my_effienct_frontier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# import needed modules\n",
    "import time\n",
    "import datetime\n",
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "start = datetime.date(2010,1,1)\n",
    "end = datetime.date.today()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  6 of 6 completed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BND</th>\n",
       "      <th>GLD</th>\n",
       "      <th>PDBC</th>\n",
       "      <th>VIG</th>\n",
       "      <th>VNQ</th>\n",
       "      <th>VT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>55.724014</td>\n",
       "      <td>107.309998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.430309</td>\n",
       "      <td>27.823154</td>\n",
       "      <td>32.810135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>55.787827</td>\n",
       "      <td>109.800003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.888996</td>\n",
       "      <td>27.704998</td>\n",
       "      <td>33.579185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>55.950951</td>\n",
       "      <td>109.699997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.865669</td>\n",
       "      <td>27.673901</td>\n",
       "      <td>33.678162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>55.929672</td>\n",
       "      <td>111.510002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.888996</td>\n",
       "      <td>27.624149</td>\n",
       "      <td>33.845695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>55.887089</td>\n",
       "      <td>110.820000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.075592</td>\n",
       "      <td>27.922667</td>\n",
       "      <td>33.716244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-19</th>\n",
       "      <td>76.089996</td>\n",
       "      <td>171.910004</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>145.210007</td>\n",
       "      <td>94.230003</td>\n",
       "      <td>89.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-20</th>\n",
       "      <td>76.320000</td>\n",
       "      <td>172.029999</td>\n",
       "      <td>18.950001</td>\n",
       "      <td>145.800003</td>\n",
       "      <td>94.830002</td>\n",
       "      <td>89.349998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-23</th>\n",
       "      <td>76.050003</td>\n",
       "      <td>172.830002</td>\n",
       "      <td>19.059999</td>\n",
       "      <td>148.300003</td>\n",
       "      <td>95.930000</td>\n",
       "      <td>90.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-24</th>\n",
       "      <td>76.669998</td>\n",
       "      <td>174.130005</td>\n",
       "      <td>19.059999</td>\n",
       "      <td>148.630005</td>\n",
       "      <td>96.739998</td>\n",
       "      <td>89.980003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25</th>\n",
       "      <td>76.980003</td>\n",
       "      <td>173.080002</td>\n",
       "      <td>19.180000</td>\n",
       "      <td>149.369995</td>\n",
       "      <td>97.559998</td>\n",
       "      <td>90.620003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3122 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  BND         GLD       PDBC         VIG        VNQ         VT\n",
       "Date                                                                          \n",
       "2009-12-31  55.724014  107.309998        NaN   36.430309  27.823154  32.810135\n",
       "2010-01-04  55.787827  109.800003        NaN   36.888996  27.704998  33.579185\n",
       "2010-01-05  55.950951  109.699997        NaN   36.865669  27.673901  33.678162\n",
       "2010-01-06  55.929672  111.510002        NaN   36.888996  27.624149  33.845695\n",
       "2010-01-07  55.887089  110.820000        NaN   37.075592  27.922667  33.716244\n",
       "...               ...         ...        ...         ...        ...        ...\n",
       "2022-05-19  76.089996  171.910004  18.900000  145.210007  94.230003  89.099998\n",
       "2022-05-20  76.320000  172.029999  18.950001  145.800003  94.830002  89.349998\n",
       "2022-05-23  76.050003  172.830002  19.059999  148.300003  95.930000  90.750000\n",
       "2022-05-24  76.669998  174.130005  19.059999  148.630005  96.739998  89.980003\n",
       "2022-05-25  76.980003  173.080002  19.180000  149.369995  97.559998  90.620003\n",
       "\n",
       "[3122 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ticker_info = yf.Ticker(\"9984.T\")\n",
    "# hist = ticker_info.history(period=\"max\")\n",
    "# VYM VTI VNQI VNQ\n",
    "# tickerList = ['VT','BND','','GLD','','','VIG','']\n",
    "tickerList = ['VT','BND','GLD','VIG','PDBC','VNQ']\n",
    "data = yf.download(tickerList, start=start, end=end)[\"Adj Close\"]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate daily and annual returns of the stocks\n",
    "#returns_daily = table.pct_change()\n",
    "returns_daily = data.pct_change()\n",
    "returns_annual = returns_daily.mean() * 250\n",
    "\n",
    "# get daily and covariance of returns of the stock\n",
    "cov_daily = returns_daily.cov()\n",
    "cov_annual = cov_daily * 250\n",
    "\n",
    "# empty lists to store returns, volatility and weights of imiginary portfolios\n",
    "port_returns = []\n",
    "port_volatility = []\n",
    "sharpe_ratio = []\n",
    "stock_weights = []\n",
    "\n",
    "# set the number of combinations for imaginary portfolios\n",
    "num_assets = len(tickerList)\n",
    "num_portfolios = 50000\n",
    "\n",
    "#set random seed for reproduction's sake\n",
    "np.random.seed(101)\n",
    "\n",
    "weights = np.random.random(num_assets)\n",
    "weights /= np.sum(weights)\n",
    "\n",
    "# populate the empty lists with each portfolios returns,risk and weights\n",
    "for single_portfolio in range(num_portfolios):\n",
    "   weights = np.random.random(num_assets)\n",
    "   weights /= np.sum(weights)\n",
    "   returns = np.dot(weights, returns_annual)\n",
    "   volatility = np.sqrt(np.dot(weights.T, np.dot(cov_annual, weights)))\n",
    "   sharpe = returns / volatility # decrease risk free return\n",
    "   sharpe_ratio.append(sharpe)\n",
    "   port_returns.append(returns)\n",
    "   port_volatility.append(volatility)\n",
    "   stock_weights.append(weights)\n",
    "   \n",
    "# a dictionary for Returns and Risk values of each portfolio\n",
    "portfolio = {'Returns': port_returns,\n",
    "            'Volatility': port_volatility,\n",
    "            'Sharpe Ratio': sharpe_ratio}\n",
    "            \n",
    "# extend original dictionary to accomodate each ticker and weight in the portfolio\n",
    "for counter,symbol in enumerate(tickerList):\n",
    "   portfolio[symbol+' Weight'] = [Weight[counter] for Weight in stock_weights]\n",
    "\n",
    "# make a nice dataframe of the extended dictionary\n",
    "df = pd.DataFrame(portfolio)\n",
    "\n",
    "# get better labels for desired arrangement of columns\n",
    "column_order = ['Returns', 'Volatility', 'Sharpe Ratio'] + [stock+' Weight' for stock in tickerList]\n",
    "\n",
    "# reorder dataframe columns\n",
    "df = df[column_order]\n",
    "\n",
    "# dict_keys(['Returns', 'Volatility', 'Sharpe Ratio', 'VT Weight', 'BND Weight', 'GLD Weight', 'VIG Weight', 'PDBC Weight', 'VNQ Weight'])\n",
    "min_volatility = df['Volatility'].min()\n",
    "max_sharpe = df['Sharpe Ratio'].max()\n",
    "\n",
    "# use the min, max values to locate and create the two special portfolios\n",
    "sharpe_portfolio = df.loc[df['Sharpe Ratio'] == max_sharpe]\n",
    "min_variance_port = df.loc[df['Volatility'] == min_volatility]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # plot frontier, max sharpe & min Volatility values with a scatterplot\n",
    "# plt.style.use('seaborn-dark')\n",
    "# df.plot.scatter(x='Volatility', y='Returns', c='Sharpe Ratio',\n",
    "#                cmap='RdYlGn', edgecolors='black', figsize=(10, 8), grid=True)\n",
    "# plt.xlabel('Volatility (Std. Deviation)')\n",
    "# plt.ylabel('Expected Returns')\n",
    "# plt.title('Efficient Frontier')\n",
    "# plt.show()\n",
    "\n",
    "# plot frontier, max sharpe & min Volatility values with a scatterplot\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# plt.style.use('seaborn-dark')\n",
    "# df.plot.scatter(x='Volatility', y='Returns', c='Sharpe Ratio',\n",
    "#                cmap='RdYlGn', edgecolors='black', figsize=(10, 8), grid=True)\n",
    "# plt.scatter(x=sharpe_portfolio['Volatility'], y=sharpe_portfolio['Returns'], c='red', marker='D', s=200)\n",
    "# plt.scatter(x=min_variance_port['Volatility'], y=min_variance_port['Returns'], c='blue', marker='D', s=200 )\n",
    "# plt.xlabel('Volatility (Std. Deviation)')\n",
    "# plt.ylabel('Expected Returns')\n",
    "# plt.title('Efficient Frontier')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='相対価格'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可視化\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(font='Yu Gothic')\n",
    "\n",
    "# 可視化用コード\n",
    "# 価格推移\n",
    "# plot_df = all_df/all_df.loc['2020-11-02',:]\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_ylabel('相対価格')\n",
    "\n",
    "# calculate daily and annual returns of the stocks\n",
    "returns_daily = data.pct_change()\n",
    "\n",
    "simple_return =  returns_daily + 1\n",
    "simple_return.iloc[0,:] = 1\n",
    "#simple_return = simple_return.resample('M').sum()\n",
    "simple_return = simple_return.cumprod()\n",
    "simple_return.plot(figsize=(10,10),ax=ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 相関係数\n",
    "sns.heatmap(data.corr(),annot=True, fmt=\"1.1f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypfopt.efficient_frontier import EfficientFrontier\n",
    "from pypfopt import risk_models\n",
    "from pypfopt import expected_returns\n",
    "from pypfopt.risk_models import CovarianceShrinkage\n",
    "import math\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('BND', 0.50179), ('GLD', 0.03952), ('PDBC', 0.0), ('VIG', 0.45869), ('VNQ', 0.0), ('VT', 0.0)])\n",
      "Expected annual return: 7.0%\n",
      "Annual volatility: 7.5%\n",
      "Sharpe Ratio: 0.67\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.07017166100415764, 0.07474171826167188, 0.6712671607107814)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 諸パラメータの計算\n",
    "days=252 #リターン、リスクの計算期間\n",
    "# mu = expected_returns.mean_historical_return(data,frequency=days) # リターンの計算\n",
    "mu = expected_returns.mean_historical_return(data) # リターンの計算\n",
    "\n",
    "# S = risk_models.sample_cov(data) # リスク：標本共分散の計算\n",
    "S = risk_models.sample_cov(data)\n",
    "\n",
    "# risks = (data.pct_change().dropna(how='all')).std() # 各株ごとの平均リスク（標準偏差）の計算\n",
    "# risks = ((risks*risks)*days).apply(math.sqrt) # 指定期間での各株ごとのリスク（標準偏差）の計算\n",
    "\n",
    "# ef = EfficientFrontier(mu, S)\n",
    "# # ポートフォリオの期待リターンが目標リターン以上となる制約のもとポートフォリオの分散を最小化する\n",
    "# ef.efficient_return(target_return=0.1)\n",
    "\n",
    "# Optimize for maximal Sharpe ratio\n",
    "ef = EfficientFrontier(mu, S)\n",
    "raw_weights = ef.max_sharpe()\n",
    "cleaned_weights = ef.clean_weights()\n",
    "# ef.save_weights_to_file(\"weights.csv\")  # saves to file\n",
    "print(cleaned_weights)\n",
    "ef.portfolio_performance(verbose=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete allocation: {'BND': 65, 'VIG': 31, 'GLD': 2}\n",
      "Funds remaining: $19.67\n"
     ]
    }
   ],
   "source": [
    "from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices\n",
    "\n",
    "df = data\n",
    "latest_prices = get_latest_prices(df)\n",
    "\n",
    "da = DiscreteAllocation(cleaned_weights, latest_prices, total_portfolio_value=10000)\n",
    "allocation, leftover = da.greedy_portfolio()\n",
    "print(\"Discrete allocation:\", allocation)\n",
    "print(\"Funds remaining: ${:.2f}\".format(leftover))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#expected returns\n",
    "#covariance matrix\n",
    "e_r = expected_returns.mean_historical_return(data)\n",
    "cov_matrix = CovarianceShrinkage(data).ledoit_wolf()\n",
    "\n",
    "from pypfopt.efficient_frontier import EfficientFrontier\n",
    "\n",
    "#border problem solution\n",
    "#selectiong portfolio with better sharpe ratio\n",
    "ef = EfficientFrontier(e_r, cov_matrix)\n",
    "# weights = ef.max_sharpe()\n",
    "# weights\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from pypfopt import plotting\n",
    "# ef = EfficientFrontier(mu, S, weight_bounds=(None, None))\n",
    "# ef.add_constraint(lambda w: w[0] >= 0.2)\n",
    "# ef.add_constraint(lambda w: w[2] == 0.15)\n",
    "# ef.add_constraint(lambda w: w[3] + w[4] <= 0.10)\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# plotting.plot_efficient_frontier(ef, ax=ax, show_assets=True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot pickle 'osqp.OSQP_results' object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\U683552\\Desktop\\python\\mycollection\\my_effienct_frontier.ipynb Cell 12'\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/U683552/Desktop/python/mycollection/my_effienct_frontier.ipynb#ch0000012?line=2'>3</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpypfopt\u001b[39;00m \u001b[39mimport\u001b[39;00m plotting\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/U683552/Desktop/python/mycollection/my_effienct_frontier.ipynb#ch0000012?line=3'>4</a>\u001b[0m fig, ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots()\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/U683552/Desktop/python/mycollection/my_effienct_frontier.ipynb#ch0000012?line=4'>5</a>\u001b[0m ef_max_sharpe \u001b[39m=\u001b[39m copy\u001b[39m.\u001b[39;49mdeepcopy(ef)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/U683552/Desktop/python/mycollection/my_effienct_frontier.ipynb#ch0000012?line=5'>6</a>\u001b[0m plotting\u001b[39m.\u001b[39mplot_efficient_frontier(ef, ax\u001b[39m=\u001b[39max, show_assets\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=169'>170</a>\u001b[0m                 y \u001b[39m=\u001b[39m x\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=170'>171</a>\u001b[0m             \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=171'>172</a>\u001b[0m                 y \u001b[39m=\u001b[39m _reconstruct(x, memo, \u001b[39m*\u001b[39;49mrv)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=173'>174</a>\u001b[0m \u001b[39m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=174'>175</a>\u001b[0m \u001b[39mif\u001b[39;00m y \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m x:\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:270\u001b[0m, in \u001b[0;36m_reconstruct\u001b[1;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=267'>268</a>\u001b[0m \u001b[39mif\u001b[39;00m state \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=268'>269</a>\u001b[0m     \u001b[39mif\u001b[39;00m deep:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=269'>270</a>\u001b[0m         state \u001b[39m=\u001b[39m deepcopy(state, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=270'>271</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(y, \u001b[39m'\u001b[39m\u001b[39m__setstate__\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=271'>272</a>\u001b[0m         y\u001b[39m.\u001b[39m__setstate__(state)\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=143'>144</a>\u001b[0m copier \u001b[39m=\u001b[39m _deepcopy_dispatch\u001b[39m.\u001b[39mget(\u001b[39mcls\u001b[39m)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=144'>145</a>\u001b[0m \u001b[39mif\u001b[39;00m copier \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=145'>146</a>\u001b[0m     y \u001b[39m=\u001b[39m copier(x, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=146'>147</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=147'>148</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(\u001b[39mcls\u001b[39m, \u001b[39mtype\u001b[39m):\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:230\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[1;34m(x, memo, deepcopy)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=227'>228</a>\u001b[0m memo[\u001b[39mid\u001b[39m(x)] \u001b[39m=\u001b[39m y\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=228'>229</a>\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m x\u001b[39m.\u001b[39mitems():\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=229'>230</a>\u001b[0m     y[deepcopy(key, memo)] \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=230'>231</a>\u001b[0m \u001b[39mreturn\u001b[39;00m y\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=169'>170</a>\u001b[0m                 y \u001b[39m=\u001b[39m x\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=170'>171</a>\u001b[0m             \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=171'>172</a>\u001b[0m                 y \u001b[39m=\u001b[39m _reconstruct(x, memo, \u001b[39m*\u001b[39;49mrv)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=173'>174</a>\u001b[0m \u001b[39m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=174'>175</a>\u001b[0m \u001b[39mif\u001b[39;00m y \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m x:\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:270\u001b[0m, in \u001b[0;36m_reconstruct\u001b[1;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=267'>268</a>\u001b[0m \u001b[39mif\u001b[39;00m state \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=268'>269</a>\u001b[0m     \u001b[39mif\u001b[39;00m deep:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=269'>270</a>\u001b[0m         state \u001b[39m=\u001b[39m deepcopy(state, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=270'>271</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(y, \u001b[39m'\u001b[39m\u001b[39m__setstate__\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=271'>272</a>\u001b[0m         y\u001b[39m.\u001b[39m__setstate__(state)\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=143'>144</a>\u001b[0m copier \u001b[39m=\u001b[39m _deepcopy_dispatch\u001b[39m.\u001b[39mget(\u001b[39mcls\u001b[39m)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=144'>145</a>\u001b[0m \u001b[39mif\u001b[39;00m copier \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=145'>146</a>\u001b[0m     y \u001b[39m=\u001b[39m copier(x, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=146'>147</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=147'>148</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(\u001b[39mcls\u001b[39m, \u001b[39mtype\u001b[39m):\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:230\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[1;34m(x, memo, deepcopy)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=227'>228</a>\u001b[0m memo[\u001b[39mid\u001b[39m(x)] \u001b[39m=\u001b[39m y\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=228'>229</a>\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m x\u001b[39m.\u001b[39mitems():\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=229'>230</a>\u001b[0m     y[deepcopy(key, memo)] \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=230'>231</a>\u001b[0m \u001b[39mreturn\u001b[39;00m y\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=169'>170</a>\u001b[0m                 y \u001b[39m=\u001b[39m x\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=170'>171</a>\u001b[0m             \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=171'>172</a>\u001b[0m                 y \u001b[39m=\u001b[39m _reconstruct(x, memo, \u001b[39m*\u001b[39;49mrv)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=173'>174</a>\u001b[0m \u001b[39m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=174'>175</a>\u001b[0m \u001b[39mif\u001b[39;00m y \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m x:\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:270\u001b[0m, in \u001b[0;36m_reconstruct\u001b[1;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=267'>268</a>\u001b[0m \u001b[39mif\u001b[39;00m state \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=268'>269</a>\u001b[0m     \u001b[39mif\u001b[39;00m deep:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=269'>270</a>\u001b[0m         state \u001b[39m=\u001b[39m deepcopy(state, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=270'>271</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(y, \u001b[39m'\u001b[39m\u001b[39m__setstate__\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=271'>272</a>\u001b[0m         y\u001b[39m.\u001b[39m__setstate__(state)\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=143'>144</a>\u001b[0m copier \u001b[39m=\u001b[39m _deepcopy_dispatch\u001b[39m.\u001b[39mget(\u001b[39mcls\u001b[39m)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=144'>145</a>\u001b[0m \u001b[39mif\u001b[39;00m copier \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=145'>146</a>\u001b[0m     y \u001b[39m=\u001b[39m copier(x, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=146'>147</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=147'>148</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(\u001b[39mcls\u001b[39m, \u001b[39mtype\u001b[39m):\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:230\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[1;34m(x, memo, deepcopy)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=227'>228</a>\u001b[0m memo[\u001b[39mid\u001b[39m(x)] \u001b[39m=\u001b[39m y\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=228'>229</a>\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m x\u001b[39m.\u001b[39mitems():\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=229'>230</a>\u001b[0m     y[deepcopy(key, memo)] \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=230'>231</a>\u001b[0m \u001b[39mreturn\u001b[39;00m y\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=143'>144</a>\u001b[0m copier \u001b[39m=\u001b[39m _deepcopy_dispatch\u001b[39m.\u001b[39mget(\u001b[39mcls\u001b[39m)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=144'>145</a>\u001b[0m \u001b[39mif\u001b[39;00m copier \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=145'>146</a>\u001b[0m     y \u001b[39m=\u001b[39m copier(x, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=146'>147</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=147'>148</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(\u001b[39mcls\u001b[39m, \u001b[39mtype\u001b[39m):\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:230\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[1;34m(x, memo, deepcopy)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=227'>228</a>\u001b[0m memo[\u001b[39mid\u001b[39m(x)] \u001b[39m=\u001b[39m y\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=228'>229</a>\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m x\u001b[39m.\u001b[39mitems():\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=229'>230</a>\u001b[0m     y[deepcopy(key, memo)] \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=230'>231</a>\u001b[0m \u001b[39mreturn\u001b[39;00m y\n",
      "File \u001b[1;32mc:\\Users\\U683552\\AppData\\Local\\Programs\\Python\\Python38\\lib\\copy.py:161\u001b[0m, in \u001b[0;36mdeepcopy\u001b[1;34m(x, memo, _nil)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=158'>159</a>\u001b[0m reductor \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(x, \u001b[39m\"\u001b[39m\u001b[39m__reduce_ex__\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=159'>160</a>\u001b[0m \u001b[39mif\u001b[39;00m reductor \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=160'>161</a>\u001b[0m     rv \u001b[39m=\u001b[39m reductor(\u001b[39m4\u001b[39;49m)\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=161'>162</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/U683552/AppData/Local/Programs/Python/Python38/lib/copy.py?line=162'>163</a>\u001b[0m     reductor \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(x, \u001b[39m\"\u001b[39m\u001b[39m__reduce__\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot pickle 'osqp.OSQP_results' object"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import copy\n",
    "import osqp\n",
    "from pypfopt import plotting\n",
    "fig, ax = plt.subplots()\n",
    "ef_max_sharpe = copy.deepcopy(ef)\n",
    "plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)\n",
    "\n",
    "# # Find the tangency portfolio\n",
    "# # ef_max_sharpe.max_sharpe()\n",
    "# ret_tangent, std_tangent, _ = ef_max_sharpe.portfolio_performance()\n",
    "# ax.scatter(std_tangent, ret_tangent, marker=\"*\", s=100, c=\"r\", label=\"Max Sharpe\")\n",
    "\n",
    "# # Generate random portfolios\n",
    "# n_samples = 10000\n",
    "# w = np.random.dirichlet(np.ones(ef.n_assets), n_samples)\n",
    "# rets = w.dot(ef.expected_returns)\n",
    "# stds = np.sqrt(np.diag(w @ ef.cov_matrix @ w.T))\n",
    "# sharpes = rets / stds\n",
    "# ax.scatter(stds, rets, marker=\".\", c=sharpes, cmap=\"viridis_r\")\n",
    "\n",
    "# # Output\n",
    "# ax.set_title(\"Efficient Frontier with random portfolios\")\n",
    "# ax.legend()\n",
    "# plt.tight_layout()\n",
    "# plt.savefig(\"ef_scatter.png\", dpi=200)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3.8.8 64-bit",
   "language": "python",
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