Stock_technical_analysis.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/TheAmirHK/Portfolio-analysis-and-optimization/blob/main/Stock_technical_analysis.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": [
        "This article analyzes important financial ratios of a single stock compared to the market (e.g. GSPC),,\n",
        "    as well as others in the technology, motor production, and healthcare domains. This information can then be used for portfolio optimization."
      ],
      "metadata": {
        "id": "VOxYrR2EJ7fr"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jgZI29e7w3qH"
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      "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": [
        "# 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\n"
      ],
      "metadata": {
        "id": "Td63b-Nn0ddH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the correlation matrix\n",
        "\n",
        "def evaluate_corr(data):\n",
        "    Correlation = data.corr()\n",
        "    Correlation = np.tril(Correlation,-1)\n",
        "    sns.heatmap(Correlation, annot=True, mask = (Correlation ==0), xticklabels = stocks, yticklabels = stocks, label= \"Stocks' correleation\" )\n",
        "    return"
      ],
      "metadata": {
        "id": "FnR03wJF1VE4"
      },
      "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": "ayV036Gf1VK_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To calculate the beta, we need the covariance between the specific stock and the market (e.g. GSPC).\n",
        "\n",
        "def calculate_covariance(data):\n",
        "    covariance = pd.DataFrame()\n",
        "    log_returns = daily_return(data)\n",
        "    covariance = log_returns.cov()*252 #Annualize by multiplying by 252 (trading days in a year)\n",
        "    return covariance"
      ],
      "metadata": {
        "id": "lN7DCxJP2T2_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# A function to calculate annualized flunctuation of the price of a specific period\n",
        "def calculate_volatility (data):\n",
        "    log_returns = daily_return(data)\n",
        "    volatility_list = (log_returns.std()*np.sqrt(252)).dropna()\n",
        "    return volatility_list"
      ],
      "metadata": {
        "id": "BCA-KC9T2y8E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Beta value\n",
        "beta is a measure of a stock's or portfolio's volatility in relation to the overall market. It is used to assess the systematic risk, or market risk, of an investment compared to the market as a whole.\n",
        "A beta of 1 indicates that the investment's price will move in line with the market. A beta greater than 1 suggests that the investment is more volatile than the market, while a beta less than 1 indicates lower volatility compared to the market. For example, a stock with a beta of 1.2 is theoretically 20% more volatile than the overall market, while a stock with a beta of 0.8 is theoretically 20% less volatile.\n",
        "In portfolio optimization, beta is used to assess the diversification benefits of adding a particular stock to a portfolio. By combining assets with different betas, investors can potentially reduce the overall risk (beta) of their portfolio. This is based on the idea that assets with low or negative correlations to each other can offset each other's risk."
      ],
      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "def calculate_beta (data):\n",
        "    beta = pd.DataFrame()\n",
        "    covariance = calculate_covariance(data)\n",
        "    vol =  calculate_volatility (data)\n",
        "    stock_covariance = covariance.iloc[:,-1]\n",
        "    market_var = covariance.iloc[-1,-1]\n",
        "    beta = stock_covariance/market_var\n",
        "    return beta"
      ],
      "metadata": {
        "id": "nFAMm9P62zEG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Capital Asset Pricing Model (CAPM)\n",
        "The Capital Asset Pricing Model (CAPM) is a widely used model in finance that describes the relationship between systematic risk and expected return for assets, particularly stocks. It provides a framework for determining the expected return on an investment based on its risk and the risk-free rate of return.\n",
        "In portfolio optimization, the CAPM is used to calculate the expected return of an individual asset or a portfolio. The model suggests that the expected return of an asset is equal to the risk-free rate plus a risk premium, which is determined by the asset's beta (systematic risk) and the market risk premium.\n",
        "The CAPM is a fundamental tool for portfolio managers and investors in determining the appropriate expected return for an asset given its risk. It also helps in evaluating whether an asset is providing adequate compensation for the risk it carries, and it plays a key role in the modern portfolio theory and asset pricing."
      ],
      "metadata": {
        "id": "B_wOUUAPKRd6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def CAPM(data, Rf):\n",
        "    daily_log = daily_return(data)\n",
        "    beta = calculate_beta(data)\n",
        "    var = (daily_log.var())*252\n",
        "    expected_return = daily_log.mean()*252\n",
        "    CAPM_value = Rf + beta*(expected_return.iloc[-1] - Rf)\n",
        "    sharpe_ratio = ( expected_return - Rf ) /np.sqrt(var)\n",
        "    sharr =    ( expected_return.dropna() - Rf ) /np.sqrt(var)\n",
        "    ten = (expected_return.dropna() - Rf)/beta\n",
        "    return CAPM_value"
      ],
      "metadata": {
        "id": "znRnNYDx2zJI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sharpe ratio\n",
        "Sharpe ratio is a measure used in finance to evaluate the performance of an investment or a trading strategy. It was developed by Nobel laureate William F. Sharpe. The ratio is calculated by subtracting the risk-free rate of return from the investment's return and then dividing the result by the investment's standard deviation. The Sharpe ratio essentially measures the risk-adjusted return of an investment. It provides insight into how well the return of an investment compensates the investor for the risk taken. A higher Sharpe ratio indicates better risk-adjusted performance, as it means the investment has generated more return for the amount of risk taken. The Sharpe ratio is widely used by investors and portfolio managers to compare the risk-adjusted returns of different investments or strategies. It helps in evaluating whether the excess return of an investment is sufficient given the level of risk involved, and it can be a valuable tool for making informed investment decisions."
      ],
      "metadata": {
        "id": "dHn5AqAEKnfK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Sharpe_ratio(data, Rf):\n",
        "    daily_log = daily_return(data)\n",
        "    var = (daily_log.var())*252\n",
        "    expected_return = daily_log.mean()*252\n",
        "    sharpe_value = ( expected_return.dropna() - Rf ) /np.sqrt(var)\n",
        "    return sharpe_value"
      ],
      "metadata": {
        "id": "vrrrpLrM5Ceh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Treynor ratio\n",
        "The Treynor ratio provides a measure of how much excess return an investment or portfolio generates for each unit of systematic risk it carries. A higher Treynor ratio indicates a better risk-adjusted return, as it means the investment is providing more return for the level of systematic risk it bears.\n",
        "The Treynor ratio is particularly useful for comparing the risk-adjusted performance of different investments or portfolios, especially those with similar levels of systematic risk. It is a valuable tool for investors and portfolio managers in evaluating the performance of their investments and making informed decisions about portfolio allocation."
      ],
      "metadata": {
        "id": "AX6xmtMhKwQb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Treynor_ratio(data, Rf):\n",
        "    daily_log = daily_return(data)\n",
        "    beta = calculate_beta(data)\n",
        "    expected_return = daily_log.mean()*252\n",
        "    tren = (expected_return.dropna() - Rf)/beta\n",
        "    return tren"
      ],
      "metadata": {
        "id": "6r8sXbGT5ds2"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# M-squared\n",
        "M-squared is a measure that adjusts a portfolio's return for risk, particularly in comparison to a benchmark. It is similar to the Sharpe ratio but is designed to be used when the benchmark is a market index. M-squared takes into account the systematic risk of the portfolio and compares it to the systematic risk of the benchmark index.\n",
        "M-squared provides a measure of the portfolio's risk-adjusted return relative to a specific benchmark. It helps in evaluating the performance of investment managers and their ability to generate returns in relation to the risk they take on."
      ],
      "metadata": {
        "id": "u2VrVlNOKzhG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def M2(data, Rf):\n",
        "    daily_log = daily_return(data)\n",
        "    beta = calculate_beta(data)\n",
        "    std_stock = (daily_log.std())*np.sqrt(252)\n",
        "    expected_return = daily_log.mean()*252\n",
        "    m2_value = ((expected_return.dropna() - Rf)*std_stock/std_stock['GSPC']) - (expected_return['GSPC'] - Rf)\n",
        "    return m2_value"
      ],
      "metadata": {
        "id": "exVgcWDa6C1h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Jensen's Alpha\n",
        "In finance, Jensen's Alpha, named after the economist Michael Jensen, is a risk-adjusted performance measure that evaluates the return of an investment or portfolio relative to its expected return based on its level of systematic risk, as measured by its beta.\n",
        "Jensen's Alpha is calculated using the Capital Asset Pricing Model (CAPM) and is expressed as the excess return of the investment over and above what would be expected given its beta and the market return. Jensen's Alpha provides a measure of the portfolio's risk-adjusted return, taking into account the systematic risk of the portfolio as well as the risk-free rate of return. A positive Jensen's Alpha indicates that the portfolio has outperformed its expected return given its level of risk, while a negative value suggests underperformance."
      ],
      "metadata": {
        "id": "TB5k_f2JLQTD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Jensens_alpha (data, Rf):\n",
        "    daily_log = daily_return(data)\n",
        "    beta = calculate_beta(data)\n",
        "    expected_return = daily_log.mean()*252\n",
        "    Jensens_alpha_value = expected_return - (Rf + beta * (expected_return['GSPC'] -Rf) )\n",
        "    return Jensens_alpha_value"
      ],
      "metadata": {
        "id": "F7LGjqPVADX0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Information ratio\n",
        "Information ratio measures is a key performance metric used by investors and portfolio managers to evaluate the skill of an investment strategy in generating excess returns.\n",
        "The information ratio is calculated by dividing the excess return of the investment or portfolio by its tracking error, which measures the volatility of the active return relative to the benchmark. The information ratio provides a measure of the portfolio manager's ability to generate excess returns relative to the benchmark, taking into account the level of risk or volatility involved. A higher information ratio indicates that the portfolio is generating more excess return for each unit of risk taken."
      ],
      "metadata": {
        "id": "94sQHP3wLaPr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Information_ratio(data):\n",
        "    daily_log = daily_return(data)\n",
        "    expected_return = daily_log.mean()*252\n",
        "    dif = expected_return.iloc[:-1] - expected_return['GSPC']\n",
        "    error = dif.std()*np.sqrt(252)\n",
        "    Info_ratio = dif/error\n",
        "    return dif"
      ],
      "metadata": {
        "id": "c__yqoQMJ4bn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sortino Ratio\n",
        "Sortino ratio is a measure used in finance to evaluate the risk-adjusted return of an investment or a portfolio, particularly in the context of downside risk. It is similar to the Sharpe ratio, but while the Sharpe ratio considers the total volatility of an investment, the Sortino ratio focuses only on the downside volatility.\n",
        "The Sortino ratio is calculated by dividing the excess return of the investment by the downside deviation, which measures the volatility of negative returns. The Sortino ratio provides a measure of the risk-adjusted return of an investment, focusing specifically on the risk of experiencing negative returns. A higher Sortino ratio indicates that the investment is providing more return for each unit of downside risk taken."
      ],
      "metadata": {
        "id": "wNv0mefsLyp7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Sortino_ratio(data, Rf):\n",
        "    daily_log = daily_return(data)\n",
        "    expected_return = (daily_log.mean())*252\n",
        "    std_neg = (daily_log[daily_log<0].std())*np.sqrt(252)\n",
        "    sortino_value = (expected_return - Rf)/std_neg\n",
        "    return sortino_value"
      ],
      "metadata": {
        "id": "mMPnSJZeMtP9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def comaprison (data, Rf):\n",
        "    result = pd.DataFrame()\n",
        "    result[\"Volatility\"] = calculate_volatility (data)\n",
        "    result[\"Beta\"] = calculate_beta (data)\n",
        "    result[\"CAPM\"] = CAPM(data, Rf)\n",
        "    result[\"Sharpe ratio\"] = Sharpe_ratio(data, Rf)\n",
        "    result[\"Treynor ratio\"] = Treynor_ratio(data, Rf)\n",
        "    result[\"M2\"] = M2(data, Rf)\n",
        "    result[\"Jensen's alpha\"] = Jensens_alpha (data, Rf)\n",
        "    result[\"Sortino ratio\"] = Sortino_ratio(data, Rf)\n",
        "    result[\"Information ratio\"] = Information_ratio(data)\n",
        "    result.plot(kind=\"barh\", subplots=True, layout=(3,3),\n",
        "                figsize=(14, 14), xlabel= \"Ratio\", sharex = False, grid = \"True\", fontsize = 7)\n",
        "    plt.figure(\"Stocks' correlation\")\n",
        "    fig = evaluate_corr(data)\n",
        "    return result\n"
      ],
      "metadata": {
        "id": "QTWIuTf2VjAC"
      },
      "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",
        "# 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",
        "stock_market_data = stock_data.assign( GSPC = market_data['^GSPC'])"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start date:2018-11-26 20:30:56.034295tr   End date:2023-11-25 20:30:56.034295tr\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comaprison (stock_market_data, Rf)"
      ],
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          "height": 1000
        },
        "id": "BJgPnUGC1Uo4",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Volatility      Beta      CAPM  Sharpe ratio  Treynor ratio        M2  \\\n",
              "AAPL     0.327162  1.223031  0.154532      1.031286       0.275870  0.399978   \n",
              "MSFT     0.309434  1.190411  0.150944      0.940493       0.244470  0.306044   \n",
              "GOOG     0.320369  1.133381  0.144671      0.707597       0.200014  0.225534   \n",
              "AMZN     0.360444  1.092875  0.140215      0.468696       0.154582  0.171330   \n",
              "TSLA     0.651242  1.498910  0.184878      1.012892       0.440078  1.874699   \n",
              "TM       0.236512  0.660272  0.092629      0.408686       0.146393 -0.004379   \n",
              "MBGYY    0.408842  1.268378  0.159520      0.344318       0.110986  0.155901   \n",
              "VWAGY    0.422702  1.129920  0.144290      0.173877       0.065047  0.033536   \n",
              "VLVLY    0.358296  1.135418  0.144895      0.595564       0.187938  0.243233   \n",
              "GM       0.421016  1.272062  0.159925      0.073728       0.024402 -0.049622   \n",
              "LLY      0.311225  0.643766  0.090814      1.211741       0.585809  0.432258   \n",
              "JNJ      0.205322  0.533259  0.078658      0.199035       0.076635 -0.071233   \n",
              "AZN      0.265688  0.544276  0.079870      0.520639       0.254150  0.059797   \n",
              "GSPC     0.216448  1.000000  0.129999      0.508200       0.109999  0.000000   \n",
              "\n",
              "       Jensen's alpha  Sortino ratio  Information ratio  \n",
              "AAPL         0.202865       1.434052           0.227398  \n",
              "MSFT         0.160077       1.315073           0.181022  \n",
              "GOOG         0.102022       0.974444           0.116694  \n",
              "AMZN         0.048724       0.680704           0.058940  \n",
              "TSLA         0.494759       1.461537           0.549639  \n",
              "TM           0.024030       0.602050          -0.013340  \n",
              "MBGYY        0.001252       0.442863           0.030773  \n",
              "VWAGY       -0.050792       0.258679          -0.036501  \n",
              "VLVLY        0.088494       0.810281           0.103390  \n",
              "GM          -0.108885       0.106412          -0.078958  \n",
              "LLY          0.306310       1.971358           0.267125  \n",
              "JNJ         -0.017792       0.256104          -0.069133  \n",
              "AZN          0.078458       0.721603           0.028329  \n",
              "GSPC         0.000000       0.623998                NaN  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Volatility</th>\n",
              "      <th>Beta</th>\n",
              "      <th>CAPM</th>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <th>Treynor ratio</th>\n",
              "      <th>M2</th>\n",
              "      <th>Jensen's alpha</th>\n",
              "      <th>Sortino ratio</th>\n",
              "      <th>Information ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.327162</td>\n",
              "      <td>1.223031</td>\n",
              "      <td>0.154532</td>\n",
              "      <td>1.031286</td>\n",
              "      <td>0.275870</td>\n",
              "      <td>0.399978</td>\n",
              "      <td>0.202865</td>\n",
              "      <td>1.434052</td>\n",
              "      <td>0.227398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.309434</td>\n",
              "      <td>1.190411</td>\n",
              "      <td>0.150944</td>\n",
              "      <td>0.940493</td>\n",
              "      <td>0.244470</td>\n",
              "      <td>0.306044</td>\n",
              "      <td>0.160077</td>\n",
              "      <td>1.315073</td>\n",
              "      <td>0.181022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GOOG</th>\n",
              "      <td>0.320369</td>\n",
              "      <td>1.133381</td>\n",
              "      <td>0.144671</td>\n",
              "      <td>0.707597</td>\n",
              "      <td>0.200014</td>\n",
              "      <td>0.225534</td>\n",
              "      <td>0.102022</td>\n",
              "      <td>0.974444</td>\n",
              "      <td>0.116694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMZN</th>\n",
              "      <td>0.360444</td>\n",
              "      <td>1.092875</td>\n",
              "      <td>0.140215</td>\n",
              "      <td>0.468696</td>\n",
              "      <td>0.154582</td>\n",
              "      <td>0.171330</td>\n",
              "      <td>0.048724</td>\n",
              "      <td>0.680704</td>\n",
              "      <td>0.058940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TSLA</th>\n",
              "      <td>0.651242</td>\n",
              "      <td>1.498910</td>\n",
              "      <td>0.184878</td>\n",
              "      <td>1.012892</td>\n",
              "      <td>0.440078</td>\n",
              "      <td>1.874699</td>\n",
              "      <td>0.494759</td>\n",
              "      <td>1.461537</td>\n",
              "      <td>0.549639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TM</th>\n",
              "      <td>0.236512</td>\n",
              "      <td>0.660272</td>\n",
              "      <td>0.092629</td>\n",
              "      <td>0.408686</td>\n",
              "      <td>0.146393</td>\n",
              "      <td>-0.004379</td>\n",
              "      <td>0.024030</td>\n",
              "      <td>0.602050</td>\n",
              "      <td>-0.013340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MBGYY</th>\n",
              "      <td>0.408842</td>\n",
              "      <td>1.268378</td>\n",
              "      <td>0.159520</td>\n",
              "      <td>0.344318</td>\n",
              "      <td>0.110986</td>\n",
              "      <td>0.155901</td>\n",
              "      <td>0.001252</td>\n",
              "      <td>0.442863</td>\n",
              "      <td>0.030773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VWAGY</th>\n",
              "      <td>0.422702</td>\n",
              "      <td>1.129920</td>\n",
              "      <td>0.144290</td>\n",
              "      <td>0.173877</td>\n",
              "      <td>0.065047</td>\n",
              "      <td>0.033536</td>\n",
              "      <td>-0.050792</td>\n",
              "      <td>0.258679</td>\n",
              "      <td>-0.036501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VLVLY</th>\n",
              "      <td>0.358296</td>\n",
              "      <td>1.135418</td>\n",
              "      <td>0.144895</td>\n",
              "      <td>0.595564</td>\n",
              "      <td>0.187938</td>\n",
              "      <td>0.243233</td>\n",
              "      <td>0.088494</td>\n",
              "      <td>0.810281</td>\n",
              "      <td>0.103390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GM</th>\n",
              "      <td>0.421016</td>\n",
              "      <td>1.272062</td>\n",
              "      <td>0.159925</td>\n",
              "      <td>0.073728</td>\n",
              "      <td>0.024402</td>\n",
              "      <td>-0.049622</td>\n",
              "      <td>-0.108885</td>\n",
              "      <td>0.106412</td>\n",
              "      <td>-0.078958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LLY</th>\n",
              "      <td>0.311225</td>\n",
              "      <td>0.643766</td>\n",
              "      <td>0.090814</td>\n",
              "      <td>1.211741</td>\n",
              "      <td>0.585809</td>\n",
              "      <td>0.432258</td>\n",
              "      <td>0.306310</td>\n",
              "      <td>1.971358</td>\n",
              "      <td>0.267125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JNJ</th>\n",
              "      <td>0.205322</td>\n",
              "      <td>0.533259</td>\n",
              "      <td>0.078658</td>\n",
              "      <td>0.199035</td>\n",
              "      <td>0.076635</td>\n",
              "      <td>-0.071233</td>\n",
              "      <td>-0.017792</td>\n",
              "      <td>0.256104</td>\n",
              "      <td>-0.069133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AZN</th>\n",
              "      <td>0.265688</td>\n",
              "      <td>0.544276</td>\n",
              "      <td>0.079870</td>\n",
              "      <td>0.520639</td>\n",
              "      <td>0.254150</td>\n",
              "      <td>0.059797</td>\n",
              "      <td>0.078458</td>\n",
              "      <td>0.721603</td>\n",
              "      <td>0.028329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GSPC</th>\n",
              "      <td>0.216448</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.129999</td>\n",
              "      <td>0.508200</td>\n",
              "      <td>0.109999</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.623998</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf4b0524-39c8-4020-8885-5038b2a967c9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-bf4b0524-39c8-4020-8885-5038b2a967c9 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-bf4b0524-39c8-4020-8885-5038b2a967c9');\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-4d5d26be-eb4d-4f5f-a06c-178a28819493\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4d5d26be-eb4d-4f5f-a06c-178a28819493')\"\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-4d5d26be-eb4d-4f5f-a06c-178a28819493 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": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1400 with 9 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}