Q_Learning_Stock_Trading.ipynb

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    "colab": {
      "name": "Q-Learning_Stock_Trading.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "T8ldRTOODvtH"
      ],
      "toc_visible": true,
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyN4eGdPVW+hZP4NHtmcMbhQ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rishipython/StockTradingAI/blob/main/Q_Learning_Stock_Trading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-wzC5aNKEps",
        "outputId": "ceb15500-b3aa-4f78-e0fe-e1d692cd23e4"
      },
      "source": [
        "!pip install yfinance"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/a7/ee/315752b9ef281ba83c62aa7ec2e2074f85223da6e7e74efb4d3e11c0f510/yfinance-0.1.59.tar.gz\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/c0/d0526314971fc661b083ab135747dc68446a3022686da8c16d25fcf6ef07/lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3MB 11.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Building wheels for collected packages: yfinance\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.59-py2.py3-none-any.whl size=23442 sha256=947a99b06d416c2f8f70423096711ad01e68773f1c5873244c0223614567c4a4\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/2a/0f/4b5a86e1d52e451757eb6bc17fd899629f0925c777741b6d04\n",
            "Successfully built yfinance\n",
            "Installing collected packages: lxml, yfinance\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed lxml-4.6.3 yfinance-0.1.59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QyyYygRD4vX"
      },
      "source": [
        "import pandas as pd\n",
        "import datetime\n",
        "from datetime import date\n",
        "import yfinance as yf\n",
        "from matplotlib import pyplot as plt\n",
        "import keras\n",
        "from keras.initializers import HeUniform\n",
        "from keras.layers import Input, Dense\n",
        "from keras.losses import Huber\n",
        "from keras.optimizers import Adam\n",
        "from keras.models import Sequential, Model\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from pickle import dump, load\n",
        "import numpy as np\n",
        "import random\n",
        "from collections import deque\n",
        "from IPython.display import clear_output\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Se_JDOp5HC2e",
        "outputId": "f83f80a9-6290-40a5-e480-a12ba4d7d8e5"
      },
      "source": [
        "# Access GPU\n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon May 24 03:12:05 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   47C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xnGdDBGHNkp",
        "outputId": "98c301c0-9225-45af-fdd2-081c01b1d068"
      },
      "source": [
        "# Access more memory\n",
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"')\n",
        "  print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')\n",
        "  print('re-execute this cell.')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your runtime has 27.3 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKgc9PIqHSSp",
        "outputId": "33f8aa8c-8404-400f-bee0-606852e62261"
      },
      "source": [
        "# See if Tensorflow is using GPU\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bhrdpyWHTXX",
        "outputId": "08a9e681-bbbc-42a8-cba6-f30b150dcc93"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SXDFzHSH37R"
      },
      "source": [
        "drive_dir = f\"{os.sep}content{os.sep}drive{os.sep}My Drive{os.sep}S&P 500 Stock Trading{os.sep}\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qD7TeVaTH9Ru",
        "outputId": "db0f73b3-a2c6-4470-a091-b3b86995ff1b"
      },
      "source": [
        "ls \"/content/drive/My Drive/S&P 500 Stock Trading/\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mmodel_1\u001b[0m/  \u001b[01;34mmodel_4\u001b[0m/          x.npy        x_val.npy   y_train.npy\n",
            "\u001b[01;34mmodel_2\u001b[0m/  normalizer_x.pkl  x_test.npy   y.npy       y_val.npy\n",
            "\u001b[01;34mmodel_3\u001b[0m/  normalizer_y.pkl  x_train.npy  y_test.npy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T8ldRTOODvtH"
      },
      "source": [
        "# Get S&P 500 Stock Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJ0O_u_yKjYL"
      },
      "source": [
        "class Company:\n",
        "  def __init__(self, symbol, name, stock):\n",
        "    self.symbol = symbol\n",
        "    self.name = name\n",
        "    self.stock = stock"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_V_90epHsGO"
      },
      "source": [
        "symbols = []\n",
        "names = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MSD_Eu0wFY2m",
        "outputId": "030cf405-7e74-4b3e-b997-9248c5b14a6a"
      },
      "source": [
        "today = datetime.datetime.now()\n",
        "print(\"Today is \" + str(today.strftime(\"%b-%d-%Y\")))\n",
        "fifteen_years_ago = today - datetime.timedelta(days=15*365)\n",
        "print(\"The day fifteen years ago was \" + str(fifteen_years_ago.strftime(\"%b-%d-%Y\")))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Today is May-24-2021\n",
            "The day fifteen years ago was May-28-2006\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VM5kOqJ6Dye4"
      },
      "source": [
        "# Get symbols and names of all S&P 500 companies started 15 years or more from today\n",
        "table=pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
        "df = table[0]\n",
        "for i in range(len(df)):\n",
        "  if df.loc[i, \"Symbol\"] == \"BF.B\": # finance can't find data for BF.B, symbol may be delisted\n",
        "    continue\n",
        "  date_first_added = df.loc[i, \"Date first added\"]\n",
        "  if not isinstance(date_first_added, str):\n",
        "    continue\n",
        "  date_first_added = date_first_added[:10]\n",
        "  date_first_added = datetime.datetime.strptime(date_first_added, \"%Y-%m-%d\")\n",
        "  if date_first_added > fifteen_years_ago:\n",
        "    continue\n",
        "  symbols.append(df.loc[i, \"Symbol\"])\n",
        "  names.append(df.loc[i, \"Security\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uICkXScyJunz",
        "outputId": "5cac0ac2-91cb-4f58-9f27-227ff948f9e6"
      },
      "source": [
        "print(len(symbols))\n",
        "print(len(names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "205\n",
            "205\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTw1Ir1yKtbg"
      },
      "source": [
        "str_symbols = \" \".join(symbols)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMFOjV7pLiUa",
        "outputId": "a33d869c-77e8-4a80-df0b-e9cff0670b01"
      },
      "source": [
        "data = yf.download(str_symbols, start=fifteen_years_ago, end=today)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  205 of 205 completed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "B7zayGFNLyhm",
        "outputId": "7d08ff30-69cd-45f9-ab8f-2ab461c76e48"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <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",
              "      <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",
              "      <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>2006-05-30</th>\n",
              "      <td>21.045671</td>\n",
              "      <td>1.880117</td>\n",
              "      <td>17.134293</td>\n",
              "      <td>14.199324</td>\n",
              "      <td>28.350000</td>\n",
              "      <td>22.932713</td>\n",
              "      <td>28.532423</td>\n",
              "      <td>24.633875</td>\n",
              "      <td>36.490002</td>\n",
              "      <td>25.334345</td>\n",
              "      <td>18.456390</td>\n",
              "      <td>13.769714</td>\n",
              "      <td>16.104511</td>\n",
              "      <td>815.853394</td>\n",
              "      <td>38.105556</td>\n",
              "      <td>12.737465</td>\n",
              "      <td>52.514687</td>\n",
              "      <td>32.148579</td>\n",
              "      <td>34.639999</td>\n",
              "      <td>60.376598</td>\n",
              "      <td>29.445019</td>\n",
              "      <td>52.513725</td>\n",
              "      <td>40.790096</td>\n",
              "      <td>39.431587</td>\n",
              "      <td>42.301666</td>\n",
              "      <td>89.300003</td>\n",
              "      <td>58.884193</td>\n",
              "      <td>36.002277</td>\n",
              "      <td>15.592568</td>\n",
              "      <td>35.332912</td>\n",
              "      <td>46.452850</td>\n",
              "      <td>45.270000</td>\n",
              "      <td>25.431051</td>\n",
              "      <td>8.093652</td>\n",
              "      <td>14.136931</td>\n",
              "      <td>20.320000</td>\n",
              "      <td>46.983498</td>\n",
              "      <td>379.524567</td>\n",
              "      <td>11.041979</td>\n",
              "      <td>33.798218</td>\n",
              "      <td>...</td>\n",
              "      <td>2107600</td>\n",
              "      <td>699600</td>\n",
              "      <td>5282400</td>\n",
              "      <td>725400</td>\n",
              "      <td>2320200</td>\n",
              "      <td>8234900</td>\n",
              "      <td>288400</td>\n",
              "      <td>2913400</td>\n",
              "      <td>1163506</td>\n",
              "      <td>814700</td>\n",
              "      <td>219200</td>\n",
              "      <td>1105000</td>\n",
              "      <td>2239400</td>\n",
              "      <td>16948000</td>\n",
              "      <td>662400</td>\n",
              "      <td>778600</td>\n",
              "      <td>6114300</td>\n",
              "      <td>7102800</td>\n",
              "      <td>1976900</td>\n",
              "      <td>1957200</td>\n",
              "      <td>1637600</td>\n",
              "      <td>8673000</td>\n",
              "      <td>1960800</td>\n",
              "      <td>4070400</td>\n",
              "      <td>1807600</td>\n",
              "      <td>1638029</td>\n",
              "      <td>532000</td>\n",
              "      <td>2611700</td>\n",
              "      <td>1124200</td>\n",
              "      <td>8348727</td>\n",
              "      <td>2662900</td>\n",
              "      <td>8249600</td>\n",
              "      <td>10261408</td>\n",
              "      <td>19274000</td>\n",
              "      <td>1022600</td>\n",
              "      <td>5460000</td>\n",
              "      <td>17866300</td>\n",
              "      <td>2669607</td>\n",
              "      <td>1646000</td>\n",
              "      <td>240600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-05-31</th>\n",
              "      <td>21.545885</td>\n",
              "      <td>1.835586</td>\n",
              "      <td>17.272976</td>\n",
              "      <td>14.252732</td>\n",
              "      <td>28.629999</td>\n",
              "      <td>23.007750</td>\n",
              "      <td>29.028208</td>\n",
              "      <td>24.704510</td>\n",
              "      <td>36.389999</td>\n",
              "      <td>25.666273</td>\n",
              "      <td>18.802044</td>\n",
              "      <td>14.114916</td>\n",
              "      <td>16.352592</td>\n",
              "      <td>820.577271</td>\n",
              "      <td>38.363590</td>\n",
              "      <td>12.889922</td>\n",
              "      <td>53.111889</td>\n",
              "      <td>33.043823</td>\n",
              "      <td>34.610001</td>\n",
              "      <td>61.007298</td>\n",
              "      <td>29.594448</td>\n",
              "      <td>53.045155</td>\n",
              "      <td>41.552586</td>\n",
              "      <td>39.773132</td>\n",
              "      <td>42.741982</td>\n",
              "      <td>90.730003</td>\n",
              "      <td>59.687218</td>\n",
              "      <td>36.151642</td>\n",
              "      <td>15.596706</td>\n",
              "      <td>36.870338</td>\n",
              "      <td>46.895180</td>\n",
              "      <td>46.630001</td>\n",
              "      <td>25.600534</td>\n",
              "      <td>8.245695</td>\n",
              "      <td>14.131168</td>\n",
              "      <td>20.680000</td>\n",
              "      <td>46.762512</td>\n",
              "      <td>381.692352</td>\n",
              "      <td>11.105866</td>\n",
              "      <td>34.186535</td>\n",
              "      <td>...</td>\n",
              "      <td>3218700</td>\n",
              "      <td>1103100</td>\n",
              "      <td>5697800</td>\n",
              "      <td>1107400</td>\n",
              "      <td>2277600</td>\n",
              "      <td>9757900</td>\n",
              "      <td>662700</td>\n",
              "      <td>2519700</td>\n",
              "      <td>1684008</td>\n",
              "      <td>1345900</td>\n",
              "      <td>528500</td>\n",
              "      <td>1962800</td>\n",
              "      <td>2492200</td>\n",
              "      <td>17016800</td>\n",
              "      <td>817600</td>\n",
              "      <td>1652200</td>\n",
              "      <td>6290700</td>\n",
              "      <td>8181600</td>\n",
              "      <td>1665400</td>\n",
              "      <td>2453300</td>\n",
              "      <td>4918000</td>\n",
              "      <td>9254900</td>\n",
              "      <td>1672100</td>\n",
              "      <td>6802000</td>\n",
              "      <td>2632100</td>\n",
              "      <td>1448143</td>\n",
              "      <td>688600</td>\n",
              "      <td>2838000</td>\n",
              "      <td>909100</td>\n",
              "      <td>11499542</td>\n",
              "      <td>3664600</td>\n",
              "      <td>14804400</td>\n",
              "      <td>5570412</td>\n",
              "      <td>12049600</td>\n",
              "      <td>1620100</td>\n",
              "      <td>9996400</td>\n",
              "      <td>25875200</td>\n",
              "      <td>3746241</td>\n",
              "      <td>1355200</td>\n",
              "      <td>565800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-06-01</th>\n",
              "      <td>21.632332</td>\n",
              "      <td>1.909291</td>\n",
              "      <td>17.372042</td>\n",
              "      <td>14.426297</td>\n",
              "      <td>28.719999</td>\n",
              "      <td>23.294241</td>\n",
              "      <td>30.187384</td>\n",
              "      <td>25.025063</td>\n",
              "      <td>37.560001</td>\n",
              "      <td>25.920380</td>\n",
              "      <td>18.944691</td>\n",
              "      <td>14.268340</td>\n",
              "      <td>16.481876</td>\n",
              "      <td>825.570923</td>\n",
              "      <td>38.530964</td>\n",
              "      <td>13.080490</td>\n",
              "      <td>54.054855</td>\n",
              "      <td>33.671902</td>\n",
              "      <td>35.070000</td>\n",
              "      <td>62.260155</td>\n",
              "      <td>30.167234</td>\n",
              "      <td>53.364017</td>\n",
              "      <td>42.494495</td>\n",
              "      <td>39.592319</td>\n",
              "      <td>43.072205</td>\n",
              "      <td>91.680000</td>\n",
              "      <td>59.966862</td>\n",
              "      <td>36.375740</td>\n",
              "      <td>15.791146</td>\n",
              "      <td>36.814682</td>\n",
              "      <td>47.570320</td>\n",
              "      <td>48.119999</td>\n",
              "      <td>25.831661</td>\n",
              "      <td>8.221458</td>\n",
              "      <td>14.476539</td>\n",
              "      <td>20.450001</td>\n",
              "      <td>47.668495</td>\n",
              "      <td>385.795807</td>\n",
              "      <td>11.169748</td>\n",
              "      <td>34.605488</td>\n",
              "      <td>...</td>\n",
              "      <td>2661200</td>\n",
              "      <td>597900</td>\n",
              "      <td>5847900</td>\n",
              "      <td>563000</td>\n",
              "      <td>1419000</td>\n",
              "      <td>8289800</td>\n",
              "      <td>405000</td>\n",
              "      <td>3747900</td>\n",
              "      <td>1330937</td>\n",
              "      <td>1063900</td>\n",
              "      <td>383300</td>\n",
              "      <td>1420900</td>\n",
              "      <td>2082200</td>\n",
              "      <td>28728500</td>\n",
              "      <td>2611200</td>\n",
              "      <td>1260000</td>\n",
              "      <td>5121900</td>\n",
              "      <td>8881600</td>\n",
              "      <td>1339500</td>\n",
              "      <td>2536700</td>\n",
              "      <td>2596200</td>\n",
              "      <td>11753300</td>\n",
              "      <td>2356300</td>\n",
              "      <td>6716000</td>\n",
              "      <td>1871700</td>\n",
              "      <td>3268411</td>\n",
              "      <td>643400</td>\n",
              "      <td>2763600</td>\n",
              "      <td>1112000</td>\n",
              "      <td>12954526</td>\n",
              "      <td>4846700</td>\n",
              "      <td>11070400</td>\n",
              "      <td>5713935</td>\n",
              "      <td>13468200</td>\n",
              "      <td>779700</td>\n",
              "      <td>7580400</td>\n",
              "      <td>17877100</td>\n",
              "      <td>4551908</td>\n",
              "      <td>1418000</td>\n",
              "      <td>440100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-06-02</th>\n",
              "      <td>21.934923</td>\n",
              "      <td>1.893629</td>\n",
              "      <td>17.328453</td>\n",
              "      <td>14.396259</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>23.492058</td>\n",
              "      <td>29.565891</td>\n",
              "      <td>24.856628</td>\n",
              "      <td>36.529999</td>\n",
              "      <td>26.215992</td>\n",
              "      <td>19.147684</td>\n",
              "      <td>14.391078</td>\n",
              "      <td>16.516813</td>\n",
              "      <td>824.760925</td>\n",
              "      <td>38.433319</td>\n",
              "      <td>12.989016</td>\n",
              "      <td>54.361301</td>\n",
              "      <td>33.556400</td>\n",
              "      <td>34.759998</td>\n",
              "      <td>62.081200</td>\n",
              "      <td>29.909893</td>\n",
              "      <td>53.870937</td>\n",
              "      <td>42.545742</td>\n",
              "      <td>39.009686</td>\n",
              "      <td>43.025028</td>\n",
              "      <td>91.699997</td>\n",
              "      <td>60.081551</td>\n",
              "      <td>36.794006</td>\n",
              "      <td>15.766331</td>\n",
              "      <td>36.592064</td>\n",
              "      <td>47.236645</td>\n",
              "      <td>47.709999</td>\n",
              "      <td>25.754629</td>\n",
              "      <td>8.197218</td>\n",
              "      <td>14.545611</td>\n",
              "      <td>20.700001</td>\n",
              "      <td>48.596573</td>\n",
              "      <td>388.195892</td>\n",
              "      <td>11.140262</td>\n",
              "      <td>34.498180</td>\n",
              "      <td>...</td>\n",
              "      <td>2441300</td>\n",
              "      <td>583500</td>\n",
              "      <td>4906200</td>\n",
              "      <td>739000</td>\n",
              "      <td>2130300</td>\n",
              "      <td>7621100</td>\n",
              "      <td>311100</td>\n",
              "      <td>2748500</td>\n",
              "      <td>1414437</td>\n",
              "      <td>839900</td>\n",
              "      <td>490300</td>\n",
              "      <td>853400</td>\n",
              "      <td>2281400</td>\n",
              "      <td>13232100</td>\n",
              "      <td>684200</td>\n",
              "      <td>1453600</td>\n",
              "      <td>4914500</td>\n",
              "      <td>7123200</td>\n",
              "      <td>1390600</td>\n",
              "      <td>3074300</td>\n",
              "      <td>1796800</td>\n",
              "      <td>12669300</td>\n",
              "      <td>1360100</td>\n",
              "      <td>7211200</td>\n",
              "      <td>2190200</td>\n",
              "      <td>2184746</td>\n",
              "      <td>744200</td>\n",
              "      <td>1702500</td>\n",
              "      <td>1328700</td>\n",
              "      <td>9677504</td>\n",
              "      <td>6774700</td>\n",
              "      <td>11030600</td>\n",
              "      <td>8944308</td>\n",
              "      <td>13776000</td>\n",
              "      <td>1049000</td>\n",
              "      <td>6572300</td>\n",
              "      <td>17354500</td>\n",
              "      <td>3247985</td>\n",
              "      <td>1549700</td>\n",
              "      <td>591900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-06-05</th>\n",
              "      <td>21.206238</td>\n",
              "      <td>1.842649</td>\n",
              "      <td>17.031263</td>\n",
              "      <td>14.172624</td>\n",
              "      <td>29.469999</td>\n",
              "      <td>22.414305</td>\n",
              "      <td>28.979330</td>\n",
              "      <td>24.666473</td>\n",
              "      <td>35.130001</td>\n",
              "      <td>26.255388</td>\n",
              "      <td>18.845938</td>\n",
              "      <td>14.260671</td>\n",
              "      <td>16.387535</td>\n",
              "      <td>815.043640</td>\n",
              "      <td>37.826599</td>\n",
              "      <td>12.661244</td>\n",
              "      <td>53.025455</td>\n",
              "      <td>32.646740</td>\n",
              "      <td>33.490002</td>\n",
              "      <td>61.024361</td>\n",
              "      <td>29.254089</td>\n",
              "      <td>51.925064</td>\n",
              "      <td>41.648697</td>\n",
              "      <td>38.672585</td>\n",
              "      <td>42.144390</td>\n",
              "      <td>89.760002</td>\n",
              "      <td>58.884193</td>\n",
              "      <td>36.405617</td>\n",
              "      <td>15.542932</td>\n",
              "      <td>36.056408</td>\n",
              "      <td>46.716713</td>\n",
              "      <td>45.389999</td>\n",
              "      <td>25.207632</td>\n",
              "      <td>7.937196</td>\n",
              "      <td>14.482294</td>\n",
              "      <td>20.379999</td>\n",
              "      <td>48.673897</td>\n",
              "      <td>384.169983</td>\n",
              "      <td>11.155006</td>\n",
              "      <td>34.145653</td>\n",
              "      <td>...</td>\n",
              "      <td>1605400</td>\n",
              "      <td>777000</td>\n",
              "      <td>6150900</td>\n",
              "      <td>767200</td>\n",
              "      <td>1920000</td>\n",
              "      <td>9900600</td>\n",
              "      <td>378900</td>\n",
              "      <td>2352100</td>\n",
              "      <td>1155425</td>\n",
              "      <td>740300</td>\n",
              "      <td>642900</td>\n",
              "      <td>1095100</td>\n",
              "      <td>2411000</td>\n",
              "      <td>12447800</td>\n",
              "      <td>620400</td>\n",
              "      <td>1296500</td>\n",
              "      <td>3390100</td>\n",
              "      <td>5423200</td>\n",
              "      <td>2633600</td>\n",
              "      <td>3024400</td>\n",
              "      <td>2547800</td>\n",
              "      <td>7065100</td>\n",
              "      <td>1485000</td>\n",
              "      <td>5498400</td>\n",
              "      <td>2053400</td>\n",
              "      <td>1462162</td>\n",
              "      <td>1056000</td>\n",
              "      <td>1990700</td>\n",
              "      <td>846800</td>\n",
              "      <td>7370657</td>\n",
              "      <td>4228500</td>\n",
              "      <td>7216600</td>\n",
              "      <td>4796614</td>\n",
              "      <td>12445900</td>\n",
              "      <td>842800</td>\n",
              "      <td>5928000</td>\n",
              "      <td>20504100</td>\n",
              "      <td>2444543</td>\n",
              "      <td>1501900</td>\n",
              "      <td>662200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1230 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Adj Close                       ...   Volume                 \n",
              "                    A      AAPL        ABC  ...      YUM      ZBH    ZION\n",
              "Date                                        ...                          \n",
              "2006-05-30  21.045671  1.880117  17.134293  ...  2669607  1646000  240600\n",
              "2006-05-31  21.545885  1.835586  17.272976  ...  3746241  1355200  565800\n",
              "2006-06-01  21.632332  1.909291  17.372042  ...  4551908  1418000  440100\n",
              "2006-06-02  21.934923  1.893629  17.328453  ...  3247985  1549700  591900\n",
              "2006-06-05  21.206238  1.842649  17.031263  ...  2444543  1501900  662200\n",
              "\n",
              "[5 rows x 1230 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52HaavTNOSdZ"
      },
      "source": [
        "companies = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWnb3KWwMh9S"
      },
      "source": [
        "for i in range(len(symbols)):\n",
        "  symbol = symbols[i]\n",
        "  name = names[i]\n",
        "  companies.append(Company(symbol, name, list(data[\"Close\"][symbol][1:])))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ihbW24lQbZt"
      },
      "source": [
        "def plot_stock(symbol):\n",
        "  for i in range(len(companies)):\n",
        "    if companies[i].symbol == symbol:\n",
        "      plt.plot(companies[i].stock)\n",
        "      plt.title(companies[i].name + \" Stock\")\n",
        "      plt.ylabel(\"Stock Price (USD)\")\n",
        "      plt.xlabel(f\"Number of days (after {fifteen_years_ago.month}/{fifteen_years_ago.day}/{fifteen_years_ago.year})\")\n",
        "      plt.show()\n",
        "      return\n",
        "  print(symbol + \" stock data not found\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0zyF5SDQRngE",
        "outputId": "8617c680-498e-425f-b2f2-faeafa51be07"
      },
      "source": [
        "plot_stock(\"AAPL\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "yYpIUdX4Rthh",
        "outputId": "77dae4d9-1b04-42d5-ec74-d4489998d8d9"
      },
      "source": [
        "plot_stock(\"MSFT\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zt7jZzQNRv7O",
        "outputId": "e3f60aef-6caa-4151-bb6a-86652db29cec"
      },
      "source": [
        "plot_stock(\"GOOG\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "nwSQDWm7SCte",
        "outputId": "cd0b5fda-035f-4c35-99f0-6f0e1d447695"
      },
      "source": [
        "plot_stock(\"JNJ\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jsxkc0qQO1x3XiTtP6+6v++i6gRUm28FgfgqGYaQkxSXKzW/PCigrKo59MF+9bS//+W4FExfmc8ZhLQPqIt3lpxVb2bX/AMd5BvGSEo0YnE5V2Xug2B8ELz2CgTjaIpFPmfRsVZ/ebRtGaZ1YEjZTEJFXRGSTiMwJU3eLiKiI5LrnIiJPisgSEflFRI5IlFyGYVQNlubvDinbf6A4TMvw+JZ1RAiZKZREmClc8NwPXDlqGp/NKQ1b8cHMtRH7KChy/BFqZznv1+kRlMfCjaXOd7l1s0LqfbaFSNdXJolcPhoFnBJcKCJtgZOAVZ7i4UBn93M18GwC5TIMowpwkscD2UdBHLt1fLuW6mRlMGPVtsDKKBOOP7wxw398yzuzIrZblu9ENvU5rZU1o/Bx6qEtQ+p9NobgeEnJIGESqOo3wNYwVY8DtxH4azkLeE0dfgQaikjokzMMo0ZTUBT7TGGL6yg2e+0O/5ZU39p9OEPzwWwPff3HlQD+iKiRXvS9NoWPZ60Lqff5N2SmwG6kSlVLInIWsFZVg1Vva2C153yNW2YYhuFn/4HYB+5/fBa6ndS3O8i7JXVfYTHPTVpKlzs/C3ufE3s0j9iHz5jcoJYTRymSTaF5/Rx+e/QhAAzv1SKk3rd8lAozhUozNItIbeAvOEtH5bnP1ThLTLRr164CJDMMo6oQz0xh2ebApDXHdW3qf5P3reaoKt3vGlfmfcoKnHf+kW14d/oaerV2UnCWlS3Npy+6tQj1YvbZErJSQClUpgQdgfbALBFZAbQBZohIC2At0NbTto1bFoKqvqCqfVW1b9OmTRMssmEYlU2aQMemdfznT158OK9d2R+Ib6ZwRu9WAecTFuYj7j4gn6F5Zww5DLIyIg/0+w4U0z63VNZIMwVvn+H0xm5Xjq8XbooqT6KptJmCqs4G/AtrrmLoq6qbReRj4I8i8hZwFLBDVddXlmyGYaQGqkqJwpAuTVnqGnHP7N3K7x0cz0xhT0GYAT9opuBLhRmOgZ2asHTTnrDRTgFmrd7OmF8Ch6myZgq+PsPFNfLFTzqqfeOI11cWCVMKIvImMBTIFZE1wN2q+nKE5mOBU4ElwF7gikTJZRhG6uIbfxvWCty2mZYmZKRJzMbgHXsP+PMiB9wnaEAOTqMZIEuJs6wTyft58pLNYe4fWaY/Ht+JVVv3hsxgoHQW0feQaqwUVPXiKPV5nmMFrkuULIZhpBbb9hQycdEm1m3fz4COTTiiXSMAFrn7+T+fu4EnLupDywalcYXSyhigg+l93xeAs9XTF5jumiEd/E5kvkH4nGe+L/M+IpF9Gnw7jryRToP9DM49vHS/TMsGtXh9xFFh73VSjxa8O30NZx8eqjAqm+RbNQzDqHE8PWEJN789i0c/X8i5noF55RZnyWje+p2c1ac1/T3LKUL84Sl+O+AQ//Htp3TD3fmJauR7+XYHZaQLaSL+pSsfn/6yjryRY1juGrJf+O2R/rrgmUjzBjkxyZmXW4cvbj6WTs3qxdQ+kZhSMAyj0glxJgPen7GGp75eAsAzvwkNapAmEnceBF/4CXBmGl5Ds9dWcM2xHfzHgzs7G1gy09NIk1Cfhrd/cnbP/7LGCePt2+YKoTOFjBTwUI4XUwqGYVQ63hwJAH/9YDZ/Gj3L72TmW5rxkiZOHKLdBUUxG5yDI6z6XuQVKHTDcPc9pBG3ndwNgLvP6IFvV2hGmrBiy94QZ7N9hU7fvqB9XsUTvPuoItN+VhYx2RREpBkwEGgF7APmANNUE5WqwjCM6owvPISPN6asCjjPCZO4Pk0cm0Kvuz+nQ9M6fH3L0LD39s4AFntiDkHpzh9VGP7EtwDUzckgPU1Y8fBpAIye5swEMiOkxNwfpJC8sgbrgGo3UxCR40Tkc2AMTnyilkAP4E5gtojcKyLh88kZhmEcJL5cx15EYJa7ZBOsVLx4czqf3rsVXZvXY2hXZ0nIN0SrKiu3OCk6zzk8MHiCr01mmtCucW0a18ni4c8WsGTTbvYUFPlnH4c0qU2X5nWDZBR6t2ngPy9ri2qqEm2mcCpwlaquCq4QkQzgdOBE4L0EyGYYRg2lRf1QA+3O/UVMXxlqiwjGtxX1giPbcNvJXR0Dszs2e5ePfDSpkx1wvc/+nJGexqGtGzBm9nqem7SUcXPWM/qaAf52PqUSjNcPYfnmyMorVSlTKajqn8uoKwI+rHCJDMOo1kRLqdmgVuZBv2HvKSjyRzjt1boBGUFhI9I8y0f+a4Kysu1yHclKSpSNO/f7y1ds2cuBGPbEepeQfloRLiZoahPV0CwiXUXkMREZ437+KSKplT/OMIwqwwbPQBsOr+G2LPJGjuGp8Ysj3js7jE0g2E8BoHOzwCWg5yc5KTjfn7mWaUEzk4EPfx2TbD4i+TikMtFsCgOAicAu4AXgRWAPMEFEjk64dIZhVDuiLQHFkzPhpcnLA859O4MgfMIa39LONje8xQ3Hd6JD00Cl4LMxDO/Vgov7tyVevL2WVMGtONFsCncBF6vqRE/ZhyLyNXA3jvHZMAwjZl77wclB8PqI/kxbsY0flm1hWf5uNu92BuqdQdtVyyJ44N/rUQrhYh+t2ebYAX794hQAamWFDoF3nNqdgZ1yOaZjE/7+6bwy+z+lZ2gY7KpOtOWjjkEKAQBVnQR0CG1uGIZRNt1bOl67gzs35eYTuzD6mgEB8YCK41hyCY5d5FUou8MohS/mbgw4j7RldEiXpmSkp0W1ITx6wWEhZeEC3lUloimFXWXUVT2zumEYlYqq8vykpf43dIBd+4s4pEntgHZ3DO/u39lz47DOMd/fu/0T4HevTfMfH98tNDlOnezY7BU+ohnF64XJteBVCfGG5UgFoi0ftRWRJ8OUC5YZzTCMKGzeXchDny3g7Wmr/c5mO/cdCElck5WRRv/2jf0OZLEya80OSko0ZLfSR9cNpEerUBeqm07o4vdEjoXO5YxFdGafqjdMRpsp/BmYHuYzDSfPsmEYRkR8u298zmbfLdnMhIX5FerUNWa2k9PAG7jOlwktmKI4Lb+XHZPH21cfzUmelJzHdik7uZd39ej8I6ueUojmp/BqcJmINAK2a1WcFxmGUakE5z/4zUuOgXfW6u0V1sf1b87ktENb8p/vV/jLwu08AshrUifgPNryf1ZGGkd1aEKd7Ay+mOfYIx45/zCOenB8yFZW/z0DFpCqnn0h2pbUu0Skm3uc7e46WgpsFJETKkNAwzCqLsGB73xEe9sui95tG4aU7TtQzKRF0ZeFmtQN9F4+OcbdQ71aN/BvVW1eP4fZ95zE2BsHR2xbStV7d462fHQhsNA9vgxH7TUFjgUeTKBchmFUAzbsKHUme3f6GvrlOcl0/nN5v7jvdf6RbejQtA6n9godyPcWFtOtRXzr/+NuGkzbxrWjN3R5/MI+fptHvZxMMtPDD58Z6aWzgx37oueATjWiGZoLPctEJwNvqWoxMN+NfWQYRg1hxeY9DP3nRLLS01j0QGwuSt5tobe+MwuArs3rHZRN4Z8X9Abg2YlLQ+qWb95Dx6Z1QsrLIlx47oqgcZ2s6I1SmGgzhQIR6SUiTYHjgC88dbGrWMMwqjzvTHdCShdG2abpJZyvQGZG+dbZT+zRLKRsWf7uAMe1WAi3nbQi+N2g9gm5b2URTSncBLwLLAAeV9XlACJyKjAzwbIZhpFCNKtXGrl0WoyB3p76enFIWcGB8sV+6NSsHpcc3Y4bPP4MI9+fzXsz1gDw3u+PKdf9y0twEL6qRpnSq+qPqtpNVZuo6t895WNV9eLEi2cYRqqweFOpL+sVo36K6ZqNOwsA+M8VpTaExZt2l1uW+88+lBuO7xRQNmftTtIEjmgXaohOFlVxk2aZdgER+VNQkQKbgcm+WYNhGNUfVeW/P5amVekZxjEsGJ83cG7dbJrVy47SOn7CvZGXaGqFmWjXpOqtskeb59QL+tQH+gKfichFCZbNMIwUwZtQpm3jWmGT4ASzwr1m8+4CsjwD+EfXDawwufrnNY77mmd/cwRjbwi/nbSi8S65VRWiOa/dG65cRBoDXwFvRbpWRF7Bycy2SVV7uWWPAmcAhTj+Dleo6na37g5gBFAM3KCqn8f9bQzDSAhrt+8D4JXL+3LlqGkcKIplWcRpc8Owzn7jdJfmdcP6GRwso68dQN7IMXFdM/zQlhXWf3XkoCwiqrqV6K56o4BTgsq+BHqp6mHAIuAOABHpAVwE9HSveUZE4otcZRhGwvAphQa1nO2W0RLlAOx3Dcq9WtUnr0kdWjbI4Z4zeyZOSKB+mNzORnwc1BMUkeOAMjNlqOo3IpIXVObd0vojcL57fBaOD0QBsFxElgD9gR8ORj7DMCoWn8G0RYPS5ZDCohIy0wUR4bJXpnJCj+b89uhD/PW+ZDnZmenUyc7ghzuGJVTGb/58XJVcw081ohmaZxPqp90YWAdcWs6+rwTedo9b4ygJH2uIEIVVRK4GrgZo165dOUUwDCMWCoudYSArPY2jOzTmx2Vb6XLnZwA0r5/Nxp0FTFqUH6AU5qzdAUBxJaUfSyH7cpUm2kzh9KBzBbaoarlyKYjIX4Ei4I14r1XVF3BSg9K3b9+qt9/LMKogvsB2WRlpDO3ajB+Xlfop+LadBrNlt1PePAajdHnoe0gjpq3cRptGifFQrmlEUwpbVLXMTcUiUjdam6D2l+Mom2GeEBprAW8y1DZumWEYKcC+QsczuVZmOp2aho8OGkztbGd4aZ8bX/iJeHk3yc5q1Y1ohuaPROQxERkiIv7frIh0EJERIvI5ocbkiIjIKTh5GM5U1b2eqo+Bi9xIrO2BzsDU2L+GYRiJxDcbyMpI44QezWkag9/Bjn0HyEwXamXanpGqRDSP5mHAeOAaYK6I7BCRLcB/gRbAZar6brhrReRNHENxVxFZIyIjgKdx/B2+FJGfReQ5t5+5wGhgHjAOuM4NvGcYRpKZsGATr/+4MqCsrH3+c9buYM22vexwM6ylkjOZEZ2ou49UdSwwNt4bRwiD8XIZ7R8AHoi3H8MwEku4kBZN62Wz7MFT+ce4BTz/zTJ/+f4DxZz+1GQATj+sJQ1qJSbonJE4qnbkJsMwkkZamnDaYYGOYN3+Ns5/vGPfAeqbUqhymFIwDOOgycqIPITs3F9kSqEKYkrBMIywrNi8xx9Col9eIxaHSaxTVhjsWau32/JRFSRmpSAig0TkCve4qbtLyDCMakhJifLMxCX+8x4t64dNP9k1TArMC/uW7i63sBNVj5iUgojcDdyOG6sIyMTZgWQYRjXkle+WM3raGv95pB1E2WGWj4Z0aeo/9nk1G1WHWGcK5wBnAnsAVHUdztZSwzCqIdv2FgacvzNtddh2IsJzlxzB307v4S/z2hnuO6tXYgQ0EkasSqHQ9T5WAK8jm2EY1Y+62YG2gI/+OChi21N6teTSAaUxjzLTS2cVFRkm26gcYl3wGy0izwMNReQqnGB2LyZOLMMwksm+A47v6Bm9W3Fyz+Z0alZ2aAuvvWH/AfM7rcrEpBRU9Z8iciKwE+gK3KWqXyZUMsMwksa+wiJyMtN46uLD4762bnYmc+49OSDbWk2jWb1sNu0KHygw1YlJKbg7jb71KQIRqSUieaq6IpHCGYaRHOav30V6nOEplj54Kt8uzmdQ59wESVV1+Pb249AqGsM5VlX+DuDdkFzslhmGUc3YXVDE5CWb2VMY3zJQepowtGuzBElVtcjOSCenigYCjFUpZKiqfzuCe5yVGJEMw1BVpi53chbkjRxD3sgxjJuznpKSxL9+9rrb0qPXZGJVCvkicqbvRETOAjYnRiTDMF6evJxfPf8D4+as95dd+98Z3PfpvEqT4YmL+lRaX0bqEKtSuBb4i4isEpHVOI5s1yROLMOo2dw/Zj4As4Ocv0Z9vyKh/T7+5SL/8Vl9wmbENao5se4+WgocLSJ13fOYM60ZhnHwBPsLZGekoaoJyVGwNH83T4xf7O/HqJmU+ZsXkUvcn38SkT8BVwNXe84Nw6gArn19OreMnuU/P66rEyrii3kbAtoVFJUw+JEJ/pzJFcm4OaV9vXRZ3wq/v1E1iPY64PNcrhfhYxhGOVmyaRfj5m7gvRmlsYZ85mTftsb7zy4NF7Fm24V29nsAACAASURBVD5mrNpW4XI8+vlC//Hgzk3LaGlUZ8pcPlLV50UkHdipqo9XkkyGUaM44V/f+I837y4gt242ExfmA/Dz6u0AtGqYE3BNRcwUFm3cxUmPf8MXNw+hS/PSd7yDcVgzqg9RFw7dXMnhUmsahlHB7Cko4oOZa0LKg/MSfDZnQ0ibeBnzi7Oz6dNf1vPEV4v95cN7tSj3vY2qS6zWpO9E5GkRGSwiR/g+CZXMMGoAPy7bEnA+d91Obn57Vki7YKXw5tRVIW12FxQx4KHxfL8ktt3iaa6x+snxi3n8q9JdRxk1ODyFEXtAPN+G5fs8ZQocX7HiGEbN4vO5gW/8yzfvCdsuXFrLH5Zu4dvF+Qzt2oz+7RuzdNNu1u/Yz/1j5jP2xsFR+/YqAh8dm1oA5JpOrErhAlU1ZzXDqGC27gnMW/DGjyvDtguX1vLiF38E4JmJS/nouoEUxejtvLugKKLX8sBOFreophNtS+oZIpIP/CIia0TkmEqSyzBqBMED+bod+wEYd9Ng6maXvrNlZ6Tz7W3H8dbVR4e9z1n//o6l+Y77UFqU1Z+ywlikJcD/wahaRFs8fAAYrKqtgPOAh2K9sYi8IiKbRGSOp6yxiHwpIovdn43cchGRJ0VkiYj8YvYKo7rz8GcLyBs5xm/sPaN3q4D6xnWyeOjcQwPK2jauzdEdmkS8523v/gLAnLU7AZi7bgcbd+6PS64eLevH1d6ofkRTCkWqugBAVacQn2/CKOCUoLKRwHhV7QyMd88BhgOd3c/VwLNx9GMYVY7nJi0NOD/jsJYB5w1qZQaktfTSs1X0gTt/VwGnPTmZox4cz/z1O2OW6+zDLbRFTSeaUmjm8152PZiDzyOiqt8AW4OKzwJedY9fBc72lL+mDj/iZHhriWFUQzQo0H7trHTaNq4dUJadke5XChlpgUs6N5/QJWof/R74yn88/Ilv/dnQgncm/fOC3gHn3lSaRs0kmlJ4kUAP5uDzeGmuqr6wjxuA5u5xa8CbGXyNWxaCiFwtItNEZFp+fv5BiGAYyWXHvgMB59+PPJ7uLetzzxk9Asp9mcsyg7aIHtct/pwFt77jbHPN312aDWzk8G70advAf/6vX/VOSEwlo2oRzaP53kR1rKoqInEHh1fVF4AXAPr27VtFcxsZNZk+95Vmsp1461Aa1nZSk1w+sD2/6teWWm5yFv9MIejtPT1NyK2bxebdgTuXyuLTX9bz9K/x3/vT6wfRq3UDNuwotTmYf4IBsW9JrSg2ikhLVV3vLg9tcsvXAm097dq4ZYZRrdixt3SW8N8RR5GXG+gXUDur9F/StxNo1/6ikPsc0zGXj2etA5yBftKfh5KZnsaWPQUBYTN8NKztbGl9Y4rj9FYry1EO9XJK+wtepjJqJpWtFD4GLgMedn9+5Cn/o4i8BRwF7PAsMxlGteGd6aWrpIc0qV1GS9hUxs6hR84/jN8P7ciKzXvo3bYhzeo7sZGCjdOnH9aST39ZzyDX/2DSImfJ1ZcqsnZWacpIUwoGxBjmQkSyw5Q1jnLNm8APQFfXx2EEjjI4UUQWAye45wBjgWXAEhy7xR9i/gaGUYXwJc+B0AE8mDrZkd/ZcjLT6d6yPsMPbUmrhrUCrrnrdMc2cWjrBjz96yPo2rweRcXOSmt7d2bSqoGjRLw2hHRTCgaxzxTeF5GzVfUAgLv08ylwZKQLVDVSEL1hYdoqcF2MshhGleWE7s35av5GAJrWDXnXCsD3dn/jsM5x9XHloPZcOai9/3zd9n0s3LgLcHYXdW1eL6xBefrKbQzr3jyk3KhZxGpZ+hAYLSLpIpIHfA7ckSihDKMqU1KiPDR2vt/D2ItPIXxx8xDSoryZp6UJKx4+jZtPjL4FtSx2FTg2iW17Clm0cbdfQfjo3bYhABf2axtyrVHziEkpqOqLwFc4yuET4FpV/SKRghlGVWXWmu08/80yrv/fTFZv3UveyDGs3ro3oI03f0Fl8Z8I+Z2fvKgPd5/Rg3aNy7ZxGDWDaLGPvI5qOUA74GecfM2WjtOo0UxfuY1/fbEwpNyXwWze+p0MfmQCAPd8PBeAlg1yQtpXFk+OXxy2/JAmdbhiYHvzUTCA6DaF4NeZ9yOUG0aN47xnvwfgqiEdqJdTGsX0+6VbQtoO6pxLYVEJ63fEF4soEQzpYqk2jcgkzXnNMKoLY2ev58J+7QC4ZXRoghyA8fM3ce8n8ypTrIhccUxeskUwUphYt6R+KSINPeeNRCRy/F3DqObsKyz2H9/+3mz/8XszQlNpAkz2xBy65tgOiRMsBg4mTIZRc4h191FTVd3uO1HVbYD9ZRk1lpmrt4WUeY3Jo68ZEPHaP5/UNSEyGUZFEKtSKBaRdr4TETkEJx2nYdRIvKEnjuvqrNF78yaXZVC2GENGKhOr89pfgckiMgkQYDBO3gPDqJGs2lI6K/DlVe7UrC4Aj1/Ym6b1wjumPXL+YYkXrgx6tbYkOkbZxKQUVHWcmw3NlwvwJsvZbNRkHhhbGq5ixZa93PH+bOat2wFAv7zG5GSm8+A5h9KmUS2e/noJU1ds5c8nd+VXfSvfQezT6wexbvs+hnRpaqEsjKjEExDvGGCI5/zTCpbFMKoMLernsMETsM67dJTrhq/49VHOiuulr0wFIDtKrKNE0at1A3q1bhC9oWEQ++6jh4EbgXnu50YReTCRghlGqrFgw04mLcpHVdldUMSlAw4hLyjSaU5mmj8CaTAWhdSoCsT66nIqcKKqvqKqr+DkXj49cWIZRupxyv99y2WvTGX/gRJ2FxTRokEOIzyB56A0iY2X490toCW2NcOoAsQzn23oOba5qFFjmb9hJwCNamfRwM2a5mPb3gMh7X15E0wnGFWBWG0KDwEzRWQCzu6jIViUVKMG4UR3dzj3GSe8RcNamdTNif4vJEjIPQwjVYk1SuqbODuP3gfeAwao6luJFMwwUontYWYAzepnk50R3n7gZVh3Z/noqPZNKlwuw6hoYjU0j1fV9ar6sfvZICLjEy2cYaQCB4pLOPzvX4aUd29Zn16t65NbN4tXLu8b8fqBnXJZ+uCpHNrGVl2N1KfMua+I5AC1gVwRaQT4tk/UB1onWDbDSAn+76tFYctrZzn/PtPuPBGAh889lIZBNgYf5h9gVBWiLYheA9wEtAKmU6oUdgJPJ1Auw0gZFm8MzaAWjov6t4veyDBSnGihs58AnhCR61X1qUqSyTBSii/mOSk0e7aqz9x1O5MsjWEklmiZ1/qJSAufQhCRS0XkIxF5UkQaV46IhpE8SjzOBR/8YSAXWR5jo5oTzdD8PFAIICJDgIeB14AdwAuJFc0wks/t7/3iP87KSOPh85yAdg1rZ0a6xDCqNNFsCumqutU9vhB4QVXfA94TkZ8TK5phJJ93pocmzfnfVUfRIbduEqQxjMQTbaaQLiI+xTEM+NpTF08wvQBE5GYRmSsic0TkTRHJEZH2IjJFRJaIyNsiEn4bh2FUIrWzQv0QjumYS4sy8iUYRlUmmlJ4E5gkIh8B+4BvAUSkE84SUtyISGvgBqCvqvYC0oGLgH8Aj6tqJ2AbMOJg7m8YFUm7xk6Iis9uHJxkSQyjcihTKajqA8AtwChgkJb66acB15ej3wygljsLqQ2sB44H3nXrXwXOLsf9DaNC2FNYxNl9WtG9pSWnMWoGUZeAVPXHMGXhvXliQFXXisg/gVU4s48vcHwgtquqL8fhGiI4x4nI1bhZ39q1s33hRuJYsXkPq7fu88cuMoyaQKVn/XA9o88C2uM4xdXBCcUdE6r6gqr2VdW+TZs2TZCUhgEXveC8D63aujdKS8OoPiQjFdQJwHJVzVfVAzhB9gYCDT1G7TbA2iTIZhh+2jauBcC8+05OsiSGUXkkQymsAo4WkdoiIji7muYBE4Dz3TaXAR8lQTbDAKCouISfVmwDSmMcGUZNoNKVgqpOwTEozwBmuzK8ANwO/ElElgBNgJcrWzbD8NHpr58BcGKP5kmWxDAql6S8Aqnq3cDdQcXLgP5JEMcwIvKbo2wzg1GzSMbykWGkNFOWbfEfd2pmnstGzcKUgmEEcc1/pwPQu00DWjeslWRpDKNyMaVgGEH4Um9+eN1AnL0QhlFzMKVgGB427dzvPzaFYNRETCkYhocHxs4H4NRDWyRZEsNIDqYUqhCqyts/rWJvYVH0xkZUvluymbyRY9jomR189PM6AK4a3CFZYhlGUjGlUEX4esFG2t8xltvfm83fPpybbHGqBW9OXQXAG1Ocn0XFJf66w9s1SopMhpFsTClUEa4cNc1//N6M0MQvRvw0qeOk7Hhy/GJ+XLbF77B26YBDkimWYSQVUwpVlGJP7mDj4GhcJ9t/7At+B/Cbo0wpGDUXUwpVhPo5gc7ne8yuEBf7DxQzfeW2AGU6cdGmsG1bNzLfBKPmYkohDCUlyr++XMSu/QeSLQoAq7fuZef+Im4c1tlfduTfv0yiRFWPq16bxnnPfk/Hv4yloKiYvJFjmLlqe9i2dbMtAJ5RczGlEIZvl2zmyfGLOfSeL9iwY3/0C+JgxqptjJuzHoA5a3dQmsyulLXb9wWcD35kAuDkC77lxC4AHCi25aN4+HbxZv/xEPd5BvP8b49k/C3HVpZIhpGSmFIIQ0ZaqdPS5CXOYLKnoIgJC8MvN8TK9r2FnPvM91z73xnkjRzD6U9NZvS01QFtJizYxMCHv2b8/I0ArN9RqiAmLcqvEbF4vl+6mdvenRVWYR4M01duCzjfuLMg4HzxA8N5fUR/Tu7Zgo5Nq//zNYyysHlyGHbuK102mrQonzN7t6Ln3Z8D8On1g+jVusFB3bfPfaFLPsED1M2jfwbg+6Vb6Ne+MWu3lSqFFy/tS2Z69dTj89bt5J3pq0kX4aXJywHo0bI+lw9sX+573/DmzLDlT158ONv2FJKZnsbgzpbFzzDAlEJYdu0vNeJ+Mmsdn8xa5z8//anJrHj4tArrq5G7LRIcY6gv7s7Lk5fz8uTl5DWpDUDjOlnUqcZr3ac++W1I2T2fzCu3Uli0cZd/Oe6ifm0ZMag9Jz7+DRcc2YYze7cq170NozpSfUeZg0RV+XzuhjLbrN+xj5YNYtuhsmV3AUfe/xUA9bIz2FVQxLe3Hee3E+wtKFVA3f42LuT6FVuc/MDP//ZIf1m97Awa1M6Mqf+KpKRE+XjWOqav3Ma9Z/YkLa1iYgPNWbsjYt2abXuZunwr5x7RJqRuxKifqJ2dwbBuzRjYKZem9bJ5ZuISPpq5js9vHgLAqi2l+ZUfPu8wgApV6oZR3TClEMSHP69l/IKybQcDHvqaFQ+fhqqyZts+2jauHbHtx55Zxq6CIs7s3YqWDXL8ZXsKiwG44j9T/WXnHt6a92cGpqjue0iph+1RHZqwLsgYnQgWbdzFp7PWcfOJXRARjnn4aza4ISEOFJf4B9nycuWonyLWDfqHozy7NK9HiwY59L3/K84/sg2Pnn+Y//fkm8ldf3wnnvp6CQBvTV1F28a1KSpxvJTfvXZAhchqGNWd6rlAfRD89uUp5I0cw1tTSw2/yx86lUfOO4xjuzTlf1cdxcL7Twm45q2fVjP4kQk8Mm5BxPve+8m8gPO12/eRkZ7GwvtPoVZmun+mMGFhvr/N7cO7hdzHG7EzOyONXQUH2H+gOL4vGSPTVmwlb+QYTnr8G578egkzVm1nztodfoUAznePZng/UFzC3z+dF1WBHfCEl/ARnMfg9Kcmc+4z3wPw7vQ1tL9jbMg1PoUAMPL92fzmpSnscO1DLTyK2DCMyJhSAPYVFvu3LE5ZvtVfLiL8ql9bXr2yP8d0zCU7I53ebRv6r5nh7mp5ZuJS/9bVouKSgMG6Qa3AZR7fTpjsjHTqZGewp7A4YAvqA+f0onn9HI5o1zCivNkZaazeuo9ufxvHjr0V70tx/nM/BJyf9+z3nP7U5JB2Zb3hA5z/7Pe8PHk5xzz8dZnttrnfYc69J7P8oVMZfc0Abjyhc0i7VVv3hpQB9Gkb+VlNWeb8PoN/D4ZhhMeUAvCHN6YHnPdqXZ8lDwwP2/Ywd+fRmm17aV6/9O3z6IfGM3raav74v5kBtoHdBUWcf2TpevgVA/P8x8UlJbw5dRUDPYPmWX1aA/D+Hwb62x55SGBwtl0eO8RLk5fF8hVjpiRK+IxP/jjIf9zEEyYimLs/msOsNaW2gnFzNjBj1TaW5u+OeE3d7AxEhP7tG9OsXuR7e7ljeDc+vG4g0+48gRuO78S/f31EQP37M9eSnibmkGYYMVLj/1NU1b90c2yXpizbvJsP/zCQjAhbP0/o0ZzXf1zJ9n0HyN8VuJ30tnd/8R+XlChb9hRSXKJ8MXcDSx88lV37D1Avp/SNdVvQW/65h7cOGLzuPK0HAFcG7cDxmnef+noJfxjaiVpZ6bF/6TAUFpWQnia87CqZfnmNuGFYZ377cqmt4/Jj8ji0TQOeuvhwrn9zJq0aRl6SefWHlQHn1/63VPG+edXRDOjYxH/euE5WSP6CpjEqhUuOduIU5dbN5k8ndQXguv8FtikuUUuYYxgxUuNnCvm7Swf2V6/sz7e3HR9RIQC0cGcHH8xcG3BtMB3+MpZ+Dzi7jq45tiPpaULD2lmkl7FjJ3h5JD1NuPuMniGG7OzMQAXwxpTAAfhg6HLnZ1z+n6k8ONaxj/z55G70y2sc0Gaka+s4o3crOjerS9O64Qful751FEuv1vWZ8pdhIfUXv/hjwHlhUQlZ6YHfyasUHj0/vEH785uGhN2m+9mNg3nukiM4qUfzsNcZhhGZpCgFEWkoIu+KyAIRmS8iA0SksYh8KSKL3Z8JDWj/4Nj55I0cwzMTlgLw9K8Pj+m6du4A/b8pq5i9dgfDe0XP0HXaoS3Dll81OHAG8Ouj2sUkQ05G4K+tMIyhtiz2H3Bi/4z6bjn7Cov9eQS8oSD6t29MTmY6P991IjcO68ySB4aT41FGuXWz2enGhlq5ZU9AnKj7xzjZy3bsOxCwxOZlwYadfm/twqISsoK+k3dpqp4nGOBXfxriP+7aol7Ye3dvWZ9TerW0nAiGcRAka6bwBDBOVbsBvYH5wEhgvKp2Bsa75wnjhW+ct9lR368A4ITusb1Vepdp8ncV0L99Y0Zd0Y8P/nBMxGsOaRJ+y+pfT+vBuJsGc4Frc4g1xELwALp++/4QA3dZPO3u0rnnk3l0v2ucP4+Aj86eUBoNa2dx84ldQmZP9WtlsHOfY9s49tGJnP+sY5z27iTyre9fcGQb2jSqxYhB7enRsj4Ap/zftwx46GtUlcLiUKXgnVEN7JRL7zYNGHPDIDo1q0djj8NfWVwzpAN/O70H8+47Oab2hmEkwaYgIg2AIcDlAKpaCBSKyFnAULfZq8BE4PZEyPD90s0hZTmZsa/JX3tsR56b5MwwujSvx8BOuWHbNamTxaTbjitzPbtbi/o8cv5hXDu0Y8xKISNoCer1H1eyetteJi7Mj8kxK9qgOqhz+O/jpX5OJgs37vKnBl24cRf5uwrY7RrBHz3/MA5r4+wKevSC3v7r8kaOCbjPezMcf4yVW/ZE7KteTiYfeQzcX948hC17CqPKmJYmjBhU/jAZhlGTSMZMoT2QD/xHRGaKyEsiUgdorqrr3TYbgIQtCM9fvyvg/OgOjSO0DM91x3X0H7fPreM//uWek5h9z0n+8+l/OzGmXS8iElcgtvS00F/bRNdYHm7PfzAFRWW3CTZsh6N9U+d73/z2z/6yfg98xb8nOLOQPM9z8dIwyBP71ndmAeF3Mh3bpSm1wijrJnWz6dI8/NKRYRjlIxm7jzKAI4DrVXWKiDxB0FKRqqqIhN0bKSJXA1cDtGsX2xp8MCMGteeE7s049tGJALx1dXzervVyMvlu5PEs2rCLVh4nq/ruzqI7T+seUF7RZKZHnnks3LArasC+f7jOdq0b1mLnvgNcdkweCzbs5Kv5jjNaJDuAlz7uLODzuRsDyt+d7qQKbRHhHh/+YSDPf7OUN6cGRof1btv18eqV/aPKYRhGxZIMpbAGWKOqU9zzd3GUwkYRaamq60WkJRDWXVZVXwBeAOjbt+9Bx1Y+pEkd3vjdUQftFdy6Ya0Qr1sfvxvc4WDFiomMMpTC6U9NJiNNWPLgqVHv893I4wPOfUs7wev74ciJsgU20pbSvNw6PHTuYdx9Rk+/P8ffz+pJj1b1o/ZpGEbiqXSloKobRGS1iHRV1YXAMGCe+7kMeNj9+VGiZYlkC0h1MsIsH3kpKlFKSjRiwLq62Rlc0Df0zTyeQHHtPNtkrx7SwW+49xHNRuOt/+2AvJj7NQwjsSTLee164A0RyQKWAVfg2DdGi8gIYCXwqyTJlvL4DM3N6mWzaVd4X4mpK7ZydIcmIeWTF29md0ERTWLcwROJ3LrZvD6iP+u376dLi3ohSiFWzJfAMFKLpCgFVf0Z6BumKtTTyQjBtz30nCNa8/yk8INxUZh0nYVFJVzysrNqNyNCfuJ48CWm2eQGynvwnEP5ywezOaVndN8NgGUPnoo5GhtGalHjw1xURXwzBe/A37FpHZbml27r3F1Q6kxWUFTMgIe+ZqtnG+fvKnCrZrP6OSy6fzhZGWlc0LcN6TGO9BWVj8EwjIqjxoe5qIqc6C65nHN4a3/ZvWf2YvQ1AxjYyVky8sZVmrtuZ4BCADimgu0pPuN0ZnqaDfaGUYUxpVAFycutw4qHT6NX6wZ8efMQhvdqQf/2jenfvjEvXdoPCExW78tD4OP0w8KH3TAMwzClUMXp3Lwez15ypP9N3ReGw+cvEI7HftU7Yp1hGDUbsylUY6Ys20JfT6TTF357JCfFaAQ2DKNmYkqhGtK8fjYbdxZw4QulIaqvObaDKQTDMKJiy0fVkGM6hhqR61nmMcMwYsCUQjUkO0yYCm/GN8MwjEiYUqiGhFMKsURPNQzDMKVQDfHGFerohriOJzS3YRg1F1toroZ4ZwrvXnsMuwuKQvI8G4ZhhMOUQjVk+z7Hm7lX6/o0qpNFo3IGvzMMo+Zgy0fVkNd+WAnAnLU7kyyJYRhVDVMK1ZDgHM6GYRixYkqhGnLTCZ0BqJdjq4OGYcSHKYVqyNlu9NRI6UINwzAiYa+S1ZDWDWvxpxO7cHaf1tEbG4ZheDClUA0REW4Y1jnZYhiGUQWx5SPDMAzDjykFwzAMw48pBcMwDMOPKQXDMAzDjykFwzAMw48pBcMwDMOPKQXDMAzDjykFwzAMw4+oarJlOGhEJB9YeZCX5wKbK1CcRJDqMqa6fJD6Mpp85SfVZUxF+Q5R1abhKqq0UigPIjJNVfsmW46ySHUZU10+SH0ZTb7yk+oyprp8wdjykWEYhuHHlIJhGIbhpyYrhReSLUAMpLqMqS4fpL6MJl/5SXUZU12+AGqsTcEwDMMIpSbPFAzDMIwgTCkYhmEYfmqkUhCRU0RkoYgsEZGRSZRjhYjMFpGfRWSaW9ZYRL4UkcXuz0ZuuYjIk67Mv4jIEQmS6RUR2SQiczxlccskIpe57ReLyGUJlu8eEVnrPsefReRUT90drnwLReRkT3lC/gZEpK2ITBCReSIyV0RudMtT4hmWIV8qPcMcEZkqIrNcGe91y9uLyBS3v7dFJMstz3bPl7j1edFkT5B8o0RkuecZ9nHLK/3/pFyoao36AOnAUqADkAXMAnokSZYVQG5Q2SPASPd4JPAP9/hU4DNAgKOBKQmSaQhwBDDnYGUCGgPL3J+N3ONGCZTvHuDWMG17uL/fbKC9+3tPT+TfANASOMI9rgcscuVIiWdYhnyp9AwFqOseZwJT3GczGrjILX8O+L17/AfgOff4IuDtsmRPoHyjgPPDtK/0/5PyfGriTKE/sERVl6lqIfAWcFaSZfJyFvCqe/wqcLan/DV1+BFoKCItK7pzVf0G2FpOmU4GvlTVraq6DfgSOCWB8kXiLOAtVS1Q1eXAEpzff8L+BlR1varOcI93AfOB1qTIMyxDvkgk4xmqqu52TzPdjwLHA++65cHP0Pds3wWGiYiUIXui5ItEpf+flIeaqBRaA6s952so+58ikSjwhYhMF5Gr3bLmqrrePd4ANHePkyl3vDIlQ9Y/ulPzV3xLM8mWz13GOBznTTLlnmGQfJBCz1BE0kXkZ2ATzmC5FNiuqkVh+vPL4tbvAJokUsZg+VTV9wwfcJ/h4yKSHSxfkBypNBb5qYlKIZUYpKpHAMOB60RkiLdSnTlmSu0ZTkWZgGeBjkAfYD3wWHLFARGpC7wH3KSqO711qfAMw8iXUs9QVYtVtQ/QBuftvlsy5QkmWD4R6QXcgSNnP5wloduTKOJBUxOVwlqgree8jVtW6ajqWvfnJuADnD/+jb5lIffnJrd5MuWOV6ZKlVVVN7r/pCXAi5QuESRFPhHJxBlw31DV993ilHmG4eRLtWfoQ1W3AxOAATjLLhlh+vPL4tY3ALZUhowe+U5xl+ZUVQuA/5AizzBeaqJS+Ano7O5kyMIxTH1c2UKISB0Rqec7Bk4C5riy+HYhXAZ85B5/DFzq7mQ4GtjhWY5INPHK9Dlwkog0cpchTnLLEkKQbeUcnOfok+8id3dKe6AzMJUE/g24a9kvA/NV9V+eqpR4hpHkS7Fn2FREGrrHtYATcWwfE4Dz3WbBz9D3bM8HvnZnY5FkT4R8CzxKX3DsHd5nmPT/k5ipTKt2qnxwdgMswlmn/GuSZOiAszNiFjDXJwfOWuh4YDHwFdDYLRfg367Ms4G+CZLrTZzlgwM4a5wjDkYm4Eocw94S4IoEy/e62/8vOP+ALT3t/+rKtxAYnui/AWAQztLQL8DP7ufUVHmGZciXSs/wMGCmK8sc4C7P/8xU93m8A2S75Tnu+RK3vkM02RMk39fuM5wD/JfSHUqV/n9Sno+FuTAMwzD81MTlI8MwDCMCphQMwzAMP6YUDMMwDD+mFAzDMAw/phQMwzAMP6YUDD8ioiLymOf8VhG5p4LuPUpEFxzsngAABntJREFUzo/estz9XCAi80VkQjLlEZGzReSuKG2yReQrN6LmhSLylwroN2K0U7d+uog0EJExIrJAnCifD3vq24kTRXWmG64h3uvjjlgqIg1F5F33fvNFZIBb/k8ROb68z8SID1MKhpcC4FwRyU22IF48XqyxMAK4SlWPS5Q8MXIb8EyUNocDqGofVX0biFspiEh6mOLH3Xv2UdWxnrbtcTxmDwD/VNVurgwDRWS42+xOYLSqHo7jkPZMnNePALapaifgceAf7rU93Pv1xAn69oxH9ieAce79euM4qgE8hRNR1qhETCkYXopw8sneHFwR/GYtIrvdn0NFZJKIfCQiy0TkYRH5jTjx5meLSEfPbU4QkWkiskhETnevTxeRR0XkJ/fN9BrPfb8VkY+BeWHkudi9/xwR8Q08d+E4Z70sIo8GtRcRedp9S/0KaOapu8vtf46IvOC27SgiMzxtOvvO3e84z5X3n2Fk6wIUqOpm9/wM9615pjszaC4izXAcnPq5b/TvALXc4zfc6y5xn+PPIvK8bxAVkd0i8piIzMIJ/xArp+AMvntVdQKAOhFOZ+CEWADHsa2+e9wAWBfn9XFFLBWRBjjh0F/23U+d0BGo6kqgiYi0iOM7GuUl2d5z9kmdD7AbZ0BYgTMg3Arc49aNwhMrHtjt/hwKbMeJ05+N8yZ5r1t3I/B/nuvH4byIdMbxRs4BrgbudNtkA9NwYt8PBfYA7cPI2QpYBTQFMnA8Sc926yYSxtsbOBcn2ma6e/123/fB9S52j18HznCPJwB93OMHgetxPJMXUprfvGGYvq4AHvOcN/K0/52vzv2OnwY/U/e4O/AJkOmePwNc6h4r8KsIv8N73N/fL8AreOLz44SF6BDUviFOHP8O7nlLHK/bNcA24Mg4r58DtPHULwVygaeBSzzlL+OEpOiD44U8CsdL+CWgjqfdi8B5yf7fqEkfmykYAagTMfM14IY4LvtJnWBgBTiDwBdu+Wwgz9NutKqWqOpinIGkG068l0vFCUM8BWfQ7ey2n6rOW2Uw/YCJqpqvTqjkN3DeNstiCPCmOkHf1uEoEh/HuW/ys3Fi9vd0y18CrnDf0C8E/ocTlnk/zmzkXGBvmL5aAvme8zbA5+79/+y5f1kMA44EfnKfzTCcMA8AxTgB7cIRNtqpOPGJ2qjqMl9Dd1nuTeBJT/nFwChVbYMb/kJE0uK4Pl4ycJImPavOktUeApeMNuEocaOSMKVghOP/cNaG63jKinD/XkQkDSfblo8Cz3GJ57wE55/eR3BMFcWJC3O9lq6Bt1dVn1LZU65vEQMikoPzFn6+qh6K82aa41a/hxPW/HRguqpucZVQf5ylkdNxZj/B7PPcA5y18afd+18TVBdRNOBVz3Ppqqr3uHX7VbU43EUaOdrpYGByUPMXgMWq+n+eshE4Gc5Q1R9cWXPjuD7eiKVrgDVamo/gXRwl4SMH53kalYQpBSMEVd2KMzCM8BSvwHlzBTgTJ9tUvFzgvnV2xHnrXYgTFfL34oRzRkS6iBM1tiymAseKSK77Fn8xMCnKNd8AF7o2jJaAzxDtG6A3i5NjwG83UdX9rnzP4oRC9uUhaKCOAfdmHMNoMPOBTp7zBpSGRC4rD+8B33PACZ53vmt78OV4PiTKdywr2ukpOCkhfe3ud+W6KegWq3BmJYhId5znkx/H9XFFLFXVDcBqEenqXjOMQBtSF893MCqBeHZ1GDWLx4A/es5fBD5yjZvjOLi3+FU4A3p94FpV3S8iL+EsMc1wDZL5lKZZDIuqrhcnUfwEnDfqMar6UVnX4OSrOB5nwFkF/ODea7uIvIgz8GzACQnt5Q2cwdU3e6mH8xxy3L7/FKavb4DHRETcAfEe4B0R2YazbNU+gowvAL+IyAxV/Y2I3ImTmS8NZ8fPdcDKKN/zEXESxiuOIr/GLR8K3AUgIm1woocuwHnu4MxkXgJuAV4UkZvde1yuqioisV7/Ms6S0xKctKkXAajqXBEZjfP8i4DrPLOd64E33CWqZTg2GV/eh044diajkrAoqYZRBiJyK87M4G9xXvcE8ImqfpUYyeKSpQ3woqoOj9o4AdcfLCJyDnBEvM/eKB+mFAwjAiLyAY7R9nh1t5fGcW1z4ChVrfQETtUFEbkAJ//x9mTLUpMwpWAYhmH4MUOzYRiG4ceUgmEYhuHHlIJhGIbhx5SCYRiG4ceUgmEYhuHn/wGj0Jk1IbJvpQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "08pnlsyvSici",
        "outputId": "11e88875-7507-493e-8669-4629a97d297b"
      },
      "source": [
        "plot_stock(\"INTC\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "JBH9EcmHSrWq",
        "outputId": "4d36fe1e-f200-4ce2-b2e1-be0c88d98c01"
      },
      "source": [
        "plot_stock(\"DIS\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qu7geLWcVynF"
      },
      "source": [
        "# Train Deep Q-Learning Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAxWZcqVI1C1"
      },
      "source": [
        "num_days_per_company = 30"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fuovPi1JPdC",
        "outputId": "e6dc271d-e93a-4044-f360-864295767653"
      },
      "source": [
        "input_shape = num_days_per_company*len(companies)\n",
        "print(input_shape)\n",
        "output_shape = len(companies)\n",
        "print(output_shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6150\n",
            "205\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gyOSQqz-V3Jf"
      },
      "source": [
        "def make_model(layers=[100, 100, 100], # number of hidden layers and number of neurons for hidden layers\n",
        "               input_shape=input_shape, # input shape of model\n",
        "               output_shape=output_shape, # output shape of model\n",
        "               lr=0.001, # learning rate\n",
        "               kernel_initializer=HeUniform, # kernel initializer\n",
        "               hidden_activation='relu', # activation function for hidden layers\n",
        "               output_activation='linear', # activation function for output layer\n",
        "               loss=Huber,\n",
        "               optimizer=Adam,\n",
        "               metrics='accuracy'\n",
        "              ):\n",
        "  if not isinstance(metrics, list):\n",
        "    metrics = [metrics]\n",
        "  input_layer = Input(shape=input_shape)\n",
        "  model = input_layer\n",
        "  for layer in layers:\n",
        "    model = Dense(units=layer, activation=hidden_activation, kernel_initializer=kernel_initializer())(model)\n",
        "  model = Dense(output_shape, activation=output_activation, kernel_initializer=kernel_initializer())(model)\n",
        "  model = Model(input_layer, model)\n",
        "  model.compile(loss=loss(), optimizer=optimizer(lr=lr), metrics=metrics)\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Qb9Ivfck9iD",
        "outputId": "424f5525-1ddb-45d7-d172-b4dd96472e9a"
      },
      "source": [
        "make_model().summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_67\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_76 (InputLayer)        [(None, 6150)]            0         \n",
            "_________________________________________________________________\n",
            "dense_277 (Dense)            (None, 100)               615100    \n",
            "_________________________________________________________________\n",
            "dense_278 (Dense)            (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dense_279 (Dense)            (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dense_280 (Dense)            (None, 205)               20705     \n",
            "=================================================================\n",
            "Total params: 656,005\n",
            "Trainable params: 656,005\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "INFO:tensorflow:Assets written to: model_2_test/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25vZk36JL21q"
      },
      "source": [
        "\"\"\"\n",
        "x = []\n",
        "y = []\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXFbl9xrM1tF"
      },
      "source": [
        "\"\"\"\n",
        "for i in range(num_days_per_company, len(companies[0].stock)):\n",
        "  x_i = []\n",
        "  y_i = []\n",
        "  for j in range(len(companies)):\n",
        "    x_i.extend(companies[j].stock[i-num_days_per_company:i])\n",
        "    y_i.append(companies[j].stock[i])\n",
        "  x.append(x_i)\n",
        "  y.append(y_i)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yr3qoqV6OhVc"
      },
      "source": [
        "\"\"\"\n",
        "x = np.array(x)\n",
        "y = np.array(y)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJPiQiJwOnCL",
        "outputId": "6ceb810c-8622-4e61-e9e8-b13e6dd9c19b"
      },
      "source": [
        "\"\"\"\n",
        "print(x.shape)\n",
        "print(y.shape)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3741, 6150)\n",
            "(3741, 205)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4_sOz_OIVFa"
      },
      "source": [
        "\"\"\"\n",
        "np.save(drive_dir+\"x.npy\", x)\n",
        "np.save(drive_dir+\"y.npy\", y)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bH7oID_yOyOf"
      },
      "source": [
        "train_portion = 2.0/3.0\n",
        "val_portion = 1.0/6.0\n",
        "test_portion = 1.0/6.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_D5S7FufPZs-"
      },
      "source": [
        "train_idx = int(x.shape[0]*train_portion)\n",
        "val_idx = int(x.shape[0]*val_portion)+train_idx\n",
        "test_idx = x.shape[0]+1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siVGdKN-Q7Rh"
      },
      "source": [
        "\"\"\"\n",
        "x_train = x[:train_idx]\n",
        "x_val = x[train_idx:val_idx]\n",
        "x_test = x[val_idx:test_idx]\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb4lAxiARnRa"
      },
      "source": [
        "\"\"\"\n",
        "y_train = y[:train_idx]\n",
        "y_val = y[train_idx:val_idx]\n",
        "y_test = y[val_idx:test_idx]\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOadr3CcRwp7",
        "outputId": "287e77bf-97fa-41d8-8b81-03ccc88f5a81"
      },
      "source": [
        "\"\"\"\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2494, 6150)\n",
            "(2494, 205)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEzexNr2R0Jc",
        "outputId": "9882ae16-2a59-4ef3-8f82-c342607190f5"
      },
      "source": [
        "\"\"\"\n",
        "print(x_val.shape)\n",
        "print(y_val.shape)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(623, 6150)\n",
            "(623, 205)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wSwgcvpAR3dH",
        "outputId": "984dc67e-1a6d-45ed-d010-207678999a6f"
      },
      "source": [
        "\"\"\"\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(624, 6150)\n",
            "(624, 205)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3U_MeHQOr_I"
      },
      "source": [
        "\"\"\"\n",
        "normalizer_x = Normalizer()\n",
        "normalizer_y = Normalizer()\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Lyc6QGxOvjF"
      },
      "source": [
        "\"\"\"\n",
        "x_train = normalizer_x.fit_transform(x_train)\n",
        "y_train = normalizer_y.fit_transform(y_train)\n",
        "\n",
        "x_val = normalizer_x.transform(x_val)\n",
        "y_val = normalizer_y.transform(y_val)\n",
        "\n",
        "x_test = normalizer_x.transform(x_test)\n",
        "y_test = normalizer_y.transform(y_test)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zu9uCVZLIv19"
      },
      "source": [
        "\"\"\"\n",
        "dump(normalizer_x, open(drive_dir+'normalizer_x.pkl', 'wb'))\n",
        "dump(normalizer_y, open(drive_dir+'normalizer_y.pkl', 'wb'))\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEkzNqzNIsD6"
      },
      "source": [
        "\"\"\"\n",
        "np.save(drive_dir+\"x_train.npy\", x_train)\n",
        "np.save(drive_dir+\"y_train.npy\", y_train)\n",
        "\n",
        "np.save(drive_dir+\"x_val.npy\", x_val)\n",
        "np.save(drive_dir+\"y_val.npy\", y_val)\n",
        "\n",
        "np.save(drive_dir+\"x_test.npy\", x_test)\n",
        "np.save(drive_dir+\"y_test.npy\", y_test)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQvG5oJQS8wg"
      },
      "source": [
        "def get_q_values(model, stock):\n",
        "  return model.predict(np.array([stock]))[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hARwTEJ9TXs-"
      },
      "source": [
        "def train(replay_memory, model, target_model, end, lr=0.7, discount=0.618, min_replay_size=1000, batch_size=128):\n",
        "\n",
        "    if len(replay_memory) < min_replay_size:\n",
        "        return\n",
        "\n",
        "    mini_batch = random.sample(replay_memory, batch_size)\n",
        "    current_states = np.array([transition[0] for transition in mini_batch])\n",
        "    current_qs_list = model.predict(current_states)\n",
        "    new_current_states = np.array([transition[3] for transition in mini_batch])\n",
        "    future_qs_list = target_model.predict(new_current_states)\n",
        "\n",
        "    X = []\n",
        "    Y = []\n",
        "    for index, (observation, action, reward, new_observation, end) in enumerate(mini_batch):\n",
        "        if not end:\n",
        "            max_future_q = reward + discount * np.max(future_qs_list[index])\n",
        "        else:\n",
        "            max_future_q = reward\n",
        "\n",
        "        current_qs = current_qs_list[index]\n",
        "        current_qs[action] = (1 - lr) * current_qs[action] + lr * max_future_q\n",
        "\n",
        "        X.append(observation)\n",
        "        Y.append(current_qs)\n",
        "    model.fit(np.array(X), np.array(Y), batch_size=batch_size, verbose=0, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tB_ptp0xfvV3"
      },
      "source": [
        "def tanh(x):\n",
        "  return (np.exp(2*x)-1)/(np.exp(2*x)+1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "_gLgNcOAf5Ou",
        "outputId": "94da7cfb-2589-4708-c14c-81877112c381"
      },
      "source": [
        "plt.plot([tanh(0.05*i) for i in range(-100, 101)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f280ff88610>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 261
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QT31FhbdcrnE"
      },
      "source": [
        "def step(i, action, a=0.05):\n",
        "  stock_price = x_train[i][num_days_per_company*action + num_days_per_company - 1]\n",
        "  next_day_stock_price = y_train[i][action]\n",
        "  diff = next_day_stock_price - stock_price\n",
        "  percent_diff = diff/stock_price\n",
        "  reward = tanh(a*percent_diff)\n",
        "  done = False\n",
        "  new_i = i+1\n",
        "  if new_i >= x_train.shape[0]-1:\n",
        "    done = True\n",
        "  new_observation = x_train[new_i]\n",
        "  return [new_i, new_observation, reward, done]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIoaKyEAXOTN"
      },
      "source": [
        "def fit_model(\n",
        "    layers=[100, 100, 100], # hidden layers for model\n",
        "    train_episodes = 30,\n",
        "    epsilon=1, # initial epsilon value\n",
        "    max_epsilon=1, # maximum epsilon value\n",
        "    min_epsilon=0.01, # minimum epsilon value\n",
        "    decay = 0.01,\n",
        "    update=4, # updates model every update steps\n",
        "    target_update=100 # updates target_model every target_update steps\n",
        "):\n",
        "\n",
        "  model = make_model(layers=layers)\n",
        "  target_model = make_model(layers=layers)\n",
        "  target_model.set_weights(model.get_weights())\n",
        "\n",
        "  replay_memory = deque(maxlen=50000)\n",
        "\n",
        "  target_update_counter = 0\n",
        "\n",
        "  X = []\n",
        "  y = []\n",
        "\n",
        "  steps = 0\n",
        "\n",
        "  for episode in range(train_episodes):\n",
        "      total_training_rewards = 0\n",
        "      i = 0\n",
        "      observation = x_train[i]\n",
        "      done = False\n",
        "      while not done:\n",
        "          progress_bar = list('**********')\n",
        "          for k in range(int(10*i/x_train.shape[0])):\n",
        "            progress_bar[k] = '-'\n",
        "          progress_bar = \"\".join(progress_bar)\n",
        "          print(f\"Episode {episode+1}/{train_episodes}: {i}/{x_train.shape[0]} {progress_bar[:5]}{round(100*i/x_train.shape[0], 2)}%{progress_bar[5:]}\")\n",
        "          steps += 1\n",
        "\n",
        "          random_number = np.random.rand()\n",
        "          if random_number <= epsilon:\n",
        "              action = np.random.randint(len(companies))\n",
        "          else:\n",
        "              predicted = get_q_values(model, observation)\n",
        "              action = np.argmax(predicted)\n",
        "          new_i, new_observation, reward, done = step(i, action)\n",
        "          replay_memory.append([observation, action, reward, new_observation, done])\n",
        "\n",
        "          # 3. Update the Main Network using the Bellman Equation\n",
        "          if steps % update == 0 or done:\n",
        "              train(replay_memory, model, target_model, done)\n",
        "\n",
        "          observation = new_observation\n",
        "          i = new_i\n",
        "          total_training_rewards += reward\n",
        "\n",
        "          if done:\n",
        "              print('Total training rewards: {} after n steps = {} with final reward = {}'.format(total_training_rewards, episode, reward))\n",
        "              total_training_rewards += 1\n",
        "\n",
        "              if steps >= 100:\n",
        "                  print('Copying main network weights to the target network weights')\n",
        "                  target_model.set_weights(model.get_weights())\n",
        "                  steps = 0\n",
        "              break\n",
        "\n",
        "      epsilon = min_epsilon + (max_epsilon - min_epsilon) * np.exp(-decay * episode)\n",
        "  return model, target_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPxjfWSQxf3z"
      },
      "source": [
        "# models = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABd9FzzvGFST",
        "outputId": "31a0d25b-012c-4dc8-eea6-3cc3c6a0ca5c"
      },
      "source": [
        "\"\"\"\n",
        "for i in range(1, 5): # number of 100 neuron hidden layers for model\n",
        "  model, target_model = fit_model(layers=[100 for k in range(i)])\n",
        "  model.save(drive_dir+f\"model_{i}\")\n",
        "  models.append(model)\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Episode 28/30: 2486/2494 -----99.68%----*\n",
            "Episode 28/30: 2487/2494 -----99.72%----*\n",
            "Episode 28/30: 2488/2494 -----99.76%----*\n",
            "Episode 28/30: 2489/2494 -----99.8%----*\n",
            "Episode 28/30: 2490/2494 -----99.84%----*\n",
            "Episode 28/30: 2491/2494 -----99.88%----*\n",
            "Episode 28/30: 2492/2494 -----99.92%----*\n",
            "Total training rewards: 549.5802711986884 after n steps = 27 with final reward = 0.2187912620635053\n",
            "Copying main network weights to the target network weights\n",
            "Episode 29/30: 0/2494 *****0.0%*****\n",
            "Episode 29/30: 1/2494 *****0.04%*****\n",
            "Episode 29/30: 2/2494 *****0.08%*****\n",
            "Episode 29/30: 3/2494 *****0.12%*****\n",
            "Episode 29/30: 4/2494 *****0.16%*****\n",
            "Episode 29/30: 5/2494 *****0.2%*****\n",
            "Episode 29/30: 6/2494 *****0.24%*****\n",
            "Episode 29/30: 7/2494 *****0.28%*****\n",
            "Episode 29/30: 8/2494 *****0.32%*****\n",
            "Episode 29/30: 9/2494 *****0.36%*****\n",
            "Episode 29/30: 10/2494 *****0.4%*****\n",
            "Episode 29/30: 11/2494 *****0.44%*****\n",
            "Episode 29/30: 12/2494 *****0.48%*****\n",
            "Episode 29/30: 13/2494 *****0.52%*****\n",
            "Episode 29/30: 14/2494 *****0.56%*****\n",
            "Episode 29/30: 15/2494 *****0.6%*****\n",
            "Episode 29/30: 16/2494 *****0.64%*****\n",
            "Episode 29/30: 17/2494 *****0.68%*****\n",
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            "Episode 29/30: 2492/2494 -----99.92%----*\n",
            "Total training rewards: 549.8749608324406 after n steps = 28 with final reward = 0.21475476346878922\n",
            "Copying main network weights to the target network weights\n",
            "Episode 30/30: 0/2494 *****0.0%*****\n",
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            "Total training rewards: 549.6899161665012 after n steps = 29 with final reward = 0.21768687091187172\n",
            "Copying main network weights to the target network weights\n",
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/S&P 500 Stock Trading/model_4/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vA8_i3DIWch9"
      },
      "source": [
        "# Load data and models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeGjVFpHJI72"
      },
      "source": [
        "normalizer_x = load(open(drive_dir+\"normalizer_x.pkl\", \"rb\"))\n",
        "normalizer_y = load(open(drive_dir+\"normalizer_y.pkl\", \"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPnBbCoCKMWv"
      },
      "source": [
        "x = np.load(drive_dir+\"x.npy\")\n",
        "y = np.load(drive_dir+\"y.npy\")\n",
        "\n",
        "x_train = np.load(drive_dir+\"x_train.npy\")\n",
        "y_train = np.load(drive_dir+\"y_train.npy\")\n",
        "\n",
        "x_val = np.load(drive_dir+\"x_val.npy\")\n",
        "y_val = np.load(drive_dir+\"y_val.npy\")\n",
        "\n",
        "x_test = np.load(drive_dir+\"x_test.npy\")\n",
        "y_test = np.load(drive_dir+\"y_test.npy\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9h5YE49Wfrv"
      },
      "source": [
        "models = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyMKLd3RWh6W"
      },
      "source": [
        "for i in range(1, 5):\n",
        "  models.append(keras.models.load_model(drive_dir+f\"model_{i}\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DxL-KHKIJnDB"
      },
      "source": [
        "# Test Deep Q-Learning Models on Validation Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSNi91PlAcG9"
      },
      "source": [
        "def stock_growth(company, i, j):\n",
        "  return companies[company].stock[j]/companies[company].stock[i]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8c6ubfngANUg"
      },
      "source": [
        "def invest(model, stocks):\n",
        "  return np.argmax(model.predict(np.array([stocks]))[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0zpO2XrABA5"
      },
      "source": [
        "def get_balances(model, stocks_x, buffer_idx):\n",
        "  balances = []\n",
        "  balance = 1\n",
        "  balances.append(balance)\n",
        "  for i in range(stocks_x.shape[0]-1):\n",
        "    balance *= stock_growth(invest(model, stocks_x[i]), i+buffer_idx+num_days_per_company, i+buffer_idx+num_days_per_company+1)\n",
        "    balances.append(balance)\n",
        "  return (balances, balance)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HmcXFl9DaMR"
      },
      "source": [
        "balances = []\n",
        "final_balances = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmUjnhIHDcON"
      },
      "source": [
        "for i in range(len(models)):\n",
        "  balances_i, final_balance_i = get_balances(models[i], x_val, train_idx)\n",
        "  balances.append(balances_i)\n",
        "  final_balances.append(final_balance_i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Km-H5bsYDz6j",
        "outputId": "335a7941-2cdb-4bdc-f514-bb249f3469f5"
      },
      "source": [
        "for balance in balances:\n",
        "  plt.plot(balance)\n",
        "plt.title(\"Total balance of Stock Bot accounts\")\n",
        "plt.ylabel(\"Balance (USD)\")\n",
        "plt.xlabel(\"Days\")\n",
        "plt.legend([f\"model_{i+1}\" for i in range(len(models))])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "K8pqOzIkFMDh",
        "outputId": "34d45332-7d5c-461b-8e80-d635f113679a"
      },
      "source": [
        "bar_width = 10\n",
        "for i, final_balance in enumerate(final_balances):\n",
        "  print(f\"Final balance for model_{i+1}: {final_balance}\")\n",
        "  plt.bar((i+0.1)*bar_width, final_balance, width=0.8*bar_width)\n",
        "plt.xlabel(\"Stock Bots\")\n",
        "plt.ylabel(\"Final balance (USD)\")\n",
        "plt.legend([f\"model_{i+1}\" for i in range(len(models))])\n",
        "plt.title(\"Final balances for each Stock Bot\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final balance for model_1: 1.2726472913661824\n",
            "Final balance for model_2: 0.9025167711931644\n",
            "Final balance for model_3: 1.454180194785426\n",
            "Final balance for model_4: 1.3297656946515373\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "oumjDGE5Gh2u",
        "outputId": "c5e6c29a-adce-4c1c-cf91-64e58f22911a"
      },
      "source": [
        "bar_width = 10\n",
        "for i, final_balance in enumerate(final_balances):\n",
        "  print(f\"Percent return over 623 days for model_{i+1}: {100*(final_balance-1)}%\")\n",
        "  plt.bar((i+0.1)*bar_width, 100*(final_balance-1), width=0.8*bar_width)\n",
        "plt.xlabel(\"Stock Bots\")\n",
        "plt.ylabel(\"Percent return over 623 days (%)\")\n",
        "plt.legend([f\"model_{i+1}\" for i in range(len(models))])\n",
        "plt.title(\"Percent return over 623 days for each Stock Bot\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percent return over 623 days for model_1: 27.264729136618236%\n",
            "Percent return over 623 days for model_2: -9.748322880683558%\n",
            "Percent return over 623 days for model_3: 45.418019478542604%\n",
            "Percent return over 623 days for model_4: 32.97656946515373%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "JSxvJS0CHIhz",
        "outputId": "95a7f5c4-1fdc-464b-e2c0-3f0246a6c347"
      },
      "source": [
        "bar_width = 10\n",
        "for i, final_balance in enumerate(final_balances):\n",
        "  print(f\"Average daily percent return: {100*(final_balance**(1/x_val.shape[0])-1)}%\")\n",
        "  plt.bar((i+0.1)*bar_width, 100*(final_balance**(1/x_val.shape[0])-1), width=0.8*bar_width)\n",
        "plt.xlabel(\"Stock Bots\")\n",
        "plt.ylabel(\"Average daily percent return (%)\")\n",
        "plt.legend([f\"model_{i+1}\" for i in range(len(models))])\n",
        "plt.title(\"Average daily percent return for each Stock Bot\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average daily percent return: 0.038707202361898396%\n",
            "Average daily percent return: -0.01646220918085639%\n",
            "Average daily percent return: 0.06012116373690368%\n",
            "Average daily percent return: 0.04575729648248483%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "hrZxIjcnJOs9",
        "outputId": "b9845563-96c2-4275-91f5-fdc4364c6e3c"
      },
      "source": [
        "bar_width = 10\n",
        "for i, final_balance in enumerate(final_balances):\n",
        "  print(f\"Average yearly percent return: {100*(final_balance**(365/x_val.shape[0])-1)}%\")\n",
        "  plt.bar((i+0.1)*bar_width, 100*(final_balance**(365/x_val.shape[0])-1), width=0.8*bar_width)\n",
        "plt.xlabel(\"Stock Bots\")\n",
        "plt.ylabel(\"Average yearly percent return (%)\")\n",
        "plt.legend([f\"model_{i+1}\" for i in range(len(models))])\n",
        "plt.title(\"Average yearly percent return for each Stock Bot\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average yearly percent return: 15.171709246599608%\n",
            "Average yearly percent return: -5.832211407311272%\n",
            "Average yearly percent return: 24.529980536686512%\n",
            "Average yearly percent return: 18.17258246511706%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ps0gIrqvHnhS"
      },
      "source": [
        "Best model: model_3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCgEl4haH0Md"
      },
      "source": [
        "# Test Best Model (model_3) on Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rm-IkBEbH8ZL"
      },
      "source": [
        "best_model = 2 # determined to be the best model based on performance on the validation set"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svNW1KsNIKtd"
      },
      "source": [
        "balance, final_balance = get_balances(models[best_model], x_test, val_idx)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "f9QxzwK5IRmM",
        "outputId": "e23814bb-75e2-4e9a-fd5c-568742bccea0"
      },
      "source": [
        "plt.plot(balance)\n",
        "plt.title(\"Total balance of Stock Bot\")\n",
        "plt.ylabel(\"Balance (USD)\")\n",
        "plt.xlabel(\"Days\")\n",
        "plt.legend([f\"model_{best_model+1}\"])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AbAkB0IJAo9Fo0saT7x2mrs3fY39ti5/JJbnDpoeKFgQajUaTBtr8Qb50z9t8/Hev9zjWHghR4B0+lnktCDQajSYNHGo2NIHdpmPYTkdXiIIsRQglIm2CQETuEJE6EdmU5LzjRCQkIh9J11g0Go0m0xxq9kW3g+FIzLGOQDhroaKJSKdGcCdwTl8niIgT+B/gmTSOQ6PRaDLOQZsgaLBVGgVo84coyElPI/qBkDZBoJR6GTiS5LSvAg8Bdekah0aj0WSDwy3dgqC2NVYQdATGiGkoGSIyGbgE+G22xqDRaDTporG9K7pd19odORSOKHzBsWMaSsYvgf+nlIokO1FErhSR9SKyXjeo12g0I4GG9i5KzUqidW3dGkFHVwhAawQmS4H7RWQv8BHgNyJycaITlVK3K6WWKqWWVlRkpyiTRqPR9IfGjgBzxxcgEqsRtPsNQTCcNIKsjUQpNcPaFpE7gX8opf6erfFoNBrNUNLY3sWx00ooy8+J0QhafEEAiryZb0nZG2kTBCJyH7ASKBeRamAN4AZQSt2WrvtqNBrNcOBIRxdl+TmML+oWBC9sreOW53cAMLU0N5vDiyFtgkAptbof5342XePQaDSaTNMVitAeCDEuz6gnVGuahi67s7s38fSy/GwNrwc6s1ij0WiGmPaA4Qco9LqoLPRGNQJ7kbni3OFjGtKCQKPRaIaYNr/hByj0uhlflENje4BQOMJEswHNzz96dF8vzzhaEGg0Gs0Q02ZGBhV4XVQUeYkoaOzoos0X5IIlE/nwB6ZkeYSxDJ/4JY1GoxklWIKg0OvCKjRd1xqg1R+kaBiZhCy0INBoNJohJBJR7KpvB4wQ0TyPMc1e/JvXcMjw8g1YaEGg0Wg0Q8gX/vIWz75fCxjZwx6XYYEPRxRhhlf+gIX2EWg0Gs0QYgkBMExDE4u9HFc1LrpvOGoEWhBoNBrNEBGOqJjnBV4XIsIfLj2OQq+L8gIPZy0cn6XR9Y42DWk0Gs0Q0WkWlAM446jx5LiMngPFuW7+/d1VRNTwKjZnMfxGpNFoNCOUzq4wAN87/yg+d9KMmGOW03g4ok1DGo1GM0R0mBnFZQUeRCTJ2cMHLQg0Go1miLA0guG8+k+EFgQajUYzRHQLguHTjzgVtCDQaDSaIcJyFmuNQKPRaMYoB5uNhvX5OVoj0Gg0mjHH4RYf//3IJgDy3FojAEBE7hCROhHZ1Mvxi0TkXRHZYDamPyldY9FoNJp086vndkS3c7WPIMqdwDl9HF8LHK2UOga4HPhDGsei0Wg0aeXpzTWUF+Rw6twKxuUNvzISfZHOVpUvi0hVH8fbbU/zAdXbuRqNRjNc2XSwheJcN82+IF89fQ7fPHNutofUb7JqyBKRS4CbgErg/D7OuxK4EmDatGmZGZxGoxm17G/s5JF3DvK1VbMHnfh1wf+9Gt0uHWGagEVWncVKqUeUUvOBi4Ef9XHe7UqppUqppRUVFZkboEajGXV0hSKc8r8v8IvntnPgiG9Q11Iq1pAxLt8zqOtli2ERNaSUehmYKSLl2R6LRqMZ3Ty+8VB02wr3HCgdZgKZxbg8LQj6hYjMFlMnE5EPADlAY7bGo9Foxga1bf7odnVT56Cu1dgeiHleOkI1grT5CETkPmAlUC4i1cAawA2glLoN+A/gMyISBHzAx1S8nqXRaDRDTLvZT1hk8BpBQ3tXzHMtCOJQSq1Ocvx/gP9J1/01Gs3oo67NT0tnkDnjCwd8jTZ/iJI8N16Xk+qmwQkCu0ZQZHYjG4mkJAhEpBI4EZiEsXrfBKxXSkXSODaNRqOJEgpHWPbjtQBs/dE5eN0DS9pq8wcp9LqoLPQO2jTU4gsCMH9CIZ8/eeaIKj1tp09BICKnAd8BSoF3gDrAixHlM0tEHgR+ppRqTfdANRrN2GZvY/ekva+xk3kTBqYVtAdCFOS4mTIul7f3Nw1qTL6g4Sz+yxXHU16QM6hrZZNkGsF5wOeVUvvjD4iIC7gAOBN4KA1j02g0mig7atui27vq2wcsCFr9IQq9LqaMy+Wf7x4mHFE4HQNbyY/UstPx9CkIlFLf6uNYCPj7kI9Io9FoErC7oSO6vce23V/a/SEmlXipLPQSiiiaO7soG+Bq3mcKAq9rFAsCABGZh5HVO9/ctQX4vVJqWzoHptFoNMFwhH2Nnbx/uJX/fXobOS4HTofQ1NGV/MW90BYIUpBTQHGukQXc4gsOXBAEw3jdDhwD1CiGC8l8BCuAh4HfAbcDAhwLvCAiH1ZKvZ7+IWo0mrHKTU9s5Y7X9kSfhyKKsnwPzaaTdiB0BsLk5bgoyjWmv5bBXKsrNOKa0CQiWULZD4DVSqnrlFKPKqX+rpRaA6zGyAvQaDSatPHOgVhnbjiiKMp109w58MnbFwyT53ZGNYJLfrOOYHhgAZCdXWFyBxi9NJxIJghmKaVejN+plHoJmJmWEWk0Go1JQU7P1XZJnpvWAa7ilVL4gmFyPd2CAGC7zRHdH3xd4RHvKIbkgqCvT2fg3hqNRqNJgUSTbEmuh2bfwHwEgVAEpcDrdlJkEwTvVrcM6Hqdo0QQJDNuTRWRWxLsF2ByGsaj0Wg0UfLj7O9VZXkU57p72PWfe7+WG5/cwseWTuVzJ83A5Uy8xvWbcf+5bidF3m5BsPFAM6uX9b/Eva8rPOK6kSUimUbwLeCtBI/1wLfTOzSNRjPWybM1gZ9TWcADX1hBcV5PH8FvX9rF7voObnpyK797eXev1/MHDV9ArscZk5l8/5sHaIgrIJcKncHR4SxOlkdwV/w+ERkHNOsCcRqNJt3YNYJzF01gfJGXghwXgVCEUDgSXfl32spB95ZjsK2mjU/83gh0tBy8675zOve/eYBb1u7gL6/v4+oz+tddrDMQJrdslGsEIvIDEZlvbueIyPPALqBWRM7IxAA1Gs3YxeXsjs8vNE05+aYD2d4L4HBLd/E4tzNxTP+tL+yk0cw/sLSBSSW5XHXabACODCA3odkXHHH9iRORzDT0McBKHLsUwzdQAZwK3JjGcWk0Gg32qM4CryEA8k2bfEfAKCft6wrHmIrq2xJP6JNKcqPbdru+x+VgWmlev/MJImZW8khtRmMnmSDospmAzgbuV0qFlVJbyHK/Y41GM/oJR7olQaElCEyNoLPLEASHTG3g2+fMY1ppXox2YMeeKxAf+1+S19MBnYxWf5CIGrldyewkEwQBEVkkIhXAacAztmN56RuWRqPRGJnEFlZOQb7pQG4PGKahw81Gx7Fjp47j5DnlHOql2UxTZ7emEC8IigeQpNZknj8uf/Sbhq4GHgS2Ar9QSu0BEJHzMMpS94qI3CEidSKyqZfjnxSRd0XkPRFZJyJHD2D8Go1mFBO2CYKoj8B0IFumIUsjmFTiZVJJLk2dwWgxODsttok+xx079RXlutlwoLlffgLr3JLRrhEopV5XSs1XSpUppX5k2/9Esg5kwJ3AOX0c3wOcqpRaDPwIo5aRRqPRRAnFCIJY01B7IEQkoqIawYRiL5NKjA5hh1p8tPqDtPqNyd8fDLN2a130WvEaQW2LcY3v/z3hujUhzaaGMRpMQ8mKzn0zbpcCGoBXLe2gN5RSL4tIVR/H19mevg5M6XOkGo1mzBEOdwsCa/K2BMFV975NMKz46NIplBd4yHE5mVRsOIRf3dHAmsc2k+NysO2Gc7lr3V4ALjpmEl9aOYuppbGW7XkTClm/rymqXaSC1Shnss0JPVJJZhoqjHsUAUuBJ0Xk40M4js8BT/Z2UESuFJH1IrK+vr5+CG+r0WiGM2FbulJFoVEq2ooaCppC4q/rq5k/oQiAuWYv4zWPbQaMkhIAT2yqoTTfw/UXLoyea+f7FyzguKpxUc0gFXbUtlGa74mOaySTLKHs+kT7RaQUeA64f7ADMNthfg44qY9x3I5pOlq6dKlOZNNoxgjhiGJaaR4vf/u06L78BIXoxuV7on+ryvJi2loC7Klv5+JjJ/dqz/e6nXxweikbD/Rp6IhS3dTJA+sPsKyqNNW3MqxJphEkRCl1BCOnYFCIyBLgD8BFSqnGwV5Po9GMbJ57v5aq7/yTvWZ2cCiicMU1fUlU5O2aM7szgmdXxrawrG8L0OoPJTXheJxCMBIhlaIJGw40oxQDqk80HBmQIDBX8YPq+iwi0zCa3nxaKbV9MNfSaDSjg7+uPwDA+4dbASOPIL6fsEjs8ynjcqkqz48+v+r02THHNxxoBmDyuL4FgdvpQKnYSKXeaPUZEUsrZpUlPXckkMxZ/B6Gg9hOKXAI+EyS194HrATKRaQao5GNG0ApdRtG05sy4DfmFxtSSi3t/1vQaDSjBZ+tOihAKJy8sfzXVs2JeX7M1BL23HQeL26v57I/vcnPnjGKI0wvzU/08ihul7EuDoYVyVoQW9FI9gqmI5lk2cEXxD1XQKNSKmkvgmThpUqpK4Arkl1Ho9GMHazcgC4zCzgc6VsQPPuNU5gzvrDHfhFhwUTDKby1po1jppawaHJPJ7Edt1nAriscIZe+JUGrL4jbKXjdAzKqDDuSCYJGpVR7XyeISEGyczQajSYVrCqiVvmIsOrpI7BTWeTt9dj4Ii9TxuVS3eRj2YzSHialeDxmsbpkbSv3NnTw/uFWCr3upNccKSQTZ4+KyM9E5BQRiepVIjJTRD4nIk/Td9KYRqPRpIwlCDrM8hG9aQRWtE6Rt++17NRxRr5AZQohnlZJ674EQSgcYeVPX+TFbfVJ7z2SSBY+usosJ/EF4ESzF0EIoyLpP4FLlVI16R+mRqMZC1iTsKURhMIKl6PnevVPlx3HkY6upCtyqy9xKmUgLNNQMNS7s/gd0/EMxLS6HOkkFWlKqSeAJzIwFo1GM8axSkok0wjyc1wJ8wnimTuhkKc210ST0PrC6mPQ1YdG0GjrYlY4VjQCjUajySTtfkMTiGoEkQge18CnqatOm83UcbmcvXBC0nM9KZiGWs3xgeGDGC2MDpe3RqOJsru+na01rdkeRr8JhSPR8FGr+1iyqKFkeFwO/nPpVBwpXMMyDW0+1Ptn12YTBOUFI7+0hIUWBBrNKOP0n73EOb98JdvD6Dftge5J1gojTRY1NJRYeQTX/m1jr9nFbf7uUtYLJ/UdjjqSSFkQiMhJInKZuV0hIjPSNyyNRjMW2HCgmXW7GoDY1fbzW+to8wdTSigbKuy9jpfftJZHNxzktZ0N/Om1PVHB0O4PkeNycOdlx3Hh0ZMyMq5MkJLxTUTWYFQdnQf8CSND+C/AiekbmkajGe1cfOtrAGy6/uyoILjw6Ek8tvEQ1U0+whEV08A+nVg+AoDa1gBfv38Dc8cXsL22nVkVBZwyt4I2f4jiXDcr51VmZEyZIlUvzCXAscDbAEqpQyLSM51Po9FoBsD5t7zCxGLD+Tq+yLC9+4Jh00eQGQu229nzPlYZ6z0NHUwrzWNjdfOoihaySPUddSmllIgoAHtymUajGZ4Ew5GEk9twwR6ds6+xk31m6WgrGsffFSYUUWRIIUj4WdW1GuGidW1+Vv38JcIRxdFTijMzoAySqiD4q4j8DigRkc8DlwO/T9+wNBpNf2jpDLK7oT3a1xcMh+tw7qdb00sTmOllxjqzsyuzGoHH1VPiWFFMda2BaFXSY6eNy8h4MklKgkAp9VMRORNoxfAT/EAp9WxaR6bRaFLm7F++TE1r7MTa5h++gkApxR9fTdwEZmqpUS7aMg1lKmooUQazRX17gNJ8DyV5bv77/KMyMp5MkqqzeAbwijX5i0iuiFQppfamc3AajSY14oUAxIZjDjd21LVz57q9LJ5czHsHW2KOFZgZw75gmK5wJGPOYit81KIkz01zpxEuerDJR3NnF588ftqwNrcNlFTf0d8Ae7pd2Nyn0WiyTG8x78NZEDR1dAHwnXPnc+1ZczlxdneDlzyPKQi6wgSCYbzu5OUhhgJ7+Oh1H1rAdFuD+x117UQUlOUPTw1rsKQqCFxKqS7ribk9Oj8RjWaEUW+rfxOzvy3x/uGAJaQKvS6uOn0O91yxnFPmVvCFU2dGm9L4gmH8oUjGa/6X5Xv47IkzmGoKAntf4rJRlE1sJ9VPuF5ELrSeiMhFQENfLxCRO0SkTkQ29XJ8voj8S0QCInJt6kPWaDR29jZ0Joq0t8gAACAASURBVNwfb3IZTliCoMBWOO7uy5fxX+ceRY5pomnzBwlHFN5k7cKGiJJcwwew5sKFANEex8tndguC0ZRNbCfVqKEvAveIyK8xmtYfIEmrSuBO4NfA3b0cPwJ8Dbg4xTFoNJoEWI3e/3TZcVz2pzej+zcNY0FgJY8VJIjJdziMzl9Npn0+J0MagcflYMMPzoo+n2QKgvr2Lp6++hQ6ukLMrCjIyFgyTapRQ7uA5SJSYD5P2pFMKfWyiFT1cbwOqBOR81MbqkajScSexg5cDmHRpNj4dnulzOFG1DSUk7imf57HRXOnYY3OlI8gnmOnlQAwqyKfeRNGd/5sqlFDOcB/AFWAy2oGoZT6YdpGFnv/K4ErAaZNm5aJW2o0I4a9DR1MLc2j1ObIrCzMIZSk5WI2afeHcDp67/mb63bS1GFoBJkyDcWzZEoJz33zFGaUj04twE6qOtejwEUY3ck6bI+MoJS6XSm1VCm1tKKiIlO3HTRt/uCwVs81o4M9DR1UleXFFGebWOxN2nt3sBxu8XGkoyv5iXEopXhg/QGcDum1w5hhGjKunSnTUCJmVxZmrOhdNknVRzBFKaV7E/eD13c38vHbXwdg943npVQPXaPpL0op9jV2smJWWcz+8UVedtYlteAOGH8wzIqbnqc4183GNWclf4GNHXXtSSOa8jyu6Dk5WdIIxhKpCoJ1IrJYKfVeWkczivjmAxui253BcEx0hEYzVNS3BfAFw1SVxZb/Kshx9dlycSAcbPZx4s3Px+xr8QV7Obt37DX9eyPX7YxqBJkOHx2LpDo7nQR8VkT2AAGMyCGllFrS2wtE5D5gJVAuItXAGozy1SilbhORCcB6oAiIiMjVwAKl1MhrrZSAiiIvh8xaKh2BkBYEmrTQ0G5MlpWFsfHtLqcQCvfehD0V/rWrkQ9OH4fHDOd8dUd9r+fuqm+nJNedUpx9u9mP+L7PL+/1HK/HGa38qTWC9JPq7HRufy+slFqd5HgNMKW/1x0JHDjSycYDzdHn7YEQ47M4Hs3I5enNNeysa+fLK2fF2NNb/UE+etu/OM5MdrIcxQ9cuZwWX5CXttcPykewp6GD1b9/nY8uncJPPnI0ADtqDVPTC9euZFtNK1/8y9uAYZ5a9bOXmFDk5fXvrkp6basvcWkfWbp5tkghrRGkn5Q+YaXUPqXUPsAHKNtDg6Ee+80qha3+ICf/5IWY4x3DONVfM7z5wp/f4n+f3kZta6xN/W/rq9la08afX98HQFmBMakeP7OMsxZOwO10DEoQWK99bWdjdN87B5o5ekoxM8rzOWfRRL5nFl+rM235Na1+7v33/qTXtv4f8nN6X+nnerqPaY0g/aQkCETkQhHZAewBXgL2Ak+mcVwjik/94d9c//j7APzF/McEo2gVDO+aL73xzOaaETnu0UpHV+x38czmmpjn4+KqjLqdQnAQpqEu0yxT12aYNw+3+HhrXxNnLujWbSvNvgFv72uK7vvuI8ndiG1JcgggNndAawTpJ9VP+EfAcmC7UmoGsAp4PW2jGmHsa+xg/d4jAGw93EZxrpsnv34yt37iAwB0mDbRkcL+xk6u/PNbXPvXjdkeypjGXkyu0/YbCoTCvL2/ic+smB7dF19u2u10EIoMXCOw7POWMFlnagZn2ATBnEojvv75rXX9unZKGoFNENi1A016SFUQBJVSjYBDRBxKqRcwehiPeULhCK3+ELvq2/EHw2yvbePYaSUcNbGICWbrvUSmofeqW7j/jf29Vo7MJla0xnrbSm8sEImoqIlvOOAPdk/kdo2grjVAMKxYNKk4qnXGx7q7nA6CYTXg31cgFPs5rN93hCKvi7mV3Rm2sysL8LgcPPHe4Zhzd9X3HbbaHgjhdTtw9VHOOc82+fflS9AMDakKgmazvMTLGDWHfkUGE8qGM1b4XETBxgPN7KpvZ/4EozCVFSl09QMb+N1Lu2Je9/1HN/Gdh9/juS39W01lgsYOw+bb4ut/stBI5alNNcz87hPM//5T2R5KFJ9NKO0/0hkVUpa5prIohxevXcnaa07t8VqPWVI5FBmoIOgWQnet28t9bxxg/oSimHwYt9PBjLJ8OrpihcY9r/ftJ2hPIYrO0gLyPE7tI8gAqQqCizAcxd8AngJ2AR9K16BGEs22OOqP3f46wbDig9ONVnb5th/7Xev2Rrf9wTC7zVWTVTBsOGGFJAbDatT6CSIRxYf+71V+/fwOAP746u7osXBE8eqOBv62/kC2hgdAp00L+PaD73Lxra/x6IaDUcfx+CIvJXkeZiUohGattgfqMA7YtJE1j20GDMETj+WktmPVCOqNNn9yQWD5CHTYdWZINWqoQykVVkqFlFJ3KaVuMU1FY57aBJ2hjqsyBYFNvc2z/aA/8fvXowXBfvzElmiTjuFCY3v3eL7/902jUhg8/M5B3jvYwk+f2Y6vKxwtOQxw3xv7+dQf/823Hnw3qZkjnfjiVtpba9r4+v0b+Oe7hikmPnfAjjsqCIbGNAQ9HdKQ2GyTrNhdbaufykJvn+dYpiGPSzuKM0Gfn7KItIlIa4JHm4iMisSvwdDSGeQTv/93zL6bPrw46rizx31bQsFw9DXHvObPtkij4UBje4A8j5PVy6bxyDsHeeit6mwPaUg52OyLiW7ZXtsWLXkMhiCwWPWzl/q81rm/eoWfPr1tyMZmz9T19eKv+Od7hxFJPDFbWN22BqwRhHq+LpzA32Ct3CtMoTSjPJ/2QN+Zw7WtfsYX9y0ILEf0vPGju+rncKFPQaCUKlRKFSV4FCqlRmeHhn5w9QPvRLetlVEiNR26bZ77Gns2ERluK+42f4hCr4sbL1nE+KIc1jy2mT+8sjv5C0cIl/3pDbpCEW77lBHVta2mLcbEt/lQKyfNLk96nV317Ww53MqvX9g5JON67v1ajr7+Gd4ynfSdXb07rp0ifdavsjSCgWYXBxIIIU8C564lML5zznw2X382M8vzo70GEqGUoqbFz8QkgmBpVSkPXLmc68wmMZr00i+9S0QqRWSa9UjXoEYK7x1s4bzFE9h78/lcc9ZcAGZW5Cc81+rD2mAm33x55Syev+ZUHAK70lgcbCD4Q0afWBFh5dxKAG7455Ysj2poCIUj7Krv4MPHTubMBRMoyHHxys4Gmju7KLQ1Sfng9HF86+x5AL1GElkTdvkQtS/cWG1oii9vN0o5vLBt4IEELsfANYJb1u7g+48afgHL3zWzPJ+rz5jT41zrs8n1OMnPcVHgdfUpCFp8QQKhCOOL+hYEYCTHTbX1DdakD51QNkC6QhEa2ruYa6qunzx+OttvOLfHpGA1t7D+MRtMf8CHPzCZmRUFnLt4IjuzaIdOhD8YjsZxX3fhQnJcDgpzXPz5X3vZXtuW3cENkrq2AOGIYmlVKU6H8J9Lp/D4xkPsa+yMMbXMKM+PNipv7MWHU93kA6A4d2gcmpZ5xepB/LuXBq6FWbZ1SxAEQmEe33gIpRSRiGJ/YydPxoV9AlQ3dfLzZ7dHn//io8dw7+ePZ+01p/bIVQC45qy5LJlSzElzDA2q0OvqU8N9xyy9MquXBZMmO+iEsgFihfBNsK1sEjm27r58GfkeJ89tqaXNH6TR/Ccvyzf+6WdXFMSEBg4H/MEIOaYgyPU4+fhxU2kLhPj+o5s56xcvs25nn+2qhzWHmo3Je1KJ8b0tnd7dj3bKuG6H8eRxuYwzBUEiZ36rP8gta42II3u8/2CwhG9da4CILezzhxct5IcX9c9E4nLEOotv+McWvnrfO7y1r4l73tjPKf/7Al+6520Ot/hiXvf67iMxz6eW5nLCrPJe+wbMn1DEY1edRJHXyGco9Lpp8wd7zV94YWsdBTmuHmWzNdlFJ5QNECtaKJnTq9DrZkKxl4iCq+/fQGN7F06HUJxr/OPMrixAKdhdP3zCSH3BMF6bUPPGZXbuaRw+YwWjSubd/9qb0rkHTUFgRQlVlXebHv7brJ0DhlDoSyN4cH23A32ofDxWzH99eyDGabx62bRocTlrPv7U8uk9Xm/HchZfde/bfOWet6Nmp4gimgUPhlajlOKr973DqzsaeGNPbDBgbwKgNwq9LoJhxT291Byqaw0wqcSrcwOGGanqtPEJZXWM0ISyXfXtFHndUTV8oFghluX5qZTdNSaK9fuayPU4Kc33RB19lg30ULOPBZOGh/89EAzHmAHy3LE/k7rWvpuKZJrVvzeU008vn5504jrUbAjwiZYgMOv4F+e6Y7S7ykJv1FmbKC7eCistL/DQ1GmsgK17v7itjjnjC2NCUlPBMuM0tAWiSX23rD4Wt9MRda4qBZuvPztpH1/LWbyjrp0dNh9UMByJrt7BMAVNL83j8Y2HeHzjIQAcYgiMgXDmUeP5yVPbeGtfE6fPr4w2gLdoCwQp9PZeY0iTHfqTUNbJKEgoW/WzlzglrjroQLBU7lTinK2yuy2+IE+8d5jzF0+MHrM0g7YkIXeZxB+MxBT6sqf7Vxbm9DAnDBfiK3Qm4lCzj+JcdzRRKT/HxY8vWcRDX1pBab6Hz6yYzvfOPwqnQ8g3HfyJakVtq2lj2YxSPn/yTMIRFRPq+dk/vclZP+877DQRVqG3+rYA9W3WQsMQyNbv5FPLp5Gf40raPrG3iKKOQIiG9gBTS40JuvqIj/1HYiPZVh018KLpc8YXMn9CIY+8c5ATbn4++p4srIg0zfAipW9EKWWt/iMi8k+gUSUpYiIidwAXAHVKqUUJjgvwK+A8DCHzWaXU2/0ZfH+xhtxbfHZ/sFZvlgreF/YU/PwcF984c270ufVP0erLfAhpXZufIq+7x+rSHwonLPpVkONiUkluj4kj2+S4HARCETYdbKHFF8TrdjC9LLEz8lCzr8cq9ZPHd5tZfnhR90/Vet+dXT2/mzZ/iKryvOjK+4ePv8/N/9Hdpym+7EIqWIuLrnCE6ibjMy4t6M5J2X7DudGgg2TYJ9uPfHAKHpeDe/+9H18wTH1bgMkluUQisK22rcfncdSEQp59v7bf47ewO90vu/MNclxO7vjscYDxufX23WiyR7KEsuUi8qKIPCwix4rIJmATUCsiyXoY3wn0dc65wBzzcSXw29SHPTDsk22izMn+0BUVBP3LfJxZnh9d3UH3P6y9fd+NT2xJe5JZOKJY9uO1fPmenrLX1xWOEQ6WUCjyujiuahxv7WtKWkYgk0wvM8xrV9y9nrN/+TKn/u+LvZ57sNnH5JLkoYvQrQnFZ/iCsZjIdTuZN8GIGntuy8AnTgv76vlbD74LwDRb+KTH5Ui59/UHpo1jxUzDIbtoUhFfO90I/ezsCtPQHqC8IIdjppaw4UBzNPrJYvGUEi45djJPfO3kAb0Pe7bxazsbeX5rXfS9tfqCWiMYhiSbxX4N3AjcBzwPXKGUmgCcAtzU1wuVUi8DR/o45SLgbmXwOlAiIhP7OH9QRCKKT/6xO9DpwJHBmTcsjSAV09AJtgiJrrgEnxyXkxyXI5qW3xEIcfvLu/n+3zcNanzJ2FZjhIEmKiHsD8YKAmtCLPS6OXfxRIJhxSs7hk/kUH6K9Wj++e5htta09Zr0F4/b6cDtFDoTaJDWZ3Ti7HLmVBZw9BQjTHgw1WQTxfxb+ScD4aJjJgGGP8TSbjoCIQ6ZCV2LJhdT3eRjT0M74/K6FycVhTn84mPHDNhnNS6/pw/AqmirTUPDk2SzmEsp9YxS6m9AjTlho5TaOgT3ngzYq3pVm/vSwoNvV7PpYHdVjEQ1gvpDMJS6RvDHS4/jkS+fAMCq+ZU9jhfluqMawZu2iI7QEDcft/O1+7uzouMnL38oQo67Z9RQUa6LJZOLyfM4+ep97/Cjf7yftvH1B19XmNPmVfDQl06I7gsn8HZ+5V5D+1laVdrjWG/kup28va8pJpwTYoVlUa4bv6lhJrpvqsQLgpPnJM9u7ouPHTeVB7+4grMWjI8K8xv+uYWuUISq8nzKTbPTzvr2mPyX0j5KV6RCotcf6ejCHwzTFees1gwPks1i9l9m/BI6Y4X0ReRKEVkvIuvr63tvoN0XFyyJVTYGLQjMlX0qPoJcj5Njp43jlW+fFuMfsCj0uqJmq6013Qlb30xTY5jG9gA7bZEkR2zhkZGIoisUifERWJUoi3PduJwOFporxT++uict4+svgVCEQq+bD04fx/VmSYIH3uy9cmh/Ythb/SH+vecId7wW+17twjLH5Yh+RgMt+wzd5kaLX6/+wICvBYZfYWlVKSLSY8FSVdZtotxV1xFTRTTRir4/xPscwMjFsDKOtUYw/EgmCI62iswBS+xF54DFg7z3QWCq7fkUc18PlFK3K6WWKqWWVlRUDOhmeR4X2284l9s+9UHA6K86GAbiI5hampcw2qPI66bVH+TxjYe4+cluZeuxjYd6rESHAium3OpwZfWche7aMXbTUJH5j7vENH/YQ2//z0yqyibG6tz4HqyV7XcfeY/3D3VrgJaN+poz5w6otLE9o9oSll4zFt7rdqZFI/B60ld5c3pZXjRE2BcMU1aQE82bGGzp56MmdpuU/vbFFYCRi2EFGUxIobyEJrMkKzrntBWZc8UVnRusfvcY8BkxWA60KKV65rwPIR6Xg3MWTaAwx0Vty2A1goE5ixMxLs/NvsZOrvmboQHMG1/IzR825OyBpqGP0Lntpd2My3NzhhkmaBcElonKXkL7hNnl3HPF8XzltNkAFOd2rx5/9uz2aFnkbOGzmWnsGtp5t7wS7TdgObdLBtjtqiDHENZfu+8dDpuLCMvunuNyRLOL7RU6+yvEg6HY8xMVeRsqJhbnxgQtlOd7ePSqE7njs0v7nUQWj+VA/8C0EmaUGxFCRzq6eP+wIZgXTi4e1PU1Q0/adDQRuQ9YCZSLSDWwBnADKKVuA57ACB3diRE+elm6xhJPRWFOtPnKQAmFFU6HJI3nToUJxbm8sM0weV1z5lzOWjiBvWb2bl8FvAZCfVuAN/Yc4dqz5kajbeps2tEes1FOfLGvE23VOONDGL9y79tMKz2JxVOy8w9ur420YlYZZxw1HofAM+/X8tNntnPWwglEzAl6oPbvAq+LB9dX89jGQ1hv38q+9rqd0Si0sC0Y4MdPbOEjH5wSs0Lui65wJKbp/GAn5HhcDomarpwOiba5BKOkxpRxeUwZN/gib163k79+YQUzK/Kj/oA2f5B9jZ0U57qZlCQbX5N50iYIlFKrkxxXwFfSdf++KCvwRDM3B0rQ/KcdCuwleT9x/DTKCnKoN1fpifodD4YfPGpEI506tzLaHMTSCMIRFe1G1VdkjTU/leS5aTbr+A82HHegKKViaiMVet384dKlHOnowiHv8dTmGv7v+Z2ccZThpLdHx/SHHJcjajKxEtcsLcTr7tYI7D6CP766hz++uoe9N5+f0j0sR2pvRe4Gy/YbzmXmd5+IPrdrBIuGeJW+bEa3Q97jdNAWCPHKjgZOmFU25AJOM3jGZPuf0nxPjIN0IBirt6H5+CbYBIEVg52fYyUzDd0E+/ruRp7cVMPJc8pZPKWYXI+TQq8rKnR+9/KuqLO6r/II1r/xpSuqovsGYxsfKJGI4ruPGILNngkNxud426c/yMXHTOLxjYf4+v0bACgbYMnogM05XGsWHLQEQY7LGa3fHxlM+GgoEjM5DzUOh/DjSxZxzxXHA7F+oKEWBHbyc5zsru+gptWvi80NU8ak+76sIIf1e5sGdY1gODJkNlz7pGutlqzY+KEqaNbiC/Lx2408irMXTojuryzM4c51e3nnQDMbDzQzd3wB1124sM/EpctPmsGrOxv45PHT+JXpLB6KbO3+8uyW2mg3sZbOxCU6Jto+22mlecwdn1oOgcXvP7OUz9+9nkAoHI0Mqo9qBGbUkNuBPzT4qKFgOJJyTsRAsWdRAzzzjVPoCqU3pDM/x8UB01GcrEWlJjuMSY2gLN9DU2fXoCJygiGFa4hMQ8tmlHLq3AouO7Equs+aEBKVNxgIL9qanNjLLVv32WjWif/GGXM5YVbf8evTy/JZe81KKm3RH4myb9NJbaufL/z5rejz3hqdXHxMd2rKl1bO6rdZ4swF4ynyuggEI9Hvos0Uzjk2jaArFEEpFeMj6A+RiJGkN1TmxlSZO74wrdoAGFFIliBI1ONYk33GrCCIKCPJ7D9+u25AXZyCQ2gacjsd3HX5MtZ8qLvmvBW1056g4Fl/iEQU63Y2sMtW5nrhpO5//HhndDIhEM+DZnhgpjWCN/Z0J97d9/nl0VDYeOZNKORrq4zyCuOLBmYWyjGdwb64vgO5Nh8BGOajRH19UzGbPfN+DaGI6tHPejSQ53FGay+VDjJHQZMexqRpyIqM+LZZz+VQs6/fhbC6htA0lAirtEDnIE1D9/x7H99/dDM5LgeTS3J57TunxxxvNeveP/SlFUQUFPfTmWrVwhlKX0Yq2AXBcVXjcPXxXVx12mzmji/gtHk9s7pTwQoP9cVpZ1ZilFVbPxCMEI70XFQEwxGcjr7LRidqFj9asJu7SlMo267JPGNSI5gR1ybPalbSH4ZSI0iEx+XA43TQ3k/TkFKKJ947zN/WH+CxjYeivWcDoUg0ptvO9RctZHJJLkumlESbn/QHq/xEpjusvbn3CLluJ989b36fQgCMz/KCJZMGHK3ijWoEse/RiqyyNAJ/KJzQR5CK38AKGf2JrYLpaMGKthIhrc5wzcAZkxrB1LhYaatZSX8IhhVuV3rtufk5Tjr7aRr60T+29CiHYGH3QVhcsGQSFyyZNJDhAd3mkUxqBB2BEFtr2vjGGXO58pRZab+fVUIi/j1aUTfWZ+DrCic0AwVDEUiyELb8D6cfVcmtn/hATMmHkY6lEZTle4Yk70Yz9IxJjcDjcnDavIroj/Jg0/DTCMAwD3X0UyNI1Fx+YrGXOz67dFANR3rDqtD582e388zmmiG/fiKsbOtZlZmpa2+VkLA0gvFFOXzx1G4BVGCL8EooCJL4oF7YVhftDpbncXL+koksnzl6wiytz2dmef8itjSZY0xqBAB3fPY42gMhTvnJCwNKLusKpV8QeFyOqMkgVdoCIU6YVca6XUbv2Se/fnLKma0DxRrjlX9+K+XkqcGwv9EQBNNKB58FmwqWRuDrCjOnsoBnv3lqzPECq7mQP5iwhWR8Mbl4LvvTm4BhOvGOwl6+x0w1alRlI8RYkxpjUiMAI16/0OumJM/oOdsfalv9HGrxpdVZDEZGZrCfTsSOQCimdECqtfeHCnuDnaHmD6/s5rxfvRItg5EpQWBpBJ1d4Wh9ITtWDH67P5QwJDmUojDPdTtTbjwzkjhlrlEo8pPHT8vySDS9MWY1AoviXHe/u20df+NaAOZPSO9K2+2Sfoe2tvtD0V67kFrjnMFy6twK1u1qIBhWHG7xp6U5eSSiuOGfWwBj5T25JDdjjsdcj5OOQBinI5jwnpbpo80fimoHdlL9Dh2jtPRCab6HXTeep/0Dw5gxqxFYlOS5afENbBU736yymC7cTkdSs0I87QFjMnrwiyu47/PL0zSyWO66fBl3XbYMgMZBFvPrjYb2bvNddZOPy06syljNmtI8oyTJkY5AwoQoK4y0PRDiE7//d4/j2xL4bRIxVFnkwxEtBIY3WhDkdhdO6y8zK9LrrHQ7HTF9bJMRiSg6ukIU5rhYWlWa0bouVpP1wRbz643quBDfT/eSQJYOSvM9tPiC1LUmFgQFCfpO27nq3ncS7tdohgtaEOR5+mUa2mLWVJ9dWcBFR6etsyZgOCl7Myt0hSKc8fOXeGZzDX94ZTf1bQE6g2GUSr2H71BSZiYK2duBDiX2yK5T5lZEk7gygRXKGQhFYlo6Wlhjue+N7q5ov/r4MZy5ILUoLd2oRZNttI8g102r3wj7S0V9PfdXrwDw2ROq0u7Yczt7jxqqbfWzs66dK816O1tr2rj2rHkACe3U6cYq73zbS7v40spZQ26/P2RqBE9ffQpV5ZlxElvYtYC+auXYExNnlhfwuZNm8Oz7tUmvb/2M4tupajSZQmsE5gTWmoKf4JF3qqPbmXDCup0SYxpSSnHxra/xn7etoylOi/G4HNF9g201OBDs2b2D7QediIPNPoq8LuZNKMyoNgCxk39ZL4Jg6fRxMc+djtg+waqP8tRdYcUnjp/Grz8xuB7FGs1ASetsJiLniMg2EdkpIt9JcHy6iKwVkXdF5EURmZLO8STCEgTxE2sivvFAdzP5TIQuGhpBtyAIhCJsONDMm3ubephgHAIPv12N0yF8MG5SyhTfPW8+EOvYHSoONvkSNkXPBLNtIbjHz0jsd/mqWdjOwuWUmEqifRWeC4YjuLUzVZNF0iYIRMQJ3AqcCywAVovIgrjTfgrcrZRaAvwQuCld4+mNErP/bnM/Iodu//QHM5L56XHFRg3Zo0rW7WqIOXf/ER/3/ns/FyyZOCTtBgfCqXONom6DbfoTzwtb61i3qzGmfHYmqSzy8vhVJ/HgF1f0WpSvMM4c55BYjSBRVVKLTGSpazR9kc5f3zJgp1Jqt1KqC7gfuCjunAXA8+b2CwmOpx3rH9tqbBIKR/ivh99jW01syJ/V1/c7587nLFtjl3TicTqobvLxv09vBYwcAYtXdxqCYOOas1hWVcobexrp6AoPqm7QYLGcqkMZQhoKR7jszjfxBcNZ0wgAFk8pZmkfRfkK48xxrjjTkFWUtCMQ4uxfvMy71d3lpkNhhTsDpkaNpjfS+eubDBywPa8299nZCHzY3L4EKBSRHkttEblSRNaLyPr6+vohHWSJ6dRs9hmT1xObarjvjf18+6F3Y877yr1vAzCnMnOZutZEcusLu4BYjaC5M4jbKRR5XYzLd0d75vbVYjLdjMvzIGKYhiIRxTcf2MBrOxuSv7AP9jZ291GYmaB66nAh3kHvdEhM5rmlEWw40My22jb+5ylDuCulhrTtqUYzELL967sWOFVE3gFOBQ4CPQqSKKVuV0otVUotCLYHJgAAES1JREFUraioGNIBjMszTUOdQQ4c6eRr9xkx33m2mjEN7QHe2tfE8plGJ7FMEd8BzWoiU1FohDB+7/wFiMT6BCYWZy8U0ekQZpTl83/P7+R7j27i4XcOcu3fNiZ/YR9sq2mPbs+uTG8C32CId9A7HRLz/Vk+Ait72HpulajWPgJNNkmnIDgITLU9n2Lui6KUOqSU+rBS6ljgv819GW3RVJTrRgTq2wI8ZlaABGLq9WyvaSOi4Gur5iStfT+UxOcQWBrBzz96NH//yolcekIVQEwp5pJ+NpYZam5ZfSwA9/7b6CUcbzvfdLCFUD+ypQ82d0a3M1VtdCDYy3pAItOQMeFbwsESBNZ3rE1DmmySzl/fm8AcEZkhIh7g48Bj9hNEpFxErDH8F3BHGseTEKdDOHpKCa/saKCmpTvs0V57/rC5f1JxZs0u/mC8IDD8GFPG5UUrOlqcOLsMj8uRsbILvbFocnGMCedwsz8aOlnT4ueC/3uVa/qhJTSajud7rzieiRn+/PtDfE6JoxfTUPR5VBCYGoE2DWmySNp+fUqpEHAV8DSwBfirUmqziPxQRC40T1sJbBOR7cB44MfpGk9fnDavkvcOtlDfFmBaaR6nzq2IhpN2hSLRiWtChs0u8e0LLdNQ/Cob4O7Lj2fz9WdnZFzJKLQlk7UFQtSYjnarj8CjGw4lfF0imjq6GF+Uwwmz+9dLORvk2syJkYiKMQ1ZGoGVF/L2/maefb82qhF4Mty0XqOxk9ZliFLqCaXUXKXULKXUj819P1BKPWZuP6iUmmOec4VSKj2FapIw2QxL3FXfTkGOi7J8TzTy5e39TdHzEtWaTyeBuPrtliBIlDAWn8CUTYqivXyN8WyvNez8h20a11/fPNDzhQk40tE1YvrcrvvO6SybYUQWFXrdMUmHlkZgTxD8/Su7o4IgkyZHjSYe/euju9bLjrp2CrwuSvM90Vj4wy397142VPjjNILaVj9FXlfGBVJ/KTI1ghPNVfwOs/pmje2z/PZD7/aZbWthCIKR0ed2XL6Hv35hBXtvPp9cjxO30xFNsrNMQXYtr6mjK9qrYLgIcc3YRP/6gAnF3SvOwhwX4/I9+IJhfF1hDhwxJq9smF3iG8Ifas5edm1/sLpsHTWxkLJ8Dztq26lp8VMd1xK0ri25AjiSNIJEWGO38gjsCYJNnV3R525tGtJkES0IMDJHLQq8rmg9mSOdXbx/qJUJRd6sVPT8xDKjo5NlYjjY7M9qnkCq7D9ixP4vnlzCnPEFvLargeU3reXuf+2LOW/93qZEL48SiRiNbsYXjlxBYC30LdOQveNcVyjC1sOGtpTubncaTV/oXx+GFmDF4neFItEiY396dQ9Pba7hwmOyk6178bGT+dTyaRTmuDjU7GPL4dYRoRFMNUtcfGBaCfPGF8ZoAkdPKY4Ktl+/sLPP6xxs9hEIRZiVwSS+oSY+b8CuEXhcjmiiovYRaLKJ/vVh9C++5qy5AOxr7IwKgj+8ugcgGq+fDbwuJ/5gmIfeMiqffnTp1CSvyD4/vHgRj3z5BCqLvKyYFRvtM29CIdtvOJerTpvN9tq2HuYvi3BERfM6Mt13eSixSptHEjiL7SXG+9suVaMZSsZ8PwKLBRON/sPzJxQyzlZq+OyF47NqjslxOwiEIjS0Byjyulg8pThrY0mVghwXx04zNKxT5pYzrTSPPI+TrTVt0bagR00sIhxR7KxrZ9Hknu9p7veejK6i56W5JWg6ccZrBDZB4AuGOWpiEVsOt3LuYt2LQJM9tCAwKcnz8NTVJzOtNA+Xw8GZC8Zz1WmzOToucSvT5LichCKK2tZAwu5Yw508j4uXv30adW1+lv14LZcca5SbmjveWOXvqGtLKAisifMbZ8zNWJP6dGAlmlnvpy3QXSakvi2A0wEr51VkpYeERmOhTUM25k8oIs/jwuNy8PvPLM26EADwuo2v6FCLL1rdcyRSWehlz03ncc4iY+U7tTQPEcMU1xfp7gudbiyNIKIU9W0Bblm7A4ArTpoBQKsvpENHNVlH/wKHOVY3roNNvmhf4JGKvfyF1+1kQpGX/UkEQV+tIUcCTptGYG/Yk+cxvtdWf1BHDGmyjv4FDnOssNXGji5KR7BGkIhppXnsO9JTENiL0h3XRw+AkYDdNGTvUpZrFqlr8QV1DoEm62hBMMyx28dHQg5Bf5g8LjfalB66+/r6zEii/z7vqIz0hk4ndmexvZChVZdIKZ1VrMk++hc4zCmyFZibmoE+yZlkUnEudW0BQuEIW2taWfCDp3l+a21UEHg9w7uURio4bAllHV3djYXybO9N5xBoso3+BQ5z7D1yp40yQTCh2Es4ovjBY5tZu6UOXzDMTU9sxd9lmIZyh3lNpVRwmZIgEgGfqRFMKPLG1IvSlUc12UbHrA1ziryjVxBMKjFKe1hNbMBYOXcGjZVz3ijQCOwlJizT0F+/sIIjtgQybRrSZBstCIY5dh/BSI+giWd2Rc9Esd31HZzzy1eA0aERWCUmIhGFzzQN5XqclEn3d6m7k2myjRYEw5zRsCrujWlleVy6YjrvHWyhND+HYDjCS9vro8dzR8F7t4ePWhpBnsdJWb6HPI+Tzq6w1gg0WSetv0AROUdEtonIThH5ToLj00TkBRF5R0TeFZHz0jmekYgVe3/ynOHfoWsgXH/RIh7+8on84dKlLJxUFHNsuPddSAVLI/jKvW9HBUGu24nDIdEaStpHoMk2adMIRMQJ3AqcCVQDb4rIY0qp922nfQ+jheVvRWQB8ARQla4xjVQ2XX92tNvXaObSE6rYVd9OnsfFI+8cHBXx9ZZGEAhF6OwK4XU7orkFlWZ5ba0RaLJNOk1Dy4CdSqndACJyP3ARYBcECrCWgcVA6s1sxxBjpQ7N+CIvv/v0UkLhCB/+wGQWThr+BfaS4bQ1tW/xBcnzdH+XVnisFgSabJPOX+BkwN6YttrcZ+c64FMiUo2hDXw10YVE5EoRWS8i6+vr6xOdohlFuJwOTp5Tke1hDAkOW1mNxvauGAe4tT0aNB/NyCbbS5HVwJ1KqSnAecCfRaTHmJRStyulliqlllZUjI4JQjM2sGsEjR1dMSY+SxCEIsl7N2s06SSdguAgYO+iMsXcZ+dzwF8BlFL/ArzA6PSKasYkTptG0NTZRY5NI7Aiwny9NOfRaDJFOgXBm8AcEZkhIh7g48BjcefsB1YBiMhRGIJA2340owaH7T/sSHucRmAKAn+XFgSa7JI2QaCUCgFXAU8DWzCigzaLyA9F5ELztGuAz4vIRuA+4LPKqjym0YwC7KahtkAooWmoUwsCTZZJaziKUuoJDCewfd8PbNvvAyemcwwaTTaxm4YgNjdirtmCc8YIb76jGfmMjbhEjSZLOByxgsCuEZw2r5LHrzqJRZOL4l+m0WQULQg0mjQSrxHkxGVLL54y8nMlNCOfbIePajSjmniNwDsGMsQ1Iw/9q9Ro0kh87EOOW//LaYYf+lep0aSRkjwP3zp7XvR5jmvkF9LTjD60INBo0sxXTpsddQh7tUagGYboX6VGkwEKc4wGQ1oj0AxHtCDQaDKAVU5iLJQT14w89K9So8kAlUVGf2anQ1ca1Qw/dB6BRpMB/uu8+eR5nJy5YHy2h6LR9EALAo0mAxR53Xz/ggXZHoZGkxBtGtJoNJoxjhYEGo1GM8bRgkCj0WjGOFoQaDQazRhHCwKNRqMZ42hBoNFoNGMcLQg0Go1mjKMFgUaj0YxxZKT1iheRemDfAF9eDjQM4XCygX4PwwP9HoYH+j38//buP2Svso7j+PvDns2ftamtGM2aoyfFIB/HqK2kH+sHS8KEJJLKEYP9Y2AShEMo+ifon0wzrSiTxbTIWckIzTaJ/qjZ1KlzT2uzBk7cngi3frDCbZ/+uK573Txuj1t7es45ns8LDve5rnNu+H6fnd3fc6773Nc5eW+2Pf94GzpXCE6HpK22lzYdx+lIDu2QHNohOUyPDA1FRPRcCkFERM/1rRB8t+kApkFyaIfk0A7JYRr06juCiIh4ub5dEURExCQpBBERPdebQiBppaSdknZLuqnpeE5E0l2SJiRtH+o7X9LDknbV1/NqvyTdVnN6StKS5iI/FuuFkh6RtEPSM5JuqP1dyuFMSY9KerLm8JXaf5GkLTXWH0uaU/vPqO3ddfuiJuMfJmmWpCckbaztTuUgaY+kpyVtk7S19nXmWAKQNE/SfZL+IGlc0vK25dCLQiBpFvAt4CPApcC1ktr6uKi7gZWT+m4CNtkeBTbVNpR8RuuyBrhzhmKcymHgC7YvBZYB19e/dZdy+DewwvZlwBiwUtIy4GvALbbfArwIrK77rwZerP231P3a4gZgfKjdxRzeb3ts6F77Lh1LALcCD9q+BLiM8u/Rrhxsv+oXYDnw0FB7LbC26bimiHcRsH2ovRNYUNcXADvr+neAa4+3X1sW4OfAh7qaA3A28DjwTsqvP0cmH1PAQ8Dyuj5S91MLYl9I+ZBZAWwE1MEc9gCvm9TXmWMJmAv8efLfsm059OKKAHgj8NxQe2/t64o32H6hru8DBk9Ab3VedXjhcmALHcuhDqlsAyaAh4FngQO2D9ddhuM8lkPdfhC4YGYjPq5vAF8Ejtb2BXQvBwO/lPSYpDW1r0vH0kXAX4Af1CG670k6h5bl0JdC8KrhcprQ+nt+JZ0LbAA+b/tvw9u6kIPtI7bHKGfV7wAuaTikUyLpo8CE7ceajuU0XWF7CWXI5HpJ7xne2IFjaQRYAtxp+3Lgn/x3GAhoRw59KQTPAxcOtRfWvq7YL2kBQH2dqP2tzEvSbEoRWG/7/trdqRwGbB8AHqEMo8yTNFI3Dcd5LIe6fS7w1xkOdbJ3A1dJ2gP8iDI8dCvdygHbz9fXCeCnlKLcpWNpL7DX9pbavo9SGFqVQ18Kwe+B0XrHxBzgk8ADDcd0Kh4AVtX1VZRx90H/dfVOg2XAwaHLzUZIEvB9YNz214c2dSmH+ZLm1fWzKN9xjFMKwjV1t8k5DHK7Bthcz/IaY3ut7YW2F1GO9822P0WHcpB0jqTXDNaBDwPb6dCxZHsf8Jyki2vXB4AdtC2HJr9ImckFuBL4I2Ws9+am45kiznuBF4CXKGcTqyljtZuAXcCvgPPrvqLcDfUs8DSwtAXxX0G5zH0K2FaXKzuWw9uBJ2oO24Ev1f7FwKPAbuAnwBm1/8za3l23L246h0n5vA/Y2LUcaqxP1uWZwf/bLh1LNa4xYGs9nn4GnNe2HDLFREREz/VlaCgiIk4ghSAioudSCCIiei6FICKi51IIIiJ6buSVd4noL0lHKLfxzaZMqLeOMmnb0SnfGNEhKQQRUzvkMtUEkl4P3AO8Fvhyo1FFTKMMDUWcJJdpDtYAn6u//Fwk6TeSHq/LuwAkrZN09eB9ktZL+pikt6k852BbnWt+tKlcIoblB2URU5D0D9vnTuo7AFwM/B04avtf9UP9XttLJb0XuNH21ZLmUn5dPUqZ5/93ttfXqU5m2T40sxlFvFyGhiL+d7OB2yWNAUeAtwLY/rWkOyTNBz4ObLB9WNJvgZslLQTut72rscgjhmRoKOIUSFpM+dCfAG4E9lOeOrUUmDO06zrg08BngbsAbN8DXAUcAn4hacXMRR5xYrkiiDhJ9Qz/28Dttl2HffbaPippFTBraPe7KZO37bO9o75/MfAn27dJehNlcrvNM5pExHGkEERM7az6pLLB7aM/BAbTa98BbJB0HfAg5aEjANjeL2mcMtvkwCeAz0h6ifJUqq/OQPwRryhfFkf8H0g6m/L7gyW2DzYdT8RU8h1BxDST9EHKg2y+mSIQXZArgoiInssVQUREz6UQRET0XApBRETPpRBERPRcCkFERM/9B3HlaWP5uKcyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylBo0BVgKHL6",
        "outputId": "dd99c24b-a0f0-4af2-9809-0f29f06d352a"
      },
      "source": [
        "print(f\"Final balance for model_{best_model+1}: {final_balance}\")\n",
        "print(f\"Percent return over 623 days for model_{i+1}: {100*(final_balance-1)}%\")\n",
        "print(f\"Average daily percent return: {100*(final_balance**(1/x_val.shape[0])-1)}%\")\n",
        "print(f\"Average yearly percent return: {100*(final_balance**(365/x_val.shape[0])-1)}%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final balance for model_3: 1.5159985887840186\n",
            "Percent return over 623 days for model_5: 51.59985887840186%\n",
            "Average daily percent return: 0.06680791752400328%\n",
            "Average yearly percent return: 27.604758945441986%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhjvYTKkePhE",
        "outputId": "79a15fca-5069-4afb-b5d1-8c2e830a7e45"
      },
      "source": [
        "print(companies[invest(models[best_model], x_test[-1])].name) # company that the model recommends investing in today"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Anthem\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}