{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "taruma_udemy_rnn_1_0_0",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "TPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "PNqS1SfWDenV",
"colab_type": "text"
},
"source": [
"# Recurrent Neural Networks (LSTM)\n",
"\n",
"Notebook ini berdasarkan kursus __Deep Learning A-Z™: Hands-On Artificial Neural Networks__ di Udemy. [Lihat kursus](https://www.udemy.com/deeplearning/).\n",
"\n",
"## Informasi Notebook\n",
"\n",
"- __notebook name__: `taruma_udemy_rnn_1_0_0`\n",
"- __notebook version/date__: `1.0.0`/`20190719`\n",
"- __notebook server__: Google Colab\n",
"- __python version__: `3.7`\n",
"- __keras version__: `2.2.4`"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VAQQ3wSCQrMY",
"colab_type": "text"
},
"source": [
"## STEP 0: SETUP"
]
},
{
"cell_type": "code",
"metadata": {
"id": "48yAKo5ggIva",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "8b149046-81b7-492f-8661-aaff8447a7f3"
},
"source": [
"# DESKRIPSI PROYEK\n",
"NOTEBOOK_TITLE = 'taruma_udemy_rnn'\n",
"NOTEBOOK_VERSION = '1.0.0'\n",
"NOTEBOOK_NAME = \"{}_{}\".format(NOTEBOOK_TITLE, NOTEBOOK_VERSION.replace('.','_'))\n",
"print(f\"NOTEBOOK_NAME = {NOTEBOOK_NAME}\")"
],
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"text": [
"NOTEBOOK_NAME = taruma_udemy_rnn_1_0_0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Tb9bSMIaEPL1",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "e8889a56-6f06-441b-841f-42a8a03a2295"
},
"source": [
"# MEMERIKSA VERSI PADA SISTEM\n",
"import sys\n",
"import keras\n",
"print(\"python version: {}\".format(sys.version))\n",
"print(\"keras version: {}\".format(keras.__version__))"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"python version: 3.6.8 (default, Jan 14 2019, 11:02:34) \n",
"[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]]\n",
"keras version: 2.2.4\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1QLngy5sJGay",
"colab_type": "code",
"colab": {}
},
"source": [
"# MENGGUNAKAN DATAFRAME INTERAKTIF\n",
"%load_ext google.colab.data_table"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7SVlD3XmGHSi",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 122
},
"outputId": "806c4d15-5188-4ab7-b098-8e6bd62b9974"
},
"source": [
"# MENGHUBUNGKAN DENGAN GOOGLE DRIVE\n",
"from google.colab import drive\n",
"drive.mount('/content/gdrive')\n",
"DROP_DRIVE = '/content/gdrive/My Drive/Colab Notebooks/_dropbox'"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
"\n",
"Enter your authorization code:\n",
"··········\n",
"Mounted at /content/gdrive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "phq8K3WV8x3M",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 289
},
"outputId": "556e7aad-a2cf-4a00-869f-e24a2e63131b"
},
"source": [
"# AMBIL DATASET\n",
"!wget -O superdatascience_rnn_udemy.zip \"https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P16-Recurrent-Neural-Networks.zip\"\n",
"!unzip superdatascience_rnn_udemy.zip"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"--2019-07-19 04:01:09-- https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P16-Recurrent-Neural-Networks.zip\n",
"Resolving sds-platform-private.s3-us-east-2.amazonaws.com (sds-platform-private.s3-us-east-2.amazonaws.com)... 52.219.88.40\n",
"Connecting to sds-platform-private.s3-us-east-2.amazonaws.com (sds-platform-private.s3-us-east-2.amazonaws.com)|52.219.88.40|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 24237 (24K) [application/zip]\n",
"Saving to: ‘superdatascience_rnn_udemy.zip’\n",
"\n",
"\r superdata 0%[ ] 0 --.-KB/s \rsuperdatascience_rn 100%[===================>] 23.67K --.-KB/s in 0.03s \n",
"\n",
"2019-07-19 04:01:09 (860 KB/s) - ‘superdatascience_rnn_udemy.zip’ saved [24237/24237]\n",
"\n",
"Archive: superdatascience_rnn_udemy.zip\n",
" creating: Recurrent_Neural_Networks/\n",
" inflating: Recurrent_Neural_Networks/Google_Stock_Price_Test.csv \n",
" inflating: Recurrent_Neural_Networks/Google_Stock_Price_Train.csv \n",
" inflating: Recurrent_Neural_Networks/rnn.py \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "oELabPAI9S6V",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "da0a93d7-5e5a-4c10-80de-1b50d6f0b4c4"
},
"source": [
"# MENENTUKAN DATASET PATH\n",
"DROP_DIRECTORY = 'Recurrent_Neural_Networks'\n",
"FILE_TRAIN = 'Google_Stock_Price_Train.csv'\n",
"FILE_TEST = 'Google_Stock_Price_Test.csv'\n",
"\n",
"path_train = \"{}/{}\".format(DROP_DIRECTORY, FILE_TRAIN)\n",
"path_test = \"{}/{}\".format(DROP_DIRECTORY, FILE_TEST)\n",
"print(f'Training dataset: {path_train}')\n",
"print(f'Test dataset: {path_test}')"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"Training dataset: Recurrent_Neural_Networks/Google_Stock_Price_Train.csv\n",
"Test dataset: Recurrent_Neural_Networks/Google_Stock_Price_Test.csv\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DWP-WzolIkwb",
"colab_type": "text"
},
"source": [
"## STEP 1: IMPORT LIBRARIES"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ib30h9CgImRF",
"colab_type": "code",
"colab": {}
},
"source": [
"# IMPORT THE LIBRARIES\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "mRBX4e1XId5e",
"colab_type": "text"
},
"source": [
"## STEP 2: IMPORT DATASET"
]
},
{
"cell_type": "code",
"metadata": {
"id": "atenwWVNCyem",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"outputId": "6f1c23e9-640a-49a1-af0a-1df354f4eb66"
},
"source": [
"# IMPORT TRAINING SET\n",
"dataset_train = pd.read_csv(path_train)\n",
"dataset_train"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" Low | \n",
" Close | \n",
" Volume | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 1/3/2012 | \n",
" 325.25 | \n",
" 332.83 | \n",
" 324.97 | \n",
" 663.59 | \n",
" 7,380,500 | \n",
"
\n",
" \n",
" | 1 | \n",
" 1/4/2012 | \n",
" 331.27 | \n",
" 333.87 | \n",
" 329.08 | \n",
" 666.45 | \n",
" 5,749,400 | \n",
"
\n",
" \n",
" | 2 | \n",
" 1/5/2012 | \n",
" 329.83 | \n",
" 330.75 | \n",
" 326.89 | \n",
" 657.21 | \n",
" 6,590,300 | \n",
"
\n",
" \n",
" | 3 | \n",
" 1/6/2012 | \n",
" 328.34 | \n",
" 328.77 | \n",
" 323.68 | \n",
" 648.24 | \n",
" 5,405,900 | \n",
"
\n",
" \n",
" | 4 | \n",
" 1/9/2012 | \n",
" 322.04 | \n",
" 322.29 | \n",
" 309.46 | \n",
" 620.76 | \n",
" 11,688,800 | \n",
"
\n",
" \n",
" | 5 | \n",
" 1/10/2012 | \n",
" 313.70 | \n",
" 315.72 | \n",
" 307.30 | \n",
" 621.43 | \n",
" 8,824,000 | \n",
"
\n",
" \n",
" | 6 | \n",
" 1/11/2012 | \n",
" 310.59 | \n",
" 313.52 | \n",
" 309.40 | \n",
" 624.25 | \n",
" 4,817,800 | \n",
"
\n",
" \n",
" | 7 | \n",
" 1/12/2012 | \n",
" 314.43 | \n",
" 315.26 | \n",
" 312.08 | \n",
" 627.92 | \n",
" 3,764,400 | \n",
"
\n",
" \n",
" | 8 | \n",
" 1/13/2012 | \n",
" 311.96 | \n",
" 312.30 | \n",
" 309.37 | \n",
" 623.28 | \n",
" 4,631,800 | \n",
"
\n",
" \n",
" | 9 | \n",
" 1/17/2012 | \n",
" 314.81 | \n",
" 314.81 | \n",
" 311.67 | \n",
" 626.86 | \n",
" 3,832,800 | \n",
"
\n",
" \n",
" | 10 | \n",
" 1/18/2012 | \n",
" 312.14 | \n",
" 315.82 | \n",
" 309.90 | \n",
" 631.18 | \n",
" 5,544,000 | \n",
"
\n",
" \n",
" | 11 | \n",
" 1/19/2012 | \n",
" 319.30 | \n",
" 319.30 | \n",
" 314.55 | \n",
" 637.82 | \n",
" 12,657,800 | \n",
"
\n",
" \n",
" | 12 | \n",
" 1/20/2012 | \n",
" 294.16 | \n",
" 294.40 | \n",
" 289.76 | \n",
" 584.39 | \n",
" 21,231,800 | \n",
"
\n",
" \n",
" | 13 | \n",
" 1/23/2012 | \n",
" 291.91 | \n",
" 293.23 | \n",
" 290.49 | \n",
" 583.92 | \n",
" 6,851,300 | \n",
"
\n",
" \n",
" | 14 | \n",
" 1/24/2012 | \n",
" 292.07 | \n",
" 292.74 | \n",
" 287.92 | \n",
" 579.34 | \n",
" 6,134,400 | \n",
"
\n",
" \n",
" | 15 | \n",
" 1/25/2012 | \n",
" 287.68 | \n",
" 288.27 | \n",
" 282.13 | \n",
" 567.93 | \n",
" 10,012,700 | \n",
"
\n",
" \n",
" | 16 | \n",
" 1/26/2012 | \n",
" 284.92 | \n",
" 286.17 | \n",
" 281.22 | \n",
" 566.54 | \n",
" 6,476,500 | \n",
"
\n",
" \n",
" | 17 | \n",
" 1/27/2012 | \n",
" 284.32 | \n",
" 289.08 | \n",
" 283.60 | \n",
" 578.39 | \n",
" 7,262,000 | \n",
"
\n",
" \n",
" | 18 | \n",
" 1/30/2012 | \n",
" 287.95 | \n",
" 288.92 | \n",
" 285.63 | \n",
" 576.11 | \n",
" 4,678,400 | \n",
"
\n",
" \n",
" | 19 | \n",
" 1/31/2012 | \n",
" 290.41 | \n",
" 290.91 | \n",
" 286.50 | \n",
" 578.52 | \n",
" 4,300,700 | \n",
"
\n",
" \n",
" | 20 | \n",
" 2/1/2012 | \n",
" 291.38 | \n",
" 291.66 | \n",
" 288.49 | \n",
" 579.24 | \n",
" 4,658,700 | \n",
"
\n",
" \n",
" | 21 | \n",
" 2/2/2012 | \n",
" 291.34 | \n",
" 292.11 | \n",
" 289.95 | \n",
" 583.51 | \n",
" 4,847,400 | \n",
"
\n",
" \n",
" | 22 | \n",
" 2/3/2012 | \n",
" 294.23 | \n",
" 297.42 | \n",
" 292.93 | \n",
" 594.7 | \n",
" 6,360,700 | \n",
"
\n",
" \n",
" | 23 | \n",
" 2/6/2012 | \n",
" 296.39 | \n",
" 304.27 | \n",
" 295.90 | \n",
" 607.42 | \n",
" 7,386,700 | \n",
"
\n",
" \n",
" | 24 | \n",
" 2/7/2012 | \n",
" 302.44 | \n",
" 303.56 | \n",
" 300.75 | \n",
" 605.11 | \n",
" 4,199,700 | \n",
"
\n",
" \n",
" | 25 | \n",
" 2/8/2012 | \n",
" 303.18 | \n",
" 304.53 | \n",
" 301.24 | \n",
" 608.18 | \n",
" 3,686,400 | \n",
"
\n",
" \n",
" | 26 | \n",
" 2/9/2012 | \n",
" 304.87 | \n",
" 306.10 | \n",
" 303.36 | \n",
" 609.79 | \n",
" 4,546,300 | \n",
"
\n",
" \n",
" | 27 | \n",
" 2/10/2012 | \n",
" 302.81 | \n",
" 302.93 | \n",
" 300.87 | \n",
" 604.25 | \n",
" 4,667,700 | \n",
"
\n",
" \n",
" | 28 | \n",
" 2/13/2012 | \n",
" 304.11 | \n",
" 305.77 | \n",
" 303.87 | \n",
" 610.52 | \n",
" 3,646,100 | \n",
"
\n",
" \n",
" | 29 | \n",
" 2/14/2012 | \n",
" 304.63 | \n",
" 304.86 | \n",
" 301.25 | \n",
" 608.09 | \n",
" 3,620,900 | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 1228 | \n",
" 11/17/2016 | \n",
" 766.92 | \n",
" 772.70 | \n",
" 764.23 | \n",
" 771.23 | \n",
" 1,304,000 | \n",
"
\n",
" \n",
" | 1229 | \n",
" 11/18/2016 | \n",
" 771.37 | \n",
" 775.00 | \n",
" 760.00 | \n",
" 760.54 | \n",
" 1,547,100 | \n",
"
\n",
" \n",
" | 1230 | \n",
" 11/21/2016 | \n",
" 762.61 | \n",
" 769.70 | \n",
" 760.60 | \n",
" 769.2 | \n",
" 1,330,600 | \n",
"
\n",
" \n",
" | 1231 | \n",
" 11/22/2016 | \n",
" 772.63 | \n",
" 776.96 | \n",
" 767.00 | \n",
" 768.27 | \n",
" 1,593,100 | \n",
"
\n",
" \n",
" | 1232 | \n",
" 11/23/2016 | \n",
" 767.73 | \n",
" 768.28 | \n",
" 755.25 | \n",
" 760.99 | \n",
" 1,478,400 | \n",
"
\n",
" \n",
" | 1233 | \n",
" 11/25/2016 | \n",
" 764.26 | \n",
" 765.00 | \n",
" 760.52 | \n",
" 761.68 | \n",
" 587,400 | \n",
"
\n",
" \n",
" | 1234 | \n",
" 11/28/2016 | \n",
" 760.00 | \n",
" 779.53 | \n",
" 759.80 | \n",
" 768.24 | \n",
" 2,188,200 | \n",
"
\n",
" \n",
" | 1235 | \n",
" 11/29/2016 | \n",
" 771.53 | \n",
" 778.50 | \n",
" 768.24 | \n",
" 770.84 | \n",
" 1,616,600 | \n",
"
\n",
" \n",
" | 1236 | \n",
" 11/30/2016 | \n",
" 770.07 | \n",
" 772.99 | \n",
" 754.83 | \n",
" 758.04 | \n",
" 2,392,900 | \n",
"
\n",
" \n",
" | 1237 | \n",
" 12/1/2016 | \n",
" 757.44 | \n",
" 759.85 | \n",
" 737.03 | \n",
" 747.92 | \n",
" 3,017,900 | \n",
"
\n",
" \n",
" | 1238 | \n",
" 12/2/2016 | \n",
" 744.59 | \n",
" 754.00 | \n",
" 743.10 | \n",
" 750.5 | \n",
" 1,452,500 | \n",
"
\n",
" \n",
" | 1239 | \n",
" 12/5/2016 | \n",
" 757.71 | \n",
" 763.90 | \n",
" 752.90 | \n",
" 762.52 | \n",
" 1,394,200 | \n",
"
\n",
" \n",
" | 1240 | \n",
" 12/6/2016 | \n",
" 764.73 | \n",
" 768.83 | \n",
" 757.34 | \n",
" 759.11 | \n",
" 1,690,700 | \n",
"
\n",
" \n",
" | 1241 | \n",
" 12/7/2016 | \n",
" 761.00 | \n",
" 771.36 | \n",
" 755.80 | \n",
" 771.19 | \n",
" 1,761,000 | \n",
"
\n",
" \n",
" | 1242 | \n",
" 12/8/2016 | \n",
" 772.48 | \n",
" 778.18 | \n",
" 767.23 | \n",
" 776.42 | \n",
" 1,488,100 | \n",
"
\n",
" \n",
" | 1243 | \n",
" 12/9/2016 | \n",
" 780.00 | \n",
" 789.43 | \n",
" 779.02 | \n",
" 789.29 | \n",
" 1,821,900 | \n",
"
\n",
" \n",
" | 1244 | \n",
" 12/12/2016 | \n",
" 785.04 | \n",
" 791.25 | \n",
" 784.35 | \n",
" 789.27 | \n",
" 2,104,100 | \n",
"
\n",
" \n",
" | 1245 | \n",
" 12/13/2016 | \n",
" 793.90 | \n",
" 804.38 | \n",
" 793.34 | \n",
" 796.1 | \n",
" 2,145,200 | \n",
"
\n",
" \n",
" | 1246 | \n",
" 12/14/2016 | \n",
" 797.40 | \n",
" 804.00 | \n",
" 794.01 | \n",
" 797.07 | \n",
" 1,704,200 | \n",
"
\n",
" \n",
" | 1247 | \n",
" 12/15/2016 | \n",
" 797.34 | \n",
" 803.00 | \n",
" 792.92 | \n",
" 797.85 | \n",
" 1,626,500 | \n",
"
\n",
" \n",
" | 1248 | \n",
" 12/16/2016 | \n",
" 800.40 | \n",
" 800.86 | \n",
" 790.29 | \n",
" 790.8 | \n",
" 2,443,800 | \n",
"
\n",
" \n",
" | 1249 | \n",
" 12/19/2016 | \n",
" 790.22 | \n",
" 797.66 | \n",
" 786.27 | \n",
" 794.2 | \n",
" 1,232,100 | \n",
"
\n",
" \n",
" | 1250 | \n",
" 12/20/2016 | \n",
" 796.76 | \n",
" 798.65 | \n",
" 793.27 | \n",
" 796.42 | \n",
" 951,000 | \n",
"
\n",
" \n",
" | 1251 | \n",
" 12/21/2016 | \n",
" 795.84 | \n",
" 796.68 | \n",
" 787.10 | \n",
" 794.56 | \n",
" 1,211,300 | \n",
"
\n",
" \n",
" | 1252 | \n",
" 12/22/2016 | \n",
" 792.36 | \n",
" 793.32 | \n",
" 788.58 | \n",
" 791.26 | \n",
" 972,200 | \n",
"
\n",
" \n",
" | 1253 | \n",
" 12/23/2016 | \n",
" 790.90 | \n",
" 792.74 | \n",
" 787.28 | \n",
" 789.91 | \n",
" 623,400 | \n",
"
\n",
" \n",
" | 1254 | \n",
" 12/27/2016 | \n",
" 790.68 | \n",
" 797.86 | \n",
" 787.66 | \n",
" 791.55 | \n",
" 789,100 | \n",
"
\n",
" \n",
" | 1255 | \n",
" 12/28/2016 | \n",
" 793.70 | \n",
" 794.23 | \n",
" 783.20 | \n",
" 785.05 | \n",
" 1,153,800 | \n",
"
\n",
" \n",
" | 1256 | \n",
" 12/29/2016 | \n",
" 783.33 | \n",
" 785.93 | \n",
" 778.92 | \n",
" 782.79 | \n",
" 744,300 | \n",
"
\n",
" \n",
" | 1257 | \n",
" 12/30/2016 | \n",
" 782.75 | \n",
" 782.78 | \n",
" 770.41 | \n",
" 771.82 | \n",
" 1,770,000 | \n",
"
\n",
" \n",
"
\n",
"
1258 rows × 6 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Date | \n",
" Open | \n",
" High | \n",
" Low | \n",
" Close | \n",
" Volume | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 1/3/2017 | \n",
" 778.81 | \n",
" 789.63 | \n",
" 775.80 | \n",
" 786.14 | \n",
" 1,657,300 | \n",
"
\n",
" \n",
" | 1 | \n",
" 1/4/2017 | \n",
" 788.36 | \n",
" 791.34 | \n",
" 783.16 | \n",
" 786.90 | \n",
" 1,073,000 | \n",
"
\n",
" \n",
" | 2 | \n",
" 1/5/2017 | \n",
" 786.08 | \n",
" 794.48 | \n",
" 785.02 | \n",
" 794.02 | \n",
" 1,335,200 | \n",
"
\n",
" \n",
" | 3 | \n",
" 1/6/2017 | \n",
" 795.26 | \n",
" 807.90 | \n",
" 792.20 | \n",
" 806.15 | \n",
" 1,640,200 | \n",
"
\n",
" \n",
" | 4 | \n",
" 1/9/2017 | \n",
" 806.40 | \n",
" 809.97 | \n",
" 802.83 | \n",
" 806.65 | \n",
" 1,272,400 | \n",
"
\n",
" \n",
" | 5 | \n",
" 1/10/2017 | \n",
" 807.86 | \n",
" 809.13 | \n",
" 803.51 | \n",
" 804.79 | \n",
" 1,176,800 | \n",
"
\n",
" \n",
" | 6 | \n",
" 1/11/2017 | \n",
" 805.00 | \n",
" 808.15 | \n",
" 801.37 | \n",
" 807.91 | \n",
" 1,065,900 | \n",
"
\n",
" \n",
" | 7 | \n",
" 1/12/2017 | \n",
" 807.14 | \n",
" 807.39 | \n",
" 799.17 | \n",
" 806.36 | \n",
" 1,353,100 | \n",
"
\n",
" \n",
" | 8 | \n",
" 1/13/2017 | \n",
" 807.48 | \n",
" 811.22 | \n",
" 806.69 | \n",
" 807.88 | \n",
" 1,099,200 | \n",
"
\n",
" \n",
" | 9 | \n",
" 1/17/2017 | \n",
" 807.08 | \n",
" 807.14 | \n",
" 800.37 | \n",
" 804.61 | \n",
" 1,362,100 | \n",
"
\n",
" \n",
" | 10 | \n",
" 1/18/2017 | \n",
" 805.81 | \n",
" 806.21 | \n",
" 800.99 | \n",
" 806.07 | \n",
" 1,294,400 | \n",
"
\n",
" \n",
" | 11 | \n",
" 1/19/2017 | \n",
" 805.12 | \n",
" 809.48 | \n",
" 801.80 | \n",
" 802.17 | \n",
" 919,300 | \n",
"
\n",
" \n",
" | 12 | \n",
" 1/20/2017 | \n",
" 806.91 | \n",
" 806.91 | \n",
" 801.69 | \n",
" 805.02 | \n",
" 1,670,000 | \n",
"
\n",
" \n",
" | 13 | \n",
" 1/23/2017 | \n",
" 807.25 | \n",
" 820.87 | \n",
" 803.74 | \n",
" 819.31 | \n",
" 1,963,600 | \n",
"
\n",
" \n",
" | 14 | \n",
" 1/24/2017 | \n",
" 822.30 | \n",
" 825.90 | \n",
" 817.82 | \n",
" 823.87 | \n",
" 1,474,000 | \n",
"
\n",
" \n",
" | 15 | \n",
" 1/25/2017 | \n",
" 829.62 | \n",
" 835.77 | \n",
" 825.06 | \n",
" 835.67 | \n",
" 1,494,500 | \n",
"
\n",
" \n",
" | 16 | \n",
" 1/26/2017 | \n",
" 837.81 | \n",
" 838.00 | \n",
" 827.01 | \n",
" 832.15 | \n",
" 2,973,900 | \n",
"
\n",
" \n",
" | 17 | \n",
" 1/27/2017 | \n",
" 834.71 | \n",
" 841.95 | \n",
" 820.44 | \n",
" 823.31 | \n",
" 2,965,800 | \n",
"
\n",
" \n",
" | 18 | \n",
" 1/30/2017 | \n",
" 814.66 | \n",
" 815.84 | \n",
" 799.80 | \n",
" 802.32 | \n",
" 3,246,600 | \n",
"
\n",
" \n",
" | 19 | \n",
" 1/31/2017 | \n",
" 796.86 | \n",
" 801.25 | \n",
" 790.52 | \n",
" 796.79 | \n",
" 2,160,600 | \n",
"
\n",
" \n",
"
\n",
"