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committedAug 10, 2023
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‎2_Stock_Market_Analysis.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"authorship_tag": "ABX9TyOeVStG6ntWcwzmsPaoMdtB",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/derkaiser9423/PythonPracticeProject/blob/master/2_Stock_Market_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"It focuses on using API to get data, handling data, and the most important visualizing for further analysis."
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],
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"metadata": {
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"id": "AIyxedTiUjQa"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from IPython import get_ipython\n",
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"get_ipython().magic('reset -sf')"
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],
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"metadata": {
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"id": "4RwilnSDXnZT"
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},
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"execution_count": 14,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"*This* code will remove all variables. It a good start to monitor variable's changes.\n",
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"First, it imports the IPython kernel object. Then, it calls the \"get_ipython()\" function, which returns the IPython kernel object.\n",
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"The magic() method allows us to execute the IPython magic commands. The magic's command \"reset -sf\" deletes all variables for a whole new session."
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],
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"metadata": {
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"id": "LEj2XcEBXnGD"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "4ychhVqwURS5",
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"outputId": "45f60f37-2f11-4295-b8aa-5aa1072010b0"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Requirement already satisfied: yfinance in /usr/local/lib/python3.10/dist-packages (0.2.27)\n",
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"Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.5.3)\n",
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"Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.23.5)\n",
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"Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.31.0)\n",
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"Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.10/dist-packages (from yfinance) (0.0.11)\n",
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"Requirement already satisfied: lxml>=4.9.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.9.3)\n",
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"Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.4.4)\n",
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"Requirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2022.7.1)\n",
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"Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.3.8)\n",
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"Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.11.2)\n",
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"Requirement already satisfied: html5lib>=1.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.1)\n",
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"Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (2.4.1)\n",
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"Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.10/dist-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
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"Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
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"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (3.2.0)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (3.4)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (1.26.16)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (2023.7.22)\n"
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]
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}
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],
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"source": [
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"!pip install yfinance"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"First, we need to install the ifinance API, which is not available on the current environment."
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],
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"metadata": {
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"id": "juvNLW-bVDey"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"import yfinance as yf\n",
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"import datetime as datetime"
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],
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"metadata": {
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"id": "Oq31gZLQVK7T"
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},
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"execution_count": 15,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"Here we import Pandas for data manipulation, yfinance for the API (to get real-time data), and the datetime library. Even the datetime is the built-in library in Python, it is still a good practice to import it for readability and maintenance of the code."
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],
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"metadata": {
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"id": "X_u-4IAEVuGP"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"start_date = datetime.datetime.now() - pd.DateOffset(months=3)\n",
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"end_date = datetime.datetime.now()"
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],
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"metadata": {
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"id": "WVhfTCeqWNOy"
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},
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"execution_count": 16,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"We need two first key points of time: the start date and the end date, which can be referred to the open and close date of the stock market.\n",
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"\n",
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"There is the \"pd.dateoffset()\" function in Pandas, which is used to create a constant increment object of period of time for adding or subtracting.\n",
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"\n",
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"The code line `start_date = datetime.now() - pd.dateoffset(months=3)` creates a timestamp object called \"start_date\".\n",
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"The second one creates the datetime object called \"end_date\"."
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],
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"metadata": {
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"id": "O5KdPVYBWTvP"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"tickers = ['AAPL', 'MSFT', 'NFLX', 'GOOG']"
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],
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"metadata": {
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"id": "ph1lyoTqY_D2"
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},
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"execution_count": 20,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"Create the list of stocks (abbreviation, 4 letters)."
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],
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"metadata": {
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"id": "CzdpEipmZDDz"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"df_list = []"
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],
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"metadata": {
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"id": "6bRcvMRzZXtW"
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},
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"execution_count": 22,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"Create an empty list to get the data of 4 stocks above."
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],
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"metadata": {
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"id": "p3RTA08RZegw"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"for ticker in tickers:\n",
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" data = yf.download(ticker, start=start_date, end=end_date)\n",
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" df_list.append(data)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Iz3YNn1MZmTv",
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"outputId": "4384841f-720f-4167-a3f3-a19d0c26e0b3"
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},
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"execution_count": 23,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[*********************100%***********************] 1 of 1 completed\n",
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"[*********************100%***********************] 1 of 1 completed\n",
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"[*********************100%***********************] 1 of 1 completed\n",
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"[*********************100%***********************] 1 of 1 completed\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Why list for dateframe?"
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],
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"metadata": {
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"id": "ex2LqbbEaBnd"
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}
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}
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]
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}

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