Crash_course_MansiThorat.ipynb

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        "**Title: \"A Guide to Causality and Its Application in Finance\"**\n",
        "\n",
        "**Causality**  \n",
        "Causality refers to the relationship between two events or phenomena where one (the cause) leads to the production or occurrence of another (the effect). In this relationship, the cause is considered partly responsible for the effect, while the effect depends on the cause to some extent. A process typically has multiple causes, also called causal factors, all of which are found in its past. In turn, an effect may become a cause for other effects, which will occur in its future.\n",
        "\n",
        "**Causal Inference**  \n",
        "Causal inference is an academic discipline that focuses on how to make causal conclusions from data. It involves understanding the assumptions, study designs, and estimation techniques needed to draw these conclusions accurately. Recently, causal inference has advanced significantly, especially with the introduction of Judea Pearl's Causal Diagrams (also known as Directed Acyclic Graphs, or DAGs) and Rubin's Causal Model. These frameworks provide a systematic way to analyze and model causal relationships, enabling researchers to better understand how variables interact and influence one another.\n",
        "\n",
        "**Causal Inference in Finance**  \n",
        "In finance, causal inference methods are applied to understand how various factors—such as economic policies, events, or market trends—affect financial outcomes like asset prices, market volatility, investment returns, and economic indicators. By identifying cause-and-effect relationships, financial professionals and researchers can make more informed decisions, devise effective strategies, and create well-grounded economic policies. Causal inference is especially helpful in answering key questions about how different variables in the financial ecosystem are interconnected.\n",
        "\n",
        "**The Importance of Causal Inference in Finance**  \n",
        "Causal inference plays a crucial role in finance for several reasons, particularly in helping stakeholders understand the cause-and-effect relationships between financial variables. This understanding is essential for effective decision-making, risk management, policy formulation, and developing investment strategies. Here are some key reasons why causal inference is indispensable in the financial world:\n",
        "\n",
        "- **Investment Decisions:** Investors rely on causal inference to predict the effects of factors like interest rate changes, inflation, and geopolitical events on asset prices and returns. This understanding enables them to make more informed and strategic investment decisions.\n",
        "  \n",
        "- **Policy Analysis:** Policymakers use causal inference to evaluate the impact of changes in fiscal or monetary policies on the economy, financial markets, and specific sectors. It allows them to assess the success of these policies and make necessary adjustments.\n",
        "  \n",
        "- **Risk Management:** Financial institutions use causal analysis to identify the root causes of market volatility and credit risk. By understanding what triggers market downturns or financial distress, they can improve their risk management and mitigation strategies.\n",
        "  \n",
        "- **Performance Attribution:** Asset managers use causal inference to decompose the sources of investment performance, helping them distinguish between returns driven by market trends, economic events, or their own skill.\n",
        "  \n",
        "- **Market Regulation:** Regulators apply causal inference to understand how regulatory changes impact financial markets and institutions. This knowledge is vital for ensuring that regulations achieve their intended goals and don’t lead to unintended consequences.\n",
        "  \n",
        "- **Product Development and Pricing:** Financial institutions utilize causal analysis to gauge demand for financial products and understand the factors that influence pricing. This helps them develop competitive financial products and set optimal prices.\n",
        "  \n",
        "- **Economic Forecasting:** Causal inference plays a significant role in forecasting the effects of economic shocks, policy changes, or innovations on growth, inflation, and unemployment rates. It helps anticipate future economic trends and outcomes.\n",
        "  \n",
        "- **Behavioral Finance:** Understanding the causal drivers of investor behavior, including the influence of psychological biases, can improve financial advice and the design of products that better align with investor needs.\n",
        "\n",
        "In summary, causal inference is a foundational tool in finance that enables professionals to make data-driven decisions, manage risks, create sound policies, and develop effective investment strategies."
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      "source": [
        "__Importing all the required Libraries__"
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        "import numpy as np\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "import matplotlib.pyplot as plt\n",
        "from statsmodels.tsa.api import VAR\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "from scipy.stats import pearsonr\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from statsmodels.tsa.stattools import grangercausalitytests\n",
        "import networkx as nx"
      ]
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      "id": "8fd29d83",
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        "nse_data = pd.read_csv(\"/content/NSE (1).csv\")\n",
        "ads_index = pd.read_excel('/content/ads_index (2).xlsx')"
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              "         Date         Open         High          Low        Close  \\\n",
              "0  25/03/2019  11395.65039  11395.65039  11311.59961  11354.25000   \n",
              "1  26/03/2019  11375.20020  11496.75000  11352.45020  11483.25000   \n",
              "2  27/03/2019  11531.45020  11546.20020  11413.00000  11445.04981   \n",
              "3  28/03/2019  11463.65039  11588.50000  11452.45020  11570.00000   \n",
              "4  01/04/2019  11665.20020  11738.09961  11644.75000  11669.15039   \n",
              "\n",
              "     Adj_Close    Volume  \n",
              "0  11354.25000  294500.0  \n",
              "1  11483.25000  282600.0  \n",
              "2  11445.04981  350400.0  \n",
              "3  11570.00000  527700.0  \n",
              "4  11669.15039  379600.0  "
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              "        Date  ADS_Index\n",
              "0 2019-03-25  -0.365959\n",
              "1 2019-03-26  -0.378829\n",
              "2 2019-03-27  -0.393147\n",
              "3 2019-03-28  -0.408918\n",
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              "        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>Date</th>\n",
              "      <th>ADS_Index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-03-25</td>\n",
              "      <td>-0.365959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-03-26</td>\n",
              "      <td>-0.378829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-03-27</td>\n",
              "      <td>-0.393147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-03-28</td>\n",
              "      <td>-0.408918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-03-29</td>\n",
              "      <td>-0.426147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-36e87ceb-d4fe-40da-b74d-800aad3a17a0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-36e87ceb-d4fe-40da-b74d-800aad3a17a0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --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",
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              "    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",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "      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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      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-02fdcadd-f5ce-4449-9863-5844510d8cf9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ads_index",
              "summary": "{\n  \"name\": \"ads_index\",\n  \"rows\": 1812,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-03-25 00:00:00\",\n        \"max\": \"2024-03-09 00:00:00\",\n        \"num_unique_values\": 1812,\n        \"samples\": [\n          \"2023-08-16 00:00:00\",\n          \"2021-03-01 00:00:00\",\n          \"2024-02-18 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ADS_Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.05620980060231,\n        \"min\": -26.5195,\n        \"max\": 9.45389,\n        \"num_unique_values\": 1811,\n        \"samples\": [\n          -0.809778,\n          -0.164917,\n          -0.16752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "ads_index.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2877e62c",
      "metadata": {
        "id": "2877e62c"
      },
      "source": [
        "__Converting dataset into date time frame__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "b65f0ea8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b65f0ea8",
        "outputId": "cbf400de-a15e-434e-8f9c-e4ad403bb562"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-4911edfa00f5>:1: UserWarning: Parsing dates in %d/%m/%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
            "  nse_data['Date'] = pd.to_datetime(nse_data['Date'])\n"
          ]
        }
      ],
      "source": [
        "nse_data['Date'] = pd.to_datetime(nse_data['Date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "78312d39",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "78312d39",
        "outputId": "8ed31b74-a54a-416a-a104-73d7e348c92f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date  ADS_Index\n",
              "0 2019-03-25  -0.365959\n",
              "1 2019-03-26  -0.378829\n",
              "2 2019-03-27  -0.393147\n",
              "3 2019-03-28  -0.408918\n",
              "4 2019-03-29  -0.426147"
            ],
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              "      background-color: #434B5C;\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "    fill: var(--disabled-fill-color);\n",
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              "  }\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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "      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-ee4ccd61-3272-4807-89e0-1c91fc6e332c button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"ads_index\",\n  \"rows\": 1812,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-03-25 00:00:00\",\n        \"max\": \"2024-03-09 00:00:00\",\n        \"num_unique_values\": 1812,\n        \"samples\": [\n          \"2023-08-16 00:00:00\",\n          \"2021-03-01 00:00:00\",\n          \"2024-02-18 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ADS_Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.05620980060231,\n        \"min\": -26.5195,\n        \"max\": 9.45389,\n        \"num_unique_values\": 1811,\n        \"samples\": [\n          -0.809778,\n          -0.164917,\n          -0.16752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "ads_index.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "0268aae2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0268aae2",
        "outputId": "91e49da4-317e-40f6-f696-407ad599f486"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date         Open         High          Low        Close    Adj_Close  \\\n",
              "0 2019-03-25  11395.65039  11395.65039  11311.59961  11354.25000  11354.25000   \n",
              "1 2019-03-26  11375.20020  11496.75000  11352.45020  11483.25000  11483.25000   \n",
              "2 2019-03-27  11531.45020  11546.20020  11413.00000  11445.04981  11445.04981   \n",
              "3 2019-03-28  11463.65039  11588.50000  11452.45020  11570.00000  11570.00000   \n",
              "4 2019-04-01  11665.20020  11738.09961  11644.75000  11669.15039  11669.15039   \n",
              "\n",
              "     Volume  \n",
              "0  294500.0  \n",
              "1  282600.0  \n",
              "2  350400.0  \n",
              "3  527700.0  \n",
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              "      <th>0</th>\n",
              "      <td>2019-03-25</td>\n",
              "      <td>11395.65039</td>\n",
              "      <td>11395.65039</td>\n",
              "      <td>11311.59961</td>\n",
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              "      <th>1</th>\n",
              "      <td>2019-03-26</td>\n",
              "      <td>11375.20020</td>\n",
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              "      <td>11352.45020</td>\n",
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              "      <td>11483.25000</td>\n",
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              "      <td>2019-03-27</td>\n",
              "      <td>11531.45020</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-03-28</td>\n",
              "      <td>11463.65039</td>\n",
              "      <td>11588.50000</td>\n",
              "      <td>11452.45020</td>\n",
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              "      <td>11570.00000</td>\n",
              "      <td>527700.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-04-01</td>\n",
              "      <td>11665.20020</td>\n",
              "      <td>11738.09961</td>\n",
              "      <td>11644.75000</td>\n",
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              "        document.querySelector('#df-73c266f5-cc54-4dd8-b0f1-1427b7307266 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-73c266f5-cc54-4dd8-b0f1-1427b7307266');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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-103d26ca-3616-47fd-b6cd-d6ca2d94ed67 button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"nse_data\",\n  \"rows\": 1236,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-03-25 00:00:00\",\n        \"max\": \"2024-03-22 00:00:00\",\n        \"num_unique_values\": 1236,\n        \"samples\": [\n          \"2020-06-08 00:00:00\",\n          \"2021-10-12 00:00:00\",\n          \"2024-02-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3482.1295634166427,\n        \"min\": 7735.149902,\n        \"max\": 22517.5,\n        \"num_unique_values\": 1229,\n        \"samples\": [\n          15840.5,\n          19545.19922,\n          8451.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3481.6443888009144,\n        \"min\": 8036.950195,\n        \"max\": 22526.59961,\n        \"num_unique_values\": 1230,\n        \"samples\": [\n          15895.75,\n          17617.80078,\n          9038.900391\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3482.4636579480375,\n        \"min\": 7511.100098,\n        \"max\": 22430.0,\n        \"num_unique_values\": 1232,\n        \"samples\": [\n          15505.65039,\n          18861.34961,\n          8304.900391\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3484.4237950350393,\n        \"min\": 7610.25,\n        \"max\": 22493.55078,\n        \"num_unique_values\": 1227,\n        \"samples\": [\n          19189.05078,\n          10167.4502,\n          19753.80078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj_Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3484.4237950350393,\n        \"min\": 7610.25,\n        \"max\": 22493.55078,\n        \"num_unique_values\": 1227,\n        \"samples\": [\n          19189.05078,\n          10167.4502,\n          19753.80078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 218102.35761511003,\n        \"min\": 0.0,\n        \"max\": 1811000.0,\n        \"num_unique_values\": 1091,\n        \"samples\": [\n          169000.0,\n          233200.0,\n          426700.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "nse_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "e9a1c0fd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "e9a1c0fd",
        "outputId": "7d1ded0d-35ca-4adb-f631-06891142479b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date         0\n",
              "Open         2\n",
              "High         2\n",
              "Low          2\n",
              "Close        2\n",
              "Adj_Close    2\n",
              "Volume       2\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Open</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Close</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adj_Close</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Volume</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "nse_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "e8fe9bb9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e8fe9bb9",
        "outputId": "902a12fc-215b-4ddc-8260-c00b98924c45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date         0\n",
            "Open         0\n",
            "High         0\n",
            "Low          0\n",
            "Close        0\n",
            "Adj_Close    0\n",
            "Volume       0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "mean_values = nse_data[['Open', 'High', 'Low', 'Close', 'Adj_Close', 'Volume']].mean()\n",
        "\n",
        "# Fill missing values in these columns with their respective means\n",
        "nse_data.fillna(mean_values, inplace=True)\n",
        "\n",
        "# Check to ensure no more missing values exist\n",
        "print(nse_data.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "1a1c1367",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "1a1c1367",
        "outputId": "5830ed55-4fe6-4d3a-90b6-32db86bbc693"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date         0\n",
              "ADS_Index    0\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ADS_Index</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "ads_index.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "f4215812",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "f4215812",
        "outputId": "49dea925-3e1c-472a-cbdc-1f8991d534f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "nse_data = nse_data[['Date', 'Close']]\n",
        "ads_index = ads_index[['Date', 'ADS_Index']]\n",
        "\n",
        "# Merge datasets on the 'Date' column\n",
        "data_merged = pd.merge(nse_data, ads_index, on='Date', how='inner')\n",
        "plt.figure(figsize=(14,7))\n",
        "plt.plot(data_merged['Date'], data_merged['Close'], label='NSE Close', color='blue')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('NSE Close Price')\n",
        "plt.twinx()\n",
        "plt.plot(data_merged['Date'], data_merged['ADS_Index'], label='ADS Index', color='red')\n",
        "plt.ylabel('ADS Index')\n",
        "plt.title('NSE Close vs ADS Index Over Time')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27d32dff",
      "metadata": {
        "id": "27d32dff"
      },
      "source": [
        "**Calculating the Pearson Correlation Coefficient**\n",
        "\n",
        "The Pearson correlation coefficient, denoted as **r**, is a statistical measure used to assess the linear relationship between two variables. It quantifies the strength and direction of the relationship. The value of the Pearson correlation coefficient ranges from -1 to 1, where:\n",
        "\n",
        "- **1** indicates a perfect positive linear relationship, meaning that as one variable increases, the other variable also increases in a perfectly consistent manner.\n",
        "- **-1** indicates a perfect negative linear relationship, meaning that as one variable increases, the other variable decreases in a perfectly consistent manner.\n",
        "- **0** indicates no linear relationship between the variables, meaning that changes in one variable do not systematically relate to changes in the other.\n",
        "\n",
        "A correlation coefficient close to **1** or **-1** suggests a strong linear relationship between the two variables, while a coefficient close to **0** indicates a weak or no linear relationship."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "8829395b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8829395b",
        "outputId": "feb63acd-218e-41a7-9fb1-caf6d6200b67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearson correlation coefficient: 0.14\n"
          ]
        }
      ],
      "source": [
        "# Calculate Pearson correlation coefficient\n",
        "correlation_coef, p_value = pearsonr(data_merged['Close'], data_merged['ADS_Index'])\n",
        "print(f'Pearson correlation coefficient: {correlation_coef:.2f}')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "b0e6d0dd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "b0e6d0dd",
        "outputId": "b26eaf61-aacc-4001-8646-fc5b02de8143"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Coefficients: [122.59526631]\n",
            "Model Intercept: 15428.763674729102\n"
          ]
        }
      ],
      "source": [
        "# Prepare the data for modeling\n",
        "X = data_merged[['ADS_Index']]\n",
        "y = data_merged['Close']\n",
        "\n",
        "# Initialize and fit the linear regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X, y)\n",
        "\n",
        "# Make predictions\n",
        "predictions = model.predict(X)\n",
        "\n",
        "# Plot the results\n",
        "plt.scatter(X, y, color='blue', label='Actual data')\n",
        "# plt.plot(X, predictions, color='red', linewidth=2, label='Fitted line')\n",
        "plt.xlabel('ADS Index')\n",
        "plt.ylabel('NSE Close Price')\n",
        "plt.title('Linear Regression: NSE Close Price vs ADS Index')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "print(f'Model Coefficients: {model.coef_}')\n",
        "print(f'Model Intercept: {model.intercept_}')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a0f0d79e",
      "metadata": {
        "id": "a0f0d79e"
      },
      "source": [
        "**Model Coefficients in Linear Regression**\n",
        "\n",
        "In the context of linear regression, the model coefficient represents the slope of the line that describes the relationship between the independent variable(s) and the dependent variable. It indicates the change in the dependent variable for a one-unit increase in the independent variable, while holding all other variables constant (in the case of multiple regression).\n",
        "\n",
        "For example, if a linear regression model is used to predict house prices based on size (in square feet), and the coefficient for size is **300**, this means that for every additional square foot of size, the price of the house is expected to increase by **$300**, assuming other factors remain unchanged.\n",
        "\n",
        "Coefficients are crucial for interpreting the results of regression analysis, as they reveal both the magnitude and direction of the relationship between each independent variable and the dependent variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "7542fa1a",
      "metadata": {
        "id": "7542fa1a"
      },
      "outputs": [],
      "source": [
        "nse_data.set_index('Date', inplace=True)\n",
        "ads_index.set_index('Date', inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4f6fcc3e",
      "metadata": {
        "id": "4f6fcc3e"
      },
      "source": [
        "**Granger Causality Test**\n",
        "\n",
        "The Granger Causality Test is used to determine whether one time series can predict another, commonly applied in economic and financial time series analysis. It examines whether past values of one variable provide useful information for predicting the future values of another variable.\n",
        "\n",
        "**Predictive Causality:** The test evaluates if the historical data of one variable can help predict the future behavior of another variable.\n",
        "\n",
        "**Lag Length:** The test requires selecting the number of lags for the influencing variable to include in the model. The choice of lag length can impact the test results, and it is typically chosen based on criteria such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC).\n",
        "\n",
        "**Statistical Test:** The test utilizes regression models, often with F-tests, to compare models that include and exclude lagged terms of the supposed causal variable.\n",
        "\n",
        "The Granger Causality Test is widely used in fields like econometrics, finance, neuroscience, and other areas involving time series data analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "b4e4dba8",
      "metadata": {
        "id": "b4e4dba8"
      },
      "outputs": [],
      "source": [
        "# This step would normally be part of merging, but since we're not merging:\n",
        "common_dates = nse_data.index.intersection(ads_index.index)\n",
        "nse_close = nse_data.loc[common_dates, 'Close']  # Assuming 'Close' column exists\n",
        "ads_index_series = ads_index.loc[common_dates, 'ADS_Index']  # Assuming 'ADS_Index' column exists\n",
        "\n",
        "# Combine into a single DataFrame for the Granger causality test\n",
        "combined_data = pd.concat([nse_close, ads_index_series], axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "45c579db",
      "metadata": {
        "id": "45c579db"
      },
      "outputs": [],
      "source": [
        "# This step would normally be part of merging, but since we're not merging:\n",
        "common_dates = nse_data.index.intersection(ads_index.index)\n",
        "nse_close = nse_data.loc[common_dates, 'Close']  # Assuming 'Close' column exists\n",
        "ads_index_series = ads_index.loc[common_dates, 'ADS_Index']  # Assuming 'ADS_Index' column exists\n",
        "\n",
        "# Combine into a single DataFrame for the Granger causality test\n",
        "combined_data = pd.concat([nse_close, ads_index_series], axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "11f679fb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11f679fb",
        "outputId": "9f012766-513d-4ce1-f332-f15bf83349e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 1\n",
            "ssr based F test:         F=0.7808  , p=0.3771  , df_denom=1222, df_num=1\n",
            "ssr based chi2 test:   chi2=0.7827  , p=0.3763  , df=1\n",
            "likelihood ratio test: chi2=0.7825  , p=0.3764  , df=1\n",
            "parameter F test:         F=0.7808  , p=0.3771  , df_denom=1222, df_num=1\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 2\n",
            "ssr based F test:         F=2.0045  , p=0.1352  , df_denom=1219, df_num=2\n",
            "ssr based chi2 test:   chi2=4.0255  , p=0.1336  , df=2\n",
            "likelihood ratio test: chi2=4.0189  , p=0.1341  , df=2\n",
            "parameter F test:         F=2.0045  , p=0.1352  , df_denom=1219, df_num=2\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 3\n",
            "ssr based F test:         F=2.9033  , p=0.0338  , df_denom=1216, df_num=3\n",
            "ssr based chi2 test:   chi2=8.7601  , p=0.0327  , df=3\n",
            "likelihood ratio test: chi2=8.7289  , p=0.0331  , df=3\n",
            "parameter F test:         F=2.9033  , p=0.0338  , df_denom=1216, df_num=3\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 4\n",
            "ssr based F test:         F=2.3903  , p=0.0491  , df_denom=1213, df_num=4\n",
            "ssr based chi2 test:   chi2=9.6321  , p=0.0471  , df=4\n",
            "likelihood ratio test: chi2=9.5943  , p=0.0478  , df=4\n",
            "parameter F test:         F=2.3903  , p=0.0491  , df_denom=1213, df_num=4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1556: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# The maxlag parameter can be adjusted based on your data's frequency and size\n",
        "granger_result = grangercausalitytests(combined_data, maxlag=4, verbose=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "17ce9547",
      "metadata": {
        "id": "17ce9547"
      },
      "source": [
        "__Interest Rate vs Nifty returns over the years__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "07ed3f97",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "07ed3f97",
        "outputId": "49a2409a-befb-48da-f315-6562a03e62b3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:           NiftyReturns   R-squared:                       0.017\n",
            "Model:                            OLS   Adj. R-squared:                  0.007\n",
            "Method:                 Least Squares   F-statistic:                     1.724\n",
            "Date:                Tue, 03 Dec 2024   Prob (F-statistic):              0.192\n",
            "Time:                        04:43:28   Log-Likelihood:                 246.53\n",
            "No. Observations:                 100   AIC:                            -489.1\n",
            "Df Residuals:                      98   BIC:                            -483.8\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "================================================================================\n",
            "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------\n",
            "const         3.322e-05      0.011      0.003      0.998      -0.021       0.021\n",
            "InterestRate    -0.2706      0.206     -1.313      0.192      -0.680       0.138\n",
            "==============================================================================\n",
            "Omnibus:                        5.184   Durbin-Watson:                   1.995\n",
            "Prob(Omnibus):                  0.075   Jarque-Bera (JB):                3.000\n",
            "Skew:                           0.210   Prob(JB):                        0.223\n",
            "Kurtosis:                       2.262   Cond. No.                         99.5\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "np.random.seed(0)\n",
        "dates = pd.date_range('20200101', periods=100)\n",
        "interest_rates = np.random.normal(0.05, 0.01, 100)\n",
        "nifty_returns = -0.5 * interest_rates + np.random.normal(0.01, 0.02, 100)\n",
        "df = pd.DataFrame({'Date': dates, 'InterestRate': interest_rates, 'NiftyReturns': nifty_returns})\n",
        "df.set_index('Date', inplace=True)\n",
        "\n",
        "# Visualizing the data\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(df.index, df['InterestRate'], label='Interest Rates')\n",
        "plt.legend()\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(df.index, df['NiftyReturns'], label='NIFTY Returns', color='orange')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Preparing data for linear regression\n",
        "X = sm.add_constant(df['InterestRate'])  # Adds a constant term to the predictor\n",
        "y = df['NiftyReturns']\n",
        "\n",
        "# Applying linear regression\n",
        "model = sm.OLS(y, X).fit()\n",
        "predictions = model.predict(X)\n",
        "\n",
        "# Summary of the model\n",
        "print(model.summary())\n",
        "\n",
        "# Plotting the regression line\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(df['InterestRate'], df['NiftyReturns'], label='Actual Data')\n",
        "plt.plot(df['InterestRate'], predictions, color='red', label='Fitted line')\n",
        "plt.xlabel('Interest Rate')\n",
        "plt.ylabel('NIFTY Returns')\n",
        "plt.title('Interest Rate vs NIFTY Returns')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d6b9c417",
      "metadata": {
        "id": "d6b9c417"
      },
      "source": [
        "**Directed Acyclic Graph (DAG)**\n",
        "\n",
        "A Directed Acyclic Graph (DAG) is a key concept in computer science and statistics, especially in the field of causal inference. It is used to model relationships between variables and is instrumental in statistical analysis.\n",
        "\n",
        "- **Directed:** A DAG consists of nodes connected by edges, where each edge has a direction, indicating a causal relationship. An edge points from one node (the cause) to another (the effect).\n",
        "  \n",
        "- **Acyclic:** A DAG does not contain any cycles, meaning you cannot start at one node and follow a sequence of edges that leads back to the original node. This ensures there are no circular or paradoxical relationships, which is essential for representing causal processes.\n",
        "\n",
        "- **Graph:** A DAG is composed of nodes (vertices) connected by edges (arrows), representing relationships or interactions between the variables.\n",
        "\n",
        "### Why is it Used?\n",
        "\n",
        "- **Modeling Causal Relationships:** DAGs are used in statistics and epidemiology to represent and analyze causal relationships between variables, helping researchers understand how one variable influences another.\n",
        "\n",
        "- **Avoiding Confounding Bias:** DAGs are particularly useful in identifying confounders—variables that influence both the cause and the effect, potentially skewing results. By visualizing the relationships, DAGs help in designing studies that more accurately assess causality.\n",
        "\n",
        "- **Clarifying Assumptions:** DAGs make the assumptions about causal relationships between variables explicit, providing a clear and visual representation of these assumptions, which is essential for understanding the analysis.\n",
        "\n",
        "- **Informing Statistical Analysis:** DAGs help guide the selection of variables to include in statistical models. By understanding the causal paths, researchers can decide which variables to adjust for, improving the accuracy of their analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "1bdebe98",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "1bdebe98",
        "outputId": "d61e611a-ccb9-4c64-c22d-19207ee9acfb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a Directed Graph\n",
        "dag = nx.DiGraph()\n",
        "\n",
        "# Add nodes with the variable names\n",
        "dag.add_nodes_from([\"ADS_Index\", \"NSE_Close\", \"Interest_Rates\", \"GDP_Growth\"])\n",
        "\n",
        "# Add edges to represent assumed causal directions\n",
        "dag.add_edge(\"ADS_Index\", \"NSE_Close\")\n",
        "dag.add_edge(\"Interest_Rates\", \"NSE_Close\")\n",
        "dag.add_edge(\"GDP_Growth\", \"NSE_Close\")\n",
        "dag.add_edge(\"GDP_Growth\", \"ADS_Index\")  # Assuming GDP growth influences ADS Index\n",
        "\n",
        "# Draw the DAG\n",
        "pos = nx.spring_layout(dag)\n",
        "nx.draw(dag, pos, with_labels=True, node_size=2000, node_color='lightblue', font_size=10, font_weight='bold')\n",
        "plt.title('Simplified DAG for NSE Data and ADS Index Causal Inference')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "93535f1b",
      "metadata": {
        "id": "93535f1b"
      },
      "source": [
        "**Directed Acyclic Graph (DAG) in the Financial Context**\n",
        "\n",
        "The Directed Acyclic Graph (DAG) in the previous example serves as a simplified representation of the potential causal relationships between the Aruoba-Diebold-Scotti (ADS) Index, the closing prices of the National Stock Exchange (NSE) of India, and other economic factors such as interest rates and GDP growth. Let's break down the components and the rationale behind this DAG:\n",
        "\n",
        "### **Nodes:**\n",
        "Each node in the DAG represents a variable that could influence the financial markets or is an outcome of such influences. In this example, there are four nodes:\n",
        "\n",
        "- **ADS_Index:** This node represents the Aruoba-Diebold-Scotti (ADS) Business Conditions Index, which is an economic indicator used to assess the overall health of the economy.\n",
        "  \n",
        "- **NSE_Close:** This node represents the closing price of the NSE, which is the main outcome variable of interest. It is assumed that various factors, such as economic indicators, could influence stock prices.\n",
        "\n",
        "- **Interest_Rates:** This node represents the prevailing interest rates. Interest rates have a significant impact on financial markets, with higher rates typically reducing the attractiveness of stocks compared to bonds, potentially leading to lower stock prices.\n",
        "\n",
        "- **GDP_Growth:** This node represents the growth rate of the Gross Domestic Product (GDP), a key measure of the economy's health. A higher GDP growth rate usually correlates with increased business profits, which may drive up stock prices.\n",
        "\n",
        "### **Edges:**\n",
        "The directed edges (arrows) represent assumed causal relationships or influences between the variables. These relationships are based on economic theory, empirical research, or domain knowledge:\n",
        "\n",
        "- **ADS_Index → NSE_Close:** This arrow represents the hypothesis that changes in the ADS Index, which reflects business conditions, can influence movements in NSE stock prices. For example, an improvement in business conditions may lead to higher stock prices.\n",
        "\n",
        "- **Interest_Rates → NSE_Close:** This edge indicates that interest rates might directly affect NSE stock prices. Generally, higher interest rates can lower stock prices by increasing borrowing costs for companies and making bonds more attractive to investors, potentially diverting investment away from stocks.\n",
        "\n",
        "- **GDP_Growth → NSE_Close:** This edge suggests that economic growth, measured by GDP growth, could influence stock prices. Strong economic growth can lead to higher corporate earnings, which may positively impact stock prices.\n",
        "\n",
        "- **GDP_Growth → ADS_Index:** This edge represents the hypothesis that overall economic growth influences the business conditions captured by the ADS Index. Stronger economic growth typically improves business conditions, which would be reflected in the ADS Index.\n",
        "\n",
        "This DAG visually and conceptually represents the potential causal pathways through which economic indicators and factors might influence stock market behavior."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "bcbc7038",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859
        },
        "id": "bcbc7038",
        "outputId": "9a94e90b-9b79-43ff-c19f-5ebe25a8eb6d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a directed graph\n",
        "G = nx.DiGraph()\n",
        "\n",
        "# Add nodes with their full forms as labels\n",
        "nodes = {\n",
        "    \"IR\": \"Interest Rates\",\n",
        "    \"INF\": \"Inflation\",\n",
        "    \"ER\": \"Exchange Rates\",\n",
        "    \"GDP\": \"GDP Growth\",\n",
        "    \"UNEMP\": \"Unemployment Rate\",\n",
        "    \"CC\": \"Consumer Confidence\",\n",
        "    \"MP\": \"Monetary Policy\",\n",
        "    \"FP\": \"Fiscal Policy\",\n",
        "    \"CP\": \"Commodity Prices\",\n",
        "    \"GE\": \"Global Events\",\n",
        "    \"Nifty\": \"NSE Index\"\n",
        "}\n",
        "\n",
        "# Add nodes to the graph\n",
        "G.add_nodes_from(nodes)\n",
        "\n",
        "# Add edges representing causal relationships\n",
        "edges = [\n",
        "    (\"IR\", \"Nifty\"),\n",
        "    (\"INF\", \"Nifty\"),\n",
        "    (\"ER\", \"Nifty\"),\n",
        "    (\"GDP\", \"Nifty\"),\n",
        "    (\"UNEMP\", \"Nifty\"),\n",
        "    (\"CC\", \"Nifty\"),\n",
        "    (\"MP\", \"IR\"),\n",
        "    (\"FP\", \"GDP\"),\n",
        "    (\"CP\", \"INF\"),\n",
        "    (\"GE\", \"IR\"),\n",
        "    (\"GE\", \"ER\"),\n",
        "    (\"GE\", \"GDP\"),\n",
        "    (\"GE\", \"CP\")\n",
        "]\n",
        "\n",
        "# Add edges to the graph\n",
        "G.add_edges_from(edges)\n",
        "\n",
        "# Draw the graph\n",
        "plt.figure(figsize=(10, 8))\n",
        "pos = nx.spring_layout(G)  # positions for all nodes\n",
        "nx.draw(G, pos, with_labels=True, labels=nodes, node_size=4000, node_color=\"lightblue\", font_size=10, font_weight=\"bold\", arrowstyle=\"->\", arrowsize=20)\n",
        "plt.title(\"Directed Acyclic Graph (DAG) for Finance Variables\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f7c99e69",
      "metadata": {
        "id": "f7c99e69"
      },
      "source": [
        "### **Key Economic Factors Influencing the Nifty:**\n",
        "\n",
        "Here is a breakdown of the key economic factors and their assumed relationships with the Nifty, which is the stock market index of the National Stock Exchange (NSE) of India:\n",
        "\n",
        "#### **Interest Rates (IR):**\n",
        "Interest rates are assumed to have a direct effect on the Nifty. Generally, **higher interest rates** can reduce investment and consumer spending, which may lead to a decrease in stock prices. On the other hand, **lower interest rates** typically encourage borrowing and investment, potentially boosting the performance of the stock market.\n",
        "\n",
        "#### **Inflation (INF):**\n",
        "Inflation affects the Nifty by influencing consumers' purchasing power and corporate earnings. **High inflation** can lead to lower consumer spending and increased costs for companies, negatively impacting stock prices. Conversely, lower inflation tends to support stable economic conditions, which may favor stock market growth.\n",
        "\n",
        "#### **Exchange Rates (ER):**\n",
        "Exchange rates represent the value of a country's currency relative to others. The value of the **national currency** can affect companies' earnings, especially those involved in **import and export activities**. Fluctuations in exchange rates can thus impact the Nifty, particularly for companies with significant international operations.\n",
        "\n",
        "#### **GDP Growth (GDP):**\n",
        "The **GDP growth rate** is a key indicator of the economy's overall health. Strong GDP growth typically supports better corporate earnings and a **stronger stock market** performance, as businesses benefit from higher consumer spending and investment.\n",
        "\n",
        "#### **Unemployment Rate (UNEMP):**\n",
        "The **unemployment rate** is an indicator of economic health. A **higher unemployment rate** may signal economic struggles, potentially leading to lower stock market performance. This is due to reduced consumer spending, decreased demand for goods and services, and overall lower economic activity.\n",
        "\n",
        "#### **Consumer Confidence (CC):**\n",
        "**Consumer confidence** measures how optimistic or pessimistic consumers feel about their financial future. Higher consumer confidence typically leads to **increased spending and investment**, which positively influences stock market performance. When consumers are more confident, they tend to spend more, which drives economic growth and boosts stock prices.\n",
        "\n",
        "#### **Monetary Policy (MP) and Fiscal Policy (FP):**\n",
        "These policies, enacted by the **central bank** and **government**, respectively, have broad economic implications. Monetary policy impacts interest rates, inflation, and GDP growth, while fiscal policy influences government spending and taxation. Both policies can indirectly affect the Nifty by influencing key economic variables such as interest rates, inflation, and GDP.\n",
        "\n",
        "#### **Commodity Prices (CP):**\n",
        "Changes in **commodity prices** (e.g., oil, metals) can influence inflation and the costs for companies that rely on these commodities for production. For example, rising commodity prices can lead to higher production costs, which may squeeze corporate margins and negatively affect stock prices. Thus, **commodity price changes** can indirectly impact the Nifty.\n",
        "\n",
        "#### **Global Events (GE):**\n",
        "**Global events**, such as geopolitical tensions, international trade agreements, or global pandemics, can have significant impacts on various economic factors, including **interest rates**, **exchange rates**, **GDP growth**, and **commodity prices**. These events can create uncertainty or opportunities in the global economy, thereby affecting the Nifty through various channels.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "50a4be0f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50a4be0f",
        "outputId": "3265c36e-f7c5-4e58-95a1-a49e3ece1319"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj_Close', 'Volume'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv('/content/NSE (1).csv')\n",
        "print(df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "6e476afa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "6e476afa",
        "outputId": "7f999d6c-d09d-4dbe-a525-346cfa7259af"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Select the specific columns for the heatmap\n",
        "columns_of_interest = ['Open', 'High', 'Low', 'Adj_Close', 'Close', 'Volume']\n",
        "data_subset = df[columns_of_interest]\n",
        "\n",
        "# Calculate the correlation matrix for the selected columns\n",
        "corr_subset = data_subset.corr()\n",
        "\n",
        "# Generate a heatmap for the selected subset of data\n",
        "plt.figure(figsize=(8, 6))  # Adjust the figure size as needed\n",
        "sns.heatmap(corr_subset, annot=True, cmap='viridis', fmt=\".2f\", linewidths=.5)\n",
        "plt.title('Heatmap for Volume, High, Low, Adj Close, and Close of NSE Data')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fb48ea34",
      "metadata": {
        "id": "fb48ea34"
      },
      "source": [
        "### **Conclusion**\n",
        "\n",
        "The application of **causal inference in finance** has significant implications both for theoretical research and practical applications.\n",
        "\n",
        "#### **Theoretical Implications:**\n",
        "On a theoretical level, causal inference enhances our understanding of **financial market dynamics**. It offers valuable insights into how various factors, such as interest rates, inflation, or economic policies, influence market behavior and economic outcomes. This understanding is crucial for developing more accurate financial theories and models, which are better equipped to capture the complexities of real-world markets.\n",
        "\n",
        "#### **Practical Applications:**\n",
        "From a practical standpoint, causal inference provides financial analysts, portfolio managers, policymakers, and regulators with essential tools for making **evidence-based decisions**. By identifying the **causal relationships** between variables, stakeholders can develop strategies that are more likely to achieve desired outcomes. For example, understanding the causal impact of central bank policies on inflation or the effects of technological advancements on stock market performance can help guide more effective **policy formulation** and **investment strategies**.\n",
        "\n",
        "#### **Integration with Big Data and Machine Learning:**\n",
        "In an era where **big data** and **machine learning** dominate the financial sector, the role of causal inference is becoming increasingly significant. Traditional data analysis methods often struggle to distinguish between causation and correlation, which can lead to incorrect conclusions and suboptimal decision-making. By integrating causal inference techniques with machine learning models, financial professionals can improve the **accuracy** and **predictive power** of their analyses, thus creating more sophisticated and reliable financial tools.\n",
        "\n",
        "### **Conclusion Summary:**\n",
        "In conclusion, causal inference is crucial for both refining financial theories and improving real-world decision-making. As financial markets continue to evolve and data becomes more complex, integrating causal analysis with advanced modeling techniques will enhance the quality of financial research, policy-making, and investment strategies."
      ]
    },
    {
      "cell_type": "markdown",
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        "###**References**\n",
        "\n",
        "1. **Medium**:  \n",
        "   Medium provides articles and insights from various professionals, including those on finance, machine learning, and data analysis.  \n",
        "   [https://medium.com/](https://medium.com/)\n",
        "\n",
        "2. **Kaggle**:  \n",
        "   Kaggle is a popular platform for data science and machine learning projects, where users can share datasets, code, and solutions, including finance-related challenges.  \n",
        "   [https://www.kaggle.com/](https://www.kaggle.com/)\n",
        "\n",
        "3. **Yahoo Finance**:  \n",
        "   Yahoo Finance offers financial news, data, and analysis, which is commonly used for tracking stock markets, economic events, and financial research.  \n",
        "   [https://finance.yahoo.com/](https://finance.yahoo.com/)  \n"
      ]
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      "source": [
        "###**License**::-\n"
      ],
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