Calculation_of_daily_pivot_levels.ipynb

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    "colab": {
      "name": "Calculation of daily pivot levels.ipynb",
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      "authorship_tag": "ABX9TyPFmH9nyVVd7WK+RoGMlwC7"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        }
      },
      "source": [
        "!pip install yfinance"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.6/dist-packages (0.1.54)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.0.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.18.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas>=0.24->yfinance) (1.15.0)\n"
          ],
          "name": "stdout"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cONv05MkwBVB"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import yfinance\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qL393Ux9wE3x"
      },
      "source": [
        "ticker = yfinance.Ticker(\"FB\")\n",
        "df = ticker.history(interval=\"1d\")\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VXqcqOBxEDc",
        "outputId": "9e7f1a6a-3a96-4073-af03-a4ef655b5094",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 20,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Dividends</th>\n",
              "      <th>Stock Splits</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>293.95</td>\n",
              "      <td>296.88</td>\n",
              "      <td>291.55</td>\n",
              "      <td>293.20</td>\n",
              "      <td>17345100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>2020-09-01</th>\n",
              "      <td>294.71</td>\n",
              "      <td>301.49</td>\n",
              "      <td>292.71</td>\n",
              "      <td>295.44</td>\n",
              "      <td>17320900</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2020-09-02</th>\n",
              "      <td>298.88</td>\n",
              "      <td>303.60</td>\n",
              "      <td>293.05</td>\n",
              "      <td>302.50</td>\n",
              "      <td>24341400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2020-09-03</th>\n",
              "      <td>295.99</td>\n",
              "      <td>297.60</td>\n",
              "      <td>283.63</td>\n",
              "      <td>291.12</td>\n",
              "      <td>32294100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2020-09-04</th>\n",
              "      <td>287.25</td>\n",
              "      <td>289.00</td>\n",
              "      <td>271.14</td>\n",
              "      <td>282.73</td>\n",
              "      <td>30333700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-08</th>\n",
              "      <td>271.28</td>\n",
              "      <td>279.30</td>\n",
              "      <td>269.42</td>\n",
              "      <td>271.16</td>\n",
              "      <td>24864000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-09</th>\n",
              "      <td>275.77</td>\n",
              "      <td>278.48</td>\n",
              "      <td>271.35</td>\n",
              "      <td>273.72</td>\n",
              "      <td>22918800</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-10</th>\n",
              "      <td>275.51</td>\n",
              "      <td>279.16</td>\n",
              "      <td>267.03</td>\n",
              "      <td>268.09</td>\n",
              "      <td>24814700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-11</th>\n",
              "      <td>270.06</td>\n",
              "      <td>271.39</td>\n",
              "      <td>262.64</td>\n",
              "      <td>266.61</td>\n",
              "      <td>18913900</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-14</th>\n",
              "      <td>270.95</td>\n",
              "      <td>276.64</td>\n",
              "      <td>265.70</td>\n",
              "      <td>266.15</td>\n",
              "      <td>24093800</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>2020-09-15</th>\n",
              "      <td>270.67</td>\n",
              "      <td>274.52</td>\n",
              "      <td>269.30</td>\n",
              "      <td>272.42</td>\n",
              "      <td>18478500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2020-09-16</th>\n",
              "      <td>267.29</td>\n",
              "      <td>272.44</td>\n",
              "      <td>261.79</td>\n",
              "      <td>263.52</td>\n",
              "      <td>29183400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2020-09-17</th>\n",
              "      <td>258.28</td>\n",
              "      <td>261.50</td>\n",
              "      <td>250.19</td>\n",
              "      <td>254.82</td>\n",
              "      <td>31281400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2020-09-18</th>\n",
              "      <td>258.40</td>\n",
              "      <td>259.20</td>\n",
              "      <td>250.05</td>\n",
              "      <td>252.53</td>\n",
              "      <td>28130800</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2020-09-21</th>\n",
              "      <td>247.54</td>\n",
              "      <td>249.95</td>\n",
              "      <td>244.13</td>\n",
              "      <td>248.15</td>\n",
              "      <td>24709400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2020-09-22</th>\n",
              "      <td>253.31</td>\n",
              "      <td>255.32</td>\n",
              "      <td>248.22</td>\n",
              "      <td>254.75</td>\n",
              "      <td>30293100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-23</th>\n",
              "      <td>255.26</td>\n",
              "      <td>257.99</td>\n",
              "      <td>248.15</td>\n",
              "      <td>249.02</td>\n",
              "      <td>19641300</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-24</th>\n",
              "      <td>246.50</td>\n",
              "      <td>252.24</td>\n",
              "      <td>245.62</td>\n",
              "      <td>249.53</td>\n",
              "      <td>20006800</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-25</th>\n",
              "      <td>249.40</td>\n",
              "      <td>255.75</td>\n",
              "      <td>246.61</td>\n",
              "      <td>254.82</td>\n",
              "      <td>18351300</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-28</th>\n",
              "      <td>259.40</td>\n",
              "      <td>259.60</td>\n",
              "      <td>254.82</td>\n",
              "      <td>256.82</td>\n",
              "      <td>18807800</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "              Open    High     Low   Close    Volume  Dividends  Stock Splits\n",
              "Date                                                                         \n",
              "2020-08-31  293.95  296.88  291.55  293.20  17345100          0             0\n",
              "2020-09-01  294.71  301.49  292.71  295.44  17320900          0             0\n",
              "2020-09-02  298.88  303.60  293.05  302.50  24341400          0             0\n",
              "2020-09-03  295.99  297.60  283.63  291.12  32294100          0             0\n",
              "2020-09-04  287.25  289.00  271.14  282.73  30333700          0             0\n",
              "2020-09-08  271.28  279.30  269.42  271.16  24864000          0             0\n",
              "2020-09-09  275.77  278.48  271.35  273.72  22918800          0             0\n",
              "2020-09-10  275.51  279.16  267.03  268.09  24814700          0             0\n",
              "2020-09-11  270.06  271.39  262.64  266.61  18913900          0             0\n",
              "2020-09-14  270.95  276.64  265.70  266.15  24093800          0             0\n",
              "2020-09-15  270.67  274.52  269.30  272.42  18478500          0             0\n",
              "2020-09-16  267.29  272.44  261.79  263.52  29183400          0             0\n",
              "2020-09-17  258.28  261.50  250.19  254.82  31281400          0             0\n",
              "2020-09-18  258.40  259.20  250.05  252.53  28130800          0             0\n",
              "2020-09-21  247.54  249.95  244.13  248.15  24709400          0             0\n",
              "2020-09-22  253.31  255.32  248.22  254.75  30293100          0             0\n",
              "2020-09-23  255.26  257.99  248.15  249.02  19641300          0             0\n",
              "2020-09-24  246.50  252.24  245.62  249.53  20006800          0             0\n",
              "2020-09-25  249.40  255.75  246.61  254.82  18351300          0             0\n",
              "2020-09-28  259.40  259.60  254.82  256.82  18807800          0             0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5G7JJfUsA0i"
      },
      "source": [
        "last_day = df.tail(1).copy()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGfvJQKK5Hh3"
      },
      "source": [
        "https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/ \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyRdpLqq5B9z"
      },
      "source": [
        "last_day['Pivot'] = (last_day['High'] + last_day['Low'] + last_day['Close'])/3\n",
        "last_day['R1'] = 2*last_day['Pivot'] - last_day['Low']\n",
        "last_day['S1'] = 2*last_day['Pivot'] - last_day['High']\n",
        "last_day['R2'] = last_day['Pivot'] + (last_day['High'] - last_day['Low'])\n",
        "last_day['S2'] = last_day['Pivot'] - (last_day['High'] - last_day['Low'])\n",
        "last_day['R3'] = last_day['Pivot'] + 2*(last_day['High'] - last_day['Low'])\n",
        "last_day['S3'] = last_day['Pivot'] - 2*(last_day['High'] - last_day['Low'])\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MO4FoBnrs3tG",
        "outputId": "122f2ba4-7583-448c-8851-1f4b2bef8958",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "last_day"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
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              "      <th>2020-09-28</th>\n",
              "      <td>259.4</td>\n",
              "      <td>259.6</td>\n",
              "      <td>254.82</td>\n",
              "      <td>256.82</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>257.08</td>\n",
              "      <td>259.34</td>\n",
              "      <td>254.56</td>\n",
              "      <td>261.86</td>\n",
              "      <td>252.3</td>\n",
              "      <td>266.64</td>\n",
              "      <td>247.52</td>\n",
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              "             Open   High     Low   Close  ...      R2     S2      R3      S3\n",
              "Date                                      ...                               \n",
              "2020-09-28  259.4  259.6  254.82  256.82  ...  261.86  252.3  266.64  247.52\n",
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              "[1 rows x 14 columns]"
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          "execution_count": 22
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