Portfolio_optimization_Non_linear_Programming(Faster_technique).ipynb

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    {
      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/TheAmirHK/Portfolio-analysis-and-optimization/blob/main/Portfolio_optimization_Non_linear_Programming(Faster_technique).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QCPaQpn6YouP"
      },
      "outputs": [],
      "source": [
        "import pandas_datareader.data as web\n",
        "import datetime\n",
        "from datetime import datetime, date, timedelta\n",
        "import yfinance as yf\n",
        "yf.pdr_override() # <== that's all it takes :-)\n",
        "from pandas_datareader import data as pdr\n",
        "import pandas as pd\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', None)\n",
        "import numpy as np\n",
        "from scipy.optimize import minimize\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# For warnings suppression\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyomo\n",
        "from pyomo.environ import *\n",
        "import matplotlib.pyplot as plt\n",
        "!wget -N -q \"https://matematica.unipv.it/gualandi/solvers/ipopt-linux64.zip\"\n",
        "!unzip -o -q ipopt-linux64"
      ],
      "metadata": {
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        },
        "id": "-sSaEfh2Z0w1",
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      },
      "execution_count": null,
      "outputs": [
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          "text": [
            "Collecting pyomo\n",
            "  Downloading Pyomo-6.6.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.7/12.7 MB\u001b[0m \u001b[31m37.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ply (from pyomo)\n",
            "  Downloading ply-3.11-py2.py3-none-any.whl (49 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ply, pyomo\n",
            "Successfully installed ply-3.11 pyomo-6.6.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A function to download stocks' adjusted close price\n",
        "def receive_data(tickers, start, end):\n",
        "    adj_close_df = pd.DataFrame()\n",
        "    for ticker in tickers:\n",
        "        data = pdr.get_data_yahoo(ticker, start, end, progress=False)\n",
        "        adj_close_df[ticker] = data[\"Adj Close\"]\n",
        "    return adj_close_df"
      ],
      "metadata": {
        "id": "t1tRaX2nZL39"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# A function to normalize prices\n",
        "def daily_return(data):\n",
        "    log_returns = data.pct_change().dropna()\n",
        "    return log_returns"
      ],
      "metadata": {
        "id": "DWsrSd-IZL61"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# One portfolio test and evaluation\n",
        "def evaluation  (data, Rf = 0.02, weights = None):\n",
        "    result = pd.DataFrame()\n",
        "    log_return = daily_return(data)\n",
        "    if weights is None:\n",
        "        result[\"ExpRet\"] = log_return.mean()*252\n",
        "        result[\"ExpVol\"] = log_return.std() *np.sqrt(252)\n",
        "        result[\"ExpSha\"] = (result[\"ExpRet\"] - Rf) / result[\"ExpVol\"]\n",
        "    else:\n",
        "        result[\"ExpRet\"] = [np.sum(log_return.mean()*weights)*252]\n",
        "        result[\"ExpVol\"] = [np.sqrt(weights.T @ log_return.cov() @ weights) * np.sqrt(252)]\n",
        "        result[\"ExpSha\"] = (result[\"ExpRet\"] - Rf) / result[\"ExpVol\"]\n",
        "    return result"
      ],
      "metadata": {
        "id": "qyzYpOhQZMAu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate optimal share volumes using the obtained weights from simulation.\n",
        "def share_volume(stock_data, results, wealth):\n",
        "    Optimal_weights = results\n",
        "    Optimal_weights = np.array(Optimal_weights)\n",
        "    evaluation = pd.DataFrame()\n",
        "    evaluation['Price'] = stock_data.iloc[-1]\n",
        "    evaluation['Optimal Weights'] = Optimal_weights.T\n",
        "    evaluation['Optimal Volume'] = (evaluation['Optimal Weights'] *wealth / evaluation['Price'])\n",
        "    evaluation['Optimal Volume'] = evaluation['Optimal Volume'].apply(math.floor)\n",
        "    evaluation['Total pay'] = evaluation['Optimal Volume'] * evaluation['Price']\n",
        "    left = wealth - np.sum(evaluation['Total pay'])\n",
        "    print(\"Not allocated budegt: %0.2f $ out of %0.2f $\"%(left,wealth ))\n",
        "    return evaluation"
      ],
      "metadata": {
        "id": "dxBJ3AuPZYQX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare portfolio performance against market\n",
        "def portfolio_vs_market(optimization_results, market_data):\n",
        "\n",
        "    portfolio = optimization_results\n",
        "    #portfolio = (pd.DataFrame(portfolio)).T\n",
        "\n",
        "    market_ev = evaluation(market_data)\n",
        "\n",
        "    df = pd.DataFrame({\n",
        "    'Portfolio Vs Market': ['ExpRet', 'ExpVol', 'ExpSha'],\n",
        "    'Portfolio': portfolio.iloc[0].values.tolist(),\n",
        "    'Market': (market_data.iloc[0]).values.tolist()\n",
        "    })\n",
        "\n",
        "    # plotting graph\n",
        "    df.plot(x=\"Portfolio Vs Market\", y=[\"Portfolio\", \"Market\"], kind=\"bar\", grid=True)\n",
        "    plt.show()\n",
        "\n",
        "    return"
      ],
      "metadata": {
        "id": "sVZIYAg0ZYwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Eahc individual stocks to be analyzed !\n",
        "stocks = [\"AAPL\", \"MSFT\", \"GOOG\", \"AMZN\",   # Technology stocks\n",
        "          \"TSLA\", \"TM\", \"MBGYY\", \"VWAGY\", \"VLVLY\", \"GM\",  # Motor manufacturing stocks\n",
        "          \"LLY\", \"JNJ\", \"AZN\"] # Health market stocks\n",
        "market = ['^GSPC'] # S&P500 index\n",
        "\n",
        "# define the number of years to be studied\n",
        "NYears = 5\n",
        "\n",
        "# Risk-free asset rate\n",
        "Rf = 0.02\n",
        "\n",
        "# Wealth value (Budget)\n",
        "wealth = 5000\n",
        "\n",
        "# Define the time period of study\n",
        "end = datetime.today()\n",
        "start = end - timedelta(days = NYears*365)\n",
        "print(\"Start date:%str   End date:%str\" %(start, end))\n",
        "\n",
        "stock_data = receive_data(stocks, start, end)\n",
        "market_data = receive_data(market, start, end)\n",
        "log_stock_data = daily_return(stock_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3wGiE2aZcWV",
        "outputId": "6d7d866b-d173-453e-862a-7f16911a3c3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start date:2018-11-27 22:03:30.632943tr   End date:2023-11-26 22:03:30.632943tr\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's optimize the portfolio using pyomo\n",
        "import pyomo\n",
        "import math\n",
        "from pyomo.environ import *\n",
        "\n",
        "def Input_ExpRet (model, i):\n",
        "    return log_stock_data.mean()[i]\n",
        "\n",
        "# create a function to return covariance by index i and j\n",
        "def get_covariance(model, i, j):\n",
        "    return (log_stock_data.cov())[i][j]\n",
        "\n",
        "# create an instance of ConcreteModel\n",
        "model = ConcreteModel(\"Portfolio_Optimization\")\n",
        "\n",
        "# create a set of assets\n",
        "model.i = Set(initialize = stocks, doc = 'List of assets')\n",
        "\n",
        "# create parameter r_i\n",
        "model.r = Param(model.i, rule = Input_ExpRet, doc = 'Expected annual return')\n",
        "\n",
        "# create parameter cov_ij\n",
        "model.cov = Param(model.i, model.i, rule = get_covariance, doc = 'Covariance')\n",
        "\n",
        "# create decision variable x_i\n",
        "model.X = Var(model.i, bounds = (0,1))\n",
        "\n",
        "# create constraint that sum of all asset ratio = 1\n",
        "model.C1 = Constraint(expr = sum(model.X[i] for i in model.i) == 1)\n",
        "\n",
        "# define expected return\n",
        "Return = sum(model.X[i]*model.r[i] for i in model.i)*252\n",
        "\n",
        "# define expected voatility\n",
        "Risk = sqrt(sum([model.X[i]*model.X[j]*model.cov[i,j] for i in model.i for j in model.i]) + 0.000000001)*sqrt(252)\n",
        "\n",
        "sharp = (Return - Rf)/ Risk\n",
        "\n",
        "# define objective function\n",
        "model.obj = Objective(expr =sharp, sense = maximize)\n",
        "\n",
        "# call solver to optimize\n",
        "SolverFactory('ipopt').solve(model) # applicable solvers: bonmin, cbc, ipopt\n",
        "\n",
        "# display and save optimal weights\n",
        "model.obj.display()\n",
        "model.X.display()\n",
        "optimal_values = [value(model.X[key]) for key in model.X]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-oYQaqo_YvEf",
        "outputId": "8feb406f-c036-4877-a03e-c687b725cccb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "obj : Size=1, Index=None, Active=True\n",
            "    Key  : Active : Value\n",
            "    None :   True : 1.5151837671173247\n",
            "X : Size=13, Index=i\n",
            "    Key   : Lower : Value                  : Upper : Fixed : Stale : Domain\n",
            "     AAPL :     0 :    0.15842316457916134 :     1 : False : False :  Reals\n",
            "     AMZN :     0 :  7.268600905510082e-08 :     1 : False : False :  Reals\n",
            "      AZN :     0 :  2.356796829358341e-07 :     1 : False : False :  Reals\n",
            "       GM :     0 :  3.415160228459873e-08 :     1 : False : False :  Reals\n",
            "     GOOG :     0 : 2.1679150799319694e-07 :     1 : False : False :  Reals\n",
            "      JNJ :     0 :  7.607963872817863e-08 :     1 : False : False :  Reals\n",
            "      LLY :     0 :      0.620498916787023 :     1 : False : False :  Reals\n",
            "    MBGYY :     0 :  8.893105959791624e-08 :     1 : False : False :  Reals\n",
            "     MSFT :     0 :  7.329346810107888e-07 :     1 : False : False :  Reals\n",
            "       TM :     0 :  2.088192083755397e-07 :     1 : False : False :  Reals\n",
            "     TSLA :     0 :     0.2210755129484806 :     1 : False : False :  Reals\n",
            "    VLVLY :     0 :  6.915657384475649e-07 :     1 : False : False :  Reals\n",
            "    VWAGY :     0 :  4.804620654493985e-08 :     1 : False : False :  Reals\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the share_volume\n",
        "share_volume(stock_data, optimal_values, wealth)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "7YRd1XyYaL9W",
        "outputId": "4bf7f560-13b6-40d0-8f45-aa9ddecc4c1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not allocated budegt: 292.82 $ out of 5000.00 $\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Price  Optimal Weights  Optimal Volume    Total pay\n",
              "AAPL   189.970001     1.584232e-01               4   759.880005\n",
              "MSFT   377.429993     7.329347e-07               0     0.000000\n",
              "GOOG   138.220001     2.167915e-07               0     0.000000\n",
              "AMZN   146.740005     7.268601e-08               0     0.000000\n",
              "TSLA   235.449997     2.210755e-01               4   941.799988\n",
              "TM     188.300003     2.088192e-07               0     0.000000\n",
              "MBGYY   15.980000     8.893106e-08               0     0.000000\n",
              "VWAGY   12.950000     4.804621e-08               0     0.000000\n",
              "VLVLY   22.809999     6.915657e-07               0     0.000000\n",
              "GM      28.180000     3.415160e-08               0     0.000000\n",
              "LLY    601.099976     6.204989e-01               5  3005.499878\n",
              "JNJ    152.500000     7.607964e-08               0     0.000000\n",
              "AZN     64.500000     2.356797e-07               0     0.000000"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Optimal Weights</th>\n",
              "      <th>Optimal Volume</th>\n",
              "      <th>Total pay</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>189.970001</td>\n",
              "      <td>1.584232e-01</td>\n",
              "      <td>4</td>\n",
              "      <td>759.880005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>377.429993</td>\n",
              "      <td>7.329347e-07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>GOOG</th>\n",
              "      <td>138.220001</td>\n",
              "      <td>2.167915e-07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMZN</th>\n",
              "      <td>146.740005</td>\n",
              "      <td>7.268601e-08</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TSLA</th>\n",
              "      <td>235.449997</td>\n",
              "      <td>2.210755e-01</td>\n",
              "      <td>4</td>\n",
              "      <td>941.799988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TM</th>\n",
              "      <td>188.300003</td>\n",
              "      <td>2.088192e-07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MBGYY</th>\n",
              "      <td>15.980000</td>\n",
              "      <td>8.893106e-08</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VWAGY</th>\n",
              "      <td>12.950000</td>\n",
              "      <td>4.804621e-08</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VLVLY</th>\n",
              "      <td>22.809999</td>\n",
              "      <td>6.915657e-07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GM</th>\n",
              "      <td>28.180000</td>\n",
              "      <td>3.415160e-08</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LLY</th>\n",
              "      <td>601.099976</td>\n",
              "      <td>6.204989e-01</td>\n",
              "      <td>5</td>\n",
              "      <td>3005.499878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JNJ</th>\n",
              "      <td>152.500000</td>\n",
              "      <td>7.607964e-08</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AZN</th>\n",
              "      <td>64.500000</td>\n",
              "      <td>2.356797e-07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f015333e-1aa4-4723-98ba-28cc94a9cbae button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f015333e-1aa4-4723-98ba-28cc94a9cbae');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a73b3230-e17b-4cb0-9272-c5094fdc6ee0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a73b3230-e17b-4cb0-9272-c5094fdc6ee0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a73b3230-e17b-4cb0-9272-c5094fdc6ee0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate market and optimal portfolio performances adn compare them\n",
        "portfolio = evaluation(stock_data, weights = np.asarray(optimal_values) )\n",
        "market_ev = evaluation(market_data)\n",
        "portfolio_vs_market(portfolio,market_ev)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "X9onwjQ6aqEY",
        "outputId": "7e40a6f7-4f25-44a7-e1fa-2490589b5e1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}