Extracting Stock Data Using a Python Library.ipynb

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   "source": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/Images/SN_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\">\n",
    "</center>\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Extracting Stock Data Using a Python Library</h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A company's stock share is a piece of the company more precisely:\n",
    "<p><b>A stock (also known as equity) is a security that represents the ownership of a fraction of a corporation. This\n",
    "entitles the owner of the stock to a proportion of the corporation's assets and profits equal to how much stock they own. Units of stock are called \"shares.\" [1]</p></b>\n",
    "\n",
    "An investor can buy a stock and sell it later. If the stock price increases, the investor profits, If it decreases,the investor with incur a loss.  Determining the stock price is complex; it depends on the number of outstanding shares, the size of the company's future profits, and much more. People trade stocks throughout the day the stock ticker is a report of the price of a certain stock, updated continuously throughout the trading session by the various stock market exchanges. \n",
    "<p>You are a data scientist working for a hedge fund; it's your job to determine any suspicious stock activity. In this lab you will extract stock data using a Python library. We will use the <coode>yfinance</code> library, it allows us to extract data for stocks returning data in a pandas dataframe. You will use the lab to extract.</p>\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "<h2>Table of Contents</h2>\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ul>\n",
    "        <li>Using yfinance to Extract Stock Info</li>\n",
    "        <li>Using yfinance to Extract Historical Share Price Data</li>\n",
    "        <li>Using yfinance to Extract Historical Dividends Data</li>\n",
    "        <li>Exercise</li>\n",
    "    </ul>\n",
    "<p>\n",
    "    Estimated Time Needed: <strong>30 min</strong></p>\n",
    "</div>\n",
    "\n",
    "<hr>\n"
   ]
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     "text": [
      "Collecting yfinance==0.2.4\n",
      "  Downloading yfinance-0.2.4-py2.py3-none-any.whl (51 kB)\n",
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      "Requirement already satisfied: pycparser in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance==0.2.4) (2.21)\n",
      "Installing collected packages: multitasking, appdirs, html5lib, frozendict, yfinance\n",
      "Successfully installed appdirs-1.4.4 frozendict-2.3.8 html5lib-1.1 multitasking-0.0.11 yfinance-0.2.4\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance==0.2.4\n",
    "#!pip install pandas==1.3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the yfinance Library to Extract Stock Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `Ticker` module we can create an object that will allow us to access functions to extract data. To do this we need to provide the ticker symbol for the stock, here the company is Apple and the ticker symbol is `AAPL`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "apple = yf.Ticker(\"AAPL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can access functions and variables to extract the type of data we need. You can view them and what they represent here https://aroussi.com/post/python-yahoo-finance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-09-29 14:27:50--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/data/apple.json\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5699 (5.6K) [application/json]\n",
      "Saving to: ‘apple.json’\n",
      "\n",
      "apple.json          100%[===================>]   5.57K  --.-KB/s    in 0s      \n",
      "\n",
      "2023-09-29 14:27:50 (44.7 MB/s) - ‘apple.json’ saved [5699/5699]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/data/apple.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stock Info\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the attribute  <code>info</code> we can extract information about the stock as a Python dictionary.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'zip': '95014',\n",
       " 'sector': 'Technology',\n",
       " 'fullTimeEmployees': 100000,\n",
       " 'longBusinessSummary': 'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. It also sells various related services. In addition, the company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; AirPods Max, an over-ear wireless headphone; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, HomePod, and iPod touch. Further, it provides AppleCare support services; cloud services store services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. Additionally, the company offers various services, such as Apple Arcade, a game subscription service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was incorporated in 1977 and is headquartered in Cupertino, California.',\n",
       " 'city': 'Cupertino',\n",
       " 'phone': '408 996 1010',\n",
       " 'state': 'CA',\n",
       " 'country': 'United States',\n",
       " 'companyOfficers': [],\n",
       " 'website': 'https://www.apple.com',\n",
       " 'maxAge': 1,\n",
       " 'address1': 'One Apple Park Way',\n",
       " 'industry': 'Consumer Electronics',\n",
       " 'ebitdaMargins': 0.33890998,\n",
       " 'profitMargins': 0.26579002,\n",
       " 'grossMargins': 0.43019,\n",
       " 'operatingCashflow': 112241000448,\n",
       " 'revenueGrowth': 0.112,\n",
       " 'operatingMargins': 0.309,\n",
       " 'ebitda': 128217997312,\n",
       " 'targetLowPrice': 160,\n",
       " 'recommendationKey': 'buy',\n",
       " 'grossProfits': 152836000000,\n",
       " 'freeCashflow': 80153247744,\n",
       " 'targetMedianPrice': 199.5,\n",
       " 'currentPrice': 177.77,\n",
       " 'earningsGrowth': 0.25,\n",
       " 'currentRatio': 1.038,\n",
       " 'returnOnAssets': 0.19875,\n",
       " 'numberOfAnalystOpinions': 44,\n",
       " 'targetMeanPrice': 193.53,\n",
       " 'debtToEquity': 170.714,\n",
       " 'returnOnEquity': 1.45567,\n",
       " 'targetHighPrice': 215,\n",
       " 'totalCash': 63913000960,\n",
       " 'totalDebt': 122797998080,\n",
       " 'totalRevenue': 378323009536,\n",
       " 'totalCashPerShare': 3.916,\n",
       " 'financialCurrency': 'USD',\n",
       " 'revenuePerShare': 22.838,\n",
       " 'quickRatio': 0.875,\n",
       " 'recommendationMean': 1.8,\n",
       " 'exchange': 'NMS',\n",
       " 'shortName': 'Apple Inc.',\n",
       " 'longName': 'Apple Inc.',\n",
       " 'exchangeTimezoneName': 'America/New_York',\n",
       " 'exchangeTimezoneShortName': 'EDT',\n",
       " 'isEsgPopulated': False,\n",
       " 'gmtOffSetMilliseconds': '-14400000',\n",
       " 'quoteType': 'EQUITY',\n",
       " 'symbol': 'AAPL',\n",
       " 'messageBoardId': 'finmb_24937',\n",
       " 'market': 'us_market',\n",
       " 'annualHoldingsTurnover': None,\n",
       " 'enterpriseToRevenue': 7.824,\n",
       " 'beta3Year': None,\n",
       " 'enterpriseToEbitda': 23.086,\n",
       " '52WeekChange': 0.4549594,\n",
       " 'morningStarRiskRating': None,\n",
       " 'forwardEps': 6.56,\n",
       " 'revenueQuarterlyGrowth': None,\n",
       " 'sharesOutstanding': 16319399936,\n",
       " 'fundInceptionDate': None,\n",
       " 'annualReportExpenseRatio': None,\n",
       " 'totalAssets': None,\n",
       " 'bookValue': 4.402,\n",
       " 'sharesShort': 111286790,\n",
       " 'sharesPercentSharesOut': 0.0068,\n",
       " 'fundFamily': None,\n",
       " 'lastFiscalYearEnd': 1632528000,\n",
       " 'heldPercentInstitutions': 0.59397,\n",
       " 'netIncomeToCommon': 100554997760,\n",
       " 'trailingEps': 6.015,\n",
       " 'lastDividendValue': 0.22,\n",
       " 'SandP52WeekChange': 0.15217662,\n",
       " 'priceToBook': 40.38392,\n",
       " 'heldPercentInsiders': 0.0007,\n",
       " 'nextFiscalYearEnd': 1695600000,\n",
       " 'yield': None,\n",
       " 'mostRecentQuarter': 1640390400,\n",
       " 'shortRatio': 1.21,\n",
       " 'sharesShortPreviousMonthDate': 1644883200,\n",
       " 'floatShares': 16302795170,\n",
       " 'beta': 1.185531,\n",
       " 'enterpriseValue': 2959991898112,\n",
       " 'priceHint': 2,\n",
       " 'threeYearAverageReturn': None,\n",
       " 'lastSplitDate': 1598832000,\n",
       " 'lastSplitFactor': '4:1',\n",
       " 'legalType': None,\n",
       " 'lastDividendDate': 1643932800,\n",
       " 'morningStarOverallRating': None,\n",
       " 'earningsQuarterlyGrowth': 0.204,\n",
       " 'priceToSalesTrailing12Months': 7.668314,\n",
       " 'dateShortInterest': 1647302400,\n",
       " 'pegRatio': 1.94,\n",
       " 'ytdReturn': None,\n",
       " 'forwardPE': 27.099087,\n",
       " 'lastCapGain': None,\n",
       " 'shortPercentOfFloat': 0.0068,\n",
       " 'sharesShortPriorMonth': 108944701,\n",
       " 'impliedSharesOutstanding': 0,\n",
       " 'category': None,\n",
       " 'fiveYearAverageReturn': None,\n",
       " 'previousClose': 178.96,\n",
       " 'regularMarketOpen': 178.55,\n",
       " 'twoHundredDayAverage': 156.03505,\n",
       " 'trailingAnnualDividendYield': 0.004833482,\n",
       " 'payoutRatio': 0.1434,\n",
       " 'volume24Hr': None,\n",
       " 'regularMarketDayHigh': 179.61,\n",
       " 'navPrice': None,\n",
       " 'averageDailyVolume10Day': 93823630,\n",
       " 'regularMarketPreviousClose': 178.96,\n",
       " 'fiftyDayAverage': 166.498,\n",
       " 'trailingAnnualDividendRate': 0.865,\n",
       " 'open': 178.55,\n",
       " 'toCurrency': None,\n",
       " 'averageVolume10days': 93823630,\n",
       " 'expireDate': None,\n",
       " 'algorithm': None,\n",
       " 'dividendRate': 0.88,\n",
       " 'exDividendDate': 1643932800,\n",
       " 'circulatingSupply': None,\n",
       " 'startDate': None,\n",
       " 'regularMarketDayLow': 176.7,\n",
       " 'currency': 'USD',\n",
       " 'trailingPE': 29.55445,\n",
       " 'regularMarketVolume': 92633154,\n",
       " 'lastMarket': None,\n",
       " 'maxSupply': None,\n",
       " 'openInterest': None,\n",
       " 'marketCap': 2901099675648,\n",
       " 'volumeAllCurrencies': None,\n",
       " 'strikePrice': None,\n",
       " 'averageVolume': 95342043,\n",
       " 'dayLow': 176.7,\n",
       " 'ask': 178.53,\n",
       " 'askSize': 800,\n",
       " 'volume': 92633154,\n",
       " 'fiftyTwoWeekHigh': 182.94,\n",
       " 'fromCurrency': None,\n",
       " 'fiveYearAvgDividendYield': 1.13,\n",
       " 'fiftyTwoWeekLow': 122.25,\n",
       " 'bid': 178.4,\n",
       " 'tradeable': False,\n",
       " 'dividendYield': 0.005,\n",
       " 'bidSize': 3200,\n",
       " 'dayHigh': 179.61,\n",
       " 'regularMarketPrice': 177.77,\n",
       " 'preMarketPrice': 178.38,\n",
       " 'logo_url': 'https://logo.clearbit.com/apple.com'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "with open('apple.json') as json_file:\n",
    "    apple_info = json.load(json_file)\n",
    "    # Print the type of data variable    \n",
    "    #print(\"Type:\", type(apple_info))\n",
    "apple_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the <code>'country'</code> using the key country\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'United States'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_info['country']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Share Price\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A share is the single smallest part of a company's stock  that you can buy, the prices of these shares fluctuate over time. Using the <code>history()</code> method we can get the share price of the stock over a certain period of time. Using the `period` parameter we can set how far back from the present to get data. The options for `period` are 1 day (1d), 5d, 1 month (1mo) , 3mo, 6mo, 1 year (1y), 2y, 5y, 10y, ytd, and max.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "apple_share_price_data = apple.history(period=\"max\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The format that the data is returned in is a Pandas DataFrame. With the `Date` as the index the share `Open`, `High`, `Low`, `Close`, `Volume`, and `Stock Splits` are given for each day.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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       "      <th>1980-12-12 00:00:00-05:00</th>\n",
       "      <td>0.099450</td>\n",
       "      <td>0.099882</td>\n",
       "      <td>0.099450</td>\n",
       "      <td>0.099450</td>\n",
       "      <td>469033600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15 00:00:00-05:00</th>\n",
       "      <td>0.094694</td>\n",
       "      <td>0.094694</td>\n",
       "      <td>0.094261</td>\n",
       "      <td>0.094261</td>\n",
       "      <td>175884800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16 00:00:00-05:00</th>\n",
       "      <td>0.087775</td>\n",
       "      <td>0.087775</td>\n",
       "      <td>0.087343</td>\n",
       "      <td>0.087343</td>\n",
       "      <td>105728000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17 00:00:00-05:00</th>\n",
       "      <td>0.089504</td>\n",
       "      <td>0.089937</td>\n",
       "      <td>0.089504</td>\n",
       "      <td>0.089504</td>\n",
       "      <td>86441600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18 00:00:00-05:00</th>\n",
       "      <td>0.092099</td>\n",
       "      <td>0.092532</td>\n",
       "      <td>0.092099</td>\n",
       "      <td>0.092099</td>\n",
       "      <td>73449600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Open      High       Low     Close     Volume  \\\n",
       "Date                                                                           \n",
       "1980-12-12 00:00:00-05:00  0.099450  0.099882  0.099450  0.099450  469033600   \n",
       "1980-12-15 00:00:00-05:00  0.094694  0.094694  0.094261  0.094261  175884800   \n",
       "1980-12-16 00:00:00-05:00  0.087775  0.087775  0.087343  0.087343  105728000   \n",
       "1980-12-17 00:00:00-05:00  0.089504  0.089937  0.089504  0.089504   86441600   \n",
       "1980-12-18 00:00:00-05:00  0.092099  0.092532  0.092099  0.092099   73449600   \n",
       "\n",
       "                           Dividends  Stock Splits  \n",
       "Date                                                \n",
       "1980-12-12 00:00:00-05:00        0.0           0.0  \n",
       "1980-12-15 00:00:00-05:00        0.0           0.0  \n",
       "1980-12-16 00:00:00-05:00        0.0           0.0  \n",
       "1980-12-17 00:00:00-05:00        0.0           0.0  \n",
       "1980-12-18 00:00:00-05:00        0.0           0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_share_price_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can reset the index of the DataFrame with the `reset_index` function. We also set the `inplace` paramter to `True` so the change takes place to the DataFrame itself.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "apple_share_price_data.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the `Open` price against the `Date`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple_share_price_data.plot(x=\"Date\", y=\"Open\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Dividends\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dividends are the distribution of a companys profits to shareholders. In this case they are defined as an amount of money returned per share an investor owns. Using the variable `dividends` we can get a dataframe of the data. The period of the data is given by the period defined in the 'history` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1987-05-11 00:00:00-04:00    0.000536\n",
       "1987-08-10 00:00:00-04:00    0.000536\n",
       "1987-11-17 00:00:00-05:00    0.000714\n",
       "1988-02-12 00:00:00-05:00    0.000714\n",
       "1988-05-16 00:00:00-04:00    0.000714\n",
       "                               ...   \n",
       "2022-08-05 00:00:00-04:00    0.230000\n",
       "2022-11-04 00:00:00-04:00    0.230000\n",
       "2023-02-10 00:00:00-05:00    0.230000\n",
       "2023-05-12 00:00:00-04:00    0.240000\n",
       "2023-08-11 00:00:00-04:00    0.240000\n",
       "Name: Dividends, Length: 80, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.dividends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the dividends overtime:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tWvJyj7lhYCEiokbhZFYeAKCDqyOsuZjW7PA7RkREjcKJvy8H8VKQeapTYImPj0f79u1hb2+PkJAQ7Nixo9qxGRkZeOSRR+Dv7w+1Wo3JkydXGrNs2TKoVKpKH0VFRXUpj4iIqJLy9St+7k4KV0J1YXRgSUhIwOTJkzFjxgzs378fffv2RVRUFFJTU6scr9Vq4erqihkzZiAoKKja8zo7OyMjI6PCh709t0QmIqL6UX5JyM+dMyzmyOjA8tFHH2HcuHF48sknERgYiLlz58Lb2xsLFy6scny7du0wb948jBkzBi4uLtWeV6VSwcPDo8IHERFRfbj+DqFObpxhMUdGBZbi4mIkJycjMjKywvHIyEjs3r37lgrJz8+Hj48P2rRpg3vuuQf79++vcbxWq4VGo6nwQUREVJXyO4RsrFS8Q8hMGRVYsrOzodPp4O7uXuG4u7s7MjMz61xEQEAAli1bhh9++AGrVq2Cvb09+vTpg5MnT1b7mLi4OLi4uBg+vL296/zvExGRZTPcIdSqKe8QMlN1+q6pVKoKn4tIpWPGCAsLw2OPPYagoCD07dsXa9asgZ+fHz755JNqHzN9+nTk5uYaPtLSarfVMhERNT7ll4N8uX7FbBm1022rVq1gZWVVaTYlKyur0qzLrVCr1ejZs2eNMyx2dnaws+O7aBIR0c2d4B1CZs+oGRZbW1uEhIQgKSmpwvGkpCRERETUW1EiggMHDqB169b1dk4iImq8yi8J+XIPFrNl9HsJTZ06FTExMQgNDUV4eDgWL16M1NRUxMbGAii7VJOeno7ly5cbHnPgwAEAZQtrL1++jAMHDsDW1hadO3cGAMyaNQthYWHw9fWFRqPB/PnzceDAASxYsKAeWiQiosZMrxecyiq/JMQZFnNldGCJjo7GlStX8OabbyIjIwNdu3ZFYmIifHx8AJRtFHfjniw9evQw/HdycjK+/vpr+Pj44Ny5cwCAnJwcjB8/HpmZmXBxcUGPHj2wfft29OrV6xZaIyIiKrtD6Fox7xAydyoREaWLqA8ajQYuLi7Izc2Fs7Oz0uUQEZGJ+DklC08s2wd/dydsmnKX0uXQDWr7+s17u4iIyKKduPT3+hXeIWTWjL4kREREZApyrhVj9OJfkf5XYY3jikp1AABf7nBr1hhYiIjILP1y4jJSMvNqNVatAu70bXWbK6LbiYGFiIjM0pH0XADAfcFeeG6gb41jXZrYoLmjbUOURbcJAwsREZmlw38HlrAOLdGulaPC1dDtxkW3RERkdvR6wZ/pZW96283LReFqqCEwsBARkdk5d6UAedpS2FmruXttI8HAQkREZqf8clBga2e++3Ijwe8yERGZnfIFt7wc1HgwsBARkdkpn2Hp1oaBpbFgYCEiIrPCBbeNEwMLERGZFS64bZwYWIiIyKxwwW3jxO80ERGZFS64bZwYWIiIyKxwwW3jxMBCRERmgwtuGy++lxAREZmE7Hwt/nfwIop1+mrH5BeVcsFtI8XAQkREJuHdDSlYm3yhVmO7eHLBbWPDwEJERCYh+fxfAID+/q5o4Whb7ThrtQqP9PZpqLLIRDCwEBGR4vKKSnAmuwAA8OGDQWjZ1E7hisjUcD6NiIgU9+fFsoW0Xs2aMKxQlRhYiIhIcYcvlN2q3NXLWeFKyFQxsBARkeIOczM4ugkGFiIiUlz57rVdGVioGgwsRESkqOsX3HKGharDwEJERIrigluqDQYWIiJSFBfcUm0wsBARkaK44JZqg4GFiIgUxQW3VBsMLEREpBguuKXaYmAhIiLFcMEt1RYDCxERKYYLbqm2+OaHRERU7/R6wRPL9uHXM1dqHFeqFwC8HEQ3x8BCRET17sjFXPxy4nKtxtpYqTAgwO02V0TmjoGFiIjq3daULADAwAA3vDmiS41jnZvYwNnepiHKIjPGwEJERPXu578Dy5Au7mjT3EHhasgScNEtERHVq8t5Whz8ezHtAH9e6qH6wcBCRET1qnztSlcvZ7g52ytcDVkKBhYiIqpX5ZeDOLtC9YmBhYiI6k2JTo/tJ8tmWHjnD9UnBhYiIqo3yef/Ql5RKVo42iKoTTOlyyELwsBCRET1pvxyUD8/V1ipVQpXQ5aEtzUTEdFN6fSCnaeykVtYUuO4TX9mAuDlIKp/DCxERHRTPxxMx5SEg7Uaq1YB/Xxdb3NF1NgwsBAR0U39fu4vAIBPSwd4NWtS49jBnd3h4sCda6l+MbAQEdFNHc/MAwBMHeyHEd29FK6GGiMuuiUiohqJCI5fKgss/h5OCldDjRUDCxER1Sgjtwh5RaWwVqvQoVVTpcuhRoqBhYiIalR+Oaija1PYWvNlg5TBnzwiIqpRyt+BxY+Xg0hBDCxERFSj45kaAEAAAwspqE6BJT4+Hu3bt4e9vT1CQkKwY8eOasdmZGTgkUcegb+/P9RqNSZPnlzluG+++QadO3eGnZ0dOnfujHXr1tWlNCIiqmflMyz+7gwspByjA0tCQgImT56MGTNmYP/+/ejbty+ioqKQmppa5XitVgtXV1fMmDEDQUFBVY7Zs2cPoqOjERMTg4MHDyImJgYPPfQQ9u7da2x5RERUj0p0epy+nA+AdwiRslQiIsY8oHfv3ggODsbChQsNxwIDAzFy5EjExcXV+Nj+/fuje/fumDt3boXj0dHR0Gg02LBhg+HY0KFD0bx5c6xatapWdWk0Gri4uCA3NxfOzs61b4iIiKp18lIeBn+8HY62Vjj8xhCo+f5AVM9q+/pt1AxLcXExkpOTERkZWeF4ZGQkdu/eXbdKUTbDcuM5hwwZUuM5tVotNBpNhQ8iIqpf1y+4ZVghJRkVWLKzs6HT6eDu7l7huLu7OzIzM+tcRGZmptHnjIuLg4uLi+HD29u7zv8+ERFVrfyWZi64JaXVadGtSlUxZYtIpWO3+5zTp09Hbm6u4SMtLe2W/n0iIqqMC27JVBj1XkKtWrWClZVVpZmPrKysSjMkxvDw8DD6nHZ2drCzs6vzv0lERDd3/FLZ5XbuwUJKM2qGxdbWFiEhIUhKSqpwPCkpCREREXUuIjw8vNI5N2/efEvnJCKiW5OvLUXa1UIAQIAHb2YgZRn9bs1Tp05FTEwMQkNDER4ejsWLFyM1NRWxsbEAyi7VpKenY/ny5YbHHDhwAACQn5+Py5cv48CBA7C1tUXnzp0BAJMmTcJdd92Fd999FyNGjMD333+PLVu2YOfOnfXQIhER1cXJv9/w0NXJDi0cbRWuhho7owNLdHQ0rly5gjfffBMZGRno2rUrEhMT4ePjA6Bso7gb92Tp0aOH4b+Tk5Px9ddfw8fHB+fOnQMAREREYPXq1Xj11Vfx2muvoWPHjkhISEDv3r1voTUiIroZEcH6wxnYmpIFvb7iLhfpOeWzK7wcRMozeh8WU8V9WIiIjPP7uauYvf4YDqTl1Dhu4oCOeHFIQMMURY1ObV+/jZ5hISIi83b+SgHe2ZCCDUfKbnZwsLVCTJgPXJ0q38jgYGuNe4NaN3SJRJUwsBARNRI514rxydZTWL7nHEp0ArUKiO7pjSmD/ODmbK90eUQ1YmAhIrJwxaV6fPXrecz/6SRyC0sAAHf5ueKVYQG8+4fMBgMLEZGFEhFsPJKJdzam4PyVawDKNoB7ZXgg+vm5KlwdkXEYWIiILNCBtBy8vf4o9p37C0DZrcnPD/bDg6HesOJ7ApEZYmAhIrIgaVev4b1Nx/HjwYsAAHsbNcb37YCn+3WEox1/5ZP54k8vEZEF0BSVYMHPp7B01zkUl+qhUgH3B7fB85F+aO3SROnyiG4ZAwsRkRkr0emx6rdUzN1yElcLigEAER1b4pVhgejq5aJwdUT1h4GFiMgMiQi2HMtC3IZjOHO5AADQya0pXhkWgAH+bjW+2z2ROWJgISIyM4cv5OLtxKP49cxVAEBLR1tMHuyHh3t6w9rKqPe0JTIbDCxERGbiYk4hPth0HN/uTwcA2Fqr8eSd7fFM/45wsrdRuDqi24uBhYjIxOVrS7Fw2yl8vuMstKV6AMDI7p54cWgAvJpxQS01DgwsREQmqlSnR8Lvafg46QSy88sW1PZq1wIzhgciyLuZssURNTAGFiIiEyMi2HbiMuasP4aTWfkAgPatHPFyVAAiO7tzQS01SgwsREQm5OhFDeYkHsPOU9kAgGYONph0ty8e7e0DW2suqKXGi4GFiMgEXNIU4cPNx7E2+QJEAFsrNcb2aYeJ/TvBxYELaokYWIiIFHStuBSLt5/Bol/OoLBEBwAYfkdrvDQkAG1bOihcHZHpYGAhIlKATi/4JvkCPth8HFl5WgBAcNtmmDG8M0J8mitcHZHpYWAhImpgO09mY/b6o0jJzAMAeLdogpeHBmJYNw8uqCWqBgMLEVEDOXEpD3MSj2Hb8csAAGd7azx3ty9iwn1gZ22lcHVEpo2BhYjoNrucp8XHW05g9W+p0AtgrVYhJtwHzw30RXNHW6XLIzILDCxERLdJYbEOS3aewcJtp1FQXLagdmgXD7wUFYD2rRwVro7IvDCwEBHVM71e8N2BdLy/6TgycosAAEFtXDBjeGf0at9C4eqIzBMDCxFRPdpz+greTjyKI+kaAIBXsyaYNtQf997hCbWaC2qJ6oqBhYioHpy+nI+4xBRsOXYJAOBkZ40JAzrhiT7tYG/DBbVEt4qBhYjoFlzJ12LeTyexcm8qdHqBlVqFR3q1xeRBvmjZ1E7p8ogsBgMLEVEdFJXosGz3OSzYegp52lIAwKBAN7wcFYBObk4KV0dkeRhYiIiMICL48VAG3t2QgvScQgBAF09nzBgWiIhOrRSujshyMbAQEdXS7+eu4q31x3AwLQcA4OFsjxeG+OO+Hl5cUEt0mzGwEBHdxPkrBXhnQwo2HMkEADjYWuGZfh3xZN8OaGLLBbVEDYGBhYioGjnXijH/p1P46tdzKNEJ1CogumdbTBnsCzcne6XLI2pUGFiIiG6gLdXhqz3n8cnWU8gtLAEA9PNzxSvDAuHvwQW1REpgYCEi+puIYMORTLyzIQWpV68BAAI8nPDKsEDc5eeqcHVEjRsDCxERgP2pf+Ht9cfw+/m/AACuTnZ4IdIPD4R4w4oLaokUx8BCRI1a2tVreHdjCv53KAMAYG+jxvi7OuLpuzrA0Y6/IolMBZ+NRNQo5RaWIP7nU1i66xyKdXqoVMADwW3wfKQ/PFy4oJbI1DCwEFGjUqLTY+Wv5zHvp5P461rZgto+nVrilWGB6OLponB1RFQdBhYiahREBElHL+GdDSk4k10AAOjk1hSvDAvAAH83qFRcp0JkyhhYiMjiHb6Qi9nrj2Lv2asAgJaOtpgy2A+je3rD2kqtcHVEVBsMLERksS7mFOL9Tcexbn86AMDOWo1xd7bHM/07wsneRuHqiMgYDCxEZHHytaVYuO0UPt9xFtpSPQBgVA8vvDDEH17NmihcHRHVBQMLEVmMUp0eq/elYe6WE8jOLwYA9GrfAq8OD8QdbZopWxwR3RIGFiIyeyKCbccvY07iMZzMygcAdGjliJejAjC4szsX1BJZAAYWIjJrRy9q8HbiUew6dQUA0NzBBpPu9sWjYT6w4YJaIovBwEJEZumSpggfbDqO//5xASKArZUaT/RphwkDOsGlCRfUElkaBhYiMisF2lIs2n4G/9l+BoUlOgDAPXe0xktDA+DdwkHh6ojodmFgISKzoNML/puchg83n0BWnhYAEOLTHDOGByK4bXOFqyOi242BhYhM3o6Tl/H2+mNIycwDALRt4YCXowIQ1dWDC2qJGgkGFiIyWScu5eHt9cfwy4nLAABne2s8d7cvYsJ9YGdtpXB1RNSQGFiIyORcztPio6QTSNiXCr0ANlYqxIS1w3N3d0IzB1ulyyMiBTCwEJHJKCzW4fMdZ/DZL6dRUFy2oHZoFw+8HBWAdq0cFa6OiJRUp00K4uPj0b59e9jb2yMkJAQ7duyocfwvv/yCkJAQ2Nvbo0OHDvjss88qfH3ZsmVQqVSVPoqKiupSHhGZGb1e8E3yBQz8cBs+TDqBgmIdgtq4YM3T4fgsJoRhhYiMn2FJSEjA5MmTER8fjz59+mDRokWIiorC0aNH0bZt20rjz549i2HDhuGpp57CihUrsGvXLkyYMAGurq64//77DeOcnZ1x/PjxCo+1t7evQ0tEZE72nL6CtxOP4ki6BgDg1awJpg31x713eEKt5oJaIiqjEhEx5gG9e/dGcHAwFi5caDgWGBiIkSNHIi4urtL4l156CT/88AOOHTtmOBYbG4uDBw9iz549AMpmWCZPnoycnJw6tgFoNBq4uLggNzcXzs7OdT4PETWMU1n5eGfDMWw5lgUAcLKzxsSBnTA2oh3sbbiglqixqO3rt1EzLMXFxUhOTsbLL79c4XhkZCR2795d5WP27NmDyMjICseGDBmCJUuWoKSkBDY2ZTtS5ufnw8fHBzqdDt27d8dbb72FHj16VFuLVquFVqs1fK7RaIxphYgUciVfi7lbTuLr31Kh0wus1Co82rstJt3ti5ZN7ZQuj4hMlFGBJTs7GzqdDu7u7hWOu7u7IzMzs8rHZGZmVjm+tLQU2dnZaN26NQICArBs2TJ069YNGo0G8+bNQ58+fXDw4EH4+vpWed64uDjMmjXLmPKJSEFFJTos3XUO8T+fQp62FAAwKNAd04cFoKNrU4WrIyJTV6e7hG7cqElEaty8qarx1x8PCwtDWFiY4et9+vRBcHAwPvnkE8yfP7/Kc06fPh1Tp041fK7RaODt7W1cI0R02+n1gh8PXcR7G48jPacQANDVyxmvDAtERMdWCldHRObCqMDSqlUrWFlZVZpNycrKqjSLUs7Dw6PK8dbW1mjZsmWVj1Gr1ejZsydOnjxZbS12dnaws+P0MZEp23fuKmb/7ygOXsgFAHg42+PFIf4Y1cOLC2qJyChGBRZbW1uEhIQgKSkJo0aNMhxPSkrCiBEjqnxMeHg4fvzxxwrHNm/ejNDQUMP6lRuJCA4cOIBu3boZUx4RmYhz2QV4Z0MKNv5Z9seKo60VnunfEePu7IAmtlxQS0TGM/qS0NSpUxETE4PQ0FCEh4dj8eLFSE1NRWxsLICySzXp6elYvnw5gLI7gj799FNMnToVTz31FPbs2YMlS5Zg1apVhnPOmjULYWFh8PX1hUajwfz583HgwAEsWLCgntokooaQc60Y8346iRW/nkeJTqBWAdE922LKYF+4OXGbAiKqO6MDS3R0NK5cuYI333wTGRkZ6Nq1KxITE+Hj4wMAyMjIQGpqqmF8+/btkZiYiClTpmDBggXw9PTE/PnzK+zBkpOTg/HjxyMzMxMuLi7o0aMHtm/fjl69etVDi0R0u2lLdfhqz3nM/+kkNEVlC2r7+7vilWGB8HN3Urg6IrIERu/DYqq4DwtRwxMRJB7OxLsbU5B69RoAIMDDCTOGB6Kvr6vC1RGRObgt+7AQEZX7I/UvvL3+GJLP/wUAcHWywwuRfnggxBtWXFBLRPWMgYWIjJJ29Rre3ZiC/x3KAAA0sbHC+Ls6YPxdHeBox18pRHR78LcLEdVKbmEJFvx8Cst2nUOxTg+VCngwpA2ej/SHuzMX1BLR7cXAQkQ1KtHpsfLX85j300n8da0EAHBnp1Z4ZVggOntyvRgRNQwGFiKqkohg89FLeGdDCs5mFwAAfN2a4pVhgejv71rj7tZERPWNgYWIKjl0IQez1x/Db2evAgBaNbXFlMF+iA71hrWVWuHqiKgxYmAhIoP0nEK8vzEF3x24CACws1bjyb7tEduvI5zsq96ZmoioITCwEBHyikqwcNtpLNl5FtpSPQDgvh5eeGGIPzybNVG4OiIiBhaiRq1Up8eqfWmYm3QCVwqKAQC927fAq8M7o1sbF4WrIyL6BwMLUSMkIvj5eBbmJKbgVFY+AKBDK0e8HBWAwZ3duaCWiEwOAwtRI/PnxVzMSTyGXaeuAACaO9hg8iA/PNK7LWy4oJaITBQDC1EjkZlbhA82H8c3f1yACGBrpcYTfdphwoBOcGnCBbVEZNoYWIgsXIG2FIu2n8F/tp9BYYkOAHBvkCemDfGHdwsHhasjIqodBhYiC6XTC9b+noYPk07gcp4WABDq0xwzhgeiR9vmCldHRGQcBhYiC7T9xGXMSTyGlMw8AIBPSwe8PDQAQ7t6cEEtEZklBhYiC3I8Mw9vJx7D9hOXAQAuTWzw7MBOGBPeDrbWXFBLROaLgYXIAmTlFeHjpBNI2JcGvQA2ViqMCW+HZwd2QjMHW6XLIyK6ZQwsRGassFiH/+w4g89+OY1rxWULaqO6euCloQFo18pR4eqIiOoPAwuRGdLrBd/uT8cHm44jU1MEAOju3QyvDg9EaLsWCldHRFT/GFiIzMzuU9mYvf4YjmZoAABezZrgpagA3HtHay6oJSKLxcBCZCZOZeUjLvEYfkrJAgA42Vlj4sBOGBvRDvY2VgpXR0R0ezGwEJm4K/lazN1yEl//lgqdXmClVuGx3m0xaZAfWjhyQS0RNQ4MLEQmqqhEhy92nUX8z6eRry0FAAwKdMf0YQHo6NpU4eqIiBoWAwuRidHrBT8euoj3Nh5Hek4hAKCrlzNmDOuM8I4tFa6OiEgZDCxEJuS3s1fx9vqjOHghFwDQ2sUeLw7xx8juXlCruaCWiBovBhYiE3A2uwDvbDiGTX9eAgA42lphwoBOGHdney6oJSICAwuRov4qKMa8n05ixa/nUaoXqFXA6F5tMWWQH1yd7JQuj4jIZDCwEClAW6rD8t3n8cnWk9AUlS2oHeDvileGBcLX3Unh6oiITA8DC1EDEhGsP5yBdzemIO1q2YLaAA8nvDq8M+70baVwdUREpouBhaiBJJ//C2+vP4o/UnMAAG5OdnhhiD/uD24DKy6oJSKqEQML0W2WeuUa3t2UgvWHMgAATWys8HS/Dhh/Vwc42PIpSERUG/xtSXSb5F4rwac/n8SXu8+jWKeHSgU8GNIGz0f6w93ZXunyiIjMCgMLUT0rLtVj5d7zmPfTSeRcKwEA9PVthVeGBSKwtbPC1RERmScGFqJ6IiLY9OclvLsxBWezCwAAvm5N8crwQPT3c+U7KRMR3QIGFqJ6cOhCDmavP4bfzl4FALRqaospg/0QHeoNayu1wtUREZk/BhaiW5CeU4j3N6bguwMXAQB21mo81bcDYvt3RFM7Pr2IiOoLf6MS1UFeUQnit53Gkp1nUVyqBwDcF+yFF4f4o7VLE4WrIyKyPAwsREYo1emxal8a5iadwJWCYgBAWIcWeHV4Z3T1clG4OiIiy8XAQlQLIoKtKVmYk3gMpy+XLajt4OqIV6ICcXegGxfUEhHdZgwsRDdxJD0XcxKPYffpKwCAFo62mDzIFw/3agsbLqglImoQDCxE1cjMLcL7m47j2/0XIALYWqvxRJ92mDigE5ztbZQuj4ioUWFgIbpBgbYUi345jcU7zqCopGxB7b+CPPHiEH94t3BQuDoiosaJgYXobzq9YM3vafhw8wlk52sBAKE+zTFjeCB6tG2ucHVERI0bAwsRgF9OXMac9cdw/FIeAMCnpQOmRwVgSBcPLqglIjIBDCzUqKVkajAnMQXbT1wGALg0scFzd/siJswHttZcUEtEZCoYWKhRysorwkebT2DN72nQC2BjpcKY8HZ4dmAnNHOwVbo8IiK6AQMLNSqFxTr8Z8cZfPbLaVwr1gEAhnXzwEtDA+DT0lHh6oiIqDoMLNQo6PWCb/64gA82H8clTdmC2h5tm+HV4YEI8WmhcHVERHQzDCxk8Xafysbs9cdwNEMDAGjTvAleGhqAe+5ozQW1RERmgoGFLNaprDzMSUzB1pQsAICTvTWeHdgJY8Lbwd7GSuHqiIjIGAwsZHGy87WYu+UEVv2WBp1eYK1W4bEwHzx3ty9aOHJBLRGROarTfZvx8fFo37497O3tERISgh07dtQ4/pdffkFISAjs7e3RoUMHfPbZZ5XGfPPNN+jcuTPs7OzQuXNnrFu3ri6lUSNWVKLDgp9Pof/727Di11To9ILBnd2xecpdeONfXRhWiIjMmNEzLAkJCZg8eTLi4+PRp08fLFq0CFFRUTh69Cjatm1bafzZs2cxbNgwPPXUU1ixYgV27dqFCRMmwNXVFffffz8AYM+ePYiOjsZbb72FUaNGYd26dXjooYewc+dO9O7d+9a7vAXaUh1E/vm8VC/IKypBbmEJNIWl0Oml+gcDEAi0JXrkFpY9Jl9bWrt/t0SHolI9ikoq/vvVKdXrUVisQ1GJHqU3qclSHb2Yi4u5RQCAbl4umDE8EGEdWipcFRER1QeVSG1eDv/Ru3dvBAcHY+HChYZjgYGBGDlyJOLi4iqNf+mll/DDDz/g2LFjhmOxsbE4ePAg9uzZAwCIjo6GRqPBhg0bDGOGDh2K5s2bY9WqVbWqS6PRwMXFBbm5uXB2djampRrdF78Lf6Tm1Nv56PbydLHHi0P9MSLIC2o1F9QSEZm62r5+GzXDUlxcjOTkZLz88ssVjkdGRmL37t1VPmbPnj2IjIyscGzIkCFYsmQJSkpKYGNjgz179mDKlCmVxsydO7faWrRaLbRareFzjUZjTCu3xFqtgksTGzjZW8PG6uZX1exs1HBpYgOXJjZwtLWGuhZ3ptjZqGFvYwV7a3Wt7mSxVqvQxNYKdjZWsGmkL9SOdtYY3NmdC2qJiCyQUYElOzsbOp0O7u7uFY67u7sjMzOzysdkZmZWOb60tBTZ2dlo3bp1tWOqOycAxMXFYdasWcaUXycrnuxd4bKPlVqFJjZWvB2WiIioAdVp0e2NL9YiUuMLeFXjbzxu7DmnT5+O3Nxcw0daWlqt6zeGg601nOxtDB8OttYMK0RERA3MqBmWVq1awcrKqtLMR1ZWVqUZknIeHh5Vjre2tkbLli1rHFPdOQHAzs4OdnZ2xpRPREREZsqoGRZbW1uEhIQgKSmpwvGkpCRERERU+Zjw8PBK4zdv3ozQ0FDY2NjUOKa6cxIREVHjYvRtzVOnTkVMTAxCQ0MRHh6OxYsXIzU1FbGxsQDKLtWkp6dj+fLlAMruCPr0008xdepUPPXUU9izZw+WLFlS4e6fSZMm4a677sK7776LESNG4Pvvv8eWLVuwc+fOemqTiIiIzJnRgSU6OhpXrlzBm2++iYyMDHTt2hWJiYnw8fEBAGRkZCA1NdUwvn379khMTMSUKVOwYMECeHp6Yv78+YY9WAAgIiICq1evxquvvorXXnsNHTt2REJCguJ7sBAREZFpMHofFlN1u/ZhISIiotuntq/fdbpLiIiIiKghMbAQERGRyWNgISIiIpPHwEJEREQmj4GFiIiITB4DCxEREZk8o/dhMVXld2c35Ls2ExER0a0pf92+2S4rFhNY8vLyAADe3t4KV0JERETGysvLg4uLS7Vft5iN4/R6PS5evAgnJ6cGfTdljUYDb29vpKWlmfWGdezDtFhCH5bQA8A+TI0l9GEJPQD114eIIC8vD56enlCrq1+pYjEzLGq1Gm3atFHs33d2djbrH7xy7MO0WEIfltADwD5MjSX0YQk9APXTR00zK+W46JaIiIhMHgMLERERmTwGlltkZ2eHmTNnws7OTulSbgn7MC2W0Icl9ACwD1NjCX1YQg9Aw/dhMYtuiYiIyHJxhoWIiIhMHgMLERERmTwGFiIiIjJ5DCxERERk8hhYiBoY17nT7cCfK9PB78XtwcByE6WlpYb/5g+hsi5cuICMjAwA5vu9yMrKMrzvFWC+fZw6dQpJSUlKl3HL0tLSkJycjIsXLypdyi3Jzc2FTqczfG6OP1cnTpxAbGwsduzYoXQpt4TP8duHgaUaxcXFePnllzFhwgTMnDkThYWFDfoeRfWhpKQES5cuxbp165CSkqJ0OXVWUlKCp59+GhEREfjqq68AwOy+F6WlpRg3bhx69eqFQYMG4dFHH0V2drbZ9QEAhw4dgp+fHx5++GGcP39e6XLqpPxnKjg4GP/+978RFBSEXbt2KV2W0UpKSjBx4kQMGzYMw4YNw1tvvQWdTmdWP1d6vR5TpkxB9+7dUVBQUOHF3pzwOX77MbBU4bvvvoOPjw9+++032Nvb4/3338f48eMhImaTlhctWgR3d3d88cUXmDx5Mu6//36sWbMGQNkvCHORlpaGPn364PDhw1i7di0efvhhs/o+AGW/yMaOHYujR4/iyy+/xMMPP4xDhw7hvvvuw7Fjx5Quz2jFxcUYMmQIbGxs8N577yldjtHy8/PxwAMP4OTJk9i8eTPWrFmD4OBgvPbaawDM5y/ipKQkdO7cGX/++SdefPFFeHt7Y+XKlXjjjTcAmE8fGzZswL59+7BhwwZ89dVXGDZsmOFr5tIDn+MNRKiCoqIiiYqKkldeecVw7LvvvhMHBwcpLCxUsLLaKSkpkY8//li6desmK1euFBGRgwcPyrPPPishISGi0+kUrtA4n3/+uQwaNEj0er2IiKSlpUlxcbHCVRknNTVVfH195auvvjIcy8jIEC8vL3n22WclMzNTweqMt2jRInn44Yflp59+Emtra9m7d6/SJRll79694uvrK1u3bjUc+89//iP/+te/zOb5kZubK08++aRMnDjR8HzQarUyc+ZMGTJkiBQUFChcYe2NHDlSJk6cKCIi27Ztk1dffVWWLl0q58+fV7iy2uNzvGFwhuUGhw4dwrZt23D33XcbjmVmZmL8+PEmPzMhIigpKTH8BTl69GgAwB133IEuXbrA2toaly9fVrjKm5PrZlB+//13BAUFIScnBw899BAGDx6MXr16Yfz48cjMzFS40tq5cuUKLly4gLCwMACAVquFh4cHpk+fjs2bN2P79u0KV3hz1//s29nZwcfHBwMHDkTPnj0xa9YsAGVvNW8OiouLcerUKcN24tnZ2ViwYAE8PT3xxRdfoLCwUOEKb05EcOedd+LJJ5+EjY0NRAS2trYoKipCYWEhHBwczGJ2Ii8vD9nZ2bj77rsxe/ZsjB49GocPH8brr7+OgQMH4scff1S6xFqxhOf49T8vpvocb/SBZfPmzTh48KBhwVrPnj3RokULfPrpp9iwYQNefPFFTJgwAVu3boWvry8WLlxoeNE3lV8Ip0+fhl6vh0qlgr29PR599FG8/vrrUKvVhhqbN2+O/Px8uLm5KVxt9U6fPg0RgUqlMlz3PXLkCABg7ty5AIBPP/0UsbGx+PHHHzFz5kykp6cDMJ3vxZw5czBz5kysXr3acCwwMBBubm5YsWIFAECtLnvaTZw4EU5OTtiwYQO0Wq0i9Vbnxj7KawaAP/74A/n5+QCAlStXYuPGjYiKisKQIUNMbq1UVd+PO++8E/369cMTTzyBqKgouLu7w8PDA7a2tpg+fToef/xxHD58WMGqK0tMTATwT3B0cXHB448/ju7du1c4npubiw4dOgAwvXVe5T1c/1x1cnJCSUkJPv/8c5w4cQLffvst/vvf/+L8+fPo2LEjvvjiC5P7mVq8eDH+85//VAghvr6+8PDwMKvneHkfv/zyC4Cyn5fynyOTfY4rM7GjvKVLl4qHh4d069ZNnJycZMKECZKWliYiZdOSEyZMkF69ekmnTp3kp59+kuPHj8vs2bPF19dXvvzyS4WrL7NkyRJp27athISESO/evWX58uWGSyciUmF6+4knnpDHHntMRMTkLqnc2MeKFStEq9WKiMgHH3wgVlZW4ufnJ/v27TM8ZunSpdKlSxf58ccflSq7gr1790rbtm0lODhYoqKixMnJSe6//345ffq0iIi88MIL4ufnJ5cuXRIRMVxe/PLLL6VZs2Ymc7mxqj4eeOABOXnypGHM6NGjZcuWLSJSdimlSZMmYmNjI//973+VKruS6vpISUkRERGNRiMnT56UiIgI+eCDDwyP279/v3To0EHWrFmjVOkV/O9//xMvLy9RqVSya9cuEZEqL1uVP+979+4tn3/+eYVjSquqB71eb6hvyZIlolKpxM/PT7KysgyP2759u7Ru3Vp2796tSN03+vrrr8XNzU3Cw8Ole/fu4urqKm+//baIlF2imzZtmlk8x6vqY86cOSIiht+7pvocb5SB5fPPP5dOnTrJqlWr5PLly7Jy5UpxdHSUAwcOGMaUlJRIZGRkpXDSpUuXCutblDJ37lxDDzt37pTXX39d1Gq1LFiwwBBI9Hq9lJaWSklJiQQHB8uiRYsqnUfpa/ZV9aFSqWTBggVSWloqf/75pwQFBUm7du0kPT29wmO9vLxk4cKFClVe0dSpU2X48OEiUvb/6eHDh8XHx0diY2MlJydHfv31VwkODpYJEyaIyD8vJj///LO4ubnJwYMHFav9etX18cwzz8iFCxdEROSxxx6TmJgY6dmzp7i6uspbb70lzZs3r/DCr7Sa+rh48aKIiOzbt0/8/f0lKyvL8P0oLS01mV527NghQ4cOlf/7v/+TqKgoCQ0NrXH82bNnxdXV1RDKRMQQmJV6ntemh6NHj0r//v2lc+fOkpGRYTheWFgoTZs2lbVr1zZkyVVauXKlBAUFyWeffSYiIunp6fLpp5+Ko6Oj5ObmiohIUlKS9OzZ06Sf4zX1odFoDOMef/xxk3yON6rAUv4C/sgjj0hMTEyFr/n5+VUILBcvXpTmzZsbFn6VlpZKTk6OhIaGGlK1UgoKCmTw4MEyc+ZMEfnnidG3b1/x8fGR7777rsLxjIwMadOmjeEX2f79++Xxxx9v8LpvVFMf3t7e8r///U9ERN577z2xsrKq8FdvVlaWdOvWTVasWNHgdV9Pr9dLTk6O3HnnnfLCCy+IyD8vDvHx8dKjRw/DL4ePP/5YHBwc5NtvvzX8JTN79mzp37+/4n8N36yPkJAQ+eSTT0REZNSoUdKiRQuZOHGiIcS88847olKp5OzZs4rUX642fcydO1dERFJSUkSlUklycrLh8evWrZPg4GD5448/Gr74v5X/LJw4cUI++ugjOXPmjPz+++/i4OBgmD2pKoAsXLhQgoODRUTkjz/+kF69eomrq6uUlJQ0XPF/q00PpaWlhv/97rvvxM7OTmbOnGn4mUpISJDw8HDDjIUSyvtYtmyZjB8/Xq5du2b42s6dO8XPz0/27NkjImUB6+OPPxZHR0eTe47Xpo/yhbXXrl2TUaNGScuWLU3uOd6oAku57t27y5NPPmlYuf3ss8+Kv7+/vPHGG7Jnzx4pKCgQrVYrd9xxh0RFRcnBgwfl3LlzMm7cOAkMDJQjR44oWr9Wq5UWLVrI119/LSL/TD3ef//94unpKWPGjKkwtfrVV19J3759RaPRyL///W+xsbGRESNGiE6nU/RJdLM+YmJi5K+//pL8/HwZNWqUeHt7y8yZM2X//v0ybtw46dGjh+Gv5YaUnJwsOTk5FY6FhobK008/LSJld5qJlF16u+++++Rf//qXpKenS3Fxsbz44ovi5OQk/fr1kwcffFCaNGkiCxYsEJGGn8KvSx9//fWXHDp0SA4fPlzhcUVFRfLee+8p8pe8sX2MHDlSzp8/LwUFBRIdHS0ODg4SGxsrY8aMEScnJ3n99dcVeV5U1Uf5i3pJSYk8//zz4urqauinXHmtzz77rDzwwAMyZcoUUavVMm7cuEpjbzdje7j+52X+/Pni6ekp/v7+MmrUKHF0dFTsj8Pk5GT566+/DJ/n5OQY+ih34MAB8fDwkKtXrxqOaTQamTZtmkk9x+vSx2+//SZ//vlnhXFKPsfLWXRgWbNmjTz55JMyd+5cOXTokOH46tWrxcfHRyIjI6Vly5YSEBAgb775pgwYMECCgoLknXfeEZGya6iurq7i5+cnbdq0kQEDBlS4lq9kDw8//LAEBAQY0u+KFStkwIAB8uSTT4qfn5/s37/fMHb06NFiZWUlTk5OEhoaKseOHWvQHkTq1oevr6+hj+LiYnnuueckJCRE/P39pV+/fnLq1KkG7eG///2vtGnTRjp27Cht27aV119/3VD3vHnzpGnTpobbScv/uvrmm2+kTZs2hmv3IiJr166VmTNnSmxsrCLfi7r24eXlZTLrCURu7ftR3kdBQYFMmzZNxo4dK2PGjJHjx4+bRB/ll0auX+tx5swZ8fb2lueff97wtXI6nU58fHxEpVJJ//79K73YmGoPN774/frrrxIfHy/Tp083ie/Fa6+9VuGW5Ovr/eijj6RPnz4i8s/PVzlTe47Xto+GDrjGssjAkp2dLQ888IB4eHhIbGys3HnnneLp6SlLly41jMnKypL3339f+vXrV+Ha3VNPPSUjR46U7OxsERE5f/68/Pbbb/Lbb78p3kPr1q1l+fLlIlI21dqhQwfp0KGDeHp6ioODg3zzzTciImJtbS3r168XkbJfFg8//LC0a9fOcMwc+yiXn5/f4EFFpGy9Q0BAgMydO1cOHjwo8fHx4urqKs8884zk5OTI+fPnpWPHjoa/6q9f2NyyZUtZsmRJg9dcFfZR1kf5ZYlySlw2Eam5jytXrojIPzMUer1e4uPjxdraWs6cOSMiZS+UBQUFUlhYKHPmzJFNmzaZZQ/X/w5WSm360Ol0hp+VUaNGGfaPMSWW0kdVLDKwrF27Vnr16mX4a0tEZMSIEdK+fXv59ttvRaTsF9To0aNl9uzZIvJPQp46dap07NhR8vPzG77w61TXQ7t27WTdunUiUraJ2qZNm+TLL780/ELOysqqdJfDiRMnGrT2691qH0ovuCv/y3DhwoXSpk0bwwI7EZFPP/1UevXqJXFxcSIismDBArGyspJffvnFMOb06dPSsWNHQwhTCvsoYy59hIWFyVtvvVXpcVeuXJGIiAgZMWKEJCcny+DBgytsVtaQ6quHyMhI+eqrrxS7PG1sH+WX0jt27GhYZ3f8+HEZPXq0pKamNmzx17GUPmpikYFl1KhRct9994mISF5enoiULTZSqVRy9913GxZxDR48WEaOHGl4XGZmptxzzz0yY8aMhi/6BjfroXyNyo1TqgkJCRIQEFBhtb2SLKWPadOmycCBAyvsIJqfny8TJ06UsLAwOX78uOj1enn00UfFw8NDZs2aJfv375enn35aunXrVukOJ6WwD/PpIyIiwrBe7vp1B0uXLhWVSiVqtVruuecexXe1rY8erl8EqpTa9iFSdtt8t27d5OLFizJp0iSxs7OTwYMHm8QlFUvpoypmv3Hc9u3bsWnTpgrvquzr64s///wTANC0aVMAQEpKCgYOHIiioiJ89913AIDp06dj/fr16NOnDyZMmIDQ0FBoNBqMHz/ebHpQq9W4fPkyUlJS8Omnn2LKlCm477770KpVqwbfTM0S+khKSsJzzz2HefPm4bfffjMc79OnD3bv3m3YXVen08HR0REjRoyAWq3G+vXroVKpsGLFCjz44INYt24dHnzwQezbtw8rV66Ep6dng/XAPiyjD5VKhc2bNwMArKysUFxcjPj4eIwbNw533XUXDh06hB9//BEODg5m30OTJk0apIf66AMo2wTvyJEj8Pf3R1JSEnbt2oXNmzcbdk9mH7eJ0ompri5fvixjxowRlUolQUFBFW61On36tLi6ukq/fv3k3XfflfDwcGnfvr389NNPEhQUJK+++qph7Lp16+Sll16SRx55pME3i7qVHl577TXD2OTkZBk5cqS0b99ekelhS+jj4sWLcs8994ibm5s8+uij0q1bN3FxcTHc6ldYWCgBAQEyfvx4Eak4I9S3b1955plnDJ/rdDopKCiosB9GQ2EfltVH+Z4eImUzwJMmTWrwjSstoQeR+u1j9uzZ4urqqsilRUvpoy7MMrCUlJRIfHy8DBkyRFavXi0ODg4SFxdXYRpr586d8tRTT0lwcLD83//9n1y+fFlERGJiYuT+++9XqnSD+u5BqX0jLKGPgoICefzxxyU6OtqwEFBEpGfPnjJ27FgRKZvOXr58uajV6gp3/IiIPProozJgwADD50pdi2cfZSy1DyVYQg8i9dNH//79DZ9fv21EQ7KUPurKLAOLSNntb+Xbss+aNUtcXV0r3Mpb7vrbzS5duiRdu3Y1LLRVepfX+uhBqbsbrmcJfYwfP142bNhQoZZZs2ZJ7969DWOKiopk1KhREhgYKNu2bRO9Xi8ZGRnSq1evSnedKIV9sI/6Zgk9iLAPU+ujLsw2sNz4V5Onp6eMHz/ecHvc9V8vLCyU4uJiw86j1+8DoiRL6EHEMvq4/rbX8nofe+wxeeqppyocKywslP79+4ubm5tERkaKp6enhIWFmcyqevbBPuqbJfQgwj5MrY+6MNvAUq78r/Y1a9aItbW1bN68ucLXL1y4IPHx8RIaGlphV1VTYgk9iFhOH+X69u1r2Lun/G0dRMquw2/evFnefvttWblypYIV1g77MC2W0Icl9CDCPsyN2QeW64WHh8ugQYMMty2XX5/7+uuvFX/TptqyhB5EzL+P06dPi7u7u/z++++GYzfuZmkO2IdpsYQ+LKEHEfZhjiwisJRfxzty5IhYWVnJvHnz5LnnnpPg4OBK73liqiyhBxHz76N8OvXLL7+Ujh07Go6/8cYbEhsbq+gbsRmDfZgWS+jDEnoQYR/mzCICy/V69uwpKpVKfHx8ZOPGjUqXUyeW0IOIefcxceJEmTZtmmzevFnatWsnbm5uimx7fqvYh2mxhD4soQcR9mGOLCawnDp1Srp27Vrh7cvNjSX0IGL+fRQWFkqnTp1EpVKJnZ2d4c0wzQ37MC2W0Icl9CDCPsyVtdIb19UXKysr3H///XjppZcadNfE+mQJPQDm34e9vT3atWuHwYMH46OPPoK9vb3SJdUJ+zAtltCHJfQAsA9zpRJp4P3bicyATqeDlZWV0mXcMvZhWiyhD0voAWAf5oiBhYiIiEye2b/5IREREVk+BhYiIiIyeQwsREREZPIYWIiIiMjkMbAQERGRyWNgISIiIpPHwEJEREQmj4GFiIiITB4DCxE1iLFjx0KlUkGlUsHGxgbu7u4YPHgwvvjiC+j1+lqfZ9myZWjWrNntK5SITBIDCxE1mKFDhyIjIwPnzp3Dhg0bMGDAAEyaNAn33HMPSktLlS6PiEwYAwsRNRg7Ozt4eHjAy8sLwcHBeOWVV/D9999jw4YNWLZsGQDgo48+Qrdu3eDo6Ahvb29MmDAB+fn5AIBt27bhiSeeQG5urmG25o033gAAFBcXY9q0afDy8oKjoyN69+6Nbdu2KdMoEdU7BhYiUtTAgQMRFBSEb7/9FgCgVqsxf/58HDlyBF9++SW2bt2KadOmAQAiIiIwd+5cODs7IyMjAxkZGXjhhRcAAE888QR27dqF1atX49ChQ3jwwQcxdOhQnDx5UrHeiKj+8M0PiahBjB07Fjk5Ofjuu+8qfW306NE4dOgQjh49Wulra9euxTPPPIPs7GwAZWtYJk+ejJycHMOY06dPw9fXFxcuXICnp6fh+KBBg9CrVy/MmTOn3vshooZlrXQBREQiApVKBQD4+eefMWfOHBw9ehQajQalpaUoKipCQUEBHB0dq3z8H3/8ARGBn59fheNarRYtW7a87fUT0e3HwEJEijt27Bjat2+P8+fPY9iwYYiNjcVbb72FFi1aYOfOnRg3bhxKSkqqfbxer4eVlRWSk5NhZWVV4WtNmza93eUTUQNgYCEiRW3duhWHDx/GlClT8Pvvv6O0tBQffvgh1OqyJXZr1qypMN7W1hY6na7CsR49ekCn0yErKwt9+/ZtsNqJqOEwsBBRg9FqtcjMzIROp8OlS5ewceNGxMXF4Z577sGYMWNw+PBhlJaW4pNPPsG9996LXbt24bPPPqtwjnbt2iE/Px8//fQTgoKC4ODgAD8/Pzz66KMYM2YMPvzwQ/To0QPZ2dnYunUrunXrhmHDhinUMRHVF94lREQNZuPGjWjdujXatWuHoUOH4ueff8b8+fPx/fffw8rKCt27d8dHH32Ed999F127dsXKlSsRFxdX4RwRERGIjY1FdHQ0XF1d8d577wEAli5dijFjxuD555+Hv78//vWvf2Hv3r3w9vZWolUiqme8S4iIiIhMHmdYiIiIyOQxsBAREZHJY2AhIiIik8fAQkRERCaPgYWIiIhMHgMLERERmTwGFiIiIjJ5DCxERERk8hhYiIiIyOQxsBAREZHJY2AhIiIik/f/TdWGViKqE34AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple.dividends.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the `Ticker` module create an object for AMD (Advanced Micro Devices) with the ticker symbol is `AMD` called; name the object <code>amd</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "amd = yf.Ticker(\"AMD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-09-29 14:27:53--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/data/amd.json\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5838 (5.7K) [application/json]\n",
      "Saving to: ‘amd.json’\n",
      "\n",
      "amd.json            100%[===================>]   5.70K  --.-KB/s    in 0s      \n",
      "\n",
      "2023-09-29 14:27:53 (35.5 MB/s) - ‘amd.json’ saved [5838/5838]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/data/amd.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'zip': '95054',\n",
       " 'sector': 'Technology',\n",
       " 'fullTimeEmployees': 15500,\n",
       " 'longBusinessSummary': 'Advanced Micro Devices, Inc. operates as a semiconductor company worldwide. The company operates in two segments, Computing and Graphics; and Enterprise, Embedded and Semi-Custom. Its products include x86 microprocessors as an accelerated processing unit, chipsets, discrete and integrated graphics processing units (GPUs), data center and professional GPUs, and development services; and server and embedded processors, and semi-custom System-on-Chip (SoC) products, development services, and technology for game consoles. The company provides processors for desktop and notebook personal computers under the AMD Ryzen, AMD Ryzen PRO, Ryzen Threadripper, Ryzen Threadripper PRO, AMD Athlon, AMD Athlon PRO, AMD FX, AMD A-Series, and AMD PRO A-Series processors brands; discrete GPUs for desktop and notebook PCs under the AMD Radeon graphics, AMD Embedded Radeon graphics brands; and professional graphics products under the AMD Radeon Pro and AMD FirePro graphics brands. It also offers Radeon Instinct, Radeon PRO V-series, and AMD Instinct accelerators for servers; chipsets under the AMD trademark; microprocessors for servers under the AMD EPYC; embedded processor solutions under the AMD Athlon, AMD Geode, AMD Ryzen, AMD EPYC, AMD R-Series, and G-Series processors brands; and customer-specific solutions based on AMD CPU, GPU, and multi-media technologies, as well as semi-custom SoC products. It serves original equipment manufacturers, public cloud service providers, original design manufacturers, system integrators, independent distributors, online retailers, and add-in-board manufacturers through its direct sales force, independent distributors, and sales representatives. The company was incorporated in 1969 and is headquartered in Santa Clara, California.',\n",
       " 'city': 'Santa Clara',\n",
       " 'phone': '408 749 4000',\n",
       " 'state': 'CA',\n",
       " 'country': 'United States',\n",
       " 'companyOfficers': [],\n",
       " 'website': 'https://www.amd.com',\n",
       " 'maxAge': 1,\n",
       " 'address1': '2485 Augustine Drive',\n",
       " 'industry': 'Semiconductors',\n",
       " 'ebitdaMargins': 0.24674,\n",
       " 'profitMargins': 0.19240999,\n",
       " 'grossMargins': 0.48248002,\n",
       " 'operatingCashflow': 3520999936,\n",
       " 'revenueGrowth': 0.488,\n",
       " 'operatingMargins': 0.22198,\n",
       " 'ebitda': 4055000064,\n",
       " 'targetLowPrice': 107,\n",
       " 'recommendationKey': 'buy',\n",
       " 'grossProfits': 7929000000,\n",
       " 'freeCashflow': 3122749952,\n",
       " 'targetMedianPrice': 150,\n",
       " 'currentPrice': 119.22,\n",
       " 'earningsGrowth': -0.454,\n",
       " 'currentRatio': 2.024,\n",
       " 'returnOnAssets': 0.21327,\n",
       " 'numberOfAnalystOpinions': 38,\n",
       " 'targetMeanPrice': 152.02,\n",
       " 'debtToEquity': 9.764,\n",
       " 'returnOnEquity': 0.47428,\n",
       " 'targetHighPrice': 200,\n",
       " 'totalCash': 3608000000,\n",
       " 'totalDebt': 732000000,\n",
       " 'totalRevenue': 16433999872,\n",
       " 'totalCashPerShare': 3.008,\n",
       " 'financialCurrency': 'USD',\n",
       " 'revenuePerShare': 13.548,\n",
       " 'quickRatio': 1.49,\n",
       " 'recommendationMean': 2.2,\n",
       " 'exchange': 'NMS',\n",
       " 'shortName': 'Advanced Micro Devices, Inc.',\n",
       " 'longName': 'Advanced Micro Devices, Inc.',\n",
       " 'exchangeTimezoneName': 'America/New_York',\n",
       " 'exchangeTimezoneShortName': 'EDT',\n",
       " 'isEsgPopulated': False,\n",
       " 'gmtOffSetMilliseconds': '-14400000',\n",
       " 'quoteType': 'EQUITY',\n",
       " 'symbol': 'AMD',\n",
       " 'messageBoardId': 'finmb_168864',\n",
       " 'market': 'us_market',\n",
       " 'annualHoldingsTurnover': None,\n",
       " 'enterpriseToRevenue': 8.525,\n",
       " 'beta3Year': None,\n",
       " 'enterpriseToEbitda': 34.551,\n",
       " '52WeekChange': 0.51966953,\n",
       " 'morningStarRiskRating': None,\n",
       " 'forwardEps': 4.72,\n",
       " 'revenueQuarterlyGrowth': None,\n",
       " 'sharesOutstanding': 1627360000,\n",
       " 'fundInceptionDate': None,\n",
       " 'annualReportExpenseRatio': None,\n",
       " 'totalAssets': None,\n",
       " 'bookValue': 6.211,\n",
       " 'sharesShort': 27776129,\n",
       " 'sharesPercentSharesOut': 0.0171,\n",
       " 'fundFamily': None,\n",
       " 'lastFiscalYearEnd': 1640390400,\n",
       " 'heldPercentInstitutions': 0.52896,\n",
       " 'netIncomeToCommon': 3161999872,\n",
       " 'trailingEps': 2.57,\n",
       " 'lastDividendValue': 0.005,\n",
       " 'SandP52WeekChange': 0.15217662,\n",
       " 'priceToBook': 19.194977,\n",
       " 'heldPercentInsiders': 0.00328,\n",
       " 'nextFiscalYearEnd': 1703462400,\n",
       " 'yield': None,\n",
       " 'mostRecentQuarter': 1640390400,\n",
       " 'shortRatio': 0.24,\n",
       " 'sharesShortPreviousMonthDate': 1644883200,\n",
       " 'floatShares': 1193798619,\n",
       " 'beta': 1.848425,\n",
       " 'enterpriseValue': 140104957952,\n",
       " 'priceHint': 2,\n",
       " 'threeYearAverageReturn': None,\n",
       " 'lastSplitDate': 966902400,\n",
       " 'lastSplitFactor': '2:1',\n",
       " 'legalType': None,\n",
       " 'lastDividendDate': 798940800,\n",
       " 'morningStarOverallRating': None,\n",
       " 'earningsQuarterlyGrowth': -0.453,\n",
       " 'priceToSalesTrailing12Months': 11.805638,\n",
       " 'dateShortInterest': 1647302400,\n",
       " 'pegRatio': 0.99,\n",
       " 'ytdReturn': None,\n",
       " 'forwardPE': 25.258476,\n",
       " 'lastCapGain': None,\n",
       " 'shortPercentOfFloat': 0.0171,\n",
       " 'sharesShortPriorMonth': 88709340,\n",
       " 'impliedSharesOutstanding': 0,\n",
       " 'category': None,\n",
       " 'fiveYearAverageReturn': None,\n",
       " 'previousClose': 123.23,\n",
       " 'regularMarketOpen': 123.04,\n",
       " 'twoHundredDayAverage': 116.6998,\n",
       " 'trailingAnnualDividendYield': 0,\n",
       " 'payoutRatio': 0,\n",
       " 'volume24Hr': None,\n",
       " 'regularMarketDayHigh': 125.66,\n",
       " 'navPrice': None,\n",
       " 'averageDailyVolume10Day': 102167370,\n",
       " 'regularMarketPreviousClose': 123.23,\n",
       " 'fiftyDayAverage': 115.95,\n",
       " 'trailingAnnualDividendRate': 0,\n",
       " 'open': 123.04,\n",
       " 'toCurrency': None,\n",
       " 'averageVolume10days': 102167370,\n",
       " 'expireDate': None,\n",
       " 'algorithm': None,\n",
       " 'dividendRate': None,\n",
       " 'exDividendDate': 798940800,\n",
       " 'circulatingSupply': None,\n",
       " 'startDate': None,\n",
       " 'regularMarketDayLow': 118.59,\n",
       " 'currency': 'USD',\n",
       " 'trailingPE': 46.389107,\n",
       " 'regularMarketVolume': 99476946,\n",
       " 'lastMarket': None,\n",
       " 'maxSupply': None,\n",
       " 'openInterest': None,\n",
       " 'marketCap': 194013855744,\n",
       " 'volumeAllCurrencies': None,\n",
       " 'strikePrice': None,\n",
       " 'averageVolume': 102428813,\n",
       " 'dayLow': 118.59,\n",
       " 'ask': 117.24,\n",
       " 'askSize': 1100,\n",
       " 'volume': 99476946,\n",
       " 'fiftyTwoWeekHigh': 164.46,\n",
       " 'fromCurrency': None,\n",
       " 'fiveYearAvgDividendYield': None,\n",
       " 'fiftyTwoWeekLow': 72.5,\n",
       " 'bid': 117.24,\n",
       " 'tradeable': False,\n",
       " 'dividendYield': None,\n",
       " 'bidSize': 900,\n",
       " 'dayHigh': 125.66,\n",
       " 'regularMarketPrice': 119.22,\n",
       " 'preMarketPrice': 116.98,\n",
       " 'logo_url': 'https://logo.clearbit.com/amd.com'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "with open('amd.json') as json_file:\n",
    "    amd_info = json.load(json_file)\n",
    "    # Print the type of data variable    \n",
    "    #print(\"Type:\", type(apple_info))\n",
    "amd_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b>Question 1</b> Use the key  <code>'country'</code> to find the country the stock belongs to, remember it as it will be a quiz questio<n.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unexpected EOF while parsing (3456636957.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/tmp/ipykernel_381/3456636957.py\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(\"Country\", stock_country\u001b[0m\n\u001b[0m                                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
     ]
    }
   ],
   "source": [
    "stock_country = amd_info['country']\n",
    "print(\"Country\", stock_country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 2</b> Use the key  <code>'sector'</code> to find the sector the stock belongs to, remember it as it will be a quiz question.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "stock_sector = amd_info['sector']\n",
    "print(\"Sector:\", stock_sector)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 3</b> Obtain stock data for AMD using the `history` function, set the `period` to max. Find the `Volume` traded on the first day (first row).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "# Create an object for AMD with the ticker symbol \"AMD\"\n",
    "amd = yf.Ticker(\"AMD\")\n",
    "\n",
    "# Use the history function to fetch historical stock data with the period set to max\n",
    "amd_history = amd.history(period=\"max\")\n",
    "\n",
    "# Extract the Volume traded on the first day (first row)\n",
    "first_day_volume = amd_history['Volume'].iloc[0]\n",
    "\n",
    "print(\"Volume traded on the first day:\", first_day_volume)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0220ENSkillsNetwork900-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n",
    "\n",
    "Azim Hirjani\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description        |\n",
    "| ----------------- | ------- | ------------- | ------------------------- |\n",
    "| 2020-11-10        | 1.1     | Malika Singla | Deleted the Optional part |\n",
    "| 2020-08-27        | 1.0     | Malika Singla | Added lab to GitLab       |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "\n",
    "<p>\n"
   ]
  }
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