Sharpe_ratio_gain.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4dd4b81c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: dask in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (2021.4.0)\n",
      "Requirement already satisfied: fsspec>=0.6.0 in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (from dask) (0.9.0)\n",
      "Requirement already satisfied: pyyaml in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (from dask) (5.4.1)\n",
      "Requirement already satisfied: cloudpickle>=1.1.1 in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (from dask) (1.6.0)\n",
      "Requirement already satisfied: partd>=0.3.10 in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (from dask) (1.2.0)\n",
      "Requirement already satisfied: toolz>=0.8.2 in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (from dask) (0.11.1)\n",
      "Requirement already satisfied: locket in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages\\locket-0.2.1-py3.8.egg (from partd>=0.3.10->dask) (0.2.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (1.19.5)\n",
      "Requirement already satisfied: pandas in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (1.3.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (from pandas) (2021.1)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (from pandas) (1.19.5)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\emile ndoumbe\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install dask\n",
    "!pip install numpy\n",
    "!pip install pandas\n",
    "!pip install \"plotly>=4\" numpy pandas\n",
    "!pip install -U kaleido\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "import dask.dataframe as dd\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from timeit import default_timer as timer\n",
    "import warnings\n",
    "from pandas import plotting\n",
    "\n",
    "import os\n",
    "\n",
    "if not os.path.exists(\"images\"):\n",
    "    os.mkdir(\"images\")\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13a34527",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa44d88b",
   "metadata": {},
   "source": [
    "# File of OoR_sq results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25202591",
   "metadata": {},
   "outputs": [],
   "source": [
    "file=pd.read_csv(r'C:/Users/Emile Ndoumbe/Dropbox/Oo_R_sq.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ecd0f02",
   "metadata": {},
   "source": [
    "# Sharpe_ratio_gain of sDOC model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cca8c5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the path\n",
    "\n",
    "path1 = r'C:/Users/Emile Ndoumbe/Dropbox/output_forecasting/sDOC_result'                     \n",
    "all_files1 = glob.glob(path1 + \"/*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "abd26f8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c3f9fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpe_ratio_gain1=[]\n",
    "for i in range(63):\n",
    "    fil=pd.read_csv(all_files1[i])\n",
    "    sharpe=[]\n",
    "    for j in range(fil.shape[0]):\n",
    "        SR_0=(fil.iloc[j,5]**2)/fil.iloc[j,3]\n",
    "        sharpe.append(SR_0)\n",
    "    sharpe_1=np.mean(sharpe)\n",
    "    sharpe_2=(sharpe_1+file.iloc[i,3])/(1-file.iloc[i,3])\n",
    "    SR_1=math.sqrt(sharpe_2)- math.sqrt(sharpe_1)\n",
    "    \n",
    "    sharpe_ratio_gain1.append(SR_1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23f796af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fa49bfac",
   "metadata": {},
   "source": [
    "# Sharpe_ratio_gain of PC model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "be3c3526",
   "metadata": {},
   "outputs": [],
   "source": [
    "path2 = r'C:/Users/Emile Ndoumbe/Dropbox/output_forecasting/resultspcs'                     \n",
    "all_files2 = glob.glob(path2 + \"/*.csv\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c2925d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpe_ratio_gain2=[]\n",
    "for i in range(63):\n",
    "    fil=pd.read_csv(all_files2[i])\n",
    "    sharpe=[]\n",
    "    for j in range(fil.shape[0]):\n",
    "        SR_0=(fil.iloc[j,5]**2)/fil.iloc[j,3]\n",
    "        sharpe.append(SR_0)\n",
    "    sharpe_1=np.mean(sharpe)\n",
    "    sharpe_2=(sharpe_1+file.iloc[i,2])/(1-file.iloc[i,2])\n",
    "    SR_1=math.sqrt(sharpe_2)- math.sqrt(sharpe_1) \n",
    "    \n",
    "    sharpe_ratio_gain2.append(SR_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "774f6bb8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ea02b570",
   "metadata": {},
   "source": [
    "# Sharpe_ratio_gain of random forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1de1c432",
   "metadata": {},
   "outputs": [],
   "source": [
    "path3 = r'C:/Users/Emile Ndoumbe/Dropbox/output_forecasting/resultsRFR'                     \n",
    "all_files3 = glob.glob(path3 + \"/*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96da1143",
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpe_ratio_gain3=[]\n",
    "for i in range(63):\n",
    "    fil=pd.read_csv(all_files3[i])\n",
    "    sharpe=[]\n",
    "    for j in range(fil.shape[0]):\n",
    "        SR_0=(fil.iloc[j,5]**2)/fil.iloc[j,3]\n",
    "        sharpe.append(SR_0)\n",
    "    sharpe_1=np.mean(sharpe)\n",
    "    sharpe_2=(sharpe_1+file.iloc[i,0])/(1-file.iloc[i,0])\n",
    "    SR_1=math.sqrt(sharpe_2)- math.sqrt(sharpe_1) \n",
    "    \n",
    "    sharpe_ratio_gain3.append(SR_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba72c2e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "34c2818f",
   "metadata": {},
   "source": [
    "# Sharpe_ratio_gain of xgboost with cv model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fd388d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "path4 = r'C:/Users/Emile Ndoumbe/Dropbox/output_forecasting/resultxgboostcv'                     \n",
    "all_files4 = glob.glob(path4 + \"/*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "531b55b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpe_ratio_gain4=[]\n",
    "for i in range(63):\n",
    "    fil=pd.read_csv(all_files4[i])\n",
    "    sharpe=[]\n",
    "    for j in range(fil.shape[0]):\n",
    "        SR_0=(fil.iloc[j,5]**2)/fil.iloc[j,3]\n",
    "        sharpe.append(SR_0)\n",
    "    sharpe_1=np.mean(sharpe)\n",
    "    sharpe_2=(sharpe_1+file.iloc[i,1])/(1-file.iloc[i,1])\n",
    "    SR_1=math.sqrt(sharpe_2)- math.sqrt(sharpe_1) \n",
    "    \n",
    "    sharpe_ratio_gain4.append(SR_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f0e4e265",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b4e0d208",
   "metadata": {},
   "source": [
    "# Sharpe_ratio_gain of neural network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34df3ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "path5 = r'C:/Users/Emile Ndoumbe/Dropbox/output_forecasting/resultNeural'                     \n",
    "all_files5 = glob.glob(path5 + \"/*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "daa2e84e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpe_ratio_gain5_11=[]\n",
    "for i in range(12):\n",
    "    fil=pd.read_csv(all_files5[i])\n",
    "    sharpe=[]\n",
    "    for j in range(fil.shape[0]):\n",
    "        SR_0=(fil.iloc[j,5]**2)/fil.iloc[j,3]\n",
    "        sharpe.append(SR_0)\n",
    "    sharpe_1=np.mean(sharpe)\n",
    "    sharpe_2=(sharpe_1+file.iloc[i,4])/(1-file.iloc[i,4])\n",
    "    SR_1=math.sqrt(sharpe_2)- math.sqrt(sharpe_1) \n",
    "    \n",
    "    sharpe_ratio_gain5_11.append(SR_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "120d62c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5503edbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpe_ratio_gain5_62=[]\n",
    "for i in range(12,62):\n",
    "    fil=pd.read_csv(all_files5[i])\n",
    "    sharpe=[]\n",
    "    for j in range(fil.shape[0]):\n",
    "        SR_0=(fil.iloc[j,5]**2)/fil.iloc[j,3]\n",
    "        sharpe.append(SR_0)\n",
    "    sharpe_1=np.mean(sharpe)\n",
    "    sharpe_2=(sharpe_1+file.iloc[i+1,4])/(1-file.iloc[i+1,4])\n",
    "    SR_1=math.sqrt(sharpe_2)- math.sqrt(sharpe_1) \n",
    "    \n",
    "    sharpe_ratio_gain5_62.append(SR_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55f4fa3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a23860aa",
   "metadata": {},
   "source": [
    "# Diagram of evaluation criteria\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3727eb39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Categories = [\"RF\",\"xgboost_cv\",\"PCR\",\"sDOC\",\"NN\"] # 5 methods\n",
    "\n",
    "Sous_categories = [\"ind_6\",\"ind_7\",\"ind_8\",\"ind_9\",\"ind_10\"] \n",
    "#Sous_categories = [\"ind_61\",\"ind_62\",\"ind_63\"] \n",
    "# Valeurs pour chaque catégories\n",
    "\n",
    "y1 = [-2.94,-2.14,-4.70,-4.58,-4.44]; y2 = [-1.44,-17,3.08,-2.96,2.07] ; y3 = [0.04,0.38,0.04,0.13,-0.07];y4=[-0.16,2.84,3.05,0.51,0.54];y5=[-0.79,1.87,2.87,0.86,0]\n",
    "\n",
    "from math import * \n",
    "\n",
    "nb_categories = len(Categories)\n",
    "\n",
    "largeur_barre = floor(1*5/nb_categories)/10\n",
    "\n",
    "x1 = range(len(y1))\n",
    "\n",
    "x2 = [i + largeur_barre for i in x1]\n",
    "\n",
    "x3 = [i + 2*largeur_barre for i in x1]\n",
    "\n",
    "x4 = [i + 3*largeur_barre for i in x1]\n",
    "\n",
    "x5 = [i + 4*largeur_barre for i in x1]\n",
    "\n",
    "\n",
    "\n",
    "plt.bar(x1, y1, width = largeur_barre, color = 'red',\n",
    "\n",
    "           edgecolor = 'black', linewidth = 2)\n",
    "\n",
    "plt.bar(x2, y2, width = largeur_barre, color = 'blue',\n",
    "\n",
    "           edgecolor = 'black', linewidth = 2)\n",
    "\n",
    "plt.bar(x3, y3, width = largeur_barre, color = 'green',\n",
    "\n",
    "           edgecolor = 'black', linewidth = 2)\n",
    "\n",
    "plt.bar(x4, y4, width = largeur_barre, color = 'yellow',\n",
    "\n",
    "           edgecolor = 'black', linewidth = 2)\n",
    "\n",
    "plt.bar(x5, y5, width = largeur_barre, color = 'pink',\n",
    "\n",
    "           edgecolor = 'black', linewidth = 2)\n",
    "\n",
    "plt.xticks([r + largeur_barre / nb_categories for r in range(len(y1))],\n",
    "\n",
    "              Sous_categories)\n",
    "\n",
    "plt.legend(Categories,loc=4)\n",
    "\n",
    "plt.savefig('grapheco2.pdf')\n",
    "\n",
    "plt.show()\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aec6d40",
   "metadata": {},
   "source": [
    "# Graphical representation of DOC-GARCH(1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "3e82c379",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r'C:/Users/Emile Ndoumbe/Dropbox/garch_month'                     \n",
    "all_files = glob.glob(path + \"/*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "3aa829af",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>excess_log_return</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10/30/1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.998126</td>\n",
       "      <td>0.998126</td>\n",
       "      <td>0.998126</td>\n",
       "      <td>0.998126</td>\n",
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       "      <td>0.998126</td>\n",
       "      <td>0.998126</td>\n",
       "      <td>0.998126</td>\n",
       "      <td>0.998126</td>\n",
       "      <td>0.998126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/30/1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.936047</td>\n",
       "      <td>0.926647</td>\n",
       "      <td>0.900540</td>\n",
       "      <td>0.944631</td>\n",
       "      <td>0.927186</td>\n",
       "      <td>0.910627</td>\n",
       "      <td>0.941280</td>\n",
       "      <td>0.937883</td>\n",
       "      <td>0.911292</td>\n",
       "      <td>0.881439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12/31/1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.877999</td>\n",
       "      <td>0.860722</td>\n",
       "      <td>0.813173</td>\n",
       "      <td>0.894749</td>\n",
       "      <td>0.861418</td>\n",
       "      <td>0.830886</td>\n",
       "      <td>0.888657</td>\n",
       "      <td>0.881498</td>\n",
       "      <td>0.832137</td>\n",
       "      <td>0.779794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/29/1999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.823732</td>\n",
       "      <td>0.799950</td>\n",
       "      <td>0.735025</td>\n",
       "      <td>0.848279</td>\n",
       "      <td>0.800457</td>\n",
       "      <td>0.758223</td>\n",
       "      <td>0.839998</td>\n",
       "      <td>0.828739</td>\n",
       "      <td>0.759993</td>\n",
       "      <td>0.691430</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/26/1999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.773011</td>\n",
       "      <td>0.743965</td>\n",
       "      <td>0.665202</td>\n",
       "      <td>0.805027</td>\n",
       "      <td>0.743960</td>\n",
       "      <td>0.692019</td>\n",
       "      <td>0.795064</td>\n",
       "      <td>0.779387</td>\n",
       "      <td>0.694252</td>\n",
       "      <td>0.614805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>8/31/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.048790</td>\n",
       "      <td>1.184008</td>\n",
       "      <td>1.282349</td>\n",
       "      <td>2.572745</td>\n",
       "      <td>1.143793</td>\n",
       "      <td>3.519205</td>\n",
       "      <td>2.188677</td>\n",
       "      <td>1.645280</td>\n",
       "      <td>1.142573</td>\n",
       "      <td>6.084613</td>\n",
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       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>9/30/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983429</td>\n",
       "      <td>1.098216</td>\n",
       "      <td>1.155310</td>\n",
       "      <td>2.419969</td>\n",
       "      <td>1.062258</td>\n",
       "      <td>3.209165</td>\n",
       "      <td>2.049394</td>\n",
       "      <td>1.544119</td>\n",
       "      <td>1.042989</td>\n",
       "      <td>5.339827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>10/30/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.922304</td>\n",
       "      <td>1.019011</td>\n",
       "      <td>1.041391</td>\n",
       "      <td>2.276589</td>\n",
       "      <td>0.986650</td>\n",
       "      <td>2.926464</td>\n",
       "      <td>1.919483</td>\n",
       "      <td>1.449315</td>\n",
       "      <td>0.952193</td>\n",
       "      <td>4.686450</td>\n",
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       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>11/30/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.865150</td>\n",
       "      <td>0.945916</td>\n",
       "      <td>0.939297</td>\n",
       "      <td>2.142048</td>\n",
       "      <td>0.916546</td>\n",
       "      <td>2.668694</td>\n",
       "      <td>1.798346</td>\n",
       "      <td>1.360478</td>\n",
       "      <td>0.869420</td>\n",
       "      <td>4.113296</td>\n",
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       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>12/31/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.811721</td>\n",
       "      <td>0.878490</td>\n",
       "      <td>0.847862</td>\n",
       "      <td>2.015823</td>\n",
       "      <td>0.851556</td>\n",
       "      <td>2.433659</td>\n",
       "      <td>1.685427</td>\n",
       "      <td>1.277242</td>\n",
       "      <td>0.793972</td>\n",
       "      <td>3.610554</td>\n",
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       "<p>267 rows × 12 columns</p>\n",
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      "text/plain": [
       "           date  excess_log_return        X1        X2        X3        X4  \\\n",
       "0    10/30/1998                0.0  0.998126  0.998126  0.998126  0.998126   \n",
       "1    11/30/1998                0.0  0.936047  0.926647  0.900540  0.944631   \n",
       "2    12/31/1998                0.0  0.877999  0.860722  0.813173  0.894749   \n",
       "3     1/29/1999                0.0  0.823732  0.799950  0.735025  0.848279   \n",
       "4     2/26/1999                0.0  0.773011  0.743965  0.665202  0.805027   \n",
       "..          ...                ...       ...       ...       ...       ...   \n",
       "262   8/31/2020                0.0  1.048790  1.184008  1.282349  2.572745   \n",
       "263   9/30/2020                0.0  0.983429  1.098216  1.155310  2.419969   \n",
       "264  10/30/2020                0.0  0.922304  1.019011  1.041391  2.276589   \n",
       "265  11/30/2020                0.0  0.865150  0.945916  0.939297  2.142048   \n",
       "266  12/31/2020                0.0  0.811721  0.878490  0.847862  2.015823   \n",
       "\n",
       "           X5        X6        X7        X8        X9       X10  \n",
       "0    0.998126  0.998126  0.998126  0.998126  0.998126  0.998126  \n",
       "1    0.927186  0.910627  0.941280  0.937883  0.911292  0.881439  \n",
       "2    0.861418  0.830886  0.888657  0.881498  0.832137  0.779794  \n",
       "3    0.800457  0.758223  0.839998  0.828739  0.759993  0.691430  \n",
       "4    0.743960  0.692019  0.795064  0.779387  0.694252  0.614805  \n",
       "..        ...       ...       ...       ...       ...       ...  \n",
       "262  1.143793  3.519205  2.188677  1.645280  1.142573  6.084613  \n",
       "263  1.062258  3.209165  2.049394  1.544119  1.042989  5.339827  \n",
       "264  0.986650  2.926464  1.919483  1.449315  0.952193  4.686450  \n",
       "265  0.916546  2.668694  1.798346  1.360478  0.869420  4.113296  \n",
       "266  0.851556  2.433659  1.685427  1.277242  0.793972  3.610554  \n",
       "\n",
       "[267 rows x 12 columns]"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file=pd.read_csv(all_files[62])\n",
    "file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "e54f17c7",
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
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   ],
   "source": [
    "fig=go.Figure()\n",
    "trace6=go.Scatter(x=file['date'],y=file['X1'],name='garch_doc1')\n",
    "trace7=go.Scatter(x=file['date'],y=file['X2'],name='garch_doc2')\n",
    "trace8=go.Scatter(x=file['date'],y=file['X3'],name='garch_doc3')\n",
    "trace9=go.Scatter(x=file['date'],y=file['X4'],name='garch_doc4')\n",
    "trace10=go.Scatter(x=file['date'],y=file['X5'],name='garch_doc5')\n",
    "fig.add_trace(trace6)\n",
    "fig.add_trace(trace7)\n",
    "fig.add_trace(trace8)\n",
    "fig.add_trace(trace9)\n",
    "fig.add_trace(trace10)\n",
    "fig.show()\n",
    "fig.write_image('images/garchdoc_63_1.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f623cffb",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a421368d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "69f25cc7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a12f6b7",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2a4a231",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c31a22de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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