class_6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "from pandas_datareader import data as pdr\n",
    "\n",
    "import yfinance as yf\n",
    "yf.pdr_override() # <== that's all it takes :-)\n",
    "\n",
    "# download dataframe\n",
    "data = pdr.get_data_yahoo(\"AAPL\", start=\"2003-10-30\", end=\"2023-10-30\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, fig = plt.subplots(figsize=(16,8))\n",
    "data['Adj Close'].plot(kind=\"line\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl = data['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.graphics.tsa.plot_pacf(aapl, lags=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\srika\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Users\\srika\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Users\\srika\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:              Adj Close   No. Observations:                 5033\n",
      "Model:                ARIMA(1, 1, 10)   Log Likelihood               -8034.173\n",
      "Date:                Sat, 09 Dec 2023   AIC                          16092.346\n",
      "Time:                        15:01:49   BIC                          16170.629\n",
      "Sample:                             0   HQIC                         16119.774\n",
      "                               - 5033                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.5608      0.117     -4.811      0.000      -0.789      -0.332\n",
      "ma.L1          0.5337      0.117      4.568      0.000       0.305       0.763\n",
      "ma.L2         -0.0516      0.008     -6.860      0.000      -0.066      -0.037\n",
      "ma.L3         -0.0287      0.008     -3.428      0.001      -0.045      -0.012\n",
      "ma.L4         -0.0128      0.007     -1.793      0.073      -0.027       0.001\n",
      "ma.L5          0.0363      0.007      5.327      0.000       0.023       0.050\n",
      "ma.L6          0.0041      0.008      0.500      0.617      -0.012       0.020\n",
      "ma.L7          0.0182      0.007      2.434      0.015       0.004       0.033\n",
      "ma.L8         -0.0339      0.008     -4.496      0.000      -0.049      -0.019\n",
      "ma.L9          0.0227      0.008      2.718      0.007       0.006       0.039\n",
      "ma.L10        -0.0013      0.011     -0.116      0.908      -0.023       0.020\n",
      "sigma2         1.4267      0.010    148.160      0.000       1.408       1.446\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):             62243.80\n",
      "Prob(Q):                              0.95   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):             533.33   Skew:                            -0.09\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                        20.23\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "aapl_tsa = sm.tsa.arima.ARIMA(aapl, order=(1, 1, 10))\n",
    "aapl_res = aapl_tsa.fit()\n",
    "print(aapl_res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\srika\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  return get_prediction_index(\n",
      "c:\\Users\\srika\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:836: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
      "  return get_prediction_index(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5033    168.116318\n",
       "5034    168.100088\n",
       "5035    168.122005\n",
       "5036    167.883556\n",
       "5037    167.986418\n",
       "5038    167.950400\n",
       "5039    168.074833\n",
       "5040    167.876644\n",
       "5041    168.018508\n",
       "5042    167.937511\n",
       "Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_model = aapl_res.get_forecast(steps=10)\n",
    "forecast_CI = forecast_model.conf_int()\n",
    "forecast_mean = forecast_model.predicted_mean\n",
    "\n",
    "\n",
    "forecast_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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