FFT.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install alpaca-py"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "sy2GEovCbjqh",
        "outputId": "1c3c4d90-a424-4f62-a581-778c2501f29a"
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      "execution_count": 1,
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          "text": [
            "Requirement already satisfied: alpaca-py in /usr/local/lib/python3.10/dist-packages (0.16.0)\n",
            "Requirement already satisfied: msgpack<2.0.0,>=1.0.3 in /usr/local/lib/python3.10/dist-packages (from alpaca-py) (1.0.7)\n",
            "Requirement already satisfied: pandas>=1.5.3 in /usr/local/lib/python3.10/dist-packages (from alpaca-py) (1.5.3)\n",
            "Requirement already satisfied: pydantic<3.0.0,>=2.0.3 in /usr/local/lib/python3.10/dist-packages (from alpaca-py) (2.6.1)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.30.0 in /usr/local/lib/python3.10/dist-packages (from alpaca-py) (2.31.0)\n",
            "Requirement already satisfied: sseclient-py<2.0.0,>=1.7.2 in /usr/local/lib/python3.10/dist-packages (from alpaca-py) (1.8.0)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.5.3->alpaca-py) (2023.4)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.5.3->alpaca-py) (1.25.2)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0.0,>=2.0.3->alpaca-py) (0.6.0)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.30.0->alpaca-py) (3.6)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.30.0->alpaca-py) (2024.2.2)\n",
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      "metadata": {
        "id": "nv-VgLv3bky7"
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    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Bm0M9L3xY5AZ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.fftpack as ft\n",
        "\n",
        "from alpaca.data import StockHistoricalDataClient, TimeFrame\n",
        "from alpaca.data.requests import StockQuotesRequest, StockBarsRequest"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ALPACA_API_KEY_ID = 'API_KEY'\n",
        "ALPACA_API_SECRET_KEY = 'SECRET_KEY'\n",
        "\n",
        "# Instantiate a data client\n",
        "data_client = StockHistoricalDataClient(ALPACA_API_KEY_ID, ALPACA_API_SECRET_KEY)\n",
        "\n",
        "# Set the start time\n",
        "start_time = pd.to_datetime(\"2023-02-26\").tz_localize('America/New_York')# It's generally best to explicitly provide an end time but will default to 'now' if not\n",
        "request_params = StockBarsRequest(\n",
        "    symbol_or_symbols=['SOYB'],\n",
        "    timeframe=TimeFrame.Day,\n",
        "    start=start_time\n",
        "    )"
      ],
      "metadata": {
        "id": "FPYxXYDIZEId"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bars_df = data_client.get_stock_bars(request_params).df.tz_convert('America/New_York', level=1)"
      ],
      "metadata": {
        "id": "7uskSH6RbzAk"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bars_df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCKhV1tBm1dH",
        "outputId": "72fa31b7-9645-4491-ea96-acad98a37a56"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiIndex([('SOYB', '2023-02-27 00:00:00-05:00'),\n",
              "            ('SOYB', '2023-02-28 00:00:00-05:00'),\n",
              "            ('SOYB', '2023-03-01 00:00:00-05:00'),\n",
              "            ('SOYB', '2023-03-02 00:00:00-05:00'),\n",
              "            ('SOYB', '2023-03-03 00:00:00-05:00'),\n",
              "            ('SOYB', '2023-03-06 00:00:00-05:00'),\n",
              "            ('SOYB', '2023-03-07 00:00:00-05:00'),\n",
              "            ('SOYB', '2023-03-08 00:00:00-05:00'),\n",
              "            ('SOYB', '2023-03-09 00:00:00-05:00'),\n",
              "            ('SOYB', '2023-03-10 00:00:00-05:00'),\n",
              "            ...\n",
              "            ('SOYB', '2024-02-12 00:00:00-05:00'),\n",
              "            ('SOYB', '2024-02-13 00:00:00-05:00'),\n",
              "            ('SOYB', '2024-02-14 00:00:00-05:00'),\n",
              "            ('SOYB', '2024-02-15 00:00:00-05:00'),\n",
              "            ('SOYB', '2024-02-16 00:00:00-05:00'),\n",
              "            ('SOYB', '2024-02-20 00:00:00-05:00'),\n",
              "            ('SOYB', '2024-02-21 00:00:00-05:00'),\n",
              "            ('SOYB', '2024-02-22 00:00:00-05:00'),\n",
              "            ('SOYB', '2024-02-23 00:00:00-05:00'),\n",
              "            ('SOYB', '2024-02-26 00:00:00-05:00')],\n",
              "           names=['symbol', 'timestamp'], length=251)"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OAzx7DbsvTdh"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Assuming bars_df is your DataFrame\n",
        "# Convert the index to datetime\n",
        "bars_df.index = pd.to_datetime(bars_df.index.get_level_values(1))\n",
        "bars_df.index = bars_df.index.date\n",
        "\n",
        "# Now your index is converted to a datetime index\n",
        "print(bars_df.index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jB6eaPknzuz",
        "outputId": "661abf3a-0459-47a5-af96-2193efe5cf19"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index([2023-02-27, 2023-02-28, 2023-03-01, 2023-03-02, 2023-03-03, 2023-03-06,\n",
            "       2023-03-07, 2023-03-08, 2023-03-09, 2023-03-10,\n",
            "       ...\n",
            "       2024-02-12, 2024-02-13, 2024-02-14, 2024-02-15, 2024-02-16, 2024-02-20,\n",
            "       2024-02-21, 2024-02-22, 2024-02-23, 2024-02-26],\n",
            "      dtype='object', length=251)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bars_df"
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      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             open     high      low    close    volume  trade_count       vwap\n",
              "2023-02-27  28.26  28.2600  27.9900  28.0300   21054.0        235.0  28.048651\n",
              "2023-02-28  27.87  27.8700  27.4400  27.5000  143104.0        683.0  27.534114\n",
              "2023-03-01  27.56  27.8100  27.5600  27.7000  114477.0        400.0  27.700634\n",
              "2023-03-02  27.50  28.0700  27.5000  27.9600   24130.0        196.0  27.945100\n",
              "2023-03-03  28.00  28.2044  28.0000  28.1800   11492.0        155.0  28.141768\n",
              "...           ...      ...      ...      ...       ...          ...        ...\n",
              "2024-02-20  24.77  24.8990  24.6450  24.7842   17988.0        212.0  24.808517\n",
              "2024-02-21  24.58  24.5800  24.3601  24.4100   16501.0        199.0  24.460454\n",
              "2024-02-22  24.43  24.4700  24.1300  24.2200   38349.0        265.0  24.261405\n",
              "2024-02-23  24.14  24.1401  23.9400  24.0500   28601.0        298.0  24.031474\n",
              "2024-02-26  23.95  24.1000  23.8478  24.0350   43328.0        207.0  23.988511\n",
              "\n",
              "[251 rows x 7 columns]"
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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>trade_count</th>\n",
              "      <th>vwap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2023-02-27</th>\n",
              "      <td>28.26</td>\n",
              "      <td>28.2600</td>\n",
              "      <td>27.9900</td>\n",
              "      <td>28.0300</td>\n",
              "      <td>21054.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>28.048651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-28</th>\n",
              "      <td>27.87</td>\n",
              "      <td>27.8700</td>\n",
              "      <td>27.4400</td>\n",
              "      <td>27.5000</td>\n",
              "      <td>143104.0</td>\n",
              "      <td>683.0</td>\n",
              "      <td>27.534114</td>\n",
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              "    <tr>\n",
              "      <th>2023-03-01</th>\n",
              "      <td>27.56</td>\n",
              "      <td>27.8100</td>\n",
              "      <td>27.5600</td>\n",
              "      <td>27.7000</td>\n",
              "      <td>114477.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>27.700634</td>\n",
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              "    <tr>\n",
              "      <th>2023-03-02</th>\n",
              "      <td>27.50</td>\n",
              "      <td>28.0700</td>\n",
              "      <td>27.5000</td>\n",
              "      <td>27.9600</td>\n",
              "      <td>24130.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>27.945100</td>\n",
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              "    <tr>\n",
              "      <th>2023-03-03</th>\n",
              "      <td>28.00</td>\n",
              "      <td>28.2044</td>\n",
              "      <td>28.0000</td>\n",
              "      <td>28.1800</td>\n",
              "      <td>11492.0</td>\n",
              "      <td>155.0</td>\n",
              "      <td>28.141768</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",
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              "    <tr>\n",
              "      <th>2024-02-20</th>\n",
              "      <td>24.77</td>\n",
              "      <td>24.8990</td>\n",
              "      <td>24.6450</td>\n",
              "      <td>24.7842</td>\n",
              "      <td>17988.0</td>\n",
              "      <td>212.0</td>\n",
              "      <td>24.808517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-02-21</th>\n",
              "      <td>24.58</td>\n",
              "      <td>24.5800</td>\n",
              "      <td>24.3601</td>\n",
              "      <td>24.4100</td>\n",
              "      <td>16501.0</td>\n",
              "      <td>199.0</td>\n",
              "      <td>24.460454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-02-22</th>\n",
              "      <td>24.43</td>\n",
              "      <td>24.4700</td>\n",
              "      <td>24.1300</td>\n",
              "      <td>24.2200</td>\n",
              "      <td>38349.0</td>\n",
              "      <td>265.0</td>\n",
              "      <td>24.261405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-02-23</th>\n",
              "      <td>24.14</td>\n",
              "      <td>24.1401</td>\n",
              "      <td>23.9400</td>\n",
              "      <td>24.0500</td>\n",
              "      <td>28601.0</td>\n",
              "      <td>298.0</td>\n",
              "      <td>24.031474</td>\n",
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              "      <th>2024-02-26</th>\n",
              "      <td>23.95</td>\n",
              "      <td>24.1000</td>\n",
              "      <td>23.8478</td>\n",
              "      <td>24.0350</td>\n",
              "      <td>43328.0</td>\n",
              "      <td>207.0</td>\n",
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              "      --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-04bb631e-8ffe-4f62-a93f-6ed872f40211 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2d13dcca-7527-451f-aea2-e38b2e4d254c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('bars_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2d13dcca-7527-451f-aea2-e38b2e4d254c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('bars_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "bars_df",
              "summary": "{\n  \"name\": \"bars_df\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.244051040014034,\n        \"min\": 23.87,\n        \"max\": 29.22,\n        \"num_unique_values\": 201,\n        \"samples\": [\n          29.04,\n          27.02,\n          27.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2407626506616054,\n        \"min\": 24.1,\n        \"max\": 29.43,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          27.02,\n          27.72,\n          27.48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2338531950373612,\n        \"min\": 23.83,\n        \"max\": 28.96,\n        \"num_unique_values\": 207,\n        \"samples\": [\n          28.01,\n          26.8,\n          27.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2428395364306126,\n        \"min\": 24.035,\n        \"max\": 29.31,\n        \"num_unique_values\": 213,\n        \"samples\": [\n          27.857,\n          25.69,\n          24.6442\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20678.229541018347,\n        \"min\": 2707.0,\n        \"max\": 143104.0,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          13039.0,\n          13777.0,\n          21085.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trade_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76.06847533794169,\n        \"min\": 71.0,\n        \"max\": 683.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          246.0,\n          157.0,\n          197.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vwap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.237259315978657,\n        \"min\": 23.988511,\n        \"max\": 29.209306,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          26.86992,\n          28.121487,\n          27.565305\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bars_df['close'].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "brKEgZico8Wd",
        "outputId": "09e7a6c0-7a7a-45bb-aa78-70698472a11d"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#making data frame of stock prices into 2-dim list\n",
        "stock_data = bars_df.values.tolist()\n",
        "print(stock_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaNk5wJkcDg3",
        "outputId": "0b72298c-0e93-4e04-d3c7-32e51ce97465"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[28.26, 28.26, 27.99, 28.03, 21054.0, 235.0, 28.048651], [27.87, 27.87, 27.44, 27.5, 143104.0, 683.0, 27.534114], [27.56, 27.81, 27.56, 27.7, 114477.0, 400.0, 27.700634], [27.5, 28.07, 27.5, 27.96, 24130.0, 196.0, 27.9451], [28.0, 28.2044, 28.0, 28.18, 11492.0, 155.0, 28.141768], [27.95, 28.46, 27.95, 28.32, 10517.0, 177.0, 28.251501], [28.32, 28.32, 28.07, 28.11, 13777.0, 148.0, 28.121487], [28.02, 28.35, 28.02, 28.1, 15352.0, 159.0, 28.206552], [28.22, 28.26, 27.9, 28.0247, 13244.0, 168.0, 28.017925], [27.83, 27.92, 27.76, 27.857, 97439.0, 309.0, 27.866577], [27.8, 27.92, 27.591, 27.64, 48996.0, 324.0, 27.705456], [27.58, 27.63, 27.501, 27.62, 24450.0, 486.0, 27.59177], [27.405, 27.51, 27.33, 27.347, 42734.0, 324.0, 27.37323], [27.31, 27.39, 27.15, 27.34, 22040.0, 272.0, 27.283226], [27.34, 27.34, 27.0675, 27.0718, 23799.0, 183.0, 27.148131], [27.02, 27.1405, 26.8, 27.09, 21902.0, 208.0, 27.035473], [27.43, 27.43, 26.8, 26.81, 19168.0, 257.0, 26.922124], [26.67, 26.67, 26.3201, 26.33, 21112.0, 247.0, 26.43481], [26.4, 26.64, 25.92, 25.9645, 26105.0, 266.0, 26.077453], [25.96, 26.345, 25.96, 26.29, 17559.0, 177.0, 26.191456], [26.36, 26.57, 26.27, 26.5078, 14516.0, 155.0, 26.498167], [26.67, 26.91, 26.6201, 26.8859, 29805.0, 276.0, 26.785456], [26.73, 26.89, 26.73, 26.8365, 6173.0, 157.0, 26.821257], [26.95, 26.95, 26.81, 26.885, 17214.0, 141.0, 26.849861], [26.87, 27.368, 26.87, 27.135, 16213.0, 219.0, 27.146994], [27.36, 27.54, 27.3399, 27.4395, 17988.0, 286.0, 27.428805], [27.3, 27.35, 27.16, 27.32, 16152.0, 158.0, 27.25174], [27.15, 27.38, 27.1456, 27.249, 13017.0, 127.0, 27.244252], [26.9494, 27.1, 26.9494, 27.03, 14487.0, 109.0, 27.063106], [27.03, 27.1199, 26.97, 27.0779, 11151.0, 141.0, 27.045172], [27.25, 27.34, 27.075, 27.15, 21260.0, 149.0, 27.182529], [27.12, 27.13, 27.0195, 27.095, 3467.0, 87.0, 27.052714], [27.19, 27.27, 27.0604, 27.18, 8695.0, 102.0, 27.184128], [27.0, 27.02, 26.88, 26.98, 6555.0, 126.0, 26.998147], [26.98, 27.21, 26.98, 27.21, 17338.0, 166.0, 27.122127], [27.31, 27.37, 27.25, 27.37, 10153.0, 121.0, 27.32023], [27.18, 27.26, 27.13, 27.18, 5309.0, 83.0, 27.170237], [26.88, 27.0, 26.85, 27.0, 14587.0, 99.0, 26.980127], [26.82, 26.91, 26.53, 26.71, 9336.0, 129.0, 26.704247], [26.35, 26.82, 26.35, 26.5, 18815.0, 163.0, 26.659154], [26.37, 26.37, 26.1105, 26.28, 34781.0, 165.0, 26.242402], [26.24, 26.3385, 26.21, 26.29, 21379.0, 178.0, 26.254045], [26.05, 26.2086, 26.05, 26.15, 7410.0, 110.0, 26.116742], [26.14, 26.35, 26.05, 26.32, 9973.0, 126.0, 26.265261], [26.11, 26.59, 26.11, 26.59, 8759.0, 110.0, 26.372214], [26.81, 26.81, 26.29, 26.41, 20044.0, 106.0, 26.421743], [26.21, 26.51, 26.2, 26.51, 11854.0, 112.0, 26.398154], [26.43, 26.4348, 26.295, 26.39, 11139.0, 99.0, 26.41823], [26.38, 26.6893, 26.38, 26.633, 38098.0, 224.0, 26.626359], [26.51, 26.77, 26.51, 26.517, 6930.0, 127.0, 26.60829], [26.5, 26.5, 26.11, 26.25, 11666.0, 130.0, 26.261452], [26.2, 26.305, 26.14, 26.165, 15343.0, 154.0, 26.230237], [25.98, 26.205, 25.84, 26.2, 13438.0, 149.0, 26.043869], [26.09, 26.12, 25.66, 25.675, 31787.0, 243.0, 25.848458], [25.66, 25.895, 25.66, 25.73, 5899.0, 88.0, 25.784665], [25.73, 25.73, 25.215, 25.2685, 24397.0, 217.0, 25.337584], [25.01, 25.33, 24.81, 24.8489, 27315.0, 290.0, 25.002673], [24.76, 24.9901, 24.695, 24.855, 22559.0, 135.0, 24.863901], [25.13, 25.16, 24.6, 24.6475, 20970.0, 240.0, 24.75629], [24.81, 25.173, 24.81, 25.04, 15157.0, 188.0, 25.042084], [25.12, 25.12, 24.795, 24.9066, 21651.0, 178.0, 24.91785], [24.89, 25.045, 24.725, 24.8268, 9186.0, 107.0, 24.918358], [24.7, 24.82, 24.51, 24.6485, 24064.0, 150.0, 24.672379], [24.83, 24.99, 24.8101, 24.92, 5408.0, 108.0, 24.912984], [24.66, 24.66, 24.21, 24.28, 26479.0, 243.0, 24.323812], [23.87, 24.11, 23.83, 24.0621, 13560.0, 182.0, 24.000044], [24.42, 24.57, 24.39, 24.429, 20276.0, 138.0, 24.480203], [24.52, 24.8, 24.52, 24.7324, 122219.0, 223.0, 24.716646], [24.74, 24.84, 24.61, 24.6442, 19150.0, 179.0, 24.696941], [24.68, 24.82, 24.6, 24.69, 10387.0, 130.0, 24.70474], [24.61, 24.719, 24.53, 24.53, 8032.0, 99.0, 24.658518], [24.5, 24.83, 24.5, 24.8101, 9802.0, 117.0, 24.617787], [24.85, 25.1799, 24.85, 25.1136, 14320.0, 175.0, 25.066537], [25.18, 25.3786, 25.1501, 25.2704, 12225.0, 158.0, 25.255023], [25.55, 25.989, 25.51, 25.84, 38835.0, 277.0, 25.783531], [25.7, 25.9221, 25.62, 25.8711, 8681.0, 148.0, 25.811654], [26.26, 26.79, 26.26, 26.77, 41488.0, 341.0, 26.505959], [27.18, 27.58, 27.18, 27.57, 47799.0, 413.0, 27.462608], [27.6, 27.7, 27.31, 27.64, 39001.0, 302.0, 27.550871], [27.78, 28.19, 27.75, 28.19, 41904.0, 367.0, 28.019189], [27.49, 27.69, 27.29, 27.6846, 132483.0, 395.0, 27.611789], [27.05, 27.2, 26.62, 27.06, 25767.0, 232.0, 27.023677], [27.05, 27.58, 27.05, 27.43, 36867.0, 200.0, 27.423537], [26.81, 27.04, 26.66, 26.9072, 38482.0, 210.0, 26.868497], [26.5, 26.5, 26.191, 26.3, 15907.0, 187.0, 26.342504], [26.25, 26.4465, 26.25, 26.27, 35528.0, 129.0, 26.34328], [26.681, 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17518.0, 140.0, 28.408769], [28.28, 28.3, 27.98, 28.03, 13796.0, 148.0, 28.047119], [27.93, 27.93, 27.551, 27.59, 113371.0, 178.0, 27.653395], [27.46, 27.64, 27.46, 27.59, 15650.0, 160.0, 27.565209], [27.788, 27.788, 27.51, 27.66, 12788.0, 133.0, 27.685945], [27.4, 27.47, 27.2, 27.23, 52166.0, 164.0, 27.235195], [27.25, 27.35, 27.2, 27.26, 9683.0, 118.0, 27.276816], [27.24, 27.41, 27.1, 27.37, 13413.0, 148.0, 27.261721], [27.45, 27.5, 27.2438, 27.44, 7314.0, 108.0, 27.38226], [27.54, 27.77, 27.51, 27.51, 14280.0, 151.0, 27.593003], [27.39, 27.53, 27.37, 27.53, 14475.0, 149.0, 27.464804], [27.42, 27.54, 27.06, 27.07, 81458.0, 191.0, 27.482804], [27.03, 27.22, 26.82, 27.1824, 12691.0, 166.0, 27.098916], [27.11, 27.136, 26.91, 27.1089, 21059.0, 154.0, 27.003322], [27.05, 27.12, 26.98, 27.07, 18474.0, 105.0, 27.032787], [27.06, 27.19, 26.8876, 27.19, 25085.0, 215.0, 27.039601], [27.19, 27.19, 26.89, 26.9147, 9920.0, 131.0, 27.0525], [26.99, 26.99, 26.8001, 26.82, 2802.0, 85.0, 26.86992], [26.845, 26.99, 26.845, 26.965, 6743.0, 123.0, 26.907564], [26.87, 26.88, 26.594, 26.64, 17781.0, 157.0, 26.689865], [26.78, 27.46, 26.7, 27.3, 38232.0, 206.0, 27.253841], [27.44, 27.44, 26.98, 27.1404, 6441.0, 123.0, 27.159545], [27.13, 27.27, 27.11, 27.27, 12371.0, 115.0, 27.18535], [27.29, 27.48, 27.29, 27.4695, 16170.0, 148.0, 27.445898], [27.61, 27.69, 27.33, 27.68, 18766.0, 151.0, 27.590445], [27.6, 27.76, 27.4716, 27.73, 14569.0, 131.0, 27.640786], [27.5918, 27.68, 27.46, 27.46, 22384.0, 294.0, 27.565305], [27.31, 27.51, 27.25, 27.2606, 34612.0, 182.0, 27.312437], [27.38, 27.42, 27.25, 27.395, 22784.0, 105.0, 27.35552], [27.43, 27.44, 27.12, 27.295, 12923.0, 127.0, 27.244611], [27.491, 27.491, 27.15, 27.19, 8073.0, 113.0, 27.246022], [27.4601, 27.66, 27.4601, 27.5559, 7311.0, 120.0, 27.557472], [27.56, 27.5916, 27.34, 27.38, 6927.0, 100.0, 27.409936], [27.305, 27.55, 27.305, 27.4359, 2707.0, 73.0, 27.444502], [27.46, 27.5599, 27.46, 27.4946, 5376.0, 109.0, 27.50322], [27.7, 27.84, 27.66, 27.78, 33659.0, 203.0, 27.79961], [28.05, 28.2899, 27.986, 28.2673, 31929.0, 246.0, 28.142594], [28.49, 28.53, 28.35, 28.46, 27139.0, 235.0, 28.461883], [28.4, 28.658, 28.21, 28.37, 37696.0, 236.0, 28.432259], [28.6, 28.635, 28.32, 28.3668, 11302.0, 140.0, 28.5229], [28.33, 28.48, 27.85, 28.07, 42987.0, 207.0, 28.131635], [28.11, 28.11, 27.923, 28.07, 21616.0, 132.0, 28.025088], [28.15, 28.82, 28.15, 28.82, 30968.0, 191.0, 28.542493], [28.59, 28.91, 28.52, 28.91, 18880.0, 207.0, 28.716359], [28.81, 28.885, 28.75, 28.8525, 18055.0, 129.0, 28.821929], [28.59, 28.6385, 28.24, 28.3424, 18575.0, 136.0, 28.417152], [28.18, 28.192, 28.02, 28.12, 50801.0, 214.0, 28.107201], [28.34, 28.575, 28.1533, 28.5683, 20131.0, 249.0, 28.318356], [28.81, 28.949, 28.5519, 28.758, 16675.0, 108.0, 28.726533], [28.63, 28.6599, 28.42, 28.5, 21031.0, 118.0, 28.535022], [28.43, 28.43, 27.934, 27.934, 7772.0, 130.0, 28.049851], [28.04, 28.04, 27.83, 28.0, 13902.0, 153.0, 27.927182], [28.01, 28.34, 28.01, 28.32, 11829.0, 155.0, 28.234626], [28.28, 28.32, 28.14, 28.3, 6974.0, 122.0, 28.246259], [28.19, 28.275, 28.19, 28.25, 6429.0, 71.0, 28.234093], [28.0, 28.13, 27.86, 27.98, 16338.0, 175.0, 27.983157], [27.84, 27.99, 27.54, 27.65, 35309.0, 172.0, 27.759419], [27.71, 27.7251, 27.361, 27.63, 22899.0, 214.0, 27.600731], [27.56, 27.56, 27.36, 27.44, 19168.0, 107.0, 27.403912], [27.46, 27.75, 27.44, 27.72, 4397.0, 94.0, 27.604428], [27.82, 27.9805, 27.43, 27.63, 46531.0, 199.0, 27.639787], [27.73, 28.16, 27.73, 28.16, 21354.0, 155.0, 28.003997], [27.99, 28.06, 27.7901, 27.92, 40572.0, 277.0, 27.900438], [27.72, 27.72, 27.53, 27.71, 34104.0, 228.0, 27.598522], [27.71, 27.85, 27.66, 27.84, 11814.0, 120.0, 27.726335], [27.49, 27.9, 27.49, 27.72, 5803.0, 90.0, 27.674358], [27.77, 27.93, 27.601, 27.93, 15350.0, 115.0, 27.766419], [27.63, 27.9899, 27.48, 27.53, 12375.0, 161.0, 27.570023], [27.55, 27.55, 27.34, 27.34, 8917.0, 111.0, 27.465929], [27.39, 27.4, 27.08, 27.15, 12049.0, 155.0, 27.200342], [27.19, 27.28, 27.15, 27.15, 5758.0, 89.0, 27.215049], [27.33, 27.48, 27.12, 27.48, 18529.0, 136.0, 27.369753], [27.3158, 27.449, 27.3, 27.4194, 6512.0, 75.0, 27.371303], [27.48, 27.57, 27.24, 27.28, 14576.0, 122.0, 27.430909], [27.22, 27.22, 26.95, 27.0107, 21311.0, 161.0, 27.054995], [27.02, 27.02, 26.37, 26.55, 17510.0, 213.0, 26.538599], [26.46, 26.67, 26.4412, 26.64, 18937.0, 115.0, 26.556231], [26.39, 26.525, 26.24, 26.47, 29859.0, 186.0, 26.424006], [26.13, 26.4, 26.13, 26.18, 14977.0, 157.0, 26.287872], [25.97, 26.05, 25.805, 26.05, 21102.0, 182.0, 25.980883], [25.9, 26.11, 25.85, 26.11, 9995.0, 134.0, 25.997236], [25.85, 26.06, 25.85, 25.93, 13779.0, 181.0, 25.919018], [25.85, 26.04, 25.85, 26.0, 13531.0, 140.0, 25.986778], [26.27, 26.27, 25.38, 25.85, 37983.0, 363.0, 25.660985], [25.9, 25.9899, 25.66, 25.83, 74434.0, 420.0, 25.830984], [25.5, 25.66, 25.34, 25.39, 22861.0, 232.0, 25.499201], [25.4, 25.55, 25.3467, 25.54, 9699.0, 97.0, 25.479938], [25.75, 25.82, 25.46, 25.55, 14092.0, 148.0, 25.58037], [25.15, 25.73, 25.15, 25.71, 21288.0, 211.0, 25.561863], [25.5, 25.99, 25.5, 25.99, 14807.0, 148.0, 25.804751], [26.06, 26.07, 25.8627, 25.97, 10802.0, 129.0, 25.949113], [26.13, 26.13, 25.522, 25.62, 28112.0, 170.0, 25.707933], [25.39, 25.5099, 25.15, 25.37, 13918.0, 186.0, 25.395536], [25.22, 25.38, 25.0701, 25.24, 13617.0, 233.0, 25.179817], [25.4, 25.72, 25.24, 25.69, 22229.0, 165.0, 25.580223], [25.57, 25.73, 25.47, 25.73, 29989.0, 196.0, 25.611635], [25.57, 25.57, 25.31, 25.39, 17223.0, 157.0, 25.434973], [25.29, 25.3016, 25.0, 25.06, 33273.0, 297.0, 25.146635], [25.05, 25.19, 24.82, 25.16, 39717.0, 247.0, 25.070944], [25.25, 25.319, 25.1705, 25.28, 11075.0, 111.0, 25.256885], [25.11, 25.12, 24.88, 25.01, 15232.0, 149.0, 25.023772], [25.08, 25.2886, 24.84, 25.17, 21674.0, 210.0, 25.097655], [25.1, 25.11, 24.85, 24.87, 14619.0, 202.0, 24.923761], [25.03, 25.1167, 25.0, 25.06, 15897.0, 164.0, 25.059965], [25.07, 25.07, 24.86, 24.96, 24914.0, 157.0, 24.941771], [24.98, 24.98, 24.53, 24.61, 29255.0, 233.0, 24.686168], [24.54, 24.65, 24.36, 24.47, 31643.0, 228.0, 24.486633], [24.569, 24.65, 24.52, 24.65, 16522.0, 113.0, 24.56504], [24.77, 24.899, 24.645, 24.7842, 17988.0, 212.0, 24.808517], [24.58, 24.58, 24.3601, 24.41, 16501.0, 199.0, 24.460454], [24.43, 24.47, 24.13, 24.22, 38349.0, 265.0, 24.261405], [24.14, 24.1401, 23.94, 24.05, 28601.0, 298.0, 24.031474], [23.95, 24.1, 23.8478, 24.035, 43328.0, 207.0, 23.988511]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Detrending\n",
        "\n",
        "## Check for the existence of a trend"
      ],
      "metadata": {
        "id": "HbzVa4PTcMno"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import numpy as np\n",
        "# import statsmodels.api as sm\n",
        "\n",
        "# # Assuming bars_df is your DataFrame containing the data\n",
        "# # Extract 'close' column\n",
        "# X = bars_df['close']\n",
        "\n",
        "# # Extract index as the dependent variable\n",
        "# Y = bars_df.index\n",
        "\n",
        "# # Add a constant to the independent variable (X) to fit an intercept\n",
        "# X = sm.add_constant(X)\n",
        "\n",
        "# # Fit the linear regression model\n",
        "# model = sm.OLS(Y, X).fit()\n",
        "\n",
        "# # Print the summary of the regression results\n",
        "# print(model.summary())\n",
        "\n",
        "# # Extract the p-value of the coefficient for the independent variable\n",
        "# p_value = model.pvalues[1]  # Index 1 corresponds to the independent variable coefficient\n",
        "# print(\"P-value:\", p_value)"
      ],
      "metadata": {
        "id": "N1DD6GarfMbc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# #detrending the data for open and close prices\n",
        "# dtrend_open, dtrend_close = [], []\n",
        "\n",
        "# for i in range(len(stock_data) - 1):\n",
        "#     dtrend_open.append(float(stock_data[i][0]) - float(stock_data[i + 1][0]))\n",
        "#     dtrend_close.append(float(stock_data[i][3]) - float(stock_data[i + 1][3]))"
      ],
      "metadata": {
        "id": "BHVzUFBzcNuX"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print(dtrend_open)"
      ],
      "metadata": {
        "id": "xvanEAwOpgXY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "FFT"
      ],
      "metadata": {
        "id": "NamZ3tOIcXqp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dtrend_close = bars_df['close'].values.tolist()\n",
        "print(dtrend_close)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mD4FrP1MppSq",
        "outputId": "1803b65a-1dd0-451a-819d-32b5d4a999da"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[28.03, 27.5, 27.7, 27.96, 28.18, 28.32, 28.11, 28.1, 28.0247, 27.857, 27.64, 27.62, 27.347, 27.34, 27.0718, 27.09, 26.81, 26.33, 25.9645, 26.29, 26.5078, 26.8859, 26.8365, 26.885, 27.135, 27.4395, 27.32, 27.249, 27.03, 27.0779, 27.15, 27.095, 27.18, 26.98, 27.21, 27.37, 27.18, 27.0, 26.71, 26.5, 26.28, 26.29, 26.15, 26.32, 26.59, 26.41, 26.51, 26.39, 26.633, 26.517, 26.25, 26.165, 26.2, 25.675, 25.73, 25.2685, 24.8489, 24.855, 24.6475, 25.04, 24.9066, 24.8268, 24.6485, 24.92, 24.28, 24.0621, 24.429, 24.7324, 24.6442, 24.69, 24.53, 24.8101, 25.1136, 25.2704, 25.84, 25.8711, 26.77, 27.57, 27.64, 28.19, 27.6846, 27.06, 27.43, 26.9072, 26.3, 26.27, 27.3227, 27.9241, 27.754, 27.68, 27.19, 27.59, 27.94, 27.2945, 28.1169, 28.25, 28.3, 28.6, 28.89, 28.86, 28.79, 29.31, 29.19, 29.22, 28.8, 28.51, 27.65, 27.86, 27.4, 27.53, 27.88, 27.2, 27.272, 27.4033, 27.55, 27.34, 27.57, 27.24, 27.62, 27.89, 28.31, 28.42, 28.0, 28.4, 28.61, 28.57, 29.14, 28.94, 28.82, 28.41, 28.52, 28.49, 28.57, 28.36, 28.48, 28.6, 28.16, 28.25, 28.49, 28.03, 27.59, 27.59, 27.66, 27.23, 27.26, 27.37, 27.44, 27.51, 27.53, 27.07, 27.1824, 27.1089, 27.07, 27.19, 26.9147, 26.82, 26.965, 26.64, 27.3, 27.1404, 27.27, 27.4695, 27.68, 27.73, 27.46, 27.2606, 27.395, 27.295, 27.19, 27.5559, 27.38, 27.4359, 27.4946, 27.78, 28.2673, 28.46, 28.37, 28.3668, 28.07, 28.07, 28.82, 28.91, 28.8525, 28.3424, 28.12, 28.5683, 28.758, 28.5, 27.934, 28.0, 28.32, 28.3, 28.25, 27.98, 27.65, 27.63, 27.44, 27.72, 27.63, 28.16, 27.92, 27.71, 27.84, 27.72, 27.93, 27.53, 27.34, 27.15, 27.15, 27.48, 27.4194, 27.28, 27.0107, 26.55, 26.64, 26.47, 26.18, 26.05, 26.11, 25.93, 26.0, 25.85, 25.83, 25.39, 25.54, 25.55, 25.71, 25.99, 25.97, 25.62, 25.37, 25.24, 25.69, 25.73, 25.39, 25.06, 25.16, 25.28, 25.01, 25.17, 24.87, 25.06, 24.96, 24.61, 24.47, 24.65, 24.7842, 24.41, 24.22, 24.05, 24.035]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "freq = ft.fftfreq(len(dtrend_close))\n",
        "spec = np.abs(ft.fft(dtrend_close))\n",
        "\n",
        "# Find indices of dominant frequencies\n",
        "num_dom_freq = 5  # Adjust as needed\n",
        "dominant_indices = np.argsort(spec)[::-1][:num_dom_freq]"
      ],
      "metadata": {
        "id": "lr-Cmv-hcOgM"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract dominant frequencies\n",
        "dominant_freq = freq[dominant_indices]\n",
        "\n",
        "print(\"Dominant frequencies:\", dominant_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYd94vY1cRgF",
        "outputId": "6e97ff29-cd86-46d7-fc9f-00e220586b55"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dominant frequencies: [ 0.          0.01195219 -0.01195219  0.00398406 -0.00398406]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Forecasting up to 45 periods in advance\n",
        "forecast_periods = 25\n",
        "\n",
        "# Extend the spectrum for forecasting\n",
        "extended_spec = np.concatenate((spec, np.zeros(forecast_periods)))\n",
        "\n",
        "# Inverse FFT to get the forecasted data\n",
        "forecast = np.real(ft.ifft(extended_spec))\n",
        "\n",
        "# Plotting the original and forecasted data\n",
        "plt.plot(dtrend_close, label='Original Data')\n",
        "plt.plot(range(len(dtrend_close), len(dtrend_close) + forecast_periods), forecast[-forecast_periods:], label='Forecasted Data')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "Wn32X_0ocTXY",
        "outputId": "ba8703e6-7b7e-41b2-b4c1-8198d01491a6"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4Eyf0xIVqBCE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}