eYJ8K27gReL1jepfXpKssq+vYMTt50YsqRNYyP6RYmMkHbu1IkjEO8cprmSYVCDerfTnLqpVPe9tQYAUFZ+nysLy/oBhsBfnt5g/d3i+yr7oySHAQCLOn4BABDaKHtkUrMObRwd0SdKZIS0Y/K6avT78wW18gter3Pbp5wV9b/YTQIAVHirTtVU+L+3cTn+WR1G2Xy1FY0/u7uangtp/nVgkT2qjykXxZz7+quKDYbBEZ8PAQCuwUN1EifRD4Zl28+LIh4eHsjN1T7VDCEmZ5G9xmL201IwAsW/Y3efycDpA/+Bz5OvY+YTAQDLQlpZCtFqb60fe+edfHRxsNJJyE3y2HUdl/XEUOEllbLr4/6EPPlHBGVufuzYMm5TLmdRWlUHJ1sa5GFomvN9Tj0ywrvyOzdQlH5BrfxCdilyS6s0HEGaIv+G9mmmHiUxABjb2xuTZnyM2MH+DysZiGwbntHi2L7NbRFi2/EehNsuQ1SKWLkEliU3NLd/SCBgKIm1A5TICO/svusDl61DUCtVrP8kryyBtOwuwv8rxtX4MTxHZ7w6/W84t10wKwMZoe9qbCcQMOjZ1QEiYdO/DrrVNpwgdCn7uvIl7WpYAgC6eXWF5/SduG/milvmPQAAsrpadCs7rXLsNeen9Rco0RtKZMRgbF8RCwAQrPKGaE0AACBG2L5mltCX0txUbvuwrDfcXJzgM+YjlEa+DXZeRos/NztQ8XfUreoiIKlupLVu3N0+GwCwVzYA8pmncTc4Fp5DpoERWcBhfhrKfZ4BAJRXqvfmy7yGq5UR40eJjPBC06PZqbLdeGfhYrXyzIw0fYTUrtzbrZzN/skFBxQbIgs4jl4KxsZJy1ENu8/aQNRTMStI57KLwLJOkC1ybHWszVFdK0EfgaI32G/OFti4eqPz+FWAUDm3A/Nwu/9fL6sdH2aWo59AiV5RIiO8eOObg5j+8SJUVpSrlH/FxKu1vVd8T62MNMzv4cS4158/CjML6zb5zPxX/oCdRw+VMiHkqKqTtsnnN4atq4LVCuXM9Z1cnDW2E4jMtX6Gda8JWuuI8dJ5IpszZw7EYjEYhsHVq1e58rS0NPTr1w/+/v6IiopCSkpKk+qI8ZNLavHDvVex0XwNzn3+TKPte/8eg9uFxjFVkr6xLIuEH95H4p51XNm1X9dy2z6BES3+7N4132BMrfJF4YCAHrCxUh+pOGL5Ly0+x+NYaS2ufD8DubdvqdVVFCtHsN2w66f1M7Q967tqHgo4+7Y+SGJwdJ7Ixo8fj4SEBHh5eamUT58+HbGxsUhNTcX777+PadOmNamOGL+qHOUw6cHCy006xmuDGLkF1DOrL/PsQTCLHTAgZyP6XlmgKKwqQdB5xQzu1bCAhVnLp1MtgiMus6ovSNcf7fjIHva9Fp/jcVXLvBGSux0emyLV6lIzMrltv/LTavWPmLESjeUiueZyYvx0nsgGDRoEDw8PlbLCwkIkJydj4sSJAIBx48YhMzMTWVlZDdYR4yORySGVKde6qiovge2PWmaZaERVWWFbhWXUau7ewIPcFHgffEWlPC09DSd//FS5/+xvrTrPd5N7YUIfT7zgtAe+NT9qbefK3Nda11w2rPbZ9iOPvshtCzWsn/aIdWW2xvJyx8CWB0YMGi+z3+fk5MDd3R0ikeL0DMPA09MT2dnZsLGx0VonFotVPic+Ph7x8cpnKhUVFXq7BtI0+xc9iwLGCbM+2wwAuPHLSjR2s+vKkB/glv0bbJ5eDJtvQrny08kX4eXTHRYioe4CNnClualw/L7Pw0Hnqvy29oLfw+2jliMwLDyqVecaFuiGYYFukMuDUSfTnjjaSm1NJVTe6GJZgGEUdVVlKnUpky9DW1qqlKj/+/zqoI0I62dYs5OQtsPbYA/m4S/oI/VHsTVUV19cXBxyc3O5P7a2thrbEX7k3b6F8cITmCVQPkMpq6pr9Lj80gq4Tt4MGxfV29GTb7yFM19NQF78ILDS2jaP19DdOv8HHL/v3aS2nqM+bLPzCgQMLM2U/3jIs1DM+HFO7t9m5wAAdqXqTCI3zhzmtv/e8pmyYlEZAn1UfzfqK6uVqZUFP/EyzC15nImE6BQviaxr167Izc2FVKoY7cSyLHJycuDp6dlgHTEiLIv0K8r1oPKWBKIo7zaGFmxq4CCFyJAeWusGVRyCe/klXLvWtGdr7Ynvr2PVyirfTNTQEuge3PJBHo2xG6OY/d5h2DyurDUz3V3asRh562Ige+wjiouLuO3hBd8DAApEXRr9vJ5iN5X9u2zDs5QQ48dLInN1dUV4eDi2bdsGANizZw/EYjHEYnGDdcTwSWqrFfPgLXaAx6WvuHJ32R08+I/yZdSSVw9qPL6o/2J09O3T6Hlqzm9vfbAGLvfbl5C15xNc3rkUrFz91l7WmN2w6RII+buqs2zccNHt7BW2QSOARWUwc+vOlUnlLUxkLIue1+PhXpyIaljgDqt8x+3HkxnY/d8vVeZVdIzd3+hHWgyYDZm9J+6bdwIASGj94HZP54ls1qxZ3OSPTz31FHx9FcNfN27ciI0bN8Lf3x8rV67EDz8ol4ZoqI4Ytoz/vc9te0tUh1D7CPK5bUe/fkgKWcTtL7aYh7U9fobLsHeadJ5e2Yrfiay0qzh1ZFcrIjZM1376EB55hyG+shahKavAfKZ88bhozDZgURnEEYpBMwL7zqhlFE/NrkcsRPe3ftZLjEKB8najTNL4LWNNjvy+k9t2ZspRaCHm9jear8H47M9U2pu7+qFR1h0hfPcKTg/YDABYKZ/UotiI8aDZ70mb+ueT/hgovNpgmxKzTug4/6Zi59G/tuvNSF7fz3t/gd2F/2CUUMNw60VlSP80AN0Ed1E0JxMuHY3zFlL1/SKkb3oTHZ//HO7i7jhz5Qb67NHcK/21w0sYPfdbtXJJWT5qywpg69lT1+EqySTAEsVLyWnjjsAvpPGedH1ph/8Nv9Oqz/KOySLwlDBZY/s8phPcF95s8udn3qvE0NV/4d2n/PGvp5qQAIlBodnvCS/y79xuNIkBwCXbQdx25bsZKH9H/eXXR14d+zxGLDqE7P4rVcrPW0QDALoJ7gIAbvy9U+1YQ3dtSR+ULuwCq698EVx2HLd/mAq2rlJrEgOAqKkrNJab2XfSbxIDAKEZt1nyoHmrFBw/uEMtiQGAryAPqdGar7HQuXmJ0tvZBjeWxGDOk/QSdHtHiYy0mU7fhTbeCMDQUmXSsbF3gp2DS4PtzYQCdIpSHegQWXsa108p35MaeOmDZkTKv/y0ZATJbsCRUb4y0leYAma5u0q7X/2Xq+y7OBpmr9POsnnPoYaejdVYnjDoJ60DOsx7T252XJZmQrVR0KT9oURGWi0lLQPvfPyRxrrUnm2TYMys1F+t6HFEdd48eQuf0+jbg8Lb6PRT4ysSv103G6NfnYXzPrO4MoHAML+UJY8POWzA0sXvq+xf6KucXuulIRHIE7g/fggAoKZa+8vSxLRRIiOt5rJtML4y36BS9mDSUeT2Wwb/5z8GZp5B2fM/cXVngj59/CMaxZjb4FjAZzgT86vWNjeW9ELS9fRmf7a+Vex6q9E20k9KsXbZUgBAxMSlON3vO1TOu6Pr0JrtYnfF4ByptGnTP7EsiwXsRm7/PmuL8BHKnpaZUIAe/pqfZ1k0ZaAHMUmUyEiruTCqM9jfDZ6ODt2i4DFcsW4UXANg33MUci0VX0Qdg59q0Xmeevlf6BM9CLms5lnPAwW34fW/J1r02W0l7fRvuLB9MeTaZsKQSVBXqRjYctF2IHKiPkFa8Dt4ME31fTCRUMDdEmMEAkQPfxE2Nob3wr9ApHhOJm1ibzgzXXWwhsOjW6vTTwCzzwEAQj0c8E2E8vWMXwf9ih97/oSgHjTFFNGMXrAgrSNV/wLrPP4LjU1d5vyB7Kyb8OvRukEJl6PXwOOMckh1EWsPF0aRHFyZ+5BIpTAT8fCrvciemyIKS+JRxtih1roTXCtTcfXp3QjuNRTVS7rAC4pZSQLf3g1zC/XJpmqnn1adqsmAMULFkilSSdNmWrmwZS586s0wdtJzBvoDQGfV34lZY/qj4pobbGsL8MyA3hCY06wcRDvqkZFWqco60+S2Ftb28Axs3fx/ABAoVn2GInvsheCTaxTPzu5cO4lrXwzDiRXP4mpyArDIHle2f9Lq82tSfEd91WV7thyulYqVmoMPjQeWOMEKyi98tST28V1gXgYsOmuf2cTQ1MgUXyHf/HGjkZYApHUYJ0zgdk+4vIq+UzSPUAQA2zmJwIyTlMRIo6hHRlql5MhqPFq2sUTgBMcPr0DXwxHKpaq/tp0crCFlBRAxitt5QyoPQy6pQ5ddI8GNfzvwFwAg5OZaAEvQ1py+C2/9h5hbK/4YkfI6xSAPM6jPb6hmqXJ06rcdZuHNmcsaHlFo46T4Q0gjqEdGmuzojyuwd9s33H554mZ4FP4FAFjX4R04fpIOxtxG53H4dFOfrFa0qATVjgHcfs4K7XMNXryQ1OYx1TycWaPCxrTmBBVBMSfqUrP/ArKmrxQ9cOjTNCyetBlKZKRJ5LWVGJaxEmNvfcyVlZcr16EKFHvo7YvJ1lpDr4VhYDXrH27XS56j9fiw/S0bbKKNRCKBJVujiG3eFbX67I79ue0jAUvxR8R63HrpRJvGwBfnSsXL7B7MPdT9/WWTjqlhzdAjYqAuwyImhhIZaZLiTa+olVUXKlfs5etf1yftRip3ROZNP7DeRLStwbIscpYqXgQvZRWjCmvevYXyty4i16E3UpyGo+tbe7n2IYPH4skxk+DbygEvhkIuUj7nSz9/VGu7+svuVP6r6dNMEdIUlMiIVsUVtUg8exq1FSVwyf+bKz95SDHzvF/6Zq6sZ3fvxw/XC8bSTmX/pkj1tmP51ONaj62sqm7VuYvKq8EsdoAPkwcAOC4aAACwtHeBnZs3PN45hsC3d4Exs0Rqt9dww34AOndufBkSoyJQTlNVKdH8UvSy+W+BWeoKAPhOOhJOHem5F2lblMiIVmc+H4W+B0fAYrVqkpLknAPqLStyf8CncAp6Uq+xVU/7C+mOAxE+UXUORolQ9bajnTgC9+yDNX5GWXbL1zQrLy6AS3wnlbKY9zZrbe8/6SsEvPt7i89nqFihshecVW2N7LsFqKlVvpJRcq8A882Us/E7unXVa3zENFAiIxpV19RhpPCsxrohed+jrKyU23d4aq6+wuJYdQ1Ht3/9Bis7R5VyqVD9+ZnzjN9QEv0hyv6lOusHe6T5M4w8YrdOfcCJtZUJDhOvN3HweOEJeG70R+I3b3BlgnWqozndO3XWW2jEdFAiIxrlXz7SYP2tTW8CAI6wvfURTpNJ6s0wcS/g4VyMVo7oGPMR7B2dIXtNeV1dSjQsDdOADd/9G+c+7Y0ftm5RKT8b+DGk72VqOap9YwXqzyWHlisXv3RgVOdHFFq3zbNJQuqjREY0yk1r+LZbZPkfAIBBuKiPcJpMWqNYTuSQxdNwfnmDWr3Qqw8O92/+ki91Ujlm3vkQvQSpmJY+hyuXsEJEvfgBRLaGOSu97jVvkI/c3K7xRoQ0EyUyotHAtM8BAOlTksF+nAd20Dywb19Qa3dlxP/0HVqD+gpTAABBcu0zTfSKHsxtS6sfNOlzU04fUiv7S94T10YdaGaE7YtM2MBkWpIa9SINt34JaS1KZETNvTzlbbKuHmIw5jZgnlgAxskHp7qrLtfiGdBL3+E16C6r6BnlO2qPy7mDcsi46HOPJn2uRaX6zPNDPjuBsN4Dmhlh+3LfPkBjec73r6JiVRC3nzLuD1zuNA79B8foKzRiQiiRETXO34Zx2+ZmQpU6G4HqJMGWlrqfyaM5pAJFkpJZNv1WX9F348BWK17uLiivQU6J+mrHPRLnAQASLAbjnllnXPF5Q62NKWI8+2os75r7O2zr7nH7gSG9EDrjvxAK6SuHtD36rSJa5UxWnxDYujJXZd/S3LB+hTZZvAoASHNqeOHKAzLlF7DLnWNI2a1Y+yt91VBs+vJ9JB7/DZXViltjZZnJXFuPF1fBef4NhExu2iwW7d2Q7g2v7g0A6azmhTIJaSuG9S1EeHH2z31IS7uJO18OxM3Fyt5YVx/120ZuRadU9i1EQrU2fPrbbCDENT+j2Lpbg+0sn1musp9XUo6CXxejnzAFn5ptRd+/J+D3jfMBAPZblEmxc1d+Xvw2VAzDoAiODbZJH723wXpCWotmvzdhqQUP0MmiBlEnpjT5mAL7UNhVa5/HkG9rXwnH0t+uY3JfrwbbDY+OAA4r94eV7gDOq7YJKDuB8ru38Gic3WFZb8SYN2MaLBNhMTsRWK/5WRkADO9lPMvSEONEPTITdXL31/D/twdq10Q26zjXZ5fgjqgrpte9i341a3UUXcsFudtje2w0nGxbvzTlPftQ2G1U/nyGLTzcQGvTZeesfMm5VmhYz0yJaaAemSliWfS/qpjV4tHKyo9Lf+EYNN2cs+/cDfYLrmLyrXvoYNm+f31EZVkq+0I+Vp02MpUzknH5u4noXadYKicdHhp/jwhpS9QjM0FnExqetQMArDprv1UEAP19nRHq4dBWIRmUTPGLAIBB7DmuLNljMl/hGBXGogOXxACg8wureIyGmApKZCYo6o8XtdZtiDqK0fgKznameYvo1sSz8J76nVq5/3Pv8xCN8XHsoPrCs1UPem+M6B4lMhOTl6W+FlSZUDnqbObIKPy66DWYi0zzV8Ncy3txts40a3uDfB8uVsowKLVXvgjNCEzz94joF/2WmZj7ucqpmyQf3gE+uYekzorJdXdav8RXWLxKtY7gts0tFT2Ke1NPcmXJcl+9x2R0JuwGFhQBAEqHLOM5GGJqKJGZmMBjimc914Pfg5mlLSA0wzn3CYitexc/WU7gOTp+FEV/zG1bWSl6ZM7iYFwZvgMAUN77XV7iMioMw63QbW6yEygTvtAwLBNydcNEPFpiUhD0HFf+bLgHnj7RG/95Qn2NrfZsat08vCI8Djgo33OyMFf+LxHSLwYVkcUYbG5YL30bOgdHSmREvyiRtXNVuVdg/f0AHHd/E0MLf+XKu/cI4bZ7dLZD5oqRYJjmLclh7Ma+9Do++S0aR/3ccLb3GpRmXcHwx54N2lrQ/yLNZePgyncIxMQwLMuyfAfRVjw8PJCbm9t4Q1OySH0hw5w3U9C1SxcegiGmIuHHRTBzD0Gfp8bxHQoxUs35Puf1GZlYLEZAQADCwsIQFhaGHTsUzyTS0tLQr18/+Pv7IyoqCikpKXyGabQunz6msZySGNG1AZMXURIjesP7fZPdu3cjODhYpWz69OmIjY3F1KlTsXv3bkybNg2JiYk8RWh85DI5Eg5uxaDzcxpvTAghRs7gRi0WFhYiOTkZEydOBACMGzcOmZmZyMrK4jcwI5K050uVJHbMK47HaAghRLd4T2QTJkxASEgI3njjDRQVFSEnJwfu7u4QPZzXjmEYeHp6Ijs7m+dIjUftlf0q+0+9tpDbvtxvnb7DIYQQneI1kZ04cQKXLl1CcnIynJycMGWKYjmRx0fPaRuPEh8fDw8PD+5PRUWFzmM2dL/u34VBwivc/tXnFc/JJO/exO2+SxE6bBJfoRFCiE4YzKjFu3fvwt/fH+np6fDz80NxcTFEIhFYlkXnzp1x+vRpiMXiBj/D1Ect3r51DV7b+gEAalgznIpajyeeeZnnqAghpPmMYtRiZWUl7t+/z+1v374d4eHhcHV1RXh4OLZt2wYA2LNnD8RicaNJzBQlfjYUuz8ZjfSLJ8BK67gkBgB3XzpMSYwQYhJ465FlZGRg3LhxkMlkYFkWPj4++PrrryEWi3Hz5k1MnToVxcXFsLOzw5YtWxAUFNToZ7b3HlnN/QKcuXAR7oH9IM86ie6HNM+NWDe/GOZmvA9IJYSQFmvO97nB3FpsC+09kWl6uflxl81CETr/Hz0EQwghumMUtxZJ07FyOR6UlWisO287RGVf+uxGPURECCGGg+4/GThZbSWEK9zRQUPdr91XYPCQ4cDGSEXBojJEaGhHCCHtGSUyA1ZTcR+Wq71Uym6/8H/wCooGWBajH76mcGVaFizMhDCtuesJIUSBEpkBkkokEC1zhuVj5TcZH3QPilbs1HvXLqSrIwghxFRRIjMgNaV3Yfl1gNpfyv9sJ+Pl99ahOy9REUKIYaNEZiBqHxTD8usAlbL13hswOCIIL4eE8RQVIYQYPkpkBuBaylUE7eyvUna77xLMHjGBp4gIIcR4UCLjWeKhbeh7Zha3nz32ADxDB8OrgWMIIYQoUSLjiVwmx631z6Fv6d9cWcozvyAwdDCPURFCiPGhRNYGpJWlENloHjlYWloCmaQWHWyt8aA4H85du6OmqgKWX3ThhsuXs1awWpCDQDMz/QVNCCHtBCWyFrh9ajcq5WaQ3Pg/9Mz9ifshHrF9Dk+8818U5WXifmE2JIw5Qn99hjvO4uF/6w+rv9h1MsKm0RphhBDSUjTXogYFWddh6ewNO2sz5JzaCWFHMaprJfDdP7oNolTKik2F2N2tTT+TEELag+Z8n1OPrJ7TZxMRfTAG9VOLZyPHlAvskRm9FD1Pva2x/qA8GsHDX0P1P+tQau6O6AdHcMVxGEL+tRvitgqcEEJMGCWyejokLG2wPtNpEITDFsLdJxgic8UNQjsAPQGU9RuPG18MhRmksBm9EqJ712EXPhYjO3koDh6gXBssREfxE0KIKaJbi/Vcv3kDNvunoShsJjx9ApCRuA9Bz8yCbcdObRglIYSQxtB6ZIQQQowarUdGCCHEZFAiI4QQYtQokRFCCDFqlMgIIYQYNUpkhBBCjBolMkIIIUaNEhkhhBCjRomMEEKIUaNERgghxKi1q5k9LCws4OLi0qrPqKiogK2tbRtFxD+6HsNG12PY6Hr4U1RUhNra2ia1bVeJrC20t2mu6HoMG12PYaPrMQ50a5EQQohRo0RGCCHEqAkXLVq0iO8gDE3fvn35DqFN0fUYNroew0bXY/joGRkhhBCjRrcWCSGEGDVKZIQQQowaJbJ60tLS0K9fP/j7+yMqKgopKSl8h6Rizpw5EIvFYBgGV69e5cobiruldfpQU1OD5557Dv7+/ggLC0NMTAyysrIAAIWFhYiJiYGfnx+Cg4ORkJDAHdfSOn0YPnw4QkNDERYWhoEDB+LixYsAjPfvCAAWL16s8jtnzNciFosREBCAsLAwhIWFYceOHUZ9TbW1tZg9ezb8/PwQFBSEiRMnGvX1tBhLOEOHDmU3bdrEsizL7tq1i42OjuY3oMf8/fffbE5ODuvl5cVeuXKFK28o7pbW6UN1dTX7+++/s3K5nGVZll23bh07bNgwlmVZ9rXXXmMXLlzIsizLnj17lvX09GQlEkmr6vShtLSU2/7ll1/Y8PBwlmWN9+/o/PnzbExMDOvp6cn9zhnrtbAsq/b/ziPGek3vvPMO+/bbb3P/D+Xl5bUqZr6vp6UokT1UUFDA2tvbc196crmcdXNzYzMzM/kNTIP6/zM2FHdL6/iSlJTEduvWjWVZlrWxsWELCwu5ut69e7PHjx9vVZ2+bd68mY2MjDTav6Oamho2OjqazcjI4H7njPVaHtGUyIz1mioqKlh7e3v2wYMH7eJ6WkPEd4/QUOTk5MDd3R0ikeJHwjAMPD09kZ2dDbFYzG9wDWgobhsbmxbV8XW9a9euxejRo1FcXAy5XK4y3ZhYLEZ2dnaL6/Rp8uTJOH78OADg8OHDRvt39Omnn2LixInw9vbmyoz1WuqbMGEC5HI5+vTpgxUrVhjtNaWnp8PJyQlLly7FsWPHYGVlhUWLFsHBwcEor6c16BlZPQzDqOyzRvJmQkNxt7RO35YvX460tDQsW7YMgHFf048//oicnBwsXboU8+bNazQuQ7yexMREJCUlYebMmWp1xnYt9Z04cQKXLl1CcnIynJycMGXKlEZjM9RrkkgkyMjIQGBgIM6dO4f169fj5ZdfhlQqNcrraRU99wANVkFBAWtnZ2cU3erHby1qi7uldfq2atUqNjIyUuX5krW1tdZbhC2t44OlpSWbn59vdH9HK1asYDt37sx6eXmxXl5erFAoZN3d3dnNmzcb3bVok5eXx9ra2hrt/0NFRUWsQCBgpVIpV9a7d292x44dRnk9rUGJrJ7BgwerPOjs06cPvwFp8fh9/obibmmdvnz55ZdsREQEW1JSolI+ZcoUlUEbXbt25f4Ha2mdrpWVlbF37tzh9vfu3ct26dKFlcvlRv13xLKqv3PGei0VFRUq/1j68ssv2YEDBzYamyFf07Bhw9jff/+dZVmWzcrKYp2dndm8vDyjvV+vSTEAAAMXSURBVJ6WokRWz40bN9jo6GjWz8+PjYyMZK9evcp3SCpmzpzJdunShRUKhaybmxs3MKKhuFtapw85OTksANbHx4ft2bMn27NnTzYqKoplWZbNz89nhw0bxvr6+rKBgYHsX3/9xR3X0jpdy87OZnv37s0GBwezoaGh7JNPPsleuHCBZVnj/Tt6pH4iM9ZrSU9PZ8PCwtiQkBA2ODiYHTNmDNfbMOZrGjx4MBscHMz27NmT3bt3r1FfT0vRFFWEEEKMGg32IIQQYtQokRFCCDFqlMgIIYQYNUpkhBBCjBolMkIIIUaNEhkhPHs0I3vPnj3h5+eHZ599FqdOnWr0uM2bNyM1NVUPERJi2CiREWIAdu/ejUuXLiEtLQ2vv/46Ro4ciTNnzjR4DCUyQhQokRFiYJ599lnMnDkTq1evxh9//IG+ffsiPDwcwcHB2LRpEwDg+++/x7lz5zBnzhyEhYXh4MGDAIDVq1cjKioKERERGDlyJHJycvi8FEL0gma/J8QA9e7dG/v27UNERAQSEhIgFApRUlKCiIgIxMTE4I033sC2bdvw3nvvYdSoUQCAn3/+GampqUhMTIRQKMTWrVsxe/Zs7N+/n+erIUS3KJERYoAeTbhTXFyMadOmITU1FSKRCPfu3cO1a9fQuXNntWP27duHc+fOITIyEgAgk8kgFAr1GjchfKBERogBSkpKQnBwMGbMmIHRo0djz549YBgGERERqKmp0XgMy7JYsGABXn/9dT1HSwi/6BkZIQZm//79+Pe//424uDiUlpbCy8sLDMNwa2k9Ymdnh7KyMm5/zJgx2LBhA0pKSgAo1qu6cOGC3uMnRN+oR0aIARg/fjwsLCxQWVmJwMBAHDx4ENHR0Vi5ciVmzpyJlStXIjAwEH369OGOiY2Nxdy5c7Fq1SosX74ckyZNQnFxMYYMGQKGYSCVSjFt2jSEh4fzeGWE6B7Nfk8IIcSo0a1FQgghRo0SGSGEEKNGiYwQQohRo0RGCCHEqFEiI4QQYtQokRFCCDFqlMgIIYQYNUpkhBBCjBolMkIIIUbt/wFq6QZZH8QYOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pricing = pd.read_csv(\n",
    "    '../data/Stocks/spy.csv',\n",
    "    header=0,\n",
    "    parse_dates=[\"Date\"],\n",
    "    #index_col=0,\n",
    "    usecols=['Date','Adj_Open', 'Adj_Close'])\n",
    "figure(num=None, figsize=(6, 4), dpi=80, facecolor='w', edgecolor='k')\n",
    "plt.plot(pricing.Adj_Open, label='Spy Open')\n",
    "plt.plot(pricing.Adj_Close, label='Spy Close')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual inspection of the DataFrame. \n",
    "\n",
    "Adjust to see wider and different selection of rows.  e.g pricing.tail(10) shows \n",
    "the final ten rows of data. See 10 Minutes to Pandas https://pandas.pydata.org/pandas-docs/stable/10min.html \n",
    "or the Pandas Cheat Sheet https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Date</th>\n",
       "      <th>Adj_Close</th>\n",
       "      <th>Adj_Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993-01-29</td>\n",
       "      <td>27.112253</td>\n",
       "      <td>27.131505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993-02-01</td>\n",
       "      <td>27.305082</td>\n",
       "      <td>27.131502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1993-02-02</td>\n",
       "      <td>27.362904</td>\n",
       "      <td>27.285771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993-02-03</td>\n",
       "      <td>27.652185</td>\n",
       "      <td>27.401472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1993-02-04</td>\n",
       "      <td>27.767893</td>\n",
       "      <td>27.748579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Adj_Close   Adj_Open\n",
       "0 1993-01-29  27.112253  27.131505\n",
       "1 1993-02-01  27.305082  27.131502\n",
       "2 1993-02-02  27.362904  27.285771\n",
       "3 1993-02-03  27.652185  27.401472\n",
       "4 1993-02-04  27.767893  27.748579"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pricing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shift the Adjusted Close down one row \n",
    "\n",
    "so that it aligns with the next day's Adjusted Open.  That way we can calculate the signals using the previous day's Close and enter the trade on the next day's Open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Date</th>\n",
       "      <th>Adj_Close</th>\n",
       "      <th>Adj_Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6508</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>273.980011</td>\n",
       "      <td>273.809998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6509</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>275.649994</td>\n",
       "      <td>280.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6510</th>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>279.299988</td>\n",
       "      <td>278.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6511</th>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>270.250000</td>\n",
       "      <td>265.920013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6512</th>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>269.839996</td>\n",
       "      <td>269.459991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Adj_Close    Adj_Open\n",
       "6508 2018-11-30  273.980011  273.809998\n",
       "6509 2018-12-03  275.649994  280.279999\n",
       "6510 2018-12-04  279.299988  278.369995\n",
       "6511 2018-12-06  270.250000  265.920013\n",
       "6512 2018-12-07  269.839996  269.459991"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock=pricing.copy()\n",
    "stock.Adj_Close=stock.Adj_Close.shift(1)\n",
    "\n",
    "stock.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create three or more separate blocks of data for testing. \n",
    "\n",
    "Reserve one of these for out of sample testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Date</th>\n",
       "      <th>Adj_Close</th>\n",
       "      <th>Adj_Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-09-04</td>\n",
       "      <td>81.720024</td>\n",
       "      <td>81.505194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-09-05</td>\n",
       "      <td>81.197357</td>\n",
       "      <td>81.397842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-09-06</td>\n",
       "      <td>81.397842</td>\n",
       "      <td>80.646166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-09-07</td>\n",
       "      <td>79.300270</td>\n",
       "      <td>78.763331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-09-10</td>\n",
       "      <td>77.832664</td>\n",
       "      <td>77.102443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Adj_Close   Adj_Open\n",
       "0 2001-09-04  81.720024  81.505194\n",
       "1 2001-09-05  81.197357  81.397842\n",
       "2 2001-09-06  81.397842  80.646166\n",
       "3 2001-09-07  79.300270  78.763331\n",
       "4 2001-09-10  77.832664  77.102443"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_1=stock.iloc[0:2170].copy()\n",
    "stock_2=stock.iloc[2170:4342].copy().reset_index(drop=True)\n",
    "stock_3=stock.iloc[4342:6513].copy().reset_index(drop=True) \n",
    "stock_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here is the code for the function which loops through the stock data and executes the trades.  \n",
    "\n",
    "You can experiment with different parameters such as changing the maximum position size or starting capital.\n",
    "\n",
    "Experiment with changing references:\n",
    "\n",
    "1.from both trading and signal generation at the Close; to \n",
    "\n",
    "2.signal generation at the Close but trading at the next Open. \n",
    "\n",
    "You will not be happy with the result suggesting you may need to automate trading to generate signals and trade intraday so you can execute a trade immediately a signal is given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pixiedust": {
     "displayParams": {}
    }
   },
   "outputs": [],
   "source": [
    "# Trade using a simple mean-reversion strategy\n",
    "def trade(stock, length):\n",
    "\n",
    "    temp_dict = {}\n",
    "    # If window length is 0, algorithm doesn't make sense, so exit\n",
    "    if length == 0:\n",
    "        return 0\n",
    "\n",
    "    # Compute rolling means and rolling standard deviation\n",
    "    #sma and lma are filters to prevent taking long or short positions against the longer term trend\n",
    "    rolling_window = stock.Adj_Close.rolling(window=length)\n",
    "    mu = rolling_window.mean()\n",
    "    sma = stock.Adj_Close.rolling(window=length*1).mean()\n",
    "    lma = stock.Adj_Close.rolling(window=length * 10).mean()\n",
    "    std = rolling_window.std()\n",
    "\n",
    "    #If you don't use a maximum position size the positions will keep on pyramidding.\n",
    "    #Set max_position to a high number (1000?) to disable this parameter\n",
    "    #Need to beware of unintended leverage\n",
    "    max_position = 1\n",
    "    percent_per_trade = 1.0\n",
    "\n",
    "    #Slippage and commission adjustment  - simply reduces equity by a percentage guess\n",
    "    # a setting of 1 means no slippage, a setting of 0.999 gives 0.1% slippage\n",
    "    slippage_adj = 1\n",
    "\n",
    "    # Compute the z-scores for each day using the historical data up to that day\n",
    "    zscores = (stock.Adj_Close - mu) / std\n",
    "\n",
    "    # Simulate trading\n",
    "    # Start with your chosen starting capital and no positions\n",
    "    money = 1000.00\n",
    "    position_count = 0\n",
    "\n",
    "    for i, row in enumerate(stock.itertuples(), 0):\n",
    "\n",
    "        #set up position size so that each position is a fixed position of your account equity\n",
    "        equity = money + (stock.Adj_Close[i] * position_count)\n",
    "        if equity > 0:\n",
    "            fixed_frac = (equity * percent_per_trade) / stock.Adj_Close[i]\n",
    "        else:\n",
    "            fixed_frac = 0\n",
    "        fixed_frac = int(round(fixed_frac))\n",
    "\n",
    "        #exit all positions if zscore flips from positive to negative or vice versa without going through\n",
    "        #the neutral zone\n",
    "        if i > 0:\n",
    "            if (zscores[i - 1] > 0.5\n",
    "                    and zscores[i] < -0.5) or (zscores[i - 1] < -0.5\n",
    "                                               and zscores[i] > 0.5):\n",
    "\n",
    "                if position_count > 0:\n",
    "                    money += position_count * stock.Adj_Close[i] * slippage_adj\n",
    "                elif position_count < 0:\n",
    "                    money += position_count * stock.Adj_Close[i] * (\n",
    "                        1 / slippage_adj)\n",
    "                position_count = 0\n",
    "\n",
    "        # Sell short if the z-score is > 1 and if the longer term trend is negative\n",
    "        if (zscores[i] > 1) & (position_count > max_position * -1) & (sma[i] <\n",
    "                                                                      lma[i]):\n",
    "\n",
    "            position_count -= fixed_frac\n",
    "            money += fixed_frac * stock.Adj_Close[i] * slippage_adj\n",
    "\n",
    "        # Buy long if the z-score is < 1 and the longer term trend is positive\n",
    "        elif zscores[i] < -1 and position_count < max_position and sma[i] > lma[i]:\n",
    "\n",
    "            position_count += fixed_frac\n",
    "            money -= fixed_frac * stock.Adj_Close[i] * (1 / slippage_adj)\n",
    "\n",
    "        # Clear positions if the z-score between -.5 and .5\n",
    "        elif abs(zscores[i]) < 0.5:\n",
    "            #money += position_count * stock.Adj_Close[i]\n",
    "            if position_count > 0:\n",
    "                money += position_count * stock.Adj_Close[i] * slippage_adj\n",
    "            elif position_count < 0:\n",
    "                money += position_count * stock.Adj_Close[i] * (\n",
    "                    1 / slippage_adj)\n",
    "            position_count = 0\n",
    "\n",
    "        #fill dictionary with the trading results.\n",
    "        temp_dict[stock.Date[i]] = [\n",
    "            stock.Adj_Open[i], stock.Adj_Close[i], mu[i], std[i], zscores[i],\n",
    "            money, position_count, fixed_frac, sma[i], lma[i]\n",
    "        ]\n",
    "    #create a dataframe to return for use in calculating and charting the trading results\n",
    "    pr = pd.DataFrame(data=temp_dict).T\n",
    "    pr.index.name = 'Date'\n",
    "    pr.index = pd.to_datetime(pr.index)\n",
    "    pr.columns = [\n",
    "        'Open', 'Close', 'mu', 'std', 'zscores', 'money', 'position_count',\n",
    "        'fixed_frac', 'sma', 'lma'\n",
    "    ]\n",
    "    pr['equity'] = pr.money + (pr.Close * pr.position_count)\n",
    "    #\n",
    "    return pr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The next cell calls the function. \n",
    "\n",
    "Experiment with different moving averages by altering the number in brackets.\n",
    "\n",
    "profit = trade(stock, moving_average) runs the back test with the entire price series\n",
    "profit = trade(stock_1, moving_average) runs the test using only the first third of the data and so forth through \n",
    "stock_2 and stock_3\n",
    "\n",
    "You will need to amend the function \"profit.to_csv\" to your own raletive or absolute file address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pixiedust": {
     "displayParams": {}
    }
   },
   "outputs": [],
   "source": [
    "moving_average=10\n",
    "profit = trade(stock, moving_average)\n",
    "profit.to_csv('../data/mean_reversion_profit.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect the reults data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>mu</th>\n",
       "      <th>std</th>\n",
       "      <th>zscores</th>\n",
       "      <th>money</th>\n",
       "      <th>position_count</th>\n",
       "      <th>fixed_frac</th>\n",
       "      <th>sma</th>\n",
       "      <th>lma</th>\n",
       "      <th>equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>273.809998</td>\n",
       "      <td>273.980011</td>\n",
       "      <td>269.269998</td>\n",
       "      <td>4.334340</td>\n",
       "      <td>1.086674</td>\n",
       "      <td>15886.752402</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>269.269998</td>\n",
       "      <td>280.604163</td>\n",
       "      <td>7941.332083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-03</th>\n",
       "      <td>280.279999</td>\n",
       "      <td>275.649994</td>\n",
       "      <td>269.532999</td>\n",
       "      <td>4.655090</td>\n",
       "      <td>1.314045</td>\n",
       "      <td>15886.752402</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>269.532999</td>\n",
       "      <td>280.604539</td>\n",
       "      <td>7892.902576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-04</th>\n",
       "      <td>278.369995</td>\n",
       "      <td>279.299988</td>\n",
       "      <td>270.089996</td>\n",
       "      <td>5.474237</td>\n",
       "      <td>1.682425</td>\n",
       "      <td>15886.752402</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>270.089996</td>\n",
       "      <td>280.616429</td>\n",
       "      <td>7787.052750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-06</th>\n",
       "      <td>265.920013</td>\n",
       "      <td>270.250000</td>\n",
       "      <td>270.204996</td>\n",
       "      <td>5.463197</td>\n",
       "      <td>0.008238</td>\n",
       "      <td>8049.502402</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.204996</td>\n",
       "      <td>280.535628</td>\n",
       "      <td>8049.502402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-07</th>\n",
       "      <td>269.459991</td>\n",
       "      <td>269.839996</td>\n",
       "      <td>270.776996</td>\n",
       "      <td>5.038219</td>\n",
       "      <td>-0.185978</td>\n",
       "      <td>8049.502402</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>270.776996</td>\n",
       "      <td>280.453216</td>\n",
       "      <td>8049.502402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open       Close          mu       std   zscores  \\\n",
       "Date                                                                 \n",
       "2018-11-30  273.809998  273.980011  269.269998  4.334340  1.086674   \n",
       "2018-12-03  280.279999  275.649994  269.532999  4.655090  1.314045   \n",
       "2018-12-04  278.369995  279.299988  270.089996  5.474237  1.682425   \n",
       "2018-12-06  265.920013  270.250000  270.204996  5.463197  0.008238   \n",
       "2018-12-07  269.459991  269.839996  270.776996  5.038219 -0.185978   \n",
       "\n",
       "                   money  position_count  fixed_frac         sma         lma  \\\n",
       "Date                                                                           \n",
       "2018-11-30  15886.752402           -29.0        29.0  269.269998  280.604163   \n",
       "2018-12-03  15886.752402           -29.0        29.0  269.532999  280.604539   \n",
       "2018-12-04  15886.752402           -29.0        28.0  270.089996  280.616429   \n",
       "2018-12-06   8049.502402             0.0        30.0  270.204996  280.535628   \n",
       "2018-12-07   8049.502402             0.0        30.0  270.776996  280.453216   \n",
       "\n",
       "                 equity  \n",
       "Date                     \n",
       "2018-11-30  7941.332083  \n",
       "2018-12-03  7892.902576  \n",
       "2018-12-04  7787.052750  \n",
       "2018-12-06  8049.502402  \n",
       "2018-12-07  8049.502402  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a new dataframe \n",
    "\n",
    "which contains trhe equity curve as its only column to feed to FFN the stats library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "series=profit[['equity']].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stats and Charts\n",
    "\n",
    "The following charts and stats are allproduced by the FFN Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stat                 equity\n",
      "-------------------  ----------\n",
      "Start                1993-02-01\n",
      "End                  2018-12-07\n",
      "Risk-free rate       0.00%\n",
      "\n",
      "Total Return         704.95%\n",
      "Daily Sharpe         0.85\n",
      "Daily Sortino        1.33\n",
      "CAGR                 8.40%\n",
      "Max Drawdown         -14.44%\n",
      "Calmar Ratio         0.58\n",
      "\n",
      "MTD                  1.36%\n",
      "3m                   -0.78%\n",
      "6m                   -1.30%\n",
      "YTD                  -4.45%\n",
      "1Y                   -4.45%\n",
      "3Y (ann.)            1.78%\n",
      "5Y (ann.)            3.69%\n",
      "10Y (ann.)           7.96%\n",
      "Since Incep. (ann.)  8.40%\n",
      "\n",
      "Daily Sharpe         0.85\n",
      "Daily Sortino        1.33\n",
      "Daily Mean (ann.)    8.58%\n",
      "Daily Vol (ann.)     10.10%\n",
      "Daily Skew           0.42\n",
      "Daily Kurt           24.86\n",
      "Best Day             8.87%\n",
      "Worst Day            -7.28%\n",
      "\n",
      "Monthly Sharpe       1.06\n",
      "Monthly Sortino      1.96\n",
      "Monthly Mean (ann.)  8.41%\n",
      "Monthly Vol (ann.)   7.93%\n",
      "Monthly Skew         0.31\n",
      "Monthly Kurt         7.35\n",
      "Best Month           15.75%\n",
      "Worst Month          -9.52%\n",
      "\n",
      "Yearly Sharpe        0.83\n",
      "Yearly Sortino       9.86\n",
      "Yearly Mean          8.82%\n",
      "Yearly Vol           10.69%\n",
      "Yearly Skew          1.97\n",
      "Yearly Kurt          4.80\n",
      "Best Year            44.31%\n",
      "Worst Year           -4.45%\n",
      "\n",
      "Avg. Drawdown        -2.37%\n",
      "Avg. Drawdown Days   45.15\n",
      "Avg. Up Month        1.86%\n",
      "Avg. Down Month      -1.36%\n",
      "Win Year %           84.00%\n",
      "Win 12m %            87.33%\n"
     ]
    }
   ],
   "source": [
    "stats = series.calc_stats()\n",
    "stats.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices =series\n",
    "ax = stats.prices.to_drawdown_series().plot(figsize=(15, 8),title='Drawdown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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up8ujW15fboe3rtFhx/wZlbUuRYUGaeU93qX1Q44yf+yi0amatTxP958/VDuKKrV5X4UevXRE+wxsh5h5coY+27BPD180XI9+tlm/Ost/Qz9bk2m4sV2gZGVlWUuXLg10GQAAAIBf/H3eFv3ts82Hvf/jCb30SoOFQiSpT1KUvvjllKO+u7S6Tt9nF2tvWY1+995aSc3vubMsS0WVzuMKi2hZxphllmVlHa0dPXAAAACAn63ZVaqkmDDNnJihv3yyqdH9Q8ObJFXVupv17l+/vUqfrt93XHUZYwhv7QwBDgAAAPCj57/O1qfr92l87wTdPDVTLrel3SXV+s05AzV/U77+761VjZ6ZPixFX20usM9dbk+Ty+lXOV36dP0+jenVRSFBRt9tL/brd0HgHd+mCgAAAACO6F9fbVPGnR/ovvfXS5J+NqWvJOkXZ/TTwxcPV0JUqC4cndrks32SolTpdKna6dbzX2cr8+6PNL2JzbjfWJIrSTp/VE/994YJfvomaEvogQMAAAD84IEPN9rHoUEOTR2Q3GS7q05K16vf7ZTkXep/dV6piiud8ljSXe+u1nsrd0uS1u0uU8adH2jWjRP19ZZCPfb5wTl1p2R6V1e8Y9oAud2BX+MC/kOAAwAAAFrQ+6t366kvtvpcc7o9h21/33lD7QB33sieOm9kT03963xJssNbQ3e9u1oVNS6fa727RkmSbpqSeSKlox1gCCUAAADQQl5bvEO3vL5CG/eW69xh3fWzyd5hk3efO+iwzxhj9NCFw/TWTw8Ogbwk6+DQyvAQ31/ZN++r0O7SGl0wqqfQ+dADBwAAgE7NsiztKKpSRn0v1omY06DH7J8/GiNJuvOcgUd97vJx6T7nWb0S7OOw4CC9e+PJ+uXbq7RhT5kk6e2fTdCwnnHq3TVKZw7udsJ1o/0gwAEAAKBT+2DNHt3y+gr99tyBun5Sn+PaxLrO7dG5TyzU1oIKSdJDFw47oZoa7tV81zkDNbhHrD76xSTlFlepoKJWo9O7SJJuPb3fCX0O2h8CHAAAADotj8fSn+Z6V4l84MONSomL0IwRPY75PXn7q7Ulv0JnDOqmm6f21aj6gHW8sjIO9sAN7hFrH6clRCotIfKE3o32jTlwAAAA6LQWbClQQXmtfZ5TWHlc73lm/jZJ0s8m9znh8CZJQY6DvYApcREn/D50HAQ4AAAAdFp5+6t9zkuq647rPW8u9e7H1pK9YzFh3sFyiVGhLfZOtH8MoQQAAECnVOf26PfvrfW59vzX2fr9DwY32X5rfoU27ytXWXWdHA6jzORoXf6v7+R0ebcISIkLV7fY8Bar7/1bT9GWfRVyOI59Th46LgIcAAAAOo3tBRXaVVKtSf2S9MaSncf07M9eXaat+RX2+S9O7yeny6MbTu2jyNCg45o7dyS9EqPUK/HEV8ZExxLQAGeMmSFpRmYmGw4CAADAv55ZsE0PfbRRkhQREqTqOrckaeEdU/XYZ5v17opdh33Wsixtza/QBaN6qkd8uP7x5TY9MW+LJOmXZ/VXWHCQ/78AoAAHOMuy5kqam5WVdX0g6wAAAEDH4XR5tKukutH11xbvsI8n9E1UZnK0JvRJVFpCpB69bKRiI0L00qIcWZbVaCuBlbklkqRuseEa0P3gqpDXnpxBeEOrYgglAAAAOgSPx9L2wko98vFGfbp+3xHb/vTUPhrfJ9Hn2oGVH7/aUqjJ/ZPs606XR3e8s1qSdObgbhrSYFn/e2YMaanygWYhwAEAAKBDePKLLXr8c++wxvG9E3TFuHSf+0EOo4l9E/V9zv5G4U2SkmPCJEkLNhVocv8kzd+Ur9cW79Rn9WEwyGE0KCVGYcEs5I7AIcABAACg3Zi9Ik+Ltxfr/87qr+QY3xUfD4S3f1w5WmN7d2l0/4BpQ7s3ef3qCb304EcbtWBzvqTBmrV8lx3eJOmxy0YqMtT76/NPJ/fR5H5JTb4H8CcCHAAAANqNP8xZp/Ial7IyEnTxmFT7emWtyz6ePjzluN59IJxtK6jUd9uLNHfVbp/7p2R2tY/vOmfQcX0GcKLo/wUAAECbtnZXqcbc95nOeWKhymu8Qa2kyunT5sn6FSEHdo85oc967+aTJUmX/+s7n+uj0uOVwIbaaAMIcAAAAGjTfvD3r1VU6VRMWLDOGtxNkvTN1kL7/qOfbtKzX21XREiQnp859oQ+a2Ra/Ak9D/gbAQ4AAABt0jvL8vRE/bw2SXrx2rH614+zJElfbiqQ22NJkuZvLpAkfXr7qeoZH3HCn3vbGf0aXevNhtpoI5gDBwAAgDansKJWv3p7lSQp2GH0/Myxigrz/dX1rMcWKCMxSqvzSnVKZlelJUS2yGdfN6mPkmLCdPfstZKkf/84SxMzG69aCQQCAQ4AAABtRllNnVbuLNG2ggpJ0oszx2py/yQ5HKZR220FlQqt30Q7o2vLhDdJig4L1o/G97ID3Bn1wzaBtoAABwAAgDbjLx9v0n++2yFJMkYa0D2mUXj7ww8G697312vqgCS9eO045RZXqWt0mF/q6ZXYcsEQaAkEOAAAALSat77P1b6yGvs8IjRIV53US+Eh3p60OSt3SZJm3ThBcRGh6tHEnLadxVWSJIfxBruWGjp5qI33TbM/A2grCHAAAABoFbnFVbpj1upG1zOTozVlQLKcLo/K6rcJGNMr4bDvuXJ8ul5alKPf/WCw32qVZIdKoC0hwAEAAMDviiudmvTIl5Kk+84fqivGpmnj3nL94O9fa+aL3x/Tu/p3i1HOQ9P9USbQ5rGNAAAAAPxua36FfRwW7FBwkEOx4SFNtp3cP6m1ygLaHXrgAAAA4Hdl1XX2cf9uMZKktISD89tenDlWw1LjNH9TgS4ek9rq9QHtBQEOAAAAfmVZlq57Zakk6flrsjQyLV6SZBosEDJ1YLIkEd6AoyDAAQAAoMW5PZYWZxep1uXR99nF9vXTB/nuqbb2T2fL6fK0dnlAu0WAAwAAQIv7YM0e3frGiqO2iw4LlvyzhRvQIbGICQAAAFrcgfA268aJmn3TxABXA3Qc9MABAADAb8b06iLJu3fbwO4xAa4GaP8IcAAAAGhRG/eWSZIm9etqX3vggmGBKgfoUAhwAAAAaDHLdhTrgQ83SpJ+ckrvAFcDdDwEOAAAAJywaqdbW/MrdO/c9Vq7u0xnDOqmqQOSA10W0OGwiAkAAEAAvL96t2b8/Wst21Gsc55YqDeW7FRRRa0qal2BLu243P3eGs146mutyivV9ZP66N/XZAW6JKBDMpZlBe7DjZkhaUZmZub1W7ZsCVgdAAAAranO7VG/uz+SJJ09pJs+WbfPvtc1OlRLf3dmoEo7bpc+863Kaup0x7QBGtc70bs9AIBmM8YssyzrqP/yEdAeOMuy5lqWdUNcXFwgywAAAGhVq/NK7OPP1u/zuVdY4dT+Smdrl3TCCitr1btrlE4b2I3wBvgRQygBAABa2Z8/2GAfe5oYDLVpX3krVnPi3B5LecXVSk+IDHQpQIfHP48AAAC0svzyWvWMj9Cukuom77elHrg6t0f/W7lbI9LilZkcLUlyuT36fEO+aurckqSymjo53R5ldI0KZKlAp0CAAwAAaCUVtS59tGaP8vZX67Yz+unxz33XABiZFq+VuSWqa6pbLkCWZBfrl2+vkiT94vR+uv3M/vpue7F+9uqyRm2H9Iht7fKATocABwAA0Epe+TZHj3y8SZI0ZUCyzh7SXec8sVAJUaEqrnQqb7+3R67O5QlglQfN35SvmS9+b58/MW+LZk7M0N6yGknS69ePV0pchCQpMjRI3WLDA1In0JkQ4AAAAPzo6fnb9PzX2/XYZSPt8Pb1b6YqtYt3vtiK35+pJTnF+ul/likmPFiFFbVyebwBzrIs3fjqcm0tqFBmUrSevmq0jDF+rff7nGJt3FOm389Z1+T9Ufd9pgl9EiVJI1LjFcWCJUCrYhETAACAJszflK+MOz/QeU99rTveWXXY+WpHsjW/Qg9/vFGFFU5d/fwS+/qB8CZJXaJC7cU/fjQ+XZLkdFv1//fo43V7tTW/Qh+v26vnFm4/ka90VIu2FeqSZ771CW99kqL0wkzflc1X5pZoRGqcIkOD/FoPgMYIcAAAAId4/utse+jgqrxSvbU0T19s2HeUpxpbvnO/fTyxb+Jh2w1KidWqe87SRaNTJXkXCZGkylq3T7sHPtx4zDUcjdtj6dHPNuueOWt19+y1kqR/XDlakvS3S0bos9snKyEqzG5/4aie2nDfNM255RS/9wYCaIw+bwAAgEO89t0O+/gfV47Wza8v1+/nrNPVEzKO6T3OBnPZkmLC9O1dp6m8xtVk27iIEFXUeu+56nvgKuvPLxqdqlnL85r9uaVVdfpo7R65PJYcxuiMwclKjml6ftqW/HI9OW+LokKDFBrs0J9+OETTh6do+vDpdpu+SVHqlRipP58/TCdnHj6IAvA/AhwAAEC9xz7brLmrdmt7YaUk6fyRPTR9eIpufv343pdT/x5J6hkfoZS4CKXEHb59sMPbo+Ws74Grcnp74FK7RNhtDgznfO26kw77nreW5urPHx7ca+6DNYm6eWqmkmPC7a0ADqip837WU1eO1tSByU2+LyY8RAt+PfXwhQNoNQQ4AACAel9uyveZ63bf+UN97te5PQoJOvoMlLveXaPZK/LscCSpWZtcH3j3gR64DXvKJPkuz//W0qP3xBVXORXsMFp052ka98A8fbO1SN9sLZIk5Tw03aft/irvnnOhwcysAdoDAhwAAIC84WxvaY3SEyK1Jb9CseHBigkPkSRdMS5NbyzJ1brdZRqZFm8/s7/Sqd/NWauqWt9hkYuzi5WRGKWNe8vta+EhR1/wI8hh5DDeWpbmFOu2N1dK8g6vPJRlWZq/qUCfrt/b6N6yHfsVEx6s5CaW9c/bX+WziMreUu+WAGwBALQPBDgAANDp1bk9mvDgPBVWONW7a5Qk6ewh3e37V47rpTeW5Cq/fv+zA1bk7tcHq/eoX3K0IhqsyNgvOVq3nt5P2wsq1ScpSst27Nf04SnNqiU4yCGn22MP45TU5FL9m/dV6OkF27Ri5351iQxtdH/KAN/hkA9fNEy/mbVGX28pVK/EKOXur9IlY1K1ZlepJCktIaLROwC0PQQ4AADQac1dtVvhIUEamRavwgrvUMJbTsvUH6JClZEYZbc7EM5u+M8y/fWSEfb1f9cv6//s1WPUJ8l3bpkknT7owP+7NbumLpEhWpNXave6zbpxgob2bDxxbsHmfC3JLtbk/kl6+Sfjjvres4d0129mrdHnG/L1ef2Kmk/P36bs+qAYFsyWAEB7QIADAACd1s/fWCFJ+vJXUyRJj102QpP6JTVq16frwTD3q7dXNbrfNSas0bXjNTglVgUVtSoor1VMeLDG9EqQJI3LSNCSnGK73YEtBbYVVBzxfXedM1DR4cF2L97nDbZDOBDe7pg2oMXqB+BfBDgAANApuT2WffzwR94wFBPWeK6ZJDkcB/c7m3PzyUqIClWty6MzHl0gSYoNb/q54xHkcGjtrjKt3VWm2PCDv6rdMW2ALn7m20bt8/YfeYPxn07uax+H1g/PPNRNUzJPoGIArYkABwAAOqUDKzxK0sfrvAuBRIcf/VejQSmxCg12yNMgALakhpt/lzXYMy64GatfHk1T4a05wy8BtB2sFwsAADqlpjbUjm5isZAD/nbJCJ2cmWgvt+9wGF11UrpeaeEAVFzpbPJ6kPH2AoaH+P769peLhx/zZzxykfeZiX0TNbl/4yGjANoueuAAAECHVlPn1jdbCxUdFqyQBnudra1ffbGh5NjDz2W7aEyqLhqT6nPt/vOHtVyhh3jv5pPVI/7g0v6O+tJjwkNUU1cryRveLslKO+Z3j+7l3QphWBOLowBo2whwAACgw9iyr1xPz98mt+Ud3hjscKhnlwg9OW/LUZ89a3A3Jce0nb3QesSF+9STGh+pvklRunFKpr2QyrGGtwNz4DKTYzTn5pMJcEA7RIADAAAdxhtLcvXuil3KSIyUx5J2FlcpMergHmlPXTnKZ5jkzBe/t4/jI1tuIZKW0HDhFEmKiwzRvF9OkeRdCTMl7tjD5vxfT9HO4ipJ0ogGG5IDaD8IcAAFSrOsAAAgAElEQVQAoEMorarTC99kKyEqVPN/PVUej6W+d3+oogZzyqYN6e6zGEj32HDtrd+c+51leXrk4hGN3hsoDmMOe2/1H89SjdN9zO/sER+hHvFs2A20ZyxiAgAAOoRdJd7l9C/J8s5TcziMrAYLRc6+aWKjlRwb9rr5aVHJ43b4+ObdtiA5tu0M9wTQeuiBAwAA7dLynfs1a1meymtcigkP1tZ874bWpw/s1qjt8NQ4jUrv0uj6fecP1SVN7K0GAG0VAQ4AALRLLy/K0ZyVu32uxUeGKDM5ulHbG07t0+Q7xmYkKOeh6frHl1t15uDGwS+QjjSEEkDnRYADAADtUkWNS4NSYu0NuRf8eorSEyJlGgSfHwxP0fur96iuiQ2sG7p5aqZfaz0Wz/04S/9dslNxbWxRFQBtA3PgAABAu1TpdCmmwYqScREhPuFNksb06mLfay/OHNxNz88cG+gyALRR9MABAIB2p6bOre+2F2tkg6XwG24PcMDMiRka0D1GE/oktmZ5AOA3BDgAANDufLutSJK0MrfEvnboCpOSZIzRxL5dW60uAPA3hlACAIB2p7zWJUn65Zn9NbB7TICrAYDWE9AeOGPMDEkzMjPbzsRhAADQts1ekaf/1a8+eUlWmq6b1EfVdce+qTUAtEcB7YGzLGuuZVk3xMXFBbIMAADQTlQ73frlW6u0YHOB0hMi1SUqRBGhQUqICg10aQDQKpgDBwAA2o0t+eXyWNIzV43WtKEpgS4HAFodc+AAAEC7sK+sRne8s1qSmtysGwA6AwIcAABoF/7z7Q5t3FsuSYqPZMgkgM6JAAcAANqFgvJaSd6VJxOZ8wagk2IOHAAAaLM27CnTU19ulcdjacXOEg1KidXPT+8X6LIAIGDogQMAAG3Wx2v36oPVe7StoEKxEcH6wXAWLgHQudEDBwAA2qyaOrfCgh369PbJgS4FANoEAhwAAGgzFm8v0o7iKknShD6JqnK6FREaFOCqAKDtIMABAIA2wbIsXf38EjndnkCXAgBtFnPgAABAm1Dr8sjp9uhnk/v6XO/fjT3fAOAAAhwAAGgTaurckqTkmDD72vkjezD/DQAaIMABAIA2oabOO3QyPCRI156cIUlyGBPAigCg7WEOHAAACKiSKqd+O3uNCiuckqTwEIcGpcRKkgwBDgB80AMHAAACat3uMn24Zq/2Vzo1vneCRqV3sXvewkP4VQUAGqIHDgAABNSP/r1YkvTIxcM1Kr2LJCkmPFinD0zWjVP6HulRAOh0CHAAAKBNCAk62NvWNTpMz88cG8BqAKBtYlwCAABoExoGOABA0/hJCQAAAmbFzv32cUgQC5YAwNEQ4AAAQMDsKqm2j+mBA4Cj4yclAAAImCqn2z52OOiBA4CjIcABAICAeX5htiQpIzFSSdFhAa4GANo+AhwAAAiYTfvKJUkf/eJUhQbzawkAHA0/KQEAQMCxYTcANA/7wAEAgFbz3Ffb9fmGfZKknl0iJEl9k6JkDPPfAKA5CHAAAKDVvL0sV4UVTrk9lhZnF0uSLs1KC3BVANB+MF4BAAC0Gpfb0sS+iXrggmH2tSBWnwSAZiPAAQCAVuN0exQa5NDw1Dj7mmUFsCAAaGcIcAAAoNW43JaCg4yCgw72urGACQA0Hz8xAQBAq6lzexQS5FCw4+CvIJGhTMkHgOYiwAEAgFZzMMAd7IHrFhsewIoAoH0hwAEAgFYxb8M+ldW4VFhRq6AGQyhPzkwMYFUA0L4Q4AAAQKv409z1kqSu0WEKaTCEkj3gAKD5GHQOAABahcvt0WkDk/XbcwcFuhQAaLcIcAAAoFW4PJaSY8IUGuyQx8PeAQBwPAhwAADAb6z6Td6MMfJYlr1pt8NhdNrAZF02Ni2Q5QFAuxPQAGeMmSFpRmZmZiDLAAAAfpBTWKlzn1yoKqdbd587SLV1Hp/VJ1+YOTaA1QFA+xTQRUwsy5prWdYNcXFxgSwDAAD4wY7iKlU53ZKkP3+4QeW1LkWHM/gHAE4EP0UBAIBf1NS5fc5fmJmlsRkJAaoGADoGAhwAAPCLWpfH5/y0gd0CVAkAdBzsAwcAAPxiw56yQJcAAB0OAQ4AAPjFR2v2BLoEAOhwCHAAAMAvgoMcOqkPc94AoCUxBw4AAPjF1vwKxUV00evXj1dtnefoDwAAjooABwAA/GbZjv2a2LdroMsAgA6DIZQAAKDFuT1WoEsAgA6JAAcAAFrc/E35kqTfTBsY4EoAoGMhwAEAgBa3OLtYkjS5f1KAKwGAjoUABwAAWpzbYykqNEiDe8QGuhQA6FAIcAAAoMW5PZYcDhPoMgCgwyHAAQCAFuexLAUR4ACgxRHgAABAi3N7LAUZAhwAtDQCHAAAaHEeiyGUAOAPBDgAANDi6IEDAP8gwAEAgBbn9og5cADgBwQ4AADQ4rxDKANdBQB0PPxoBQAALY4hlADgHwQ4AADQ4jyWJQcBDgBaHAEOAAC0OFahBAD/IMABAIAWxxBKAPCP4EAXAAAAOo6VuSWavTxP6/eUKTosJNDlAECHQw8cAABoMa98m6NXvtuh8hqXxvSKD3Q5ANDh0AMHAABajNtjqVdCpOb/emqgSwGADokeOAAA0GLcHlafBAB/IsABAIAWY1li9UkA8CMCHAAAaDHeHrhAVwEAHRcBDgAAtBg3G3gDgF8R4AAAQIuxLEtBdMEBgN8Q4AAAQIthERMA8C8CHAAAaDEeFjEBAL9iHzgAAHBc6twe7S2t8bm2s7hKsREhAaoIADo+AhwAADgut7+5Uu+v3hPoMgCgUyHAAQCAY2ZZlr7bXqyT+iTo4jFp9vVfvb0qgFUBQMdHgAMAAMekvKZOH63dq8KKWt0yta8uHpNq3yPAAYB/EeAAAMAxuXv2Wv1v1W5J0tjeCQGuBgA6FwIcAAA4JgfC27d3naaUuIgAVwMAnQvbCAAAgGbbvK/cPia8AUDrI8ABAIBme3bB9qO26RYb1gqVAEDnxBBKAADQbOt2l0qSvvzVlCbvb7xvmhyGjbwBwF8IcAAAoFn2Vzq1cW+5QoMd6t01qsk24SFBrVwVAHQuDKEEAACHta2gwp73Vul0SZJ+e87AQJYEAJ0aPXAAAKBJLrdHp/9tgSQpMzla2YWVkiSn2xPIsgCgU6MHDgAANKmy1m0fJ8eEyVE/ta2owhmgigAA9MABAIAmNexp+9ulI9QtJlz/+W6HLslKDWBVANC5BbQHzhgzwxjzr9LS0kCWAQAAmvDhmj32cffYcDkcRtdMzFBkKP/+CwCBEtAAZ1nWXMuyboiLiwtkGQAAoAlLcoolSQ9fNEyGrQEAoE1gDhwAALBtzS/Xy4tyJElFFbUam9FFl41ND2xRAAAbYyAAAOjk9pXV6KGPNmpIj1jd/8EGSdI9/1snSTpnaPdAlgYAOAQBDgCATm5xdrFmr9il2St22deiQoN0zcQMnTssJYCVAQAORYADAKCTczVYbTIhKlTFlU49cOEwnTeyZwCrAgA0hQAHAEAn53Jb9vGiO09TbZ1HsRH8igAAbRE/nQEA6MRmLcvT/1btts/DQ4IUHhIUwIoAAEdCgAMAoJN6a2mu7nhntX3+xS8nB7AaAEBzEOAAAOiEluYU6w9z1kqS+iVH67P/I7wBQHvAPnAAAHRCLy3KUU2dd/GSJ68YFeBqAADNRQ8cAACdUGWtS8N6xmnuz08JdCkAgGNADxwAAJ1QpdOtyFAWKwGA9oYeOAAAOgHLsvTH/61TdlGVJGlJdrGG9YwLcFUAgGNFgAMAoBOoqfPo5W93qGd8hGIjQiRJa3eXBrgqAMCxYgglAAAdgGVZyrjzA2Xc+UGT9xdtK5Qkje+ToNeuG1//TKuVBwBoIQQ4AAA6gOcWbrePc4urGt3fsKdMkjRjRA/F1/fA9YyPaJ3iAAAthgAHAEAH8P7qPfbxo59t9rmXW1ylv37qvTalf5IcDqP//L9xeufGCa1aIwDgxBHgAABo55wuj1bnlapfcrQkafaKXdpTWi1JKqlyatIjX9ptjTGSpEn9kpQSRw8cALQ3LGICAEA79039/Lbo8IN/rU948ItAlQMA8CN64AAAaOdmL98lSfrd9MFHbHf2kG6tUQ4AwI8IcAAAtHPVdW51iw3TmF5dNKFP4mHb3Xp6v1asCgDgDwQ4AADasZIqpxZvL1J6QqQk6cVrxzZqc/nYNElSbHhIq9YGAGh5zIEDAKAd+T6nWF9vKdTynfu1o6hKO+u3DPg+Z78kKTwkSJnJ0dqaX2E/c//5Q3XdpN5Kqw95AID2iwAHAEA7ccvry322CzicSf26+gS44CCHMpNj/FkaAKCVEOAAAGgHiiudPuHt/87s77Pf29iMLvbxb88dpGsn9laty62iSmer1gkA8C8CHAAAbcCS7GJd+uy3Gtg9RteenKHLxqb73M8pqrSPgxxGwUHGPn/1/43XqPR4+zwkyKH0RO9wSZYtAYCOhQAHAECA1dS5dc//1kmSNu4t129mrdEHa/b6tNlf35MWFRqkL389RXNW7Lbv9UqMVFQYf6UDQGfAT3sAAALshW+ytWFPmc+1nMJKJUSF2udBDqNJ/brquR9nKTwkSE63x74XE85f5wDQWfATHwCAALAsSytzS/TKtzs0e8WuRvf/fMFQTeqXdNjnS6oOzm2Ljww9bDsAQMfCPnAAAATA3z7drAv+ucgObw9eOMy+d3Jmosb3PvyG3JKUEBXm1/oAAG0TPXAAALQSt8fS7pJqzVqep6e+3CpJeuyyERrYPVaDUmL1fXaxZozooakDk4/6rtMGJuvhjzfqqStH+btsAEAbYizLCnQNysrKspYuXRroMgAA8Ks/f7Bezy3Mts/fvOEkje9z5J62I/F4LDkc5ugNAQBtnjFmmWVZWUdrRw8cAACtZEt+hdITInXr6f3Uv1u0hqfGH/2hIyC8AUDnQ4ADAMDPVueV6G+fbtaCzQU6bWCyLh6TGuiSAADtFAEOAIAWkl1YqYoal73RdpDDKNhh9NI3OVqwuUCj0+N1/qiegS4TANCOEeAAAMfN5fbo9SU7VVHr0qn9kjS0Z1ygS2pVNXVu3TNnnd5dkadpQ1M0d9XuI7Z/96aTW6kyAEBHRYADABy3V77doXvfXy9JWrS1SA6H0a79VUpPiNSzV2cpv7xGL32To8E9YnXh6PY9bHD+pnzd8MoyDUyJ0ej0Lvpue5E27i237x8Ib786q78yk2Pk9lhyeTxyeyztKKpSXERIoEoHAHQgBDgAwHFZtLXQDm8jUuP09dZC+962gkp9un6vfvvuGpXVuCRJSTFh9sbUbo+lZxZs07UnZygytPX+Krp79hp9u61IH992qkKDm78V6qrcEs188XtJ0uq8Uu0oqlJ8ZNOBbNrQ7spMjmmRegEAOBQbeQMAjsuO4ir7OKNrlH3cMz5CknTL6yvs8CZJVz+/xD6et2Gf/vLJJp36yHz/F9rAa4t3anthpfaUVh/Tc/d/sN7nvLS6ThP7dtWSu0/Xp7efqhevHStJOqlPgvomRbdYvQAAHIoeOADAcams9YazG6f0VXGF077+ye2naug9n0iS+iRF6akrRuvcJxdKki7/17cKDQ7SV5sLJEmFFbWtVm+V82CYLK2uO6Znc4urddrAZKUnROqlRTmSpFFp8UqOCVdyTLj6d4vRhnunKSI0qCVLBgCgEXrgAADHZf3uMknSL8/sr95J3h64Uenxig4L1kX1891mTszQ4B6xiqwPNt9tL7bDmySFBvnvr6E6t0cfrN6jd5blKbuwUl9szLfvPfTRxma9Y+2uUo2891PtLatRWLBD8zbus+9dkuU7p4/wBgBoDfTAAQCOamlOsX47e42MjEakxWlPaY0WbvHOeQsOcuhnk/tqTK8uGpQSK0m6//yh6hEfrkuz0iRJ6++dpow7P2j03u5x4X6r+YnPt+ipL7c2eW/RtiI5XZ5G8+CqnC69v3qPkmPCdNubK+UwRiVVdQp2GN00JVPltXW68rnFCgkyMoZNtAEArY8ABwA4os37ynXxM9/a55v2ldvB58B8N0kam5FgH0eEBumXZw3wec/7Pz9Fy3bs19CesYoND9GDH23Uml2lzaphzspdGtOri1K7RB61rWVZ6n3Xh4e9HxkapCqnWy8vytH1p/axr6/MLdH5//imUfufn5ZpfxeX2yNJdjAFAKC1EeAAAEe0bMd+SdJNU/rq3wuz5XR75HR5tPG+aQp2NL8XamjPOJ994tITIvXFxnyVVDkVHxna5DM7i6r09rJc/f2LrTJGyn5w+mHf/8TnW7R0R7G27Kvwuf7MVWO0dlep3Rv35OWjdN0rSxUZ5jvk8dN1e+3jxy8bqdveXKnLstJ8gmhwkEOr/nCWosIYLgkACAwCHADAVl5TJ5fb8rn2xOdbFBMerF+fPUD/nL9NkrcXKzykZULM7W+u1O9/MNg+zy6s1Kn9kxQS5NB/vsvRcwuzJUmWJZ380Bfq2SVCTpdHw3rG6ZqJvZSZHCPLsvTsV9sUGx4ij+Vb/7Sh3XXawGQ99eVWhYc4NDEzUSFBRjuLvKto7iqp1jmPf2WvmPnjCb103sgeGt8nQckxjYd4xh1m+wAAAFoDAQ4AIMm7UfWBvc4ONb53gs+crxdmjj3hz7vtjH56aVGOvtxUoC83LWh0f9aNE7StoFI94sI1uEesPt+Qr10l1dpV4t0CYGVuif7z3Q7delqmFmwpVJXTrd9NH6wrx6fL47G0Jb9Cu+vbhgY79PPTMpWVkaDI0GANSonVs19tV9+kaD340QaV1bh00ehUXTEuTVn1Q0FT4iIa1QQAQKAR4AAAWrZjvx3e7jxnoMIbLO7htqTzR/bwad81OuyEP7PhsMlbT8tU3+Ro/eK/K+1rFz3tnXc3Kj1eGYkH95m7eEyq3lmWZ58/+cXBhUouHN1TkuRwGA3oHqMB3Q9uqN1wKOSBXro7Zq22r/3klAwN6XFwiCcAAG0RAQ4AOjnLsrQ4u8g+v35SHwUdZm7bIxcN12cb9ikzuWU3q75sXLp6xkdoTK8uOuXhLyVJqV0i9MAFw9SvW7T2ldXq3197h1Lee94Q9UqI1Mfr9mrd7jLdOKWv3l+9W7nF1c0e1hkf4Q2PT1w+Ui98na1VeaWHnYcHAEBbYqxD5goEQlZWlrV06dJAlwEAndI/52/VIx9vss9zHjr8QiEtbfO+cn2ztVDXntzbvmZZluau3qOTeicoOfbgHLTswkrFhgcrsUHvn2VZsiypvMYlp9ujpJjm9QzmFlfp1e926PYz+8sYbw/kxL5dW+6LAQBwjIwxyyzLyjpqOwIcAHRud85arY/W7tWvzh6gKf2TlJZw9KX6AQBAy2pugGMIJQB0UnNW7tKclbv1xcZ8BTmMrj6pV6BLAgAAR0GAA4BOqNrp1nMLt2trvnfPtGsmZAS2IAAA0CwEOADohKb89UvtK6uVJG28b5rCGqw6CQAA2i4CHAC0cfe/v14vLcpRdHiw3v/5KUrtcuxz1Eqr62SMvBtdeyw7vElqsQ25AQCA/xHgAKCN27C3TC6PpZKqOu0srjrmAFdT59aIP30qSTqpT4KMmt4iAAAAtH0EOABo41xuS2HBDtW6PKqt8xzz83tLa+zjNXmlGpgSq/G9EzR5QJIuHJXakqUCAAA/I8ABQBvn9liKCgtWrcupspo65e2v0sMfb5LT5dblY9M1dWDyEZ9/Y8lO+/jvV47SaQO7+btkAADgJwQ4AGgD8stqVFPnUXpi4+GRLo8lh/EOe/zFf1fqgQuGae6q3QpyGDmMOWyA21NaraIKp579art9LSyY+W4AALRnBDgACIDZK/I0dUCy4iNDVVPn1rgH5kmS3v/5KRraM86nbZXTpa7RoSqs8C48UlZTJ0nqlRCpOrfvkMq1u0p1/wfr5XJbWrpjf6PPHZwS64+vAwAAWgnrRgNAK9tWUKHb31ylX761SpJUXuOy7+Xtr/Jp6/ZY2ryvQuN7J0iSrpnQS299nytJio0IUa3Lo5o6t3YUVWpHUaVeW7xD320vVkiQ74/3LpEhynlourpEhfrzqwEAAD+jBw4AWlllrTewzduYL0mqdbntey6P5dP2H19ulST1SoxSSly4quvcCg4y6hEXrtBgh5wuj655YYkWZxf7PPf69ePV+64PJUmf3n6q+neL8dv3AQAArYcABwCtrM7tG9JqGqws6W4Q4Jbv3K/XF3sXILlsbJrufX+93lqap5jwYE0b0l17SmtU5XRpdV6pxvVO0OVj07Q6r1QpceEyxmjVPWfJ5fYoMTqsdb4YAADwOwIcALSy295cYR+v3VWq7YWV9nlpdZ2yCyv11eYC3fO/dZKkyf2TFBV28Md1eY1LQ3vGaXdptZbvLJEk7Sur0YWjU3Xh6IPbAsRFhPj7qwAAgFZGgAOAVpZbXG0f/+DvX/vc+8OcdTp3WHd9uGavfS063PdH9evXj9fEvl319rJc+9rDFw33U7UAAKAtIcABQCuyLMueuyZJPz8tU8N6xmlPaY3d41ZSVacRafFalevtXeuf7Dt/Lal+SGREiHdLgCevGKWT+iS21lcAAAABxCqUANCKymtddniTpCkDknXWkO6a2PdgAKusdSm+wfDHkmqnJGloT+8WAAdWkowM9f4bXGiQ8XvdAACgbaAHDgBaUUllnc/5gXlqDRefXJVXqunDUuzzivptBp69OksLNxeoa30PXGSotwfu0JUrAQBAxxXQAGeMmSFpRmZmZiDLAAC/yi6s1H+X7JTHsrStwLtgSVpChHKLq5UU4w1jocG+AyKiwoL0yEXDdces1bp0bJokqWd8hC4fl263OdADV+V0CwAAdA4BDXCWZc2VNDcrK+v6QNYBAP705ve5evar7YoMDbLD1uVj03XV+F52D1zvrlE+z/SIj9ClY9Ps8NaUAz1wVbWuw7YBAAAdC3PgAOA4rd1Vqv+t2q35m/JlWYcfxljldCk+MkTr752m287oJ0mqqXMrLvLwy/yPSI0/6uffNLWvJvRJ1Hkjex578QAAoF1iDhwAHIfb31yp2St22eef3X6q+nWLabJttdOtyPoVI1PiwiUdHP54OLUNFjo5nJS4CL1xw0nNLRkAAHQABDgAOA4HwtuBuWxlNY2HMb61NFdLsou1OLtI4fXDHS8ZkyaPJV3UYMPtA/5x5Wjd/PpySVKti3ltAACgMQIcADRDYUWtnvpiq8qq6/Rug563A1bs3K8xvbr4XHvss80qra5Tl8hQTRmULElyOIyuaLAQSUPTh6coJX6iLvznIvZ1AwAATSLAAUAzfL5+n15alNPoev/kGOUWV+v+DzYob3+1zhzcTSVVdfpo7R7tKa3RVSel6/7zhzX7c0and1HOQ9NbsHIAANCRsIgJADRDfnmtJOmjX0yyrw1OidVDFw23z19alKMf/Xuxbn59ud5fvUeStL/Kd983AACAE0GAA4AmVNa69OS8LVqZW6KaOrfe/D5X3WPDfZb7n3lyhrpGhzZ69hen99MzV42WJI09ZFglAADAiWAIJQA04ZuthXr0s836ZN1eXTMhQ7tKqnVKZleFBh38d68LR/WUMUZf/XqqHp+3We8u986Nu3hMqtISIvX93WcoMapxwAMAADheBDgAaEJ1nXcVyHW7y1RQ4R0++Y8rR8vhMProF5O0JLtYwfVhLj0xUo9eOlKPXjrS5x1JMWGtWzQAAOjwCHAAOrWFWwq0t7TGPt+8r1wDusfK5T64D9tfPtkkSYqN8P7IHJQSq0Epsa1bKAAAgAhwADqxkiqnrn5+SZP37pkxuNE1Y4y/SwIAADgiAhyADseyLJXVuBQdFqwgx+FDV2GFU5L0xxmDdfqgbt7j/63TvI35uvf99T5tZ07M8Fu9AAAAzcUqlAA6nL99ulkj/vSprvjXd4dts2Vfuc54dIEkqW9ytNISIpWWEKn+3WMkSZYlnT+yh92exUgAAEBbQIAD0OFkF1ZKkpbkFKugfv+2hh7/fLMu/Oci+3xYzzj7ODw4yD6+ekKGfXzT1Ew/VAoAAHBsCHAAOpya+hUkJemOd1Y1uv/ttiJFhAbpjzMGa/6vpig+8mDvWlnNwY23u8UeXEXySEMxAQAAWgtz4AB0ODWugwGutLpOdW6P9pTUaOHWAsVHhKrO7dGA7jGaeXLvRs9O6tdVz3+dLUmKjQjRTVP6amdxVavVDgAAcCQEOAAdyttLc7W9oFI94yO0q6RaPxzRQ7e/uVLvr95jt4mLCFFWry5NPj9lQLLW33u26tyWYv9/e3ceX1V17n/882ROgCTMBMI8GAZBZBAFRRxxwqmO16HWW7S2Wn/aWu+tWq1WbR3rdfZatXqr16tWcFZURLEoIILMIGOYISEMmc9Zvz/2ziEhCYRMZ8j3/Xrx4py19z7n2a8nOTnPXmuvlZLILRNzmit0ERERkYPSEEoRiRkl5QF++8YCtuwq5pi+7QGYsWJ7leINoCwQZEJOp1pfJy0pgYzUxCaNVURERKQ+VMCJSMwoKPLuX7tr0mB+f8ZAAD5burXafqcfnsVlY3o2a2wiIiIijUEFnIjEjF1+AZeemlht0pHnrhhJUrz3kbd2x95mj01ERESkMaiAE5GoVR4I8st/fMe3q/OAfT1wGamJJMZX/Xg7eVBnHr34CAAGZqU3b6AiIiIijUSTmIhIVCotD3LCQ9PJzS/ivQWb6NEuLTRbZOvkhCo9cH07tgK8oZPvXj+Ow/zFukVERESijQo4EYlKW3cXk5tfFHoeZ14v257icnKy0kmoVMC9ce0xocdDKi3aLSIiIhJtVMCJSFTaXVxe5fnRfTtw33mH17hv21ZJNbaLiIiIRBvdAyciUWn/Am7u2rwwRSIiIiLSfFTAiUhU2l3sTVjS3u9d21lYFs5wRERERJqFhlCKSFSq6IF7bfIYHvx4GQ9eMKzaPmcMzWL8gI7NHZqIiIhIk1EBJyJRqaIHLvffahgAACAASURBVCMtkWcuH1njPk9cemRzhiQiIiLS5FTAiUhUKSoNcNpfZ7BhpzcDZXpKYpgjEhEREWk+KuBEJKpsKihizY5CTszpxIkDO5OSGB/ukERERESajQo4EYkqO4u8oZOXjenJhJxOYY5GREREpHlpFkoRiSqvfbsOgHZa201ERERaIBVwIhJV8v3lAgZ3TQ9zJCIiIiLNTwWciESVkvIgw7pnkhCvjy8RERFpefQNSESiSnFZgJQEfXSJiIhIy6RJTEQk4u3YU0KcGXtLy9ldXE7HNsnhDklEREQkLFTAiUhEKykPMOKeaVXa+nTMClM0IiIiIuGlAk5EmtXctXls2FnMMX3b0yopgV/94zvW5hVyeLcMHrnoCABWbt1Nt8w0UpPi2VNcHjr2uAEdOXNoFkf3aR+u8EVERETCSgWciDSJVdv2cMJDX3DBiGweuGAYAKXlQS56ZhblQcclo3uQnpLAp0u3ArBy6x4euegIgkHHSQ/PYGTPtrzxi2MoLA2EXnNQVjoXjuwelvMRERERiQSaCUBEGtVb3+XS69b3mLM2H4D/m5sb2lZYWk550AHw6rfreGbGKgA6tPbWdDvhwemMf/BzAOaszefPHy6luGxfAbdwQ0GznIOIiIhIpFIBJyKNZuPOIh7+ZDkAC3J3Vtv+9rwNNR531djeAKzavpe+HVuH2p+a/iMnPzIj9PzIHpmNGa6IiIhI1FEBJyJ14pw74PZvVu3gmPs/Ize/CIDPlnhDIysvuH3nO4trPLZHu7TQ49vOGMRdkwZX2+fmkwdww4n9DzluERERkViie+BE5IC+XLGNn/99DsVlQabdNJ5+nVpX22dPSTnPf7W6StvGgmIAdhWX8fGizYzq1S607d3rxzGkWwZ7SsopKg2wZNOu0LbstqlceUwv/jB1EQDTbhrP4k27OGtoFmbWFKcoIiIiEjXUAyfSQhUUlvHcjFXsLCw94H7Tl22juCwIwEkPf8ENr86r1hs35fsNfLx4S43Hr88rYvLLc7nnvSWhtiHdMgBonZxAxzbJJMZ7H0Uje7YlJTG+yvHpKQlMGtZVxZuIiIgIKuBEWiTnHFe88C1/en9JrfelVVi0serEIVPnb6So0sQiALsrTfVfm48Xba51W3bbVADOGd4t1Pb4pcM5/8hsLdotIiIiUomGUIq0QK/MWsv89d4kIwfq2XLOMWtVHjld2rB08+5QeyBYtQeueL+Cria7S2ov8rq3S+OHO0+hdfK+j6Qzh3blzKFdD/q6IiIiIi2JCjiRFub9HzZx+5RFoec7C8vI27tvGGVaUnxoGONrs9cDcPKgzlUKuGCw6muWlAdJio+jNLDfhv1MGtaVodkZNW5rk5J4SOchIiIi0hKpgBNpYSqGMv721MN44KNlPDJtOY9MWx7a3iYlga9+dwIZqYks84u2q8f1JjMtiY8Wbebb1XmU71fBFZcFSE7YV8DN+O0E3pqXS1FZgGe+WBXa77YzB9KpTUpTn6KIiIhIzNI9cCItTHFZkAGdW3Pd8X1Dbecc0ZW7Jg1mXL8O7C4uZ9tubwbJzQXFdG+XSmZaEleP682kYd6QxoDbfwhlkORKk4/0aJ/GjScN4NaJObxx7dGh9qR4feSIiIiINIR64ERamOLyACmJ8VXufXvkoiMwM7IyUvhq5XZOengGa+4/gw/3m3gkIc475qsV2zllcBdmr8kDB69+uw6A6b85nuTEfUWamTGy0vIBiSrgRERERBpEBZxIC/Hhwk3MW7eT6cu20b5VEgDJCXEMzc4IFXMDs/Ytul1UWn1ikori7KbX53PN+N1VhkcC9OrQ6oAxqIATERERaRgVcCItxN3vLmFTQREAO/xJS5bdc1qVfbq3Sws9/mL51mqvkRS/b5jkloJiuqSn8PTlIzjniZl1iiExXmu5iYiIiDSELoeLtBAl5QEuGd3joPsNzc4gLSmea1/5DoAXfjoqtK1yAfb+ws10aJPEEd0z6xyDFuMWERERaRj1wIlEKOccizbuYki3fdPulwWCfLFsG3tLyzmuf0fa+kMh66K0PFinIYy5+UUUVho+ObZfh9DjysXa6UO6cNrhWQD87acjqy0tICIiIiKNTwWcSAQpLQ/y3bp8AkHHS1+v4ePFW3jpZ6MZP6AjAE9P/5GHPvGm/L9mfB/+47SBtb6Wc45lW3azt8QrxnYVl5OUEMfTl42ga2btU/lfNKo7T03/MfQ8KWFf0dcpPSW0qPdffjIstO2EnM71P2kRERERqTMVcCIR5PU567nt7YVV2q5+cTYr7z0d2HfvWpuUBIprmGSksnnrd3Lek19XaeuSnsLEIV3qHE+rpPhqba9NHsPiTbuqFHYiIiIi0jxUwIlEkB17vALttclj+HjRFv42czXlwX1rrpUHg7Tzh03uvxZbheKyAP/+0hy+WrkdgDvOHETfTq1JiDNG9Gx70BgGVZqJctEfJ1bbnpmWxDF9O1RrP5APbzyWtTsKD+kYEREREalOl9BFIkhRWYCk+DjG9GnPcQOqF0nFZUHi44w4M4I112+s2bE3VLwBjOjZlvEDOjK2XwdSEqv3qO3vzKFZ9Y6/Njld0jl1cN17/kRERESkZirgRCJIUWk5qf6wxXH+5CGjenm9ZntKynljbi7OQZx597jVZNvukgbFoJkiRURERCKXhlCKRJCisgCpfi9ZQnwcI3u2JSHOu86S79//dtyADny9cgeBWrrgKgq4Vknx7C0NEFePguzGk/rX6zgRERERaVoq4EQiQEm5d9/alyu207tDq1B7cmIcxWVB/jBlIR8u2gzA6F7t+GZVXrUhlO/M38jsNXks2bQLgBtO7M99HyylR/s0DtWNJw2o/8mIiIiISJNRASfSQB8v2szmXcVccXSvQz52+Zbd3DFlIQs37GJPSTkA2W1TQ9vjzNi6u5iX/pUfalufX4gZBPcbQvmXj5aypaCEVsnxHNW7HdeM78s14/vW76REREREJCKpgBNpoMkvzwXg8jE9q90/dsmzs/h+/c5ajy0q85YCGN27Hd+uzgPg+StHhbbvLCxjfV4RAKcO7sxHi7awt8QbFlkxY2XotUoD/GRkNveee3jDT0pEREREIpIKOJFGkl9YFpriv8LctfkM6prO6N7tajzm2RmrAHju8pEM++PHQNWFs3/YUBB6fOHI7ny0aAs5XdowbckWvli+rcprFZcFSUk4+CyTIiIiIhK9VMCJNJKK3rQKZYEgpYEgJ+Z04voT+9d4TEUBl5QQx4DOrVm+ZU+tr3/iwM58evN4+nRoxa1v/QDAH6Ys5K6zh7C7uIw9JeWkJmliWREREZFYpgJO5BC9Mmstz3+1ulr7+rxCFm4oCK13VljqFXQVywIcSHJCHO9cP45gsObtfTu28v9vDUBOlzYs3bybl/61lrvOHsIjn6wAoH2r5EM+HxERERGJHrpcL3KIvlyxje17Sji8WwaHdW4Tar/42Vlc8/JcygNeFVbkF3BpSbVfJ7nu+L4kxBlxcUZyQny1Yi8zLRGAUb2qDsE8IadT6HFJeYAtu4qJjzOuOLpnw05ORERERCKaeuBE6mhTQRELN+yipDxI7w6teOyS4QSDjj7/+X6V/e56ZzFnDM3i9TnrAWiVXHsP3C0Tc7hlYk6t22f//iS+X7+TodkZVdqP6duBJ6f/CMCWghLyC0sZ3j2ThHhdkxERERGJZSrgRGoRCDq+/nF7qCetYrZJgBE92wIQF2d0y0xlw86i0LaXZ63l5VlrQ88rhj3WR2J8XLXeN4Bx/TuEHn+6dAt5e0vJbnvo672JiIiISHRRASdSi5krt3PF376tcdvctfvWZRvQuXWVAq6yF346iiHdMmrc1lC/PrE/f/10BXe9sxiAYdmZTfI+IiIiIhI5NN5KosKWXcXc8Oo8jrp3WrO9586iMgCevmwE714/rtowxgquxlZPWh0mMKmv4wbs64W7+eQBXDO+T5O9l4iIiIhEBvXASUTbVFDEfe8vZer8jaE251y1BbObQrE/dHJodgZdM1MZlp3JglxvXbaObfbN9tguzVv77YScTsxcuZ2S8n1TSWamVV0XrjElV1rzrbZlCkREREQktqgHTiJWQWEZb87NrVK8AZQFDtTnVX9bdxWzdPOu0L81O/YCkJLoFUoXjeoe2vetXxwTepwQ7xWTpwzqXKV4m3BYRwZ0rv/9bwfTOT0FoMpMmCIiIiIS29QDJxFjy65ipny/gXH9OvLRos389dMVVbZXTBby/FerOWd4V7IyUhv0fvl7S/nT+0s4qnc7Tjs8i+Me+JzisqoLsSXEWWgYZKtk79elc3oy3dtVnzDEDG49LYf7P1hKq6R4XrhqdIPiO5iObZJ5+rIj6a8CTkRERKTFUAEnEeN/Zq3lsc9WAktDbXeeNYgBndvQOiWB+et3cvuURfz5w6Us2ljA45ceWe/3cs4x4aHp7Cws4425uSQnxlNcFmTycX0Y3n3fZCBdMlJCPXCt/EIuEHT7vda+x1ce3Yv7P1jKkf4slU1t4pCsZnkfEREREYkMKuAkIrw8ay3Tlmyt1v7Tsb1Djyvf87WnpLxB7zdv/U52FpaFnt/w6jwATh3cJbREwP6S/UIuPSWxSntF/WYYqUnxTPnlWHp3bNWg+EREREREaqICTsJu9po8bn97YZW2aTeNr1akVdxrBlBaXnWo46H6ceueGtv7dKi98MpITeSPZw9mwmGdqrSfNLATb8zN5XB/lsph3TWdv4iIiIg0DRVwEnb//eUqAPp0bMWqbd7EIf06VZ/8I77SzJN7/RkiDyQQdLwwczXnDO9Gh9bJlAeC/Pq179lYUMS8dTur7b/iT6eRGH/geX2uOLpXtbaJQ7JYds/EKj2EIiIiIiJNQQWchJVzjs+XbQPg7rOHsGTTLuJqWSKgvNK9Z7uLymrcB2DHnhICzjFz5XbueW8JuflF3DlpMJsKinnvh03kdNk36cfUX41l0uMzAQ5avB2IijcRERERaQ4q4CSsbn59fmg45DF92zO2X4da9+3ZPo1Jw7oydf5GVm3fS69b3wO8ddoq1lubsXxbteNe/HoNBUVlLNroreF262k5HN23PbuLyyn3lySo7b43EREREZFIonXgJKzemrcBgNG92h10ce7E+Dgeu2R4tfYFuQXsKipjV6VeuXvOGVKlp23u2nyKy4IM6ZbO0OxMkhPi6dA6mS4ZKTzwk6E8e/mIRjojEREREZGmox44CatObZLZuruEy47uWedj3r/hWE5/7Euy26aSm18EwNu/HAvAhws3s3bHXi4b05OLR3Wn3+8/AGDGLRNqfb0LRnavdZuIiIiISCRRASdh1a5VEkOzM5k0rGudjxnUNZ01958BQK9b32NcpWGXE4d0CT1OaMA9bSIiIiIikUgFnDSbtTv2UhZwlJYHWbF1NwB5e0vp37nNQY6s3eI/nnrAyUem/HIse0sbtmaciIiIiEikUAEnzWb8A9NrbO+Snlzv10xLOvCPsNZkExEREZFYogJOmsxb3+WyILeg1u2f3jwegJ7t0porJBERERGRqKYCThrdgtyd3P3uYmavySfOoHXyvh+ztmmJ5Bd6s0X27Vh9sW4REREREamdCjhpVAVFZdw5dRHfrdsJwOTj+nLraTmh7cGg48Wv13DByOxwhSgiIiIiErVUwEmDFZcFKPEX437kk+Wh4g3g+MM6Vtk3Ls742bjezRqfiIiIiEisUAEnDVJQWMbYP3/GnpKqMz3O+O0E4uONbpmpYYpMRERERCT2qICTevtw4SbeXbCJPSXlXHpUj9A9bcOyM+jRXhOTiIiIiIg0NhVwUi+BoOPaV74DoHu7VK4/oR9ZGeptExERERFpSirgpE4m/30OX/+4I/S8Ysjkvx3Vgz+de3i4whIRERERaVFUwMkBBYOOorIAM1dup0/H1ozu3S60LTUxnutP7BfG6EREREREWhYVcBHKOceVL8xm9fY9PHHpkQzNzmz2GFZu3c2kx2dSWBoA4MyhWVwzvm+zxyEiIiIiIh4VcBHIOcc5T8xkfm4BAE9/8SNP/tuIZnnvV2at5dFpKwBveYCyQJDfTcwhPTWBM4d2bZYYRERERESkZirgItDU+RtDxRvA+z9sbvBr7i0p57S/fsnQ7Awev/TIWvf7bOlWwHHK4C4AHNE9kwtHdm/w+4uIiIiISMOpgIswHy/azK9f+x6AC0dmM2dNPp3TUxr0mpsLivnnvA2syytkXV4hxWWzOWtYVyYO6UKpvwB3hc+WbmXCYR25VxOTiIiIiIhEHBVwEWRPSTmTX54LQM/2afzlJ8O48Ol/4XANet37PljClO83hp5PW7KVaUu2Eh9nBILVX7t1SmKD3k9ERERERJqGCrgwKy0P8vmyrTzzxY98t25nqP2+87wesOTEONblFbJwQwEZqYl0b3doC2T/7+x1TPl+I8N7ZDLPf/04g6Dz1nK77vi+tGuVFNo/Ic44d3h2I5yZiIiIiIg0NhVwYXbVi98yc6W3vtqx/TvQr1Nrfja2d6hQKywNsHZHIWf+11cAvHL1URzdtz3xcXbQ13bOcfvbi0hJjOP0IVlkt03jnfkbefCCYdz0+nwA/t/JA0iMj2uisxMRERERkcakAi5Mpi3ewseLN/Pt6jxOGtiJa8f3ZWSvdtX2m7s2v8rzy57/hvOPzOahC4cd9D1enrWW0kCQ2yYO5N+P7UNpeZC7zx5MalJ8qIBT8SYiIiIiEj307T0MnHM89Mlyps7fSFZGKtdN6Fdj8QYwpo/X3jVj30Qmb36Xy4adRQd8jw8XbuKOKYsA6JaZCkBSQhyZaUkkJ8Tzw52nsODOUxrjdEREREREpJmoB64ZzFmTx5xKPWn3f7AUgKvG9uIPZw0+4LGje7dn1qo8LhzVneuO78eA2z4AYOz9n9E2LZFHLx7O+AEdqxxTVBrg2le+A2Bkz7ZMHNKl2uu20UQlIiIiIiJRRwVcM7jt7YUs3by7SlublAT+/dg+Bz326rG9WbdjL1eN7U1SQhxf3jKBY//yOQD5hWVc+bdv+fHe01mfVxiaqzJvb0no+HvOHYLZwe+XExERERGRyKcCrol9vXI7SzfvZmBWOm/94hh+/do8Pl68hTvOHBQa2nggGX4vW4Xu7dL48/mH87s3fwi1jX/gc3Lzqw+p/N3EHHK6pDfOiYiIiIiISNipgGtkmwqKeHNuLgF/fexHpi0HID0lgdSkeH45oR8AZw3rWu/3uGhUD0b0bMtJD88ACBVvj150BAAbdhbx7oJNnD+iW73fQ0REREREIo8KuEb24sw1PDNjVbX2564cCcCw7pk8e8XIBr9Pv05tQo+7Zaby4lWj6N95X1tFoSgiIiIiIrFDBVwjcc7x+7cX8o9v1jEsO4N/XjcWgNdmr+fY/h1Ib8JJQ2bcMqFO68KJiIiIiEh0UwHXSIrKAvzjm3X0aJfGteP7EucXVJce1aPJ3vOJS4/ki+VbVbyJiIiIiLQQjV7AmdlA4NdAB+BT59xTjf0ekeid+RsBuHpcb047PKtZ3vOMoVmcMbR53ktERERERMKvTgt5m9nfzGyrmS3cr32imS0zs5VmdiuAc26Jc+5a4EKg4Td7RYH1eYWhWSH7dWod5mhERERERCRW1bUH7kXgceDvFQ1mFg88AZwM5AKzzWyqc26xmU0CbvWPiUrr8wopLgvUad8ft+0B4E/nDmFsvw5NGZaIiIiIiLRgdSrgnHMzzKzXfs2jgZXOuVUAZvYacDaw2Dk3FZhqZu8B/2i8cJvPzf83n29X5x3SMX07qvdNRERERESaTkPugesGrK/0PBc4ysyOB84DkoH3azvYzCYDkwF69Gi6iT7q68aT+pO3t7TO+6clxTOqV7smjEhERERERFq6hhRwNU196Jxz04HpBzvYOfcs8CzAyJEjXQPiaBLH9NVQSBERERERiSx1msSkFrlA90rPs4GNDQtHREREREREatOQAm420N/MeptZEnAxMLVxwhIREREREZH91XUZgVeBfwGHmVmumV3tnCsHfgV8BCwBXnfOLWq6UEVERERERFq2us5CeUkt7e9zgIlKREREREREpPE0ZAiliIiIiIiINCMVcCIiIiIiIlFCBZyIiIiIiEiUUAEnIiIiIiISJVTAiYiIiIiIRAkVcCIiIiIiIlFCBZyIiIiIiEiUUAEnIiIiIiISJVTAiYiIiIiIRAkVcCIiIiIiIlFCBZyIiIiIiEiUSAjnm5vZWcBZwC4zWxHOWFqoDsD2cAchjU55jU3Ka2xSXmOPchqblNfYFGl57VmXncw519SBSIQysznOuZHhjkMal/Iam5TX2KS8xh7lNDYpr7EpWvOqIZQiIiIiIiJRQgWciIiIiIhIlFAB17I9G+4ApEkor7FJeY1NymvsUU5jk/Iam6Iyr7oHTkREREREJEqoB05ERERERCRKqIATERERERGJEirgREQijJlZuGOQxmNmR5pZ+3DHIY3LzPQdKsaYWRf/f30GxxAzi/f/j5m86sNHJErF0gdRS2dmo8zsWTP7tZm1dro5OSaY2XAzmwZ8AySEOx5pODMbbWY3ADjnguGORxqH/7v6KXA3gD6DY4OZHW1mzwH/z8zSYymvKuBilP9H5l5dIYwtZjbIzI4D/YGJBWaWYGZPAk/hfck/FXg0vFFJQ5lZspk9DTwHPAnMAM7wt+nCS5QysxuBfwK3mdlpflt8eKOShjDPI8DfgZeccz8Pd0zSOPzvSo8DnwFdgf8ws1PDG1Xj0Zf7GGNm6Wb2BN4Pba5zLqgvDNHPzBLN7BngVeB6M/utmY3wt+n3OHo54F/A8c6554HbgKC+FEa9LGAuMM459xbwMdDezEwXXqLaSuBM4BfAfwA45wL6Gxu9/N/H1sA859zfAcysr/6uxoQRwEzn3Kt4PaudgYsrhslGO/2Axp7/BMYApzjnngT11MSIwUCGc24YcC1QhjckIE3DeKKLmV1oZjeb2RjnXMA597Jzbo9/ZfALoANwl5llhDlUOQR+Xn9jZqOdc2ucc88554r9za2B7s45p+I8epjZGDMbUKnpPWCB//+eiqGUgHIaRWrI683AUWZ2u5nNBB4AXqy4SCrRoYa8LgcyzCzLOZcP7AGSgbPDEmAjUwEXA8yst5ml+U//DmwDOpnZT8zsQTO72Mx6hDFEqQc/ryn+01bAEWYW75zbARQDg4Cr/X11BTjCmVm8md0B/A6v5+05Mzu30i5xwMXAJUB34GozS2r+SOVQ7JfXIPC8mZ3nb6v4G/s2MMm/4BIIU6hSR2aWaWbvAZ8AF5pZq4pN/kWXYuAhvN/RDs658rAFK3VWW16dc7uAJ4Dz8XpWLwE2AeebWcdwxSt1U0NeW/ublgO7gJfM7E28v6vzgDb+cVH9vUkFXBQzs15m9gHw38DLZjbIObcY+BL4CLgOWAZcAPzWzLLDF63U1X55/R8zywHmA18BT5lZH+BovHsxjvS/QKiXNcL5X9wPA252zj0M/AFvOOxAf/sHzrn3nHMlwFt4VwnLwhaw1Ektef2VmQ2s1Du+De8+jJwwhSmHphXe39Dr/ccV9x1XHu0wHZjl74OZjW7eEKUeaswrgHPuMWCCc26G/xn8NjASKAxHoHJIavt9XYHXu3of8IZz7lxgIXC8vz2qvzepgIsy+10x+A3wjXPuROBz4G7/y/0DwJ3OuROcc88Bt+MN4end7AFLnRwkr3cC2cAdeD1vf8W7v2Yq3tCd/GYNVurMzK4ws/Fmluk3bQHamlmCf2/UYuDCGg7tBXyLhmZFpLrmtVIP3B6gH17Pa9Rf+Y1FlXKa7pzbADwLvI73mXuUmXX19zMIFe73AL8zswK8i2nKa4Spa14B/GF2FUYAuYB6zCPQQfI6uiKvzrlS59zn/n1w4OX1w/BE3bhUwEWfFPBmr/OfLwJwzj2O94M5GWjlnHup4gC/V64LsK55Q5VDcKC8jgF+Cux1zt0AnO9fLVwBtAdSmz1aqZV5sszsc+BK4N+AJ/xhHduBw/EuqAD8F3Cev3+ymU00s1nAScAzGpoVOeqTV7yb5nHO5QE7gBP851F95TdW1JLTp/xRDcXOuUJgGtCWSrkzszgz6wf8A5iJN1nN08prZKhPXv3jks3seDObgzcj8P2V7mOVMKtvXv1jx5nZXOBY4N3mjr0pqICLEmZ2spl9AjxgZhf6X+zygOFmNszMhuF1DWfjf2nwj5tk3tomG4E8XSGMLIeY1yz/sICZTcKbmnwOGuIRMcy7R9HhjbHf4PeiXgcU4PWcPgmMBYb690MtA5YC5/nDdjLwvjSc5ZxbGZ6zkP01IK8XVHqZK51zDzVz6FKLA+Q0D+9qPgDOuZnAGiDHzDJs38RRu4A7nHMnOud+aP4zkJo0IK+p/mdwKXCP/xm8vPnPQGrSgLxW3L+6CrjdOXeqc25NswbfRLSwaBTwr/TdA9yL14t2i5l1wBsqeSPwJyDTf3wT3tX7pWZ2DN79GHc7594OR+xSu/rmFW8o1pXAvf5wLQkzv+f0j0C8mb0PpOMPvXHOlZvZr4DNwMN4V+0vxivI/xcoxyvEcc79b/NHL7VphLx+U/Fa/kQJEmZ1yOkNwEYzG++c+8I/7Dm8z+pPgJ5mNsI5lwtsbf4zkJo0MK/TgB5mNtw593UYwpdaNPLv68bmP4Omox64COUP0ajIz1HAXOfcFOfcPLwPm3uBFOfc3cANzrlxzrk5wNd4Y4Bxzn3tnBuh4i1yNCCvM9mX12XOufNVvEUGMxuPd09iW7x1ou7Gm3xkgvkTG/hX7O8CHvCHN38MXGFm8/AupOkKfoRRXmNPHXPq8L4w3lnp0DPwrvbPBw73vwxKhGiEvH6Pl9eY+oIf7fT7emAq4CKQmV2Fd/Ps3X7TD8AlZtbLf54A/Ag84j9f7R83GfgZ8F1zxSp118C8Xo3yGqmCwIPOuV/4kwYtxJswVFMCfwAABDNJREFU6A7gKQhNJ/8mUGhm3f2LKlfj3c94kT92XyKL8hp76prTfwLbKn02FwMnOed+7pxTr1vkUV5jk/J6ACrgIox/Q/zZwJ+B08wsxzm3AHgJuM+8RSaPxZvUoq2ZdfZvqr4R+DlwjXNOX/QjjPIa0+YCr9u+BZpnAj2ccy/iDfu43u+pyQbKnHPrAZxzm51zq8ISsdSF8hp7DiWngYp7ZfxREjPCEbDUifIam5TXA9A9cBHGObfHzG5wzq0zsyy84TkX4a1lkQEMcs59ZWbd8dYW2ukf+qxz7tHwRC0Ho7zGrhp6WU4GFviPrwJ+bmbv4q0V9iwSFZTX2FOfnJqZ+cO0JEIpr7FJeT0wFXARyDlXMd3/o8BUMzvVOfeRmRU4577yt12LN/tguX+MhupEOOU1tvlXCR3eLLBT/ebdwH8CQ4DVzluvRqKI8hp7DiWnLeXLYCxQXmOT8lozDaGMYM65zcDzeD+kOOcCZjbazKYAw4E/Om8xUYkiymvMCgKJeGuCDfWvDN4OBJ1zX+lLftRSXmOPchqblNfYpLzWwFpQsRp1zCzOORc0szeATUAJ3kyFK5xzP4Y3Oqkv5TV2mdkYvJlgvwZecM49H+aQpBEor7FHOY1NymtsUl6rUwEX4cwsDfgQGITXM/NYmEOSRqC8xiYzywYuBx523qKwEgOU19ijnMYm5TU2Ka/VqYCLcGb2G7wZdn6nH9rYobyKiIiISH2ogItwFcPtwh2HNC7lVURERETqQwWciIiIiIhIlNAslCIiIiIiIlFCBZyIiIiIiEiUUAEnIiIiIiISJVTAiYhIzDKzgJl9b2aLzGy+md1kZgf822dmvczs0uaKUURE5FCogBMRkVhW5Jw7wjk3GDgZOB34w0GO6QWogBMRkYikWShFRCRmmdke51zrSs/7ALOBDkBP4GWglb/5V865r81sFjAQWA28BDwG3A8cDyQDTzjnnmm2kxAREalEBZyIiMSs/Qs4vy0fyAF2A0HnXLGZ9Qdedc6NNLPjgd845870958MdHLO3WNmycBM4ALn3OpmPRkREREgIdwBiIiINDPz/08EHjezI4AAMKCW/U8BhprZT/znGUB/vB46ERGRZqUCTkREWgx/CGUA2Ip3L9wWYBjePeHFtR0GXO+c+6hZghQRETkATWIiIiItgpl1BJ4GHnfe/QMZwCbnXBC4HIj3d90NtKl06EfAL8ws0X+dAWbWChERkTBQD5yIiMSyVDP7Hm+4ZDnepCUP+9ueBN40swuAz4G9fvsCoNzM5gMvAn/Fm5nyOzMzYBtwTnOdgIiISGWaxERERERERCRKaAiliIiIiIhIlFABJyIiIiIiEiVUwImIiIiIiEQJFXAiIiIiIiJRQgWciIiIiIhIlFABJyIiIiIiEiVUwImIiIiIiEQJFXAiIiIiIiJR4v8DX5VTFRMSFM0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = prices.plot(logy=True,figsize=(15, 8),title='Equity Curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}