bl_ricerca_operativa_song.ipynb

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        "<a href=\"https://colab.research.google.com/github/Song-z-h/black-litterman-allocation/blob/master/bl_ricerca_operativa_song.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": "markdown",
      "source": [
        "# Black litterman portfolio Allocation  \n",
        "This model is an example of using the [Black-litterman model](https://pyportfolioopt.readthedocs.io/en/latest/BlackLitterman.html) to calculate the posterior expected return, and then solve the quadratic equition using Gurobi solver. You can find a more specified problem setting [here](https://www.notion.so/Operational-Research-Project-1658df813e6448f791aa0cf0ffc28733?pvs=4)\n",
        "\n",
        "A simple context is that we have N assets to invest in, we have 1 dollar, we want to know the fraction to be invested into each of the N assets, in order to maximize our future returns. "
      ],
      "metadata": {
        "id": "jvPGVAh322V_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install the libraries. "
      ],
      "metadata": {
        "id": "-MKe7Cay9PeX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 502,
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        "id": "qrjmyxkbqPmf",
        "outputId": "ddb47876-00d8-4f84-cd37-c3e25d93fcb2"
      },
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
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            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.10/dist-packages (0.2.18)\n",
            "Requirement already satisfied: PyPortfolioOpt in /usr/local/lib/python3.10/dist-packages (1.5.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
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            "Requirement already satisfied: requests>=2.26 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.27.1)\n",
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            "Requirement already satisfied: qdldl in /usr/local/lib/python3.10/dist-packages (from osqp>=0.4.1->cvxpy<2.0.0,>=1.1.19->PyPortfolioOpt) (0.1.7)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gurobipy in /usr/local/lib/python3.10/dist-packages (10.0.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n",
        "!pip install gurobipy  # install gurobipy, if not already installed\n",
        "\n",
        "import os\n",
        "if not os.path.isdir('data'):\n",
        "    os.system('git clone https://github.com/robertmartin8/PyPortfolioOpt.git')\n",
        "    os.chdir('PyPortfolioOpt/cookbook')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import yfinance as yf"
      ],
      "metadata": {
        "id": "8wXmvSygqUMI"
      },
      "execution_count": 503,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Selecting your stocks\n",
        "You can put some of your favourite stocks in the bracket, then choose the simulation day, that means if you were investing 1 dollar y years ago, you want to know how much you would make now!"
      ],
      "metadata": {
        "id": "BBg5tR83Aka2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tickers = [\"MSFT\", \"AMZN\", \"BAC\", \"DPZ\", \"DIS\", \"KO\", \"JPM\", \"COST\", \"SBUX\", \"AAPL\", \"TSM\", \"GS\", \"AAL\"]\n",
        "simulation_day = 365 * 5"
      ],
      "metadata": {
        "id": "0sTCaPhdqbQq"
      },
      "execution_count": 504,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you want to calculate for the future expected returns based on the data we know today, just put the simulation day to zero!"
      ],
      "metadata": {
        "id": "ET2gUVu-5dSf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare the historic data\n",
        "We want to know if we only had the data until y years ago, and did the predication based on the data we knew, how the performance would be today, you can use ctrl + f9 to do all the computation using default parameters, and check out the result at end of the page."
      ],
      "metadata": {
        "id": "QUiAclhDsXUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ohlc = yf.download(tickers, period=\"max\")\n",
        "prices = ohlc[\"Adj Close\"]\n",
        "prices.tail()"
      ],
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        "id": "J3BJRCIVqdkq",
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      },
      "execution_count": 505,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "[*********************100%***********************]  13 of 13 completed\n"
          ]
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              "              AAL        AAPL        AMZN        BAC        COST         DIS  \\\n",
              "Date                                                                           \n",
              "2023-05-08  14.36  173.260345  105.830002  27.690001  499.059998  102.970001   \n",
              "2023-05-09  14.47  171.532745  106.620003  27.660000  500.790009  102.180000   \n",
              "2023-05-10  14.34  173.320267  110.190002  27.320000  499.660004  101.139999   \n",
              "2023-05-11  14.07  173.510010  112.180000  27.389999  501.890015   92.309998   \n",
              "2023-05-12  14.02  172.570007  110.260002  27.090000  504.070007   91.989998   \n",
              "\n",
              "                   DPZ          GS         JPM         KO        MSFT  \\\n",
              "Date                                                                    \n",
              "2023-05-08  308.940002  326.510010  137.070007  63.919998  308.649994   \n",
              "2023-05-09  308.640015  324.769989  136.410004  63.389999  307.000000   \n",
              "2023-05-10  306.890015  322.549988  136.479996  63.500000  312.309998   \n",
              "2023-05-11  306.929993  320.720001  136.050003  63.860001  310.109985   \n",
              "2023-05-12  307.579987  319.500000  134.100006  64.110001  308.970001   \n",
              "\n",
              "                  SBUX        TSM  \n",
              "Date                               \n",
              "2023-05-08  106.639008  85.540001  \n",
              "2023-05-09  106.181290  85.059998  \n",
              "2023-05-10  106.440002  84.900002  \n",
              "2023-05-11  105.629997  83.430000  \n",
              "2023-05-12  106.120003  83.430000  "
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              "      <th>2023-05-08</th>\n",
              "      <td>14.36</td>\n",
              "      <td>173.260345</td>\n",
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              "      <th>2023-05-09</th>\n",
              "      <td>14.47</td>\n",
              "      <td>171.532745</td>\n",
              "      <td>106.620003</td>\n",
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              "      <th>2023-05-10</th>\n",
              "      <td>14.34</td>\n",
              "      <td>173.320267</td>\n",
              "      <td>110.190002</td>\n",
              "      <td>27.320000</td>\n",
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              "      <th>2023-05-11</th>\n",
              "      <td>14.07</td>\n",
              "      <td>173.510010</td>\n",
              "      <td>112.180000</td>\n",
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              "      <th>2023-05-12</th>\n",
              "      <td>14.02</td>\n",
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          "metadata": {},
          "execution_count": 505
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "market_prices = yf.download(\"SPY\", period=\"max\")[\"Adj Close\"]\n",
        "market_prices.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYPj5MUPqhkf",
        "outputId": "a04c5962-644b-4123-f295-dcaa79a7aad6"
      },
      "execution_count": 506,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2023-05-08    412.739990\n",
              "2023-05-09    410.929993\n",
              "2023-05-10    412.850006\n",
              "2023-05-11    412.130005\n",
              "2023-05-12    411.589996\n",
              "Name: Adj Close, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 506
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Stock information\n",
        "We get analysis's target price, market capital and sector informations from the api"
      ],
      "metadata": {
        "id": "2f45--sLtXCt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mcaps = {}\n",
        "targetPrices = {}\n",
        "sectors = {}\n",
        "for t in tickers:\n",
        "    stock = yf.Ticker(t)\n",
        "    mcaps[t] = stock.info[\"marketCap\"]\n",
        "    targetPrices[t] = stock.info[\"targetMeanPrice\"]\n",
        "    #targetPrices[t] = stock.info[\"targetLowPrice\"]\n",
        "    #targetPrices[t] = stock.info[\"targetHighPrice\"]\n",
        "    sectors[t] = stock.info[\"sector\"]\n",
        "mcaps"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKpQK89gqrOF",
        "outputId": "95da6912-264c-4853-9e4a-efd6e53967d1"
      },
      "execution_count": 507,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'MSFT': 2322178506752,\n",
              " 'AMZN': 1131311792128,\n",
              " 'BAC': 217717194752,\n",
              " 'DPZ': 10869569536,\n",
              " 'DIS': 184122572800,\n",
              " 'KO': 277248802816,\n",
              " 'JPM': 398833516544,\n",
              " 'COST': 223546490880,\n",
              " 'SBUX': 122630152192,\n",
              " 'AAPL': 2729798795264,\n",
              " 'TSM': 432674635776,\n",
              " 'GS': 107231232000,\n",
              " 'AAL': 9362051072}"
            ]
          },
          "metadata": {},
          "execution_count": 507
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stock covariance\n",
        "I used a library to calculate the covariance vector between different assets, which we will use later to compute the prior(the asset prices knowing the potential risks)"
      ],
      "metadata": {
        "id": "dKi-whV2tgC4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pypfopt import risk_models\n",
        "\n",
        "cov_matrix = risk_models.CovarianceShrinkage(prices[:-simulation_day+1]).ledoit_wolf()\n",
        "cov_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "evSdm4uLquoC",
        "outputId": "fe5c8975-9bd3-4079-b0cc-3f49a03a1b11"
      },
      "execution_count": 508,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           AAL      AAPL      AMZN       BAC      COST       DIS       DPZ  \\\n",
              "AAL   0.106536  0.014031  0.016229  0.033823  0.011090  0.015568  0.022666   \n",
              "AAPL  0.014031  0.144810  0.029662  0.028267  0.021811  0.026415  0.008475   \n",
              "AMZN  0.016229  0.029662  0.142418  0.025099  0.018505  0.021715  0.010948   \n",
              "BAC   0.033823  0.028267  0.025099  0.120020  0.021304  0.033390  0.015445   \n",
              "COST  0.011090  0.021811  0.018505  0.021304  0.062039  0.018670  0.006761   \n",
              "DIS   0.015568  0.026415  0.021715  0.033390  0.018670  0.101839  0.009190   \n",
              "DPZ   0.022666  0.008475  0.010948  0.015445  0.006761  0.009190  0.032402   \n",
              "GS    0.022053  0.019330  0.023247  0.036888  0.013445  0.018628  0.011850   \n",
              "JPM   0.027485  0.031018  0.027440  0.063879  0.023115  0.030977  0.014831   \n",
              "KO    0.007939  0.016689  0.007531  0.020986  0.011955  0.026067  0.004676   \n",
              "MSFT  0.011055  0.035152  0.026361  0.025337  0.020781  0.024143  0.007790   \n",
              "SBUX  0.017962  0.020363  0.024798  0.024293  0.017003  0.017084  0.012007   \n",
              "TSM   0.014317  0.026846  0.032866  0.022760  0.016206  0.020264  0.009913   \n",
              "\n",
              "            GS       JPM        KO      MSFT      SBUX       TSM  \n",
              "AAL   0.022053  0.027485  0.007939  0.011055  0.017962  0.014317  \n",
              "AAPL  0.019330  0.031018  0.016689  0.035152  0.020363  0.026846  \n",
              "AMZN  0.023247  0.027440  0.007531  0.026361  0.024798  0.032866  \n",
              "BAC   0.036888  0.063879  0.020986  0.025337  0.024293  0.022760  \n",
              "COST  0.013445  0.023115  0.011955  0.020781  0.017003  0.016206  \n",
              "DIS   0.018628  0.030977  0.026067  0.024143  0.017084  0.020264  \n",
              "DPZ   0.011850  0.014831  0.004676  0.007790  0.012007  0.009913  \n",
              "GS    0.048492  0.035142  0.006592  0.017959  0.016839  0.022780  \n",
              "JPM   0.035142  0.090484  0.018335  0.028383  0.023124  0.026138  \n",
              "KO    0.006592  0.018335  0.055784  0.015779  0.009713  0.006814  \n",
              "MSFT  0.017959  0.028383  0.015779  0.068339  0.018648  0.022445  \n",
              "SBUX  0.016839  0.023124  0.009713  0.018648  0.071734  0.018626  \n",
              "TSM   0.022780  0.026138  0.006814  0.022445  0.018626  0.078518  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3323db6b-92f0-4c7d-b171-4272a6291285\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAL</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>BAC</th>\n",
              "      <th>COST</th>\n",
              "      <th>DIS</th>\n",
              "      <th>DPZ</th>\n",
              "      <th>GS</th>\n",
              "      <th>JPM</th>\n",
              "      <th>KO</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>SBUX</th>\n",
              "      <th>TSM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAL</th>\n",
              "      <td>0.106536</td>\n",
              "      <td>0.014031</td>\n",
              "      <td>0.016229</td>\n",
              "      <td>0.033823</td>\n",
              "      <td>0.011090</td>\n",
              "      <td>0.015568</td>\n",
              "      <td>0.022666</td>\n",
              "      <td>0.022053</td>\n",
              "      <td>0.027485</td>\n",
              "      <td>0.007939</td>\n",
              "      <td>0.011055</td>\n",
              "      <td>0.017962</td>\n",
              "      <td>0.014317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.014031</td>\n",
              "      <td>0.144810</td>\n",
              "      <td>0.029662</td>\n",
              "      <td>0.028267</td>\n",
              "      <td>0.021811</td>\n",
              "      <td>0.026415</td>\n",
              "      <td>0.008475</td>\n",
              "      <td>0.019330</td>\n",
              "      <td>0.031018</td>\n",
              "      <td>0.016689</td>\n",
              "      <td>0.035152</td>\n",
              "      <td>0.020363</td>\n",
              "      <td>0.026846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMZN</th>\n",
              "      <td>0.016229</td>\n",
              "      <td>0.029662</td>\n",
              "      <td>0.142418</td>\n",
              "      <td>0.025099</td>\n",
              "      <td>0.018505</td>\n",
              "      <td>0.021715</td>\n",
              "      <td>0.010948</td>\n",
              "      <td>0.023247</td>\n",
              "      <td>0.027440</td>\n",
              "      <td>0.007531</td>\n",
              "      <td>0.026361</td>\n",
              "      <td>0.024798</td>\n",
              "      <td>0.032866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BAC</th>\n",
              "      <td>0.033823</td>\n",
              "      <td>0.028267</td>\n",
              "      <td>0.025099</td>\n",
              "      <td>0.120020</td>\n",
              "      <td>0.021304</td>\n",
              "      <td>0.033390</td>\n",
              "      <td>0.015445</td>\n",
              "      <td>0.036888</td>\n",
              "      <td>0.063879</td>\n",
              "      <td>0.020986</td>\n",
              "      <td>0.025337</td>\n",
              "      <td>0.024293</td>\n",
              "      <td>0.022760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COST</th>\n",
              "      <td>0.011090</td>\n",
              "      <td>0.021811</td>\n",
              "      <td>0.018505</td>\n",
              "      <td>0.021304</td>\n",
              "      <td>0.062039</td>\n",
              "      <td>0.018670</td>\n",
              "      <td>0.006761</td>\n",
              "      <td>0.013445</td>\n",
              "      <td>0.023115</td>\n",
              "      <td>0.011955</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.017003</td>\n",
              "      <td>0.016206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DIS</th>\n",
              "      <td>0.015568</td>\n",
              "      <td>0.026415</td>\n",
              "      <td>0.021715</td>\n",
              "      <td>0.033390</td>\n",
              "      <td>0.018670</td>\n",
              "      <td>0.101839</td>\n",
              "      <td>0.009190</td>\n",
              "      <td>0.018628</td>\n",
              "      <td>0.030977</td>\n",
              "      <td>0.026067</td>\n",
              "      <td>0.024143</td>\n",
              "      <td>0.017084</td>\n",
              "      <td>0.020264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DPZ</th>\n",
              "      <td>0.022666</td>\n",
              "      <td>0.008475</td>\n",
              "      <td>0.010948</td>\n",
              "      <td>0.015445</td>\n",
              "      <td>0.006761</td>\n",
              "      <td>0.009190</td>\n",
              "      <td>0.032402</td>\n",
              "      <td>0.011850</td>\n",
              "      <td>0.014831</td>\n",
              "      <td>0.004676</td>\n",
              "      <td>0.007790</td>\n",
              "      <td>0.012007</td>\n",
              "      <td>0.009913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GS</th>\n",
              "      <td>0.022053</td>\n",
              "      <td>0.019330</td>\n",
              "      <td>0.023247</td>\n",
              "      <td>0.036888</td>\n",
              "      <td>0.013445</td>\n",
              "      <td>0.018628</td>\n",
              "      <td>0.011850</td>\n",
              "      <td>0.048492</td>\n",
              "      <td>0.035142</td>\n",
              "      <td>0.006592</td>\n",
              "      <td>0.017959</td>\n",
              "      <td>0.016839</td>\n",
              "      <td>0.022780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JPM</th>\n",
              "      <td>0.027485</td>\n",
              "      <td>0.031018</td>\n",
              "      <td>0.027440</td>\n",
              "      <td>0.063879</td>\n",
              "      <td>0.023115</td>\n",
              "      <td>0.030977</td>\n",
              "      <td>0.014831</td>\n",
              "      <td>0.035142</td>\n",
              "      <td>0.090484</td>\n",
              "      <td>0.018335</td>\n",
              "      <td>0.028383</td>\n",
              "      <td>0.023124</td>\n",
              "      <td>0.026138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KO</th>\n",
              "      <td>0.007939</td>\n",
              "      <td>0.016689</td>\n",
              "      <td>0.007531</td>\n",
              "      <td>0.020986</td>\n",
              "      <td>0.011955</td>\n",
              "      <td>0.026067</td>\n",
              "      <td>0.004676</td>\n",
              "      <td>0.006592</td>\n",
              "      <td>0.018335</td>\n",
              "      <td>0.055784</td>\n",
              "      <td>0.015779</td>\n",
              "      <td>0.009713</td>\n",
              "      <td>0.006814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.011055</td>\n",
              "      <td>0.035152</td>\n",
              "      <td>0.026361</td>\n",
              "      <td>0.025337</td>\n",
              "      <td>0.020781</td>\n",
              "      <td>0.024143</td>\n",
              "      <td>0.007790</td>\n",
              "      <td>0.017959</td>\n",
              "      <td>0.028383</td>\n",
              "      <td>0.015779</td>\n",
              "      <td>0.068339</td>\n",
              "      <td>0.018648</td>\n",
              "      <td>0.022445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SBUX</th>\n",
              "      <td>0.017962</td>\n",
              "      <td>0.020363</td>\n",
              "      <td>0.024798</td>\n",
              "      <td>0.024293</td>\n",
              "      <td>0.017003</td>\n",
              "      <td>0.017084</td>\n",
              "      <td>0.012007</td>\n",
              "      <td>0.016839</td>\n",
              "      <td>0.023124</td>\n",
              "      <td>0.009713</td>\n",
              "      <td>0.018648</td>\n",
              "      <td>0.071734</td>\n",
              "      <td>0.018626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TSM</th>\n",
              "      <td>0.014317</td>\n",
              "      <td>0.026846</td>\n",
              "      <td>0.032866</td>\n",
              "      <td>0.022760</td>\n",
              "      <td>0.016206</td>\n",
              "      <td>0.020264</td>\n",
              "      <td>0.009913</td>\n",
              "      <td>0.022780</td>\n",
              "      <td>0.026138</td>\n",
              "      <td>0.006814</td>\n",
              "      <td>0.022445</td>\n",
              "      <td>0.018626</td>\n",
              "      <td>0.078518</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 508
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Risk free rates\n",
        "This will be the US 10 year treasury yield of today"
      ],
      "metadata": {
        "id": "GOBml6FftxN4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "risk_free_rates = 0.039"
      ],
      "metadata": {
        "id": "6nPxruMY5WPk"
      },
      "execution_count": 509,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Market implied risk return\n",
        "We are trying to calculate the delta  $ δ=(R−R_f) / σ2$ that reflects the potential returns taking away the part of money you can earn from US 10 year bonds."
      ],
      "metadata": {
        "id": "yVh58jjAt9Bd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def market_implied_risk_premium(market_prices, frequency=252, risk_free_rates=0.02):\n",
        "  if not isinstance(market_prices, (pd.Series, pd.DataFrame)):\n",
        "    raise TypeError(\"Please format market_prices as a pd.Series\")\n",
        "  returns = market_prices.pct_change().dropna()\n",
        "  r = returns.mean() * frequency\n",
        "  variance = returns.var() * frequency\n",
        "  delta = (r - risk_free_rates) / variance\n",
        "  return delta\n",
        "# 252 are the number of trading days in a year (us stock market)\n",
        "delta = market_implied_risk_premium(market_prices[:-simulation_day+1], 252, risk_free_rates)\n",
        "print(delta)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lr0rJ0RzsxUx",
        "outputId": "85a753e6-c74d-4cbc-86ee-b0584df06f8b"
      },
      "execution_count": 510,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.6866207436044989\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can see that by changing the risk_free_rate, you you will have a different delta, so this delta is dependent on the Fed's decision at every FOMC meeting."
      ],
      "metadata": {
        "id": "HoMvIv-ev-G6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Market implied excess equilibrium returns\n",
        "Here we will get the prior, which is the potential future returns we can get, considering the factors of each component, they are market capitals, covariance between assets. We get the prior by aplying this formula : $ Π=δΣwmkt $"
      ],
      "metadata": {
        "id": "WvCp8ga3vMjT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def market_implied_returns(marketCap, delta, cov_matrix, risk_free_rate=0.02):\n",
        "  mcaps = pd.Series(marketCap)\n",
        "  mWeights = pd.Series(mcaps / mcaps.sum())\n",
        "  pi = delta * cov_matrix.dot(mWeights) + risk_free_rate\n",
        "  return pi\n",
        "\n",
        "prior = market_implied_returns(mcaps, delta, cov_matrix, risk_free_rates)\n",
        "tickers = prior.index.to_numpy()\n",
        "print(prior)\n",
        "tickers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QT8qaudtxf7K",
        "outputId": "fdb317da-0a0a-4c32-f6a8-e4703efd68c2"
      },
      "execution_count": 511,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AAL     0.063824\n",
            "AAPL    0.154598\n",
            "AMZN    0.111268\n",
            "BAC     0.090626\n",
            "COST    0.074916\n",
            "DIS     0.083526\n",
            "DPZ     0.054484\n",
            "GS      0.073804\n",
            "JPM     0.093911\n",
            "KO      0.065950\n",
            "MSFT    0.106917\n",
            "SBUX    0.074117\n",
            "TSM     0.085701\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['AAL', 'AAPL', 'AMZN', 'BAC', 'COST', 'DIS', 'DPZ', 'GS', 'JPM',\n",
              "       'KO', 'MSFT', 'SBUX', 'TSM'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 511
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to diversify in the market, we need to put a small portion of our money also in stocks that doesn't really have the best potential. Otherwise you will have greater flactuations"
      ],
      "metadata": {
        "id": "-LscYoXbwc2l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "potential_view = pd.Series(targetPrices) / prices.tail(1) - 1\n",
        "potential_view"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "cfNoRvPb8vyp",
        "outputId": "b646122e-5c70-4616-e872-37b1d4f171af"
      },
      "execution_count": 512,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 AAL      AAPL     AMZN       BAC      COST      DIS  \\\n",
              "Date                                                                   \n",
              "2023-05-12  0.201854 -0.052558  0.21277  0.229974  0.087408  0.23133   \n",
              "\n",
              "                 DPZ        GS       JPM        KO      MSFT      SBUX  \\\n",
              "Date                                                                     \n",
              "2023-05-12  0.145035  0.212238  0.197912  0.089222 -0.019516  0.096306   \n",
              "\n",
              "                 TSM  \n",
              "Date                  \n",
              "2023-05-12  0.234328  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>AAL</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>BAC</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>2023-05-12</th>\n",
              "      <td>0.201854</td>\n",
              "      <td>-0.052558</td>\n",
              "      <td>0.21277</td>\n",
              "      <td>0.229974</td>\n",
              "      <td>0.087408</td>\n",
              "      <td>0.23133</td>\n",
              "      <td>0.145035</td>\n",
              "      <td>0.212238</td>\n",
              "      <td>0.197912</td>\n",
              "      <td>0.089222</td>\n",
              "      <td>-0.019516</td>\n",
              "      <td>0.096306</td>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# View vector\n",
        "We need to integrate our views on the stocks we are trying to invest. For example, if I think Apple are doing stock buy backs, I think APPL will increase 10% from now, Then I will put 0.10 in the array. For convenience, I downloaded the mean analysts' target prices, and let that price as potential gains. So you can leave this part, if you trust on wall street's analysts. "
      ],
      "metadata": {
        "id": "oX4Ia9Knw5OJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Q = np.array([0.20, 0.10, 0.05, 0.10])\n",
        "'''Q = np.array([ \n",
        "     0.10,\n",
        "     0.30,\n",
        "     0.05,\n",
        "     0.05,\n",
        "     0.20,\n",
        "     -0.05,  # I think Coca-Cola will go down 5%\n",
        "     0.15,\n",
        "     0.10,\n",
        "     0.50,  # but low confidence, which will be reflected later\n",
        "     0.10\n",
        "] )\n",
        "Q.reshape(-1, 1)'''\n",
        "Q = potential_view.to_numpy().squeeze()\n",
        "Q\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ry0RKSJ1euf",
        "outputId": "9b875f70-871e-4fc0-8c09-414cee8c68c1"
      },
      "execution_count": 513,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.20185445, -0.05255842,  0.21276979,  0.22997415,  0.08740848,\n",
              "        0.23132952,  0.14503549,  0.21223787,  0.19791195,  0.08922164,\n",
              "       -0.01951646,  0.09630604,  0.23432817])"
            ]
          },
          "metadata": {},
          "execution_count": 513
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The columns of P should be in the order as we can find in the series, which we will multiply later"
      ],
      "metadata": {
        "id": "JrfpwSRMAGTR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Picking vector\n",
        "P is the vector that associates each view to our stocks. You can find how to specify this vector [here](https://pyportfolioopt.readthedocs.io/en/latest/BlackLitterman.html) "
      ],
      "metadata": {
        "id": "I-3CvZ0dx1CP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#P = np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0 ,0],\n",
        " #            [0, 0, 0, 0, 0, 0, 0, 0, 0 ,1],\n",
        "  #           [0, 0, 0, 1, -1, 0, 0, 0, 0 ,0],\n",
        "   #          [0, 0, 0, 0, 0, 0.5, 0.5, -0.5, -0.5 ,0]])\n",
        "P = np.eye(len(tickers))\n",
        "P"
      ],
      "metadata": {
        "id": "k288pB_pDXGW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15b8e7bd-5f20-43fe-947f-d4199c7c206c"
      },
      "execution_count": 514,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 514
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q_P = Q @ P\n",
        "Q_P = pd.Series(data=Q_P, index=tickers)\n",
        "Q_P"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0shX_Do4n1Yo",
        "outputId": "a0d57d82-2cf6-49f3-e667-c49608e68518"
      },
      "execution_count": 515,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AAL     0.201854\n",
              "AAPL   -0.052558\n",
              "AMZN    0.212770\n",
              "BAC     0.229974\n",
              "COST    0.087408\n",
              "DIS     0.231330\n",
              "DPZ     0.145035\n",
              "GS      0.212238\n",
              "JPM     0.197912\n",
              "KO      0.089222\n",
              "MSFT   -0.019516\n",
              "SBUX    0.096306\n",
              "TSM     0.234328\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 515
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Confidence vector\n",
        "Confidence is that you need to specify, how certain you were about your views, if you are 100% convinced that APPL will increase 10%, then you put 1; If you think you are half convinved you put 0.5. For convenience, I put a same value for every view, you can decide your confidence vectors."
      ],
      "metadata": {
        "id": "Y6Z1ebAcEccW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "variances = []\n",
        "'''confidence_interval = [\n",
        "    (0, 0.25),\n",
        "    (0.1, 0.4),\n",
        "    (-0.1, 0.15),\n",
        "    (-0.05, 0.1),\n",
        "    (0.15, 0.25),\n",
        "    (-0.1, 0),\n",
        "    (0.1, 0.2),\n",
        "    (0.08, 0.12),\n",
        "    (0.1, 0.9),\n",
        "    (0, 0.3)\n",
        "]\n",
        "for ll, lh in confidence_interval:\n",
        "  sigma = ((lh-ll) / 2) ** 2\n",
        "  variances.append(sigma)'''\n",
        "\n",
        "'''confidences = [\n",
        "    0.6,\n",
        "    0.4,\n",
        "    0.2,\n",
        "    0.5,\n",
        "    0.7, \n",
        "    0.7, \n",
        "    0.7, \n",
        "    0.5,\n",
        "    0.1,\n",
        "    0.4\n",
        "]'''\n",
        "confidences = [0.1] * len(tickers)\n",
        "variances = confidences"
      ],
      "metadata": {
        "id": "0FFuqxCEs72w"
      },
      "execution_count": 516,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Omega uncertainty matrix\n",
        "$ Ω=τ∗PΣP^T $ is the formula I used to calcualte the uncertainty matrix, the more you are not sure about your views, the more you are uncertain to the view you gave to the asset."
      ],
      "metadata": {
        "id": "kYiM7tMVyq-X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tau = 0.05\n",
        "def idzorek_method(tau, view_uncertainty, P, Q, S):\n",
        "  view_omg = []\n",
        "  for q_id in range(len(Q)):\n",
        "    conf = view_uncertainty[q_id]\n",
        "    if conf < 0 or conf > 1:\n",
        "      raise ValueError(\"View confidences must be between 0 and 1\")\n",
        "    if conf == 0:\n",
        "      view_omg.append(1e6)\n",
        "      continue\n",
        "    alpha = (1-conf)/conf\n",
        "    P_view = P[q_id]\n",
        "    omega = tau * alpha * P_view @ S @ P_view.T\n",
        "    view_omg.append(omega)\n",
        "\n",
        "  return np.diag(view_omg)\n",
        "\n",
        "omega = idzorek_method(tau, variances, P, Q, cov_matrix)\n",
        "pd.Series(data=np.diag(omega), index=tickers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DA-iHvsWEcHk",
        "outputId": "3b711703-47be-4edd-97db-948ee5d948b0"
      },
      "execution_count": 517,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AAL     0.047941\n",
              "AAPL    0.065164\n",
              "AMZN    0.064088\n",
              "BAC     0.054009\n",
              "COST    0.027918\n",
              "DIS     0.045828\n",
              "DPZ     0.014581\n",
              "GS      0.021821\n",
              "JPM     0.040718\n",
              "KO      0.025103\n",
              "MSFT    0.030753\n",
              "SBUX    0.032280\n",
              "TSM     0.035333\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 517
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(7,7))\n",
        "im = ax.imshow(omega)\n",
        "\n",
        "# We want to show all ticks...\n",
        "ax.set_xticks(np.arange(len(tickers)))\n",
        "ax.set_yticks(np.arange(len(tickers)))\n",
        "\n",
        "ax.set_xticklabels(tickers)\n",
        "ax.set_yticklabels(tickers)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "eAsjfZV4zOqf",
        "outputId": "a5380b03-1523-405f-e673-4fa6bf6e258e"
      },
      "execution_count": 518,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Black litterman posterior excess returns\n",
        "\n",
        "$ E(R)=[(τΣ)^{-1}+P^TΩ^{-1}P]^{-1}[(τΣ)^{-1}Π+P^TΩ^{-1}Q] $\n",
        " \n",
        "$E(R)$\n",
        " is a Nx1 vector of expected returns, where N is the number of assets.\n",
        "\n",
        "$Q$\n",
        " is a Kx1 vector of views.\n",
        "\n",
        "$P$\n",
        " is the KxN picking matrix which maps views to the universe of assets. Essentially, it tells the model which view corresponds to which asset(s).\n",
        "\n",
        "$Ω$ omega\n",
        " is the KxK uncertainty matrix of views.\n",
        "\n",
        "$Π$ pi, prior\n",
        " is the Nx1 vector of prior expected returns.\n",
        "\n",
        "$Σ$ sigma\n",
        " is the NxN covariance matrix of asset returns (as always)\n",
        "\n",
        "$τ$\n",
        " is a scalar tuning constant.\n"
      ],
      "metadata": {
        "id": "zm3yUNxHzPk7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tau_S_inv = np.linalg.inv(tau * cov_matrix)\n",
        "omega_inv = np.linalg.inv(omega) \n",
        "expected_excess_returns = np.linalg.inv(tau_S_inv + (P.T @ omega_inv @ P)) @ (tau_S_inv @ prior + P.T @ omega_inv @ Q )\n",
        "rets_bl = pd.Series(data=expected_excess_returns, index=tickers)\n",
        "rets_bl "
      ],
      "metadata": {
        "id": "u6hWgLnsFm18",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "904fbcb3-365e-4c76-f547-7bc827178fac"
      },
      "execution_count": 519,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AAL     0.094407\n",
              "AAPL    0.149788\n",
              "AMZN    0.134505\n",
              "BAC     0.126318\n",
              "COST    0.084977\n",
              "DIS     0.108739\n",
              "DPZ     0.071996\n",
              "GS      0.100003\n",
              "JPM     0.124563\n",
              "KO      0.074420\n",
              "MSFT    0.109468\n",
              "SBUX    0.089045\n",
              "TSM     0.110290\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 519
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rets_df = pd.DataFrame([prior, rets_bl, Q_P], \n",
        "             index=[\"Prior\", \"Posterior\", \"Views\"]).T\n",
        "             \n",
        "rets_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "ZyuBfQpMkZot",
        "outputId": "d55f1c54-6c43-4025-acd8-1f6a3e84b566"
      },
      "execution_count": 520,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Prior  Posterior     Views\n",
              "AAL   0.063824   0.094407  0.201854\n",
              "AAPL  0.154598   0.149788 -0.052558\n",
              "AMZN  0.111268   0.134505  0.212770\n",
              "BAC   0.090626   0.126318  0.229974\n",
              "COST  0.074916   0.084977  0.087408\n",
              "DIS   0.083526   0.108739  0.231330\n",
              "DPZ   0.054484   0.071996  0.145035\n",
              "GS    0.073804   0.100003  0.212238\n",
              "JPM   0.093911   0.124563  0.197912\n",
              "KO    0.065950   0.074420  0.089222\n",
              "MSFT  0.106917   0.109468 -0.019516\n",
              "SBUX  0.074117   0.089045  0.096306\n",
              "TSM   0.085701   0.110290  0.234328"
            ],
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              "\n",
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              "      <th>Prior</th>\n",
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              "      <th>Views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAL</th>\n",
              "      <td>0.063824</td>\n",
              "      <td>0.094407</td>\n",
              "      <td>0.201854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.154598</td>\n",
              "      <td>0.149788</td>\n",
              "      <td>-0.052558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMZN</th>\n",
              "      <td>0.111268</td>\n",
              "      <td>0.134505</td>\n",
              "      <td>0.212770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BAC</th>\n",
              "      <td>0.090626</td>\n",
              "      <td>0.126318</td>\n",
              "      <td>0.229974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COST</th>\n",
              "      <td>0.074916</td>\n",
              "      <td>0.084977</td>\n",
              "      <td>0.087408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DIS</th>\n",
              "      <td>0.083526</td>\n",
              "      <td>0.108739</td>\n",
              "      <td>0.231330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DPZ</th>\n",
              "      <td>0.054484</td>\n",
              "      <td>0.071996</td>\n",
              "      <td>0.145035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GS</th>\n",
              "      <td>0.073804</td>\n",
              "      <td>0.100003</td>\n",
              "      <td>0.212238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JPM</th>\n",
              "      <td>0.093911</td>\n",
              "      <td>0.124563</td>\n",
              "      <td>0.197912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KO</th>\n",
              "      <td>0.065950</td>\n",
              "      <td>0.074420</td>\n",
              "      <td>0.089222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.106917</td>\n",
              "      <td>0.109468</td>\n",
              "      <td>-0.019516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SBUX</th>\n",
              "      <td>0.074117</td>\n",
              "      <td>0.089045</td>\n",
              "      <td>0.096306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TSM</th>\n",
              "      <td>0.085701</td>\n",
              "      <td>0.110290</td>\n",
              "      <td>0.234328</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "rets_df.plot.bar(figsize=(12, 8))"
      ],
      "metadata": {
        "colab": {
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          "height": 492
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        "id": "KQsdbnUunban",
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      },
      "execution_count": 521,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 521
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To make sure that the calculations are correct, the posterior excess return should be the bayesian average between prior and our views. If it's not, the result may be not so accurate theoretically"
      ],
      "metadata": {
        "id": "KT5HJtSt0Zag"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Update the posterior covarance matrix"
      ],
      "metadata": {
        "id": "mGcY647x0mMF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_tau_sigma_P = tau * cov_matrix @ P.T\n",
        "_A = (P @ _tau_sigma_P) + omega\n",
        "\n",
        "b = _tau_sigma_P.T\n",
        "part1 = tau * cov_matrix \n",
        "part2 = _tau_sigma_P @ np.linalg.solve(_A, b)\n",
        "M = part1.values - part2.values\n",
        "posterior_cov = cov_matrix + M\n",
        "S_bl = pd.DataFrame(posterior_cov, index=tickers, columns=tickers)\n",
        "\n",
        "print(S_bl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6ryyBdpc76D",
        "outputId": "751c7f92-0769-4432-88f2-c365457bd8ec"
      },
      "execution_count": 522,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           AAL      AAPL      AMZN       BAC      COST       DIS       DPZ  \\\n",
            "AAL   0.111129  0.014410  0.016698  0.034950  0.011405  0.016020  0.023509   \n",
            "AAPL  0.014410  0.151044  0.030627  0.029100  0.022523  0.027255  0.008708   \n",
            "AMZN  0.016698  0.030627  0.148567  0.025812  0.019090  0.022381  0.011284   \n",
            "BAC   0.034950  0.029100  0.025812  0.124921  0.021950  0.034461  0.015923   \n",
            "COST  0.011405  0.022523  0.019090  0.021950  0.064699  0.019269  0.006958   \n",
            "DIS   0.016020  0.027255  0.022381  0.034461  0.019269  0.106178  0.009460   \n",
            "DPZ   0.023509  0.008708  0.011284  0.015923  0.006958  0.009460  0.033796   \n",
            "GS    0.022788  0.019911  0.024002  0.038147  0.013849  0.019199  0.012238   \n",
            "JPM   0.028357  0.031983  0.028266  0.066162  0.023853  0.031962  0.015296   \n",
            "KO    0.008160  0.017234  0.007708  0.021680  0.012349  0.027019  0.004809   \n",
            "MSFT  0.011334  0.036384  0.027234  0.026094  0.021484  0.024929  0.008011   \n",
            "SBUX  0.018540  0.021001  0.025630  0.025040  0.017568  0.017603  0.012412   \n",
            "TSM   0.014730  0.027733  0.034015  0.023413  0.016722  0.020902  0.010222   \n",
            "\n",
            "            GS       JPM        KO      MSFT      SBUX       TSM  \n",
            "AAL   0.022788  0.028357  0.008160  0.011334  0.018540  0.014730  \n",
            "AAPL  0.019911  0.031983  0.017234  0.036384  0.021001  0.027733  \n",
            "AMZN  0.024002  0.028266  0.007708  0.027234  0.025630  0.034015  \n",
            "BAC   0.038147  0.066162  0.021680  0.026094  0.025040  0.023413  \n",
            "COST  0.013849  0.023853  0.012349  0.021484  0.017568  0.016722  \n",
            "DIS   0.019199  0.031962  0.027019  0.024929  0.017603  0.020902  \n",
            "DPZ   0.012238  0.015296  0.004809  0.008011  0.012412  0.010222  \n",
            "GS    0.050469  0.036347  0.006744  0.018518  0.017367  0.023548  \n",
            "JPM   0.036347  0.094102  0.018925  0.029289  0.023833  0.026953  \n",
            "KO    0.006744  0.018925  0.058208  0.016311  0.010009  0.006978  \n",
            "MSFT  0.018518  0.029289  0.016311  0.071201  0.019246  0.023184  \n",
            "SBUX  0.017367  0.023833  0.010009  0.019246  0.074802  0.019225  \n",
            "TSM   0.023548  0.026953  0.006978  0.023184  0.019225  0.081854  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Diversifications\n",
        "If you put assets that belongs to different sectors, that's good, we can cover each sectors with a percentage, you can find the description of these constraints [here](https://childlike-browser-849.notion.site/Operations-Research-Project-1658df813e6448f791aa0cf0ffc28733), there were only alpha, beta and gamma in the original exercise, now we scale it into a matrix"
      ],
      "metadata": {
        "id": "Rm7K4QBk0rzH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#type_parameters = [0.2, 0.2, 0.2, 0.1, 0.0]\n",
        "type_constraints = pd.Series(sectors).unique().astype('U')\n",
        "type_parameters = [0.3 / len(type_constraints)]*len(type_constraints)\n",
        "constrains_param = [(i, j) for i, j in zip(type_constraints, type_parameters)] \n",
        "constrains_param"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWh7r7FQtle3",
        "outputId": "c8e508d3-55bb-4ec1-dfa8-d09216b8eeb9"
      },
      "execution_count": 523,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Technology', 0.049999999999999996),\n",
              " ('Consumer Cyclical', 0.049999999999999996),\n",
              " ('Financial Services', 0.049999999999999996),\n",
              " ('Communication Services', 0.049999999999999996),\n",
              " ('Consumer Defensive', 0.049999999999999996),\n",
              " ('Industrials', 0.049999999999999996)]"
            ]
          },
          "metadata": {},
          "execution_count": 523
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "constraints_table = pd.DataFrame(data=sectors, index=type_constraints)\n",
        "#constraints_table.loc['Technology'] = 1 if constraints_table.iloc[]\n",
        "for j in range(len(tickers)):\n",
        "  for i in range(len(type_constraints)):\n",
        "    constraints_table.iloc[i, j] = 1 if constraints_table.iloc[i, j] == constraints_table.index[i] else 0\n",
        "\n",
        "constraints_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "ouucBMocpooE",
        "outputId": "0cc01e99-4cef-4bc9-b285-8467185d7faf"
      },
      "execution_count": 524,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       MSFT AMZN BAC DPZ DIS KO JPM COST SBUX AAPL TSM GS AAL\n",
              "Technology                1    0   0   0   0  0   0    0    0    1   1  0   0\n",
              "Consumer Cyclical         0    1   0   1   0  0   0    0    1    0   0  0   0\n",
              "Financial Services        0    0   1   0   0  0   1    0    0    0   0  1   0\n",
              "Communication Services    0    0   0   0   1  0   0    0    0    0   0  0   0\n",
              "Consumer Defensive        0    0   0   0   0  1   0    1    0    0   0  0   0\n",
              "Industrials               0    0   0   0   0  0   0    0    0    0   0  0   1"
            ],
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              "\n",
              "  <div id=\"df-8cef994a-e6bb-4b1e-87c3-da2a52f5ef0f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>MSFT</th>\n",
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              "      <th>Financial Services</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>Communication Services</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>Consumer Defensive</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can check if they belongs to the right sector"
      ],
      "metadata": {
        "id": "_AvM2wQb1PeZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Solve the optimization problem\n",
        "I used gurobi optimizer, you can use any commercial solver to solve this, the objective function is a typical formula derived from the theory of mean-variance optimization. The formula is \n",
        "\n",
        "$ max z = W_{t+1} = \\sum_{i=1}^N x_{i,t}r_{i, t+1} - x^TΣx \\\\s.t.\\quad \\\\  \\sum_{i=1}^N x_{i,t} = 1\\\\ \\sum_{i=1}^N x_{i,t}a_{i,t} \\ge \\alpha W_t \\\\\\sum_{i=1}^N x_{i,t}b_{i,t} \\ge \\beta W_t \\\\ \\sum_{i=1}^N x_{i,t}c_{i,t} \\ge \\gamma W_t  \\\\ x_{i,t} \\ge 0 \\quad \\forall i = 1...n $\n",
        "\n",
        "$r$ is the posterior excess returns we just calculated from the black litterman mdoel\n",
        "\n",
        "$Σ$ is the posterior covarience matrix"
      ],
      "metadata": {
        "id": "9-zMk9_31ULD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gurobipy as gp\n",
        "from gurobipy import GRB\n",
        "\n",
        "\n",
        "# Define the optimization problem\n",
        "r = rets_bl.to_numpy() # objective function coefficients\n",
        "\n",
        "# Create a Gurobi model\n",
        "model = gp.Model()\n",
        "x = model.addMVar(len(tickers))\n",
        "\n",
        "quad_objective = r.T @ x -  x.T @ S_bl.to_numpy() @ x\n",
        "# Set the objective function\n",
        "#model.setObjective(gp.quicksum(r[i]*x[i] for i in range(length)), sense=gp.GRB.MAXIMIZE)\n",
        "model.setObjective(quad_objective, GRB.MAXIMIZE);\n",
        "\n",
        "# Add constraints\n",
        "model.addConstr(gp.quicksum(x[i] for i in range(len(tickers))) == 1)\n",
        "for i in range(len(type_parameters)):\n",
        "  model.addConstr(gp.quicksum(x[j] * constraints_table.iloc[i, j] for j in range(len(tickers))) >= constrains_param[i][1])\n",
        "\n",
        "\n",
        "# Optimize the model\n",
        "model.optimize()\n",
        "\n",
        "# Extract the results\n",
        "weights = []\n",
        "if model.status == gp.GRB.OPTIMAL:\n",
        "    print(f\"Optimal objective value:{model.objVal}%\")\n",
        "    print(\"Optimal decision variables:\")\n",
        "    weights = [x[i].x.item() for i in range(len(tickers))]\n",
        "    \n",
        "weights_me = [round(w, 5) for w in weights]\n",
        "weights_me"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLQUYwbIH3x3",
        "outputId": "c20c6ad1-5707-4349-ca81-edf2e447df68"
      },
      "execution_count": 525,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning for adding constraints: zero or small (< 1e-13) coefficients, ignored\n",
            "Gurobi Optimizer version 10.0.1 build v10.0.1rc0 (linux64)\n",
            "\n",
            "CPU model: AMD EPYC 7B12, instruction set [SSE2|AVX|AVX2]\n",
            "Thread count: 1 physical cores, 2 logical processors, using up to 2 threads\n",
            "\n",
            "Optimize a model with 7 rows, 13 columns and 26 nonzeros\n",
            "Model fingerprint: 0x9370e943\n",
            "Model has 91 quadratic objective terms\n",
            "Coefficient statistics:\n",
            "  Matrix range     [1e+00, 1e+00]\n",
            "  Objective range  [7e-02, 1e-01]\n",
            "  QObjective range [2e-02, 3e-01]\n",
            "  Bounds range     [0e+00, 0e+00]\n",
            "  RHS range        [5e-02, 1e+00]\n",
            "Presolve removed 2 rows and 0 columns\n",
            "Presolve time: 0.02s\n",
            "Presolved: 5 rows, 13 columns, 24 nonzeros\n",
            "Presolved model has 91 quadratic objective terms\n",
            "Ordering time: 0.00s\n",
            "\n",
            "Barrier statistics:\n",
            " Free vars  : 12\n",
            " AA' NZ     : 1.200e+02\n",
            " Factor NZ  : 1.530e+02\n",
            " Factor Ops : 1.785e+03 (less than 1 second per iteration)\n",
            " Threads    : 1\n",
            "\n",
            "                  Objective                Residual\n",
            "Iter       Primal          Dual         Primal    Dual     Compl     Time\n",
            "   0  -5.01640933e+05  5.03616216e+05  9.00e+03 2.53e-01  9.99e+05     0s\n",
            "   1  -3.05120355e+04  3.16414797e+04  1.66e+03 7.13e-05  1.92e+05     0s\n",
            "   2   1.16988955e+00  1.06776943e+03  5.97e+00 2.56e-07  7.64e+02     0s\n",
            "   3   7.81432344e-02  8.90027319e+02  5.97e-06 2.56e-13  5.24e+01     0s\n",
            "   4   7.81425874e-02  1.02299656e+00  3.69e-10 1.77e-13  5.56e-02     0s\n",
            "   5   7.89933375e-02  1.14845950e-01  9.66e-12 4.92e-15  2.11e-03     0s\n",
            "   6   8.27527090e-02  9.00550677e-02  5.55e-17 1.39e-17  4.30e-04     0s\n",
            "   7   8.34360219e-02  8.39882332e-02  5.64e-17 5.55e-17  3.25e-05     0s\n",
            "   8   8.35918482e-02  8.36257513e-02  5.61e-15 2.78e-17  1.99e-06     0s\n",
            "   9   8.36096701e-02  8.36111650e-02  1.94e-15 2.78e-17  8.79e-08     0s\n",
            "  10   8.36108422e-02  8.36109098e-02  2.85e-14 5.55e-17  3.98e-09     0s\n",
            "  11   8.36109058e-02  8.36109059e-02  6.37e-14 1.39e-17  8.58e-12     0s\n",
            "\n",
            "Barrier solved model in 11 iterations and 0.04 seconds (0.00 work units)\n",
            "Optimal objective 8.36109058e-02\n",
            "\n",
            "Optimal objective value:0.08361090579198362%\n",
            "Optimal decision variables:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05135,\n",
              " 0.19059,\n",
              " 0.1369,\n",
              " 0.07523,\n",
              " 0.05,\n",
              " 0.07443,\n",
              " 0.02484,\n",
              " 0.09097,\n",
              " 0.091,\n",
              " 0.0,\n",
              " 0.10498,\n",
              " 0.0,\n",
              " 0.10972]"
            ]
          },
          "metadata": {},
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        }
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    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(weights_me, tickers).plot.pie(figsize=(10,10), autopct='%1.1f%%');"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "7p5RW-YkWcQ3",
        "outputId": "bffd187b-a3a8-4dfc-d0eb-818c76489e6f"
      },
      "execution_count": 526,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Result & Comparison\n",
        "\n",
        "If I invested using the weights calculated from the model, that's amount of money I would have today, remember I had 1 dollar."
      ],
      "metadata": {
        "id": "Qc72FVGB3vb0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bought_shares = weights_me / prices.iloc[-simulation_day: -simulation_day+1]\n",
        "value_today = prices.tail(1).to_numpy() * bought_shares.to_numpy()\n",
        "value_today = pd.Series(value_today.squeeze(), index=tickers)\n",
        "print(f'I would have {np.sum(prices.tail(1).to_numpy() * bought_shares.to_numpy())} today')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "or2EreCokN2x",
        "outputId": "95ffa89a-92c3-404a-e686-a356f82bfa15"
      },
      "execution_count": 527,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I would have 4.405464838769153 today\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(weights_me)\n",
        "rets_today_df = pd.DataFrame([pd.Series(weights_me, index=tickers), value_today], \n",
        "             index=[f\"{simulation_day}days ago\", \"today\"]).T\n",
        "\n",
        "rets_today_df.plot.bar(figsize=(12, 8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "AD0dDS69po4i",
        "outputId": "98defa80-d0f4-4aec-ae94-8ce0cd58d0a5"
      },
      "execution_count": 528,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.05135, 0.19059, 0.1369, 0.07523, 0.05, 0.07443, 0.02484, 0.09097, 0.091, 0.0, 0.10498, 0.0, 0.10972]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 528
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can clearly see which company in the long term made more money."
      ],
      "metadata": {
        "id": "kE8PCp8V4Fvk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If invested a the same fraction for all assets equally, that's the amount of money I would have now, always had 1 dollar on the start"
      ],
      "metadata": {
        "id": "r2CoTpx94KQR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bought_shares = [1 / len(tickers)]*len(tickers) / prices.iloc[-simulation_day:-simulation_day+1]\n",
        "value_today = prices.tail(1).to_numpy() * bought_shares.to_numpy()\n",
        "value_today = pd.Series(value_today.squeeze(), index=tickers)\n",
        "print(f'I would have {np.sum(prices.tail(1).to_numpy() * bought_shares.to_numpy())} today')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6KAN4Ldpowc",
        "outputId": "cb07aa81-4e02-40ca-952a-2499a6324483"
      },
      "execution_count": 529,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I would have 3.405369332731043 today\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print([1 / len(tickers)]*len(tickers))\n",
        "rets_today_df = pd.DataFrame([pd.Series([1 / len(tickers)]*len(tickers), index=tickers), value_today], \n",
        "             index=[f\"{simulation_day}days ago\", \"today\"]).T\n",
        "\n",
        "rets_today_df.plot.bar(figsize=(12, 8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "EkL7GtG3skrs",
        "outputId": "99623a92-c790-46dc-bf82-5330aac6bc03"
      },
      "execution_count": 530,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 530
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n",
        "- Since I am using analysts' view, some controversial stocks have really diverse views, so it might not be accurate, like tesla 3 years ago, it was the most shorted stock, but today it had a lot of gains.\n",
        "- View changes the output dramatically, even if I use low confidence, the output still tries to \"believe\" that your views are true and accurate.\n",
        "- If you don't know how to invest, the average in each stock equally actually beats the wrong model.  \n",
        "- Time span of investment is essential, you have to pick the right stocks for the long run.\n",
        "- Stocks with little historic data can be computed, but less accurate.\n",
        "- Fun fact, If you have fear, put the lowers target price, and the model will output more weight on defensive stocks; If you are greedy, the model tries to all in on those stocks with the most gains \n",
        "# Parameters are put to demonstrate a demo, don't trust the result you see on this page. No financial advice!!!!"
      ],
      "metadata": {
        "id": "hKMGt5jjHZKw"
      }
    }
  ]
}