Quantitative_Trading_An_Introduction_Chapter_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantitative Trading Strategies Using Python\n",
    "## Technical Analysis, Statistical Testing,and Machine Learning\n",
    "—\n",
    "### Peng Liu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#######################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CHAPTER 1\n",
    "## Quantitative Trading: An Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Started with Financial Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Downloading Stock Price Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: yfinance in ./Library/Python/3.9/lib/python/site-packages (0.2.40)\n",
      "Requirement already satisfied: pandas>=1.3.0 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (2.2.0)\n",
      "Requirement already satisfied: numpy>=1.16.5 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (1.26.2)\n",
      "Requirement already satisfied: requests>=2.31 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (2.31.0)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: lxml>=4.9.1 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (5.2.2)\n",
      "Requirement already satisfied: platformdirs>=2.0.0 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (3.5.1)\n",
      "Requirement already satisfied: pytz>=2022.5 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (2023.3.post1)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (2.4.4)\n",
      "Requirement already satisfied: peewee>=3.16.2 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (3.17.5)\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (4.12.3)\n",
      "Requirement already satisfied: html5lib>=1.1 in ./Library/Python/3.9/lib/python/site-packages (from yfinance) (1.1)\n",
      "Requirement already satisfied: soupsieve>1.2 in ./Library/Python/3.9/lib/python/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5)\n",
      "Requirement already satisfied: six>=1.9 in /Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (1.15.0)\n",
      "Requirement already satisfied: webencodings in ./Library/Python/3.9/lib/python/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in ./Library/Python/3.9/lib/python/site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in ./Library/Python/3.9/lib/python/site-packages (from pandas>=1.3.0->yfinance) (2023.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in ./Library/Python/3.9/lib/python/site-packages (from requests>=2.31->yfinance) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in ./Library/Python/3.9/lib/python/site-packages (from requests>=2.31->yfinance) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in ./Library/Python/3.9/lib/python/site-packages (from requests>=2.31->yfinance) (1.26.19)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in ./Library/Python/3.9/lib/python/site-packages (from requests>=2.31->yfinance) (2023.11.17)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install botorch\n",
    "!pip install -U statsmodels\n",
    "!pip install ta\n",
    "!pip install yfinance\n",
    "from botorch.acquisition import ExpectedImprovement\n",
    "from botorch.acquisition import qExpectedImprovement\n",
    "from botorch.acquisition import UpperConfidenceBound\n",
    "from botorch.acquisition.knowledge_gradient import qKnowledgeGradient\n",
    "from botorch.fit import fit_gpytorch_mll\n",
    "from bs4 import BeautifulSoup\n",
    "from datetime import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "from itertools import combinations\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import random\n",
    "import requests\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "import statsmodels.api as sm\n",
    "import ta\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Use the Ticker module to access ticker data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'address1': 'One Microsoft Way',\n",
       " 'city': 'Redmond',\n",
       " 'state': 'WA',\n",
       " 'zip': '98052-6399',\n",
       " 'country': 'United States',\n",
       " 'phone': '425 882 8080',\n",
       " 'website': 'https://www.microsoft.com',\n",
       " 'industry': 'Software - Infrastructure',\n",
       " 'industryKey': 'software-infrastructure',\n",
       " 'industryDisp': 'Software - Infrastructure',\n",
       " 'sector': 'Technology',\n",
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       " 'sectorDisp': 'Technology',\n",
       " 'longBusinessSummary': 'Microsoft Corporation develops and supports software, services, devices and solutions worldwide. The Productivity and Business Processes segment offers office, exchange, SharePoint, Microsoft Teams, office 365 Security and Compliance, Microsoft viva, and Microsoft 365 copilot; and office consumer services, such as Microsoft 365 consumer subscriptions, Office licensed on-premises, and other office services. This segment also provides LinkedIn; and dynamics business solutions, including Dynamics 365, a set of intelligent, cloud-based applications across ERP, CRM, power apps, and power automate; and on-premises ERP and CRM applications. The Intelligent Cloud segment offers server products and cloud services, such as azure and other cloud services; SQL and windows server, visual studio, system center, and related client access licenses, as well as nuance and GitHub; and enterprise services including enterprise support services, industry solutions, and nuance professional services. The More Personal Computing segment offers Windows, including windows OEM licensing and other non-volume licensing of the Windows operating system; Windows commercial comprising volume licensing of the Windows operating system, windows cloud services, and other Windows commercial offerings; patent licensing; and windows Internet of Things; and devices, such as surface, HoloLens, and PC accessories. Additionally, this segment provides gaming, which includes Xbox hardware and content, and first- and third-party content; Xbox game pass and other subscriptions, cloud gaming, advertising, third-party disc royalties, and other cloud services; and search and news advertising, which includes Bing, Microsoft News and Edge, and third-party affiliates. The company sells its products through OEMs, distributors, and resellers; and directly through digital marketplaces, online, and retail stores. The company was founded in 1975 and is headquartered in Redmond, Washington.',\n",
       " 'fullTimeEmployees': 221000,\n",
       " 'companyOfficers': [{'maxAge': 1,\n",
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       "   'age': 56,\n",
       "   'title': 'Chairman & CEO',\n",
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       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Bradford L. Smith LCA',\n",
       "   'age': 64,\n",
       "   'title': 'President & Vice Chairman',\n",
       "   'yearBorn': 1959,\n",
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       "  {'maxAge': 1,\n",
       "   'name': 'Ms. Amy E. Hood',\n",
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       "   'title': 'Executive VP & CFO',\n",
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       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Judson B. Althoff',\n",
       "   'age': 49,\n",
       "   'title': 'Executive VP & Chief Commercial Officer',\n",
       "   'yearBorn': 1974,\n",
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       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Christopher David Young',\n",
       "   'age': 51,\n",
       "   'title': 'Executive Vice President of Business Development, Strategy & Ventures',\n",
       "   'yearBorn': 1972,\n",
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       "  {'maxAge': 1,\n",
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       "  {'maxAge': 1,\n",
       "   'name': 'Brett  Iversen',\n",
       "   'title': 'Vice President of Investor Relations',\n",
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       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Hossein  Nowbar',\n",
       "   'title': 'Chief Legal Officer',\n",
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       "   'exercisedValue': 0,\n",
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       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Frank X. Shaw',\n",
       "   'title': 'Chief Communications Officer',\n",
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       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0}],\n",
       " 'auditRisk': 3,\n",
       " 'boardRisk': 4,\n",
       " 'compensationRisk': 2,\n",
       " 'shareHolderRightsRisk': 2,\n",
       " 'overallRisk': 1,\n",
       " 'governanceEpochDate': 1719792000,\n",
       " 'compensationAsOfEpochDate': 1703980800,\n",
       " 'irWebsite': 'http://www.microsoft.com/investor/default.aspx',\n",
       " 'maxAge': 86400,\n",
       " 'priceHint': 2,\n",
       " 'previousClose': 460.77,\n",
       " 'open': 459.66,\n",
       " 'dayLow': 458.965,\n",
       " 'dayHigh': 468.35,\n",
       " 'regularMarketPreviousClose': 460.77,\n",
       " 'regularMarketOpen': 459.66,\n",
       " 'regularMarketDayLow': 458.965,\n",
       " 'regularMarketDayHigh': 468.35,\n",
       " 'dividendRate': 3.0,\n",
       " 'dividendYield': 0.0064,\n",
       " 'exDividendDate': 1723680000,\n",
       " 'payoutRatio': 0.24780001,\n",
       " 'fiveYearAvgDividendYield': 0.92,\n",
       " 'beta': 0.894,\n",
       " 'trailingPE': 40.481384,\n",
       " 'forwardPE': 35.15489,\n",
       " 'volume': 15662166,\n",
       " 'regularMarketVolume': 15662166,\n",
       " 'averageVolume': 18836435,\n",
       " 'averageVolume10days': 18437860,\n",
       " 'averageDailyVolume10Day': 18437860,\n",
       " 'bid': 467.3,\n",
       " 'ask': 467.52,\n",
       " 'bidSize': 100,\n",
       " 'askSize': 200,\n",
       " 'marketCap': 3475050856448,\n",
       " 'fiftyTwoWeekLow': 309.45,\n",
       " 'fiftyTwoWeekHigh': 468.35,\n",
       " 'priceToSalesTrailing12Months': 14.688444,\n",
       " 'fiftyDayAverage': 427.2934,\n",
       " 'twoHundredDayAverage': 392.13004,\n",
       " 'trailingAnnualDividendRate': 2.93,\n",
       " 'trailingAnnualDividendYield': 0.006358921,\n",
       " 'currency': 'USD',\n",
       " 'enterpriseValue': 3501264994304,\n",
       " 'profitMargins': 0.36426997,\n",
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       " 'sharesShort': 62396959,\n",
       " 'sharesShortPriorMonth': 43764126,\n",
       " 'sharesShortPreviousMonthDate': 1715731200,\n",
       " 'dateShortInterest': 1718323200,\n",
       " 'sharesPercentSharesOut': 0.0084,\n",
       " 'heldPercentInsiders': 0.00054000004,\n",
       " 'heldPercentInstitutions': 0.73635,\n",
       " 'shortRatio': 3.41,\n",
       " 'shortPercentOfFloat': 0.0084,\n",
       " 'impliedSharesOutstanding': 7432309760,\n",
       " 'bookValue': 34.058,\n",
       " 'priceToBook': 13.728346,\n",
       " 'lastFiscalYearEnd': 1688083200,\n",
       " 'nextFiscalYearEnd': 1719705600,\n",
       " 'mostRecentQuarter': 1711843200,\n",
       " 'earningsQuarterlyGrowth': 0.199,\n",
       " 'netIncomeToCommon': 86181003264,\n",
       " 'trailingEps': 11.55,\n",
       " 'forwardEps': 13.3,\n",
       " 'pegRatio': 2.4,\n",
       " 'lastSplitFactor': '2:1',\n",
       " 'lastSplitDate': 1045526400,\n",
       " 'enterpriseToRevenue': 14.799,\n",
       " 'enterpriseToEbitda': 27.792,\n",
       " '52WeekChange': 0.40903473,\n",
       " 'SandP52WeekChange': 0.26253593,\n",
       " 'lastDividendValue': 0.75,\n",
       " 'lastDividendDate': 1715731200,\n",
       " 'exchange': 'NMS',\n",
       " 'quoteType': 'EQUITY',\n",
       " 'symbol': 'MSFT',\n",
       " 'underlyingSymbol': 'MSFT',\n",
       " 'shortName': 'Microsoft Corporation',\n",
       " 'longName': 'Microsoft Corporation',\n",
       " 'firstTradeDateEpochUtc': 511108200,\n",
       " 'timeZoneFullName': 'America/New_York',\n",
       " 'timeZoneShortName': 'EDT',\n",
       " 'uuid': 'b004b3ec-de24-385e-b2c1-923f10d3fb62',\n",
       " 'messageBoardId': 'finmb_21835',\n",
       " 'gmtOffSetMilliseconds': -14400000,\n",
       " 'currentPrice': 467.56,\n",
       " 'targetHighPrice': 600.0,\n",
       " 'targetLowPrice': 436.0,\n",
       " 'targetMeanPrice': 488.97,\n",
       " 'targetMedianPrice': 480.89,\n",
       " 'recommendationMean': 1.7,\n",
       " 'recommendationKey': 'buy',\n",
       " 'numberOfAnalystOpinions': 46,\n",
       " 'totalCash': 80013000704,\n",
       " 'totalCashPerShare': 10.766,\n",
       " 'ebitda': 125981999104,\n",
       " 'totalDebt': 106228998144,\n",
       " 'quickRatio': 1.132,\n",
       " 'currentRatio': 1.242,\n",
       " 'totalRevenue': 236583993344,\n",
       " 'debtToEquity': 41.963,\n",
       " 'revenuePerShare': 31.834,\n",
       " 'returnOnAssets': 0.1541,\n",
       " 'returnOnEquity': 0.38487998,\n",
       " 'freeCashflow': 61997998080,\n",
       " 'operatingCashflow': 110122999808,\n",
       " 'earningsGrowth': 0.2,\n",
       " 'revenueGrowth': 0.17,\n",
       " 'grossMargins': 0.69894,\n",
       " 'ebitdaMargins': 0.5325,\n",
       " 'operatingMargins': 0.44588003,\n",
       " 'financialCurrency': 'USD',\n",
       " 'trailingPegRatio': 2.3053}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msft = yf.Ticker(\"MSFT\")\n",
    "# get stock info\n",
    "msft.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Access a specific attribute, such as market capitilisation, from the dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3475050856448"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msft.info[\"marketCap\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Download daily stock price data by passing in specified ticker and date range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-07-06\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "today_date = datetime.today().strftime('%Y-%m-%d')\n",
    "print(today_date)\n",
    "data = yf.download(\"MSFT\", start=\"2022-01-01\", end=today_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>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>2024-06-28</th>\n",
       "      <td>453.070007</td>\n",
       "      <td>455.380005</td>\n",
       "      <td>446.410004</td>\n",
       "      <td>446.950012</td>\n",
       "      <td>446.950012</td>\n",
       "      <td>28362300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-01</th>\n",
       "      <td>448.660004</td>\n",
       "      <td>457.369995</td>\n",
       "      <td>445.660004</td>\n",
       "      <td>456.730011</td>\n",
       "      <td>456.730011</td>\n",
       "      <td>17662800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-02</th>\n",
       "      <td>453.200012</td>\n",
       "      <td>459.589996</td>\n",
       "      <td>453.109985</td>\n",
       "      <td>459.279999</td>\n",
       "      <td>459.279999</td>\n",
       "      <td>13979800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-03</th>\n",
       "      <td>458.190002</td>\n",
       "      <td>461.019989</td>\n",
       "      <td>457.880005</td>\n",
       "      <td>460.769989</td>\n",
       "      <td>460.769989</td>\n",
       "      <td>9932800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-05</th>\n",
       "      <td>459.609985</td>\n",
       "      <td>468.350006</td>\n",
       "      <td>458.970001</td>\n",
       "      <td>467.559998</td>\n",
       "      <td>467.559998</td>\n",
       "      <td>15980200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2024-06-28  453.070007  455.380005  446.410004  446.950012  446.950012   \n",
       "2024-07-01  448.660004  457.369995  445.660004  456.730011  456.730011   \n",
       "2024-07-02  453.200012  459.589996  453.109985  459.279999  459.279999   \n",
       "2024-07-03  458.190002  461.019989  457.880005  460.769989  460.769989   \n",
       "2024-07-05  459.609985  468.350006  458.970001  467.559998  467.559998   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2024-06-28  28362300  \n",
       "2024-07-01  17662800  \n",
       "2024-07-02  13979800  \n",
       "2024-07-03   9932800  \n",
       "2024-07-05  15980200  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Check data dimension/size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(629, 6)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Stock Price Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot closing price as a time series chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "fig = go.Figure(data=go.Scatter(x=data.index,y=data['Close'],mode='lines'))\n",
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  },
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   "metadata": {},
   "source": [
    "##### Overlay the trading volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "name": "Price",
         "type": "scatter",
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     "metadata": {},
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   ],
   "source": [
    "fig2 = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "fig2.add_trace(go.Scatter(x=data.index,y=data['Close'],name='Price'),secondary_y=False)\n",
    "fig2.add_trace(go.Bar(x=data.index,y=data['Volume'],name='Volume'),secondary_y=True)\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Rescale volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
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          "2022-01-03T00:00:00",
          "2022-01-04T00:00:00",
          "2022-01-05T00:00:00",
          "2022-01-06T00:00:00",
          "2022-01-07T00:00:00",
          "2022-01-10T00:00:00",
          "2022-01-11T00:00:00",
          "2022-01-12T00:00:00",
          "2022-01-13T00:00:00",
          "2022-01-14T00:00:00",
          "2022-01-18T00:00:00",
          "2022-01-19T00:00:00",
          "2022-01-20T00:00:00",
          "2022-01-21T00:00:00",
          "2022-01-24T00:00:00",
          "2022-01-25T00:00:00",
          "2022-01-26T00:00:00",
          "2022-01-27T00:00:00",
          "2022-01-28T00:00:00",
          "2022-01-31T00:00:00",
          "2022-02-01T00:00:00",
          "2022-02-02T00:00:00",
          "2022-02-03T00:00:00",
          "2022-02-04T00:00:00",
          "2022-02-07T00:00:00",
          "2022-02-08T00:00:00",
          "2022-02-09T00:00:00",
          "2022-02-10T00:00:00",
          "2022-02-11T00:00:00",
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          "2022-02-22T00:00:00",
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          "2022-03-03T00:00:00",
          "2022-03-04T00:00:00",
          "2022-03-07T00:00:00",
          "2022-03-08T00:00:00",
          "2022-03-09T00:00:00",
          "2022-03-10T00:00:00",
          "2022-03-11T00:00:00",
          "2022-03-14T00:00:00",
          "2022-03-15T00:00:00",
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          "2022-03-31T00:00:00",
          "2022-04-01T00:00:00",
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          "yaxis": {
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   "source": [
    "fig2.update_yaxes(range=[0,500000000],secondary_y=True)\n",
    "fig2.update_yaxes(visible=True, secondary_y=True)\n",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Switch to candlestick chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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            "gridwidth": 2,
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            "showbackground": true,
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           "yaxis": {
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         "side": "right"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig3 = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "fig3.add_trace(go.Candlestick(x=data.index,\n",
    "open=data['Open'],\n",
    "high=data['High'],\n",
    "low=data['Low'],\n",
    "close=data['Close'],\n",
    "))\n",
    "fig3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Overlay the trading volume and rescale volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          0.94
         ]
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ]
        },
        "yaxis2": {
         "anchor": "x",
         "overlaying": "y",
         "range": [
          0,
          200000000
         ],
         "side": "right",
         "visible": true
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4 = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "fig4.add_trace(go.Scatter(x=apple.index,y=data['Close'],name='Price'),secondary_y=False)\n",
    "fig4.add_trace(go.Bar(x=apple.index,y=data['Volume'],name='Volume'),secondary_y=True)\n",
    "\n",
    "fig4.update_yaxes(range=[0,200000000],secondary_y=True)\n",
    "fig4.update_yaxes(visible=True, secondary_y=True)\n",
    "fig4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Switch to candlestick chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "fig5 = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "fig5.add_trace(go.Candlestick(x=data.index,\n",
    "open=data['Open'],\n",
    "high=data['High'],\n",
    "low=data['Low'],\n",
    "close=data['Close'],\n",
    "))\n",
    "fig5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Get year-to-date (YTD) average stock price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "apple = yf.download(\"AAPL\", start=\"2022-01-01\", end=today_date)\n",
    "end_year_apple = apple.groupby(pd.Grouper(level='Date', freq='YE')).nth(-1).reset_index()\n",
    "start_year_apple= apple.groupby(pd.Grouper(level='Date', freq='YE')).nth(0).reset_index()\n",
    "\n",
    "ytd = ((end_year_apple['Close'] - start_year_apple['Close']) / start_year_apple['Close']) *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -28.613814\n",
       "1    53.937794\n",
       "2    21.924153\n",
       "Name: Close, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ytd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#######################################################################################################################"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}