Build_A_Killer_Stock_Portfolio_Using_Python.ipynb

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      "name": "Build A Killer Stock Portfolio Using Python.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhml0IgOYAsH"
      },
      "source": [
        "# This program optimizes a stock portfolio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojneFIrmTC1g"
      },
      "source": [
        "!pip install yfinance"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMQfGwb2SvWr"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import requests"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gif7r-USS87Z"
      },
      "source": [
        "def get_sp500():\n",
        "    \"\"\"\n",
        "    Get list of S&P 500 stocks from wikipedia\n",
        "    \"\"\"\n",
        "    sp500_tickers = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
        "    sp500_tickers = sp500_tickers[0]\n",
        "\n",
        "    tickers = sp500_tickers['Symbol'].values.tolist()\n",
        "    return tickers\n",
        "\n",
        "companies = get_sp500()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQ4ASOJnS83K",
        "outputId": "da95cf20-811a-4236-d581-f6271341a3ed"
      },
      "source": [
        "import os\n",
        "from pandas_datareader import data as pdr\n",
        "import pandas as pd\n",
        "#import fix_yahoo_finance as yf\n",
        "import yfinance as yf\n",
        "\n",
        "yf.pdr_override()\n",
        "\n",
        "START_DATE = \"2021-01-01\"\n",
        "END_DATE = \"2021-04-01\"\n",
        "\n",
        "def build_stock_dataset(start=START_DATE, end=END_DATE):\n",
        "    \"\"\"\n",
        "    Creates the dataset containing all stock prices\n",
        "    :returns: stock_prices.csv\n",
        "    \"\"\"\n",
        "    ticker_list = get_sp500()\n",
        "\n",
        "    # Get all Adjusted Close prices for all the tickers in our list,\n",
        "    # between START_DATE and END_DATE\n",
        "    all_data = pdr.get_data_yahoo(ticker_list, start, end)\n",
        "    stock_data = all_data[\"Adj Close\"]\n",
        "\n",
        "    # Remove any columns that hold no data, and print their tickers.\n",
        "    stock_data.dropna(how=\"all\", axis=1, inplace=True)\n",
        "    missing_tickers = [\n",
        "        ticker for ticker in ticker_list if ticker.upper() not in stock_data.columns\n",
        "    ]\n",
        "    print(f\"{len(missing_tickers)} tickers are missing: \\n {missing_tickers} \")\n",
        "    # If there are only some missing datapoints, forward fill.\n",
        "    stock_data.ffill(inplace=True)\n",
        "    stock_data.to_csv(\"stock_prices.csv\")\n",
        "    \n",
        "build_stock_dataset()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  505 of 505 completed\n",
            "\n",
            "2 Failed downloads:\n",
            "- BF.B: No data found for this date range, symbol may be delisted\n",
            "- BRK.B: No data found, symbol may be delisted\n",
            "2 tickers are missing: \n",
            " ['BRK.B', 'BF.B'] \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4327: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  downcast=downcast,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1-whFIUSysm"
      },
      "source": [
        "# Load data\n",
        "df = pd.read_csv(\"stock_prices.csv\")\n",
        "# Set date as index\n",
        "df = df.set_index(pd.DatetimeIndex(df['Date'].values))\n",
        "# remove date column\n",
        "df.drop(columns=['Date'], axis=1, inplace=True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "OK-X5JwSTh7T",
        "outputId": "84967dc7-f985-4d98-f45a-449dfa9a81b5"
      },
      "source": [
        "df"
      ],
      "execution_count": 11,
      "outputs": [
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              "      <td>174.651627</td>\n",
              "      <td>540.390015</td>\n",
              "      <td>51.520000</td>\n",
              "      <td>109.183670</td>\n",
              "      <td>116.952499</td>\n",
              "      <td>157.759995</td>\n",
              "      <td>90.657944</td>\n",
              "      <td>11.658796</td>\n",
              "      <td>90.330002</td>\n",
              "      <td>124.046158</td>\n",
              "      <td>229.911880</td>\n",
              "      <td>195.940140</td>\n",
              "      <td>213.781525</td>\n",
              "      <td>3138.379883</td>\n",
              "      <td>285.869995</td>\n",
              "      <td>349.899994</td>\n",
              "      <td>327.227509</td>\n",
              "      <td>205.879745</td>\n",
              "      <td>...</td>\n",
              "      <td>211.982513</td>\n",
              "      <td>86.954048</td>\n",
              "      <td>40.088181</td>\n",
              "      <td>56.990417</td>\n",
              "      <td>160.406525</td>\n",
              "      <td>35.875751</td>\n",
              "      <td>199.725037</td>\n",
              "      <td>199.509995</td>\n",
              "      <td>226.649994</td>\n",
              "      <td>47.972218</td>\n",
              "      <td>18.490000</td>\n",
              "      <td>58.037601</td>\n",
              "      <td>78.918762</td>\n",
              "      <td>262.470001</td>\n",
              "      <td>42.623661</td>\n",
              "      <td>53.410000</td>\n",
              "      <td>89.864555</td>\n",
              "      <td>63.479488</td>\n",
              "      <td>32.515942</td>\n",
              "      <td>177.466232</td>\n",
              "      <td>203.083511</td>\n",
              "      <td>117.203575</td>\n",
              "      <td>21.138729</td>\n",
              "      <td>145.478897</td>\n",
              "      <td>66.482994</td>\n",
              "      <td>46.038570</td>\n",
              "      <td>293.815247</td>\n",
              "      <td>22.904140</td>\n",
              "      <td>33.121216</td>\n",
              "      <td>110.849998</td>\n",
              "      <td>65.967941</td>\n",
              "      <td>141.220001</td>\n",
              "      <td>43.235222</td>\n",
              "      <td>57.863960</td>\n",
              "      <td>103.476448</td>\n",
              "      <td>105.902420</td>\n",
              "      <td>160.006393</td>\n",
              "      <td>394.820007</td>\n",
              "      <td>47.908611</td>\n",
              "      <td>166.643158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-07</th>\n",
              "      <td>125.968323</td>\n",
              "      <td>15.380000</td>\n",
              "      <td>167.439621</td>\n",
              "      <td>130.502991</td>\n",
              "      <td>104.209320</td>\n",
              "      <td>109.270058</td>\n",
              "      <td>323.559998</td>\n",
              "      <td>110.447891</td>\n",
              "      <td>261.494385</td>\n",
              "      <td>477.739990</td>\n",
              "      <td>154.946228</td>\n",
              "      <td>52.066040</td>\n",
              "      <td>167.987534</td>\n",
              "      <td>315.200012</td>\n",
              "      <td>73.689629</td>\n",
              "      <td>78.495201</td>\n",
              "      <td>24.434729</td>\n",
              "      <td>44.365757</td>\n",
              "      <td>40.619877</td>\n",
              "      <td>138.590912</td>\n",
              "      <td>121.848701</td>\n",
              "      <td>102.809998</td>\n",
              "      <td>183.528824</td>\n",
              "      <td>558.359985</td>\n",
              "      <td>51.320000</td>\n",
              "      <td>109.263084</td>\n",
              "      <td>120.033089</td>\n",
              "      <td>155.750000</td>\n",
              "      <td>94.380875</td>\n",
              "      <td>11.559825</td>\n",
              "      <td>95.160004</td>\n",
              "      <td>124.195915</td>\n",
              "      <td>230.670868</td>\n",
              "      <td>198.308380</td>\n",
              "      <td>216.378067</td>\n",
              "      <td>3162.159912</td>\n",
              "      <td>295.970001</td>\n",
              "      <td>365.019989</td>\n",
              "      <td>336.257355</td>\n",
              "      <td>205.660706</td>\n",
              "      <td>...</td>\n",
              "      <td>213.168945</td>\n",
              "      <td>87.749237</td>\n",
              "      <td>40.008400</td>\n",
              "      <td>58.031025</td>\n",
              "      <td>161.653778</td>\n",
              "      <td>34.958614</td>\n",
              "      <td>200.633514</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>232.899994</td>\n",
              "      <td>47.049866</td>\n",
              "      <td>18.520000</td>\n",
              "      <td>57.907001</td>\n",
              "      <td>81.700607</td>\n",
              "      <td>260.790009</td>\n",
              "      <td>44.832603</td>\n",
              "      <td>54.740002</td>\n",
              "      <td>86.939011</td>\n",
              "      <td>62.221294</td>\n",
              "      <td>33.252003</td>\n",
              "      <td>181.291367</td>\n",
              "      <td>204.628830</td>\n",
              "      <td>117.492203</td>\n",
              "      <td>21.178059</td>\n",
              "      <td>145.468964</td>\n",
              "      <td>65.903931</td>\n",
              "      <td>45.620491</td>\n",
              "      <td>303.724274</td>\n",
              "      <td>22.646568</td>\n",
              "      <td>33.439594</td>\n",
              "      <td>109.750000</td>\n",
              "      <td>64.677574</td>\n",
              "      <td>149.710007</td>\n",
              "      <td>43.574436</td>\n",
              "      <td>57.564457</td>\n",
              "      <td>108.173134</td>\n",
              "      <td>105.096275</td>\n",
              "      <td>159.057846</td>\n",
              "      <td>409.100006</td>\n",
              "      <td>49.370266</td>\n",
              "      <td>166.493607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-25</th>\n",
              "      <td>121.714798</td>\n",
              "      <td>22.770000</td>\n",
              "      <td>185.649994</td>\n",
              "      <td>120.385513</td>\n",
              "      <td>102.632133</td>\n",
              "      <td>116.907799</td>\n",
              "      <td>294.140015</td>\n",
              "      <td>118.614487</td>\n",
              "      <td>267.781250</td>\n",
              "      <td>450.989990</td>\n",
              "      <td>149.899994</td>\n",
              "      <td>56.880001</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>262.190002</td>\n",
              "      <td>81.589996</td>\n",
              "      <td>84.008553</td>\n",
              "      <td>25.978989</td>\n",
              "      <td>51.139999</td>\n",
              "      <td>46.500000</td>\n",
              "      <td>143.539993</td>\n",
              "      <td>124.959999</td>\n",
              "      <td>99.540001</td>\n",
              "      <td>148.929993</td>\n",
              "      <td>517.869995</td>\n",
              "      <td>67.750000</td>\n",
              "      <td>114.709999</td>\n",
              "      <td>123.480003</td>\n",
              "      <td>152.970001</td>\n",
              "      <td>119.720001</td>\n",
              "      <td>11.580000</td>\n",
              "      <td>76.220001</td>\n",
              "      <td>123.050003</td>\n",
              "      <td>244.530624</td>\n",
              "      <td>226.713928</td>\n",
              "      <td>226.047928</td>\n",
              "      <td>3046.260010</td>\n",
              "      <td>292.000000</td>\n",
              "      <td>327.880005</td>\n",
              "      <td>366.869995</td>\n",
              "      <td>224.550354</td>\n",
              "      <td>...</td>\n",
              "      <td>207.668350</td>\n",
              "      <td>78.110001</td>\n",
              "      <td>66.349998</td>\n",
              "      <td>71.515900</td>\n",
              "      <td>165.589996</td>\n",
              "      <td>45.805660</td>\n",
              "      <td>174.779999</td>\n",
              "      <td>193.380005</td>\n",
              "      <td>212.360001</td>\n",
              "      <td>53.139374</td>\n",
              "      <td>14.020000</td>\n",
              "      <td>56.769245</td>\n",
              "      <td>77.754852</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>51.709999</td>\n",
              "      <td>64.050003</td>\n",
              "      <td>92.525063</td>\n",
              "      <td>70.578911</td>\n",
              "      <td>39.214451</td>\n",
              "      <td>216.119995</td>\n",
              "      <td>223.820572</td>\n",
              "      <td>127.430000</td>\n",
              "      <td>23.940001</td>\n",
              "      <td>133.485779</td>\n",
              "      <td>75.269997</td>\n",
              "      <td>51.860001</td>\n",
              "      <td>275.302185</td>\n",
              "      <td>24.700001</td>\n",
              "      <td>34.790001</td>\n",
              "      <td>128.100006</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>120.029999</td>\n",
              "      <td>55.373322</td>\n",
              "      <td>60.549999</td>\n",
              "      <td>101.930000</td>\n",
              "      <td>107.379997</td>\n",
              "      <td>157.639999</td>\n",
              "      <td>461.260010</td>\n",
              "      <td>54.422073</td>\n",
              "      <td>152.652649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-26</th>\n",
              "      <td>125.449112</td>\n",
              "      <td>22.930000</td>\n",
              "      <td>187.320007</td>\n",
              "      <td>121.004463</td>\n",
              "      <td>104.706917</td>\n",
              "      <td>118.292686</td>\n",
              "      <td>301.399994</td>\n",
              "      <td>121.623436</td>\n",
              "      <td>279.903717</td>\n",
              "      <td>469.089996</td>\n",
              "      <td>157.110001</td>\n",
              "      <td>57.910000</td>\n",
              "      <td>192.690002</td>\n",
              "      <td>269.010010</td>\n",
              "      <td>81.459999</td>\n",
              "      <td>84.276283</td>\n",
              "      <td>26.177908</td>\n",
              "      <td>51.560001</td>\n",
              "      <td>46.820000</td>\n",
              "      <td>144.649994</td>\n",
              "      <td>125.699997</td>\n",
              "      <td>102.699997</td>\n",
              "      <td>150.399994</td>\n",
              "      <td>528.340027</td>\n",
              "      <td>68.320000</td>\n",
              "      <td>116.029999</td>\n",
              "      <td>126.750000</td>\n",
              "      <td>154.880005</td>\n",
              "      <td>128.639999</td>\n",
              "      <td>11.780000</td>\n",
              "      <td>77.410004</td>\n",
              "      <td>127.320000</td>\n",
              "      <td>251.094482</td>\n",
              "      <td>231.582977</td>\n",
              "      <td>237.588104</td>\n",
              "      <td>3052.030029</td>\n",
              "      <td>311.269989</td>\n",
              "      <td>342.959991</td>\n",
              "      <td>371.309998</td>\n",
              "      <td>227.823364</td>\n",
              "      <td>...</td>\n",
              "      <td>213.220291</td>\n",
              "      <td>80.209999</td>\n",
              "      <td>48.230000</td>\n",
              "      <td>72.197289</td>\n",
              "      <td>170.339996</td>\n",
              "      <td>46.764679</td>\n",
              "      <td>178.630005</td>\n",
              "      <td>199.679993</td>\n",
              "      <td>214.639999</td>\n",
              "      <td>54.418762</td>\n",
              "      <td>14.360000</td>\n",
              "      <td>57.560726</td>\n",
              "      <td>78.383919</td>\n",
              "      <td>281.220001</td>\n",
              "      <td>52.029999</td>\n",
              "      <td>67.279999</td>\n",
              "      <td>92.366196</td>\n",
              "      <td>72.790695</td>\n",
              "      <td>39.673450</td>\n",
              "      <td>223.089996</td>\n",
              "      <td>228.665863</td>\n",
              "      <td>129.800003</td>\n",
              "      <td>24.559999</td>\n",
              "      <td>134.601410</td>\n",
              "      <td>75.820000</td>\n",
              "      <td>53.580002</td>\n",
              "      <td>280.159576</td>\n",
              "      <td>25.370001</td>\n",
              "      <td>35.439999</td>\n",
              "      <td>127.430000</td>\n",
              "      <td>66.309998</td>\n",
              "      <td>123.139999</td>\n",
              "      <td>56.881351</td>\n",
              "      <td>61.279999</td>\n",
              "      <td>104.760002</td>\n",
              "      <td>108.059998</td>\n",
              "      <td>161.320007</td>\n",
              "      <td>476.959991</td>\n",
              "      <td>55.525623</td>\n",
              "      <td>155.917770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-29</th>\n",
              "      <td>125.229446</td>\n",
              "      <td>22.910000</td>\n",
              "      <td>185.059998</td>\n",
              "      <td>121.184158</td>\n",
              "      <td>105.447906</td>\n",
              "      <td>118.611511</td>\n",
              "      <td>305.769989</td>\n",
              "      <td>121.782860</td>\n",
              "      <td>278.677551</td>\n",
              "      <td>469.320007</td>\n",
              "      <td>153.850006</td>\n",
              "      <td>57.090000</td>\n",
              "      <td>192.500000</td>\n",
              "      <td>270.779999</td>\n",
              "      <td>82.669998</td>\n",
              "      <td>85.119141</td>\n",
              "      <td>25.929256</td>\n",
              "      <td>51.340000</td>\n",
              "      <td>46.580002</td>\n",
              "      <td>143.770004</td>\n",
              "      <td>126.839996</td>\n",
              "      <td>103.099998</td>\n",
              "      <td>146.410004</td>\n",
              "      <td>516.000000</td>\n",
              "      <td>67.500000</td>\n",
              "      <td>116.800003</td>\n",
              "      <td>126.279999</td>\n",
              "      <td>155.570007</td>\n",
              "      <td>125.709999</td>\n",
              "      <td>11.800000</td>\n",
              "      <td>77.139999</td>\n",
              "      <td>128.210007</td>\n",
              "      <td>253.179825</td>\n",
              "      <td>228.297119</td>\n",
              "      <td>240.582565</td>\n",
              "      <td>3075.729980</td>\n",
              "      <td>307.690002</td>\n",
              "      <td>342.230011</td>\n",
              "      <td>369.369995</td>\n",
              "      <td>233.132034</td>\n",
              "      <td>...</td>\n",
              "      <td>214.198868</td>\n",
              "      <td>78.779999</td>\n",
              "      <td>45.009998</td>\n",
              "      <td>70.962898</td>\n",
              "      <td>170.899994</td>\n",
              "      <td>45.301430</td>\n",
              "      <td>180.320007</td>\n",
              "      <td>201.179993</td>\n",
              "      <td>216.710007</td>\n",
              "      <td>53.704689</td>\n",
              "      <td>14.320000</td>\n",
              "      <td>58.203812</td>\n",
              "      <td>78.014465</td>\n",
              "      <td>284.019989</td>\n",
              "      <td>52.849998</td>\n",
              "      <td>65.349998</td>\n",
              "      <td>94.113640</td>\n",
              "      <td>71.094658</td>\n",
              "      <td>38.356323</td>\n",
              "      <td>216.850006</td>\n",
              "      <td>233.889999</td>\n",
              "      <td>130.339996</td>\n",
              "      <td>24.040001</td>\n",
              "      <td>136.135376</td>\n",
              "      <td>75.889999</td>\n",
              "      <td>52.919998</td>\n",
              "      <td>280.359497</td>\n",
              "      <td>24.790001</td>\n",
              "      <td>35.209999</td>\n",
              "      <td>123.500000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>122.230003</td>\n",
              "      <td>56.575806</td>\n",
              "      <td>62.060001</td>\n",
              "      <td>104.269997</td>\n",
              "      <td>109.209999</td>\n",
              "      <td>160.210007</td>\n",
              "      <td>467.070007</td>\n",
              "      <td>53.577007</td>\n",
              "      <td>158.154449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-30</th>\n",
              "      <td>124.650322</td>\n",
              "      <td>24.120001</td>\n",
              "      <td>186.070007</td>\n",
              "      <td>119.696686</td>\n",
              "      <td>105.507179</td>\n",
              "      <td>118.621468</td>\n",
              "      <td>309.880005</td>\n",
              "      <td>119.311928</td>\n",
              "      <td>277.690582</td>\n",
              "      <td>465.459991</td>\n",
              "      <td>152.610001</td>\n",
              "      <td>57.900002</td>\n",
              "      <td>187.889999</td>\n",
              "      <td>268.429993</td>\n",
              "      <td>81.330002</td>\n",
              "      <td>84.018471</td>\n",
              "      <td>26.734884</td>\n",
              "      <td>51.830002</td>\n",
              "      <td>47.340000</td>\n",
              "      <td>144.710007</td>\n",
              "      <td>125.629997</td>\n",
              "      <td>101.180000</td>\n",
              "      <td>145.389999</td>\n",
              "      <td>524.330017</td>\n",
              "      <td>69.940002</td>\n",
              "      <td>117.110001</td>\n",
              "      <td>125.290001</td>\n",
              "      <td>153.449997</td>\n",
              "      <td>126.760002</td>\n",
              "      <td>11.740000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>127.440002</td>\n",
              "      <td>248.006195</td>\n",
              "      <td>231.802032</td>\n",
              "      <td>235.926697</td>\n",
              "      <td>3055.290039</td>\n",
              "      <td>298.859985</td>\n",
              "      <td>331.890015</td>\n",
              "      <td>364.089996</td>\n",
              "      <td>231.595306</td>\n",
              "      <td>...</td>\n",
              "      <td>211.592651</td>\n",
              "      <td>80.129997</td>\n",
              "      <td>46.610001</td>\n",
              "      <td>70.883888</td>\n",
              "      <td>172.229996</td>\n",
              "      <td>46.042942</td>\n",
              "      <td>175.949997</td>\n",
              "      <td>194.869995</td>\n",
              "      <td>212.990005</td>\n",
              "      <td>54.270000</td>\n",
              "      <td>14.150000</td>\n",
              "      <td>57.966362</td>\n",
              "      <td>79.602119</td>\n",
              "      <td>282.510010</td>\n",
              "      <td>52.980000</td>\n",
              "      <td>65.459999</td>\n",
              "      <td>92.723633</td>\n",
              "      <td>72.334450</td>\n",
              "      <td>39.304256</td>\n",
              "      <td>220.240005</td>\n",
              "      <td>232.309998</td>\n",
              "      <td>129.279999</td>\n",
              "      <td>23.680000</td>\n",
              "      <td>135.209015</td>\n",
              "      <td>76.320000</td>\n",
              "      <td>53.220001</td>\n",
              "      <td>278.190643</td>\n",
              "      <td>24.840000</td>\n",
              "      <td>35.450001</td>\n",
              "      <td>125.449997</td>\n",
              "      <td>66.010002</td>\n",
              "      <td>120.300003</td>\n",
              "      <td>55.875996</td>\n",
              "      <td>63.540001</td>\n",
              "      <td>104.879997</td>\n",
              "      <td>109.769997</td>\n",
              "      <td>161.220001</td>\n",
              "      <td>474.829987</td>\n",
              "      <td>55.585274</td>\n",
              "      <td>156.806442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-31</th>\n",
              "      <td>126.946831</td>\n",
              "      <td>23.900000</td>\n",
              "      <td>183.490005</td>\n",
              "      <td>121.942871</td>\n",
              "      <td>106.920006</td>\n",
              "      <td>117.635117</td>\n",
              "      <td>318.730011</td>\n",
              "      <td>119.401596</td>\n",
              "      <td>275.397675</td>\n",
              "      <td>475.369995</td>\n",
              "      <td>155.080002</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>188.470001</td>\n",
              "      <td>277.149994</td>\n",
              "      <td>81.360001</td>\n",
              "      <td>83.988716</td>\n",
              "      <td>26.665262</td>\n",
              "      <td>51.180000</td>\n",
              "      <td>46.209999</td>\n",
              "      <td>141.770004</td>\n",
              "      <td>124.769997</td>\n",
              "      <td>101.900002</td>\n",
              "      <td>146.110001</td>\n",
              "      <td>541.530029</td>\n",
              "      <td>69.209999</td>\n",
              "      <td>114.900002</td>\n",
              "      <td>125.620003</td>\n",
              "      <td>152.910004</td>\n",
              "      <td>133.600006</td>\n",
              "      <td>11.680000</td>\n",
              "      <td>78.500000</td>\n",
              "      <td>127.730003</td>\n",
              "      <td>247.072754</td>\n",
              "      <td>231.453522</td>\n",
              "      <td>237.826859</td>\n",
              "      <td>3094.080078</td>\n",
              "      <td>301.890015</td>\n",
              "      <td>339.559998</td>\n",
              "      <td>358.950012</td>\n",
              "      <td>229.619537</td>\n",
              "      <td>...</td>\n",
              "      <td>211.422897</td>\n",
              "      <td>79.919998</td>\n",
              "      <td>45.099998</td>\n",
              "      <td>70.706139</td>\n",
              "      <td>168.750000</td>\n",
              "      <td>44.876297</td>\n",
              "      <td>176.690002</td>\n",
              "      <td>198.759995</td>\n",
              "      <td>214.889999</td>\n",
              "      <td>53.340000</td>\n",
              "      <td>13.970000</td>\n",
              "      <td>57.531048</td>\n",
              "      <td>79.042946</td>\n",
              "      <td>284.170013</td>\n",
              "      <td>54.900002</td>\n",
              "      <td>66.750000</td>\n",
              "      <td>92.922203</td>\n",
              "      <td>71.045067</td>\n",
              "      <td>38.984955</td>\n",
              "      <td>220.350006</td>\n",
              "      <td>228.880005</td>\n",
              "      <td>129.020004</td>\n",
              "      <td>23.690001</td>\n",
              "      <td>135.298660</td>\n",
              "      <td>75.349998</td>\n",
              "      <td>52.049999</td>\n",
              "      <td>281.628784</td>\n",
              "      <td>24.660000</td>\n",
              "      <td>35.599998</td>\n",
              "      <td>125.370003</td>\n",
              "      <td>66.510002</td>\n",
              "      <td>123.900002</td>\n",
              "      <td>55.028351</td>\n",
              "      <td>63.810001</td>\n",
              "      <td>105.180000</td>\n",
              "      <td>108.180000</td>\n",
              "      <td>160.080002</td>\n",
              "      <td>485.179993</td>\n",
              "      <td>54.640793</td>\n",
              "      <td>157.245789</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>62 rows × 503 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     A        AAL  ...       ZION         ZTS\n",
              "2020-12-31         NaN        NaN  ...        NaN         NaN\n",
              "2021-01-04  118.459747  15.130000  ...  42.397789  163.093948\n",
              "2021-01-05  119.428276  15.430000  ...  43.069359  164.230499\n",
              "2021-01-06  122.703293  15.520000  ...  47.908611  166.643158\n",
              "2021-01-07  125.968323  15.380000  ...  49.370266  166.493607\n",
              "...                ...        ...  ...        ...         ...\n",
              "2021-03-25  121.714798  22.770000  ...  54.422073  152.652649\n",
              "2021-03-26  125.449112  22.930000  ...  55.525623  155.917770\n",
              "2021-03-29  125.229446  22.910000  ...  53.577007  158.154449\n",
              "2021-03-30  124.650322  24.120001  ...  55.585274  156.806442\n",
              "2021-03-31  126.946831  23.900000  ...  54.640793  157.245789\n",
              "\n",
              "[62 rows x 503 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6eX_FXg_T37V"
      },
      "source": [
        "assets = df.columns"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "re-sxsqxUDpN",
        "outputId": "de31043e-646d-49fb-c42f-11d09432fa3f"
      },
      "source": [
        "!pip install PyPortfolioOpt"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting PyPortfolioOpt\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/46/55/7d39d78d554ee33a7317e345caf01339da11406c28f18bc48794fe967935/PyPortfolioOpt-1.4.1-py3-none-any.whl (56kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 2.2MB/s \n",
            "\u001b[?25hCollecting cvxpy<2.0.0,>=1.1.10\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/83/47/fd1e818b8da30ef18695a0fbf9b66611ab18506f0a44fc69480a75f4db1b/cvxpy-1.1.12.tar.gz (1.3MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3MB 5.2MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.1.5)\n",
            "Requirement already satisfied: numpy<2.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.19.5)\n",
            "Requirement already satisfied: scipy<2.0,>=1.3 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.4.1)\n",
            "Requirement already satisfied: osqp>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (0.6.2.post0)\n",
            "Requirement already satisfied: scs>=1.1.6 in /usr/local/lib/python3.7/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (2.1.3)\n",
            "Requirement already satisfied: ecos>=2 in /usr/local/lib/python3.7/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (2.0.7.post1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2.8.1)\n",
            "Requirement already satisfied: qdldl in /usr/local/lib/python3.7/dist-packages (from osqp>=0.4.1->cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (0.1.5.post0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->PyPortfolioOpt) (1.15.0)\n",
            "Building wheels for collected packages: cvxpy\n",
            "  Building wheel for cvxpy (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cvxpy: filename=cvxpy-1.1.12-cp37-cp37m-linux_x86_64.whl size=2731667 sha256=82bb4edca631991a621218772742828b4d05dec1382ad584e1963df38507cefb\n",
            "  Stored in directory: /root/.cache/pip/wheels/9b/62/55/1da181c05c710c5d99bd560edebec3bd6a61cb69acef9dc00e\n",
            "Successfully built cvxpy\n",
            "Installing collected packages: cvxpy, PyPortfolioOpt\n",
            "  Found existing installation: cvxpy 1.0.31\n",
            "    Uninstalling cvxpy-1.0.31:\n",
            "      Successfully uninstalled cvxpy-1.0.31\n",
            "Successfully installed PyPortfolioOpt-1.4.1 cvxpy-1.1.12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "849iC9DWUJKp"
      },
      "source": [
        "# Optimize the portfolio\n",
        "from pypfopt.efficient_frontier import EfficientFrontier\n",
        "from pypfopt import risk_models, expected_returns"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K75yhtBKUL1U",
        "outputId": "02895a32-5172-4c9c-fe74-cfc105b6621a"
      },
      "source": [
        "# Calculate expected anuallized returns\n",
        "\n",
        "mu = expected_returns.mean_historical_return(df)\n",
        "S = risk_models.sample_cov(df)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pypfopt/risk_models.py:69: UserWarning: The covariance matrix is non positive semidefinite. Amending eigenvalues.\n",
            "  \"The covariance matrix is non positive semidefinite. Amending eigenvalues.\"\n",
            "/usr/local/lib/python3.7/dist-packages/pypfopt/risk_models.py:88: UserWarning: Could not fix matrix. Please try a different risk model.\n",
            "  \"Could not fix matrix. Please try a different risk model.\", UserWarning\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Sd5YUCNUfsn",
        "outputId": "b283c727-4a68-4c61-c319-42ccdb4b7746"
      },
      "source": [
        "# Optimize for maximal Sharpe ratio\n",
        "ef = EfficientFrontier(mu, S)\n",
        "weights = ef.max_sharpe()\n",
        "\n",
        "cleaned_weights = ef.clean_weights()\n",
        "print(cleaned_weights)\n",
        "ef.portfolio_performance(verbose=True)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OrderedDict([('A', 0.0), ('AAL', 0.08165), ('AAP', 0.0), ('AAPL', 0.0), ('ABBV', 0.0), ('ABC', 0.0), ('ABMD', 0.0), ('ABT', 0.0141), ('ACN', 0.0), ('ADBE', 0.0), ('ADI', 0.0), ('ADM', 0.0), ('ADP', 0.0), ('ADSK', 0.0), ('AEE', 0.02477), ('AEP', 0.0), ('AES', 0.0), ('AFL', 0.0), ('AIG', 0.0), ('AIZ', 0.0), ('AJG', 0.0), ('AKAM', 0.0), ('ALB', 0.0), ('ALGN', 0.0), ('ALK', 0.0), ('ALL', 0.0), ('ALLE', 0.0), ('ALXN', 0.0), ('AMAT', 0.05891), ('AMCR', 0.0), ('AMD', 0.0), ('AME', 0.0), ('AMGN', 0.0), ('AMP', 0.0), ('AMT', 0.0), ('AMZN', 0.0), ('ANET', 0.0), ('ANSS', 0.0), ('ANTM', 0.0), ('AON', 0.0), ('AOS', 0.0), ('APA', 0.0), ('APD', 0.0), ('APH', 0.0), ('APTV', 0.0), ('ARE', 0.0), ('ATO', 0.0), ('ATVI', 0.0), ('AVB', 0.0), ('AVGO', 0.0), ('AVY', 0.0), ('AWK', 0.0), ('AXP', 0.0), ('AZO', 0.0), ('BA', 0.0), ('BAC', 0.0), ('BAX', 0.0), ('BBY', 0.0), ('BDX', 0.0), ('BEN', 0.0), ('BIIB', 0.0), ('BIO', 0.0), ('BK', 0.0), ('BKNG', 0.0), ('BKR', 0.0), ('BLK', 0.0), ('BLL', 0.0), ('BMY', 0.0), ('BR', 0.0), ('BSX', 0.0), ('BWA', 0.0), ('BXP', 0.0), ('C', 0.0), ('CAG', 0.0), ('CAH', 0.0), ('CARR', 0.0), ('CAT', 0.0), ('CB', 0.0), ('CBOE', 0.0), ('CBRE', 0.0), ('CCI', 0.02668), ('CCL', 0.0), ('CDNS', 0.0), ('CDW', 0.0), ('CE', 0.0), ('CERN', 0.0), ('CF', 0.0), ('CFG', 0.0), ('CHD', 0.0), ('CHRW', 0.0), ('CHTR', 0.0), ('CI', 0.0), ('CINF', 0.0), ('CL', 0.0), ('CLX', 0.0), ('CMA', 0.0), ('CMCSA', 0.0), ('CME', 0.0), ('CMG', 0.0), ('CMI', 0.0), ('CMS', 0.0), ('CNC', 0.0), ('CNP', 0.0), ('COF', 0.0), ('COG', 0.0), ('COO', 0.0), ('COP', 0.0), ('COST', 0.0), ('CPB', 0.0), ('CPRT', 0.0), ('CRM', 0.0), ('CSCO', 0.0), ('CSX', 0.0), ('CTAS', 0.0), ('CTLT', 0.0), ('CTSH', 0.0), ('CTVA', 0.0), ('CTXS', 0.0), ('CVS', 0.0), ('CVX', 0.0), ('CZR', 0.0), ('D', 0.0), ('DAL', 0.0), ('DD', 0.0), ('DE', 0.0), ('DFS', 0.0), ('DG', 0.0), ('DGX', 0.0), ('DHI', 0.0), ('DHR', 0.0), ('DIS', 0.0), ('DISCA', 0.04315), ('DISCK', 0.0), ('DISH', 0.0), ('DLR', 0.07324), ('DLTR', 0.0), ('DOV', 0.0), ('DOW', 0.0), ('DPZ', 0.0), ('DRE', 0.0), ('DRI', 0.0), ('DTE', 0.0), ('DUK', 0.0), ('DVA', 0.0), ('DVN', 0.0), ('DXC', 0.0), ('DXCM', 0.0), ('EA', 0.0), ('EBAY', 0.0), ('ECL', 0.0), ('ED', 0.02068), ('EFX', 0.0), ('EIX', 0.0), ('EL', 0.0), ('EMN', 0.0), ('EMR', 0.0), ('ENPH', 0.0), ('EOG', 0.0), ('EQIX', 0.0), ('EQR', 0.0), ('ES', 0.0), ('ESS', 0.0), ('ETN', 0.0), ('ETR', 0.0), ('ETSY', 0.0), ('EVRG', 0.00021), ('EW', 0.0), ('EXC', 0.0), ('EXPD', 0.0), ('EXPE', 0.0), ('EXR', 0.13205), ('F', 0.0), ('FANG', 0.0), ('FAST', 0.0), ('FB', 0.0), ('FBHS', 0.0), ('FCX', 0.0), ('FDX', 0.0), ('FE', 0.05121), ('FFIV', 0.0), ('FIS', 0.0), ('FISV', 0.0), ('FITB', 0.0), ('FLIR', 0.0), ('FLT', 0.0), ('FMC', 0.0), ('FOX', 0.0), ('FOXA', 0.0), ('FRC', 0.0), ('FRT', 0.0), ('FTNT', 0.0), ('FTV', 0.0), ('GD', 0.0), ('GE', 0.0), ('GILD', 0.0), ('GIS', 0.0), ('GL', 0.0), ('GLW', 0.0), ('GM', 0.0), ('GNRC', 0.00902), ('GOOG', 0.0), ('GOOGL', 0.0), ('GPC', 0.0), ('GPN', 0.0), ('GPS', 0.0), ('GRMN', 0.0), ('GS', 0.0), ('GWW', 0.0), ('HAL', 0.0), ('HAS', 0.0), ('HBAN', 0.0), ('HBI', 0.0), ('HCA', 0.0), ('HD', 0.00385), ('HES', 0.0), ('HFC', 0.0), ('HIG', 0.02863), ('HII', 0.0), ('HLT', 0.0), ('HOLX', 0.0), ('HON', 0.0), ('HPE', 0.0), ('HPQ', 0.0), ('HRL', 0.0), ('HSIC', 0.0), ('HST', 0.0), ('HSY', 0.0), ('HUM', 0.0), ('HWM', 0.0), ('IBM', 0.0), ('ICE', 0.0), ('IDXX', 0.0), ('IEX', 0.0), ('IFF', 0.0), ('ILMN', 0.0), ('INCY', 0.0), ('INFO', 0.0), ('INTC', 0.0), ('INTU', 0.0), ('IP', 0.0), ('IPG', 0.0), ('IPGP', 0.0), ('IQV', 0.0), ('IR', 0.0), ('IRM', 0.07047), ('ISRG', 0.0), ('IT', 0.0), ('ITW', 0.0), ('IVZ', 0.0), ('J', 0.0), ('JBHT', 0.0), ('JCI', 0.0), ('JKHY', 0.0), ('JNJ', 0.0), ('JNPR', 0.0), ('JPM', 0.0), ('K', 0.0), ('KEY', 0.0), ('KEYS', 0.0), ('KHC', 0.02209), ('KIM', 0.0), ('KLAC', 0.0), ('KMB', 0.0), ('KMI', 0.0), ('KMX', 0.0), ('KO', 0.0), ('KR', 0.00189), ('KSU', 0.0), ('L', 0.0), ('LB', 0.0718), ('LDOS', 0.0), ('LEG', 0.0), ('LEN', 0.0), ('LH', 0.08388), ('LHX', 0.0), ('LIN', 0.0), ('LKQ', 0.0), ('LLY', 0.01844), ('LMT', 0.0), ('LNC', 0.0), ('LNT', 0.0), ('LOW', 0.0), ('LRCX', 0.0), ('LUMN', 0.0), ('LUV', 0.0), ('LVS', 0.0), ('LW', 0.0), ('LYB', 0.0), ('LYV', 0.0), ('MA', 0.0), ('MAA', 0.0), ('MAR', 0.0), ('MAS', 0.0), ('MCD', 0.0), ('MCHP', 0.0), ('MCK', 0.0), ('MCO', 0.0), ('MDLZ', 0.0), ('MDT', 0.0), ('MET', 0.0), ('MGM', 0.0), ('MHK', 0.0), ('MKC', 0.0), ('MKTX', 0.0), ('MLM', 0.0), ('MMC', 0.0), ('MMM', 0.0), ('MNST', 0.0), ('MO', 0.0), ('MOS', 0.0), ('MPC', 0.0), ('MPWR', 0.0), ('MRK', 0.0), ('MRO', 0.0), ('MS', 0.0), ('MSCI', 0.0), ('MSFT', 0.0), ('MSI', 0.0), ('MTB', 0.0), ('MTD', 0.0), ('MU', 0.0), ('MXIM', 0.0), ('NCLH', 0.0), ('NDAQ', 0.0), ('NEE', 0.0), ('NEM', 0.0), ('NFLX', 0.0), ('NI', 0.0), ('NKE', 0.0), ('NLOK', 0.0), ('NLSN', 0.0), ('NOC', 0.0), ('NOV', 0.0), ('NOW', 0.0), ('NRG', 0.0), ('NSC', 0.0), ('NTAP', 0.0), ('NTRS', 0.0), ('NUE', 0.03029), ('NVDA', 0.0), ('NVR', 0.0), ('NWL', 0.0), ('NWS', 0.0), ('NWSA', 0.0), ('NXPI', 0.0), ('O', 0.0), ('ODFL', 0.0), ('OKE', 0.0), ('OMC', 0.0), ('ORCL', 0.0), ('ORLY', 0.0), ('OTIS', 0.0), ('OXY', 0.0), ('PAYC', 0.0), ('PAYX', 0.0), ('PBCT', 0.0), ('PCAR', 0.0), ('PEAK', 0.0), ('PEG', 0.0), ('PENN', 0.0), ('PEP', 0.0), ('PFE', 0.0), ('PFG', 0.0), ('PG', 0.0), ('PGR', 0.0), ('PH', 0.0), ('PHM', 0.0), ('PKG', 0.0), ('PKI', 0.0), ('PLD', 0.0), ('PM', 0.0), ('PNC', 0.0), ('PNR', 0.0), ('PNW', 0.0), ('POOL', 0.0), ('PPG', 0.0), ('PPL', 0.0), ('PRGO', 0.0), ('PRU', 0.0), ('PSA', 0.01246), ('PSX', 0.0), ('PTC', 0.0), ('PVH', 0.0), ('PWR', 0.0), ('PXD', 0.03703), ('PYPL', 0.0), ('QCOM', 0.0), ('QRVO', 0.0), ('RCL', 0.0), ('RE', 0.0), ('REG', 0.0), ('REGN', 0.0), ('RF', 0.0), ('RHI', 0.0), ('RJF', 0.0), ('RL', 0.0), ('RMD', 0.0), ('ROK', 0.0), ('ROL', 0.0), ('ROP', 0.0), ('ROST', 0.0), ('RSG', 0.0), ('RTX', 0.0), ('SBAC', 0.0), ('SBUX', 0.0), ('SCHW', 0.0), ('SEE', 0.0), ('SHW', 0.0), ('SIVB', 0.0), ('SJM', 0.04416), ('SLB', 0.0), ('SNA', 0.0), ('SNPS', 0.0), ('SO', 0.0), ('SPG', 0.0), ('SPGI', 0.0), ('SRE', 0.0), ('STE', 0.0), ('STT', 0.0), ('STX', 0.01071), ('STZ', 0.0), ('SWK', 0.0), ('SWKS', 0.0), ('SYF', 0.0), ('SYK', 0.0), ('SYY', 0.0), ('T', 0.0), ('TAP', 0.0), ('TDG', 0.0), ('TDY', 0.0), ('TEL', 0.0), ('TER', 0.0), ('TFC', 0.0), ('TFX', 0.0), ('TGT', 0.0), ('TJX', 0.0), ('TMO', 0.0), ('TMUS', 0.0), ('TPR', 0.0), ('TRMB', 0.0), ('TROW', 0.0), ('TRV', 0.0), ('TSCO', 0.0), ('TSLA', 0.0), ('TSN', 0.0), ('TT', 0.0), ('TTWO', 0.0), ('TWTR', 0.0), ('TXN', 0.0), ('TXT', 0.0), ('TYL', 0.0), ('UA', 0.0), ('UAA', 0.0), ('UAL', 0.0), ('UDR', 0.0), ('UHS', 0.0), ('ULTA', 0.0), ('UNH', 0.0), ('UNM', 0.0), ('UNP', 0.0), ('UPS', 0.0), ('URI', 0.0), ('USB', 0.0), ('V', 0.0), ('VFC', 0.0), ('VIAC', 0.0), ('VLO', 0.0), ('VMC', 0.0), ('VNO', 0.0), ('VRSK', 0.0), ('VRSN', 0.0), ('VRTX', 0.0), ('VTR', 0.0), ('VTRS', 0.0), ('VZ', 0.0), ('WAB', 0.0), ('WAT', 0.0), ('WBA', 0.00722), ('WDC', 0.0), ('WEC', 0.02143), ('WELL', 0.0), ('WFC', 0.0), ('WHR', 0.0), ('WLTW', 0.0), ('WM', 0.0), ('WMB', 0.0), ('WMT', 0.0), ('WRB', 0.0), ('WRK', 0.0), ('WST', 0.0), ('WU', 0.0), ('WY', 0.0), ('WYNN', 0.0), ('XEL', 0.0), ('XLNX', 0.0), ('XOM', 0.0), ('XRAY', 0.0), ('XYL', 0.0), ('YUM', 0.0), ('ZBH', 0.0), ('ZBRA', 0.0), ('ZION', 0.0), ('ZTS', 0.0)])\n",
            "Expected annual return: 237.5%\n",
            "Annual volatility: 11.0%\n",
            "Sharpe Ratio: 21.44\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.375066079764527, 0.10985521722442311, 21.437908360359117)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8BBtajxVSxN",
        "outputId": "76160ab7-4b98-45b4-f4c4-8768555f91ff"
      },
      "source": [
        "!pip install pulp"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pulp\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/c4/0eec14a0123209c261de6ff154ef3be5cad3fd557c084f468356662e0585/PuLP-2.4-py3-none-any.whl (40.6MB)\n",
            "\u001b[K     |████████████████████████████████| 40.6MB 105kB/s \n",
            "\u001b[?25hCollecting amply>=0.1.2\n",
            "  Downloading https://files.pythonhosted.org/packages/f3/c5/dfa09dd2595a2ab2ab4e6fa7bebef9565812722e1980d04b0edce5032066/amply-0.1.4-py3-none-any.whl\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.7/dist-packages (from amply>=0.1.2->pulp) (2.4.7)\n",
            "Requirement already satisfied: docutils>=0.3 in /usr/local/lib/python3.7/dist-packages (from amply>=0.1.2->pulp) (0.17.1)\n",
            "Installing collected packages: amply, pulp\n",
            "Successfully installed amply-0.1.4 pulp-2.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFSkMjq5Vb7d",
        "outputId": "b7fcfc04-00b4-4448-b5eb-f563f8511399"
      },
      "source": [
        "# Get discrete allocation of each share per stock\n",
        "\n",
        "from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices\n",
        "\n",
        "#How much money I want to put in USD\n",
        "portfolio_val = 5000\n",
        "\n",
        "latest_prices=get_latest_prices(df)\n",
        "weights = cleaned_weights\n",
        "da = DiscreteAllocation(weights, latest_prices, total_portfolio_value= portfolio_val)\n",
        "allocation, leftover = da.lp_portfolio()\n",
        "print('Discrete Allocation ', allocation)\n",
        "print('Funds Aemaining: $', leftover)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Discrete Allocation  {'AAL': 17, 'ABT': 1, 'AEE': 1, 'AMAT': 2, 'CCI': 1, 'DISCA': 5, 'DLR': 3, 'ED': 1, 'EXR': 5, 'FE': 8, 'HIG': 2, 'IRM': 10, 'KHC': 3, 'LB': 6, 'LH': 2, 'NUE': 2, 'PXD': 1, 'SJM': 2, 'STX': 1, 'WBA': 1, 'WEC': 1}\n",
            "Funds Aemaining: $ 1.9444828033447266\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0lvefevWJe-",
        "outputId": "f6ebe75c-ddf7-4c6c-fdef-c3eb63a42cdd"
      },
      "source": [
        "# Create a function to get companies name\n",
        "import requests\n",
        "def get_company_name(symbol):\n",
        "    url = \"http://d.yimg.com/autoc.finance.yahoo.com/autoc?query={}&region=1&lang=en\".format(symbol)\n",
        "\n",
        "    result = requests.get(url).json()\n",
        "    \n",
        "    for x in result['ResultSet']['Result']:\n",
        "        if x['symbol'] == symbol:\n",
        "            return x['name']\n",
        "\n",
        "company = get_company_name(\"MSFT\")\n",
        "\n",
        "print(company)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Microsoft Corporation\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34_ZjCJmV07T"
      },
      "source": [
        "# Store company name in a list\n",
        "company_name = []\n",
        "for symbol in allocation:\n",
        "  company_name.append(get_company_name(symbol))"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPJjR9gDW43f"
      },
      "source": [
        "# Get the discrete allocation values\n",
        "discrete_allocation_list = []\n",
        "for symbol in allocation:\n",
        "  discrete_allocation_list.append(allocation.get(symbol))"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaIEjNvGW2Qo"
      },
      "source": [
        "# Create a df for the portfolio\n",
        "\n",
        "portfolio_df = pd.DataFrame(columns=[\"Company_name\", \"Company_ticker\", \"Discrete_val\"+str(portfolio_val)])\n",
        "portfolio_df['Company_name']= company_name\n",
        "portfolio_df['Company_ticker']= allocation\n",
        "portfolio_df[\"Discrete_val\"+str(portfolio_val)]= discrete_allocation_list"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "xlKNP3rSXk6Y",
        "outputId": "058fef31-b9f8-4991-d59c-6ec9415763f5"
      },
      "source": [
        "portfolio_df"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company_name</th>\n",
              "      <th>Company_ticker</th>\n",
              "      <th>Discrete_val5000</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>American Airlines Group Inc.</td>\n",
              "      <td>AAL</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abbott Laboratories</td>\n",
              "      <td>ABT</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ameren Corporation</td>\n",
              "      <td>AEE</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Applied Materials, Inc.</td>\n",
              "      <td>AMAT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Crown Castle International Corp. (REIT)</td>\n",
              "      <td>CCI</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Discovery, Inc.</td>\n",
              "      <td>DISCA</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Digital Realty Trust, Inc.</td>\n",
              "      <td>DLR</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Consolidated Edison, Inc.</td>\n",
              "      <td>ED</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Extra Space Storage Inc.</td>\n",
              "      <td>EXR</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>FirstEnergy Corp.</td>\n",
              "      <td>FE</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>The Hartford Financial Services Group, Inc.</td>\n",
              "      <td>HIG</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Iron Mountain Incorporated</td>\n",
              "      <td>IRM</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>The Kraft Heinz Company</td>\n",
              "      <td>KHC</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>L Brands, Inc.</td>\n",
              "      <td>LB</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Laboratory Corporation of America Holdings</td>\n",
              "      <td>LH</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Nucor Corporation</td>\n",
              "      <td>NUE</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Pioneer Natural Resources Company</td>\n",
              "      <td>PXD</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>The J. M. Smucker Company</td>\n",
              "      <td>SJM</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Seagate Technology plc</td>\n",
              "      <td>STX</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Walgreens Boots Alliance, Inc.</td>\n",
              "      <td>WBA</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>WEC Energy Group, Inc.</td>\n",
              "      <td>WEC</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   Company_name  ... Discrete_val5000\n",
              "0                  American Airlines Group Inc.  ...               17\n",
              "1                           Abbott Laboratories  ...                1\n",
              "2                            Ameren Corporation  ...                1\n",
              "3                       Applied Materials, Inc.  ...                2\n",
              "4       Crown Castle International Corp. (REIT)  ...                1\n",
              "5                               Discovery, Inc.  ...                5\n",
              "6                    Digital Realty Trust, Inc.  ...                3\n",
              "7                     Consolidated Edison, Inc.  ...                1\n",
              "8                      Extra Space Storage Inc.  ...                5\n",
              "9                             FirstEnergy Corp.  ...                8\n",
              "10  The Hartford Financial Services Group, Inc.  ...                2\n",
              "11                   Iron Mountain Incorporated  ...               10\n",
              "12                      The Kraft Heinz Company  ...                3\n",
              "13                               L Brands, Inc.  ...                6\n",
              "14   Laboratory Corporation of America Holdings  ...                2\n",
              "15                            Nucor Corporation  ...                2\n",
              "16            Pioneer Natural Resources Company  ...                1\n",
              "17                    The J. M. Smucker Company  ...                2\n",
              "18                       Seagate Technology plc  ...                1\n",
              "19               Walgreens Boots Alliance, Inc.  ...                1\n",
              "20                       WEC Energy Group, Inc.  ...                1\n",
              "\n",
              "[21 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    }
  ]
}