bl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pytickersymbols in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (1.13.0)\n",
      "Requirement already satisfied: wheel==0.38.4 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from pytickersymbols) (0.38.4)\n",
      "Requirement already satisfied: packaging in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from pytickersymbols) (21.3)\n",
      "Requirement already satisfied: PyYAML==6.0 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from pytickersymbols) (6.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from packaging->pytickersymbols) (3.0.9)\n",
      "Requirement already satisfied: pandas_datareader in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (0.10.0)\n",
      "Requirement already satisfied: lxml in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from pandas_datareader) (4.9.1)\n",
      "Requirement already satisfied: pandas>=0.23 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from pandas_datareader) (1.4.4)\n",
      "Requirement already satisfied: requests>=2.19.0 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from pandas_datareader) (2.28.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->pandas_datareader) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->pandas_datareader) (2023.3)\n",
      "Requirement already satisfied: numpy>=1.18.5 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->pandas_datareader) (1.24.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->pandas_datareader) (1.26.11)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->pandas_datareader) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->pandas_datareader) (2022.9.24)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->pandas_datareader) (2.0.4)\n",
      "Requirement already satisfied: six>=1.5 in /Users/ygsharma/opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas>=0.23->pandas_datareader) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pytickersymbols\n",
    "!pip install pandas_datareader \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib_inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab\n",
    "pylab.rcParams['figure.figsize'] = (16, 9)\n",
    "import scipy.optimize\n",
    "from pandas_datareader import data as web\n",
    "import yfinance as yf\n",
    "import statistics\n",
    "import random\n",
    "# from pandas_datareader import data\n",
    "\n",
    "# import pypfopt\n",
    "from pypfopt.efficient_frontier import EfficientFrontier\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def port_mean(R,W):\n",
    "    return np.sum(R*W)\n",
    "\n",
    "def port_var(W,C):\n",
    "    return np.dot(np.dot(W,C), W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Result:\n",
    "    def __init__(self, W, tangent_mean, tangent_var, frontier_mean, frontier_var, frontier_weights):\n",
    "        self.W=W\n",
    "        self.tangent_mean=tangent_mean\n",
    "        self.tangent_var=tangent_var\n",
    "        self.frontier_mean=frontier_mean\n",
    "        self.frontier_var=frontier_var\n",
    "        self.frontier_weights=frontier_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_weights(R,C,rf):\n",
    "    # utility function which we have to optimize\n",
    "    def util_function(W,R,C,rf):\n",
    "        mean = port_mean(R,W)\n",
    "        var = port_var(W,C)\n",
    "        util = (mean - rf) / np.sqrt(var)\n",
    "        return 1/util\n",
    "\n",
    "    n = len(R)\n",
    "    W = np.ones(n)/n # initialize weights with equal values\n",
    "    bound = [(0.,1.) for i in range(n)]  #defining bounds for weights\n",
    "    print(\"x\")\n",
    "    constraint = [{'type': 'eq', 'fun': lambda W: np.sum(W)-1.}] #constraint for weights\n",
    "    print(\"y\")\n",
    "\n",
    "    optimized_weights = scipy.optimize.minimize(util_function, W, (R,C,rf), method='SLSQP', constraints=constraint, bounds=bound)\n",
    "    if not optimized_weights.success: raise BaseException(optimized_weights.message)\n",
    "    return optimized_weights.x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for different returns we will find optimal weights and mean, variance \n",
    "# which will help in plotting graph\n",
    "def optimize_frontier(R,C,rf):\n",
    "    # utility function for a particular return \n",
    "    def util_function(W,R,C,r):\n",
    "        mean = port_mean(R,W)\n",
    "        var = port_var(W,C)\n",
    "        penalty = 100 * abs(mean-r)\n",
    "        return var + penalty\n",
    "\n",
    "    frontier_means = []\n",
    "    frontier_vars = []\n",
    "    frontier_weights = []\n",
    "    n = len(R)\n",
    "\n",
    "    for r in np.linspace(min(R), max(R), num=30):\n",
    "        W = np.ones(n)/n\n",
    "        bound = [(0.,1.) for i in range(n)]  #defining bounds for weights\n",
    "        constraint = [{'type': 'eq', 'fun': lambda W: np.sum(W)-1.}] #constraint for weights\n",
    "\n",
    "        optimized_weights = scipy.optimize.minimize(util_function, W, (R,C,r), method='SLSQP', constraints=constraint,  bounds=bound)\n",
    "        \n",
    "        if not optimized_weights.success: raise BaseException(optimized_weights.message)\n",
    "\n",
    "        frontier_weights.append(optimized_weights.x)\n",
    "        frontier_means.append(r)\n",
    "        frontier_vars.append(port_var(optimized_weights.x, C))\n",
    "\n",
    "    return np.array(frontier_means), np.array(frontier_vars), np.array(frontier_weights)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def efficient_frontier_class(R,C,rf):\n",
    "    frontier_means = []\n",
    "    frontier_vars = []\n",
    "    frontier_weights = []\n",
    "    n = len(R)\n",
    "\n",
    "\n",
    "    max_value = -10\n",
    "\n",
    "    R = np.asarray(R)\n",
    "    \n",
    "    mu_one = np.c_[ R, np.ones(n) ]\n",
    "\n",
    "    middle_term = np.linalg.inv( np.dot(np.dot(mu_one.T, np.linalg.inv(C)), mu_one) )\n",
    "\n",
    "    for r in np.linspace(min(R), max(R), num=30):\n",
    "        exp_return_term = np.array([r,1])\n",
    "    \n",
    "        weights = np.dot( np.dot( np.dot( np.linalg.inv(C), mu_one), middle_term ), exp_return_term.T)\n",
    "        frontier_weights.append(weights);\n",
    "        frontier_means.append(r)\n",
    "        var = port_var(weights, C)\n",
    "        frontier_vars.append(var)\n",
    "\n",
    "        sharpe_ratio = (r - rf) / np.sqrt(var)\n",
    "        if(sharpe_ratio > max_value):\n",
    "            max_value = sharpe_ratio\n",
    "            tangent_mean = r\n",
    "            tangent_var = var\n",
    "            tangent_weights = weights\n",
    "\n",
    "\n",
    "\n",
    "        # var_mid_term = np.dot(np.dot(mu_one.T, np.linalg.inv(C)), mu_one)\n",
    "        # var = np.dot(np.dot(exp_return_term, var_mid_term), exp_return_term.T)\n",
    "        # frontier_vars.append(var)\n",
    "\n",
    "        # frontier_means.append(r)\n",
    "\n",
    "    return Result(tangent_weights, tangent_mean, tangent_var, np.array(frontier_means), np.array(frontier_vars), np.array(frontier_weights) )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_efficient_frontier_class(R,C,rf):\n",
    "\n",
    "    def util_function(C,W,r, mu_one, lamda, gamma):\n",
    "        exp_return_term = np.array([r,1])\n",
    "        lagrangians = np.array([lamda, gamma]).T\n",
    "\n",
    "        first_term = np.dot(np.dot(W.T, C), W)\n",
    "        second_term = np.dot( exp_return_term - np.dot(W.T, mu_one), lagrangians)\n",
    "\n",
    "        return first_term + second_term\n",
    "\n",
    "\n",
    "\n",
    "    frontier_means = []\n",
    "    frontier_vars = []\n",
    "    frontier_weights = []\n",
    "    n = len(R)\n",
    "\n",
    "\n",
    "    max_value = -10\n",
    "\n",
    "    R = np.asarray(R)\n",
    "    \n",
    "    mu_one = np.c_[ R, np.ones(n) ]\n",
    "\n",
    "    # middle_term = np.linalg.inv( np.dot(np.dot(mu_one.T, np.linalg.inv(C)), mu_one) )\n",
    "\n",
    "    for r in np.linspace(min(R), max(R), num=30):\n",
    "        W = np.ones(n)/n\n",
    "        lamda = np.random.randint(1,10)\n",
    "        gamma = np.random.randint(1,10)\n",
    "        bound = [(0.,1.) for i in range(n)]  #defining bounds for weights\n",
    "        constraint = [{'type': 'eq', 'fun': lambda W: np.sum(W)-1.}] #constraint for weights\n",
    "\n",
    "        optimized_weights = scipy.optimize.minimize(util_function, (W, lamda, gamma), (C,r,mu_one), method='SLSQP', constraints=constraint,  bounds=bound)\n",
    "        \n",
    "        if not optimized_weights.success: raise BaseException(optimized_weights.message)\n",
    "\n",
    "        frontier_weights.append(optimized_weights.x)\n",
    "        frontier_means.append(r)\n",
    "        var = port_var(optimized_weights.x, C)\n",
    "        frontier_vars.append()\n",
    "\n",
    "        sharpe_ratio = (r - rf) / np.sqrt(var)\n",
    "        if(sharpe_ratio > max_value):\n",
    "            max_value = sharpe_ratio\n",
    "            tangent_mean = r\n",
    "            tangent_var = var\n",
    "            tangent_weights = optimized_weights.x\n",
    "\n",
    "\n",
    "\n",
    "        # var_mid_term = np.dot(np.dot(mu_one.T, np.linalg.inv(C)), mu_one)\n",
    "        # var = np.dot(np.dot(exp_return_term, var_mid_term), exp_return_term.T)\n",
    "        # frontier_vars.append(var)\n",
    "\n",
    "        # frontier_means.append(r)\n",
    "\n",
    "    return Result(tangent_weights, tangent_mean, tangent_var, np.array(frontier_means), np.array(frontier_vars), np.array(frontier_weights) )\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pytickersymbols import PyTickerSymbols\n",
    "stock_data = PyTickerSymbols()\n",
    "stocks_list = stock_data.get_dow_jones_nyc_yahoo_tickers()\n",
    "stocks_list.remove('DOW')\n",
    "len(stocks_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = yf.Tickers(stocks_list)\n",
    "market_cap = {}\n",
    "for str in stocks_list:\n",
    "    market_cap[str] = tickers.tickers[str].info['marketCap']\n",
    "# market_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function loads historical stock prices of nine major S&P companies and returns them together\n",
    "# with their market capitalizations\n",
    "def load_data():\n",
    "    symbols = stocks_list\n",
    "    n = len(symbols)\n",
    "    prices_out, caps_out = [], []\n",
    "    for s in symbols:\n",
    "        dataframe = pd.read_csv('dow-jones/%s.csv' % s, index_col=None, parse_dates=['date'])\n",
    "        prices = list(dataframe['close'])[-2000:] # trailing window 500 days\n",
    "        prices_out.append(prices)\n",
    "        caps_out.append(market_cap[s])\n",
    "    return symbols, prices_out, caps_out\n",
    "\n",
    "names, prices, caps = load_data()\n",
    "# print(prices)\n",
    "prices = np.array(prices)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Historical Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def historical_returns(prices):\n",
    "    prices = pd.DataFrame(prices).T\n",
    "    returns = prices.pct_change()\n",
    "    cov_mat = returns.cov() * 252\n",
    "\n",
    "    # expected_returns = np.array([])\n",
    "    individual_returns = ((1 + np.mean(returns, axis=0)) ** 252)-1\n",
    "\n",
    "    return np.array(individual_returns), np.array(cov_mat)\n",
    "\n",
    "W = np.array(caps) / sum(caps) # calculate market weights from capitalizations\n",
    "# R1, C1 = historical_returns(prices)\n",
    "R, C = historical_returns(prices)\n",
    "rf = .035  # Risk-free rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_frontier(R, C, rf):\n",
    "    W = optimize_weights(R, C, rf)\n",
    "    tangent_mean = port_mean(R, W)\n",
    "    tangent_var = port_var(W, C)\n",
    "    frontier_means, frontier_vars, frontier_weights = optimize_frontier(R, C, rf)\n",
    "    return Result(W, tangent_mean, tangent_var, frontier_means, frontier_vars, frontier_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0097644 , 0.00964118, 0.009573  , 0.00955984, 0.00960171,\n",
       "       0.00969862, 0.00985055, 0.01005752, 0.01031952, 0.01063655,\n",
       "       0.01100861, 0.0114357 , 0.01191783, 0.01245498, 0.01304717,\n",
       "       0.01369438, 0.01439663, 0.01515391, 0.01596622, 0.01683356,\n",
       "       0.01775593, 0.01873333, 0.01976577, 0.02085323, 0.02199573,\n",
       "       0.02319326, 0.02444581, 0.0257534 , 0.02711602, 0.02853368])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# result1 = get_frontier(R, C, rf)\n",
    "result1 = efficient_frontier_class(R,C,rf)\n",
    "n = len(R)\n",
    "result1.frontier_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_assets(names, R, C, color='black'):\n",
    "    plt.scatter([C[i, i] ** .5 for i in range(n)], R, marker='x', color=color), plt.grid(True)  # draw assets\n",
    "    for i in range(n): \n",
    "        plt.text(C[i, i] ** .5, R[i], '  %s' % names[i], verticalalignment='center', color=color) # draw labels\n",
    "\n",
    "def display_frontier(result: Result, label=None, color='black'):\n",
    "    from collections import defaultdict\n",
    "    from IPython.core.display import HTML\n",
    "    plt.text(result.tangent_var ** .5, result.tangent_mean, ' tangent', verticalalignment='center', color=color)\n",
    "    plt.scatter(result.tangent_var ** .5, result.tangent_mean, marker='o', color=color), plt.grid(True)\n",
    "    plt.plot(list(result.frontier_var ** .5), list(result.frontier_mean), label=label, color=color), plt.grid(True)  # draw efficient frontie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_assets(names, R, C, color='blue')\n",
    "display_frontier(result1, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_stock_data(name):\n",
    "    dataframe = pd.read_csv('dow-jones/%s.csv' % name, index_col=None, parse_dates=['date'])\n",
    "    prices = list(dataframe['close'])[-2000:] # trailing window 500 days\n",
    "    return prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def market_return(prices):\n",
    "    prices = np.matrix(prices)  # create numpy matrix from prices\n",
    "    rows, cols = prices.shape\n",
    "    returns = np.empty([rows, cols - 1])\n",
    "    for r in range(rows):\n",
    "        for c in range(cols - 1):\n",
    "            p0, p1 = prices[r, c], prices[r, c + 1]\n",
    "            returns[r, c] = (p1 / p0) - 1\n",
    "    var = statistics.variance(returns[0]) * 250\n",
    "    \n",
    "    market_return = ((1 + np.mean(returns[0])) ** 250) - 1\n",
    "  \n",
    "    return market_return, var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cov_matrix(stock_prices, spy_prices):\n",
    "    \n",
    "    prices_df = pd.DataFrame(prices)\n",
    "    asset_returns = prices_df.pct_change()\n",
    "    asset_returns.drop(columns=asset_returns.columns[0], axis=0, inplace=True)\n",
    "\n",
    "    spy_df = pd.DataFrame(spy_prices).T\n",
    "    spy_returns = spy_df.pct_change()\n",
    "    spy_returns.drop(columns=spy_returns.columns[0], axis=1, inplace=True)\n",
    "\n",
    "    \n",
    "    print('Asset returns', asset_returns.shape)\n",
    "    print('Spy Return', spy_returns.shape)\n",
    "\n",
    "\n",
    "    cov_matrix = []\n",
    "    for r in range(stock_prices.shape[0]):\n",
    "        covar = np.cov(asset_returns[r+1], spy_returns)[0,1]\n",
    "        cov_matrix.append(covar)\n",
    "\n",
    "    var_spy = np.cov(spy_returns,spy_returns)[0,0]\n",
    "\n",
    "    print(cov_matrix)\n",
    "    print(var_spy)\n",
    "\n",
    "    return (np.asarray(cov_matrix) * 250) / var_spy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "spy_prices = load_stock_data('SPY')\n",
    "rm, market_var = market_return(spy_prices)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Black-litterman reverse optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.11277747 0.16278477 0.20432273 0.18886099 0.13775823 0.13766637\n",
      " 0.14897301 0.08746181 0.03836485 0.16018284 0.13983193 0.12036467\n",
      " 0.17523799 0.08539939 0.15054211 0.10294032 0.14577277 0.08827086\n",
      " 0.19502549 0.08858469 0.10973282 0.13287184 0.06660796 0.16020669\n",
      " 0.08064652 0.1158481  0.13695897 0.11271397 0.13345574 0.18376174] [-0.01139749  0.12995906  0.26771352  0.14330435  0.19238213  0.12186442\n",
      "  0.10622025  0.06863905  0.00242946  0.13351046  0.18575797  0.02421168\n",
      "  0.01385409  0.09013986  0.16080835  0.17237675  0.16905813  0.10793506\n",
      "  0.27721477  0.08804824  0.10664208  0.25998061 -0.00486174  0.19883916\n",
      "  0.08740008 -0.04320811  0.02958325  0.0985969   0.13577698  0.17975741]\n"
     ]
    }
   ],
   "source": [
    "# Calculate portfolio historical return and variance\n",
    "mean = port_mean(R, W)\n",
    "var = port_var(W,C)\n",
    "\n",
    "lmb = (mean - rf) / var  # Calculate risk aversion\n",
    "Pi = np.dot(np.dot(lmb, C), W)  # Calculate equilibrium excess returns\n",
    "print(Pi, R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tp0/69OmTp556ao119OnTJ5MnT87UqVMzZsyYddZdLBZTLBY3YKUAAAAAQEdY7x2QK1asyBVXXJHzzz8/CxYsKLXf/va3ec973pMrr7xylXsmTpyY+fPnr3H341FHHZVly5blkksu2eDCTz311HTr1i0XXnjhBt8LAAAAAHSO9d4BOXfu3CxZsiQnnHBCamtr2107/PDDM2PGjFV2I37605/OEUcckT59+qx2zr322iuTJk3KpEmT8uSTT+YTn/hE6uvrs2jRosyYMSOFQiHduq0+I+3Zs2emTp2aU045ZX2XAAAAAAB0svXeATljxoyMGDFilfAxSQ477LAsWLAgzz//fLv+qqqq9OvXr90hM2923nnn5aqrrspDDz2UMWPGZIcddsgRRxyR119/Pffdd19qamrWeO9xxx2XwYMHr+8SAAAAAIBOVmhb3Skv73AtLS1vnIZ9+rXpVuxV6XIAAOiCGqePrnQJAABd1sp8rbm5ea0bCJMNPAUbAAAAAGBDbNAp2O80j04duc6EFgAAAADYeHZAAgAAAABlI4AEAAAAAMpGAAkAAAAAlM0m/Q3IYVNucwo2AADwljk1HQDWzA5IAAAAAKBsBJAAAAAAQNkIIAEAAACAshFAAgAAAABlU9EAcsKECSkUCikUCunRo0fe9a535cADD0xDQ0Nef/310riBAwfmO9/5Tun3Qw89lDFjxmSbbbZJz549M3DgwIwbNy7PPvtsBVYBAAAAAKxJxXdAjho1KosWLUpjY2N++tOfZvjw4TnttNMyZsyYrFixYpXx//jHPzJixIj069cvt912WxYuXJiGhoZsu+22efHFFyuwAgAAAABgTaoqXUCxWExdXV2S5N3vfnc++MEP5sMf/nAOOOCAzJo1KyeeeGK78ffee29aWlpy+eWXp6rqjfIHDRqU/fffv9NrBwAAAADWruI7IFdn//33z/vf//7ccMMNq1yrq6vLihUrcuONN6atrW295mttbU1LS0u7BgAAAACUX5cMIJNk6NChaWxsXKX/wx/+cM4666x86lOfSr9+/XLwwQfnW9/6Vv7+97+vca5p06altra21Orr68tYOQAAAACwUpcNINva2lIoFFZ77Rvf+EYWL16cyy67LDvvvHMuu+yyDB06NI888shqx0+ePDnNzc2l1tTUVM7SAQAAAID/vy4bQC5cuDCDBg1a4/W+ffvmiCOOyPnnn5+FCxemf//+Oe+881Y7tlgspqampl0DAAAAAMqvSwaQt99+ex555JEcdthh6zV+s802y/bbb5/ly5eXuTIAAAAAYENU/BTs1tbWLF68OK+99lr+/ve/59Zbb820adMyZsyYHHvssauMnzt3bq6++uoceeSR2XHHHdPW1pYf//jHueWWWzJz5swKrAAAAAAAWJOKB5C33nprtt1221RVVWXLLbfM+9///lx00UU57rjj0q3bqhs0d9555/Tq1SuTJk1KU1NTisVidthhh1x++eU55phjKrACAAAAAGBNCm1tbW2VLqKztbS0vHEa9unXpluxV6XLAQAA3uYap4+udAkA0KlW5mvNzc3rPG+lS34DEgAAAAB4Z6j4K9iV9OjUkU7EBgAAAIAysgMSAAAAACgbASQAAAAAUDYCSAAAAACgbDbpb0AOm3KbU7ABACgrpyMDAJs6OyABAAAAgLIRQAIAAAAAZSOABAAAAADKRgAJAAAAAJRNhweQl112WbbYYousWLGi1Lds2bL06NEjH/3oR9uNveuuu1IoFPLEE09k4MCBKRQKKRQK6d69e/r3758TTjghS5YsWe1zdtppp2y22Wb529/+1tFLAAAAAAA6SIcHkMOHD8+yZcvy61//utR31113pa6uLg8++GBefPHFUv/8+fPTv3//7LjjjkmSs88+O4sWLcpf//rXXHnllfnlL3+Zz3/+86s84+67787LL7+cI444IrNmzeroJQAAAAAAHaTDA8iddtop/fv3z/z580t98+fPzyGHHJLtt98+9957b7v+4cOHl35vscUWqaury7vf/e4MHz48xx57bH7zm9+s8owZM2bkU5/6VI455pg0NDSkra2to5cBAAAAAHSAsnwDcr/99ssdd9xR+n3HHXdkv/32y7777lvqf+WVV3Lfffe1CyD/2d/+9rfMnTs3e+65Z7v+F154IXPmzMn48eNz4IEHZvny5e3CztVpbW1NS0tLuwYAAAAAlF/ZAsh77rknK1asyAsvvJCHHnoo++yzT/bdd99SWHj//ffnpZdeahdAnnnmmamurs7mm2+e7bbbLoVCId/+9rfbzX311Vdnhx12yC677JLu3bvnyCOPzIwZM9Zaz7Rp01JbW1tq9fX1Hb5mAAAAAGBVZQkghw8fnuXLl+fBBx/MXXfdlR133DHbbLNN9t133zz44IOlXYsDBgzI4MGDS/d94QtfyIIFC/Lwww9n3rx5SZLRo0fntddeK42ZMWNGxo8fX/o9fvz43HDDDVm6dOka65k8eXKam5tLrampqeMXDQAAAACsoqockw4ZMiTbbbdd7rjjjixZsiT77rtvkqSuri6DBg3KPffckzvuuCP7779/u/v69euXIUOGJEl22GGHfOc738lee+2VO+64IyNGjMhjjz2WX/3qV3nwwQdz5plnlu577bXX8sMf/jAnnXTSauspFospFovlWCoAAAAAsBZl2QGZvLELcv78+Zk/f37222+/Uv++++6b2267Lffff/8av/+4Uvfu3ZMkL730UpI3dj/us88++e1vf5sFCxaU2he/+MV1voYNAAAAAHS+suyATN4IIE855ZS8+uqrpR2QyRsB5EknnZSXX355lQDyhRdeyOLFi9PW1pampqZ88YtfTL9+/bL33nvn1Vdfzf/93//l7LPPzrBhw9rdd+KJJ+bcc8/Nb3/727z//e8v15IAAAAAgA1U1h2QL730UoYMGZJ3vetdpf599903L7zwQrbffvtVDoP5yle+km233Tb9+/fPmDFj0rt37/z85z9P3759c/PNN+e5557Lxz/+8VWetcMOO+R973ufXZAAAAAA0MWUbQfkwIED09bWtkr/dtttt9r+xsbGtc532GGHtTuM5s0efvjhDa4RAAAAACivsu2ABAAAAAAo2w7It4NHp45MTU1NpcsAAAAAgHcsOyABAAAAgLIRQAIAAAAAZSOABAAAAADKZpP+BuSwKbelW7FXpcsAgLedxumjK10CAADwNmEHJAAAAABQNgJIAAAAAKBsBJAAAAAAQNkIIAEAAACAsunQAPKyyy7LFltskRUrVpT6li1blh49euSjH/1ou7F33XVXCoVCnnjiiQwcODCFQiFXX331KnPusssuKRQKmTVrVubPn59CobDWNmvWrI5cEgAAAADwFnRoADl8+PAsW7Ysv/71r0t9d911V+rq6vLggw/mxRdfLPXPnz8//fv3z4477pgkqa+vz8yZM9vNd//992fx4sXp3bt3kmTvvffOokWLSu2Tn/xkRo0a1a5v3LhxHbkkAAAAAOAt6NAAcqeddkr//v0zf/78Ut/8+fNzyCGHZPvtt8+9997brn/48OGl30cffXTuvPPONDU1lfoaGhpy9NFHp6qqKkmy2Wabpa6urtQ233zzFIvFVfoAAAAAgK6hw78Bud9+++WOO+4o/b7jjjuy3377Zd999y31v/LKK7nvvvvaBZDvete7MnLkyMyePTtJ8uKLL+aaa67JxIkT33JNra2taWlpadcAAAAAgPIrSwB5zz33ZMWKFXnhhRfy0EMPZZ999sm+++5b2hl5//3356WXXmoXQCbJxIkTM2vWrLS1teW6667L9ttvn9122+0t1zRt2rTU1taWWn19/VueEwAAAABYtw4PIIcPH57ly5fnwQcfzF133ZUdd9wx22yzTfbdd988+OCDWb58eebPn58BAwZk8ODB7e4dPXp0li1bll/+8pdpaGjokN2PSTJ58uQ0NzeX2j+/5g0AAAAAlE9VR084ZMiQbLfddrnjjjuyZMmS7LvvvkmSurq6DBo0KPfcc0/uuOOO7L///qsWU1WVY445JlOmTMmvfvWr3HjjjR1SU7FYTLFY7JC5AAAAAID11+E7IJM3dkHOnz8/8+fPz3777Vfq33fffXPbbbfl/vvvX+X165UmTpyYO++8M4cccki23HLLcpQHAAAAAHSSDt8BmbwRQJ5yyil59dVXSzsgkzcCyJNOOikvv/zyGgPI9773vXn22WfTq1evcpQGAAAAAHSisgWQL730UoYOHZp3vetdpf599903L7zwQrbffvu1HgTTt2/fcpQFAAAAAHSysgSQAwcOTFtb2yr922233Wr7Gxsb1zrf0qVLV9s/a9asjagOAAAAAOgsZfkGJAAAAABAUqYdkG8Xj04dmZqamkqXAQAAAADvWHZAAgAAAABlI4AEAAAAAMpGAAkAAAAAlI0AEgAAAAAom036EJphU25Lt2KvSpcBAADAO1Dj9NGVLgGgS7ADEgAAAAAoGwEkAAAAAFA2AkgAAAAAoGzeVgHk2LFjM2LEiNVeu++++1IoFPKb3/ymk6sCAAAAANbkbRVAnnDCCbn99tvz5JNPrnKtoaEhu+22Wz74wQ9WoDIAAAAAYHXeVgHkmDFjss0222TWrFnt+l988cVcc801OeGEEypTGAAAAACwWm+rALKqqirHHntsZs2alba2tlL/nDlz8sorr+Too49e7X2tra1paWlp1wAAAACA8ntbBZBJMnHixDQ2Nmb+/PmlvoaGhnziE5/Illtuudp7pk2bltra2lKrr6/vpGoBAAAAYNP2tgsghw4dmr333jsNDQ1Jkj/96U+56667MnHixDXeM3ny5DQ3N5daU1NTZ5ULAAAAAJu0t10AmbxxGM3111+flpaWzJw5M+95z3tywAEHrHF8sVhMTU1NuwYAAAAAlN/bMoD85Cc/me7du+eqq67K7Nmzc/zxx6dQKFS6LAAAAADgTaoqXcDGqK6uzrhx43LWWWelubk5EyZMqHRJAAAAAMBqvC13QCZvvIa9ZMmSjBgxIgMGDKh0OQAAAADAarwtd0AmyV577ZW2trZKlwEAAAAArMXbdgckAAAAAND1CSABAAAAgLJ5276C3REenToyNTU1lS4DAAAAAN6x7IAEAAAAAMpGAAkAAAAAlI0AEgAAAAAom036G5DDptyWbsVelS4DgE7WOH10pUsAAADYZNgBCQAAAACUjQASAAAAACgbASQAAAAAUDZdJoAcO3ZsRowYsdpr9913XwqFQmpqalIoFNbY7rzzzk6uGgAAAABYmy5zCM0JJ5yQT3ziE3nyySfznve8p921hoaG7Lbbbpk3b15eeeWVdtdeeeWVjB49Oj179syee+7ZmSUDAAAAAOvQZXZAjhkzJttss01mzZrVrv/FF1/MNddckxNOOCFbbbVV6urq2rWvfe1reeaZZ3LjjTemZ8+elSkeAAAAAFitLhNAVlVV5dhjj82sWbPS1tZW6p8zZ05eeeWVHH300avcc8kll+SKK67IDTfckO22226Nc7e2tqalpaVdAwAAAADKr8sEkEkyceLENDY2Zv78+aW+hoaGfOITn8iWW27Zbuwvf/nLnH766bn44ouz9957r3XeadOmpba2ttTq6+vLUT4AAAAA8CZdKoAcOnRo9t577zQ0NCRJ/vSnP+Wuu+7KxIkT243761//msMPPzz//u//nhNPPHGd806ePDnNzc2l1tTUVJb6AQAAAID2ulQAmbxxGM3111+flpaWzJw5M+95z3tywAEHlK6/9NJL+fjHP55ddtkl3/nOd9ZrzmKxmJqamnYNAAAAACi/LhdAfvKTn0z37t1z1VVXZfbs2Tn++ONTKBRK10888cQ8//zzmTNnTqqquswh3gAAAADAanS5BK+6ujrjxo3LWWedlebm5kyYMKF07Vvf+lbmzJmTH//4x1mxYkUWL17c7t7a2tpsvvnmnVwxAAAAALAmXW4HZPLGa9hLlizJiBEjMmDAgFL/JZdckldffTWjRo3Ktttuu0q75pprKlg1AAAAAPBmXW4HZJLstddeaWtrW6X/L3/5SwWqAQAAAAA2VpfcAQkAAAAAvDMIIAEAAACAsumSr2B3lkenjkxNTU2lywAAAACAdyw7IAEAAACAshFAAgAAAABlI4AEAAAAAMpmk/4G5LApt6VbsVelywAAAMqscfroSpcAAJssOyABAAAAgLIRQAIAAAAAZSOABAAAAADKRgAJAAAAAJRNWQPI/fbbL6effvoq/TfddFMKhUKSZNasWSkUChk1alS7MUuXLk2hUMj8+fNLfYVCITfddNMq802YMCGHHnpoB1YOAAAAAHSELrEDsqqqKvPmzcsdd9xR6VIAAAAAgA7UJQLI3r175/jjj8+XvvSlsszf2tqalpaWdg0AAAAAKL8uEUAmyVe/+tU88sgjue666zp87mnTpqW2trbU6uvrO/wZAAAAAMCqukwA2b9//5x22mn58pe/nBUrVqxx3FFHHZXq6up27corr1zr3JMnT05zc3OpNTU1dXT5AAAAAMBqdJkAMknOPPPMPPPMM2loaFjjmAsuuCALFixo1/7t3/5trfMWi8XU1NS0awAAAABA+VWVc/Kampo0Nzev0r906dLVhoB9+vTJ5MmTM3Xq1IwZM2a1c9bV1WXIkCHt+rbYYossXbq0Q2oGAAAAADpOWXdADh06NL/+9a9X6X/wwQez0047rfaeU089Nd26dcuFF15YztIAAAAAgE5Q1gDy5JNPzp/+9Keccsop+e1vf5snnngiF198cWbMmJEvfOELq72nZ8+emTp1ai666KJylgYAAAAAdIKyBpADBw7MXXfdlT/96U856KCD8qEPfSizZs3KrFmzcsQRR6zxvuOOOy6DBw8uZ2kAAAAAQCcotLW1tVW6iM7W0tKS2tra1J9+bboVe1W6HAAAoMwap4+udAkA8I6yMl9rbm5e54HPXeoUbAAAAADgnaWsp2B3dY9OHbnOhBYAAAAA2Hh2QAIAAAAAZSOABAAAAADKRgAJAAAAAJTNJv0NyGFTbnMKNgDAO5ATjwEAug47IAEAAACAshFAAgAAAABlI4AEAAAAAMpGAAkAAAAAlE2nBZCFQmGtbcKECauMq66uzvvf//7MmjUrSfL3v/89PXr0yA9+8IPVPuMzn/lMdt11105aEQAAAACwLp0WQC5atKjUvvOd76SmpqZd34UXXlgaO3PmzCxatCi//e1vM27cuBx//PG57bbb8q53vSujR4/OzJkzV5n/pZdeytVXX50TTjihs5YEAAAAAKxDpwWQdXV1pVZbW5tCobBK30p9+vRJXV1dtt9++5x11lnZaqut8rOf/SxJcsIJJ+SOO+5IY2Nju/mvu+66vPzyyxk/fnxnLQkAAAAAWIcu/Q3I1157Lddee22ef/759OjRI0nysY99LHV1daXXsldqaGjIoYcemr59+64yT2tra1paWto1AAAAAKD8umQAedRRR6W6ujrFYjHjxo3LVlttlRNPPDFJ0r179xx77LGZNWtW2trakiR/+ctfcuedd67x9etp06altra21Orr6zttLQAAAACwKeuSAeQFF1yQBQsW5Oc//3l22223XHDBBRkyZEjp+gknnJAnn3wyt99+e5I3dj9ut912GTFixGrnmzx5cpqbm0utqampU9YBAAAAAJu6qkoXsDp1dXUZMmRIhgwZkjlz5uQDH/hA/uVf/iU777xzkmSHHXbIRz/60cycOTPDhw/P7Nmzc/zxx6dbt9XnqcViMcVisTOXAAAAAACki+6A/GdDhgzJYYcdlsmTJ7frP+GEE3LDDTfk+uuvz1NPPZXjjz++QhUCAAAAAGvS5QPIJJk0aVJ+/OMf59e//nWp74gjjkiPHj3ymc98JgcccEAGDhxYuQIBAAAAgNV6WwSQ73vf+zJixIh85StfKfX16tUrRx55ZJYsWZKJEydWsDoAAAAAYE0KbSuPkt6EtLS0vHEa9unXpluxV6XLAQCggzVOH13pEgAA3tFW5mvNzc2pqalZ69i3xQ5IAAAAAODtqUuegt1ZHp06cp0JLQAAAACw8eyABAAAAADKRgAJAAAAAJSNABIAAAAAKJtN+huQw6bc5hRsANiEOBkZAAA6nx2QAAAAAEDZCCABAAAAgLIRQAIAAAAAZSOABAAAAADKpksFkBMmTEihUEihUEiPHj0yePDgnHHGGVm+fHlpzPXXX5/9998/W265ZXr16pWddtopEydOzEMPPVTBygEAAACA1elSAWSSjBo1KosWLcqf//znfP3rX88ll1ySM844I0ly5plnZty4cdltt91y880353e/+12+//3vZ/vtt89ZZ51V4coBAAAAgDerqnQBb1YsFlNXV5ck+dSnPpU77rgjN910U4477rice+65ufDCC/P5z3++NH7QoEHZd99909bWVqmSAQAAAIA16HIB5JttvvnmefXVV/PDH/4w1dXVOfnkk1c7rlAorHGO1tbWtLa2ln63tLR0eJ0AAAAAwKq63CvY/+yBBx7IVVddlQMOOCBPPPFEBg8enKqq/5eZfvvb3051dXWpNTc3r3aeadOmpba2ttTq6+s7awkAAAAAsEnrcgHk3LlzU11dnZ49e2avvfbKPvvsk+9+97tJVt3lOHHixCxYsCD/8z//k+XLl6/xNezJkyenubm51Jqamsq+DgAAAACgC76CPXz48Fx66aXp0aNH+vfvnx49eiRJdthhh9x999159dVXS319+vRJnz598tRTT611zmKxmGKxWPbaAQAAAID2utwOyN69e2fIkCF5z3veUwoak+Soo47KsmXLcskll1SwOgAAAABgQ3S5HZBrstdee2XSpEmZNGlSnnzyyXziE59IfX19Fi1alBkzZqRQKKRbty6XpwIAAADAJu1tldidd955ueqqq/LQQw9lzJgx2WGHHXLEEUfk9ddfz3333ZeamppKlwgAAAAA/JNC25pObnkHa2lpeeM07NOvTbdir0qXAwB0ksbpoytdAgAAvCOszNeam5vXuSnwbbUDEgAAAAB4e3nbfAOyHB6dOtJr2wAAAABQRnZAAgAAAABlI4AEAAAAAMpGAAkAAAAAlM0m/Q3IYVNucwo2AAAAQIU0Th9d6RLoBHZAAgAAAABlI4AEAAAAAMpGAAkAAAAAlI0AEgAAAAAomw4JICdMmJBCoZDPfvazq1w7+eSTUygUMmHChCTJP/7xj3zmM5/JgAEDUiwWU1dXl5EjR+a+++4r3TNw4MAUCoV2bbvttstXv/rVVfrf3BobGztiSQAAAABAB+iwU7Dr6+tz9dVX54ILLsjmm2+eJHn55Zfzwx/+MAMGDCiNO+yww/Lqq69m9uzZGTx4cP7+979n3rx5ef7559vNd/bZZ+fTn/506Xf37t2z+eabtws5P/ShD+Xf//3f243beuutO2pJAAAAAMBb1GEB5Ac/+MH8+c9/zg033JCjjz46SXLDDTekvr4+gwcPTpIsXbo0d999d+bPn5999903SfKe97wne+yxxyrzbbHFFqmrq1ulv7q6uvR39+7d1zgOAAAAAKi8Dv0G5PHHH5+ZM2eWfjc0NGTixIml39XV1amurs5NN92U1tbWjnz0WrW2tqalpaVdAwAAAADKr0MDyGOOOSZ33313Ghsb8+STT+aee+7J+PHjS9erqqoya9aszJ49O3369Mm//uu/5qyzzsrDDz+8ylxnnnlmKbCsrq7ORRddtNF1TZs2LbW1taVWX1+/0XMBAAAAAOuvQwPIfv36ZfTo0Zk9e3ZmzpyZ0aNHp1+/fu3GHHbYYXn66adz8803Z+TIkZk/f34++MEPZtasWe3GfeELX8iCBQtK7dhjj93ouiZPnpzm5uZSa2pq2ui5AAAAAID112HfgFxp4sSJ+dznPpckufjii1c7pmfPnjnwwANz4IEH5itf+UpOPPHETJkypXRSdvJGmDlkyJAOqalYLKZYLHbIXAAAAADA+uvQHZBJMmrUqLzyyit55ZVXMnLkyPW6Z+edd87y5cs7uhQAAAAAoMI6fAdk9+7ds3DhwtLf/+y5557LEUcckYkTJ2bXXXfNFltskV//+tc599xzc8ghh3R0KQAAAABAhXV4AJkkNTU1q+2vrq7OnnvumQsuuCB/+tOf8uqrr6a+vj6f/vSnc9ZZZ5WjFAAAAACgggptbW1tlS6is7W0tLxxGvbp16ZbsVelywEAAADYJDVOH13pEthIK/O15ubmNW5GXKnDvwEJAAAAALBSWV7Bfrt4dOrIdSa0AAAAAMDGswMSAAAAACgbASQAAAAAUDYCSAAAAACgbDbpb0AOm3KbU7ABYBPhhEUAAKgMOyABAAAAgLIRQAIAAAAAZSOABAAAAADKRgAJAAAAAJRNWQPICRMmpFAo5LOf/ewq104++eQUCoVMmDCh3dhCoZCqqqoMGDAgJ510UpYsWdLuvoEDB+Y73/lO6XdbW1smTZqULbbYIrfffns5lwMAAAAAbKCy74Csr6/P1VdfnZdeeqnU9/LLL+eHP/xhBgwY0G7sqFGjsmjRojQ2Nubyyy/Pj3/845x88slrnPu1117LCSeckCuuuCK333579t9//7KtAwAAAADYcGUPID/4wQ9mwIABueGGG0p9N9xwQ+rr6/OBD3yg3dhisZi6urpst912OeiggzJu3Lj87Gc/W+28ra2tOeKII/Lzn/88v/zlL/OhD32orOsAAAAAADZcp3wD8vjjj8/MmTNLvxsaGjJx4sS13vPnP/85t956a3r06LHKtWXLlmX06NH53e9+l3vuuSfvfe971zpXa2trWlpa2jUAAAAAoPw6JYA85phjcvfdd6exsTFPPvlk7rnnnowfP36VcXPnzk11dXU233zzbL/99nnsscdy5plnrjLua1/7WhYsWJC77rprlde4V2fatGmpra0ttfr6+g5ZFwAAAACwdp0SQPbr1y+jR4/O7NmzM3PmzIwePTr9+vVbZdzw4cOzYMGC/OpXv8qpp56akSNH5tRTT11l3EEHHZTly5fnnHPOWa/nT548Oc3NzaXW1NT0ltcEAAAAAKxbpwSQSTJx4sTMmjUrs2fPXuPr1717986QIUOy66675qKLLkpra2umTp26yrgDDjggN998c77//e+vNqB8s2KxmJqamnYNAAAAACi/TgsgR40alVdeeSWvvPJKRo4cuV73TJkyJeedd16efvrpVa4deOCBmTt3bhoaGnLKKaekra2to0sGAAAAAN6iTgsgu3fvnoULF2bhwoXp3r37et2z3377ZZdddlnjq9b7779/fvKTn2T27NlCSAAAAADogjotgEyyUa8//+d//mf+93//d43fbdxvv/1yyy235P/+7/9y0kknCSEBAAAAoAsptG2CiV1LS8sbp2Gffm26FXtVuhwAoBM0Th9d6RIAAOAdY2W+1tzcvM4Nh526AxIAAAAA2LRUVbqASnp06kgnYgMAAABAGdkBCQAAAACUjQASAAAAACgbASQAAAAAUDYCSAAAAACgbDbpQ2iGTbkt3Yq9Kl0GAABAl9E4fXSlSwDgHcYOSAAAAACgbASQAAAAAEDZCCABAAAAgLLptABywoQJKRQKmT59erv+m266KYVCIUkyf/78FAqFLF26tHT96aefzrBhw/KRj3wkS5cuTWNjYwqFwmrb/fff31nLAQAAAADWQ6fugOzZs2e++c1vZsmSJes1/k9/+lM+8pGPZMCAAfnZz36WPn36lK794he/yKJFi9q13XffvUyVAwAAAAAbo1MDyBEjRqSuri7Tpk1b59iHH344H/nIR7LnnnvmRz/6UXr1an9add++fVNXV9eu9ejRo1ylAwAAAAAboVMDyO7du+ecc87Jd7/73Tz11FNrHHfvvfdm3333zSc+8YlceeWVbzlYbG1tTUtLS7sGAAAAAJRfpx9C8/GPfzy77bZbpkyZstYxY8eOzcUXX5xu3VZf4t57753q6up27bXXXlvt2GnTpqW2trbU6uvrO2QtAAAAAMDaVeQU7G9+85uZPXt2HnvssdVeP+SQQ3LjjTfmrrvuWuMc11xzTRYsWNCude/efbVjJ0+enObm5lJramrqkHUAAAAAAGtXkQByn332yciRI3PWWWet9vr//M//5KijjsrBBx+cO++8c7Vj6uvrM2TIkHZtTYrFYmpqato1AAAAAKD8qir14OnTp2e33XbLjjvuuMq1QqGQ//mf/0n37t3zsY99LD/5yU+y3377dX6RAAAAAMBbUrEA8n3ve1+OPvrofPe7313t9UKhkEsuuSTdu3fP6NGj8+Mf/zj7779/6fpzzz2XxYsXt7unT58+6dmzZ1nrBgAAAADWX0VewV7pa1/7Wtra2tZ4vVAo5Hvf+15OPPHEjBkzJr/4xS9K10aMGJFtt922Xbvppps6oWoAAAAAYH0V2taWAL5DtbS0vHEa9unXpluxV6XLAQAA6DIap4+udAkAvA2szNeam5vXed5KRXdAAgAAAADvbAJIAAAAAKBsKnYITVfw6NSR69wiCgAAAABsPDsgAQAAAICyEUACAAAAAGUjgAQAAAAAymaT/gbksCm3pVuxV6XLAAB422mcPrrSJQAA8DZhByQAAAAAUDYCSAAAAACgbASQAAAAAEDZlC2AnDBhQgqFQj772c+ucu3kk09OoVDIhAkTSn2LFy/OqaeemsGDB6dYLKa+vj5jx47NvHnzSmMGDhyYQqGQQqGQzTffPAMHDswnP/nJ3H777eVaBgAAAADwFpR1B2R9fX2uvvrqvPTSS6W+l19+OT/84Q8zYMCAUl9jY2N233333H777Tn33HPzyCOP5NZbb83w4cNzyimntJvz7LPPzqJFi/L444/niiuuSJ8+fTJixIh84xvfKOdSAAAAAICNUNZTsD/4wQ/mz3/+c2644YYcffTRSZIbbrgh9fX1GTx4cGncyh2RDzzwQHr37l3q32WXXTJx4sR2c26xxRapq6tLkgwYMCD77LNPtt1223zlK1/J4Ycfnp122qmcSwIAAAAANkDZvwF5/PHHZ+bMmaXfDQ0N7ULF559/PrfeemtOOeWUduHjSn369FnnM0477bS0tbXlRz/60Wqvt7a2pqWlpV0DAAAAAMqv7AHkMccck7vvvjuNjY158sknc88992T8+PGl63/84x/T1taWoUOHbvQzttpqq2yzzTZpbGxc7fVp06altra21Orr6zf6WQAAAADA+ivrK9hJ0q9fv4wePTqzZ89OW1tbRo8enX79+pWut7W1JUkKhcJbek5bW9sa55g8eXL+8z//s/S7paVFCAkAAAAAnaDsAWSSTJw4MZ/73OeSJBdffHG7azvssEMKhUIWLlyYQw89dKPmf+655/LMM89k0KBBq71eLBZTLBY3am4AAAAAYOOV/RXsJBk1alReeeWVvPLKKxk5cmS7a1tttVVGjhyZiy++OMuXL1/l3qVLl65z/gsvvDDdunXb6AATAAAAACiPTtkB2b179yxcuLD095tdcskl2XvvvbPHHnvk7LPPzq677poVK1bk5z//eS699NLSvUnywgsvZPHixXn11Vfzl7/8JT/4wQ9y+eWXZ9q0aRkyZEhnLAcAAAAAWE+dEkAmSU1NzRqvDRo0KL/5zW/yjW98I5MmTcqiRYuy9dZbZ/fdd8+ll17abuxXvvKVfOUrX8lmm22Wurq6fPjDH868efMyfPjwci8BAAAAANhAhbaVp8BsQlpaWt44Dfv0a9Ot2KvS5QAAvO00Th9d6RIAAKiglflac3PzWjceJp30DUgAAAAAYNMkgAQAAAAAyqbTvgHZFT06deQ6t4gCAAAAABvPDkgAAAAAoGwEkAAAAABA2QggAQAAAICy2aS/ATlsym3pVuxV6TIAAAA2SY3TR1e6BAA6gR2QAAAAAEDZCCABAAAAgLIRQAIAAAAAZSOABAAAAADKpiIB5IQJE3LooYeW/i4UCikUCunRo0cGDx6cM844I8uXL0+SNDY2plAopKqqKn/729/azbNo0aJUVVWlUCiksbGxk1cBAAAAAKxLl9gBOWrUqCxatCh//vOf8/Wvfz2XXHJJzjjjjHZj+vfvnyuuuKJd3+zZs/Pud7+7M0sFAAAAADZAlwggi8Vi6urqUl9fn0996lM5+uijc9NNN7Ubc9xxx2XmzJnt+mbNmpXjjjtunfO3trampaWlXQMAAAAAyq9LBJBvtvnmm+fVV19t1/dv//ZvWbJkSe6+++4kyd13353nn38+Y8eOXed806ZNS21tbanV19eXpW4AAAAAoL0uF0A+8MADueqqq3LAAQe06+/Ro0fGjx+fhoaGJElDQ0PGjx+fHj16rHPOyZMnp7m5udSamprKUjsAAAAA0F5VpQtIkrlz56a6ujorVqzIq6++mkMOOSTf/e53Vxl3wgknZK+99so555yTOXPm5L777suKFSvWOX+xWEyxWCxH6QAAAADAWnSJAHL48OG59NJL06NHj/Tv33+NuxqHDRuWoUOH5qijjsp73/veDBs2LAsWLOjcYgEAAACA9dYlXsHu3bt3hgwZkve85z3rfKV64sSJmT9/fiZOnNhJ1QEAAAAAG6tL7IDcEJ/+9KdzxBFHpE+fPpUuBQAAAABYh4oEkK+//nqqqjbu0VVVVenXr18HVwQAAAAAlENFAsh//OMfGTJkSJJk1qxZax07cODAtLW1rfH6brvtttbrAAAAAEDldOo3IJcsWZKf/OQnmT9/fkaMGNGZjwYAAAAAKqBTd0BOnDgxDz74YCZNmpRDDjmkMx+9Wo9OHZmamppKlwEAAAAA71idGkDeeOONnfk4AAAAAKDCOvUVbAAAAABg0yKABAAAAADKpiKnYHcVw6bclm7FXpUuAwBYh8bpoytdAgAAsJHsgAQAAAAAykYACQAAAACUjQASAAAAACgbASQAAAAAUDZdLoCcMGFCDj300NLfhUIh06dPbzfmpptuSqFQKP2eP39+CoVCli5d2omVAgAAAADr0uUCyDfr2bNnvvnNb2bJkiWVLgUAAAAA2EBdPoAcMWJE6urqMm3atI2eo7W1NS0tLe0aAAAAAFB+XT6A7N69e84555x897vfzVNPPbVRc0ybNi21tbWlVl9f38FVAgAAAACr0+UDyCT5+Mc/nt122y1TpkzZqPsnT56c5ubmUmtqaurgCgEAAACA1amqdAHr65vf/Gb233//TJo0aYPvLRaLKRaLZagKAAAAAFibt8UOyCTZZ599MnLkyJx11lmVLgUAAAAAWE9vmx2QSTJ9+vTstttu2XHHHStdCgAAAACwHt42OyCT5H3ve1+OPvrofPe73610KQAAAADAeuhyAeTrr7+eqqo1b8z82te+lra2tlXuSbLW+wAAAACAztflErt//OMfGTJkSJJk1qxZq1x/z3vek5dffnmVe3r37p3q6urOKBEAAAAAWE9dZgfkkiVL8pOf/CTz58/PiBEj1uue1tbWPPbYY/ne97633vcAAAAAAJ2ny+yAnDhxYh588MFMmjQphxxyyHrd89Of/jTHHHNM9t5771x00UUb/MxHp45MTU3NBt8HAAAAAKyfQtubP6i4CWhpaUltbW2am5sFkAAAAACwgTYkX+syr2ADAAAAAO88AkgAAAAAoGy6zDcgK2HYlNvSrdir0mUAAAAAG6Bx+uhKlwBsADsgAQAAAICyEUACAAAAAGUjgAQAAAAAykYACQAAAACUTdkCyAkTJuTQQw8t/V0oFDJ9+vR2Y2666aYUCoV2Y9bWkqSlpSVf/vKXM3To0PTs2TN1dXUZMWJEbrjhhrS1tZVrOQAAAADARui0HZA9e/bMN7/5zSxZsmS11y+88MIsWrSo1JJk5syZ7fqWLl2avffeO1dccUUmT56c3/zmN/nlL3+ZcePG5Ytf/GKam5s7azkAAAAAwHqo6qwHjRgxIn/84x8zbdq0nHvuuatcr62tTW1tbbu+Pn36pK6urvT75JNPTmNjY5544on079+/1L/jjjvmqKOOSs+ePcu3AAAAAABgg3XaDsju3bvnnHPOyXe/+9089dRTG3z/66+/nquvvjpHH310u/Bxperq6lRVrT5PbW1tTUtLS7sGAAAAAJRfpx5C8/GPfzy77bZbpkyZssH3Pvvss1myZEmGDh26wfdOmzattMOytrY29fX1GzwHAAAAALDhOv0U7G9+85uZPXt2HnvssQ26b+UBMysPo9kQkydPTnNzc6k1NTVt8BwAAAAAwIbr9AByn332yciRI3PWWWdt0H1bb711ttxyyyxcuHCDn1ksFlNTU9OuAQAAAADl1+kBZJJMnz49P/7xj3Pvvfeu9z3dunXLuHHjcuWVV+bpp59e5fry5cuzYsWKjiwTAAAAAHiLKhJAvu9978vRRx+d7373uxt03znnnJP6+vrsueeeueKKK/LYY4/lD3/4QxoaGrLbbrtl2bJlZaoYAAAAANgYFQkgk+RrX/ta6buO62vLLbfM/fffn/Hjx+frX/96PvCBD+SjH/1ofvjDH+Zb3/pWamtry1QtAAAAALAxCm0bmgK+A7S0tLxxGvbp16ZbsVelywEAAAA2QOP00ZUuATZ5K/O15ubmdZ63UrEdkAAAAADAO19VpQuopEenjnQiNgAAAACUkR2QAAAAAEDZCCABAAAAgLIRQAIAAAAAZbNJfwNy2JTbnIINAMA7ghNhAYCuyg5IAAAAAKBsBJAAAAAAQNkIIAEAAACAshFAAgAAAABl0+kB5IQJE3LooYeW/i4UCqXWt2/fjBo1Kg8//HC7e1Zev//++9v1t7a2pm/fvikUCpk/f34nrQAAAAAAWF8V3wE5atSoLFq0KIsWLcq8efNSVVWVMWPGrDKuvr4+M2fObNd34403prq6urNKBQAAAAA2UMUDyGKxmLq6utTV1WW33XbLmWeemaampjzzzDPtxh133HG5+uqr89JLL5X6Ghoactxxx3V2yQAAAADAeqp4APnPli1bliuvvDJDhgxJ3759213bfffdM2jQoFx//fVJkqampvzyl7/MMcccs855W1tb09LS0q4BAAAAAOVX8QBy7ty5qa6uTnV1dbbYYovcfPPNueaaa9Kt26qlHX/88WloaEiSzJw5Mx/72Mey9dZbr/MZ06ZNS21tbanV19d3+DoAAAAAgFVVPIAcPnx4FixYkAULFuRXv/pVDjrooBx88MF58sknVxk7fvz43Hffffnzn/+cWbNmZeLEiev1jMmTJ6e5ubnUmpqaOnoZAAAAAMBqVFW6gN69e2fIkCGl37vvvntqa2vzv//7v/n617/ebmzfvn0zZsyYnHDCCXn55Zdz8MEH54UXXljnM4rFYorFYofXDgAAAACsXcV3QL5ZoVBIt27d2h02888mTpyY+fPn59hjj0337t07uToAAAAAYENUfAdka2trFi9enCRZsmRJvve972XZsmUZO3bsasePGjUqzzzzTGpqajqzTAAAAABgI1Q8gLz11luz7bbbJkm22GKLDB06NHPmzMl+++232vGFQiH9+vXrxAoBAAAAgI1VaGtra6t0EZ2tpaXljdOwT7823Yq9Kl0OAAC8ZY3TR1e6BABgE7IyX2tubl7nm8pd7huQAAAAAMA7R8Vfwa6kR6eO9C1JAAAAACgjOyABAAAAgLIRQAIAAAAAZSOABAAAAADKZpP+BuSwKbc5BRsAAOgSnGQOwDuVHZAAAAAAQNkIIAEAAACAshFAAgAAAABlI4AEAAAAAMqmSwSQEyZMyKGHHrpK//z581MoFLJ06dLS34VCId26dUttbW0+8IEP5Itf/GIWLVrU+UUDAAAAAOvUJQLIDfH444/n6aefzoMPPpgzzzwzv/jFLzJs2LA88sgjlS4NAAAAAHiTt10Auc0226Suri477rhjjjzyyNxzzz3Zeuutc9JJJ1W6NAAAAADgTd52AeSbbb755vnsZz+be+65J//4xz9WO6a1tTUtLS3tGgAAAABQflWVLmCluXPnprq6ul3fa6+9tl73Dh06NEnS2NiYbbbZZpXr06ZNy9SpU996kQAAAADABukyOyCHDx+eBQsWtGuXX375et3b1taWJCkUCqu9Pnny5DQ3N5daU1NTh9UNAAAAAKxZl9kB2bt37wwZMqRd31NPPbVe9y5cuDBJMnDgwNVeLxaLKRaLb6k+AAAAAGDDdZkdkBvrpZdeyve///3ss88+2XrrrStdDgAAAADwT7rMDsj19Y9//CMvv/xyXnjhhfx//9//l3PPPTfPPvtsbrjhhkqXBgAAAAC8ydsugNxpp51SKBRSXV2dwYMH56CDDsp//ud/pq6urtKlAQAAAABv0iUCyFmzZq22f7/99isdMPPPfwMAAAAAbw9v+29AAgAAAABdV5fYAVkpj04dmZqamkqXAQAAAADvWHZAAgAAAABlI4AEAAAAAMpGAAkAAAAAlI0AEgAAAAAom036EJphU25Lt2KvSpcBAADAO1Dj9NGVLgGgS7ADEgAAAAAoGwEkAAAAAFA2AkgAAAAAoGy6XAC5ePHinHbaaRkyZEh69uyZd73rXfnIRz6Syy67LC+++GKS5KGHHsqYMWOyzTbbpGfPnhk4cGDGjRuXZ599tsLVAwAAAAD/rEsdQvPnP/85//qv/5o+ffrknHPOyfve976sWLEiTzzxRBoaGtK/f/98+MMfzogRIzJ27Njcdttt6dOnT/7yl7/k5ptvLgWUAAAAAEDXUGhra2urdBErjRo1Kr/73e/y+9//Pr17917leltbW370ox/liCOOyEsvvZSqqo3LT1taWlJbW5v60691CjYAAABl4RRs4J1sZb7W3NycmpqatY7tMq9gP/fcc/nZz36WU045ZbXhY5IUCoXU1dVlxYoVufHGG7O+2Wlra2taWlraNQAAAACg/LpMAPnHP/4xbW1t2Wmnndr19+vXL9XV1amurs6ZZ56ZD3/4wznrrLPyqU99Kv369cvBBx+cb33rW/n73/++xrmnTZuW2traUquvry/3cgAAAACAdKEAcqVCodDu9wMPPJAFCxZkl112SWtra5LkG9/4RhYvXpzLLrssO++8cy677LIMHTo0jzzyyGrnnDx5cpqbm0utqamp7OsAAAAAALpQADlkyJAUCoX8/ve/b9c/ePDgDBkyJJtvvnm7/r59++aII47I+eefn4ULF6Z///4577zzVjt3sVhMTU1NuwYAAAAAlF+XCSD79u2bAw88MN/73veyfPnyDbp3s802y/bbb7/B9wEAAAAA5dVlAsgkueSSS7JixYr8y7/8S6655posXLgwjz/+eH7wgx/k97//fbp37565c+dm/PjxmTt3bp544ok8/vjjOe+883LLLbfkkEMOqfQSAAAAAIB/UlXpAv7Z9ttvn4ceeijnnHNOJk+enKeeeirFYjE777xzzjjjjJx88slZvHhxevXqlUmTJqWpqSnFYjE77LBDLr/88hxzzDGVXgIAAAAA8E8KbW1tbZUuorO1tLS8cRr26demW7FXpcsBAADgHahx+uhKlwBQNivztebm5nWet9KlXsEGAAAAAN5ZBJAAAAAAQNl0qW9AdrZHp45c5xZRAAAAAGDj2QEJAAAAAJSNABIAAAAAKBsBJAAAAABQNpv0NyCHTbkt3Yq9Kl0GbDIap4+udAkAAABAJ7MDEgAAAAAoGwEkAAAAAFA2AkgAAAAAoGw2KoBcvHhxTjvttAwZMiQ9e/bMu971rnzkIx/JZZddlhdffDFJ8tBDD2XMmDHZZptt0rNnzwwcODDjxo3Ls88+u8Z558+fn0KhUGpbb711Dj744Pz2t78tjdlvv/1y+umnt7vvwgsvTLFYzFVXXbUxywEAAAAAymSDD6H585//nH/9139Nnz59cs455+R973tfVqxYkSeeeCINDQ3p379/PvzhD2fEiBEZO3ZsbrvttvTp0yd/+ctfcvPNN5cCyrV5/PHHU1NTk7/+9a/5/Oc/n1GjRuX3v/99amtrVxk7ZcqUfOtb38qNN96Yj33sYxu6HAAAAACgjDY4gDz55JNTVVWVX//61+ndu3ep/33ve18OO+ywtLW15Uc/+lFaWlpy+eWXp6rqjUcMGjQo+++//3o9Y5tttkmfPn1SV1eX888/Px/5yEdy//33Z+TIkaUxbW1t+fznP5//+7//y89+9rN85CMf2dClAAAAAABltkGvYD/33HP52c9+llNOOaVd+PjPCoVC6urqsmLFitx4441pa2t7SwVuvvnmSZJXX3211LdixYocc8wxmTNnTu688851ho+tra1paWlp1wAAAACA8tugAPKPf/xj2trastNOO7Xr79evX6qrq1NdXZ0zzzwzH/7wh3PWWWflU5/6VPr165eDDz443/rWt/L3v/99g4p77rnnMnXq1GyxxRbZY489Sv3/+7//mzlz5mT+/Pl5//vfv855pk2bltra2lKrr6/foDoAAAAAgI2zUYfQFAqFdr8feOCBLFiwILvssktaW1uTJN/4xjeyePHiXHbZZdl5551z2WWXZejQoXnkkUeSJLvssksptDz44IPbzbfddtuluro6/fr1y8KFCzNnzpxss802pesf+chHUl1dnf/6r//KihUr1lnv5MmT09zcXGpNTU0bs2wAAAAAYANt0DcghwwZkkKhkN///vft+gcPHpzk/70uvVLfvn1zxBFH5Igjjsi0adPygQ98IOedd15mz56dW265pfRa9Zvvu+uuu1JTU5Ott946NTU1q9Txvve9L+eff35GjBiRT37yk7nmmmvSo0ePNdZdLBZTLBY3ZKkAAAAAQAfYoB2Qffv2zYEHHpjvfe97Wb58+QY9aLPNNsv2229fuu8973lPhgwZkiFDhuTd7353u7GDBg3K9ttvv9rwcaXddtstt99+e+6+++4cccQR7b4RCQAAAAB0DRv8CvYll1ySFStW5F/+5V9yzTXXZOHChXn88cfzgx/8IL///e/TvXv3zJ07N+PHj8/cuXPzxBNP5PHHH895552XW265JYccckiHFb/rrrvmjjvuyH333ZfDDz88r7zySofNDQAAAAC8dRv0CnaSbL/99nnooYdyzjnnZPLkyXnqqadSLBaz884754wzzsjJJ5+cxYsXp1evXpk0aVKamppSLBazww475PLLL88xxxzToQvYZZddcscdd+SAAw7IYYcdluuvvz6bbbZZhz4DAAAAANg4hba2trZKF9HZWlpa3jgN+/Rr063Yq9LlwCajcfroSpcAAAAAdICV+Vpzc/NaP6OYbOQp2AAAAAAA60MACQAAAACUzQZ/A/Kd5NGpI9e5RRQAAAAA2Hh2QAIAAAAAZSOABAAAAADKRgAJAAAAAJTNJv0NyGFTbku3Yq9KlwEAdLDG6aMrXQIAAPD/ZwckAAAAAFA2AkgAAAAAoGwEkAAAAABA2QggAQAAAICy6VIB5IQJE3LooYe267vuuuvSs2fPnHvuuXnttddywQUXZNddd03Pnj3Tp0+fHHzwwbnnnnsqUzAAAAAAsFZdKoB8s8svvzxHH310vve97+ULX/hCjjzyyJx99tn5/Oc/n4ULF+bOO+9MfX199ttvv9x0002VLhcAAAAAeJOqShewJueee26+8pWv5Kqrrsphhx2Wa665Jtddd11uvvnmjB07tjTu+9//fp577rmceOKJOfDAA9O7d+9V5mptbU1ra2vpd0tLS6esAQAAAAA2dV1yB+SXvvSlfO1rX8vcuXNz2GGHJUmuuuqq7Ljjju3Cx5UmTZqU5557Lj//+c9XO9+0adNSW1tbavX19WWtHwAAAAB4Q5cLIH/605/mm9/8Zn70ox9lxIgRpf4nnngi733ve1d7z8r+J554YrXXJ0+enObm5lJramrq+MIBAAAAgFV0uVewd9111zz77LP5yle+kg996EPZYost1vveQqGw2v5isZhisdhRJQIAAAAA66nL7YB897vfnTvvvDOLFi3KqFGj8sILLyRJdtxxxzz22GOrvWfhwoVJkh122KHT6gQAAAAA1q3LBZBJMmDAgNx55535xz/+kYMOOigtLS058sgj84c//CE//vGPVxl//vnnp2/fvjnwwAMrUC0AAAAAsCZdMoBMku222y7z58/Pc889l4MOOigf+9jH8vGPfzzHHXdcZsyYkcbGxjz88MP5zGc+k5tvvjmXX375ak/ABgAAAAAqp8sGkMn/ex176dKlOfDAA/P9738/X/7yl3PBBRdk6NCh+ehHP5onn3wyd9xxRw499NBKlwsAAAAAvEmhra2trdJFdLaWlpbU1tam/vRr063Yq9LlAAAdrHH66EqXAAAA72gr87Xm5ubU1NSsdWyX3gEJAAAAALy9VVW6gEp6dOrIdSa0AAAAAMDGswMSAAAAACgbASQAAAAAUDYCSAAAAACgbDbpb0AOm3KbU7ABAADKoHH66EqXAEAXYQckAAAAAFA2AkgAAAAAoGwEkAAAAABA2QggAQAAAICyecsB5OLFi3Pqqadm8ODBKRaLqa+vz9ixYzNv3rwkyUMPPZQxY8Zkm222Sc+ePTNw4MCMGzcuzz77bLt5rr/++uy3336pra1NdXV1dt1115x99tl5/vnnS2NeeumlTJkyJTvttFOKxWL69euXww8/PL/73e/e6jIAAAAAgDJ4SwFkY2Njdt9999x+++0599xz88gjj+TWW2/N8OHDc8opp+Qf//hHRowYkX79+uW2227LwoUL09DQkG233TYvvvhiaZ4vf/nLGTduXD70oQ/lpz/9aR599NGcf/75+e1vf5v/+7//S5K0trZmxIgRaWhoyNe+9rU88cQTueWWW/Laa69lzz33zP333//W/hMAAAAAQIcrtLW1tW3szR/72Mfy8MMP5/HHH0/v3r3bXVu6dGnmz5+fI444Ii+99FKqqqpWO8cDDzyQPffcM9/5zndy2mmnrXJ96dKl6dOnT775zW9m8uTJeeihh/L+97+/dP3111/PnnvumRdffDGPPvpoCoXCKnO0tramtbW19LulpSX19fWpP/3adCv22tjlAwAAsAaN00dXugQAyqilpSW1tbVpbm5OTU3NWsdu9A7I559/PrfeemtOOeWUVcLHJOnTp0/q6uqyYsWK3HjjjVlTznnllVemuro6J5988mqv9+nTJ0ly1VVX5cADD2wXPiZJt27d8h//8R957LHH8tvf/na1c0ybNi21tbWlVl9fvwErBQAAAAA21kYHkH/84x/T1taWoUOHrnHMhz/84Zx11ln51Kc+lX79+uXggw/Ot771rfz9738vjfnDH/6QwYMHp0ePHmt93hNPPJH3vve9q722sv+JJ55Y7fXJkyenubm51Jqamta1PAAAAACgA2x0ALlyR+PqXnn+Z9/4xjeyePHiXHbZZdl5551z2WWXZejQoXnkkUdK86xrjrdaS7FYTE1NTbsGAAAAAJTfRgeQO+ywQwqFQhYuXLjOsX379s0RRxyR888/PwsXLkz//v1z3nnnJUl23HHH/OlPf8qrr7661jl23HHHPPbYY6u99vvf/75UEwAAAADQdWx0ALnVVltl5MiRufjii7N8+fJVri9dunS192222WbZfvvtS/d86lOfyrJly3LJJZesdvzKeY488sj84he/WOU7j6+//nouuOCC7Lzzzqt8HxIAAAAAqKyNDiCT5JJLLslrr72WPfbYI9dff33+8Ic/ZOHChbnooouy1157Ze7cuRk/fnzmzp2bJ554Io8//njOO++83HLLLTnkkEOSJHvuuWe++MUvZtKkSfniF7+Y++67L08++WTmzZuXI444IrNnz06S/Md//Ef22GOPjB07NnPmzMlf//rXPPjggznssMOycOHCzJgx4y2/yg0AAAAAdKyqt3LzoEGD8pvf/Cbf+MY3MmnSpCxatChbb711dt9991x66aUZMGBAevXqlUmTJqWpqSnFYjE77LBDLr/88hxzzDGleb75zW9m9913z8UXX5zLLrssr7/+erbffvscfvjhOe6445IkPXv2zO23355p06blrLPOypNPPpktttgiw4cPz/33359hw4a9tf8EAAAAANDhCm0rT3DZhLS0tKS2tjb1p1+bbsVelS4HAADgHadx+uhKlwBAGa3M15qbm9d54PNbegUbAAAAAGBt3tIr2G93j04duc6EFgAAAADYeHZAAgAAAABlI4AEAAAAAMpGAAkAAAAAlM0m/Q3IYVNucwo2AADQ4ZwADQD/jx2QAAAAAEDZCCABAAAAgLIRQAIAAAAAZSOABAAAAADKplMDyMWLF+fUU0/N4MGDUywWU19fn7Fjx+a2225Lv3798vWvf321902bNi39+vXLK6+8ki9+8YsZOHBgXnjhhXZjxo4dm3322Sevv/56ZywFAAAAAFgPnRZANjY2Zvfdd8/tt9+ec889N4888khuvfXWDB8+PKeddlrGjx+fWbNmpa2tbZV7Z86cmWOOOSabbbZZvva1r6W6ujr/+Z//Wbre0NCQO+64IzNnzky3bjZ1AgAAAEBXUdVZDzr55JNTKBTywAMPpHfv3qX+XXbZJRMnTkxTU1MuvPDC/PKXv8y+++5bun7XXXflD3/4Q0444YQkSbFYzOzZs7PXXnvlsMMOy84775z/+I//yLnnnpvtt9++s5YDAAAAAKyHTgkgn3/++dx66635xje+0S58XKlPnz7p06dPPvShD2XmzJntAsiGhobsscceGTZsWKlv9913z+TJk3PiiSdm++23z4c+9KGcdNJJa3x+a2trWltbS79bWlo6aGUAAAAAwNp0yvvKf/zjH9PW1pahQ4euddzEiRNz3XXXZdmyZUmSZcuWZc6cOaXdj//sv/7rv9KtW7f86le/SkNDQwqFwhrnnTZtWmpra0utvr7+rS0IAAAAAFgvnRJArvyu49pCwiQ56qij8vrrr+eaa65JklxzzTVpa2vLkUceucrYn//851m0aFHa2try4IMPrnXeyZMnp7m5udSampo2ciUAAAAAwIbolAByhx12SKFQyMKFC9c6rra2NocffnhmzpyZ5I3DZw4//PDU1NS0G7dkyZJ8+tOfzllnnZX//u//zsknn5xnn312jfMWi8XU1NS0awAAAABA+XVKALnVVltl5MiRufjii7N8+fJVri9durT09wknnJB77rknc+fOzT333LPa169PPfXUbLPNNvmv//qvfOlLX0p9fX0+97nPlXMJAAAAAMBG6JQAMkkuueSSvPbaa9ljjz1y/fXX5w9/+EMWLlyYiy66KHvttVdp3L777pshQ4bk2GOPzZAhQ7LPPvu0m+fGG2/MnDlzMnv27PTo0SNVVVWZNWtWbrzxxlx//fWdtRwAAAAAYD10WgA5aNCg/OY3v8nw4cMzadKkDBs2LAceeGDmzZuXSy+9tN3YiRMnZsmSJZk4cWK7/meffTaf/exnM2XKlOy6666l/mHDhmXKlCnrfBUbAAAAAOhchbaVJ8RsQlpaWt44Dfv0a9Ot2KvS5QAAAO8wjdNHV7oEACirlflac3PzOs9b6bQdkAAAAADApqeq0gVU0qNTRzoRGwAAAADKyA5IAAAAAKBsBJAAAAAAQNkIIAEAAACAstmkvwE5bMptTsEGAADKwknYAPAGOyABAAAAgLIRQAIAAAAAZSOABAAAAADKRgAJAAAAAJRNRQLIxYsX59RTT83gwYNTLBZTX1+fsWPHZt68ee3GnXPOOenevXumT59e6hs4cGAKhcIa23777dfJqwEAAAAA1qTTT8FubGzMv/7rv6ZPnz4599xzs+uuu+bVV1/NbbfdllNOOSW///3vS2NnzpyZL37xi2loaMiXvvSlJMmDDz6Y1157LUly77335rDDDsvjjz+empqaJMlmm23W2UsCAAAAANag0wPIk08+OYVCIQ888EB69+5d6t9ll10yceLE0u8777wzL730Us4+++xcccUV+eUvf5l99tknW2+9dWnMVlttlSTZZptt0qdPn05bAwAAAACwfjr1Feznn38+t956a0455ZR24eNK/xwizpgxI0cddVR69OiRo446KjNmzNjo57a2tqalpaVdAwAAAADKr1MDyD/+8Y9pa2vL0KFD1zqupaUl119/fcaPH58kGT9+fK677rqNDg6nTZuW2traUquvr9+oeQAAAACADdOpAWRbW1uSpFAorHXcVVddlcGDB+f9739/kmS33XbL4MGDc/XVV2/UcydPnpzm5uZSa2pq2qh5AAAAAIAN06kB5A477JBCoZCFCxeudVxDQ0N+97vfpaqqqtR+97vfbfRr2MViMTU1Ne0aAAAAAFB+nXoIzVZbbZWRI0fm4osvzuc///lVvgO5dOnSNDU15de//nXmz59fOmRm5bV99tknjz76aIYNG9aZZQMAAAAAG6nTT8G+5JJLsvfee2ePPfbI2WefnV133TUrVqzIz3/+81x66aUZOXJk9thjj+yzzz6r3LvXXntlxowZueCCCzq7bAAAAABgI3TqK9hJMmjQoPzmN7/J8OHDM2nSpAwbNiwHHnhg5s2blwsvvDA/+MEPcthhh6323sMOOyw/+MEP8sorr3Ry1QAAAADAxii0rTwZZhPS0tLyxmnYp1+bbsVelS4HAAB4B2qcPrrSJQBA2azM15qbm9d53kqn74AEAAAAADYdnf4NyK7k0akjnYgNAAAAAGVkByQAAAAAUDYCSAAAAACgbASQAAAAAEDZbNLfgBw25TanYAMAAACbjMbpoytdApsgOyABAAAAgLIRQAIAAAAAZSOABAAAAADKRgAJAAAAAJRNlwogJ0yYkEKhUGp9+/bNqFGj8vDDD68y9t///d/TvXv3XH311RWoFAAAAABYH10qgEySUaNGZdGiRVm0aFHmzZuXqqqqjBkzpt2YF198Mddcc02+8IUvZMaMGRWqFAAAAABYly4XQBaLxdTV1aWuri677bZbzjzzzDQ1NeWZZ54pjZkzZ0523nnnTJ48Offcc08aGxsrVzAAAAAAsEZdLoD8Z8uWLcuVV16ZIUOGpG/fvqX+GTNmZPz48amtrc3HPvaxzJw5c63ztLa2pqWlpV0DAAAAAMqvywWQc+fOTXV1daqrq7PFFlvk5ptvzjXXXJNu3d4o9Q9/+EPuv//+jBs3Lkkyfvz4zJw5M6+//voa55w2bVpqa2tLrb6+vlPWAgAAAACbui4XQA4fPjwLFizIggUL8qtf/SoHHXRQDj744Dz55JNJ3tj9OHLkyPTr1y9J8rGPfSzLly/PL37xizXOOXny5DQ3N5daU1NTp6wFAAAAADZ1VZUu4M169+6dIUOGlH7vvvvuqa2tzf/+7/9m6tSpueKKK7J48eJUVf2/0l977bXMmDEjBx100GrnLBaLKRaLZa8dAAAAAGivywWQb1YoFNKtW7e89NJLueWWW/LCCy/koYceSvfu3Utjfv/73+foo4/Oc8891+5bkQAAAABAZXW5ALK1tTWLFy9OkixZsiTf+973smzZsowdOzbf+c53Mnr06Lz//e9vd88uu+yS008/PT/4wQ9y2mmnVaJsAAAAAGA1utw3IG+99dZsu+222XbbbbPnnnvmwQcfzJw5c/Le9743P/nJT3LYYYetck+hUMgnPvGJzJgxowIVAwAAAABrUmhra2urdBGdraWl5Y3TsE+/Nt2KvSpdDgAAAECnaJw+utIl8A6xMl9rbm5OTU3NWsd2uR2QAAAAAMA7R5f7BmRnenTqyHUmtAAAAADAxrMDEgAAAAAoGwEkAAAAAFA2AkgAAAAAoGwEkAAAAABA2WzSh9AMm3JbuhV7VboMAAAAAN4hGqePrnQJXY4dkAAAAABA2QggAQAAAICyEUACAAAAAGXT4QHkvffem+7du2fUqFFrHHPVVVele/fu+exnP7vKtfnz56dQKJTa1ltvnYMPPji//e1vS2P222+/nH766R1dOgAAAADQwTo8gGxoaMipp56au+++O3/961/XOOaLX/xirr766rz44ourHfP4449n0aJF+clPfpIlS5Zk1KhRaW5u7uhyAQAAAIAy6tAAcvny5bn22mtz0kknZcyYMZk1a9YqYxobG3PvvffmS1/6UoYOHZrrrrtutXNts802qauryx577JHzzz8/ixcvzv3339+R5QIAAAAAZdahAeQ111yTnXbaKTvttFPGjx+fmTNnpq2trd2YhoaGjB49OrW1tRk/fnxmzJixznk333zzJMmrr766UXW1trampaWlXQMAAAAAyq9DA8gZM2Zk/PjxSZJRo0Zl2bJlmTdvXun666+/nlmzZpXGHHnkkbnvvvvyxz/+cY1zPvfcc5k6dWq22GKL7LHHHhtV17Rp01JbW1tq9fX1GzUPAAAAALBhOiyAfPzxx/PAAw/kyCOPTJJUVVVl3LhxaWhoKI352c9+luXLl+fggw9OkvTr1y8HHXRQuzErbbfddqmurk6/fv2ycOHCzJkzJ9tss81G1TZ58uQ0NzeXWlNT00bNAwAAAABsmKqOmmjGjBlZsWJF3v3ud5f62tra0qNHjyxZsiRbbrllGhoa8vzzz6dXr16lMa+//noeeuihfO1rX0v37t1L/XfddVdqamqy9dZbp6am5i3VViwWUywW39IcAAAAAMCG65AAcsWKFbniiity/vnn56CDDmp37bDDDsuVV16Zo446Kj/60Y9y9dVXZ5dddildf/311/PRj340P/3pTzNmzJhS/6BBg9KnT5+OKA8AAAAAqJAOCSDnzp2bJUuW5IQTTkhtbW27a4cffnhpd2Tfvn1zxBFHpFu39m9+jxkzJjNmzGgXQK7LM888kwULFrTrq6urS11d3UavAwAAAADoWB3yDcgZM2ZkxIgRq4SPyRs7IBcsWJCvfOUr+fjHP75K+LhyzNy5c/P3v/99vZ951VVX5QMf+EC7dtlll72ldQAAAAAAHavQ1tbWVukiOltLS8sbp2Gffm26FXut+wYAAAAAWA+N00dXuoROsTJfa25uXuf5LR12CjYAAAAAwJt12CnYb0ePTh35lk/YBgAAAADWzA5IAAAAAKBsBJAAAAAAQNkIIAEAAACAshFAAgAAAABls0kfQjNsym3pVuxV6TIAAACgncbpoytdAkCHsQMSAAAAACgbASQAAAAAUDYCSAAAAACgbDo9gLz33nvTvXv3jBo1ql3/Lbfcks022yy/+c1v2vWfd9556devXxYvXpwkmTBhQgqFQgqFQnr06JHBgwfnjDPOyPLlyzttDQAAAADA+un0ALKhoSGnnnpq7r777vz1r38t9X/sYx/Lsccem2OPPTatra1JkoULF+a///u/c/HFF6eurq40dtSoUVm0aFH+/Oc/5+tf/3ouueSSnHHGGZ29FAAAAABgHTo1gFy+fHmuvfbanHTSSRkzZkxmzZrV7voFF1yQZcuWZcqUKVmxYkWOPfbYjB07NuPGjWs3rlgspq6uLvX19fnUpz6Vo48+OjfddFPnLQQAAAAAWC9Vnfmwa665JjvttFN22mmnjB8/Pqeeemr++7//O4VCIUmyxRZbpKGhISNHjsxf/vKXNDU15ac//ek65918883z6quvrvF6a2traVdlkrS0tLz1xQAAAAAA69SpOyBnzJiR8ePHJ3njNeply5Zl3rx57cbsv//+Ofzww3PttdfmoosuSr9+/dY65wMPPJCrrroqBxxwwBrHTJs2LbW1taVWX1//1hcDAAAAAKxTpwWQjz/+eB544IEceeSRSZKqqqqMGzcuDQ0N7cY9/fTTufXWW9OrV6/cddddq51r7ty5qa6uTs+ePbPXXntln332yXe/+901Pnvy5Mlpbm4utaampo5bGAAAAACwRp32CvaMGTOyYsWKvPvd7y71tbW1pUePHlmyZEm23HLLJMmJJ56Y97///Zk6dWoOOOCAHH744dl3333bzTV8+PBceuml6dGjR/r3758ePXqs9dnFYjHFYrHjFwUAAAAArFWnBJArVqzIFVdckfPPPz8HHXRQu2uHHXZ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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(stocks_list, Pi)\n",
    "plt.legend('Min '+ stocks_list[ np.argmin(Pi)])\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# result2 = get_frontier(Pi+rf, C, rf)\n",
    "result2 = efficient_frontier_class(Pi+rf, C, rf)\n",
    "\n",
    "display_assets(names, R, C, color='red')\n",
    "display_frontier(result1, label='Historical returns', color='red')\n",
    "display_assets(names, Pi+rf, C, color='green')\n",
    "display_frontier(result2, label='Implied returns', color='green')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Determine views to the equilibrium returns and prepare views (Q) and link (P) matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_latest_data():\n",
    "    symbols = stocks_list\n",
    "    n = len(symbols)\n",
    "    prices_out = []\n",
    "    for s in symbols:\n",
    "        dataframe = pd.read_csv('dow-data-23/%s.csv' % s, index_col=None, parse_dates=['date'])\n",
    "        prices = list(dataframe['close']) \n",
    "        prices_out.append(prices)\n",
    "    return  prices_out\n",
    "\n",
    "latest_prices = load_latest_data()\n",
    "# print(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_absolute_views(latest_prices):\n",
    "    latest_prices = pd.DataFrame(latest_prices).T\n",
    "    latest_prices = latest_prices.pct_change()\n",
    "    views_returns = ((1 + np.mean(latest_prices, axis=0)) ** 252)-1\n",
    "\n",
    "    absolute_views = []\n",
    "    for i in range(len(views_returns)):\n",
    "        absolute_views.append((stocks_list[i], views_returns[i]))\n",
    "\n",
    "    return absolute_views\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "absolute_views = get_absolute_views(latest_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Views Matrix\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Views\n",
       "0   0.010\n",
       "1   0.220\n",
       "2   0.170\n",
       "3   0.280\n",
       "4   0.240\n",
       "5  -0.100\n",
       "6   0.170\n",
       "7   0.120\n",
       "8   0.230\n",
       "9   0.054\n",
       "10  0.164\n",
       "11  0.190"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Link Matrix\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM</th>\n",
       "      <th>AXP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CVX</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>KO</th>\n",
       "      <th>GS-PK</th>\n",
       "      <th>GS</th>\n",
       "      <th>...</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>VZ</th>\n",
       "      <th>V</th>\n",
       "      <th>WMT</th>\n",
       "      <th>WBA</th>\n",
       "      <th>DIS</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>HON</th>\n",
       "      <th>CRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    MMM  AXP  AAPL   BA  CAT  CVX  CSCO   KO  GS-PK   GS  ...  TRV  UNH   VZ  \\\n",
       "0   0.0  0.0   1.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "1   0.0  0.0   1.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "2   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "3   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "4   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  1.0  ...  0.0  0.0  0.0   \n",
       "5   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "6   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "7   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "8   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "9   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "10  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  1.0  0.0   \n",
       "11  0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "      V  WMT  WBA  DIS  AMGN  HON  CRM  \n",
       "0   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "1   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "2   0.0  0.0  0.0  0.0   0.0  0.0  1.0  \n",
       "3   0.0  0.0  0.0  1.0   0.0  0.0  0.0  \n",
       "4   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "5   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "6   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "7   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "8   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "9   0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "10  0.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "11  1.0  0.0  0.0  0.0   0.0  0.0  0.0  \n",
       "\n",
       "[12 rows x 30 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_views_and_link_matrix(names, relative_views, absolute_views):\n",
    "    r, a, c = len(relative_views), len(absolute_views), len(names)\n",
    "    Q = [relative_views[i][3] for i in range(r)]  # view matrix\n",
    "    if(absolute_views):\n",
    "        for i in absolute_views:\n",
    "            Q.append(i[1]);\n",
    "    P = np.zeros([r+a, c])\n",
    "    nameToIndex = dict()\n",
    "    for i, n in enumerate(names):\n",
    "        nameToIndex[n] = i\n",
    "    for i, v in enumerate(relative_views):\n",
    "        name1, name2 = relative_views[i][0], relative_views[i][2]\n",
    "        P[i, nameToIndex[name1]] = +1 if relative_views[i][1] == '>' else -1\n",
    "        P[i, nameToIndex[name2]] = -1 if relative_views[i][1] == '>' else +1\n",
    "    for i, v in enumerate(absolute_views):\n",
    "        name  = absolute_views[i][0]\n",
    "        P[i+r, nameToIndex[name]] += 1\n",
    "    return np.array(Q), P\n",
    "\n",
    "    \n",
    "\n",
    "relative_views = [('AAPL', '>', 'MSFT', 0.01)]\n",
    "\n",
    "absolute_views_1 = [ \n",
    "                    ('AAPL', 0.22),\n",
    "                    ('CRM', 0.17),\n",
    "                    ('DIS', 0.28),\n",
    "                    ('GS', 0.24),\n",
    "                    ('JNJ', -0.1),\n",
    "                    ('JPM', 0.17),\n",
    "                    ('MRK', 0.12),\n",
    "                    ('MSFT', 0.23),\n",
    "                    ('PG', 0.054),\n",
    "                    ('UNH', 0.164),\n",
    "                    ('V', 0.19)\n",
    "                    ]\n",
    "\n",
    "\n",
    "Q, P = create_views_and_link_matrix(names, relative_views, absolute_views_1)\n",
    "print('Views Matrix')\n",
    "display(pd.DataFrame({'Views':Q}))\n",
    "print('Link Matrix')\n",
    "P = pd.DataFrame(P)\n",
    "P.columns = stocks_list\n",
    "display(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11277747, 0.16278477, 0.20432273, 0.18886099, 0.13775823,\n",
       "       0.13766637, 0.14897301, 0.08746181, 0.03836485, 0.16018284,\n",
       "       0.13983193, 0.12036467, 0.17523799, 0.08539939, 0.15054211,\n",
       "       0.10294032, 0.14577277, 0.08827086, 0.19502549, 0.08858469,\n",
       "       0.10973282, 0.13287184, 0.06660796, 0.16020669, 0.08064652,\n",
       "       0.1158481 , 0.13695897, 0.11271397, 0.13345574, 0.18376174])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.23839134e-03  7.46312017e-04 -1.80565169e-04 -9.81068228e-06\n",
      "  2.67495815e-05 -4.46446453e-05 -1.44607586e-05 -7.22161454e-05\n",
      " -4.92079319e-04 -4.84351649e-05 -2.94566467e-05 -7.66039297e-05]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.04380616, -0.04885387, -0.15084013, -0.04688609,  0.06105693,\n",
       "       -0.0644093 , -0.00345906,  0.05840203,  0.71417161,  0.016889  ,\n",
       "       -0.04552144,  0.00989502,  0.00450231,  0.07801649,  0.01758304,\n",
       "        0.02146466, -0.01993484,  0.01324908,  0.03326674,  0.04806143,\n",
       "       -0.00104057, -0.03443667,  0.11088363,  0.01817534,  0.10808876,\n",
       "        0.02363653,  0.02252544,  0.00722653, -0.03192948,  0.03641072])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau = 0.025  # scaling factor\n",
    "\n",
    "# Calculate omega - uncertainty matrix about views\n",
    "omega = np.dot(np.dot(np.dot(tau, P), C), np.transpose(P))  # 0.025 * P * C * transpose(P)\n",
    "print(omega[0])\n",
    "\n",
    "\n",
    "# rows,cols = omega.shape\n",
    "# for r in range(rows):\n",
    "#     for c in range(cols):\n",
    "#         omega[r,c] = random.choice(range(90, 100))/1000\n",
    "# print(omega)\n",
    "# omega = np.ones(omega.shape)/11\n",
    "# Calculate equilibrium excess returns with views incorporated\n",
    "sub_a = np.linalg.inv(np.dot(tau, C))  #(tau.C) inverse\n",
    "sub_b = np.dot(np.dot(np.transpose(P), np.linalg.inv(omega)), P)  # (P^T.omega^-1).P\n",
    "sub_c = np.dot(np.linalg.inv(np.dot(tau, C)), Pi)  # ((Tau.C)^-1).Pi\n",
    "sub_d = np.dot(np.dot(np.transpose(P), np.linalg.inv(omega)), Q)   # (P^T.omega^-1).Q\n",
    "Pi_adj = np.dot(np.linalg.inv(sub_a + sub_b), (sub_c + sub_d))   \n",
    "\n",
    "\n",
    "# result3 = get_frontier(Pi_adj + rf, C, rf)\n",
    "result3 = efficient_frontier_class(Pi_adj+rf , C, rf)\n",
    "result3.W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "             MMM       AXP      AAPL        BA      CAT       CVX     CSCO  \\\n",
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       "      <td>-0.046886</td>\n",
       "      <td>0.061057</td>\n",
       "      <td>-0.064409</td>\n",
       "      <td>-0.003459</td>\n",
       "      <td>0.058402</td>\n",
       "      <td>0.714172</td>\n",
       "      <td>0.016889</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001041</td>\n",
       "      <td>-0.034437</td>\n",
       "      <td>0.110884</td>\n",
       "      <td>0.018175</td>\n",
       "      <td>0.108089</td>\n",
       "      <td>0.023637</td>\n",
       "      <td>0.022525</td>\n",
       "      <td>0.007227</td>\n",
       "      <td>-0.031929</td>\n",
       "      <td>0.036411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MMM       AXP     AAPL        BA       CAT       CVX      CSCO  \\\n",
       "Weight  0.043806 -0.048854 -0.15084 -0.046886  0.061057 -0.064409 -0.003459   \n",
       "\n",
       "              KO     GS-PK        GS  ...       TRV       UNH        VZ  \\\n",
       "Weight  0.058402  0.714172  0.016889  ... -0.001041 -0.034437  0.110884   \n",
       "\n",
       "               V       WMT       WBA       DIS      AMGN       HON       CRM  \n",
       "Weight  0.018175  0.108089  0.023637  0.022525  0.007227 -0.031929  0.036411  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display_assets(names, R, C, color='red')\n",
    "# display_frontier(result1, label='Historic returns', color='red')\n",
    "\n",
    "display_assets(names, Pi+rf, C, color='green')\n",
    "display_frontier(result2, label='Implied returns', color='green')\n",
    "\n",
    "display_assets(names, Pi_adj+rf, C, color='blue')\n",
    "display_frontier(result3, label='Implied returns (adjusted views)', color='blue')\n",
    "plt.xlabel('variance $\\sigma$'), plt.ylabel('mean $\\mu$'), plt.legend(), plt.show()\n",
    "\n",
    "# display(pd.DataFrame({'Weight': W}, index=names).T)\n",
    "\n",
    "display(pd.DataFrame({'Weight': result2.W}, index=names).T)\n",
    "display(pd.DataFrame({'Weight': result3.W}, index=names).T)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ahn1KbU83APByPPHEE3nuuefyqle9qsP4q171qmzcuHGX52zcuHGX9du2bcsTTzyRQw89tLB+gWJ1ZU74X9ddd122bt2as88+u4gWgSrpynzwhz/8IbNmzcry5ctTW+ufSrAv6cqc8Nhjj+X+++9Pnz598oMf/CBPPPFELrzwwjz11FOeAwmd5G9VYJ9QKpU6vK5UKjuNvVj9rsaBvVNn54QdFi1alDlz5uSHP/xhBg0aVFR7QBW91Pngueeey7nnnpumpqYcc8wx1WoPqLLOfEfYvn17SqVSbrrppjQ0NCRJvvCFL2TSpEmZN29eDjzwwML7hX2FABLYqw0YMCA1NTU7/a/lP/7xj53+d3OHwYMH77K+trY2/fv3L6xXoHhdmRN2WLJkSS644ILccsstOeOMM4psE6iCzs4HmzdvzsMPP5yVK1fm4osvTvJ8+FCpVFJbW5t77rknp512WlV6B7pfV74jHHrooTn88MPbw8fk+WdGViqV/PWvf83RRx9daM+wL/EMSGCvdsABB2TEiBG59957O4zfe++9edvb3rbLc0aOHLlT/T333JMTTzwxvXv3LqxXoHhdmROS51c+Tps2LQsXLsy4ceOKbhOogs7OB/X19fn1r3+dVatWtR8zZszI8OHDs2rVqrzlLW+pVutAAbryHeHtb397Hn/88WzZsqV97Pe//3169eqVI444otB+YV9jBSSw17v00ktz3nnn5cQTT8zIkSPzzW9+M+vWrcuMGTOSJLNnz87f/va3LFiwIMnzO15/7Wtfy6WXXpoPfOADefDBB1Mul7No0aKevA2gm3R2Tli0aFGmTp2aL3/5y3nrW9/avjLiwAMP7LDiAdj7dGY+6NWrV44//vgO5w8aNCh9+vTZaRzYO3X2O8K5556bT3/60zn//PPT1NSUJ554Ih//+Mczffp0P7+GThJAAnu9yZMn58knn8xVV12VDRs25Pjjj8/SpUvzmte8JkmyYcOGrFu3rr1+6NChWbp0aT760Y9m3rx5Oeyww/KVr3wlEydO7KlbALpRZ+eEb3zjG9m2bVsuuuiiXHTRRe3j73vf+3LjjTdWu32gG3V2PgD2bZ2dE/r27Zt77703M2fOzIknnpj+/fvn7LPPzmc+85meugXYa5UqO3ZeAAAAAADoZp4BCQAAAAAURgAJAAAAABRGAAkAAAAAFEYACQAAAAAURgAJAAAAABRGAAkAAAAAFEYACQAAAAAURgAJAAAAABRGAAkAAAAAFEYACQAAAAAURgAJAAAAABTm/wHhejkbrlCy6wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Before Views Weights': result2.W, 'After Views Weights ': result3.W}, index=stocks_list)\n",
    "ax = df.plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set width of bars\n",
    "barWidth = 0.35\n",
    "\n",
    " \n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(30)\n",
    "r2 = [x + barWidth for x in r1]\n",
    "\n",
    "# Make the plot\n",
    "plt.bar(r1, result2.W, color='#7f6d5f', width=barWidth, edgecolor='white', label='Before Views')\n",
    "plt.bar(r2, result3.W, color='#557f2d', width=barWidth, edgecolor='white', label='After Views')\n",
    "\n",
    "# Add xticks on the middle of the group bars\n",
    "plt.xlabel('group', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(30)], stocks_list)\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(stocks_list, result2.W, color='orange')\n",
    "plt.bar(stocks_list, result3.W, width=0.6)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Wedge sizes 'x' must be non negative values",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/9f/pss22hbs0l167ntml_wpj_kr0000gn/T/ipykernel_55809/4154835031.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Weights distribution before Applying Views'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# ...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpost_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mautopct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%1.1f%%'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Posterior Weights after Applying Views'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1410\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1411\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1412\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1414\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mpie\u001b[0;34m(self, x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, normalize)\u001b[0m\n\u001b[1;32m   3043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3044\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3045\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Wedge sizes 'x' must be non negative values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3046\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3047\u001b[0m         \u001b[0msx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Wedge sizes 'x' must be non negative values"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = stocks_list\n",
    "pre_weights = result2.W\n",
    "post_weights = result3.W\n",
    "fig, axs = plt.subplots(1, 2, figsize=(16, 8))\n",
    "# ...\n",
    "axs[0].pie(pre_weights,labels = labels, autopct = '%1.1f%%')\n",
    "axs[0].set_title('Weights distribution before Applying Views')\n",
    "# ...\n",
    "axs[1].pie(post_weights,labels = labels, autopct = '%1.1f%%')\n",
    "axs[1].set_title('Posterior Weights after Applying Views')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e50e539b80a1c06ff80119ceb247a161512d83640b1afb6e95ff03fd015a83cd"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}