{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8bf34aae",
   "metadata": {},
   "source": [
    "# <font color='367588'>Multivariate TimeSeries Forecasting with **Vector Auto Regression**</font>\n",
    "## Objective \n",
    "#### Forecasting Stock Prices of Ethereum with VAR. \n",
    "---\n",
    "\n",
    "\n",
    "The **Vector Autoregression (VAR)** model is a powerful and flexible tool for analyzing multivariate time series. It generalizes the univariate autoregressive model by allowing multiple interrelated time series to influence each other dynamically. VAR models are especially effective for economic and financial data, often outperforming univariate and structural models in forecasting.\n",
    "\n",
    "#### VAR(p) Equation:\n",
    "\n",
    "$$\n",
    "\\mathbf{y}_t = c + A_1 \\mathbf{y}_{t-1} + A_2 \\mathbf{y}_{t-2} + \\dots + A_p \\mathbf{y}_{t-p} + \\varepsilon_t\n",
    "$$\n",
    "\n",
    "Where:\n",
    "\n",
    "- $\\mathbf{y}_t$ is an $n \\times 1$ vector of endogenous variables,  \n",
    "- $c$ is a vector of intercepts,  \n",
    "- $A_i$ are coefficient matrices,  \n",
    "- $\\varepsilon_t$ is a vector of white noise errors.\n",
    "\n",
    "#### The model captures **lagged interactions** among all variables, making it suitable for dynamic systems analysis under stationarity assumptions.\n",
    "---\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be711dc",
   "metadata": {},
   "source": [
    "\n",
    "## Approach Breakdown\n",
    "\n",
    "<p align=\"center\"> \n",
    "<b>1. Data Preparation and Analysis</b> <br>\n",
    "Collect, clean, and explore the multivariate time series data. Identify missing values, visualize trends or seasonality, and perform ACF/PACF analysis. \n",
    "</p>\n",
    "\n",
    "<p align=\"center\">⬇️</p>\n",
    "\n",
    "<p align=\"center\">\n",
    "<b>2. Feature Engineering</b> <br>\n",
    "Create additional features from the data to later incorporate in VAR forecasting.\n",
    "</p>\n",
    "\n",
    "<p align=\"center\">⬇️</p>\n",
    "\n",
    "<p align=\"center\">\n",
    "<b>3. Stationarity Checks</b> <br>\n",
    "Use Augmented Dickey-Fuller (ADF) or KPSS tests to check for stationarity. Apply differencing or transformations if necessary.\n",
    "</p>\n",
    "\n",
    "<p align=\"center\">⬇️</p>\n",
    "\n",
    "<p align=\"center\">\n",
    "<b>4. Granger Causality Test for Variable Selection</b> <br>\n",
    "Identify predictive relationships between variables using Granger causality to refine model inputs.\n",
    "</p>\n",
    "\n",
    "<p align=\"center\">⬇️</p>\n",
    "\n",
    "<p align=\"center\">\n",
    "<b>5. Model Order/Lag Selection (AIC/BIC)</b> <br>\n",
    "Choose the optimal lag length based on the lowest AIC/BIC values for better model fit and efficiency.\n",
    "</p>\n",
    "\n",
    "<p align=\"center\">⬇️</p>\n",
    "\n",
    "<p align=\"center\">\n",
    "<b>6. Model Training</b> <br>\n",
    "Fit the VAR model using selected variables and optimal lag to capture dynamic interdependencies.\n",
    "</p>\n",
    "\n",
    "<p align=\"center\">⬇️</p>\n",
    "\n",
    "<p align=\"center\">\n",
    "<b>7. Forecasting</b> <br>\n",
    "Generate forecasts on the test set using the trained model.\n",
    "</p>\n",
    "\n",
    "<p align=\"center\">⬇️</p>\n",
    "\n",
    "<p align=\"center\">\n",
    "<b>8. Evaluation</b> <br>\n",
    "Assess performance using RMSE, MAE, or other relevant metrics and validate forecasts against actual data.\n",
    "</p>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f849a6",
   "metadata": {},
   "source": [
    "# Data Loading and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1f3506c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "378e24da",
   "metadata": {},
   "outputs": [],
   "source": [
    "ethereum_ts = pd.read_csv(\"I:/CQAI/TSA/TSD/TSD/archive/ETH-USD (2017-2024).csv\",parse_dates=[\"Date\"],index_col=[\"Date\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>320.670990</td>\n",
       "      <td>324.717987</td>\n",
       "      <td>294.541992</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>8.859860e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-11</th>\n",
       "      <td>298.585999</td>\n",
       "      <td>319.453003</td>\n",
       "      <td>298.191986</td>\n",
       "      <td>314.681000</td>\n",
       "      <td>314.681000</td>\n",
       "      <td>8.423010e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-12</th>\n",
       "      <td>314.690002</td>\n",
       "      <td>319.153015</td>\n",
       "      <td>298.513000</td>\n",
       "      <td>307.907990</td>\n",
       "      <td>307.907990</td>\n",
       "      <td>1.613480e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13</th>\n",
       "      <td>307.024994</td>\n",
       "      <td>328.415009</td>\n",
       "      <td>307.024994</td>\n",
       "      <td>316.716003</td>\n",
       "      <td>316.716003</td>\n",
       "      <td>1.041890e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-14</th>\n",
       "      <td>316.763000</td>\n",
       "      <td>340.177002</td>\n",
       "      <td>316.763000</td>\n",
       "      <td>337.631012</td>\n",
       "      <td>337.631012</td>\n",
       "      <td>1.069680e+09</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-17</th>\n",
       "      <td>2587.044678</td>\n",
       "      <td>2592.737061</td>\n",
       "      <td>2508.432861</td>\n",
       "      <td>2528.369385</td>\n",
       "      <td>2528.369385</td>\n",
       "      <td>1.044102e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-18</th>\n",
       "      <td>2528.593262</td>\n",
       "      <td>2546.263916</td>\n",
       "      <td>2426.135498</td>\n",
       "      <td>2467.018799</td>\n",
       "      <td>2467.018799</td>\n",
       "      <td>1.190003e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-19</th>\n",
       "      <td>2468.688965</td>\n",
       "      <td>2501.305176</td>\n",
       "      <td>2414.710938</td>\n",
       "      <td>2489.498535</td>\n",
       "      <td>2489.498535</td>\n",
       "      <td>1.140528e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-21</th>\n",
       "      <td>2469.798584</td>\n",
       "      <td>2469.798584</td>\n",
       "      <td>2464.171875</td>\n",
       "      <td>2465.659180</td>\n",
       "      <td>2465.659180</td>\n",
       "      <td>5.195812e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2264 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2017-11-10   320.670990   324.717987   294.541992   299.252991   299.252991   \n",
       "2017-11-11   298.585999   319.453003   298.191986   314.681000   314.681000   \n",
       "2017-11-12   314.690002   319.153015   298.513000   307.907990   307.907990   \n",
       "2017-11-13   307.024994   328.415009   307.024994   316.716003   316.716003   \n",
       "2017-11-14   316.763000   340.177002   316.763000   337.631012   337.631012   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2024-01-17  2587.044678  2592.737061  2508.432861  2528.369385  2528.369385   \n",
       "2024-01-18  2528.593262  2546.263916  2426.135498  2467.018799  2467.018799   \n",
       "2024-01-19  2468.688965  2501.305176  2414.710938  2489.498535  2489.498535   \n",
       "2024-01-20          NaN          NaN          NaN          NaN          NaN   \n",
       "2024-01-21  2469.798584  2469.798584  2464.171875  2465.659180  2465.659180   \n",
       "\n",
       "                  Volume  \n",
       "Date                      \n",
       "2017-11-10  8.859860e+08  \n",
       "2017-11-11  8.423010e+08  \n",
       "2017-11-12  1.613480e+09  \n",
       "2017-11-13  1.041890e+09  \n",
       "2017-11-14  1.069680e+09  \n",
       "...                  ...  \n",
       "2024-01-17  1.044102e+10  \n",
       "2024-01-18  1.190003e+10  \n",
       "2024-01-19  1.140528e+10  \n",
       "2024-01-20           NaN  \n",
       "2024-01-21  5.195812e+09  \n",
       "\n",
       "[2264 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethereum_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of nan rows:\n",
      " Open         1\n",
      "High         1\n",
      "Low          1\n",
      "Close        1\n",
      "Adj Close    1\n",
      "Volume       1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"No of nan rows:\\n\",ethereum_ts.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f5d6eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "ethereum_ts =ethereum_ts.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2263, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethereum_ts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2263 entries, 2017-11-10 to 2024-01-21\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       2263 non-null   float64\n",
      " 1   High       2263 non-null   float64\n",
      " 2   Low        2263 non-null   float64\n",
      " 3   Close      2263 non-null   float64\n",
      " 4   Adj Close  2263 non-null   float64\n",
      " 5   Volume     2263 non-null   float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 123.8 KB\n"
     ]
    }
   ],
   "source": [
    "ethereum_ts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2263.000000</td>\n",
       "      <td>2263.000000</td>\n",
       "      <td>2263.000000</td>\n",
       "      <td>2263.000000</td>\n",
       "      <td>2263.000000</td>\n",
       "      <td>2.263000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1248.213140</td>\n",
       "      <td>1283.972388</td>\n",
       "      <td>1208.851543</td>\n",
       "      <td>1248.811361</td>\n",
       "      <td>1248.970441</td>\n",
       "      <td>1.205243e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1118.835543</td>\n",
       "      <td>1150.922648</td>\n",
       "      <td>1082.560829</td>\n",
       "      <td>1118.664405</td>\n",
       "      <td>1118.566081</td>\n",
       "      <td>1.012443e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>84.279694</td>\n",
       "      <td>85.342743</td>\n",
       "      <td>82.829887</td>\n",
       "      <td>84.308296</td>\n",
       "      <td>84.308296</td>\n",
       "      <td>6.217330e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>231.636727</td>\n",
       "      <td>236.766563</td>\n",
       "      <td>227.149369</td>\n",
       "      <td>231.901916</td>\n",
       "      <td>231.901916</td>\n",
       "      <td>4.845689e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1038.186646</td>\n",
       "      <td>1090.229980</td>\n",
       "      <td>956.325012</td>\n",
       "      <td>1039.099976</td>\n",
       "      <td>1039.099976</td>\n",
       "      <td>9.401190e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1870.983582</td>\n",
       "      <td>1905.373352</td>\n",
       "      <td>1844.880860</td>\n",
       "      <td>1871.952942</td>\n",
       "      <td>1871.952942</td>\n",
       "      <td>1.657259e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4810.071289</td>\n",
       "      <td>4891.704590</td>\n",
       "      <td>4718.039063</td>\n",
       "      <td>4812.087402</td>\n",
       "      <td>4812.087402</td>\n",
       "      <td>8.448291e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close    Adj Close  \\\n",
       "count  2263.000000  2263.000000  2263.000000  2263.000000  2263.000000   \n",
       "mean   1248.213140  1283.972388  1208.851543  1248.811361  1248.970441   \n",
       "std    1118.835543  1150.922648  1082.560829  1118.664405  1118.566081   \n",
       "min      84.279694    85.342743    82.829887    84.308296    84.308296   \n",
       "25%     231.636727   236.766563   227.149369   231.901916   231.901916   \n",
       "50%    1038.186646  1090.229980   956.325012  1039.099976  1039.099976   \n",
       "75%    1870.983582  1905.373352  1844.880860  1871.952942  1871.952942   \n",
       "max    4810.071289  4891.704590  4718.039063  4812.087402  4812.087402   \n",
       "\n",
       "             Volume  \n",
       "count  2.263000e+03  \n",
       "mean   1.205243e+10  \n",
       "std    1.012443e+10  \n",
       "min    6.217330e+08  \n",
       "25%    4.845689e+09  \n",
       "50%    9.401190e+09  \n",
       "75%    1.657259e+10  \n",
       "max    8.448291e+10  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethereum_ts.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>320.670990</td>\n",
       "      <td>324.717987</td>\n",
       "      <td>294.541992</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>8.859860e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-11</th>\n",
       "      <td>298.585999</td>\n",
       "      <td>319.453003</td>\n",
       "      <td>298.191986</td>\n",
       "      <td>314.681000</td>\n",
       "      <td>314.681000</td>\n",
       "      <td>8.423010e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-12</th>\n",
       "      <td>314.690002</td>\n",
       "      <td>319.153015</td>\n",
       "      <td>298.513000</td>\n",
       "      <td>307.907990</td>\n",
       "      <td>307.907990</td>\n",
       "      <td>1.613480e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13</th>\n",
       "      <td>307.024994</td>\n",
       "      <td>328.415009</td>\n",
       "      <td>307.024994</td>\n",
       "      <td>316.716003</td>\n",
       "      <td>316.716003</td>\n",
       "      <td>1.041890e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-14</th>\n",
       "      <td>316.763000</td>\n",
       "      <td>340.177002</td>\n",
       "      <td>316.763000</td>\n",
       "      <td>337.631012</td>\n",
       "      <td>337.631012</td>\n",
       "      <td>1.069680e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-15</th>\n",
       "      <td>337.963989</td>\n",
       "      <td>340.911987</td>\n",
       "      <td>329.812988</td>\n",
       "      <td>333.356995</td>\n",
       "      <td>333.356995</td>\n",
       "      <td>7.226660e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-16</th>\n",
       "      <td>333.442993</td>\n",
       "      <td>336.158997</td>\n",
       "      <td>323.605988</td>\n",
       "      <td>330.924011</td>\n",
       "      <td>330.924011</td>\n",
       "      <td>7.972540e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17</th>\n",
       "      <td>330.166992</td>\n",
       "      <td>334.963989</td>\n",
       "      <td>327.523010</td>\n",
       "      <td>332.394012</td>\n",
       "      <td>332.394012</td>\n",
       "      <td>6.217330e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-18</th>\n",
       "      <td>331.980011</td>\n",
       "      <td>349.615997</td>\n",
       "      <td>327.687012</td>\n",
       "      <td>347.612000</td>\n",
       "      <td>347.612000</td>\n",
       "      <td>6.496390e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-19</th>\n",
       "      <td>347.401001</td>\n",
       "      <td>371.290985</td>\n",
       "      <td>344.739990</td>\n",
       "      <td>354.385986</td>\n",
       "      <td>354.385986</td>\n",
       "      <td>1.181530e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2017-11-10  320.670990  324.717987  294.541992  299.252991  299.252991   \n",
       "2017-11-11  298.585999  319.453003  298.191986  314.681000  314.681000   \n",
       "2017-11-12  314.690002  319.153015  298.513000  307.907990  307.907990   \n",
       "2017-11-13  307.024994  328.415009  307.024994  316.716003  316.716003   \n",
       "2017-11-14  316.763000  340.177002  316.763000  337.631012  337.631012   \n",
       "2017-11-15  337.963989  340.911987  329.812988  333.356995  333.356995   \n",
       "2017-11-16  333.442993  336.158997  323.605988  330.924011  330.924011   \n",
       "2017-11-17  330.166992  334.963989  327.523010  332.394012  332.394012   \n",
       "2017-11-18  331.980011  349.615997  327.687012  347.612000  347.612000   \n",
       "2017-11-19  347.401001  371.290985  344.739990  354.385986  354.385986   \n",
       "\n",
       "                  Volume  \n",
       "Date                      \n",
       "2017-11-10  8.859860e+08  \n",
       "2017-11-11  8.423010e+08  \n",
       "2017-11-12  1.613480e+09  \n",
       "2017-11-13  1.041890e+09  \n",
       "2017-11-14  1.069680e+09  \n",
       "2017-11-15  7.226660e+08  \n",
       "2017-11-16  7.972540e+08  \n",
       "2017-11-17  6.217330e+08  \n",
       "2017-11-18  6.496390e+08  \n",
       "2017-11-19  1.181530e+09  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethereum_ts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f979113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethereum_ts.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82fc82d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.figure(figsize=(20, 15))\n",
    "\n",
    "for i, col in enumerate(ethereum_ts.columns, 1):\n",
    "    plt.subplot(6, 1, i)\n",
    "    plt.plot(ethereum_ts.index, ethereum_ts[col], label=col, color=sns.color_palette()[i - 1])\n",
    "    plt.title(f\"Time series plot of {col}\")\n",
    "    plt.xlabel(\"Date\")\n",
    "    plt.ylabel(col)  \n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.suptitle(\"Time series plots of all features\", fontsize=20, y=1.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04553d82",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "580d944d",
   "metadata": {},
   "source": [
    "- Now we some additional  features from the dataset, including the percentage difference between High and Low as a measure for intra-day price movement , percentage difference between next-day Open and Close as a measure for overnight price movement and also some technical indiactors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d343cab",
   "metadata": {},
   "source": [
    "\n",
    "#### Ethereum Intraday vs. Overnight Price Movements\n",
    "\n",
    "* **High intraday volatility (2018–2021)**: Frequent spikes above 60% reflect intense trading activity and market sensitivity, tapering off in recent years.Volatility appears to taper off in later years, possibly reflecting changing market dynamics.\n",
    "\n",
    "* **Overnight movements are broader but mean-reverting**: Typically range between **-20% and +20%**, centered around zero, indicating relatively stable non-trading hours.\n",
    "\n",
    "This suggests Ethereum exhibits greater volatility during trading hours than overnight, underscoring that key price movements and reactions to information tend to occur intraday rather than after hours.\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f04ce19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#Percentage difference between High and Low (intra-day price movement)\n",
    "ethereum_ts['Intraday_Price_movement'] = ((ethereum_ts['High'] - ethereum_ts['Low']) / ethereum_ts['Low']) * 100\n",
    "\n",
    "ethereum_ts['Overnight_Price_movement'] = ((ethereum_ts['Open'].shift(1) - ethereum_ts['Close']) / ethereum_ts['Close']) * 100\n",
    "\n",
    "cols = ['Intraday_Price_movement', 'Overnight_Price_movement']\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "for i , col in enumerate(cols,1):\n",
    "    plt.subplot(2,1,i)    \n",
    "    plt.plot(ethereum_ts.index, ethereum_ts[col], label=col, color=sns.color_palette()[i - 1])\n",
    "    plt.title(col)\n",
    "    plt.xlabel(\"Date\")\n",
    "    plt.ylabel(col)\n",
    "    plt.tight_layout()  \n",
    "    \n",
    "plt.suptitle(\"Time series plots of intraday ,overnight percentage differences\", fontsize=20, y=1.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0b3532",
   "metadata": {},
   "source": [
    "#### Analyse some Technical Indicators \n",
    "\n",
    "| Category   | Indicator                    | Description                                   |\n",
    "| ---------- | ---------------------------- | --------------------------------------------- |\n",
    "| Trend      | **EMA** (Exponential MA)     | Shows smoothed price trends                   |\n",
    "| Volatility | **ATR** (Average True Range) | Measures market volatility                    |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebe23042",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import ta\n",
    "\n",
    "#Add EMA (Trtsd indicator)\n",
    "ethereum_ts['EMA'] = ta.trend.ema_indicator(close=ethereum_ts['Close'], window=365)\n",
    "\n",
    "\n",
    "#Add ATR (Volatility indicator)\n",
    "ethereum_ts['ATR'] = ta.volatility.average_true_range(high=ethereum_ts['High'], low=ethereum_ts['Low'], close=ethereum_ts['Close'], window=14)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16da1e2",
   "metadata": {},
   "source": [
    "#### Interpretation of each technical indicator from the graph:\n",
    "\n",
    "\n",
    "#### **1.EMA**\n",
    "\n",
    "* **What it shows**: This compares the actual closing price of Ethereum with its 14-day Exponential Moving Average (EMA).\n",
    "* **Insights**:\n",
    "\n",
    "  * **Trend Detection**: The EMA tracks price trends smoothly. When the Close price is **consistently above EMA**, it indicates **bullish momentum**; when **below EMA**, bearish.\n",
    "  * **Crossovers**: The crossover points (price crossing EMA) can signal potential entry/exit points.\n",
    "  * **Post-2021 trends**: Ethereum's price shows clear **bullish trends** in 2021 followed by **corrections**, visible where the price dips below the EMA.\n",
    "\n",
    "---\n",
    "\n",
    "---\n",
    "\n",
    "#### **3. ATR – Average True Range**\n",
    "\n",
    "* **What it shows**: Measures **volatility**.\n",
    "* **Insights**:\n",
    "\n",
    "  * **Low ATR** = Low volatility/stable market.\n",
    "  * **High ATR** = High volatility/large price swings.\n",
    "  * **Observation**: We notive huge strating from late 2020 followed by high spikes in 2021 —this coincides with Ethereum’s rapid price increase, reflecting massive market movement.\n",
    "  * **Post-2022**: ATR steadily declines, showing **reduced volatility**, potentially indicating market **maturity or consolidation**.\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "08c4d803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Define plot configurations\n",
    "plot_configs = [\n",
    "    {\"columns\": [\"EMA\", \"Close\"], \"title\": \"EMA and Close\", \"ylabel\": \"Price\"},\n",
    "    {\"columns\": [\"ATR\"], \"title\": \"ATR\", \"ylabel\": \"ATR\"}\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "for i, config in enumerate(plot_configs, 1):\n",
    "    plt.subplot(2, 1, i)\n",
    "    for col in config[\"columns\"]:\n",
    "        plt.plot(ethereum_ts.index, ethereum_ts[col], label=col, color=sns.color_palette()[config[\"columns\"].index(col)])\n",
    "    plt.title(config[\"title\"])\n",
    "    plt.xlabel(\"Date\")\n",
    "    plt.ylabel(config[\"ylabel\"])\n",
    "    plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.suptitle(\"Time Series Plots: EMA, Close, and ATR\", fontsize=18, y=1.03)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4adda5e",
   "metadata": {},
   "source": [
    "# Check for stationarity of Variables.\n",
    "- Before modeling with VAR it is importatnt to make sure the varaibles are staionary.\n",
    "- For this we use ADF and KPSS test on each of the variable in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8721635b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time seriess is Non_Stationary for Open\n",
      "The time seriess is Non_Stationary for High\n",
      "The time seriess is Non_Stationary for Low\n",
      "The time seriess is Non_Stationary for Close\n",
      "The time seriess is Non_Stationary for Adj Close\n",
      "The time seriess is Non_Stationary for Volume\n",
      "THe result is stationary for Intraday_Price_movement \n",
      "THe result is stationary for Overnight_Price_movement \n",
      "The time seriess is Non_Stationary for EMA\n",
      "The time seriess is Non_Stationary for ATR\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "for column in ethereum_ts.columns:\n",
    "\n",
    "        result_adfuller = adfuller(ethereum_ts[column].dropna() )\n",
    "        #print(\"ADF Statistic:\",\"\" result_adfuller[0])\n",
    "        #print(\"p-value:\", result_adfuller[1])\n",
    "        #print(\"Critical Values:\", result_adfuller[4])# this prints the critical values as key_value pair \n",
    "        if result_adfuller[0]>(result_adfuller[4])['5%']:\n",
    "            print(f\"The time seriess is Non_Stationary for {column}\")\n",
    "        else:\n",
    "            print(f\"THe result is stationary for {column} \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "be0ac41e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time series is **Non-Stationary** for Open \n",
      "The time series is **Non-Stationary** for High \n",
      "The time series is **Non-Stationary** for Low \n",
      "The time series is **Non-Stationary** for Close \n",
      "The time series is **Non-Stationary** for Adj Close \n",
      "The time series is **Non-Stationary** for Volume \n",
      "The time series is **Non-Stationary** for Intraday_Price_movement \n",
      "The time series is **Stationary** for Overnight_Price_movement \n",
      "The time series is **Non-Stationary** for EMA \n",
      "The time series is **Non-Stationary** for ATR \n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import kpss\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "for column in ethereum_ts.columns:\n",
    "    try:\n",
    "        result_kpss = kpss(ethereum_ts[column].dropna(), regression='c', nlags='auto')\n",
    "        stat = result_kpss[0]\n",
    "        p_value = result_kpss[1]\n",
    "        critical_values = result_kpss[3]\n",
    "\n",
    "        if stat > critical_values['5%']:\n",
    "            print(f\"The time series is **Non-Stationary** for {column} \")\n",
    "        else:\n",
    "            print(f\"The time series is **Stationary** for {column} \")\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(f\"KPSS test failed for {column}: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a63dffc",
   "metadata": {},
   "source": [
    "# Differncing varaibles to achieve staionarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01b9e5fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "non_stationary_cols=['Close',\"EMA\",\"High\",\"Open\",\"Volume\",\"Intraday_Price_movement\",\"EMA\",\"ATR\"]\n",
    "\n",
    "for col in non_stationary_cols:\n",
    "\n",
    "    ethereum_ts[col] = ethereum_ts[col].diff()\n",
    "    #ts[col] = ts[col] - ts[col].shift(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b5c2339e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THe result is stationary for Close \n",
      "THe result is stationary for EMA \n",
      "THe result is stationary for High \n",
      "THe result is stationary for Open \n",
      "THe result is stationary for Volume \n",
      "THe result is stationary for Intraday_Price_movement \n",
      "THe result is stationary for EMA \n",
      "THe result is stationary for ATR \n"
     ]
    }
   ],
   "source": [
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "for column in non_stationary_cols:\n",
    "\n",
    "        result_adfuller = adfuller(ethereum_ts[column].dropna() )\n",
    "        #print(\"ADF Statistic:\", result_adfuller[0])\n",
    "        #print(\"p-value:\", result_adfuller[1])\n",
    "        #print(\"Critical Values:\", result_adfuller[4])# this prints the critical values as key_value pair \n",
    "        if result_adfuller[0]>(result_adfuller[4])['5%']:\n",
    "            print(f\"The time seriess is Non_Stationary for {column}\")\n",
    "        else:\n",
    "            print(f\"THe result is stationary for {column} \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90c8723d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time series is **Stationary** for Close \n",
      "The time series is **Stationary** for EMA \n",
      "The time series is **Stationary** for High \n",
      "The time series is **Stationary** for Open \n",
      "The time series is **Stationary** for Volume \n",
      "The time series is **Stationary** for Intraday_Price_movement \n",
      "The time series is **Stationary** for EMA \n",
      "The time series is **Stationary** for ATR \n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import kpss\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "for column in non_stationary_cols:\n",
    "    try:\n",
    "        result_kpss = kpss(ethereum_ts[column].dropna(), regression='c', nlags='auto')\n",
    "        stat = result_kpss[0]\n",
    "        p_value = result_kpss[1]\n",
    "        critical_values = result_kpss[3]\n",
    "\n",
    "        if stat > critical_values['5%']:\n",
    "            print(f\"The time series is Non-Stationary for {column} \")\n",
    "        else:\n",
    "            print(f\"The time series is Stationary for {column} \")\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(f\"KPSS test failed for {column}: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa22090",
   "metadata": {},
   "source": [
    "### A Simple Preprocessing Step\n",
    "\n",
    "- Before proceeding, we ensure all variables are on a **common scale** .This step  ensures we get consistent and reliable results for the below mentioned Grangers causality test\n",
    "\n",
    "To handle this, any variable with a disproportionately large range will be **normalized between 0 and 1** using the `MinMaxScaler` from `sklearn`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "298f1230",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "all_cols =['Close',\"High\",\"Open\",\"Volume\",\"Intraday_Price_movement\",\"EMA\",\"ATR\",\"Overnight_Price_movement\"]\n",
    "\n",
    "# Initialize the scaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "# Drop NA rows and scale the data\n",
    "scaled_data = scaler.fit_transform(ethereum_ts[all_cols].dropna())\n",
    "\n",
    "# Create DataFrame with scaled values\n",
    "ethereum_ts_scaled = pd.DataFrame(\n",
    "    scaled_data,\n",
    "    columns=all_cols,  # Use the correct list of column names\n",
    "    index=ethereum_ts[all_cols].dropna().index\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f2f28024",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:\n",
      " Close                       0.0\n",
      "High                        0.0\n",
      "Open                        0.0\n",
      "Volume                      0.0\n",
      "Intraday_Price_movement     0.0\n",
      "EMA                         0.0\n",
      "ATR                         0.0\n",
      "Overnight_Price_movement    0.0\n",
      "dtype: float64\n",
      "Max:\n",
      " Close                       1.0\n",
      "High                        1.0\n",
      "Open                        1.0\n",
      "Volume                      1.0\n",
      "Intraday_Price_movement     1.0\n",
      "EMA                         1.0\n",
      "ATR                         1.0\n",
      "Overnight_Price_movement    1.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Min:\\n\", ethereum_ts_scaled.min())\n",
    "print(\"Max:\\n\", ethereum_ts_scaled.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a2a03df",
   "metadata": {},
   "source": [
    "# Determining Predictable Power between close and other variables  using **Granger Causality test**.\n",
    "- The test runs regressions for lag 1 to lag 10.\n",
    "\n",
    "- For each lag, it tests whether lagged values of the second column help explain the first (Close).\n",
    "\n",
    "- If feature X Granger-causes Y, it means that past values of X contain information that helps predict future values of Y, above and beyond what past values of Y alone can provide.\n",
    "\n",
    "- p < 0.05 → Strong evidence of Granger causality  \n",
    "\n",
    "- p > 0.05 → No significant predictive relationship\n",
    "\n",
    "**NOTE** Granger causality doesn’t mean true \"cause and effect.\" Instead, it tests predictive power:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "48427ac3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Testing whether High Granger-causes Close?:\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=972.0826, p=0.0000  , df_denom=1893, df_num=1\n",
      "ssr based chi2 test:   chi2=973.6231, p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=785.7674, p=0.0000  , df=1\n",
      "parameter F test:         F=972.0826, p=0.0000  , df_denom=1893, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=630.5803, p=0.0000  , df_denom=1890, df_num=2\n",
      "ssr based chi2 test:   chi2=1264.4970, p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=968.7126, p=0.0000  , df=2\n",
      "parameter F test:         F=630.5803, p=0.0000  , df_denom=1890, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=453.8047, p=0.0000  , df_denom=1887, df_num=3\n",
      "ssr based chi2 test:   chi2=1366.4645, p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=1028.7804, p=0.0000  , df=3\n",
      "parameter F test:         F=453.8047, p=0.0000  , df_denom=1887, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=374.5062, p=0.0000  , df_denom=1884, df_num=4\n",
      "ssr based chi2 test:   chi2=1505.1810, p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=1107.5516, p=0.0000  , df=4\n",
      "parameter F test:         F=374.5062, p=0.0000  , df_denom=1884, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=317.5168, p=0.0000  , df_denom=1881, df_num=5\n",
      "ssr based chi2 test:   chi2=1596.8682, p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=1157.7960, p=0.0000  , df=5\n",
      "parameter F test:         F=317.5168, p=0.0000  , df_denom=1881, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=273.3802, p=0.0000  , df_denom=1878, df_num=6\n",
      "ssr based chi2 test:   chi2=1651.6355, p=0.0000  , df=6\n",
      "likelihood ratio test: chi2=1187.1039, p=0.0000  , df=6\n",
      "parameter F test:         F=273.3802, p=0.0000  , df_denom=1878, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=236.4130, p=0.0000  , df_denom=1875, df_num=7\n",
      "ssr based chi2 test:   chi2=1668.1303, p=0.0000  , df=7\n",
      "likelihood ratio test: chi2=1195.7243, p=0.0000  , df=7\n",
      "parameter F test:         F=236.4130, p=0.0000  , df_denom=1875, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=204.2354, p=0.0000  , df_denom=1872, df_num=8\n",
      "ssr based chi2 test:   chi2=1648.7205, p=0.0000  , df=8\n",
      "likelihood ratio test: chi2=1185.2247, p=0.0000  , df=8\n",
      "parameter F test:         F=204.2354, p=0.0000  , df_denom=1872, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=181.2391, p=0.0000  , df_denom=1869, df_num=9\n",
      "ssr based chi2 test:   chi2=1647.7339, p=0.0000  , df=9\n",
      "likelihood ratio test: chi2=1184.5365, p=0.0000  , df=9\n",
      "parameter F test:         F=181.2391, p=0.0000  , df_denom=1869, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=163.9759, p=0.0000  , df_denom=1866, df_num=10\n",
      "ssr based chi2 test:   chi2=1658.2125, p=0.0000  , df=10\n",
      "likelihood ratio test: chi2=1189.9608, p=0.0000  , df=10\n",
      "parameter F test:         F=163.9759, p=0.0000  , df_denom=1866, df_num=10\n",
      "\n",
      "Testing whether Open Granger-causes Close?:\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=52058.3535, p=0.0000  , df_denom=1893, df_num=1\n",
      "ssr based chi2 test:   chi2=52140.8549, p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=6351.4481, p=0.0000  , df=1\n",
      "parameter F test:         F=52058.3535, p=0.0000  , df_denom=1893, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=34175.9629, p=0.0000  , df_denom=1890, df_num=2\n",
      "ssr based chi2 test:   chi2=68532.7510, p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=6851.1234, p=0.0000  , df=2\n",
      "parameter F test:         F=34175.9629, p=0.0000  , df_denom=1890, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=25703.2605, p=0.0000  , df_denom=1887, df_num=3\n",
      "ssr based chi2 test:   chi2=77395.8274, p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=7072.9893, p=0.0000  , df=3\n",
      "parameter F test:         F=25703.2605, p=0.0000  , df_denom=1887, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=20458.1509, p=0.0000  , df_denom=1884, df_num=4\n",
      "ssr based chi2 test:   chi2=82223.5235, p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=7182.1180, p=0.0000  , df=4\n",
      "parameter F test:         F=20458.1509, p=0.0000  , df_denom=1884, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=16851.0754, p=0.0000  , df_denom=1881, df_num=5\n",
      "ssr based chi2 test:   chi2=84748.0987, p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=7235.2507, p=0.0000  , df=5\n",
      "parameter F test:         F=16851.0754, p=0.0000  , df_denom=1881, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=14296.7935, p=0.0000  , df_denom=1878, df_num=6\n",
      "ssr based chi2 test:   chi2=86374.5576, p=0.0000  , df=6\n",
      "likelihood ratio test: chi2=7267.5747, p=0.0000  , df=6\n",
      "parameter F test:         F=14296.7935, p=0.0000  , df_denom=1878, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=12486.4075, p=0.0000  , df_denom=1875, df_num=7\n",
      "ssr based chi2 test:   chi2=88104.0910, p=0.0000  , df=7\n",
      "likelihood ratio test: chi2=7301.3860, p=0.0000  , df=7\n",
      "parameter F test:         F=12486.4075, p=0.0000  , df_denom=1875, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=11060.7654, p=0.0000  , df_denom=1872, df_num=8\n",
      "ssr based chi2 test:   chi2=89289.6830, p=0.0000  , df=8\n",
      "likelihood ratio test: chi2=7323.2252, p=0.0000  , df=8\n",
      "parameter F test:         F=11060.7654, p=0.0000  , df_denom=1872, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=9945.8070, p=0.0000  , df_denom=1869, df_num=9\n",
      "ssr based chi2 test:   chi2=90422.2328, p=0.0000  , df=9\n",
      "likelihood ratio test: chi2=7343.6345, p=0.0000  , df=9\n",
      "parameter F test:         F=9945.8070, p=0.0000  , df_denom=1869, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=9058.5215, p=0.0000  , df_denom=1866, df_num=10\n",
      "ssr based chi2 test:   chi2=91604.6632, p=0.0000  , df=10\n",
      "likelihood ratio test: chi2=7364.7421, p=0.0000  , df=10\n",
      "parameter F test:         F=9058.5215, p=0.0000  , df_denom=1866, df_num=10\n",
      "\n",
      "Testing whether Volume Granger-causes Close?:\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=6.0006  , p=0.0144  , df_denom=1893, df_num=1\n",
      "ssr based chi2 test:   chi2=6.0101  , p=0.0142  , df=1\n",
      "likelihood ratio test: chi2=6.0006  , p=0.0143  , df=1\n",
      "parameter F test:         F=6.0006  , p=0.0144  , df_denom=1893, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=7.1763  , p=0.0008  , df_denom=1890, df_num=2\n",
      "ssr based chi2 test:   chi2=14.3906 , p=0.0008  , df=2\n",
      "likelihood ratio test: chi2=14.3363 , p=0.0008  , df=2\n",
      "parameter F test:         F=7.1763  , p=0.0008  , df_denom=1890, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=6.7162  , p=0.0002  , df_denom=1887, df_num=3\n",
      "ssr based chi2 test:   chi2=20.2232 , p=0.0002  , df=3\n",
      "likelihood ratio test: chi2=20.1160 , p=0.0002  , df=3\n",
      "parameter F test:         F=6.7162  , p=0.0002  , df_denom=1887, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=5.8268  , p=0.0001  , df_denom=1884, df_num=4\n",
      "ssr based chi2 test:   chi2=23.4187 , p=0.0001  , df=4\n",
      "likelihood ratio test: chi2=23.2750 , p=0.0001  , df=4\n",
      "parameter F test:         F=5.8268  , p=0.0001  , df_denom=1884, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=7.6251  , p=0.0000  , df_denom=1881, df_num=5\n",
      "ssr based chi2 test:   chi2=38.3486 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=37.9651 , p=0.0000  , df=5\n",
      "parameter F test:         F=7.6251  , p=0.0000  , df_denom=1881, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=7.2659  , p=0.0000  , df_denom=1878, df_num=6\n",
      "ssr based chi2 test:   chi2=43.8970 , p=0.0000  , df=6\n",
      "likelihood ratio test: chi2=43.3953 , p=0.0000  , df=6\n",
      "parameter F test:         F=7.2659  , p=0.0000  , df_denom=1878, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=6.5336  , p=0.0000  , df_denom=1875, df_num=7\n",
      "ssr based chi2 test:   chi2=46.1010 , p=0.0000  , df=7\n",
      "likelihood ratio test: chi2=45.5478 , p=0.0000  , df=7\n",
      "parameter F test:         F=6.5336  , p=0.0000  , df_denom=1875, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=6.4666  , p=0.0000  , df_denom=1872, df_num=8\n",
      "ssr based chi2 test:   chi2=52.2023 , p=0.0000  , df=8\n",
      "likelihood ratio test: chi2=51.4941 , p=0.0000  , df=8\n",
      "parameter F test:         F=6.4666  , p=0.0000  , df_denom=1872, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=6.7734  , p=0.0000  , df_denom=1869, df_num=9\n",
      "ssr based chi2 test:   chi2=61.5807 , p=0.0000  , df=9\n",
      "likelihood ratio test: chi2=60.5977 , p=0.0000  , df=9\n",
      "parameter F test:         F=6.7734  , p=0.0000  , df_denom=1869, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=6.4578  , p=0.0000  , df_denom=1866, df_num=10\n",
      "ssr based chi2 test:   chi2=65.3047 , p=0.0000  , df=10\n",
      "likelihood ratio test: chi2=64.2001 , p=0.0000  , df=10\n",
      "parameter F test:         F=6.4578  , p=0.0000  , df_denom=1866, df_num=10\n",
      "\n",
      "Testing whether Intraday_Price_movement Granger-causes Close?:\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.5743  , p=0.4486  , df_denom=1893, df_num=1\n",
      "ssr based chi2 test:   chi2=0.5753  , p=0.4482  , df=1\n",
      "likelihood ratio test: chi2=0.5752  , p=0.4482  , df=1\n",
      "parameter F test:         F=0.5743  , p=0.4486  , df_denom=1893, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=1.3007  , p=0.2726  , df_denom=1890, df_num=2\n",
      "ssr based chi2 test:   chi2=2.6083  , p=0.2714  , df=2\n",
      "likelihood ratio test: chi2=2.6065  , p=0.2717  , df=2\n",
      "parameter F test:         F=1.3007  , p=0.2726  , df_denom=1890, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.5444  , p=0.2011  , df_denom=1887, df_num=3\n",
      "ssr based chi2 test:   chi2=4.6505  , p=0.1993  , df=3\n",
      "likelihood ratio test: chi2=4.6448  , p=0.1997  , df=3\n",
      "parameter F test:         F=1.5444  , p=0.2011  , df_denom=1887, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.9606  , p=0.0980  , df_denom=1884, df_num=4\n",
      "ssr based chi2 test:   chi2=7.8798  , p=0.0961  , df=4\n",
      "likelihood ratio test: chi2=7.8634  , p=0.0967  , df=4\n",
      "parameter F test:         F=1.9606  , p=0.0980  , df_denom=1884, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=7.0742  , p=0.0000  , df_denom=1881, df_num=5\n",
      "ssr based chi2 test:   chi2=35.5780 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=35.2476 , p=0.0000  , df=5\n",
      "parameter F test:         F=7.0742  , p=0.0000  , df_denom=1881, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=6.3188  , p=0.0000  , df_denom=1878, df_num=6\n",
      "ssr based chi2 test:   chi2=38.1754 , p=0.0000  , df=6\n",
      "likelihood ratio test: chi2=37.7951 , p=0.0000  , df=6\n",
      "parameter F test:         F=6.3188  , p=0.0000  , df_denom=1878, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=5.9963  , p=0.0000  , df_denom=1875, df_num=7\n",
      "ssr based chi2 test:   chi2=42.3101 , p=0.0000  , df=7\n",
      "likelihood ratio test: chi2=41.8435 , p=0.0000  , df=7\n",
      "parameter F test:         F=5.9963  , p=0.0000  , df_denom=1875, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=5.8657  , p=0.0000  , df_denom=1872, df_num=8\n",
      "ssr based chi2 test:   chi2=47.3516 , p=0.0000  , df=8\n",
      "likelihood ratio test: chi2=46.7678 , p=0.0000  , df=8\n",
      "parameter F test:         F=5.8657  , p=0.0000  , df_denom=1872, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=5.5083  , p=0.0000  , df_denom=1869, df_num=9\n",
      "ssr based chi2 test:   chi2=50.0791 , p=0.0000  , df=9\n",
      "likelihood ratio test: chi2=49.4265 , p=0.0000  , df=9\n",
      "parameter F test:         F=5.5083  , p=0.0000  , df_denom=1869, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=5.3345  , p=0.0000  , df_denom=1866, df_num=10\n",
      "ssr based chi2 test:   chi2=53.9457 , p=0.0000  , df=10\n",
      "likelihood ratio test: chi2=53.1890 , p=0.0000  , df=10\n",
      "parameter F test:         F=5.3345  , p=0.0000  , df_denom=1866, df_num=10\n",
      "\n",
      "Testing whether EMA Granger-causes Close?:\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=5.6142  , p=0.0179  , df_denom=1893, df_num=1\n",
      "ssr based chi2 test:   chi2=5.6231  , p=0.0177  , df=1\n",
      "likelihood ratio test: chi2=5.6148  , p=0.0178  , df=1\n",
      "parameter F test:         F=5.6142  , p=0.0179  , df_denom=1893, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=3.1845  , p=0.0416  , df_denom=1890, df_num=2\n",
      "ssr based chi2 test:   chi2=6.3858  , p=0.0411  , df=2\n",
      "likelihood ratio test: chi2=6.3751  , p=0.0413  , df=2\n",
      "parameter F test:         F=0.8802  , p=0.3483  , df_denom=1890, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=2.1875  , p=0.0876  , df_denom=1887, df_num=3\n",
      "ssr based chi2 test:   chi2=6.5868  , p=0.0863  , df=3\n",
      "likelihood ratio test: chi2=6.5754  , p=0.0867  , df=3\n",
      "parameter F test:         F=0.4150  , p=0.6604  , df_denom=1887, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.3636  , p=0.8347  , df_denom=1884, df_num=4\n",
      "ssr based chi2 test:   chi2=1.4612  , p=0.8335  , df=4\n",
      "likelihood ratio test: chi2=1.4606  , p=0.8336  , df=4\n",
      "parameter F test:         F=0.3428  , p=0.7944  , df_denom=1884, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=2.7617  , p=0.0171  , df_denom=1881, df_num=5\n",
      "ssr based chi2 test:   chi2=13.8892 , p=0.0163  , df=5\n",
      "likelihood ratio test: chi2=13.8384 , p=0.0167  , df=5\n",
      "parameter F test:         F=2.6145  , p=0.0337  , df_denom=1881, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=2.9088  , p=0.0079  , df_denom=1878, df_num=6\n",
      "ssr based chi2 test:   chi2=17.5737 , p=0.0074  , df=6\n",
      "likelihood ratio test: chi2=17.4925 , p=0.0076  , df=6\n",
      "parameter F test:         F=2.2176  , p=0.0501  , df_denom=1878, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=2.4802  , p=0.0155  , df_denom=1876, df_num=7\n",
      "ssr based chi2 test:   chi2=17.4907 , p=0.0145  , df=7\n",
      "likelihood ratio test: chi2=17.4103 , p=0.0149  , df=7\n",
      "parameter F test:         F=1.9300  , p=0.0726  , df_denom=1876, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=2.3896  , p=0.0146  , df_denom=1874, df_num=8\n",
      "ssr based chi2 test:   chi2=19.2701 , p=0.0135  , df=8\n",
      "likelihood ratio test: chi2=19.1724 , p=0.0140  , df=8\n",
      "parameter F test:         F=1.8813  , p=0.0688  , df_denom=1874, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=-1.8832 , p=1.0000  , df_denom=1872, df_num=9\n",
      "ssr based chi2 test:   chi2=-17.0933, p=1.0000  , df=9\n",
      "likelihood ratio test: chi2=-17.1712, p=1.0000  , df=9\n",
      "parameter F test:         F=1.6260  , p=0.1124  , df_denom=1872, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=2.2019  , p=0.0154  , df_denom=1870, df_num=10\n",
      "ssr based chi2 test:   chi2=22.2196 , p=0.0140  , df=10\n",
      "likelihood ratio test: chi2=22.0898 , p=0.0147  , df=10\n",
      "parameter F test:         F=1.7757  , p=0.0681  , df_denom=1870, df_num=9\n",
      "\n",
      "Testing whether ATR Granger-causes Close?:\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=6.7398  , p=0.0095  , df_denom=1893, df_num=1\n",
      "ssr based chi2 test:   chi2=6.7505  , p=0.0094  , df=1\n",
      "likelihood ratio test: chi2=6.7385  , p=0.0094  , df=1\n",
      "parameter F test:         F=6.7398  , p=0.0095  , df_denom=1893, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=6.2163  , p=0.0020  , df_denom=1890, df_num=2\n",
      "ssr based chi2 test:   chi2=12.4655 , p=0.0020  , df=2\n",
      "likelihood ratio test: chi2=12.4247 , p=0.0020  , df=2\n",
      "parameter F test:         F=6.2163  , p=0.0020  , df_denom=1890, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=5.0033  , p=0.0019  , df_denom=1887, df_num=3\n",
      "ssr based chi2 test:   chi2=15.0655 , p=0.0018  , df=3\n",
      "likelihood ratio test: chi2=15.0059 , p=0.0018  , df=3\n",
      "parameter F test:         F=5.0033  , p=0.0019  , df_denom=1887, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=3.1964  , p=0.0126  , df_denom=1884, df_num=4\n",
      "ssr based chi2 test:   chi2=12.8467 , p=0.0120  , df=4\n",
      "likelihood ratio test: chi2=12.8033 , p=0.0123  , df=4\n",
      "parameter F test:         F=3.1964  , p=0.0126  , df_denom=1884, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=12.5382 , p=0.0000  , df_denom=1881, df_num=5\n",
      "ssr based chi2 test:   chi2=63.0577 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=62.0297 , p=0.0000  , df=5\n",
      "parameter F test:         F=12.5382 , p=0.0000  , df_denom=1881, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=11.8978 , p=0.0000  , df_denom=1878, df_num=6\n",
      "ssr based chi2 test:   chi2=71.8811 , p=0.0000  , df=6\n",
      "likelihood ratio test: chi2=70.5486 , p=0.0000  , df=6\n",
      "parameter F test:         F=11.8978 , p=0.0000  , df_denom=1878, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=11.7163 , p=0.0000  , df_denom=1875, df_num=7\n",
      "ssr based chi2 test:   chi2=82.6701 , p=0.0000  , df=7\n",
      "likelihood ratio test: chi2=80.9131 , p=0.0000  , df=7\n",
      "parameter F test:         F=11.7163 , p=0.0000  , df_denom=1875, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=11.5612 , p=0.0000  , df_denom=1872, df_num=8\n",
      "ssr based chi2 test:   chi2=93.3291 , p=0.0000  , df=8\n",
      "likelihood ratio test: chi2=91.0968 , p=0.0000  , df=8\n",
      "parameter F test:         F=11.5612 , p=0.0000  , df_denom=1872, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=12.3215 , p=0.0000  , df_denom=1869, df_num=9\n",
      "ssr based chi2 test:   chi2=112.0205, p=0.0000  , df=9\n",
      "likelihood ratio test: chi2=108.8232, p=0.0000  , df=9\n",
      "parameter F test:         F=12.3215 , p=0.0000  , df_denom=1869, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=11.9975 , p=0.0000  , df_denom=1866, df_num=10\n",
      "ssr based chi2 test:   chi2=121.3250, p=0.0000  , df=10\n",
      "likelihood ratio test: chi2=117.5842, p=0.0000  , df=10\n",
      "parameter F test:         F=11.9975 , p=0.0000  , df_denom=1866, df_num=10\n",
      "\n",
      "Testing whether Overnight_Price_movement Granger-causes Close?:\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=210.3946, p=0.0000  , df_denom=1893, df_num=1\n",
      "ssr based chi2 test:   chi2=210.7280, p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=199.8188, p=0.0000  , df=1\n",
      "parameter F test:         F=210.3946, p=0.0000  , df_denom=1893, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=85.9979 , p=0.0000  , df_denom=1890, df_num=2\n",
      "ssr based chi2 test:   chi2=172.4508, p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=165.0498, p=0.0000  , df=2\n",
      "parameter F test:         F=85.9979 , p=0.0000  , df_denom=1890, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=47.3288 , p=0.0000  , df_denom=1887, df_num=3\n",
      "ssr based chi2 test:   chi2=142.5131, p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=137.4060, p=0.0000  , df=3\n",
      "parameter F test:         F=47.3288 , p=0.0000  , df_denom=1887, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=32.8706 , p=0.0000  , df_denom=1884, df_num=4\n",
      "ssr based chi2 test:   chi2=132.1105, p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=127.7044, p=0.0000  , df=4\n",
      "parameter F test:         F=32.8706 , p=0.0000  , df_denom=1884, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=26.0129 , p=0.0000  , df_denom=1881, df_num=5\n",
      "ssr based chi2 test:   chi2=130.8250, p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=126.5002, p=0.0000  , df=5\n",
      "parameter F test:         F=26.0129 , p=0.0000  , df_denom=1881, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=21.7429 , p=0.0000  , df_denom=1878, df_num=6\n",
      "ssr based chi2 test:   chi2=131.3603, p=0.0000  , df=6\n",
      "likelihood ratio test: chi2=126.9986, p=0.0000  , df=6\n",
      "parameter F test:         F=21.7429 , p=0.0000  , df_denom=1878, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=19.1728 , p=0.0000  , df_denom=1875, df_num=7\n",
      "ssr based chi2 test:   chi2=135.2835, p=0.0000  , df=7\n",
      "likelihood ratio test: chi2=130.6611, p=0.0000  , df=7\n",
      "parameter F test:         F=19.1728 , p=0.0000  , df_denom=1875, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=16.5438 , p=0.0000  , df_denom=1872, df_num=8\n",
      "ssr based chi2 test:   chi2=133.5522, p=0.0000  , df=8\n",
      "likelihood ratio test: chi2=129.0425, p=0.0000  , df=8\n",
      "parameter F test:         F=16.5438 , p=0.0000  , df_denom=1872, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=14.2167 , p=0.0000  , df_denom=1869, df_num=9\n",
      "ssr based chi2 test:   chi2=129.2512, p=0.0000  , df=9\n",
      "likelihood ratio test: chi2=125.0191, p=0.0000  , df=9\n",
      "parameter F test:         F=14.2167 , p=0.0000  , df_denom=1869, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=13.6291 , p=0.0000  , df_denom=1866, df_num=10\n",
      "ssr based chi2 test:   chi2=137.8245, p=0.0000  , df=10\n",
      "likelihood ratio test: chi2=133.0237, p=0.0000  , df=10\n",
      "parameter F test:         F=13.6291 , p=0.0000  , df_denom=1866, df_num=10\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "\n",
    "for col in all_cols:\n",
    "    if col == \"Close\":\n",
    "        continue  # Skip testing Close against it\n",
    "   \n",
    "    print(f\"\\nTesting whether {col} Granger-causes Close?:\")\n",
    "    granger = grangercausalitytests(ethereum_ts_scaled[[col, \"Close\"]], maxlag=10, verbose=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "958e982f",
   "metadata": {},
   "source": [
    "### Variable Selection\n",
    "\n",
    "Based on the p-values observed above, the following variables show statistical significance:\n",
    "\n",
    "- `high`\n",
    "- `low`\n",
    "- `open`\n",
    "- `volume`\n",
    "- `Overnight_Price_Movement`\n",
    "- `ATR`\n",
    "\n",
    "However, including both `high` and `low` may introduce redundancy, as they often capture similar information. To maintain consistency and avoid multicollinearity, we choose to **retain `high`** and **drop `low`**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "eef8a298",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "selected_cols = [\"Open\", \"Close\", \"High\", \"Volume\",\"Overnight_Price_movement\",\"ATR\"]\n",
    "\n",
    "\n",
    "forecast_df = ethereum_ts_scaled[selected_cols].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "473a9645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Overnight_Price_movement</th>\n",
       "      <th>ATR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-11</th>\n",
       "      <td>0.633679</td>\n",
       "      <td>0.631758</td>\n",
       "      <td>0.558936</td>\n",
       "      <td>0.408502</td>\n",
       "      <td>0.208957</td>\n",
       "      <td>0.176748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12</th>\n",
       "      <td>0.631354</td>\n",
       "      <td>0.631944</td>\n",
       "      <td>0.559392</td>\n",
       "      <td>0.406173</td>\n",
       "      <td>0.222597</td>\n",
       "      <td>0.176531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-13</th>\n",
       "      <td>0.631087</td>\n",
       "      <td>0.630107</td>\n",
       "      <td>0.557752</td>\n",
       "      <td>0.408961</td>\n",
       "      <td>0.233395</td>\n",
       "      <td>0.177133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-14</th>\n",
       "      <td>0.629578</td>\n",
       "      <td>0.615082</td>\n",
       "      <td>0.556463</td>\n",
       "      <td>0.420095</td>\n",
       "      <td>0.347398</td>\n",
       "      <td>0.197826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-15</th>\n",
       "      <td>0.615491</td>\n",
       "      <td>0.632172</td>\n",
       "      <td>0.538174</td>\n",
       "      <td>0.407415</td>\n",
       "      <td>0.333797</td>\n",
       "      <td>0.182778</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-16</th>\n",
       "      <td>0.658112</td>\n",
       "      <td>0.685095</td>\n",
       "      <td>0.619185</td>\n",
       "      <td>0.425178</td>\n",
       "      <td>0.176748</td>\n",
       "      <td>0.175341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-17</th>\n",
       "      <td>0.683215</td>\n",
       "      <td>0.591763</td>\n",
       "      <td>0.540033</td>\n",
       "      <td>0.398459</td>\n",
       "      <td>0.208488</td>\n",
       "      <td>0.154422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-18</th>\n",
       "      <td>0.592827</td>\n",
       "      <td>0.590367</td>\n",
       "      <td>0.515762</td>\n",
       "      <td>0.426475</td>\n",
       "      <td>0.255207</td>\n",
       "      <td>0.181964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-19</th>\n",
       "      <td>0.591854</td>\n",
       "      <td>0.648045</td>\n",
       "      <td>0.517195</td>\n",
       "      <td>0.400176</td>\n",
       "      <td>0.227556</td>\n",
       "      <td>0.157487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-21</th>\n",
       "      <td>0.632745</td>\n",
       "      <td>0.616176</td>\n",
       "      <td>0.529927</td>\n",
       "      <td>0.323252</td>\n",
       "      <td>0.215408</td>\n",
       "      <td>0.114744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1897 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open     Close      High    Volume  Overnight_Price_movement  \\\n",
       "Date                                                                           \n",
       "2018-11-11  0.633679  0.631758  0.558936  0.408502                  0.208957   \n",
       "2018-11-12  0.631354  0.631944  0.559392  0.406173                  0.222597   \n",
       "2018-11-13  0.631087  0.630107  0.557752  0.408961                  0.233395   \n",
       "2018-11-14  0.629578  0.615082  0.556463  0.420095                  0.347398   \n",
       "2018-11-15  0.615491  0.632172  0.538174  0.407415                  0.333797   \n",
       "...              ...       ...       ...       ...                       ...   \n",
       "2024-01-16  0.658112  0.685095  0.619185  0.425178                  0.176748   \n",
       "2024-01-17  0.683215  0.591763  0.540033  0.398459                  0.208488   \n",
       "2024-01-18  0.592827  0.590367  0.515762  0.426475                  0.255207   \n",
       "2024-01-19  0.591854  0.648045  0.517195  0.400176                  0.227556   \n",
       "2024-01-21  0.632745  0.616176  0.529927  0.323252                  0.215408   \n",
       "\n",
       "                 ATR  \n",
       "Date                  \n",
       "2018-11-11  0.176748  \n",
       "2018-11-12  0.176531  \n",
       "2018-11-13  0.177133  \n",
       "2018-11-14  0.197826  \n",
       "2018-11-15  0.182778  \n",
       "...              ...  \n",
       "2024-01-16  0.175341  \n",
       "2024-01-17  0.154422  \n",
       "2024-01-18  0.181964  \n",
       "2024-01-19  0.157487  \n",
       "2024-01-21  0.114744  \n",
       "\n",
       "[1897 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b24f5276",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    " \n",
    "train = forecast_df[:int(0.85*(len(ethereum_ts_scaled)))]#85 %percent for train\n",
    "test = forecast_df[int(0.9*(len(ethereum_ts_scaled))):]#15% percent for test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0114e727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open                        0\n",
       "Close                       0\n",
       "High                        0\n",
       "Volume                      0\n",
       "Overnight_Price_movement    0\n",
       "ATR                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d22f1a9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1612, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(190, 6)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train.shape)\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e8537f",
   "metadata": {},
   "source": [
    "# Modeling with **VAR**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1e6c0155",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import VAR\n",
    "model = VAR(train,freq='D')  # 'D' for daily frequency\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43de871f",
   "metadata": {},
   "source": [
    "# Selecting the Appropriate Order using AIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2f50edd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>VAR Order Selection (* highlights the minimums)</caption>\n",
       "<tr>\n",
       "   <td></td>      <th>AIC</th>         <th>BIC</th>         <th>FPE</th>        <th>HQIC</th>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>0</th>  <td>    -36.26</td>  <td>    -36.24</td>  <td> 1.795e-16</td>  <td>    -36.25</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1</th>  <td>    -40.91</td>  <td>    -40.76</td>  <td> 1.717e-18</td>  <td>    -40.85</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>  <td>    -41.62</td>  <td>    -41.36</td>  <td> 8.384e-19</td>  <td>    -41.53</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>  <td>    -41.89</td>  <td>    -41.50</td>  <td> 6.446e-19</td>  <td>    -41.74</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>  <td>    -42.11</td>  <td>    -41.60</td>  <td> 5.175e-19</td>  <td>    -41.92</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>  <td>    -42.24</td>  <td>    -41.61*</td> <td> 4.516e-19</td>  <td>    -42.01</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>6</th>  <td>    -42.34</td>  <td>    -41.59</td>  <td> 4.076e-19</td>  <td>    -42.07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>7</th>  <td>    -42.38</td>  <td>    -41.51</td>  <td> 3.925e-19</td>  <td>    -42.06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>8</th>  <td>    -42.46</td>  <td>    -41.46</td>  <td> 3.644e-19</td>  <td>    -42.09</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>9</th>  <td>    -42.53</td>  <td>    -41.42</td>  <td> 3.372e-19</td>  <td>    -42.12*</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>10</th> <td>    -42.57</td>  <td>    -41.33</td>  <td> 3.257e-19</td>  <td>    -42.11</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>11</th> <td>    -42.61</td>  <td>    -41.26</td>  <td> 3.114e-19</td>  <td>    -42.11</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>12</th> <td>    -42.66</td>  <td>    -41.18</td>  <td> 2.985e-19</td>  <td>    -42.11</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>13</th> <td>    -42.66</td>  <td>    -41.06</td>  <td> 2.966e-19</td>  <td>    -42.07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>14</th> <td>    -42.68</td>  <td>    -40.96</td>  <td> 2.922e-19</td>  <td>    -42.04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>15</th> <td>    -42.68</td>  <td>    -40.84</td>  <td> 2.907e-19</td>  <td>    -42.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>16</th> <td>    -42.71</td>  <td>    -40.74</td>  <td> 2.842e-19</td>  <td>    -41.98</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>17</th> <td>    -42.70</td>  <td>    -40.61</td>  <td> 2.861e-19</td>  <td>    -41.92</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>18</th> <td>    -42.71</td>  <td>    -40.50</td>  <td> 2.843e-19</td>  <td>    -41.89</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>19</th> <td>    -42.73</td>  <td>    -40.40</td>  <td> 2.775e-19</td>  <td>    -41.87</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>20</th> <td>    -42.76*</td> <td>    -40.31</td>  <td> 2.698e-19*</td> <td>    -41.85</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lcccc}\n",
       "\\toprule\n",
       "            & \\textbf{AIC} & \\textbf{BIC} & \\textbf{FPE} & \\textbf{HQIC}  \\\\\n",
       "\\midrule\n",
       "\\textbf{0}  &      -36.26  &      -36.24  &   1.795e-16  &       -36.25   \\\\\n",
       "\\textbf{1}  &      -40.91  &      -40.76  &   1.717e-18  &       -40.85   \\\\\n",
       "\\textbf{2}  &      -41.62  &      -41.36  &   8.384e-19  &       -41.53   \\\\\n",
       "\\textbf{3}  &      -41.89  &      -41.50  &   6.446e-19  &       -41.74   \\\\\n",
       "\\textbf{4}  &      -42.11  &      -41.60  &   5.175e-19  &       -41.92   \\\\\n",
       "\\textbf{5}  &      -42.24  &     -41.61*  &   4.516e-19  &       -42.01   \\\\\n",
       "\\textbf{6}  &      -42.34  &      -41.59  &   4.076e-19  &       -42.07   \\\\\n",
       "\\textbf{7}  &      -42.38  &      -41.51  &   3.925e-19  &       -42.06   \\\\\n",
       "\\textbf{8}  &      -42.46  &      -41.46  &   3.644e-19  &       -42.09   \\\\\n",
       "\\textbf{9}  &      -42.53  &      -41.42  &   3.372e-19  &      -42.12*   \\\\\n",
       "\\textbf{10} &      -42.57  &      -41.33  &   3.257e-19  &       -42.11   \\\\\n",
       "\\textbf{11} &      -42.61  &      -41.26  &   3.114e-19  &       -42.11   \\\\\n",
       "\\textbf{12} &      -42.66  &      -41.18  &   2.985e-19  &       -42.11   \\\\\n",
       "\\textbf{13} &      -42.66  &      -41.06  &   2.966e-19  &       -42.07   \\\\\n",
       "\\textbf{14} &      -42.68  &      -40.96  &   2.922e-19  &       -42.04   \\\\\n",
       "\\textbf{15} &      -42.68  &      -40.84  &   2.907e-19  &       -42.00   \\\\\n",
       "\\textbf{16} &      -42.71  &      -40.74  &   2.842e-19  &       -41.98   \\\\\n",
       "\\textbf{17} &      -42.70  &      -40.61  &   2.861e-19  &       -41.92   \\\\\n",
       "\\textbf{18} &      -42.71  &      -40.50  &   2.843e-19  &       -41.89   \\\\\n",
       "\\textbf{19} &      -42.73  &      -40.40  &   2.775e-19  &       -41.87   \\\\\n",
       "\\textbf{20} &     -42.76*  &      -40.31  &  2.698e-19*  &       -41.85   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{VAR Order Selection (* highlights the minimums)}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.select_order(maxlags =20).summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "24099c2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model.fit(20)  # 20th lag = Minimum AIC\n",
    "#results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e180e286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecasted values:\n",
      " [[0.66460964 0.63840181 0.59504205 0.41553697 0.1887748  0.17667433]\n",
      " [0.63838963 0.6266288  0.55339989 0.41602096 0.21226734 0.17712401]\n",
      " [0.62698215 0.61067049 0.5420583  0.39502439 0.22733903 0.17653651]\n",
      " [0.61102043 0.63832843 0.5432508  0.39318816 0.21950696 0.16870055]\n",
      " [0.63848717 0.64352157 0.57490597 0.40404019 0.20619607 0.17848422]\n",
      " [0.64085296 0.63206968 0.56080366 0.40535018 0.21049466 0.170804  ]\n",
      " [0.63108807 0.63973994 0.57109507 0.4227457  0.21122339 0.17652736]\n",
      " [0.64053338 0.62608648 0.57009495 0.4184742  0.21471765 0.18882127]\n",
      " [0.62562439 0.61912989 0.54402463 0.41278879 0.22461768 0.18912954]\n",
      " [0.61967212 0.63106892 0.54914763 0.40581836 0.22004671 0.18849684]\n",
      " [0.63026742 0.63019718 0.55319268 0.38709233 0.21559639 0.17886285]\n",
      " [0.62942506 0.6315342  0.55511158 0.39646683 0.21558187 0.176949  ]\n",
      " [0.63084997 0.63718679 0.5635481  0.41702348 0.21310279 0.18395943]\n",
      " [0.6360298  0.63861296 0.56727921 0.41829671 0.20896728 0.17864951]\n",
      " [0.6385011  0.63979066 0.57702644 0.41978943 0.20725064 0.18953132]\n",
      " [0.63865464 0.62461195 0.55123363 0.39963477 0.21366757 0.18177289]\n",
      " [0.62440774 0.63640084 0.55745406 0.39912331 0.21630051 0.18026116]\n",
      " [0.63586559 0.63167165 0.56165017 0.39984823 0.21478301 0.18119722]\n",
      " [0.63109474 0.62906174 0.55371478 0.40509816 0.21816233 0.17769877]\n",
      " [0.62849225 0.64010209 0.56345303 0.41284637 0.21375668 0.17901418]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "n_obs = 20  #no of steps to forecast \n",
    "\n",
    "lag= results.k_ar\n",
    "\n",
    "input_data = train.values[-lag:]  # last 20 lags\n",
    "forecast = results.forecast(y=input_data, steps=n_obs)\n",
    "\n",
    "print(\"Forecasted values:\\n\", forecast)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "12237c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Overnight_Price_movement</th>\n",
       "      <th>ATR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-07-15</th>\n",
       "      <td>0.587395</td>\n",
       "      <td>0.627156</td>\n",
       "      <td>0.483853</td>\n",
       "      <td>0.322742</td>\n",
       "      <td>0.246626</td>\n",
       "      <td>0.145502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-16</th>\n",
       "      <td>0.626985</td>\n",
       "      <td>0.627222</td>\n",
       "      <td>0.555973</td>\n",
       "      <td>0.406152</td>\n",
       "      <td>0.221116</td>\n",
       "      <td>0.152812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-17</th>\n",
       "      <td>0.626672</td>\n",
       "      <td>0.624297</td>\n",
       "      <td>0.554208</td>\n",
       "      <td>0.438452</td>\n",
       "      <td>0.223157</td>\n",
       "      <td>0.180827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-18</th>\n",
       "      <td>0.623960</td>\n",
       "      <td>0.622914</td>\n",
       "      <td>0.541093</td>\n",
       "      <td>0.395214</td>\n",
       "      <td>0.225918</td>\n",
       "      <td>0.163811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-19</th>\n",
       "      <td>0.622685</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.562394</td>\n",
       "      <td>0.409579</td>\n",
       "      <td>0.224457</td>\n",
       "      <td>0.163710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-20</th>\n",
       "      <td>0.626155</td>\n",
       "      <td>0.633928</td>\n",
       "      <td>0.559748</td>\n",
       "      <td>0.424985</td>\n",
       "      <td>0.217407</td>\n",
       "      <td>0.167142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-21</th>\n",
       "      <td>0.633681</td>\n",
       "      <td>0.633343</td>\n",
       "      <td>0.546612</td>\n",
       "      <td>0.370509</td>\n",
       "      <td>0.213043</td>\n",
       "      <td>0.153894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-22</th>\n",
       "      <td>0.632251</td>\n",
       "      <td>0.613886</td>\n",
       "      <td>0.551893</td>\n",
       "      <td>0.399205</td>\n",
       "      <td>0.226378</td>\n",
       "      <td>0.171430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-23</th>\n",
       "      <td>0.614669</td>\n",
       "      <td>0.649284</td>\n",
       "      <td>0.566747</td>\n",
       "      <td>0.411941</td>\n",
       "      <td>0.215605</td>\n",
       "      <td>0.174011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-24</th>\n",
       "      <td>0.647225</td>\n",
       "      <td>0.605614</td>\n",
       "      <td>0.545814</td>\n",
       "      <td>0.431923</td>\n",
       "      <td>0.221677</td>\n",
       "      <td>0.180072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-25</th>\n",
       "      <td>0.606010</td>\n",
       "      <td>0.637904</td>\n",
       "      <td>0.538510</td>\n",
       "      <td>0.377478</td>\n",
       "      <td>0.228412</td>\n",
       "      <td>0.157148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-26</th>\n",
       "      <td>0.637140</td>\n",
       "      <td>0.642499</td>\n",
       "      <td>0.578347</td>\n",
       "      <td>0.428617</td>\n",
       "      <td>0.204456</td>\n",
       "      <td>0.170331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-27</th>\n",
       "      <td>0.641645</td>\n",
       "      <td>0.624458</td>\n",
       "      <td>0.558439</td>\n",
       "      <td>0.386776</td>\n",
       "      <td>0.213177</td>\n",
       "      <td>0.165640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-28</th>\n",
       "      <td>0.624372</td>\n",
       "      <td>0.642478</td>\n",
       "      <td>0.555835</td>\n",
       "      <td>0.400235</td>\n",
       "      <td>0.213187</td>\n",
       "      <td>0.162299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-29</th>\n",
       "      <td>0.641288</td>\n",
       "      <td>0.636930</td>\n",
       "      <td>0.563602</td>\n",
       "      <td>0.389287</td>\n",
       "      <td>0.205291</td>\n",
       "      <td>0.156777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-30</th>\n",
       "      <td>0.636231</td>\n",
       "      <td>0.619213</td>\n",
       "      <td>0.558437</td>\n",
       "      <td>0.427103</td>\n",
       "      <td>0.220200</td>\n",
       "      <td>0.170726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-31</th>\n",
       "      <td>0.619189</td>\n",
       "      <td>0.628807</td>\n",
       "      <td>0.551689</td>\n",
       "      <td>0.412066</td>\n",
       "      <td>0.225549</td>\n",
       "      <td>0.164972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-01</th>\n",
       "      <td>0.628271</td>\n",
       "      <td>0.643332</td>\n",
       "      <td>0.557287</td>\n",
       "      <td>0.450492</td>\n",
       "      <td>0.209877</td>\n",
       "      <td>0.188564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-02</th>\n",
       "      <td>0.643580</td>\n",
       "      <td>0.610078</td>\n",
       "      <td>0.563989</td>\n",
       "      <td>0.390866</td>\n",
       "      <td>0.222180</td>\n",
       "      <td>0.187855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-03</th>\n",
       "      <td>0.608832</td>\n",
       "      <td>0.629858</td>\n",
       "      <td>0.539773</td>\n",
       "      <td>0.383442</td>\n",
       "      <td>0.231907</td>\n",
       "      <td>0.169762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open     Close      High    Volume  Overnight_Price_movement  \\\n",
       "Date                                                                           \n",
       "2023-07-15  0.587395  0.627156  0.483853  0.322742                  0.246626   \n",
       "2023-07-16  0.626985  0.627222  0.555973  0.406152                  0.221116   \n",
       "2023-07-17  0.626672  0.624297  0.554208  0.438452                  0.223157   \n",
       "2023-07-18  0.623960  0.622914  0.541093  0.395214                  0.225918   \n",
       "2023-07-19  0.622685  0.626667  0.562394  0.409579                  0.224457   \n",
       "2023-07-20  0.626155  0.633928  0.559748  0.424985                  0.217407   \n",
       "2023-07-21  0.633681  0.633343  0.546612  0.370509                  0.213043   \n",
       "2023-07-22  0.632251  0.613886  0.551893  0.399205                  0.226378   \n",
       "2023-07-23  0.614669  0.649284  0.566747  0.411941                  0.215605   \n",
       "2023-07-24  0.647225  0.605614  0.545814  0.431923                  0.221677   \n",
       "2023-07-25  0.606010  0.637904  0.538510  0.377478                  0.228412   \n",
       "2023-07-26  0.637140  0.642499  0.578347  0.428617                  0.204456   \n",
       "2023-07-27  0.641645  0.624458  0.558439  0.386776                  0.213177   \n",
       "2023-07-28  0.624372  0.642478  0.555835  0.400235                  0.213187   \n",
       "2023-07-29  0.641288  0.636930  0.563602  0.389287                  0.205291   \n",
       "2023-07-30  0.636231  0.619213  0.558437  0.427103                  0.220200   \n",
       "2023-07-31  0.619189  0.628807  0.551689  0.412066                  0.225549   \n",
       "2023-08-01  0.628271  0.643332  0.557287  0.450492                  0.209877   \n",
       "2023-08-02  0.643580  0.610078  0.563989  0.390866                  0.222180   \n",
       "2023-08-03  0.608832  0.629858  0.539773  0.383442                  0.231907   \n",
       "\n",
       "                 ATR  \n",
       "Date                  \n",
       "2023-07-15  0.145502  \n",
       "2023-07-16  0.152812  \n",
       "2023-07-17  0.180827  \n",
       "2023-07-18  0.163811  \n",
       "2023-07-19  0.163710  \n",
       "2023-07-20  0.167142  \n",
       "2023-07-21  0.153894  \n",
       "2023-07-22  0.171430  \n",
       "2023-07-23  0.174011  \n",
       "2023-07-24  0.180072  \n",
       "2023-07-25  0.157148  \n",
       "2023-07-26  0.170331  \n",
       "2023-07-27  0.165640  \n",
       "2023-07-28  0.162299  \n",
       "2023-07-29  0.156777  \n",
       "2023-07-30  0.170726  \n",
       "2023-07-31  0.164972  \n",
       "2023-08-01  0.188564  \n",
       "2023-08-02  0.187855  \n",
       "2023-08-03  0.169762  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new =test[:n_obs] \n",
    "test_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b2664291",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to DataFrame\n",
    "forecasted_df = pd.DataFrame(forecast, index=test_new.index, columns=forecast_df.columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f92c11",
   "metadata": {},
   "source": [
    "# Plotting results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "ab423b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 4))\n",
    "\n",
    "# Plot actual and forecasted values\n",
    "plt.plot(test_new.index[:n_obs], test_new[\"Close\"].iloc[:n_obs], label='Actual', color='blue')\n",
    "plt.plot(forecasted_df.index, forecasted_df[\"Close\"], label='Forecast', color='orange', marker='o')\n",
    "\n",
    "plt.title(f\"{\"Close\"} - Forecast vs Actual\") \n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Close\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ca458c",
   "metadata": {},
   "source": [
    "# Lets Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a51f46e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean value of close is : 0.6387984987697474. Root Mean Squared Error is :0.03219168618309014\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "import math \n",
    "from statistics import mean\n",
    "rmse_close = math.sqrt(mean_squared_error(test_new['Close'], forecasted_df['Close']))\n",
    "\n",
    "print('Mean value of close is : {}. Root Mean Squared Error is :{}'.format(mean(test_new['Close']),rmse_close))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf82a07",
   "metadata": {},
   "source": [
    "#### The rmse should be much less then the mean value and from the above results we see for forecasting 20 steps ahead we get Rmse (0.03) <<<  Mean (0.63)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}