{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.4"},"colab":{"name":"07.01-Measuring-Return.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true}},"cells":[{"cell_type":"markdown","metadata":{"pycharm":{},"id":"dC0K7BZiKe4P"},"source":["\n","*This notebook contains course material from [CBE40455](https://jckantor.github.io/CBE40455) by\n","Jeffrey Kantor (jeff at nd.edu); the content is available [on Github](https://github.com/jckantor/CBE40455.git).\n","The text is released under the [CC-BY-NC-ND-4.0 license](https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode),\n","and code is released under the [MIT license](https://opensource.org/licenses/MIT).*"]},{"cell_type":"markdown","metadata":{"pycharm":{},"id":"kywwqZ38Ke4Q"},"source":["\n","< [Risk and Diversification](http://nbviewer.jupyter.org/github/jckantor/CBE40455/blob/master/notebooks/07.00-Risk-and-Diversification.ipynb) | [Contents](toc.ipynb) | [Geometric Brownian Motion](http://nbviewer.jupyter.org/github/jckantor/CBE40455/blob/master/notebooks/07.02-Geometric-Brownian-Motion.ipynb) >
"]},{"cell_type":"markdown","metadata":{"pycharm":{},"id":"yv2KJXVdKe4S"},"source":["# Measuring Return\n","\n","How much does one earn relative to the amount invested? \n","\n","This is the basic concept of return, and one of the fundamental measurements of financial performance. This notebook examines the different ways in which return can be measured."]},{"cell_type":"markdown","metadata":{"pycharm":{},"id":"evkdzCeTKe4T"},"source":["## Pandas-datareader\n","\n","As will be shown below, [pandas-datareader](https://github.com/pydata/pandas-datareader) provides a convenient means access and manipulate financial data using the Pandas library. The pandas-datareader is normally imported separately from pandas. Typical installation is\n","\n"," pip install pandas-datareader\n","\n","from a terminal window, or executing\n","\n"," !pip install pandas-datareader\n","\n","in a Jupyter notebook cell. Google Colab environment now includes pandas-datareader, so separate installation is required."]},{"cell_type":"markdown","metadata":{"id":"1nTCtGTkJSFd"},"source":["## Imports"]},{"cell_type":"code","metadata":{"jupyter":{"outputs_hidden":true},"pycharm":{},"id":"WQGo0o7_Ke4U","executionInfo":{"status":"ok","timestamp":1604409829630,"user_tz":300,"elapsed":629,"user":{"displayName":"Jeffrey Kantor","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64","userId":"09038942003589296665"}}},"source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import seaborn as sns\n","\n","import datetime\n","\n","import pandas as pd\n","import pandas_datareader as pdr"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"pycharm":{},"id":"Eh1HgZKdKe4d"},"source":["## Where to get Price Data\n","\n","This notebook uses the price of stocks and various commodity goods for the purpose of demonstrating returns. Price data is available from a number of sources. Here we demonstrate the process of obtaining price data on financial goods from [Yahoo Finance](http://finance.yahoo.com/) and downloading price data sets from [Quandl](http://www.quandl.com/).\n","\n","The most comprehensive repositories of financial data are commercial enterprises. Some provide a free tier of service for limited use, typically 50 inquires a day or several hundred a month. Some require registration to access the free tier. These details are a constantly changing. A listing of free services is available from [awesome-quant](https://github.com/wilsonfreitas/awesome-quant#data-sources), but please note that details change quickly."]},{"cell_type":"markdown","metadata":{"pycharm":{},"id":"SOgpAYEzKe4Y"},"source":["### Stock Symbols\n","\n","Stock price data is usually indexed and accessed by stock symbols. Stock symbols are unique identifiers for a stock, commodity, or other financial good on a specific exchanges. The following function looks up details of stock symbol on yahoo finance.."]},{"cell_type":"code","metadata":{"pycharm":{},"id":"khRgx1GPKe4Z","executionInfo":{"status":"ok","timestamp":1604409830054,"user_tz":300,"elapsed":317,"user":{"displayName":"Jeffrey Kantor","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64","userId":"09038942003589296665"}},"outputId":"7f6143c7-4b16-448d-c496-c348900c319a","colab":{"base_uri":"https://localhost:8080/"}},"source":["# python libraray for accessing internet resources\n","import requests\n","\n","def lookup_yahoo(symbol):\n"," \"\"\"Return a list of all matches for a symbol on Yahoo Finance.\"\"\"\n"," url = f\"http://d.yimg.com/autoc.finance.yahoo.com/autoc?query={symbol}®ion=1&lang=en\"\n"," return requests.get(url).json()[\"ResultSet\"][\"Result\"]\n","\n","lookup_yahoo(\"XOM\")"],"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'exch': 'NYQ',\n"," 'exchDisp': 'NYSE',\n"," 'name': 'Exxon Mobil Corporation',\n"," 'symbol': 'XOM',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'},\n"," {'exch': 'NMS',\n"," 'exchDisp': 'NASDAQ',\n"," 'name': 'XOMA Corporation',\n"," 'symbol': 'XOMA',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'},\n"," {'exch': 'YHD',\n"," 'exchDisp': 'Industry',\n"," 'name': 'Exxon Mobil Corporation',\n"," 'symbol': 'XOM.BA',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'},\n"," {'exch': 'YHD',\n"," 'exchDisp': 'Industry',\n"," 'name': 'Exxon Mobil Corporation',\n"," 'symbol': 'XOM.MX',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'},\n"," {'exch': 'DUS',\n"," 'exchDisp': 'Dusseldorf Stock Exchange',\n"," 'name': 'XOMA CORP. DL -,0005',\n"," 'symbol': 'X0M1.DU',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'},\n"," {'exch': 'STU',\n"," 'exchDisp': 'Stuttgart',\n"," 'name': 'XOMA Corp. Registered Shares DL',\n"," 'symbol': 'X0M1.SG',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'},\n"," {'exch': 'TLO',\n"," 'exchDisp': 'TLX Exchange',\n"," 'name': 'Exxon Mobil Corporation',\n"," 'symbol': 'XOM-U.TI',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'},\n"," {'exch': 'VIE',\n"," 'exchDisp': 'Vienna',\n"," 'name': 'Exxon Mobil Corporation',\n"," 'symbol': 'XOM.VI',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'},\n"," {'exch': 'BUE',\n"," 'exchDisp': 'Buenos Aires',\n"," 'name': 'EXXON MOBIL CORP',\n"," 'symbol': 'XOMD.BA',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'}]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"8K6KLyOCL9CP","executionInfo":{"status":"ok","timestamp":1604409830475,"user_tz":300,"elapsed":468,"user":{"displayName":"Jeffrey Kantor","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64","userId":"09038942003589296665"}},"outputId":"10068eaa-dfe3-4904-b5fa-818d6109ec6d","colab":{"base_uri":"https://localhost:8080/"}},"source":["def get_symbol(symbol):\n"," \"\"\"Return exact match for a symbol.\"\"\"\n"," result = [r for r in lookup_yahoo(symbol) if symbol == r['symbol']]\n"," return result[0] if len(result) > 0 else None\n","\n","get_symbol('X')"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'exch': 'NYQ',\n"," 'exchDisp': 'NYSE',\n"," 'name': 'United States Steel Corporation',\n"," 'symbol': 'X',\n"," 'type': 'S',\n"," 'typeDisp': 'Equity'}"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"pycharm":{},"id":"_wDxiCroKe4d"},"source":["### Yahoo Finance\n","\n","[Yahoo Finance](http://finance.yahoo.com/) provides historical Open, High, Low, Close, and Volume date for quotes on traded securities. In addition, Yahoo Finance provides historical [Adjusted Close](http://marubozu.blogspot.com/2006/09/how-yahoo-calculates-adjusted-closing.html) price data that corrects for splits and dividend distributions. Adjusted Close is a useful tool for computing the return on long-term investments.\n","\n","The following cell demonstrates how to download historical Adjusted Close price for a selected security into a pandas DataFrame."]},{"cell_type":"code","metadata":{"pycharm":{},"id":"ugM0ykkYKe4e","executionInfo":{"status":"ok","timestamp":1604409832484,"user_tz":300,"elapsed":1296,"user":{"displayName":"Jeffrey Kantor","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64","userId":"09038942003589296665"}},"outputId":"427bb379-14a9-48ad-e0b8-289dfe9f8129","colab":{"base_uri":"https://localhost:8080/","height":293}},"source":["symbol = 'TSLA'\n","symbol = 'AAPL'\n","\n","# get symbol data\n","symbol_data = get_symbol(symbol)\n","assert symbol_data, f\"Symbol {symbol} wasn't found.\"\n","\n","# start and end of a three year interval that ends today\n","end = datetime.datetime.today().date()\n","start = end - datetime.timedelta(3*365)\n","\n","# get stock price data\n","S = pdr.data.DataReader(symbol, \"yahoo\", start, end)['Adj Close']\n","\n","# plot data\n","plt.figure(figsize=(10,4))\n","title = f\"{symbol_data['name']} ({symbol_data['exchDisp']} {symbol_data['typeDisp']} {symbol_data['symbol']})\"\n","S.plot(title=title)\n","plt.ylabel('Adjusted Close')\n","plt.grid()"],"execution_count":4,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/pla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