daytrade.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4h-M7UK4B7m"
      },
      "source": [
        "Small python script that plots the difference between close and open prices of s&p stocks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "awfvEoNa3_OE"
      },
      "outputs": [],
      "source": [
        "# Installation\n",
        "!pip install -q yfinance\n",
        "!pip install -q yahoo_fin --upgrade\n",
        "!pip install -q pip install pytickersymbols --upgrade\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "PmE5oMq40Wgp"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "## Hyper parameters\n",
        "history = \"10y\"\n",
        "buy_trigger = 3                           # times standard deviation\n",
        "sell_trigger = 1                          # times the avg cost of the security to grow before we sell\n",
        "security_age = 15                         # number of days to hold the security before we cut the losses\n",
        "lockin_gains_factor = 1000                # times the orignal amount to grow before we lockin the gains.\n",
        "mean_type = \"+ve\"                         # only consider stocks with +ve mean of ND. These stocks have been growing over the period of time\n",
        "max_stocks_to_buy = 5                     # number of stocks to buy at buy trigger. We can change this value to be more adaptive based on market cap of the security and other parameters.\n",
        "backtest_iterations = 100                 # number of backtests to run\n",
        "backtest_days = random.sample(range(100, 700), backtest_iterations) # starting days for back testing\n",
        "backtest_days.sort()\n",
        "prefer_beta = True\n",
        "above_beta_mean = False\n",
        "stop_falling_knife = False\n",
        "index_stocks = \"NASDAQ\"                    # Other legal names are NASDAQ, DOW, SP500\n",
        "\n",
        "starting_balance = 10000                  # seed money to start investing\n",
        "current_account = original_balance = starting_balance\n",
        "portfolio = {}\n",
        "\n",
        "# Display test results. Debugging Tools\n",
        "print_final_portfolio = False             # Prints the portfolio list at the end of each backtest iteration\n",
        "plot_every_test_graph = True              # Prints the model performance during the back end against s&P500\n",
        "plot_summary_graph = True                 # prints the summary graph\n",
        "dump_all_trades = False                   # dumps all sells at the end of the trade. Use it sparingly, with iteration set to 1\n",
        "\n",
        "# Deploy the initial amount gradually\n",
        "tranche_pct = 25                          # of amount to deploy\n",
        "tranche_period = 7                        # calender days between deployment\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "rOPoTPetQumr"
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      "outputs": [],
      "source": [
        "tests = [\n",
        "    {\n",
        "        \"Name\": \"Test 1\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 15,\n",
        "        \"lockin_gains_factor\": 1000,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": False,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "     {\n",
        "        \"Name\": \"Test 2\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 15,\n",
        "        \"lockin_gains_factor\": 1.1,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": False,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "     {\n",
        "        \"Name\": \"Test 3\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 1000,\n",
        "        \"lockin_gains_factor\": 1.1,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": False,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "     {\n",
        "        \"Name\": \"Test 4\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 1000,\n",
        "        \"lockin_gains_factor\": 1.1,\n",
        "        \"prefer_beta\": False,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": False,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "     {\n",
        "        \"Name\": \"Test 5\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 90,\n",
        "        \"lockin_gains_factor\": 1.1,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": False,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "     {\n",
        "        \"Name\": \"Test 6\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 90,\n",
        "        \"lockin_gains_factor\": 1.1,\n",
        "        \"prefer_beta\": False,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": False,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "    {\n",
        "        \"Name\": \"Test 7\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 90,\n",
        "        \"lockin_gains_factor\": 1.1,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"\",\n",
        "        \"above_beta_mean\": False,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "     {\n",
        "        \"Name\": \"Test 8\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 90,\n",
        "        \"lockin_gains_factor\": 1000,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": True,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "      {\n",
        "        \"Name\": \"Test 9\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 20,\n",
        "        \"lockin_gains_factor\": 1000,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": True,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "     {\n",
        "        \"Name\": \"Test 10\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 20,\n",
        "        \"lockin_gains_factor\": 1000,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": False,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "     {\n",
        "        \"Name\": \"Test 11\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 90,\n",
        "        \"lockin_gains_factor\": 1000,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": True,\n",
        "        \"stop_falling_knife\": True,\n",
        "     },\n",
        "     {\n",
        "        \"Name\": \"Test 12\",\n",
        "        \"buy_trigger\": 2,\n",
        "        \"sell_trigger\": 1,\n",
        "        \"security_age\": 20,\n",
        "        \"lockin_gains_factor\": 1000,\n",
        "        \"prefer_beta\": True,\n",
        "        \"mean_type\": \"+ve\",\n",
        "        \"above_beta_mean\": True,\n",
        "        \"stop_falling_knife\": False,\n",
        "     },\n",
        "]\n",
        "\n",
        "tests_to_run = [10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "ZtB77RObE0jz"
      },
      "outputs": [],
      "source": [
        "import yfinance as yahooFinance\n",
        "import yahoo_fin.stock_info as si\n",
        "from pytickersymbols import PyTickerSymbols\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pprint\n",
        "\n",
        "pp = pprint.PrettyPrinter(indent=4)\n",
        "\n",
        "stock_indexes = {\"SP500\": '^GSPC', 'NASDAQ': '^IXIC', 'DOW': '^DJI'}\n",
        "\n",
        "assert index_stocks in stock_indexes\n",
        "indices = list(stock_indexes.values()) + ['^VIX']\n",
        "\n",
        "if index_stocks is \"SP500\":\n",
        "  stocks = si.tickers_sp500()\n",
        "elif index_stocks is \"NASDAQ\":\n",
        "  stock_data = PyTickerSymbols()\n",
        "  stocks = stock_data.get_nasdaq_100_nyc_yahoo_tickers()\n",
        "elif index_stocks is \"DOW\":\n",
        "  stocks = si.tickers_dow()\n",
        "\n",
        "stocks_ts = pd.DataFrame()\n",
        "stocks_spread = pd.DataFrame()\n",
        "indices_ts = pd.DataFrame()\n",
        "\n",
        "for idx in indices:\n",
        "    data = yahooFinance.Ticker(idx).history(period=history)\n",
        "    d = data.copy()[['Open', 'Close']]\n",
        "    indices_ts[idx+\"_Open\"] = d['Open']\n",
        "    indices_ts[idx+\"_Close\"] = d['Close']\n",
        "\n",
        "for stock in stocks:\n",
        "    data = yahooFinance.Ticker(stock).history(period=history)\n",
        "    d = data.copy()[['Open', 'Close']]\n",
        "    stocks_ts[stock+\"_Open\"] = d['Open']\n",
        "    stocks_ts[stock+\"_Close\"] = d['Close']\n",
        "    stocks_spread[stock] = (d['Close'] - d['Open']) * 100/d['Open']\n",
        "\n",
        "    stocks_ts = stocks_ts.copy()\n",
        "    stocks_spread = stocks_spread.copy()\n",
        "\n",
        "betas = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/betas-%s.csv\" % index_stocks.lower())\n",
        "betas = betas.drop([\"Unnamed: 0\"], axis=1)\n",
        "betas = betas.set_index('Ticker')\n"
      ]
    },
    {
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      "execution_count": 105,
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6s6/jTNFxAAAAACqewhUAAAAAFWtoTnDV/KaCk4xfk+qqc+28gethm3eZFQQAAAD4cRSuAAAAAKhYQ4WrlQpXI+r6RQOzglsUrgAAAAB+LIUrAAAAACrWky5cjYp1iwcKV5t3K1wBAAAA/DgKVwAAAABUpM4zvdl19HSSZOU8hauRNFS42nbgRE51nys4DQAAAEBlU7gCAAAAoCI9NXjdasH0SZk+pa7gNOPbvOZJmdvUkL7+ch7f21F0HAAAAICKpnAFAAAAQEXaak5wVF0/eOXq0d0dxQYBAAAAqHAKVwAAAABUpKHC1UqFq1GxbtFA4WrzruMFJwEAAACobApXAAAAAFSkJ124GlXrBi9cbdl9PP395YLTAAAAAFQuhSsAAAAAKk7X2d7sPHo6icLVaFkxrzENtVXpON2bF4+cKjoOAAAAQMVSuAIAAACg4gxdt5rfPCnTp9QVnGZiqK2uynULmpMkW8wKAgAAALwqhSsAAAAAKo45wWIMzQpuVrgCAAAAeFUKVwAAAABUnK37upIkqxYoXI2mtQubkySP7ekoNAcAAABAJVO4AgAAAKDiDF24WunC1ahas6g5SfLsoRM5cba32DAAAAAAFUrhCgAAAICK0nW2NzuOnEpiUnC0zZnWkPnNk1IuJ1v3dhYdBwAAAKAiKVwBAAAAUFGGrlvNb56UGVPqCk4z8QxduXrUrCAAAADAK1K4AgAAAKCiDBWuXLcqxtqFzUmSR3d3FJoDAAAAoFIpXAEAAABQUbbu60qSrFqgcFWEtYMXrh7b05FyuVxsGAAAAIAKpHAFAAAAQEUZunC10oWrQlw7ryk1VaUcOdmdfR1nio4DAAAAUHEUrgAAAACoGJ1nerPjyKkkyXUKV4VoqK3O8rmNScwKAgAAALwShSsAAAAAKsZTg9etFs6YlOlT6gpOM3H98KwgAAAAAC+ncAUAAABAxXhisHB13fzmYoNMcGsWNidRuAIAAAB4JQpXAAAAAFSMrXsHClerFpgTLNLaRdOTJFv3dabnXH/BaQAAAAAqi8IVAAAAABXj8b0dSZLr5itcFalt5uQ0T65Nz7n+bG/vKjoOAAAAQEVRuAIAAACgIhw71ZO9x88kSa5VuCpUqVTK6gXNScwKAgAAAPxzClcAAAAAVISt+wbmBJfMmpKmSbUFp2HtouYkyWO7OwrNAQAAAFBpFK4AAAAAqAhbB+cEV7luVRHWLGxOkjzqwhUAAADAyyhcAQAAAFARntg7cOHqugUKV5VgqHC148ipdJzuKTYMAAAAQAVRuAIAAACgIgxNCrpwVRmaJ9flillTkiSPuXIFAAAAcJ7CFQAAAACFO3TibA50nk2plFyrcFUxzs8K7u4oNAcAAABAJVG4AgAAAKBwTw5et7py9tRMra8pOA1D1i5qTuLCFQAAAMAPU7gCAAAAoHBP7B0oXF3nulVFWT144erxvR0pl8vFhgEAAACoEApXAAAAABRu62DhatUChatKsqy1MXXVVek43Zu9x88UHQcAAACgIihcAQAAAFCocrmcJwYnBa9TuKoodTVVWT53WpKBK1cAAAAAKFwBAAAAULD2rrM5fKI71VWlrJircFVphq6ODc0+AgAAAEx0ClcAAAAAFGqoyHPVnKmZVFddcBr+uesWNCdJHt/TUWgOAAAAgEqhcAUAAABAobbuNSdYyVYPFq6e3NeZ/v5ysWEAAAAAKoDCFQAAAACFemLfQOFq1WCxh8py5ewpmVRbnVM9fXnxyMmi4wAAAAAUTuEKAAAAgMKUy+Vs3duRJFk134WrSlRTXZWV8xuTJI/v6Sw4DQAAAEDxFK4AAAAAKMzuY6dz/HRvaqtLWT53WtFxeBXXDV4f27pP4QoAAABA4QoAAACAwjy2pyNJsmJuY+prqosNw6u6bsHA9bHHB6+RAQAAAExkClcAAAAAFGaocLVmYXOhOXhtQxeunt7fld6+/mLDAAAAABRM4QoAAACAwjw+VLha1FxoDl5b28zJmdZQk+5z/Xmm/UTRcQAAAAAKpXAFAAAAQCF6zvXnyf1dSZLVgxeUqEylUun8rODWfZ0FpwEAAAAolsIVAAAAAIV4pv1Ees71p7GhJktmTSk6Dj/G0KzgE3s7Cs0BAAAAUDSFKwAAAAAK8die40mS1QubUyqVCk7Dj7N68MLV43tcuAIAAAAmNoUrAAAAAArx2GBxZ+3C5mKDcEFWDV64evbgiZzt7Ss2DAAAAECBFK4AAAAAKMQPX7ii8s1rasisqXU511/O0we6io4DAAAAUBiFKwAAAABGXdfZ3rxw+FSSZI3C1ZhQKpVy3eCVqyf2dBSaBQAAAKBIClcAAAAAjLonBucEF86YlJlT6wtOw4VaNb8pSfLE3s6CkwAAAAAUR+EKAAAAgFH3+N6OJMnqwYtJjA2rFw4WrvYpXAEAAAATl8IVAAAAAKPu0d0dScwJjjVDk4IvHD6Zk93nig0DAAAAUBCFKwAAAABGVblczmN7OpIoXI01s6bWZ25TQ8rl5On9XUXHAQAAACiEwhUAAAAAo2p/59kcOdmd6qpSVs5vKjoOF+naeQP/mz1pVhAAAACYoBSuAAAAABhVjw9et1rWOi0NtdXFhuGirZzfmCR5cr/CFQAAADAxKVwBAAAAMKrMCY5tqwavkj21z6QgAAAAMDEpXAEAAAAwqoYKV6sVrsakoRnI5w6dyJmevoLTAAAAAIw+hSsAAAAARs25vv5s3TswRbdW4WpMmjOtPrOm1qe/nGxrd+UKAAAAmHgUrgAAAAAYNc8ePJkzvX2ZWl+TK2dPLToOl6BUKmXl/MYkyVP7OgtOAwAAADD6FK4AAAAAGDVbdh9PkqxZ2JyqqlLBabhUK+cNzAo+uc+FKwAAAGDiUbgCAAAAYNRs2TVQuLp+UXOxQbgsK+cPFq72u3AFAAAATDwKVwAAAACMmqELV9cvnl5wEi7H0KTgswdPpPtcX8FpAAAAAEaXwhUAAAAAo+Loye7sPHo6SbJ2ocLVWDa/eVKaJ9emt6+cZ9tPFh0HAAAAYFQpXAEAAAAwKrbs7kiSLJ0zNU2Ta4sNw2UplUpZOc+sIAAAADAxKVwBAAAAMCqG5gTXLXLdajxYOX+wcLVP4QoAAACYWBSuAAAAABgVW3YNFK6uX9xcbBCGxcr5jUkUrgAAAICJR+EKAAAAgBHX29efx/d2JEmud+FqXBiaFNzWfiK9ff0FpwEAAAAYPQpXAAAAAIy47QdO5GxvfxobanLl7KlFx2EYLJoxOdPqa9Jzrj/PHzpZdBwAAACAUaNwBQAAAMCI27J7YE5w7aLpqaoqFZyG4VBVVcqKeWYFAQAAgIlH4QoAAACAEbd510Dhypzg+LJq/sCs4FP7uwpOAgAAADB6FK4AAAAAGHFDF66uX9xcbBCG1crBwtVWF64AAACACUThCgAAAIARdajrbPYeP5NSKVmzsLnoOAyjlfMHJgWf3t+Vvv5ywWkAAAAARofCFQAAAAAjaui61TUt0zKtobbgNAynJbOmZlJtdc709uXFwyeLjgMAAAAwKhSuAAAAABhRW3Z3JEnWLppebBCGXXVVKSvmDV65OtBVcBoAAACA0aFwBQAAAMCI2rJr4MLVusUKV+PRirkvzQoCAAAATAQKVwAAAACMmJ5z/XliX2eS5PpFzcWGYURc68IVAAAAMMEoXAEAAAAwYp7a35mec/2ZPrk2S2ZNKToOI+D8pOD+rpTL5YLTAAAAAIw8hSsAAAAARszmwTnBtYump1QqFZyGkXB1y7RUV5Vy9FRPDnZ1Fx0HAAAAYMQpXAEAAAAwYjbtHChcrW+bXnASRkpDbXWunD1wvezpA50FpwEAAAAYeQpXAAAAAIyIcrmcTbuOJUlubJtRcBpG0oq5L80KAgAAAIx3ClcAAAAAjIgdR07lyMme1NVUZdWCpqLjMIJWzBssXB1QuAIAAADGP4UrAAAAAEbE0Jzg6gVNqa+pLjgNI2nF3IFCnQtXAAAAwESgcAUAAADAiHhk58Cc4A3mBMe95XOnJUl2Hj2dk93nCk4DAAAAMLIUrgAAAAAYEZt2DVy4Urga/2ZOrU9rY0OSZLtZQQAAAGCcU7gCAAAAYNgdOnE2O46cSqmUXL9oetFxGAXXzmtMkjytcAUAAACMcwpXAAAAAAy7zTsHrltd0zItTZNrC07DaFgxVLjar3AFAAAAjG8KVwAAAAAMu0d2mhOcaFbMdeEKAAAAmBgqonD1qU99Km1tbWloaMhNN92Uhx9++FXf++Uvfznr169Pc3NzpkyZkjVr1uRP//RPRzEtAAAAAD/OIzuPJUnWt5kTnCiGLlxtbz+Rc339BacBAAAAGDmFF66+9KUvZcOGDbn77ruzZcuWrF69Ou94xzty6NChV3z/jBkz8hu/8Rt58MEH88QTT+SOO+7IHXfcka997WujnBwAAACAV3Ky+1ye2t+ZxIWriWTh9MmZWl+TnnP9eeHwqaLjAAAAAIyYwgtX9957bz7ykY/kjjvuyIoVK/KZz3wmkydPzn333feK73/zm9+c973vfVm+fHmuvPLK/Nt/+29z3XXX5Xvf+94oJwcAAADglTy2uyP95WR+86TMa55UdBxGSVVVKcvnTkuSPH2gs+A0AAAAACOn0MJVT09PNm/enFtvvfX8a1VVVbn11lvz4IMP/tifL5fL2bhxY5555pm86U1vesX3dHd3p6ur62VfAAAAAIycoTnBG8wJTjgr5g7MCj693zM4AAAAYPwqtHB15MiR9PX1paWl5WWvt7S0pL29/VV/rrOzM1OnTk1dXV1+5md+Jv/tv/23/ORP/uQrvveee+5JU1PT+a+FCxcO658BAAAAgJcbKlytNyc44ayYN1i4OqBwBQAAAIxfhU8KXopp06blscceyyOPPJLf+Z3fyYYNG/LAAw+84nvvvPPOdHZ2nv/as2fP6IYFAAAAmEB6+/rz6O6OJMkNClcTzoq5TUkGLlyVy+WC0wAAAACMjJoiP3zWrFmprq7OwYMHX/b6wYMH09ra+qo/V1VVlaVLlyZJ1qxZk23btuWee+7Jm9/85h95b319ferr64c1NwAAAACv7On9XTnT25emSbW5as7UouMwyq5qmZqaqlKOn+5Ne9fZzG2aVHQkAAAAgGFX6IWrurq6rFu3Lhs3bjz/Wn9/fzZu3Jibb775gn9Pf39/uru7RyIiAAAAABfh/Jzg4umpqioVnIbR1lBbnaWDRbun95sVBAAAAManQi9cJcmGDRvywQ9+MOvXr8+NN96YT37ykzl16lTuuOOOJMntt9+e+fPn55577kmS3HPPPVm/fn2uvPLKdHd35+///u/zp3/6p/n0pz9d5B8DAAAAgPxQ4cqc4IS1Ym5jtrefyFP7u/K25S1FxwEAAAAYdoUXrm677bYcPnw4d911V9rb27NmzZrcf//9aWkZeBize/fuVFW9dIjr1KlT+dVf/dXs3bs3kyZNyrJly/LFL34xt912W1F/BAAAAACSlMvlbNp5PEly45LpBaehKMvnNiaP7sv2dheuAAAAgPGpVC6Xy0WHGE1dXV1pampKZ2dnGhsbi44DAAAAMG68cPhk3vaJb6eupipb/9PbU19TXXQkCvDd5w7nA3/8cK6YNSXf/Oibi44DAAAAcEEuplNU9ZrfBQAAAIALtGlwTnDNgmZlqwlsWevAA8kdR0/lTE9fwWkAAAAAhp/CFQAAAADD4pHBOcH1beYEJ7LZ0+oza2p9yuXkmYMnio4DAAAAMOwUrgAAAAAYFkMXrm5YMqPgJBRt+dxpSZLtB7oKTgIAAAAw/BSuAAAAALhsh06czc6jp1MqJdcvcuFqolvWOli4anfhCgAAABh/FK4AAAAAuGybBucEr2mZlqZJtQWnoWjLWhuTJNtcuAIAAADGIYUrAAAAAC7bI4NzgjeaEyTJ8rkvFa7K5XLBaQAAAACGl8IVAAAAAJdtqHC1vk3hiuTKOVNSU1VK19lzOdB5tug4AAAAAMNK4QoAAACAy3Ky+1ye3j8wHXdD2/SC01AJ6muqc+XsqUmS7e1mBQEAAIDxReEKAAAAgMvy6O7j6S8n85snZW7TpKLjUCGWz52WJNl24ETBSQAAAACGl8IVAAAAAJflkZ3HkyQ3LjEnyEuWzW1Mkmw74MIVAAAAML4oXAEAAABwWR7ZcSxJst6cID9kWevAhavt7S5cAQAAAOOLwhUAAAAAl6y3rz+P7hm4cHVDmwtXvGTF4IWrFw+fzNnevoLTAAAAAAwfhSsAAAAALtlT+7tytrc/zZNrs3T21KLjUEFmT6vPjCl16S8nzx08WXQcAAAAgGGjcAUAAADAJTs/J7h4eqqqSgWnoZKUSqXzs4Lb2rsKTgMAAAAwfBSuAAAAALhkj+wcLFyZE+QVLGsdmBXcdkDhCgAAABg/FK4AAAAAuCTlcjmbdh1PktzQNr3gNFSi5XMHLlxtP3Ci4CQAAAAAw0fhCgAAAIBL8uKRUzl2qif1NVVZOb+p6DhUoOVzBy5cbW/vSrlcLjgNAAAAwPBQuAIAAADgkjyyY2BOcPXC5tTXVBechkq0dM7UVFeVcvx0bw6d6C46DgAAAMCwULgCAAAA4JI8stOcIK+tobY6V8yakiR5+kBXwWkAAAAAhofCFQAAAACXZNOugQtXN7TNKDgJlWzZ0KzggRMFJwEAAAAYHgpXAAAAAFy0Q11ns+vo6ZRKyfWLXbji1S1rnZYk2d7uwhUAAAAwPihcAQAAAHDRhuYEl7U2prGhtuA0VLLlcwcKV9tMCgIAAADjhMIVAAAAABftkZ1Dc4KuW/Halg9OCr5w+FS6z/UVnAYAAADg8ilcAQAAAHDRNu0aKlzNKDgJla61sSFNk2rT11/O84dOFh0HAAAA4LIpXAEAAABwUU6c7c3T+wfm4da7cMWPUSqVsqx1aFbwRMFpAAAAAC6fwhUAAAAAF+XR3R3pLycLpk/K3KZJRcdhDBiaFdx+oKvgJAAAAACXT+EKAAAAgIuyaefAnOCN5gS5QMvnDly42t7uwhUAAAAw9l1S4erFF18c7hwAAAAAjBEPDxau1itccYGWtQ5euGp34QoAAAAY+y6pcLV06dK85S1vyRe/+MWcPXt2uDMBAAAAUKF6zvXnsT0dSZIb2qYXG4Yx4+qWaakqJUdO9uTQCc8TAQAAgLHtkgpXW7ZsyXXXXZcNGzaktbU1v/zLv5yHH354uLMBAAAAUGGe2t+Zs739aZ5cmytnTy06DmPEpLrqtM2akiTZfsCsIAAAADC2XVLhas2aNfmDP/iD7N+/P/fdd18OHDiQN7zhDVm5cmXuvffeHD58eLhzAgAAAFABNu08niRZv3hGqqpKBadhLFluVhAAAAAYJy6pcDWkpqYm73//+/OXf/mX+d3f/d08//zz+ehHP5qFCxfm9ttvz4EDB4YrJwAAAAAV4OGdx5KYE+TiLWudliTZ5sIVAAAAMMZdVuFq06ZN+dVf/dXMnTs39957bz760Y/mhRdeyDe+8Y3s378/73nPe4YrJwAAAAAFK5fL2TRYuFrfNqPgNIw1y+cOXLjadsCFKwAAAGBsq7mUH7r33nvzuc99Ls8880ze+c535gtf+ELe+c53pqpqoL+1ZMmSfP7zn09bW9twZgUAAACgQC8cPpXjp3tTX1OVVfObio7DGLNs7sCFqxcOn0zPuf7U1VzW3wUFAAAAKMwlFa4+/elP51//63+dD33oQ5k7d+4rvmfOnDn54z/+48sKBwAAAEDl2Lxr4LrV6gXNyjJctPnNkzKtviYnus/lhcMnz1+8AgAAABhrLqlw9Y1vfCOLFi06f9FqSLlczp49e7Jo0aLU1dXlgx/84LCEBAAAAKB4m3YeT5Ksb5tecBLGolKplGVzp+WRncezvb1L4QoAAAAYsy7pryJeeeWVOXLkyI+8fuzYsSxZsuSyQwEAAABQeTbvUrji8gyVrLYfOFFwEgAAAIBLd0mFq3K5/Iqvnzx5Mg0NDZcVCAAAAIDKc+Rkd148cipJcv0ihSsuzbLWgcLV0we6Ck4CAAAAcOkualJww4YNSQbOf991112ZPHny+e/19fXloYceypo1a4Y1IAAAAADFG7puddWcqWmeXFdwGsaqZXOnJUm2t7twBQAAAIxdF1W4evTRR5MMXLjaunVr6upeerhWV1eX1atX56Mf/ejwJgQAAACgcOYEGQ7XtExLqZQcPtGdIye7M2tqfdGRAAAAAC7aRRWuvvWtbyVJ7rjjjvzBH/xBGhsbRyQUAAAAAJVl085jSZJ1i2cUnISxbEp9TRbPmJydR0/nmfYTmbVU4QoAAAAYe6ou5Yc+97nPKVsBAAAATBBne/vy5L6uJMn6xS5ccXmWtQ48V9x2oKvgJAAAAACX5oIvXL3//e/P5z//+TQ2Nub973//a773y1/+8mUHAwAAAKAybN3XmZ6+/syaWpfFMycXHYcxbtncabn/qfZsO3Ci6CgAAAAAl+SCC1dNTU0plUrn/zMAAAAAE8OmnceTJOsWTz//fAgu1fK5Axeutre7cAUAAACMTRdcuPrc5z73iv8ZAAAAgPFt865jSZL1i2cUnITxYPngpOBzB0+mt68/tdVVBScCAAAAuDiX9DTjzJkzOX369Pl/3rVrVz75yU/m61//+rAFAwAAAKB45XI5m3cNXrhqm15wGsaDBdMnZUpddXr6+rPjyKmi4wAAAABctEsqXL3nPe/JF77whSRJR0dHbrzxxnziE5/Ie97znnz6058e1oAAAAAAFOeFw6dy/HRv6muqsnJeU9FxGAeqqkpZNjgruO2AWUEAAABg7LmkwtWWLVvyxje+MUnyV3/1V2ltbc2uXbvyhS98If/1v/7XYQ0IAAAAQHGG5gRXL2hOXY3pN4bHstZpSZKnFa4AAACAMeiSnpKdPn0606YNPBT5+te/nve///2pqqrK6173uuzatWtYAwIAAABQnE07zQky/JYPXrjafuBEwUkAAAAALt4lFa6WLl2av/mbv8mePXvyta99LW9/+9uTJIcOHUpjY+OwBgQAAACgOJt3DRSu1i9WuGL4LDcpCAAAAIxhl1S4uuuuu/LRj340bW1tuemmm3LzzTcnGbh2tXbt2mENCAAAAEAxjp7szotHTiVJ1ilcMYyuGZwUPHSiO0dPdhecBgAAAODiXFLh6ud//ueze/fubNq0Kffff//519/2trflv/yX/zJs4QAAAAAoztB1q6vmTE3z5LqC0zCeTK2vyeKZk5Mk29vNCgIAAABjS82l/mBra2taW1tf9tqNN9542YEAAAAAqAzn5wTbXLdi+C1rnZZdR09n24GuvH7prKLjAAAAAFywSypcnTp1Kh/72MeycePGHDp0KP39/S/7/osvvjgs4QAAAAAozqbBwtW6xTMKTsJ4tHxuY7721MFsO+DCFQAAADC2XFLh6sMf/nC+/e1v5wMf+EDmzp2bUqk03LkAAAAAKNDZ3r5s3duZJFm/2IUrht+y1sYkyfb2roKTAAAAAFycSypc/cM//EO++tWv5vWvf/1w5wEAAACgAmzd15mevv7MmlqXxTMnFx2HcWjF3IHC1XMHT6a3rz+11VUFJwIAAAC4MJf0FGP69OmZMcMpeQAAAIDxatPOoTnB6a6bMyIWTJ+UKXXV6enrz44jp4qOAwAAAHDBLqlw9du//du56667cvr06eHOAwAAAEAF2LzrWJJk/WJ/6Y6RUVVVyrLBK1fbDpgVBAAAAMaOS5oU/MQnPpEXXnghLS0taWtrS21t7cu+v2XLlmEJBwAAAMDoK5fL2bxr8MJV2/SC0zCeLWudls27jufpA115z5r5RccBAAAAuCCXVLh673vfO8wxAAAAAKgULxw+leOne1NfU5WV85qKjsM4tnzwwtX2AycKTgIAAABw4S6pcHX33XcPdw4AAAAAKsTQnODqBc2pq6kqOA3j2fK505KYFAQAAADGlkt+YtbR0ZE/+qM/yp133pljxwYewm3ZsiX79u0btnAAAAAAjL5NO80JMjquaR24cHXoRHeOnuwuOA0AAADAhbmkwtUTTzyRq6++Or/7u7+bj3/84+no6EiSfPnLX86dd945nPkAAAAAGGWbdw0UrtYvVrhiZE2tr8nimZOTJNvbzQoCAAAAY8MlFa42bNiQD33oQ3nuuefS0NBw/vV3vvOd+c53vjNs4QAAAAAYXUdPdufFI6eSJOsUrhgFy1rNCgIAAABjyyUVrh555JH88i//8o+8Pn/+/LS3t192KAAAAACKMXTdaumcqWmeXFdwGiaC5XMHZgW3HXDhCgAAABgbLqlwVV9fn66uH/0bZ88++2xmz5592aEAAAAAKMZQ4eqGNtetGB3LWgcKV9vbXbgCAAAAxoZLKly9+93vzm/91m+lt7c3SVIqlbJ79+78+q//en7u535uWAMCAAAAMHo2DRau1i2eUXASJooVgxeunjt4Mr19/QWnAQAAAPjxLqlw9YlPfCInT57M7Nmzc+bMmfzET/xEli5dmmnTpuV3fud3hjsjAAAAAKPgbG9ftu7tTJKsX+zCFaNjwfRJmVJXnZ6+/uw4cqroOAAAAAA/Vs2l/FBTU1O+8Y1v5Pvf/34ef/zxnDx5Mtdff31uvfXW4c4HAAAAwCh5cl9nevr6M2tqXRbPnFx0HCaIqqpSls1tzOZdx7PtQFeubplWdCQAAACA13TRhav+/v58/vOfz5e//OXs3LkzpVIpS5YsSWtra8rlckql0kjkBAAAAGCEvTQnON0zHkbVstZp2bzreJ4+0JX3rJlfdBwAAACA13RRk4Llcjnvfve78+EPfzj79u3LqlWrcu2112bXrl350Ic+lPe9730jlRMAAACAEbZp50Dhav3iGQUnYaJZPrcxSbL9wImCkwAAAAD8eBd14erzn/98vvOd72Tjxo15y1ve8rLvffOb38x73/vefOELX8jtt98+rCEBAAAAGFnlcjmbdx1Lkqxrm15wGiaa5XMHZgS3HegqOAkAAADAj3dRF67++3//7/kP/+E//EjZKkne+ta35t//+3+fP/uzPxu2cAAAAACMjhcOn8rx072pr6nKynlNRcdhgrmmdeDC1aET3Tl6srvgNAAAAACv7aIKV0888UR+6qd+6lW//9M//dN5/PHHLzsUAAAAAKNr6LrV6gXNqau5qEdGcNmm1tdk0YzJSZLt7WYFAQAAgMp2UU/Pjh07lpaWllf9fktLS44fP37ZoQAAAAAYXZt2DjzTMSdIUcwKAgAAAGPFRRWu+vr6UlNT86rfr66uzrlz5y47FAAAAACja/OugcLV+sUKVxRj+dyBWcFtB1y4AgAAACrbq7enXkG5XM6HPvSh1NfXv+L3u7u7hyUUAAAAAKPn6MnuvHjkVJJkncIVBVnWOlC42t7uwhUAAABQ2S6qcPXBD37wx77n9ttvv+QwAAAAAIy+oetWS+dMTfPkuoLTMFGtGLxw9dzBk+nt609t9UUd5wcAAAAYNRdVuPrc5z43UjkAAAAAKIg5QSrBgumTMqWuOqd6+rLjyKlc3TKt6EgAAAAAr8hfEwMAAACY4DYNFq7MCVKkqqpSlg1euXp6v1lBAAAAoHIpXAEAAABMYGd7+7J1b2eS5Ia2GQWnYaIbmhV8an9nwUkAAAAAXp3CFQAAAMAEtnVfZ3r6+jNral0Wz5xcdBwmuGvnDV64OuDCFQAAAFC5FK4AAAAAJrBNOwfmBNcvnpFSqVRwGia6a+c1JUme2t+VcrlccBoAAACAV6ZwBQAAADCBbd51LEmyvm16wUkguaplaqqrSuk43Zv9nWeLjgMAAADwihSuAAAAACao/v5yNu0auHC1brHCFcVrqK3OVXOmJkme2tdZcBoAAACAV6ZwBQAAADBBvXjkZDpO96ahtur8lBsUbcW8xiTJ0we6Ck4CAAAA8MoUrgAAAAAmqE07B65brV7QnLoaj4moDEPlv6f2K1wBAAAAlcmTNAAAAIAJ6pHBwtUNbTMKTgIvuXbowpXCFQAAAFChFK4AAAAAJqjNu44lSda1TS84Cbxk+dyBwtW+jjM5fqqn4DQAAAAAP0rhCgAAAGACOnyiOzuPnk6plFy/SOGKytE0qTYLZ0xKkjx9wJUrAAAAoPIoXAEAAABMQEPXra5pmZamSbUFp4GXu3ZuUxKzggAAAEBlUrgCAAAAmIA27TyeJFm32HUrKs+18wZmBZ/a31lwEgAAAIAfpXAFAAAAMAFt2jVQuLqhbUbBSeBHrThfuHLhCgAAAKg8ClcAAAAAE8yZnr48uW/gcpALV1Sia+cNTAq+cPhkzvT0FZwGAAAA4OUUrgAAAAAmmMf3duRcfzktjfVZMH1S0XHgR7Q01mfmlLr0l5NnDp4oOg4AAADAyyhcAQAAAEwwmwfnBNe3zUipVCo4DfyoUqn0Q7OCnQWnAQAAAHg5hSsAAACACeaRnceSJOvNCVLBhmYFn9rfVXASAAAAgJdTuAIAAACYQPr7y9kydOFq8YyC08Cre+nClcIVAAAAUFkUrgAAAAAmkOcOnUzX2XOZXFed5XOnFR0HXtW1g4Wr7Qe6cq6vv+A0AAAAAC9RuAIAAACYQIbmBNcuak5NtUdDVK4lM6dkcl11us/1Z8eRU0XHAQAAADjPUzUAAACACWSzOUHGiKqqUpbPNSsIAAAAVJ6KKFx96lOfSltbWxoaGnLTTTfl4YcfftX3fvazn80b3/jGTJ8+PdOnT8+tt976mu8HAAAA4CWbdg1cuFrfNr3gJPDjrRgsXD25r7PgJAAAAAAvKbxw9aUvfSkbNmzI3XffnS1btmT16tV5xzvekUOHDr3i+x944IH8i3/xL/Ktb30rDz74YBYuXJi3v/3t2bdv3ygnBwAAABhbDnadzZ5jZ1JVStYuUrii8q1a0JQk2apwBQAAAFSQwgtX9957bz7ykY/kjjvuyIoVK/KZz3wmkydPzn333feK7/+zP/uz/Oqv/mrWrFmTZcuW5Y/+6I/S39+fjRs3jnJyAAAAgLFl086BOcHlcxsztb6m4DTw4103WLh6cl9n+vvLBacBAAAAGFBo4aqnpyebN2/Orbfeev61qqqq3HrrrXnwwQcv6HecPn06vb29mTFjxit+v7u7O11dXS/7AgAAAJiIzs8JLnbdirFh6eypaaityqmevrx45FTRcQAAAACSFFy4OnLkSPr6+tLS0vKy11taWtLe3n5Bv+PXf/3XM2/evJeVtn7YPffck6ampvNfCxcuvOzcAAAAAGPR0IWrdW2v/BfXoNLUVFfl2nlDs4IdxYYBAAAAGFT4pODl+NjHPpY///M/z1e+8pU0NDS84nvuvPPOdHZ2nv/as2fPKKcEAAAAKN6p7nN5+sDA5W8XrhhLVs0fKFw9sbez4CQAAAAAA2qK/PBZs2aluro6Bw8efNnrBw8eTGtr62v+7Mc//vF87GMfyz/+4z/muuuue9X31dfXp76+fljyAgAAAIxVm3cdT19/OQumT8q85klFx4ELdt2CwQtXClcAAABAhSj0wlVdXV3WrVuXjRs3nn+tv78/GzduzM033/yqP/d7v/d7+e3f/u3cf//9Wb9+/WhEBQAAABjTHt5xLEly05KZBSeBizNUuHpqf1fO9fUXnAYAAACgAiYFN2zYkM9+9rP5kz/5k2zbti2/8iu/klOnTuWOO+5Iktx+++258847z7//d3/3d/Mf/+N/zH333Ze2tra0t7envb09J0+eLOqPAAAAAFDxHtpxNEly05IZBSeBi7Nk1tRMqavOmd6+vHD4VNFxAAAAAIqdFEyS2267LYcPH85dd92V9vb2rFmzJvfff39aWlqSJLt3705V1Uu9sE9/+tPp6enJz//8z7/s99x99935T//pP41mdAAAAIAx4WxvXx7fMzDHdqPCFWNMdVUp185vysM7juWJvR25pnVa0ZEAAACACa7wwlWS/Nqv/Vp+7dd+7RW/98ADD7zsn3fu3DnygQAAAADGkcf2dKSnrz8tjfVZPHNy0XHgoq1eMFC42rqvM7+wfmHRcQAAAIAJrvBJQQAAAABG1kMvHkuS3LhkZkqlUsFp4OKtWtCcJHlib2exQQAAAACicAUAAAAw7j2882iS5CZzgoxR181vSpI8faArvX39BacBAAAAJjqFKwAAAIBxrOdcfzbvOp5E4Yqxa/HMyZnWUJOec/15pv1E0XEAAACACU7hCgAAAGAc27qvM2d7+zNjSl2WzpladBy4JKVSKdctGLhytXWfWUEAAACgWApXAAAAAOPYQzsG5gRvbJuRUqlUcBq4dKvmNydJntircAUAAAAUS+EKAAAAYBx7eMexJMmN5gQZ4166cNVRbBAAAABgwlO4AgAAABinzvX1Z9PO40mSm65QuGJsWzV/oHD1TPuJnO3tKzgNAAAAMJEpXAEAAACMU9sOnMjJ7nOZ1lCTZa2NRceBy7Jg+qRMn1yb3r5ynmk/UXQcAAAAYAJTuAIAAAAYpx7acTRJcmPbjFRXlQpOA5enVCpl1YLmJMkT+zqLDQMAAABMaApXAAAAAOPUQzuOJUluXGJOkPHhusFZwa17O4oNAgAAAExoClcAAAAA41B/fzmP7FS4YnxZtWCgcPX4HheuAAAAgOIoXAEAAACMQ88eOpGO072ZXFedlYNXgWCsW7uoOcnA/7+7zvYWGwYAAACYsBSuAAAAAMahHzx/NEmybvH01FZ7BMT4MGdaQxZMn5RyOXl8T0fRcQAAAIAJytM2AAAAgHHoBy8cSZK8fumsgpPA8Lp+0fQkyaO7O4oNAgAAAExYClcAAAAA48y5vv489OKxJMnrr1S4Yny5fnBWcMvu48UGAQAAACYshSsAAACAceaJfZ050X0uTZNqs2JeY9FxYFhdv/ilC1f9/eWC0wAAAAATkcIVAAAAwDjzg+cH5gRvvmJmqqtKBaeB4bV8bmMaaqvSeaY3Lx45VXQcAAAAYAJSuAIAAAAYZ77//NEkyS1LZxacBIZfbXVVrpvfnMSsIAAAAFAMhSsAAACAceRsb182D5ZQbrlyVsFpYGSsXdycJHlU4QoAAAAogMIVAAAAwDiyedfx9JzrT0tjfa6cPaXoODAirl80PUmyZVdHsUEAAACACUnhCgAAAGAc+f7zR5Ikr79yVkqlUsFpYGSsXdScJHn20Il0ne0tNgwAAAAw4ShcAQAAAIwj33/haJLklqXmBBm/5kxryILpk1IuJ0/s6Sw6DgAAADDBKFwBAAAAjBOdZ3qzdW9HkuT1S2cWGwZG2PlZwd3HC04CAAAATDQKVwAAAADjxEMvHk1/Obli1pTMbZpUdBwYUdcPzgoqXAEAAACjTeEKAAAAYJz4wfk5QdetGP+uXzxw4erR3R3p7y8XnAYAAACYSBSuAAAAAMaJ7z9/JElyy5WzCk4CI2/53MY01Fal80xvXjxyqug4AAAAwASicAUAAAAwDhzqOpvnDp1MqZTcfIULV4x/tdVVuW5+cxKzggAAAMDoUrgCAAAAGAeG5gRXzG3M9Cl1BaeB0bF2cXOS5FGFKwAAAGAUKVwBAAAAjAPfee5wkuQNS80JMnFcv2h6kmTLro5igwAAAAATisIVAAAAwBjX31/Ot58ZKFz9xNWzC04Do2eocPXsoRPpPNNbcBoAAABgolC4AgAAABjjntzfmaOnejKlrjrr22YUHQdGzexp9Vkya0rK5WTLLrOCAAAAwOhQuAIAAAAY4x4YvG51y9JZqavxuIeJ5Ya2gStXD+04VnASAAAAYKLwBA4AAABgjHvgmUNJkrdcM6fgJDD6blwyM0ny8I6jBScBAAAAJgqFKwAAAIAxrON0Tx7b05EkefM1s4sNAwW4cXBGc+u+zpzp6Ss4DQAAADARKFwBAAAAjGHfee5I+svJ1S1TM695UtFxYNQtnDEprY0N6e0r59E9x4uOAwAAAEwAClcAAAAAY9jQnOCbzQkyQZVKpdywZODK1cM7jhWcBgAAAJgIFK4AAAAAxqj+/nK+8+zhJMmbrzYnyMR142Dh6pGdClcAAADAyFO4AgAAABijntrflSMnezKlrjrr22YUHQcKc+Pg//837zqennP9BacBAAAAxjuFKwAAAIAxamhO8Jals1JX4zEPE9dVc6Zm+uTanO3tz9Z9HUXHAQAAAMY5T+IAAAAAxqgHhuYErzEnyMRWVVXKTUtmJkkefOFowWkAAACA8U7hCgAAAGAM6jjdk0d3H0+SvPmaOQWngeLdsnSwcPWiwhUAAAAwshSuAAAAAMag7z53JP3lgSm1+c2Tio4Dhbv5ioHC1aadx9N9rq/gNAAAAMB4pnAFAAAAMAY98Iw5QfhhS+dMzayp9ek+159Hd3cUHQcAAAAYxxSuAAAAAMaYc339+eb2g0mStywzJwhJUiqVcvOVg7OCL5gVBAAAAEaOwhUAAADAGLNp1/EcP92b5sm1ubFtRtFxoGIMzQoqXAEAAAAjSeEKAAAAYIz5+lMD163etqwlNdUe78CQWwYvXD2653jO9PQVnAYAAAAYrzyRAwAAABhDyuVyvv50e5Lk7de2FJwGKsvimZMzr6khvX3lPLLzWNFxAAAAgHFK4QoAAABgDNl24ET2Hj+ThtqqvOmq2UXHgYpSKpXy+qWzkiTfe/5IwWkAAACA8UrhCgAAAGAM+dpTA9et3nTV7Eyqqy44DVSeN1w1ULj6zrOHC04CAAAAjFcKVwAAAABjyNefPpgkefu1rQUngcr0hsELV9vbT+TQibMFpwEAAADGI4UrAAAAgDFiz7HT2XagK9VVpbxt2Zyi40BFmjm1PivnNyZJvm9WEAAAABgBClcAAAAAY8TQnOCNbTMyfUpdwWmgcr1h6ewkyXefU7gCAAAAhp/CFQAAAMAY8dKcYEvBSaCyvemqgVnB7z13JOVyueA0AAAAwHijcAUAAAAwBhw92Z1NO48lSX5yhcIVvJZ1bdPTUFuVQye68+zBk0XHAQAAAMYZhSsAAACAMWDj9kPpLycr5zdmwfTJRceBilZfU53XXTEzSfLAM4cKTgMAAACMNwpXAAAAAGPA159qT5K8fUVrwUlgbHjz1bOTJA88c7jgJAAAAMB4o3AFAAAAUOFOnO3Nd547kiR5+7XmBOFCvHXZwL8rj+w8lq6zvQWnAQAAAMYThSsAAACACveP2w6m51x/rpw9Jde0TCs6DowJi2ZOzhWzp+RcfznfHywsAgAAAAwHhSsAAACACve3jx9IkvzsdfNSKpUKTgNjx1uumZMk+dYzhwpOAgAAAIwnClcAAAAAFazjdE+++9zhJMm7Vs8tOA2MLW9dNlS4Opz+/nLBaQAAAIDxQuEKAAAAoIJ97an29PaVs3xuY5bOMScIF2N92/RMqavO4RPdefpAV9FxAAAAgHFC4QoAAACggr00J+i6FVys+prqvH7prCTJxm1mBQEAAIDhoXAFAAAAUKGOnOzOD144kiR513XzCk4DY9OtK1qSJN/Y1l5wEgAAAGC8ULgCAAAAqFD/sPVA+svJ6gVNWTRzctFxYEx627I5qSolT+7ryr6OM0XHAQAAAMYBhSsAAACACjU0J/iu1a5bwaWaObU+6xfPSJL849MHC04DAAAAjAcKVwAAAAAV6EDnmTyy61iS5J2r5hacBsa2t187MCv49afNCgIAAACXT+EKAAAAoAJ99YkDKZeTG9qmZ17zpKLjwJj2kysGClf/9OKxdJ7uLTgNAAAAMNYpXAEAAABUoL99YmBO8GevMycIl2vxzCm5pmVa+vrL+eYzZgUBAACAy6NwBQAAAFBhdh89ncf3dKSqlPz0qtai48C4MDQreP+TZgUBAACAy6NwBQAAAFBhvvLoviTJLVfOypxpDQWngfHhp1fOTZI88MzhnOw+V3AaAAAAYCxTuAIAAACoIOVyOV95dG+S5P3Xzy84DYwfy+dOyxWzpqT7XH82bjMrCAAAAFw6hSsAAACACrJld0d2Hj2dyXXVece15gRhuJRKpbxz1cCVq68+caDgNAAAAMBYpnAFAAAAUEG+vGXgutVPXduaKfU1BaeB8eVnrhucFXzWrCAAAABw6RSuAAAAACpE97m+/N3g5Z33X7+g4DQw/ixrHZgV7DErCAAAAFwGhSsAAACACvGt7YfSeaY3rY0NufnKmUXHgXGnVCqdv3L1t4+bFQQAAAAujcIVAAAAQIX48pZ9SZL3rJ2X6qpSwWlgfHr36nlJkm8/eyjHT/UUnAYAAAAYixSuAAAAACrA8VM9+dYzh5Ik719rThBGylUt03LtvMb09pXzd1tduQIAAAAunsIVAAAAQAX4uyf2p7evnGvnNeaa1mlFx4Fx7X1r5ydJ/ubRfQUnAQAAAMYihSsAAACACvDXg3OC77/edSsYae9aPS9VpWTzruPZffR00XEAAACAMUbhCgAAAKBgLx4+mcf2dKS6qpR3r55XdBwY91oaG/L6pbOSJF9x5QoAAAC4SApXAAAAAAX76y17kyRvumpWZk+rLzgNTAxDs4JffnRvyuVywWkAAACAsUThCgAAAKBA5/r681ebBwpXv7B+YcFpYOJ4x7WtmVJXnV1HT+ehHceKjgMAAACMIQpXAAAAAAX6znOHc7CrOzOm1OXW5S1Fx4EJY0p9Td69ZmDC8y8e2VNwGgAAAGAsUbgCAAAAKNBfPDJw3eq9a+anrsajGhhNvzh4Ve6rWw+k80xvwWkAAACAscJTPAAAAICCHDnZnX/cdjBJctsN5gRhtK1Z2JyrW6am+1x//ufj+4uOAwAAAIwRClcAAAAABfmbR/flXH85qxc255rWaUXHgQmnVCqdv3L1pUd2F5wGAAAAGCsUrgAAAAAKUC6X86VH9iRJfnH9goLTwMT1/usXpK66Kk/u68pjezqKjgMAAACMAQpXAAAAAAV4dE9Hnjt0Mg21VXnX6nlFx4EJa8aUuvzsdXOTJF94cGexYQAAAIAxQeEKAAAAoAB/uWngutU7V85NY0NtwWlgYvvAzYuTJH/3xIEcO9VTcBoAAACg0ilcAQAAAIyy0z3n8rePH0iS/OINCwtOA6xZ2JxV85vSc67//NQnAAAAwKtRuAIAAAAYZX+/tT0nu8+lbebk3LRkRtFxYMIrlUq5ffDK1Rf/aVf6+ssFJwIAAAAqmcIVAAAAwCj7i8ELOr+wfmFKpVLBaYAkedfqeZk+uTb7Os7ka0+1Fx0HAAAAqGAKVwAAAACj6MXDJ/PwzmOpKiU/d/2CouMAgxpqq/OB1w1cufrsd18sOA0AAABQyRSuAAAAAEbRX27emyT5iatnp7WpoeA0wA/7wM1tqaupyqO7O7J517Gi4wAAAAAVSuEKAAAAYJSc6+vPXw8Wrm67YWHBaYB/bva0+rxvzfwkyf/zHVeuAAAAgFemcAUAAAAwSr797OEcOtGdmVPq8tZlLUXHAV7Bh9+4JEny9acP5sXDJwtOAwAAAFQihSsAAACAUfKlR/YkSd63dn7qajyWgUp0Vcu03Lp8Tsrl5P9+4IWi4wAAAAAVyJM9AAAAgFFw+ER3vrn9UJLkF80JQkX7/7xlaZLkK4/uy55jpwtOAwAAAFQahSsAAACAUfCVR/fmXH85axY25+qWaUXHAV7D2kXT88arZqWvv5xPf9uVKwAAAODlFK4AAAAARli5XD4/J3ib61YwJvza4JWrv9y0Jwc6zxScBgAAAKgkClcAAAAAI2zL7o68cPhUJtVW52evm1t0HOAC3HTFzNy0ZEZ6+8r5b998vug4AAAAQAVRuAIAAAAYYX8xeN3qnavmZlpDbcFpgAv1795+TZKBf4d3HjlVcBoAAACgUhReuPrUpz6Vtra2NDQ05KabbsrDDz/8qu996qmn8nM/93Npa2tLqVTKJz/5ydELCgAAAHAJTnWfy989sT+JOUEYa25cMiM/cfXsnOsv55P/+GzRcQAAAIAKUWjh6ktf+lI2bNiQu+++O1u2bMnq1avzjne8I4cOHXrF958+fTpXXHFFPvaxj6W1tXWU0wIAAABcvK9uPZBTPX1ZMmtKbmibXnQc4CJ9dPDK1f94fH+2t3cVnAYAAACoBIUWru6999585CMfyR133JEVK1bkM5/5TCZPnpz77rvvFd9/ww035Pd///fzS7/0S6mvrx/ltAAAAAAXb2hO8BfWL0ipVCo4DXCxVi1oyjtXtaZcTu75++1FxwEAAAAqQGGFq56enmzevDm33nrrS2GqqnLrrbfmwQcfHLbP6e7uTldX18u+AAAAAEbDC4dPZtOu46muKuXnr19QdBzgEv0f71iW2upSvv3s4Xz72cNFxwEAAAAKVljh6siRI+nr60tLS8vLXm9paUl7e/uwfc4999yTpqam818LFy4ctt8NAAAA8Fr+YtPAdas3Xz07cxobCk4DXKq2WVNy+81tSZL//NVt6esvFxsIAAAAKFShk4Kj4c4770xnZ+f5rz179hQdCQAAAJgAevv689eb9yVJfvEGfwEMxrr/7a1XpXlybZ45eCL//eHdRccBAAAAClRY4WrWrFmprq7OwYMHX/b6wYMH09raOmyfU19fn8bGxpd9AQAAAIy0b24/lCMnuzNral3eumxO0XGAy9Q0uTb/37ddlST5vfu358jJ7oITAQAAAEUprHBVV1eXdevWZePGjedf6+/vz8aNG3PzzTcXFQsAAABgWPz54AWcn1u3ILXV4/7IOEwI/+p1i3PtvMZ0nT2X//z324qOAwAAABSk0Kd9GzZsyGc/+9n8yZ/8SbZt25Zf+ZVfyalTp3LHHXckSW6//fbceeed59/f09OTxx57LI899lh6enqyb9++PPbYY3n++eeL+iMAAAAA/Ij9HWfy7WcPJ0l+6YZFBacBhktNdVV+532rUiolX96yLz944UjRkQAAAIACFFq4uu222/Lxj388d911V9asWZPHHnss999/f1paWpIku3fvzoEDB86/f//+/Vm7dm3Wrl2bAwcO5OMf/3jWrl2bD3/4w0X9EQAAAAB+xF9u2pv+cvK6K2ZkyawpRccBhtGahc35lzcNFCn/z795Mt3n+gpOBAAAAIy2UrlcLhcdYjR1dXWlqakpnZ2daWxsLDoOAAAAMM709Zfzpt/7VvZ1nMknb1uT966dX3QkYJh1nunN2z7xQI6c7MlH3351fu2tVxUdCQAAALhMF9MpKvTCFQAAAMB4893nDmdfx5k0TarNT61sLToOMAKaJtXm//yZFUmS//bN57P76OmCEwEAAACjSeEKAAAAYBh96ZE9SZL3rZ2fhtrqgtMAI+U9a+bllitnpvtcf/7DV7Zmgg0JAAAAwISmcAUAAAAwTA6f6M43nj6YJPmlGxcWnAYYSaVSKf/Xe1emvqYq33v+SL740O6iIwEAAACjROEKAAAAYJj89Za9OddfzpqFzVnW2lh0HGCEXTF7an79p5YlSf7zV7dl19FTBScCAAAARoPCFQAAAMAwKJfL5+cE/4XrVjBhfOiWtrzuihk509uXf/cXj6ev37QgAAAAjHcKVwAAAADD4KEdx7LjyKlMqavOz143r+g4wCipqirl939+dabW12TTruP54++9WHQkAAAAYIQpXAEAAAAMgz9/eHeS5N1r5mVKfU3BaYDRtHDG5PzHn12eJPn4157NswdPFJwIAAAAGEkKVwAAAACXqfN0b/7+yfYkyS/dsKjgNEARfnH9wrx12Zz09PXnf//SY+k51190JAAAAGCEKFwBAAAAXKav/P/bu/MouQ76TNhvVe9Sq7u1b5Ysed8lr8I2MXbGwQFD4GPiYQlgIJNvYhIyxjADZMAsCQPEIcOwZBxmiZkkBEJCTFhCQkw8HkDxirzIlrHBtixLLclaepHUW1XNH71ILcly28i+LfXznNOnbt26VXp7U9W9/dbv/nhDBoaqOXVhW846pr3oOEABSqVSPvm6M9MxrSFrN3bn0997uOhIAAAAwAtE4QoAAADg51Cr1fKVO59Mkrzh/CUplUoFJwKKMq+tOZ/612clSf7k//wsP3z06YITAQAAAC8EhSsAAACAn8O9G7qyrrMnTfXlvHbl4qLjAAW74vQFedOq4VOLXvdXa7Jj10DBiQAAAIDDTeEKAAAA4OfwlTvWJ0muPHNh2qc1FJwGmAw+dOVpOX7u9Gzu7s/7/ua+1Gq1oiMBAAAAh5HCFQAAAMDz1Ns/lL+7d2OS5PXnLyk4DTBZtDTW5b++4ew01JXyjw9uzpdHipkAAADA0UHhCgAAAOB5+ta9G7N7oJLj5k7PBctnFR0HmETOWNye9/3yKUmS3/vWg3l0S0/BiQAAAIDDReEKAAAA4Hn6yzufTJK84fwlKZVKBacBJpt3XLw8v3DinPQNVvM7f7km/UOVoiMBAAAAh4HCFQAAAMDz8NCm7tz75M401JXyunOOKToOMAmVy6V8+qoVmTW9MQ9u6s4N33246EgAAADAYaBwBQAAAPA8/OUd65Mkv3Ta/MxpbSo4DTBZzWtrzqf+9VlJkv/xg8dy20+2FpwIAAAA+HkpXAEAAAA8R7v6h/L1e55KkrzpgmMLTgNMdr902vy85SXD/1e852v3Zltvf8GJAAAAgJ+HwhUAAADAc/R3925Mb/9Qls+ZnouOn110HOAI8J+uPDUnzmvN1p7+vO9v7kutVis6EgAAAPA8KVwBAAAAPAe1Wi1//i9PJEnedMHSlMulghMBR4Lmhrp89o1np7GunH96aMvY/yMAAADAkUfhCgAAAOA5uG9DV9Zu7E5jfTm/eu4xRccBjiCnLmzL+15xSpLk97/9UH6yuafgRAAAAMDzoXAFAAAA8ByMTqV51ZkLM3N6Y8FpgCPN2y9alpedNDf9Q9X8zl/+OH2DlaIjAQAAAM+RwhUAAADABHXtHsw379uYJPm1lywtOA1wJCqXS/nDq1Zk9vTGrOvsyae+u67oSAAAAMBzpHAFAAAAMEFf//GG9A1Wc8qCGTln6cyi4wBHqLkzmnLDVWclSf70h4/n1oe3FJwIAAAAeC4UrgAAAAAmoFar5S9uX58k+bWXHJtSqVRwIuBI9ounzM/bLlqWJHnv1+7N1p7+YgMBAAAAE6ZwBQAAADABtz+2PY9u6c20xrq8duWiouMAR4H3v+KUnDx/Rp7uHcgHvn5farVa0ZEAAACACVC4AgAAAJiAP/uXJ5Ikrz17cWY0NxScBjgaNDfU5b++cWUa6kr5p4e25BtrNhYdCQAAAJgAhSsAAACAZ7Gpa0+++0BnkuTNq44tOA1wNDllQVve9YsnJkk+8s212dLTV3AiAAAA4NkoXAEAAAA8iz9b/UQq1VpWLZ+V0xa1FR0HOMpcc+nxOW1hW3buHsz1N691akEAAACY5BSuAAAAAA6hb7CSv7xjfZLk7RcvLzgNcDRqqCvnD69akfpyKd9d25lv37+p6EgAAADAIShcAQAAABzCN9Y8lR27B7O4oyW/dNr8ouMAR6nTFrXlnZedkCS5/htrs623v+BEAAAAwDNRuAIAAAB4BrVaLX/6w8eTJFdfdGzqyqViAwFHtd++7IScsmBGtu8ayEe++WDRcQAAAIBnoHAFAAAA8Az+5Wfbs66zJy0NdXn9eUuLjgMc5Rrry7nhV1ekrlzKN+/dmFsf3lJ0JAAAAOAgFK4AAAAAnsGf/vCxJMnrzlmc9mkNBacBpoIzj2nP2y9aliT50DceyJ6BSrGBAAAAgAMoXAEAAAAcxJPbd+d7D21Okrz94mXFhgGmlHf/0klZ2N6cJ7fvyef/+ZGi4wAAAAD7UbgCAAAAOIj/vfrx1GrJL5w4JyfMm1F0HGAKmd5Un4/8yulJki/e9rM8srmn4EQAAADAvhSuAAAAAPbT3TeYr9z5ZBLTrYBiXHH6glx+6vwMVmr5T3/7QKrVWtGRAAAAgBEKVwAAAAD7+fN/eSI9fUM5YV5rLj1pXtFxgCnqo685PdMa63LH49vz13dvKDoOAAAAMELhCgAAAGAffYOV/K8fPJYkueZlx6dcLhWcCJiqFne05N2Xn5Qk+c9//1C29fYXnAgAAABIFK4AAAAAxvnaXU/m6d6BLO5oya+sXFR0HGCKe9vFy3LKghnZuXsw//k764qOAwAAAEThCgAAAGDMYKWaP7ntZ0mS//+S49JQ59AJUKyGunL+8+vOTKmU/M09G7L6p9uKjgQAAABTnqOGAAAAACO+ee/GbNixJ7OnN+bfnLek6DgASZJzls7Mmy5YmiT5Tzffn/6hSsGJAAAAYGpTuAIAAABIUq3W8t9u/WmS5B0vXZ6WxrqCEwHs9R9/+ZTMaW3Kz7buyn8fmcQHAAAAFEPhCgAAACDJPz20OY9s6c2Mpvq8+SXHFh0HYJz2loZ86FWnJkk+9/1Hs37b7oITAQAAwNSlcAUAAABMebVaLX88Mt3qzRcem/aWhoITARzoV1YsysUnzE7/UDXX/90DqdVqRUcCAACAKUnhCgAAAJjybnvk6ax5cmca68t5x8XLi44DcFClUikfe80Zaawr59aHt+a7D3QWHQkAAACmJIUrAAAAYEqr1Wr59D8+nCR586pjM3dGU8GJAJ7Z8XNb85svOy5J8tFvPpje/qGCEwEAAMDUo3AFAAAATGn/sHZz7tvQlWmNdXnnZccXHQfgWb3zshOydNa0dHb35b987ydFxwEAAIApR+EKAAAAmLIq1Vr+6HvD063ecfHyzGk13QqY/Job6vKx15yeJLnpR49n7caughMBAADA1KJwBQAAAExZX79nQ36yuTdtzfX5jUuOKzoOwIRdevK8XHnmwlSqtXzw5gdSrdaKjgQAAABThsIVAAAAMCXtHhjKDf8wPN3qty47Ie0tDQUnAnhuPvSq0zK9sS4/Xr8zX7nzyaLjAAAAwJShcAUAAABMSX/yf36WLT39WTKrJW+7eFnRcQCeswXtzXnPy09Oknzy7x/K0739BScCAACAqUHhCgAAAJhyOrv68ie3/TRJ8oFXnJqm+rqCEwE8P2+98NictrAt3X1D+b1vPVh0HAAAAJgSFK4AAACAKef3vv1g+garOX/ZzLzijAVFxwF43urryvnE685MuZR8Y83GfO/BzUVHAgAAgKOewhUAAAAwpdz68JZ8+75NqSuX8tFfOSOlUqnoSAA/lxVLOvIblxyXJPndv70/O3cPFJwIAAAAjm4KVwAAAMCU0TdYyfXfWJskeftFy3LaoraCEwEcHu++/KQcN3d6tvb052NOLQgAAAAvKIUrAAAAYMr4L//0k6zfvjsL2ppz7S+dVHQcgMOmuaEuN/zqipRKydfveSrfX+fUggAAAPBCUbgCAAAApoS7Ht+eL972syTJx15zelqb6gtOBHB4nXvszPz6xcuTJL/79QfStWew4EQAAABwdFK4AgAAAI56u/qH8p6v3ZtaLfnX5xyTl5++oOhIAC+I97z85CybPS2d3X35PacWBAAAgBeEwhUAAABw1Pu9bz2YJ7btzqL25nz4V04rOg7AC6alsS43XDV8asG/vntDvn3fpqIjAQAAwFFH4QoAAAA4qv313RvylTufTKmU/OFVK9LW3FB0JIAX1PnLZuWdlx6fJHn/1+/Lhh27C04EAAAARxeFKwAAAOCota6zOx+8+f4kybsvPykXnTCn4EQAL45rLz8pK5d0pKdvKNd+ZU0GK9WiIwEAAMBRQ+EKAAAAOCpt7enPv/3SXekbrOaSk+bmty87oehIAC+ahrpyPvuGszOjqT53PbEjn/jOuqIjAQAAwFFD4QoAAAA46uweGMqvf+nObNixJ8tmT8tnXr8y5XKp6FgAL6qls6fl0/9mRZLkf/3wsfzdvRsLTgQAAABHB4UrAAAA4KjSP1TJO//inty3oSszpzXkprdfkFnTG4uOBVCIl5++IO+89Pgkyfv++r7cv6Gr4EQAAABw5FO4AgAAAI4afYOV/Ls/uzu3Prw1zQ3l/I+rz8uyOdOLjgVQqPe8/ORcctLc7Bms5B1fujNP7dxTdCQAAAA4oilcAQAAAEeFnr7B/Mb/vmusbPW/rj4/5x47q+hYAIWrK5fyhTednVMWzMjWnv6840/vTNfuwaJjAQAAwBFL4QoAAAA44m3YsTu/+t9W5/8+8nRaGuryp2+7IBedMKfoWACTxozmhvzPt52fuTOa8vDmnrz1T+9IT5/SFQAAADwfClcAAADAEe376zbnNZ//YR7e3JN5M5ry1X/3klx4/OyiYwFMOos7WvJnv35BOqY15N4nd+YdN92Z3v6homMBAADAEUfhCgAAADgi9fYP5fpvPJB33HRXtu0ayOmL2vKN3744Zx3TUXQ0gEnrlAVt+fNfX5UZzfW58/EdedN//5ds6+0vOhYAAAAcURSuAAAAgCNKtVrLN9Y8lV/8w1vzv1c/kSR5x8XL8zfXXJSF7S0FpwOY/M5Y3J6/+LerMmt6Y+7b0JWrblydx5/eVXQsAAAAOGKUarVaregQL6bu7u60t7enq6srbW1tRccBAAAAJqhSreUf1nbms7c8knWdPUmSY2dPy++95oxcctLcgtMBHHl+urU3b/2fd+SpnXsyo7k+n3n9yvyrU+cXHQsAAAAK8Vw6RQpXAAAAwKS2YcfufGPNxnz59vV5aueeJElrU33+3SXH5TcuOS7NDXUFJwQ4cm3p7ss1f3FP7n5iR5Lk3750ed57xcn+bwUAAGDKUbg6BIUrAAAAmNxqtVoe2dKbf3igM99d25m1G7vHbmtvachbLzw2v/7S5emY1lhgSoCjx8BQNb//7QfHTtN63Jzp+f3/74xcdPycgpMBAADAi0fh6hAUrgAAAGByqVRrebizJ3c+vj13Pr49dz2+I53dfWO3l0vJectm5apzj8mrVywydQXgBfL9dZvzga/fn83d/UmSy0+dn//4yyfnpPkzCk4GAAAALzyFq0NQuAIAAIDi7OofyrrOnjy4qTsPberOgxu783BnT/YMVsZt11hXzktPnJMrTp+fy0+dn9mtTQUlBphaunYP5g//8eF8+Y71qVSHDx1ffuq8vOPi5XnJcbNTLpcKTggAAAAvDIWrQ1C4AgAAgBderVZLZ3dfHtw4Uqza1J2HNvXk8W27crAjEdMb63LOsTNz/rJZOW/ZzJy9ZGZaGk2yAijKo1t68ul//Em+u7Zz7P/tJbNa8tqVi3PZKfOy4piO1ClfAQAAcBRRuDoEhSsAAAA4vAaGqnl0S+8+xarhy527Bw+6/bwZTTl1YVtOW9Q2fLlwRpbPafWHe4BJ6Kdbe/M/f/BY/m7NxvT2D42tnzmtIS87aW4uOWluzl46M8tmT0up5P9xAAAAjlwKV4egcAUAAADP345dA+MmVj24qTuPbunJYOXAwwt15VJOmNuaUxfOGCtXnbqwLXOcHhDgiLNnoJJ/WNuZ7z20Obf9ZGt6+obG3d7e0pAVSzqy8pj2nLG4Pacvbs+i9mYlLAAAAI4YCleHoHAFAAAAz65areWJ7bv3OyVgdzZ19R10+xnN9Tlt4ejEquHpVSfMa01zg9MCAhxtBivV3PPEjnz/4S2547HtWbuxOwND1QO265jWkNMXteX0Re1jl8vnTDfREAAAgElJ4eoQFK4AAABgvEq1lke39GbNkzty34auPLipOw939mT3QOWg2y+dNW2sXHXqwhk5dWFbjpnZYooJwBQ1MFTNus7u3Pvkzqx5sitrN3bl0S29GaoeeOi5paEupy6ckdMXteeMxcMlrBPnt6apXkEXAACAYilcHYLCFQAAAFPdtt7+3P3Ejqx5cmfWPLkz923oSm//0AHbNdWXc8qC8acDPGXBjMxobiggNQBHkr7BSh7Z3Ju1G7vywMaurB2ZmNg3eOAkrMa6cs48pj3nHTsz5xw7M+ceO9PpZwEAAHjRKVwdgsIVAAAAU01v/1DueGxbfvjotvzw0aezrrPngG2mNdblzMXtWbmkI6cvbs9pC2dk2ezpqa8rF5AYgKNRpVrLY0/3Zu3G7qzd2J0HnhouYnXtGTxg22Wzp42Vr847dlZOnNeaslMRAgAA8AJSuDoEhSsAAACmgi3dffneQ5vzj2s350c/fTqDlfG7/yfOa805S2dm5dKOrFzSkRPntSpXAfCiq9VqeWLb7tz1xI7c/cSO3PPEjvxkS0/2P2o9o7k+Zy+dmXOXzsx5y2ZmxZKOtDbVFxMaAACAo5LC1SEoXAEAAHC0erq3P9+8d2P+7t6N+fH6neNuWzKrJRcfPycXnTAnFx0/26maAJi0uvYM5sfrhwtYo6fA3T1QGbdNuZScsqAt545MwTr32Jk5ZmZLSiVTsAAAAHh+FK4OQeEKAACAo0nfYCX/+ODm/O09G3LbI0+nUt27m79ySUdefvr8vPy0BTlhXmuBKQHg+RuqVLOusyf37FPC2rBjzwHbzZ3RlHOWduTMxe05Y+RDwRgAAICJUrg6BIUrAAAAjgaPPb0rf/EvT+Sv79mQnbsHx9avOKY9rz17cV555sLMb2suMCEAvHA2d/flnpHy1V1P7MjajV0HnD43SRa2N+eMxe05c+Tj9MVtmTfD8yMAAAAHUrg6BIUrAAAAjlSDlWr+6cHN+fPbn8gPH902tn5xR0ted87ivGblYpOsAJiS+gYruW9DV+7bsDP3P9WV+5/qymNP78rBjn7Pb2vKGYv2TsE6Y3FbFrQ1Ox0hAADAFKdwdQgKVwAAABxpNu7ck6/csT5fufPJbOnpT5KUSsllJ8/Lr61amktPnpe6sj8SA8C+evuH8uDG7tz/VFceGClh/XRr70FLWHNaG3P6ouHy1WgZ65iZLUpYAAAAU4jC1SEoXAEAAHAkqFRrue0nW/MXt6/P99dtTnVk731Oa2P+zXlL8sYLlmbJrGnFhgSAI8yu/qE8tKk7DzzVlQc2Dl8+sqU3leqBh8lnTmvIectmZdXyWTl/2aycvqgt9XXlAlIDAADwYlC4OgSFKwAAACazTV178ld3bshX71yfjV19Y+tXLZ+VN7/k2Fxx+oI01vtjLwAcLn2DleES1sburB2ZhPWTzT0ZrIw/dD6tsS7nLJ2ZC5bPyi+cOCcrjulI2YRJAACAo4bC1SEoXAEAADDZDFaque0nW/OXd6zP99dtGZtm1TGtIa87+5i88YIlOXH+jGJDAsAU0j9UydqN3bnjse2587HtufPx7enuGxq3zezpjXnZSXNz6SnzcsmJc9IxrbGgtAAAABwOCleHoHAFAADAZFCp1nLHY9vzzfs25u/v35QduwfHblu1fFbetGpprjh9QZob6gpMCQAkSbVay0+29OTOx7Zn9c+25f8+8nR69ilglUvJOUtn5vLT5ueVZyzM0tlO+wsAAHCkUbg6BIUrAAAAitLTN5gfProt/+cnW3PLQ5uzpad/7LbZ0xvzunMW5/XnL80J81oLTAkAPJvBSjX3PLEj//zw1vzzui15eHPPuNvPXNyeV565MFeeqXwFAABwpFC4OgSFKwAAAF4sO3cPZM2TO7PmyZ350U+35Z4ndmSounc3vK25Pq84Y2FevWJRXnLcrNTXlQtMCwA8X0/t3JPvr9uS7z6wKat/ui37PN3njMVteeWZC/OqMxcpXwEAAExiCleHoHAFAADA4TZUqeaJ7bvz6JbePLqlN49s7sl9G7rys6d3HbDt8jnT87KT5uZlJ83NxSfMSWO9khUAHE229fbnH9Zuzrfv33hA+WrFMe258qyFufKsRVnc0VJcSAAAAA6gcHUIClcAAAA8F7VaLTt3D2Zj155s2tmXjV17snFnXzaNXH9q555s7u4bN7lqX8tmT8vZS2fmnGNn5mUnzjXZAgCmkEOVr85Z2pFXnbUoV561MPPbmosLCQAAQBKFq0NSuAIAAGBfg5VqOruGi1NP7dgzdrmxa3h5086+7BmsPOvjtDTU5fh503PivBk5YV5rTlvYlhVLOjJreuOL8FkAAJPd1p7+fPeBTfnWfZtyx+PbM3pkvlRKzl82K686a2FeccbCzJ3RVGxQAACAKUrh6hAUrgAAAKaW3QNDeWrHnmzYuScb9ytVjU6neobhVOPMnt6YhR3NWdTekkUdLVnY3pyFHS1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          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "for stock in stocks[:1]:\n",
        "    #latest[stock] = data.iloc[-1]['Close'] - data.iloc[-1]['Open']\n",
        "\n",
        "    fig = go.Figure(data=[go.Candlestick(x=d.index,\n",
        "                open=d['Open'],\n",
        "                high=d['Open'],\n",
        "                low=d['Close'],\n",
        "                close=d['Close'])])\n",
        "    fig.update_layout(\n",
        "      title=stock,\n",
        "      yaxis_title=stock +' Stock',\n",
        "      shapes = [dict(\n",
        "          x0='2022-12-09', x1='2022-12-09', y0=0, y1=1, xref='x', yref='paper',\n",
        "          line_width=2)],\n",
        "      annotations=[dict(\n",
        "          x='2022-12-09', y=0.05, xref='x', yref='paper',\n",
        "          showarrow=False, xanchor='left', text='Increase Period Begins')]\n",
        "    )\n",
        "\n",
        "    fig.show()\n",
        "\n",
        "    stocks_spread['Date'] = stocks_spread.index\n",
        "    fig = px.line(stocks_spread, x=\"Date\", y=stock, title=\"Stocks Daily Price change\", markers=True) \n",
        "    fig.show()\n",
        "\n",
        "    x = pd.Series(stocks_spread[stock])\n",
        "    ax = x.plot.kde(figsize=(30,8))\n",
        "    #sns.set(rc={'figure.figsize':(25.7,8.27)})\n",
        "    #sns.lineplot(x=\"Date\",y=stock, data=stocks_spread, markers=True, err_style=\"bars\")\n",
        "    #sns.despine();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KiCTrDH-A4XJ",
        "outputId": "356ac804-8f84-45fc-b91a-e5c6402d870d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "#print(stocks_spread.describe())\n",
        "std = stocks_spread.describe().loc['std']\n",
        "mean = stocks_spread.describe().loc['mean']\n",
        "std = pd.DataFrame.from_dict(std)\n",
        "std.columns = ['std',]\n",
        "betas_mean = betas.describe()['Beta']['mean']\n",
        "\n",
        "mean = pd.DataFrame.from_dict(mean)\n",
        "mean.columns = ['mean',]\n",
        "\n",
        "fig = px.line(std, title=\"Daily Stock Price change\", markers=True)\n",
        "fig.show()\n",
        "fig = px.line(betas, title=\"Stocks Betas\", markers=True)\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "lp-4QD8H-HtR"
      },
      "outputs": [],
      "source": [
        "# Determine what stocks are in buy zone and what stocks are in sell zone\n",
        "# Start date of the investing\n",
        "\n",
        "def getsellbuy(index, portfolio, security_profit, security_loss):\n",
        "  latest = {}\n",
        "  for s in stocks:\n",
        "      latest[s] = (stocks_ts.loc[index][s+\"_Close\"] - stocks_ts.loc[index][s+\"_Open\"])* 100 /stocks_ts.loc[index][s+\"_Open\"]\n",
        "\n",
        "  latest = pd.Series(latest)\n",
        "  pd.DataFrame.from_dict(latest)\n",
        "  latest_diff = pd.DataFrame.from_dict(latest)\n",
        "  latest_diff.columns = ['diff',]\n",
        "  #latest_diff = latest_diff.iloc[::-1]\n",
        "\n",
        "  latest_diff['std'] = std['std']\n",
        "\n",
        "  # Find stocks that are in buy range\n",
        "  latest_diff['buy'] = np.where(((mean_type != \"+ve\" or mean['mean'] > 0) & (latest_diff['diff'] < 0) &\n",
        "                                 (above_beta_mean == False or betas_mean >= betas['Beta']) &\n",
        "                                 (latest_diff['diff'] < -buy_trigger * std['std'])), True, False)\n",
        "  latest_diff['beta'] = betas['Beta']\n",
        "\n",
        "  # Find stocks that are in sell range\n",
        "  latest_diff['sell'] = False #np.where((latest_diff['diff'] >= std['std']), True, False)\n",
        "  profit = loss = 0\n",
        "  for s in stocks:\n",
        "    if s in portfolio:\n",
        "      t = index - portfolio[s]['date']\n",
        "      avg = sum(portfolio[s]['costs'])/len(portfolio[s]['costs'])\n",
        "      if avg + sell_trigger * std['std'][s] * avg / 100 <= stocks_ts.loc[index, s+\"_Close\"]:\n",
        "        # If the avg cost of the security has grown more than std\n",
        "        #print(s, avg, portfolio[s]['costs'], std['std'][s+\"_Diff\"], stocks_ts.loc[index, s+\"_Close\"])\n",
        "        profit = len(portfolio[s]['costs']) * stocks_ts.loc[index, s+\"_Close\"] - sum(portfolio[s]['costs'])\n",
        "        if dump_all_trades:\n",
        "          print(\"Selling %s (beta %f) at profit. closing price %f. profit %f days %d\" %\n",
        "                (s, betas['Beta'][s], stocks_ts.loc[index, s+\"_Close\"], profit, t.days), portfolio[s])\n",
        "        security_profit.loc[len(security_profit.index)] = {'beta':betas['Beta'][s], 'days':t.days, 'profit':profit}\n",
        "        latest_diff.at[s, 'sell'] = True\n",
        "      elif t.days > security_age or (stop_falling_knife == True and (latest_diff['diff'][s] < -buy_trigger * std['std'][s])):\n",
        "        # if the security has aged for certain days, cut the losses\n",
        "        loss = len(portfolio[s]['costs']) * stocks_ts.loc[index, s+\"_Close\"] - sum(portfolio[s]['costs'])\n",
        "        if dump_all_trades:\n",
        "          print(\"Dumping %s(beta %f) because of age. closing price %f. loss %f\" %\n",
        "                (s, betas['Beta'][s], stocks_ts.loc[index, s+\"_Close\"], loss),\n",
        "                portfolio[s])\n",
        "        if loss > 0:\n",
        "          security_profit.loc[len(security_profit.index)] = {'beta':betas['Beta'][s], 'days':t.days, 'profit':loss}\n",
        "        else:\n",
        "          security_loss.loc[len(security_loss.index)] = {'beta':betas['Beta'][s], 'loss':loss}\n",
        "        latest_diff.at[s, 'sell'] = True\n",
        "        latest_diff.at[s, 'buy'] = False\n",
        "  \n",
        "  # Print which stocks are buy and which are sell\n",
        "  #print(\"Stock to Buy on \" + str(index))\n",
        "  #print(latest_diff[latest_diff['buy']])\n",
        "  #print()\n",
        "  #print(\"Stocks to Sell on \" + str(index))\n",
        "  #print(latest_diff[latest_diff['sell']])\n",
        "  sell_stocks = latest_diff[latest_diff['sell']]\n",
        "  buy_stocks = latest_diff[latest_diff['buy']]\n",
        "  if prefer_beta:\n",
        "    buy_stocks = buy_stocks.sort_values('beta',ascending=False)\n",
        "  return sell_stocks, buy_stocks\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "PFnwTp8seZNo"
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      "outputs": [],
      "source": [
        "# We assume that we invest $100 in each stock that is in the buy zone\n",
        "# and sell all stocks in the sell zone\n",
        "\n",
        "# We will start back testing from year back. We can always change the starting point and tune the model.\n",
        "\n",
        "def calculate_networth(backtest_start_date):\n",
        "  networth = current_account\n",
        "  for stock, value in portfolio.items():\n",
        "    networth += value['shares'] * stocks_ts.loc[backtest_start_date][stock+\"_Close\"]\n",
        "  return networth\n",
        "\n",
        "def do_one_backtesting(tradingdays_togoback):\n",
        "  global portfolio\n",
        "  global original_balance\n",
        "  global current_account\n",
        "\n",
        "  stocks_bought = pd.DataFrame(columns=stocks)\n",
        "  stocks_sold = pd.DataFrame(columns=stocks)\n",
        "  stocks_profit = pd.DataFrame(columns=['beta', 'days', 'profit'])\n",
        "  stocks_loss = pd.DataFrame(columns=['beta', 'loss'])\n",
        "\n",
        "  original_balance = starting_balance\n",
        "  current_account = 0\n",
        "  portfolio = {}\n",
        "  capital_to_be_deployed = original_balance\n",
        "\n",
        "  idx = int(len(stocks_ts.index)- tradingdays_togoback)\n",
        "  price_movement = []\n",
        "  cash_inhand = []\n",
        "  for backtest_start_date in stocks_ts.index[idx:]:\n",
        "      t = backtest_start_date - stocks_ts.index[idx]\n",
        "      if (capital_to_be_deployed > 0) & (t.days % tranche_period == 0):\n",
        "        current_account += min(tranche_pct * starting_balance / 100, capital_to_be_deployed)\n",
        "        capital_to_be_deployed -= min(tranche_pct * starting_balance / 100, capital_to_be_deployed)\n",
        "        #print(\"Day of deployment %d, current_account %d, deployed_capital %d\" % (t.days, current_account, capital_to_be_deployed))\n",
        "      sell, buy = getsellbuy(backtest_start_date, portfolio, stocks_profit, stocks_loss)\n",
        "\n",
        "      # process the stocks that are marked sell\n",
        "      for st in sell.iterrows():\n",
        "        stock = st[0].split('_')[0]\n",
        "        if stock in portfolio:\n",
        "          current_account += portfolio[stock]['shares'] * stocks_ts.loc[backtest_start_date][stock+\"_Close\"]\n",
        "          stocks_sold.loc[backtest_start_date, stock] = portfolio[stock]['shares'] * stocks_ts.loc[backtest_start_date][stock+\"_Close\"]\n",
        "          portfolio.pop(stock)\n",
        "      \n",
        "      # buy stocks that are marked by. We are buying max_stocks_to_buy number of stocks\n",
        "      # TODO: The number of stocks to be must be adaptive. Will come up with some\n",
        "      # algorithm based on:\n",
        "      # 1. Market capitalization\n",
        "      # 2. Beta\n",
        "      # and other criteria\n",
        "      # The goal is to put the money to work\n",
        "      for st in buy.iterrows():\n",
        "        stock = st[0].split('_')[0]\n",
        "        if current_account > max_stocks_to_buy * stocks_ts.loc[backtest_start_date][stock+\"_Close\"]:\n",
        "          current_account -= max_stocks_to_buy * stocks_ts.loc[backtest_start_date][stock+\"_Close\"]\n",
        "          if not stock in portfolio:\n",
        "              assert portfolio.get(stock, None) == None\n",
        "              portfolio[stock] = {'shares': 0, 'costs':[], 'date': backtest_start_date}\n",
        "          portfolio[stock]['shares'] += max_stocks_to_buy\n",
        "          stocks_bought.loc[backtest_start_date, stock] = max_stocks_to_buy * stocks_ts.loc[backtest_start_date][stock+\"_Close\"]\n",
        "          for i in range(max_stocks_to_buy):\n",
        "            portfolio[stock]['costs'].append(stocks_ts.loc[backtest_start_date][stock+\"_Close\"])\n",
        "\n",
        "      # lock in the gains after 10% increase of networth\n",
        "      nw = calculate_networth(backtest_start_date)\n",
        "      price_movement.append(nw)\n",
        "      cash_inhand.append(current_account)\n",
        "      if nw > original_balance * lockin_gains_factor:\n",
        "          #print(backtest_start_date, calculate_networth(backtest_start_date), current_account, portfolio)\n",
        "          for stock, value in portfolio.items():\n",
        "            current_account += value['shares'] * stocks_ts.loc[backtest_start_date][stock+\"_Close\"]\n",
        "            stocks_sold.loc[backtest_start_date, stock] = stocks_ts.loc[backtest_start_date][stock+\"_Close\"]\n",
        "          portfolio = {}\n",
        "          original_balance = current_account\n",
        "          #print(current_account, portfolio)\n",
        "      #print(current_account, portfolio)\n",
        "\n",
        "  if dump_all_trades:\n",
        "    print(stocks_profit)\n",
        "    print(stocks_loss)\n",
        "  print(\"Profit Distribution\")\n",
        "  print(\"===================\")\n",
        "  print(stocks_profit.describe())\n",
        "  print()\n",
        "  print(\"Loss Distribution\")\n",
        "  print(\"=================\")\n",
        "  print(stocks_loss.describe())\n",
        "  print(\"Total Profit %f and Total Loss %f\" % (stocks_profit['profit'].sum(), stocks_loss['loss'].sum()))\n",
        "  #stocks_sold.to_csv(\"/content/drive/MyDrive/Colab Notebooks/sold.csv\")\n",
        "  #stocks_bought.to_csv(\"/content/drive/MyDrive/Colab Notebooks/bought.csv\")\n",
        "  return calculate_networth(backtest_start_date), current_account, price_movement, cash_inhand"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "jf74WK5anAji"
      },
      "outputs": [],
      "source": [
        "\n",
        "def run_backtest():\n",
        "  nws = {}\n",
        "  sp_ret = {}\n",
        "  for i in backtest_days:\n",
        "    tradingdays_togoback = i\n",
        "    networth, current, pm, cih = do_one_backtesting(tradingdays_togoback)\n",
        "    # normalize s&p500 for starting balance\n",
        "    idx_list = list(indices_ts.loc[indices_ts.index[-tradingdays_togoback:],\n",
        "                                   stock_indexes[index_stocks]+ \"_Close\"] * starting_balance/indices_ts.loc[indices_ts.index[-tradingdays_togoback], stock_indexes[index_stocks]+\"_Close\"])\n",
        "    pm_pct = (pm[-1]-starting_balance)*100/starting_balance\n",
        "    d = pd.DataFrame({'Portfolio_Performance':pm,'%s_Performance' % index_stocks:idx_list, \"Cash_In_Hand\": cih})\n",
        "    d.index = indices_ts.index[-tradingdays_togoback:]\n",
        "    idx_pct = (idx_list[-1] - idx_list[0])*100/idx_list[0]\n",
        "    print(\"Total networth: %d (Cash %d) after going back %d days (%s)\" % (networth, current_account, tradingdays_togoback, stocks_ts.index[-tradingdays_togoback]))\n",
        "    print(\"Model (%f)%% vs S&P Performance (%f)%%\" % (pm_pct, idx_pct))\n",
        "    if print_final_portfolio:\n",
        "      pp.pprint(portfolio)\n",
        "    nws[i] = networth\n",
        "    sp_ret[i] = idx_list[-1]\n",
        "    if plot_every_test_graph:\n",
        "      fig = px.line(d, title=\"Model (%f)%% vs %s Performance (%f)%% starting at %s\" % (pm_pct, index_stocks, idx_pct, stocks_ts.index[-tradingdays_togoback]), markers=True)\n",
        "      fig.show()\n",
        "\n",
        "  if plot_summary_graph:\n",
        "    plt.bar(nws.keys(), nws.values(), color=\"green\", label='Model')\n",
        "    plt.bar(sp_ret.keys(), sp_ret.values(), color=\"blue\", label=index_stocks)\n",
        "    plt.title(\"Networth at the end of each iteration\")\n",
        "    plt.xlabel('Days') \n",
        "    plt.ylabel('Networth')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    print(sum(nws.values())/len(nws))\n",
        "    idx_list = []\n",
        "    for i in backtest_days:\n",
        "      idx_list.append(indices_ts.index[-i])\n",
        "    summary = pd.DataFrame(data={\"Model\":nws.values(), index_stocks:sp_ret.values()}, \n",
        "                           index=list(idx_list))\n",
        "    fig = px.bar(summary, title=\"Model vs %s Backtesting Results. Seed money $%f\" % (index_stocks, starting_balance), barmode=\"group\")\n",
        "    fig.show()\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "P5LLl7p1EI2o",
        "outputId": "ee6a51b8-7afb-4b9e-e37c-a0c284f2c167"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Parameters for Test 10\n",
            "=================================\n",
            "{   'Name': 'Test 10',\n",
            "    'above_beta_mean': False,\n",
            "    'buy_trigger': 2,\n",
            "    'lockin_gains_factor': 1000,\n",
            "    'mean_type': '+ve',\n",
            "    'prefer_beta': True,\n",
            "    'security_age': 20,\n",
            "    'sell_trigger': 1,\n",
            "    'stop_falling_knife': False}\n",
            "Profit Distribution\n",
            "===================\n",
            "             beta        days      profit\n",
            "count  212.000000  217.000000  217.000000\n",
            "mean     1.140566    5.428571   27.522393\n",
            "std      0.384320    5.354620   30.947391\n",
            "min     -0.220000    1.000000    1.299999\n",
            "25%      0.940000    1.000000    7.212124\n",
            "50%      1.150000    3.000000   16.050034\n",
            "75%      1.380000    7.000000   36.450043\n",
            "max      2.070000   21.000000  232.749939\n",
            "\n",
            "Loss Distribution\n",
            "=================\n",
            "            beta        loss\n",
            "count  61.000000   61.000000\n",
            "mean    1.229344  -81.659145\n",
            "std     0.422180  114.580133\n",
            "min    -0.220000 -557.699890\n",
            "25%     1.000000 -113.099976\n",
            "50%     1.300000  -36.300049\n",
            "75%     1.510000  -14.949989\n",
            "max     2.070000   -0.375004\n",
            "Total Profit 5972.359385 and Total Loss -4981.207817\n",
            "Total networth: 10967 (Cash 7752) after going back 250 days (2022-04-13 00:00:00-04:00)\n",
            "Model (9.678016)% vs S&P Performance (-12.377922)%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
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          "text": [
            "\n",
            "==================================\n",
            "\n"
          ]
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      ],
      "source": [
        "\n",
        "for i in tests_to_run:\n",
        "  t = tests[i-1]\n",
        "  buy_trigger = t['buy_trigger']    # times standard deviation\n",
        "  sell_trigger = t['sell_trigger']   # times the avg cost of the security to grow before we sell\n",
        "  security_age = t['security_age']  # number of days to hold the security before we cut the losses\n",
        "  lockin_gains_factor = t['lockin_gains_factor'] # times the orignal amount to grow before we lockin the gains.\n",
        "  prefer_beta = t['prefer_beta']\n",
        "  mean_type = t[\"mean_type\"]\n",
        "  stop_falling_knife = t[\"stop_falling_knife\"]\n",
        "  above_beta_mean = t[\"above_beta_mean\"]\n",
        "  print(\"Test Parameters for \" + t['Name'])\n",
        "  print(\"=================================\")\n",
        "  pp.pprint(t)\n",
        "  run_backtest()\n",
        "  print()\n",
        "  print(\"==================================\\n\")"
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