{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "dC0K7BZiKe4P", "nbpages": { "level": 0, "link": "[](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html)", "section": "" }, "pycharm": {} }, "source": [ "\n", "*This notebook contains material from [CBE40455-2020](https://jckantor.github.io/CBE40455-2020);\n", "content is available [on Github](https://github.com/jckantor/CBE40455-2020.git).*\n" ] }, { "cell_type": "markdown", "metadata": { "id": "kywwqZ38Ke4Q", "nbpages": { "level": 0, "link": "[](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html)", "section": "" }, "pycharm": {} }, "source": [ "\n", "< [7.0 Risk and Diversification](https://jckantor.github.io/CBE40455-2020/07.00-Risk-and-Diversification.html) | [Contents](toc.html) | [7.2 Geometric Brownian Motion](https://jckantor.github.io/CBE40455-2020/07.02-Geometric-Brownian-Motion.html) >
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yv2KJXVdKe4S",
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"level": 1,
"link": "[7.1 Measuring Return](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1-Measuring-Return)",
"section": "7.1 Measuring Return"
},
"pycharm": {}
},
"source": [
"# 7.1 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": {
"id": "evkdzCeTKe4T",
"nbpages": {
"level": 2,
"link": "[7.1.1 Pandas-datareader](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1.1-Pandas-datareader)",
"section": "7.1.1 Pandas-datareader"
},
"pycharm": {}
},
"source": [
"## 7.1.1 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",
"nbpages": {
"level": 2,
"link": "[7.1.2 Imports](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1.2-Imports)",
"section": "7.1.2 Imports"
}
},
"source": [
"## 7.1.2 Imports"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
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"elapsed": 360,
"status": "ok",
"timestamp": 1604434596715,
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"displayName": "Jeffrey Kantor",
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"level": 2,
"link": "[7.1.2 Imports](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1.2-Imports)",
"section": "7.1.2 Imports"
},
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"outputs": [],
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Eh1HgZKdKe4d",
"nbpages": {
"level": 2,
"link": "[7.1.3 Where to get Price Data](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1.3-Where-to-get-Price-Data)",
"section": "7.1.3 Where to get Price Data"
},
"pycharm": {}
},
"source": [
"## 7.1.3 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/). (UPDATE: [Look here for an alternative descripton of how to get live market data from Yahoo Finance](https://towardsdatascience.com/python-how-to-get-live-market-data-less-than-0-1-second-lag-c85ee280ed93).)\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. [Another useful collection of stock price data using Python](https://towardsdatascience.com/how-to-get-stock-data-using-python-c0de1df17e75)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SOgpAYEzKe4Y",
"nbpages": {
"level": 3,
"link": "[7.1.3.1 Stock Symbols](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1.3.1-Stock-Symbols)",
"section": "7.1.3.1 Stock Symbols"
},
"pycharm": {}
},
"source": [
"### 7.1.3.1 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. For example, [this is a list of symbols for the New York Stock Exchange (NYSE)](http://www.eoddata.com/symbols.aspx?AspxAutoDetectCookieSupport=1) The following function looks up details of stock symbol on yahoo finance.."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 448,
"status": "ok",
"timestamp": 1604434598578,
"user": {
"displayName": "Jeffrey Kantor",
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"userId": "09038942003589296665"
},
"user_tz": 300
},
"id": "khRgx1GPKe4Z",
"nbpages": {
"level": 3,
"link": "[7.1.3.1 Stock Symbols](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1.3.1-Stock-Symbols)",
"section": "7.1.3.1 Stock Symbols"
},
"outputId": "650453bb-9042-4e8d-a81d-128b2a96079e",
"pycharm": {}
},
"outputs": [
{
"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'}]"
]
},
"execution_count": 41,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"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\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 314,
"status": "ok",
"timestamp": 1604434599538,
"user": {
"displayName": "Jeffrey Kantor",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg_n8V7bVINy02QRuRgOoMo11Ri7NKU3OUKdC1bkQ=s64",
"userId": "09038942003589296665"
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"user_tz": 300
},
"id": "8K6KLyOCL9CP",
"nbpages": {
"level": 3,
"link": "[7.1.3.1 Stock Symbols](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1.3.1-Stock-Symbols)",
"section": "7.1.3.1 Stock Symbols"
},
"outputId": "d33cc255-f3ed-4ebb-c729-6ac322a8c5a0"
},
"outputs": [
{
"data": {
"text/plain": [
"{'exch': 'NMS',\n",
" 'exchDisp': 'NASDAQ',\n",
" 'name': 'Tesla, Inc.',\n",
" 'symbol': 'TSLA',\n",
" 'type': 'S',\n",
" 'typeDisp': 'Equity'}"
]
},
"execution_count": 42,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"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('TSLA')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_wDxiCroKe4d",
"nbpages": {
"level": 3,
"link": "[7.1.3.2 Yahoo Finance](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1.3.2-Yahoo-Finance)",
"section": "7.1.3.2 Yahoo Finance"
},
"pycharm": {}
},
"source": [
"### 7.1.3.2 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",
"execution_count": 43,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 293
},
"executionInfo": {
"elapsed": 1050,
"status": "ok",
"timestamp": 1604434601334,
"user": {
"displayName": "Jeffrey Kantor",
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"userId": "09038942003589296665"
},
"user_tz": 300
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"id": "ugM0ykkYKe4e",
"nbpages": {
"level": 3,
"link": "[7.1.3.2 Yahoo Finance](https://jckantor.github.io/CBE40455-2020/07.01-Measuring-Return.html#7.1.3.2-Yahoo-Finance)",
"section": "7.1.3.2 Yahoo Finance"
},
"outputId": "787290dc-1651-499e-9ea7-5003c3e11d2f",
"pycharm": {}
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"outputs": [
{
"data": {
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\n",
"text/plain": [
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]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "07.01-Measuring-Return.ipynb",
"provenance": [],
"toc_visible": true
},
"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"
}
},
"nbformat": 4,
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}