├── .gitignore
├── README.md
├── finished_files
    ├── 001_equal_weight_S&P_500.ipynb
    ├── 002_quantitative_momentum_strategy.ipynb
    └── 003_quantitative_value_strategy.ipynb
├── requirements.txt
└── starter_files
    ├── 001_equal_weight_S&P_500.ipynb
    ├── 002_quantitative_momentum_strategy.ipynb
    └── 003_quantitative_value_strategy.ipynb


/.gitignore:
--------------------------------------------------------------------------------
 1 | algorithmic-trading/
 2 | .ipynb_checkpoints/
 3 | sp_500_stocks.csv
 4 | **/secrets.py
 5 | .DS_Store
 6 | __pycache__
 7 | **/recommended_trades.xlsx
 8 | **/momentum_strategy.xlsx
 9 | venv/
10 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Algorithmic Trading in Python
 2 | 
 3 | This repository
 4 | 
 5 | ## Course Outline
 6 | 
 7 | * Section 1: Algorithmic Trading Fundamentals
 8 |   * What is Algorithmic Trading?
 9 |   * The Differences Between Real-World Algorithmic Trading and This Course
10 | * Section 2: Course Configuration & API Basics
11 |   * How to Install Python
12 |   * Cloning The Repository & Installing Our Dependencies
13 |   * Jupyter Notebook Basics
14 |   * The Basics of API Requests
15 | * Section 3: Building An Equal-Weight S&P 500 Index Fund
16 |   * Theory & Concepts
17 |   * Importing our Constituents
18 |   * Pulling Data For Our Constituents
19 |   * Calculating Weights
20 |   * Generating Our Output File
21 |   * Additional Project Ideas
22 | * Section 4: Building A Quantitative Momentum Investing Strategy
23 |   * Theory & Concepts
24 |   * Pulling Data For Our Constituents
25 |   * Calculating Weights
26 |   * Generating Our Output File
27 |   * Additional Project Ideas
28 | * Section 5: Building A Quantitative Value Investing Strategy
29 |   * Theory & Concepts
30 |   * Importing our Constituents
31 |   * Pulling Data For Our Constituents
32 |   * Calculating Weights
33 |   * Generating Our Output File
34 |   * Additional Project Ideas
35 | 


--------------------------------------------------------------------------------
/finished_files/001_equal_weight_S&P_500.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Equal-Weight S&P 500 Index Fund\n",
  8 |     "\n",
  9 |     "## Introduction & Library Imports\n",
 10 |     "\n",
 11 |     "The S&P 500 is the world's most popular stock market index. The largest fund that is benchmarked to this index is the SPDR® S&P 500® ETF Trust. It has more than US$250 billion of assets under management.\n",
 12 |     "\n",
 13 |     "The goal of this section of the course is to create a Python script that will accept the value of your portfolio and tell you how many shares of each S&P 500 constituent you should purchase to get an equal-weight version of the index fund.\n",
 14 |     "\n",
 15 |     "## Library Imports\n",
 16 |     "\n",
 17 |     "The first thing we need to do is import the open-source software libraries that we'll be using in this tutorial."
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "code",
 22 |    "execution_count": null,
 23 |    "metadata": {},
 24 |    "outputs": [],
 25 |    "source": [
 26 |     "import numpy as np #The Numpy numerical computing library\n",
 27 |     "import pandas as pd #The Pandas data science library\n",
 28 |     "import requests #The requests library for HTTP requests in Python\n",
 29 |     "import xlsxwriter #The XlsxWriter libarary for \n",
 30 |     "import math #The Python math module"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "markdown",
 35 |    "metadata": {},
 36 |    "source": [
 37 |     "## Importing Our List of Stocks\n",
 38 |     "\n",
 39 |     "The next thing we need to do is import the constituents of the S&P 500.\n",
 40 |     "\n",
 41 |     "These constituents change over time, so in an ideal world you would connect directly to the index provider (Standard & Poor's) and pull their real-time constituents on a regular basis.\n",
 42 |     "\n",
 43 |     "Paying for access to the index provider's API is outside of the scope of this course. \n",
 44 |     "\n",
 45 |     "There's a static version of the S&P 500 constituents available here. [Click this link to download them now](http://nickmccullum.com/algorithmic-trading-python/sp_500_stocks.csv). Move this file into the `starter-files` folder so it can be accessed by other files in that directory.\n",
 46 |     "\n",
 47 |     "Now it's time to import these stocks to our Jupyter Notebook file."
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": null,
 53 |    "metadata": {},
 54 |    "outputs": [],
 55 |    "source": [
 56 |     "stocks = pd.read_csv('sp_500_stocks.csv')"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "markdown",
 61 |    "metadata": {},
 62 |    "source": [
 63 |     "## Acquiring an API Token\n",
 64 |     "\n",
 65 |     "Now it's time to import our IEX Cloud API token. This is the data provider that we will be using throughout this course.\n",
 66 |     "\n",
 67 |     "API tokens (and other sensitive information) should be stored in a `secrets.py` file that doesn't get pushed to your local Git repository. We'll be using a sandbox API token in this course, which means that the data we'll use is randomly-generated and (more importantly) has no cost associated with it.\n",
 68 |     "\n",
 69 |     "[Click here](http://nickmccullum.com/algorithmic-trading-python/secrets.py) to download your `secrets.py` file. Move the file into the same directory as this Jupyter Notebook before proceeding."
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "code",
 74 |    "execution_count": null,
 75 |    "metadata": {},
 76 |    "outputs": [],
 77 |    "source": [
 78 |     "from secrets import IEX_CLOUD_API_TOKEN"
 79 |    ]
 80 |   },
 81 |   {
 82 |    "cell_type": "markdown",
 83 |    "metadata": {},
 84 |    "source": [
 85 |     "## Making Our First API Call\n",
 86 |     "\n",
 87 |     "Now it's time to structure our API calls to IEX cloud. \n",
 88 |     "\n",
 89 |     "We need the following information from the API:\n",
 90 |     "\n",
 91 |     "* Market capitalization for each stock\n",
 92 |     "* Price of each stock\n",
 93 |     "\n"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": null,
 99 |    "metadata": {},
100 |    "outputs": [],
101 |    "source": [
102 |     "symbol='AAPL'\n",
103 |     "api_url = f'https://sandbox.iexapis.com/stable/stock/{symbol}/quote?token={IEX_CLOUD_API_TOKEN}'\n",
104 |     "data = requests.get(api_url).json()\n",
105 |     "data"
106 |    ]
107 |   },
108 |   {
109 |    "cell_type": "markdown",
110 |    "metadata": {},
111 |    "source": [
112 |     "## Parsing Our API Call\n",
113 |     "\n",
114 |     "The API call that we executed in the last code block contains all of the information required to build our equal-weight S&P 500 strategy. \n",
115 |     "\n",
116 |     "With that said, the data isn't in a proper format yet. We need to parse it first."
117 |    ]
118 |   },
119 |   {
120 |    "cell_type": "code",
121 |    "execution_count": null,
122 |    "metadata": {},
123 |    "outputs": [],
124 |    "source": [
125 |     "data['latestPrice']\n",
126 |     "data['marketCap']"
127 |    ]
128 |   },
129 |   {
130 |    "cell_type": "markdown",
131 |    "metadata": {},
132 |    "source": [
133 |     "## Adding Our Stocks Data to a Pandas DataFrame\n",
134 |     "\n",
135 |     "The next thing we need to do is add our stock's price and market capitalization to a pandas DataFrame. Think of a DataFrame like the Python version of a spreadsheet. It stores tabular data."
136 |    ]
137 |   },
138 |   {
139 |    "cell_type": "code",
140 |    "execution_count": null,
141 |    "metadata": {},
142 |    "outputs": [],
143 |    "source": [
144 |     "my_columns = ['Ticker', 'Price','Market Capitalization', 'Number Of Shares to Buy']\n",
145 |     "final_dataframe = pd.DataFrame(columns = my_columns)\n",
146 |     "final_dataframe"
147 |    ]
148 |   },
149 |   {
150 |    "cell_type": "code",
151 |    "execution_count": null,
152 |    "metadata": {},
153 |    "outputs": [],
154 |    "source": [
155 |     "final_dataframe = final_dataframe.append(\n",
156 |     "                                        pd.Series(['AAPL', \n",
157 |     "                                                   data['latestPrice'], \n",
158 |     "                                                   data['marketCap'], \n",
159 |     "                                                   'N/A'], \n",
160 |     "                                                  index = my_columns), \n",
161 |     "                                        ignore_index = True)\n",
162 |     "final_dataframe"
163 |    ]
164 |   },
165 |   {
166 |    "cell_type": "markdown",
167 |    "metadata": {},
168 |    "source": [
169 |     "## Looping Through The Tickers in Our List of Stocks\n",
170 |     "\n",
171 |     "Using the same logic that we outlined above, we can pull data for all S&P 500 stocks and store their data in the DataFrame using a `for` loop."
172 |    ]
173 |   },
174 |   {
175 |    "cell_type": "code",
176 |    "execution_count": null,
177 |    "metadata": {},
178 |    "outputs": [],
179 |    "source": [
180 |     "final_dataframe = pd.DataFrame(columns = my_columns)\n",
181 |     "for symbol in stocks['Ticker']:\n",
182 |     "    api_url = f'https://sandbox.iexapis.com/stable/stock/{symbol}/quote?token={IEX_CLOUD_API_TOKEN}'\n",
183 |     "    data = requests.get(api_url).json()\n",
184 |     "    final_dataframe = final_dataframe.append(\n",
185 |     "                                        pd.Series([symbol, \n",
186 |     "                                                   data['latestPrice'], \n",
187 |     "                                                   data['marketCap'], \n",
188 |     "                                                   'N/A'], \n",
189 |     "                                                  index = my_columns), \n",
190 |     "                                        ignore_index = True)\n"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "code",
195 |    "execution_count": null,
196 |    "metadata": {},
197 |    "outputs": [],
198 |    "source": [
199 |     "final_dataframe"
200 |    ]
201 |   },
202 |   {
203 |    "cell_type": "markdown",
204 |    "metadata": {},
205 |    "source": [
206 |     "## Using Batch API Calls to Improve Performance\n",
207 |     "\n",
208 |     "Batch API calls are one of the easiest ways to improve the performance of your code.\n",
209 |     "\n",
210 |     "This is because HTTP requests are typically one of the slowest components of a script.\n",
211 |     "\n",
212 |     "Also, API providers will often give you discounted rates for using batch API calls since they are easier for the API provider to respond to.\n",
213 |     "\n",
214 |     "IEX Cloud limits their batch API calls to 100 tickers per request. Still, this reduces the number of API calls we'll make in this section from 500 to 5 - huge improvement! In this section, we'll split our list of stocks into groups of 100 and then make a batch API call for each group."
215 |    ]
216 |   },
217 |   {
218 |    "cell_type": "code",
219 |    "execution_count": null,
220 |    "metadata": {},
221 |    "outputs": [],
222 |    "source": [
223 |     "# Function sourced from \n",
224 |     "# https://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks\n",
225 |     "def chunks(lst, n):\n",
226 |     "    \"\"\"Yield successive n-sized chunks from lst.\"\"\"\n",
227 |     "    for i in range(0, len(lst), n):\n",
228 |     "        yield lst[i:i + n]"
229 |    ]
230 |   },
231 |   {
232 |    "cell_type": "code",
233 |    "execution_count": null,
234 |    "metadata": {},
235 |    "outputs": [],
236 |    "source": [
237 |     "symbol_groups = list(chunks(stocks['Ticker'], 100))\n",
238 |     "symbol_strings = []\n",
239 |     "for i in range(0, len(symbol_groups)):\n",
240 |     "    symbol_strings.append(','.join(symbol_groups[i]))\n",
241 |     "#     print(symbol_strings[i])\n",
242 |     "\n",
243 |     "final_dataframe = pd.DataFrame(columns = my_columns)\n",
244 |     "\n",
245 |     "for symbol_string in symbol_strings:\n",
246 |     "#     print(symbol_strings)\n",
247 |     "    batch_api_call_url = f'https://sandbox.iexapis.com/stable/stock/market/batch/?types=quote&symbols={symbol_string}&token={IEX_CLOUD_API_TOKEN}'\n",
248 |     "    data = requests.get(batch_api_call_url).json()\n",
249 |     "    for symbol in symbol_string.split(','):\n",
250 |     "        final_dataframe = final_dataframe.append(\n",
251 |     "                                        pd.Series([symbol, \n",
252 |     "                                                   data[symbol]['quote']['latestPrice'], \n",
253 |     "                                                   data[symbol]['quote']['marketCap'], \n",
254 |     "                                                   'N/A'], \n",
255 |     "                                                  index = my_columns), \n",
256 |     "                                        ignore_index = True)\n",
257 |     "        \n",
258 |     "    \n",
259 |     "final_dataframe"
260 |    ]
261 |   },
262 |   {
263 |    "cell_type": "markdown",
264 |    "metadata": {},
265 |    "source": [
266 |     "## Calculating the Number of Shares to Buy\n",
267 |     "\n",
268 |     "As you can see in the DataFrame above, we stil haven't calculated the number of shares of each stock to buy.\n",
269 |     "\n",
270 |     "We'll do that next."
271 |    ]
272 |   },
273 |   {
274 |    "cell_type": "code",
275 |    "execution_count": null,
276 |    "metadata": {},
277 |    "outputs": [],
278 |    "source": [
279 |     "portfolio_size = input(\"Enter the value of your portfolio:\")\n",
280 |     "\n",
281 |     "try:\n",
282 |     "    val = float(portfolio_size)\n",
283 |     "except ValueError:\n",
284 |     "    print(\"That's not a number! \\n Try again:\")\n",
285 |     "    portfolio_size = input(\"Enter the value of your portfolio:\")"
286 |    ]
287 |   },
288 |   {
289 |    "cell_type": "code",
290 |    "execution_count": null,
291 |    "metadata": {},
292 |    "outputs": [],
293 |    "source": [
294 |     "position_size = float(portfolio_size) / len(final_dataframe.index)\n",
295 |     "for i in range(0, len(final_dataframe['Ticker'])-1):\n",
296 |     "    final_dataframe.loc[i, 'Number Of Shares to Buy'] = math.floor(position_size / final_dataframe['Price'][i])\n",
297 |     "final_dataframe"
298 |    ]
299 |   },
300 |   {
301 |    "cell_type": "markdown",
302 |    "metadata": {},
303 |    "source": [
304 |     "## Formatting Our Excel Output\n",
305 |     "\n",
306 |     "We will be using the XlsxWriter library for Python to create nicely-formatted Excel files.\n",
307 |     "\n",
308 |     "XlsxWriter is an excellent package and offers tons of customization. However, the tradeoff for this is that the library can seem very complicated to new users. Accordingly, this section will be fairly long because I want to do a good job of explaining how XlsxWriter works.\n",
309 |     "\n",
310 |     "### Initializing our XlsxWriter Object"
311 |    ]
312 |   },
313 |   {
314 |    "cell_type": "code",
315 |    "execution_count": null,
316 |    "metadata": {},
317 |    "outputs": [],
318 |    "source": [
319 |     "writer = pd.ExcelWriter('recommended_trades.xlsx', engine='xlsxwriter')\n",
320 |     "final_dataframe.to_excel(writer, sheet_name='Recommended Trades', index = False)"
321 |    ]
322 |   },
323 |   {
324 |    "cell_type": "markdown",
325 |    "metadata": {},
326 |    "source": [
327 |     "### Creating the Formats We'll Need For Our `.xlsx` File\n",
328 |     "\n",
329 |     "Formats include colors, fonts, and also symbols like `%` and `$`. We'll need four main formats for our Excel document:\n",
330 |     "* String format for tickers\n",
331 |     "* \\\\$XX.XX format for stock prices\n",
332 |     "* \\\\$XX,XXX format for market capitalization\n",
333 |     "* Integer format for the number of shares to purchase"
334 |    ]
335 |   },
336 |   {
337 |    "cell_type": "code",
338 |    "execution_count": null,
339 |    "metadata": {},
340 |    "outputs": [],
341 |    "source": [
342 |     "background_color = '#0a0a23'\n",
343 |     "font_color = '#ffffff'\n",
344 |     "\n",
345 |     "string_format = writer.book.add_format(\n",
346 |     "        {\n",
347 |     "            'font_color': font_color,\n",
348 |     "            'bg_color': background_color,\n",
349 |     "            'border': 1\n",
350 |     "        }\n",
351 |     "    )\n",
352 |     "\n",
353 |     "dollar_format = writer.book.add_format(\n",
354 |     "        {\n",
355 |     "            'num_format':'$0.00',\n",
356 |     "            'font_color': font_color,\n",
357 |     "            'bg_color': background_color,\n",
358 |     "            'border': 1\n",
359 |     "        }\n",
360 |     "    )\n",
361 |     "\n",
362 |     "integer_format = writer.book.add_format(\n",
363 |     "        {\n",
364 |     "            'num_format':'0',\n",
365 |     "            'font_color': font_color,\n",
366 |     "            'bg_color': background_color,\n",
367 |     "            'border': 1\n",
368 |     "        }\n",
369 |     "    )"
370 |    ]
371 |   },
372 |   {
373 |    "cell_type": "markdown",
374 |    "metadata": {},
375 |    "source": [
376 |     "### Applying the Formats to the Columns of Our `.xlsx` File\n",
377 |     "\n",
378 |     "We can use the `set_column` method applied to the `writer.sheets['Recommended Trades']` object to apply formats to specific columns of our spreadsheets.\n",
379 |     "\n",
380 |     "Here's an example:\n",
381 |     "\n",
382 |     "```python\n",
383 |     "writer.sheets['Recommended Trades'].set_column('B:B', #This tells the method to apply the format to column B\n",
384 |     "                     18, #This tells the method to apply a column width of 18 pixels\n",
385 |     "                     string_format #This applies the format 'string_format' to the column\n",
386 |     "                    )\n",
387 |     "```"
388 |    ]
389 |   },
390 |   {
391 |    "cell_type": "code",
392 |    "execution_count": null,
393 |    "metadata": {},
394 |    "outputs": [],
395 |    "source": [
396 |     "# writer.sheets['Recommended Trades'].write('A1', 'Ticker', string_format)\n",
397 |     "# writer.sheets['Recommended Trades'].write('B1', 'Price', string_format)\n",
398 |     "# writer.sheets['Recommended Trades'].write('C1', 'Market Capitalization', string_format)\n",
399 |     "# writer.sheets['Recommended Trades'].write('D1', 'Number Of Shares to Buy', string_format)\n",
400 |     "# writer.sheets['Recommended Trades'].set_column('A:A', 20, string_format)\n",
401 |     "# writer.sheets['Recommended Trades'].set_column('B:B', 20, dollar_format)\n",
402 |     "# writer.sheets['Recommended Trades'].set_column('C:C', 20, dollar_format)\n",
403 |     "# writer.sheets['Recommended Trades'].set_column('D:D', 20, integer_format)\n"
404 |    ]
405 |   },
406 |   {
407 |    "cell_type": "markdown",
408 |    "metadata": {},
409 |    "source": [
410 |     "This code works, but it violates the software principle of \"Don't Repeat Yourself\". \n",
411 |     "\n",
412 |     "Let's simplify this by putting it in 2 loops:"
413 |    ]
414 |   },
415 |   {
416 |    "cell_type": "code",
417 |    "execution_count": null,
418 |    "metadata": {},
419 |    "outputs": [],
420 |    "source": [
421 |     "column_formats = { \n",
422 |     "                    'A': ['Ticker', string_format],\n",
423 |     "                    'B': ['Price', dollar_format],\n",
424 |     "                    'C': ['Market Capitalization', dollar_format],\n",
425 |     "                    'D': ['Number of Shares to Buy', integer_format]\n",
426 |     "                    }\n",
427 |     "\n",
428 |     "for column in column_formats.keys():\n",
429 |     "    writer.sheets['Recommended Trades'].set_column(f'{column}:{column}', 20, column_formats[column][1])\n",
430 |     "    writer.sheets['Recommended Trades'].write(f'{column}1', column_formats[column][0], string_format)"
431 |    ]
432 |   },
433 |   {
434 |    "cell_type": "markdown",
435 |    "metadata": {},
436 |    "source": [
437 |     "## Saving Our Excel Output\n",
438 |     "\n",
439 |     "Saving our Excel file is very easy:"
440 |    ]
441 |   },
442 |   {
443 |    "cell_type": "code",
444 |    "execution_count": null,
445 |    "metadata": {},
446 |    "outputs": [],
447 |    "source": [
448 |     "writer.save()"
449 |    ]
450 |   }
451 |  ],
452 |  "metadata": {
453 |   "kernelspec": {
454 |    "display_name": "Python 3",
455 |    "language": "python",
456 |    "name": "python3"
457 |   },
458 |   "language_info": {
459 |    "codemirror_mode": {
460 |     "name": "ipython",
461 |     "version": 3
462 |    },
463 |    "file_extension": ".py",
464 |    "mimetype": "text/x-python",
465 |    "name": "python",
466 |    "nbconvert_exporter": "python",
467 |    "pygments_lexer": "ipython3",
468 |    "version": "3.8.5"
469 |   }
470 |  },
471 |  "nbformat": 4,
472 |  "nbformat_minor": 4
473 | }
474 | 


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/finished_files/002_quantitative_momentum_strategy.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Quantitative Momentum Strategy\n",
   8 |     "\n",
   9 |     "\"Momentum investing\" means investing in the stocks that have increased in price the most.\n",
  10 |     "\n",
  11 |     "For this project, we're going to build an investing strategy that selects the 50 stocks with the highest price momentum. From there, we will calculate recommended trades for an equal-weight portfolio of these 50 stocks.\n",
  12 |     "\n",
  13 |     "\n",
  14 |     "## Library Imports\n",
  15 |     "\n",
  16 |     "The first thing we need to do is import the open-source software libraries that we'll be using in this tutorial."
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "code",
  21 |    "execution_count": 1,
  22 |    "metadata": {},
  23 |    "outputs": [],
  24 |    "source": [
  25 |     "import numpy as np #The Numpy numerical computing library\n",
  26 |     "import pandas as pd #The Pandas data science library\n",
  27 |     "import requests #The requests library for HTTP requests in Python\n",
  28 |     "import xlsxwriter #The XlsxWriter libarary for \n",
  29 |     "import math #The Python math module\n",
  30 |     "from scipy import stats #The SciPy stats module"
  31 |    ]
  32 |   },
  33 |   {
  34 |    "cell_type": "markdown",
  35 |    "metadata": {},
  36 |    "source": [
  37 |     "## Importing Our List of Stocks\n",
  38 |     "\n",
  39 |     "As before, we'll need to import our list of stocks and our API token before proceeding. Make sure the `.csv` file is still in your working directory and import it with the following command:"
  40 |    ]
  41 |   },
  42 |   {
  43 |    "cell_type": "code",
  44 |    "execution_count": 2,
  45 |    "metadata": {},
  46 |    "outputs": [],
  47 |    "source": [
  48 |     "stocks = pd.read_csv('sp_500_stocks.csv')\n",
  49 |     "from secrets import IEX_CLOUD_API_TOKEN"
  50 |    ]
  51 |   },
  52 |   {
  53 |    "cell_type": "markdown",
  54 |    "metadata": {},
  55 |    "source": [
  56 |     "## Making Our First API Call\n",
  57 |     "\n",
  58 |     "It's now time to make the first version of our momentum screener!\n",
  59 |     "\n",
  60 |     "We need to get one-year price returns for each stock in the universe. Here's how."
  61 |    ]
  62 |   },
  63 |   {
  64 |    "cell_type": "code",
  65 |    "execution_count": 3,
  66 |    "metadata": {},
  67 |    "outputs": [
  68 |     {
  69 |      "data": {
  70 |       "text/plain": [
  71 |        "{'week52change': 1.271858,\n",
  72 |        " 'week52high': 462.02,\n",
  73 |        " 'week52low': 206.85,\n",
  74 |        " 'marketcap': 1937104274094,\n",
  75 |        " 'employees': 137265,\n",
  76 |        " 'day200MovingAvg': 309.44,\n",
  77 |        " 'day50MovingAvg': 390.09,\n",
  78 |        " 'float': 4440629054,\n",
  79 |        " 'avg10Volume': 54435185.2,\n",
  80 |        " 'avg30Volume': 39067154.1,\n",
  81 |        " 'ttmEPS': 13.7084,\n",
  82 |        " 'ttmDividendRate': 3.22,\n",
  83 |        " 'companyName': 'Apple, Inc.',\n",
  84 |        " 'sharesOutstanding': 4331609946,\n",
  85 |        " 'maxChangePercent': 452.5766,\n",
  86 |        " 'year5ChangePercent': 3.0546,\n",
  87 |        " 'year2ChangePercent': 1.1867,\n",
  88 |        " 'year1ChangePercent': 1.186376,\n",
  89 |        " 'ytdChangePercent': 0.512578,\n",
  90 |        " 'month6ChangePercent': 0.407457,\n",
  91 |        " 'month3ChangePercent': 0.485051,\n",
  92 |        " 'month1ChangePercent': 0.19254,\n",
  93 |        " 'day30ChangePercent': 0.253108,\n",
  94 |        " 'day5ChangePercent': -0.008155,\n",
  95 |        " 'nextDividendDate': '2020-08-16',\n",
  96 |        " 'dividendYield': 0.007235663525925464,\n",
  97 |        " 'nextEarningsDate': '2020-10-17',\n",
  98 |        " 'exDividendDate': '2020-08-06',\n",
  99 |        " 'peRatio': 34.17,\n",
 100 |        " 'beta': 1.15885673879414}"
 101 |       ]
 102 |      },
 103 |      "execution_count": 3,
 104 |      "metadata": {},
 105 |      "output_type": "execute_result"
 106 |     }
 107 |    ],
 108 |    "source": [
 109 |     "symbol = 'AAPL'\n",
 110 |     "api_url = f'https://sandbox.iexapis.com/stable/stock/{symbol}/stats?token={IEX_CLOUD_API_TOKEN}'\n",
 111 |     "data = requests.get(api_url).json()\n",
 112 |     "data"
 113 |    ]
 114 |   },
 115 |   {
 116 |    "cell_type": "markdown",
 117 |    "metadata": {},
 118 |    "source": [
 119 |     "## Parsing Our API Call\n",
 120 |     "\n",
 121 |     "This API call has all the information we need. We can parse it using the same square-bracket notation as in the first project of this course. Here is an example."
 122 |    ]
 123 |   },
 124 |   {
 125 |    "cell_type": "code",
 126 |    "execution_count": 4,
 127 |    "metadata": {},
 128 |    "outputs": [
 129 |     {
 130 |      "data": {
 131 |       "text/plain": [
 132 |        "1.186376"
 133 |       ]
 134 |      },
 135 |      "execution_count": 4,
 136 |      "metadata": {},
 137 |      "output_type": "execute_result"
 138 |     }
 139 |    ],
 140 |    "source": [
 141 |     "data['year1ChangePercent']"
 142 |    ]
 143 |   },
 144 |   {
 145 |    "cell_type": "markdown",
 146 |    "metadata": {},
 147 |    "source": [
 148 |     "## Executing A Batch API Call & Building Our DataFrame\n",
 149 |     "\n",
 150 |     "Just like in our first project, it's now time to execute several batch API calls and add the information we need to our DataFrame.\n",
 151 |     "\n",
 152 |     "We'll start by running the following code cell, which contains some code we already built last time that we can re-use for this project. More specifically, it contains a function called `chunks` that we can use to divide our list of securities into groups of 100."
 153 |    ]
 154 |   },
 155 |   {
 156 |    "cell_type": "code",
 157 |    "execution_count": 5,
 158 |    "metadata": {},
 159 |    "outputs": [],
 160 |    "source": [
 161 |     "# Function sourced from \n",
 162 |     "# https://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks\n",
 163 |     "def chunks(lst, n):\n",
 164 |     "    \"\"\"Yield successive n-sized chunks from lst.\"\"\"\n",
 165 |     "    for i in range(0, len(lst), n):\n",
 166 |     "        yield lst[i:i + n]   \n",
 167 |     "        \n",
 168 |     "symbol_groups = list(chunks(stocks['Ticker'], 100))\n",
 169 |     "symbol_strings = []\n",
 170 |     "for i in range(0, len(symbol_groups)):\n",
 171 |     "    symbol_strings.append(','.join(symbol_groups[i]))\n",
 172 |     "#     print(symbol_strings[i])\n",
 173 |     "\n",
 174 |     "my_columns = ['Ticker', 'Price', 'One-Year Price Return', 'Number of Shares to Buy']"
 175 |    ]
 176 |   },
 177 |   {
 178 |    "cell_type": "markdown",
 179 |    "metadata": {},
 180 |    "source": [
 181 |     "Now we need to create a blank DataFrame and add our data to the data frame one-by-one."
 182 |    ]
 183 |   },
 184 |   {
 185 |    "cell_type": "code",
 186 |    "execution_count": 6,
 187 |    "metadata": {},
 188 |    "outputs": [
 189 |     {
 190 |      "data": {
 191 |       "text/html": [
 192 |        "<div>\n",
 193 |        "<style scoped>\n",
 194 |        "    .dataframe tbody tr th:only-of-type {\n",
 195 |        "        vertical-align: middle;\n",
 196 |        "    }\n",
 197 |        "\n",
 198 |        "    .dataframe tbody tr th {\n",
 199 |        "        vertical-align: top;\n",
 200 |        "    }\n",
 201 |        "\n",
 202 |        "    .dataframe thead th {\n",
 203 |        "        text-align: right;\n",
 204 |        "    }\n",
 205 |        "</style>\n",
 206 |        "<table border=\"1\" class=\"dataframe\">\n",
 207 |        "  <thead>\n",
 208 |        "    <tr style=\"text-align: right;\">\n",
 209 |        "      <th></th>\n",
 210 |        "      <th>Ticker</th>\n",
 211 |        "      <th>Price</th>\n",
 212 |        "      <th>One-Year Price Return</th>\n",
 213 |        "      <th>Number of Shares to Buy</th>\n",
 214 |        "    </tr>\n",
 215 |        "  </thead>\n",
 216 |        "  <tbody>\n",
 217 |        "    <tr>\n",
 218 |        "      <th>0</th>\n",
 219 |        "      <td>A</td>\n",
 220 |        "      <td>101.50</td>\n",
 221 |        "      <td>0.452986</td>\n",
 222 |        "      <td>N/A</td>\n",
 223 |        "    </tr>\n",
 224 |        "    <tr>\n",
 225 |        "      <th>1</th>\n",
 226 |        "      <td>AAL</td>\n",
 227 |        "      <td>13.65</td>\n",
 228 |        "      <td>-0.527621</td>\n",
 229 |        "      <td>N/A</td>\n",
 230 |        "    </tr>\n",
 231 |        "    <tr>\n",
 232 |        "      <th>2</th>\n",
 233 |        "      <td>AAP</td>\n",
 234 |        "      <td>157.72</td>\n",
 235 |        "      <td>0.088479</td>\n",
 236 |        "      <td>N/A</td>\n",
 237 |        "    </tr>\n",
 238 |        "    <tr>\n",
 239 |        "      <th>3</th>\n",
 240 |        "      <td>AAPL</td>\n",
 241 |        "      <td>453.87</td>\n",
 242 |        "      <td>1.172528</td>\n",
 243 |        "      <td>N/A</td>\n",
 244 |        "    </tr>\n",
 245 |        "    <tr>\n",
 246 |        "      <th>4</th>\n",
 247 |        "      <td>ABBV</td>\n",
 248 |        "      <td>95.71</td>\n",
 249 |        "      <td>0.476493</td>\n",
 250 |        "      <td>N/A</td>\n",
 251 |        "    </tr>\n",
 252 |        "    <tr>\n",
 253 |        "      <th>...</th>\n",
 254 |        "      <td>...</td>\n",
 255 |        "      <td>...</td>\n",
 256 |        "      <td>...</td>\n",
 257 |        "      <td>...</td>\n",
 258 |        "    </tr>\n",
 259 |        "    <tr>\n",
 260 |        "      <th>500</th>\n",
 261 |        "      <td>YUM</td>\n",
 262 |        "      <td>93.50</td>\n",
 263 |        "      <td>-0.208066</td>\n",
 264 |        "      <td>N/A</td>\n",
 265 |        "    </tr>\n",
 266 |        "    <tr>\n",
 267 |        "      <th>501</th>\n",
 268 |        "      <td>ZBH</td>\n",
 269 |        "      <td>138.91</td>\n",
 270 |        "      <td>0.003031</td>\n",
 271 |        "      <td>N/A</td>\n",
 272 |        "    </tr>\n",
 273 |        "    <tr>\n",
 274 |        "      <th>502</th>\n",
 275 |        "      <td>ZBRA</td>\n",
 276 |        "      <td>287.51</td>\n",
 277 |        "      <td>0.369427</td>\n",
 278 |        "      <td>N/A</td>\n",
 279 |        "    </tr>\n",
 280 |        "    <tr>\n",
 281 |        "      <th>503</th>\n",
 282 |        "      <td>ZION</td>\n",
 283 |        "      <td>35.73</td>\n",
 284 |        "      <td>-0.162236</td>\n",
 285 |        "      <td>N/A</td>\n",
 286 |        "    </tr>\n",
 287 |        "    <tr>\n",
 288 |        "      <th>504</th>\n",
 289 |        "      <td>ZTS</td>\n",
 290 |        "      <td>165.46</td>\n",
 291 |        "      <td>0.288770</td>\n",
 292 |        "      <td>N/A</td>\n",
 293 |        "    </tr>\n",
 294 |        "  </tbody>\n",
 295 |        "</table>\n",
 296 |        "<p>505 rows × 4 columns</p>\n",
 297 |        "</div>"
 298 |       ],
 299 |       "text/plain": [
 300 |        "    Ticker   Price  One-Year Price Return Number of Shares to Buy\n",
 301 |        "0        A  101.50               0.452986                     N/A\n",
 302 |        "1      AAL   13.65              -0.527621                     N/A\n",
 303 |        "2      AAP  157.72               0.088479                     N/A\n",
 304 |        "3     AAPL  453.87               1.172528                     N/A\n",
 305 |        "4     ABBV   95.71               0.476493                     N/A\n",
 306 |        "..     ...     ...                    ...                     ...\n",
 307 |        "500    YUM   93.50              -0.208066                     N/A\n",
 308 |        "501    ZBH  138.91               0.003031                     N/A\n",
 309 |        "502   ZBRA  287.51               0.369427                     N/A\n",
 310 |        "503   ZION   35.73              -0.162236                     N/A\n",
 311 |        "504    ZTS  165.46               0.288770                     N/A\n",
 312 |        "\n",
 313 |        "[505 rows x 4 columns]"
 314 |       ]
 315 |      },
 316 |      "execution_count": 6,
 317 |      "metadata": {},
 318 |      "output_type": "execute_result"
 319 |     }
 320 |    ],
 321 |    "source": [
 322 |     "final_dataframe = pd.DataFrame(columns = my_columns)\n",
 323 |     "\n",
 324 |     "for symbol_string in symbol_strings:\n",
 325 |     "#     print(symbol_strings)\n",
 326 |     "    batch_api_call_url = f'https://sandbox.iexapis.com/stable/stock/market/batch/?types=stats,quote&symbols={symbol_string}&token={IEX_CLOUD_API_TOKEN}'\n",
 327 |     "    data = requests.get(batch_api_call_url).json()\n",
 328 |     "    for symbol in symbol_string.split(','):\n",
 329 |     "        final_dataframe = final_dataframe.append(\n",
 330 |     "                                        pd.Series([symbol, \n",
 331 |     "                                                   data[symbol]['quote']['latestPrice'],\n",
 332 |     "                                                   data[symbol]['stats']['year1ChangePercent'],\n",
 333 |     "                                                   'N/A'\n",
 334 |     "                                                   ], \n",
 335 |     "                                                  index = my_columns), \n",
 336 |     "                                        ignore_index = True)\n",
 337 |     "        \n",
 338 |     "    \n",
 339 |     "final_dataframe"
 340 |    ]
 341 |   },
 342 |   {
 343 |    "cell_type": "markdown",
 344 |    "metadata": {},
 345 |    "source": [
 346 |     "## Removing Low-Momentum Stocks\n",
 347 |     "\n",
 348 |     "The investment strategy that we're building seeks to identify the 50 highest-momentum stocks in the S&P 500.\n",
 349 |     "\n",
 350 |     "Because of this, the next thing we need to do is remove all the stocks in our DataFrame that fall below this momentum threshold. We'll sort the DataFrame by the stocks' one-year price return, and drop all stocks outside the top 50.\n"
 351 |    ]
 352 |   },
 353 |   {
 354 |    "cell_type": "code",
 355 |    "execution_count": 7,
 356 |    "metadata": {},
 357 |    "outputs": [
 358 |     {
 359 |      "data": {
 360 |       "text/html": [
 361 |        "<div>\n",
 362 |        "<style scoped>\n",
 363 |        "    .dataframe tbody tr th:only-of-type {\n",
 364 |        "        vertical-align: middle;\n",
 365 |        "    }\n",
 366 |        "\n",
 367 |        "    .dataframe tbody tr th {\n",
 368 |        "        vertical-align: top;\n",
 369 |        "    }\n",
 370 |        "\n",
 371 |        "    .dataframe thead th {\n",
 372 |        "        text-align: right;\n",
 373 |        "    }\n",
 374 |        "</style>\n",
 375 |        "<table border=\"1\" class=\"dataframe\">\n",
 376 |        "  <thead>\n",
 377 |        "    <tr style=\"text-align: right;\">\n",
 378 |        "      <th></th>\n",
 379 |        "      <th>Ticker</th>\n",
 380 |        "      <th>Price</th>\n",
 381 |        "      <th>One-Year Price Return</th>\n",
 382 |        "      <th>Number of Shares to Buy</th>\n",
 383 |        "    </tr>\n",
 384 |        "  </thead>\n",
 385 |        "  <tbody>\n",
 386 |        "    <tr>\n",
 387 |        "      <th>0</th>\n",
 388 |        "      <td>NVDA</td>\n",
 389 |        "      <td>458.14</td>\n",
 390 |        "      <td>2.015964</td>\n",
 391 |        "      <td>N/A</td>\n",
 392 |        "    </tr>\n",
 393 |        "    <tr>\n",
 394 |        "      <th>1</th>\n",
 395 |        "      <td>DXCM</td>\n",
 396 |        "      <td>441.29</td>\n",
 397 |        "      <td>1.721219</td>\n",
 398 |        "      <td>N/A</td>\n",
 399 |        "    </tr>\n",
 400 |        "    <tr>\n",
 401 |        "      <th>2</th>\n",
 402 |        "      <td>AMD</td>\n",
 403 |        "      <td>85.46</td>\n",
 404 |        "      <td>1.632817</td>\n",
 405 |        "      <td>N/A</td>\n",
 406 |        "    </tr>\n",
 407 |        "    <tr>\n",
 408 |        "      <th>3</th>\n",
 409 |        "      <td>CARR</td>\n",
 410 |        "      <td>29.94</td>\n",
 411 |        "      <td>1.489900</td>\n",
 412 |        "      <td>N/A</td>\n",
 413 |        "    </tr>\n",
 414 |        "    <tr>\n",
 415 |        "      <th>4</th>\n",
 416 |        "      <td>AAPL</td>\n",
 417 |        "      <td>453.87</td>\n",
 418 |        "      <td>1.172528</td>\n",
 419 |        "      <td>N/A</td>\n",
 420 |        "    </tr>\n",
 421 |        "    <tr>\n",
 422 |        "      <th>5</th>\n",
 423 |        "      <td>REGN</td>\n",
 424 |        "      <td>624.50</td>\n",
 425 |        "      <td>1.031728</td>\n",
 426 |        "      <td>N/A</td>\n",
 427 |        "    </tr>\n",
 428 |        "    <tr>\n",
 429 |        "      <th>6</th>\n",
 430 |        "      <td>SWKS</td>\n",
 431 |        "      <td>153.26</td>\n",
 432 |        "      <td>0.921570</td>\n",
 433 |        "      <td>N/A</td>\n",
 434 |        "    </tr>\n",
 435 |        "    <tr>\n",
 436 |        "      <th>7</th>\n",
 437 |        "      <td>WST</td>\n",
 438 |        "      <td>283.20</td>\n",
 439 |        "      <td>0.921000</td>\n",
 440 |        "      <td>N/A</td>\n",
 441 |        "    </tr>\n",
 442 |        "    <tr>\n",
 443 |        "      <th>8</th>\n",
 444 |        "      <td>LRCX</td>\n",
 445 |        "      <td>393.68</td>\n",
 446 |        "      <td>0.892740</td>\n",
 447 |        "      <td>N/A</td>\n",
 448 |        "    </tr>\n",
 449 |        "    <tr>\n",
 450 |        "      <th>9</th>\n",
 451 |        "      <td>QRVO</td>\n",
 452 |        "      <td>132.38</td>\n",
 453 |        "      <td>0.858187</td>\n",
 454 |        "      <td>N/A</td>\n",
 455 |        "    </tr>\n",
 456 |        "    <tr>\n",
 457 |        "      <th>10</th>\n",
 458 |        "      <td>PYPL</td>\n",
 459 |        "      <td>196.30</td>\n",
 460 |        "      <td>0.829338</td>\n",
 461 |        "      <td>N/A</td>\n",
 462 |        "    </tr>\n",
 463 |        "    <tr>\n",
 464 |        "      <th>11</th>\n",
 465 |        "      <td>AMZN</td>\n",
 466 |        "      <td>3192.88</td>\n",
 467 |        "      <td>0.759898</td>\n",
 468 |        "      <td>N/A</td>\n",
 469 |        "    </tr>\n",
 470 |        "    <tr>\n",
 471 |        "      <th>12</th>\n",
 472 |        "      <td>ATVI</td>\n",
 473 |        "      <td>83.82</td>\n",
 474 |        "      <td>0.710587</td>\n",
 475 |        "      <td>N/A</td>\n",
 476 |        "    </tr>\n",
 477 |        "    <tr>\n",
 478 |        "      <th>13</th>\n",
 479 |        "      <td>ALGN</td>\n",
 480 |        "      <td>313.73</td>\n",
 481 |        "      <td>0.709093</td>\n",
 482 |        "      <td>N/A</td>\n",
 483 |        "    </tr>\n",
 484 |        "    <tr>\n",
 485 |        "      <th>14</th>\n",
 486 |        "      <td>NEM</td>\n",
 487 |        "      <td>63.72</td>\n",
 488 |        "      <td>0.698936</td>\n",
 489 |        "      <td>N/A</td>\n",
 490 |        "    </tr>\n",
 491 |        "    <tr>\n",
 492 |        "      <th>15</th>\n",
 493 |        "      <td>ODFL</td>\n",
 494 |        "      <td>196.24</td>\n",
 495 |        "      <td>0.690263</td>\n",
 496 |        "      <td>N/A</td>\n",
 497 |        "    </tr>\n",
 498 |        "    <tr>\n",
 499 |        "      <th>16</th>\n",
 500 |        "      <td>ROL</td>\n",
 501 |        "      <td>55.90</td>\n",
 502 |        "      <td>0.676072</td>\n",
 503 |        "      <td>N/A</td>\n",
 504 |        "    </tr>\n",
 505 |        "    <tr>\n",
 506 |        "      <th>17</th>\n",
 507 |        "      <td>NOW</td>\n",
 508 |        "      <td>439.05</td>\n",
 509 |        "      <td>0.656782</td>\n",
 510 |        "      <td>N/A</td>\n",
 511 |        "    </tr>\n",
 512 |        "    <tr>\n",
 513 |        "      <th>18</th>\n",
 514 |        "      <td>LOW</td>\n",
 515 |        "      <td>159.19</td>\n",
 516 |        "      <td>0.646186</td>\n",
 517 |        "      <td>N/A</td>\n",
 518 |        "    </tr>\n",
 519 |        "    <tr>\n",
 520 |        "      <th>19</th>\n",
 521 |        "      <td>DPZ</td>\n",
 522 |        "      <td>405.67</td>\n",
 523 |        "      <td>0.644884</td>\n",
 524 |        "      <td>N/A</td>\n",
 525 |        "    </tr>\n",
 526 |        "    <tr>\n",
 527 |        "      <th>20</th>\n",
 528 |        "      <td>FBHS</td>\n",
 529 |        "      <td>85.93</td>\n",
 530 |        "      <td>0.635603</td>\n",
 531 |        "      <td>N/A</td>\n",
 532 |        "    </tr>\n",
 533 |        "    <tr>\n",
 534 |        "      <th>21</th>\n",
 535 |        "      <td>FAST</td>\n",
 536 |        "      <td>49.98</td>\n",
 537 |        "      <td>0.624303</td>\n",
 538 |        "      <td>N/A</td>\n",
 539 |        "    </tr>\n",
 540 |        "    <tr>\n",
 541 |        "      <th>22</th>\n",
 542 |        "      <td>TGT</td>\n",
 543 |        "      <td>136.10</td>\n",
 544 |        "      <td>0.616877</td>\n",
 545 |        "      <td>N/A</td>\n",
 546 |        "    </tr>\n",
 547 |        "    <tr>\n",
 548 |        "      <th>23</th>\n",
 549 |        "      <td>QCOM</td>\n",
 550 |        "      <td>119.34</td>\n",
 551 |        "      <td>0.609546</td>\n",
 552 |        "      <td>N/A</td>\n",
 553 |        "    </tr>\n",
 554 |        "    <tr>\n",
 555 |        "      <th>24</th>\n",
 556 |        "      <td>URI</td>\n",
 557 |        "      <td>181.40</td>\n",
 558 |        "      <td>0.590406</td>\n",
 559 |        "      <td>N/A</td>\n",
 560 |        "    </tr>\n",
 561 |        "    <tr>\n",
 562 |        "      <th>25</th>\n",
 563 |        "      <td>CHTR</td>\n",
 564 |        "      <td>602.51</td>\n",
 565 |        "      <td>0.586074</td>\n",
 566 |        "      <td>N/A</td>\n",
 567 |        "    </tr>\n",
 568 |        "    <tr>\n",
 569 |        "      <th>26</th>\n",
 570 |        "      <td>MSCI</td>\n",
 571 |        "      <td>369.93</td>\n",
 572 |        "      <td>0.583839</td>\n",
 573 |        "      <td>N/A</td>\n",
 574 |        "    </tr>\n",
 575 |        "    <tr>\n",
 576 |        "      <th>27</th>\n",
 577 |        "      <td>VAR</td>\n",
 578 |        "      <td>174.79</td>\n",
 579 |        "      <td>0.569642</td>\n",
 580 |        "      <td>N/A</td>\n",
 581 |        "    </tr>\n",
 582 |        "    <tr>\n",
 583 |        "      <th>28</th>\n",
 584 |        "      <td>ROK</td>\n",
 585 |        "      <td>247.30</td>\n",
 586 |        "      <td>0.560821</td>\n",
 587 |        "      <td>N/A</td>\n",
 588 |        "    </tr>\n",
 589 |        "    <tr>\n",
 590 |        "      <th>29</th>\n",
 591 |        "      <td>BIO</td>\n",
 592 |        "      <td>517.62</td>\n",
 593 |        "      <td>0.558503</td>\n",
 594 |        "      <td>N/A</td>\n",
 595 |        "    </tr>\n",
 596 |        "    <tr>\n",
 597 |        "      <th>30</th>\n",
 598 |        "      <td>KSU</td>\n",
 599 |        "      <td>193.03</td>\n",
 600 |        "      <td>0.548382</td>\n",
 601 |        "      <td>N/A</td>\n",
 602 |        "    </tr>\n",
 603 |        "    <tr>\n",
 604 |        "      <th>31</th>\n",
 605 |        "      <td>KLAC</td>\n",
 606 |        "      <td>216.00</td>\n",
 607 |        "      <td>0.547432</td>\n",
 608 |        "      <td>N/A</td>\n",
 609 |        "    </tr>\n",
 610 |        "    <tr>\n",
 611 |        "      <th>32</th>\n",
 612 |        "      <td>ADBE</td>\n",
 613 |        "      <td>449.01</td>\n",
 614 |        "      <td>0.540159</td>\n",
 615 |        "      <td>N/A</td>\n",
 616 |        "    </tr>\n",
 617 |        "    <tr>\n",
 618 |        "      <th>33</th>\n",
 619 |        "      <td>ABMD</td>\n",
 620 |        "      <td>307.07</td>\n",
 621 |        "      <td>0.537076</td>\n",
 622 |        "      <td>N/A</td>\n",
 623 |        "    </tr>\n",
 624 |        "    <tr>\n",
 625 |        "      <th>34</th>\n",
 626 |        "      <td>CDNS</td>\n",
 627 |        "      <td>111.60</td>\n",
 628 |        "      <td>0.532542</td>\n",
 629 |        "      <td>N/A</td>\n",
 630 |        "    </tr>\n",
 631 |        "    <tr>\n",
 632 |        "      <th>35</th>\n",
 633 |        "      <td>NFLX</td>\n",
 634 |        "      <td>486.12</td>\n",
 635 |        "      <td>0.531236</td>\n",
 636 |        "      <td>N/A</td>\n",
 637 |        "    </tr>\n",
 638 |        "    <tr>\n",
 639 |        "      <th>36</th>\n",
 640 |        "      <td>ADSK</td>\n",
 641 |        "      <td>240.90</td>\n",
 642 |        "      <td>0.526803</td>\n",
 643 |        "      <td>N/A</td>\n",
 644 |        "    </tr>\n",
 645 |        "    <tr>\n",
 646 |        "      <th>37</th>\n",
 647 |        "      <td>FTNT</td>\n",
 648 |        "      <td>132.50</td>\n",
 649 |        "      <td>0.522105</td>\n",
 650 |        "      <td>N/A</td>\n",
 651 |        "    </tr>\n",
 652 |        "    <tr>\n",
 653 |        "      <th>38</th>\n",
 654 |        "      <td>MSFT</td>\n",
 655 |        "      <td>215.73</td>\n",
 656 |        "      <td>0.518900</td>\n",
 657 |        "      <td>N/A</td>\n",
 658 |        "    </tr>\n",
 659 |        "    <tr>\n",
 660 |        "      <th>39</th>\n",
 661 |        "      <td>EA</td>\n",
 662 |        "      <td>145.30</td>\n",
 663 |        "      <td>0.517087</td>\n",
 664 |        "      <td>N/A</td>\n",
 665 |        "    </tr>\n",
 666 |        "    <tr>\n",
 667 |        "      <th>40</th>\n",
 668 |        "      <td>TMO</td>\n",
 669 |        "      <td>422.95</td>\n",
 670 |        "      <td>0.513970</td>\n",
 671 |        "      <td>N/A</td>\n",
 672 |        "    </tr>\n",
 673 |        "    <tr>\n",
 674 |        "      <th>41</th>\n",
 675 |        "      <td>KR</td>\n",
 676 |        "      <td>36.29</td>\n",
 677 |        "      <td>0.502626</td>\n",
 678 |        "      <td>N/A</td>\n",
 679 |        "    </tr>\n",
 680 |        "    <tr>\n",
 681 |        "      <th>42</th>\n",
 682 |        "      <td>DHI</td>\n",
 683 |        "      <td>73.64</td>\n",
 684 |        "      <td>0.499570</td>\n",
 685 |        "      <td>N/A</td>\n",
 686 |        "    </tr>\n",
 687 |        "    <tr>\n",
 688 |        "      <th>43</th>\n",
 689 |        "      <td>CTXS</td>\n",
 690 |        "      <td>141.53</td>\n",
 691 |        "      <td>0.496854</td>\n",
 692 |        "      <td>N/A</td>\n",
 693 |        "    </tr>\n",
 694 |        "    <tr>\n",
 695 |        "      <th>44</th>\n",
 696 |        "      <td>LEN</td>\n",
 697 |        "      <td>77.72</td>\n",
 698 |        "      <td>0.495581</td>\n",
 699 |        "      <td>N/A</td>\n",
 700 |        "    </tr>\n",
 701 |        "    <tr>\n",
 702 |        "      <th>45</th>\n",
 703 |        "      <td>MAS</td>\n",
 704 |        "      <td>61.01</td>\n",
 705 |        "      <td>0.493611</td>\n",
 706 |        "      <td>N/A</td>\n",
 707 |        "    </tr>\n",
 708 |        "    <tr>\n",
 709 |        "      <th>46</th>\n",
 710 |        "      <td>TMUS</td>\n",
 711 |        "      <td>119.48</td>\n",
 712 |        "      <td>0.490167</td>\n",
 713 |        "      <td>N/A</td>\n",
 714 |        "    </tr>\n",
 715 |        "    <tr>\n",
 716 |        "      <th>47</th>\n",
 717 |        "      <td>BBY</td>\n",
 718 |        "      <td>107.33</td>\n",
 719 |        "      <td>0.486775</td>\n",
 720 |        "      <td>N/A</td>\n",
 721 |        "    </tr>\n",
 722 |        "    <tr>\n",
 723 |        "      <th>48</th>\n",
 724 |        "      <td>VRTX</td>\n",
 725 |        "      <td>280.26</td>\n",
 726 |        "      <td>0.486553</td>\n",
 727 |        "      <td>N/A</td>\n",
 728 |        "    </tr>\n",
 729 |        "    <tr>\n",
 730 |        "      <th>49</th>\n",
 731 |        "      <td>PWR</td>\n",
 732 |        "      <td>50.22</td>\n",
 733 |        "      <td>0.482556</td>\n",
 734 |        "      <td>N/A</td>\n",
 735 |        "    </tr>\n",
 736 |        "    <tr>\n",
 737 |        "      <th>50</th>\n",
 738 |        "      <td>OTIS</td>\n",
 739 |        "      <td>67.90</td>\n",
 740 |        "      <td>0.481000</td>\n",
 741 |        "      <td>N/A</td>\n",
 742 |        "    </tr>\n",
 743 |        "  </tbody>\n",
 744 |        "</table>\n",
 745 |        "</div>"
 746 |       ],
 747 |       "text/plain": [
 748 |        "   Ticker    Price  One-Year Price Return Number of Shares to Buy\n",
 749 |        "0    NVDA   458.14               2.015964                     N/A\n",
 750 |        "1    DXCM   441.29               1.721219                     N/A\n",
 751 |        "2     AMD    85.46               1.632817                     N/A\n",
 752 |        "3    CARR    29.94               1.489900                     N/A\n",
 753 |        "4    AAPL   453.87               1.172528                     N/A\n",
 754 |        "5    REGN   624.50               1.031728                     N/A\n",
 755 |        "6    SWKS   153.26               0.921570                     N/A\n",
 756 |        "7     WST   283.20               0.921000                     N/A\n",
 757 |        "8    LRCX   393.68               0.892740                     N/A\n",
 758 |        "9    QRVO   132.38               0.858187                     N/A\n",
 759 |        "10   PYPL   196.30               0.829338                     N/A\n",
 760 |        "11   AMZN  3192.88               0.759898                     N/A\n",
 761 |        "12   ATVI    83.82               0.710587                     N/A\n",
 762 |        "13   ALGN   313.73               0.709093                     N/A\n",
 763 |        "14    NEM    63.72               0.698936                     N/A\n",
 764 |        "15   ODFL   196.24               0.690263                     N/A\n",
 765 |        "16    ROL    55.90               0.676072                     N/A\n",
 766 |        "17    NOW   439.05               0.656782                     N/A\n",
 767 |        "18    LOW   159.19               0.646186                     N/A\n",
 768 |        "19    DPZ   405.67               0.644884                     N/A\n",
 769 |        "20   FBHS    85.93               0.635603                     N/A\n",
 770 |        "21   FAST    49.98               0.624303                     N/A\n",
 771 |        "22    TGT   136.10               0.616877                     N/A\n",
 772 |        "23   QCOM   119.34               0.609546                     N/A\n",
 773 |        "24    URI   181.40               0.590406                     N/A\n",
 774 |        "25   CHTR   602.51               0.586074                     N/A\n",
 775 |        "26   MSCI   369.93               0.583839                     N/A\n",
 776 |        "27    VAR   174.79               0.569642                     N/A\n",
 777 |        "28    ROK   247.30               0.560821                     N/A\n",
 778 |        "29    BIO   517.62               0.558503                     N/A\n",
 779 |        "30    KSU   193.03               0.548382                     N/A\n",
 780 |        "31   KLAC   216.00               0.547432                     N/A\n",
 781 |        "32   ADBE   449.01               0.540159                     N/A\n",
 782 |        "33   ABMD   307.07               0.537076                     N/A\n",
 783 |        "34   CDNS   111.60               0.532542                     N/A\n",
 784 |        "35   NFLX   486.12               0.531236                     N/A\n",
 785 |        "36   ADSK   240.90               0.526803                     N/A\n",
 786 |        "37   FTNT   132.50               0.522105                     N/A\n",
 787 |        "38   MSFT   215.73               0.518900                     N/A\n",
 788 |        "39     EA   145.30               0.517087                     N/A\n",
 789 |        "40    TMO   422.95               0.513970                     N/A\n",
 790 |        "41     KR    36.29               0.502626                     N/A\n",
 791 |        "42    DHI    73.64               0.499570                     N/A\n",
 792 |        "43   CTXS   141.53               0.496854                     N/A\n",
 793 |        "44    LEN    77.72               0.495581                     N/A\n",
 794 |        "45    MAS    61.01               0.493611                     N/A\n",
 795 |        "46   TMUS   119.48               0.490167                     N/A\n",
 796 |        "47    BBY   107.33               0.486775                     N/A\n",
 797 |        "48   VRTX   280.26               0.486553                     N/A\n",
 798 |        "49    PWR    50.22               0.482556                     N/A\n",
 799 |        "50   OTIS    67.90               0.481000                     N/A"
 800 |       ]
 801 |      },
 802 |      "execution_count": 7,
 803 |      "metadata": {},
 804 |      "output_type": "execute_result"
 805 |     }
 806 |    ],
 807 |    "source": [
 808 |     "final_dataframe.sort_values('One-Year Price Return', ascending = False, inplace = True)\n",
 809 |     "final_dataframe = final_dataframe[:51]\n",
 810 |     "final_dataframe.reset_index(drop = True, inplace = True)\n",
 811 |     "final_dataframe"
 812 |    ]
 813 |   },
 814 |   {
 815 |    "cell_type": "markdown",
 816 |    "metadata": {},
 817 |    "source": [
 818 |     "## Calculating the Number of Shares to Buy\n",
 819 |     "\n",
 820 |     "Just like in the last project, we now need to calculate the number of shares we need to buy. The one change we're going to make is wrapping this functionality inside a function, since we'll be using it again later in this Jupyter Notebook.\n",
 821 |     "\n",
 822 |     "Since we've already done most of the work on this, try to complete the following two code cells without watching me do it first!"
 823 |    ]
 824 |   },
 825 |   {
 826 |    "cell_type": "code",
 827 |    "execution_count": 8,
 828 |    "metadata": {},
 829 |    "outputs": [
 830 |     {
 831 |      "name": "stdout",
 832 |      "output_type": "stream",
 833 |      "text": [
 834 |       "Enter the value of your portfolio:1000000\n",
 835 |       "1000000\n"
 836 |      ]
 837 |     }
 838 |    ],
 839 |    "source": [
 840 |     "def portfolio_input():\n",
 841 |     "    global portfolio_size\n",
 842 |     "    portfolio_size = input(\"Enter the value of your portfolio:\")\n",
 843 |     "\n",
 844 |     "    try:\n",
 845 |     "        val = float(portfolio_size)\n",
 846 |     "    except ValueError:\n",
 847 |     "        print(\"That's not a number! \\n Try again:\")\n",
 848 |     "        portfolio_size = input(\"Enter the value of your portfolio:\")\n",
 849 |     "\n",
 850 |     "portfolio_input()\n",
 851 |     "print(portfolio_size)"
 852 |    ]
 853 |   },
 854 |   {
 855 |    "cell_type": "code",
 856 |    "execution_count": 9,
 857 |    "metadata": {},
 858 |    "outputs": [
 859 |     {
 860 |      "name": "stderr",
 861 |      "output_type": "stream",
 862 |      "text": [
 863 |       "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/indexing.py:494: SettingWithCopyWarning: \n",
 864 |       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
 865 |       "Try using .loc[row_indexer,col_indexer] = value instead\n",
 866 |       "\n",
 867 |       "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
 868 |       "  self.obj[item] = s\n"
 869 |      ]
 870 |     },
 871 |     {
 872 |      "data": {
 873 |       "text/html": [
 874 |        "<div>\n",
 875 |        "<style scoped>\n",
 876 |        "    .dataframe tbody tr th:only-of-type {\n",
 877 |        "        vertical-align: middle;\n",
 878 |        "    }\n",
 879 |        "\n",
 880 |        "    .dataframe tbody tr th {\n",
 881 |        "        vertical-align: top;\n",
 882 |        "    }\n",
 883 |        "\n",
 884 |        "    .dataframe thead th {\n",
 885 |        "        text-align: right;\n",
 886 |        "    }\n",
 887 |        "</style>\n",
 888 |        "<table border=\"1\" class=\"dataframe\">\n",
 889 |        "  <thead>\n",
 890 |        "    <tr style=\"text-align: right;\">\n",
 891 |        "      <th></th>\n",
 892 |        "      <th>Ticker</th>\n",
 893 |        "      <th>Price</th>\n",
 894 |        "      <th>One-Year Price Return</th>\n",
 895 |        "      <th>Number of Shares to Buy</th>\n",
 896 |        "    </tr>\n",
 897 |        "  </thead>\n",
 898 |        "  <tbody>\n",
 899 |        "    <tr>\n",
 900 |        "      <th>0</th>\n",
 901 |        "      <td>NVDA</td>\n",
 902 |        "      <td>458.14</td>\n",
 903 |        "      <td>2.015964</td>\n",
 904 |        "      <td>42</td>\n",
 905 |        "    </tr>\n",
 906 |        "    <tr>\n",
 907 |        "      <th>1</th>\n",
 908 |        "      <td>DXCM</td>\n",
 909 |        "      <td>441.29</td>\n",
 910 |        "      <td>1.721219</td>\n",
 911 |        "      <td>44</td>\n",
 912 |        "    </tr>\n",
 913 |        "    <tr>\n",
 914 |        "      <th>2</th>\n",
 915 |        "      <td>AMD</td>\n",
 916 |        "      <td>85.46</td>\n",
 917 |        "      <td>1.632817</td>\n",
 918 |        "      <td>229</td>\n",
 919 |        "    </tr>\n",
 920 |        "    <tr>\n",
 921 |        "      <th>3</th>\n",
 922 |        "      <td>CARR</td>\n",
 923 |        "      <td>29.94</td>\n",
 924 |        "      <td>1.489900</td>\n",
 925 |        "      <td>654</td>\n",
 926 |        "    </tr>\n",
 927 |        "    <tr>\n",
 928 |        "      <th>4</th>\n",
 929 |        "      <td>AAPL</td>\n",
 930 |        "      <td>453.87</td>\n",
 931 |        "      <td>1.172528</td>\n",
 932 |        "      <td>43</td>\n",
 933 |        "    </tr>\n",
 934 |        "    <tr>\n",
 935 |        "      <th>5</th>\n",
 936 |        "      <td>REGN</td>\n",
 937 |        "      <td>624.50</td>\n",
 938 |        "      <td>1.031728</td>\n",
 939 |        "      <td>31</td>\n",
 940 |        "    </tr>\n",
 941 |        "    <tr>\n",
 942 |        "      <th>6</th>\n",
 943 |        "      <td>SWKS</td>\n",
 944 |        "      <td>153.26</td>\n",
 945 |        "      <td>0.921570</td>\n",
 946 |        "      <td>127</td>\n",
 947 |        "    </tr>\n",
 948 |        "    <tr>\n",
 949 |        "      <th>7</th>\n",
 950 |        "      <td>WST</td>\n",
 951 |        "      <td>283.20</td>\n",
 952 |        "      <td>0.921000</td>\n",
 953 |        "      <td>69</td>\n",
 954 |        "    </tr>\n",
 955 |        "    <tr>\n",
 956 |        "      <th>8</th>\n",
 957 |        "      <td>LRCX</td>\n",
 958 |        "      <td>393.68</td>\n",
 959 |        "      <td>0.892740</td>\n",
 960 |        "      <td>49</td>\n",
 961 |        "    </tr>\n",
 962 |        "    <tr>\n",
 963 |        "      <th>9</th>\n",
 964 |        "      <td>QRVO</td>\n",
 965 |        "      <td>132.38</td>\n",
 966 |        "      <td>0.858187</td>\n",
 967 |        "      <td>148</td>\n",
 968 |        "    </tr>\n",
 969 |        "    <tr>\n",
 970 |        "      <th>10</th>\n",
 971 |        "      <td>PYPL</td>\n",
 972 |        "      <td>196.30</td>\n",
 973 |        "      <td>0.829338</td>\n",
 974 |        "      <td>99</td>\n",
 975 |        "    </tr>\n",
 976 |        "    <tr>\n",
 977 |        "      <th>11</th>\n",
 978 |        "      <td>AMZN</td>\n",
 979 |        "      <td>3192.88</td>\n",
 980 |        "      <td>0.759898</td>\n",
 981 |        "      <td>6</td>\n",
 982 |        "    </tr>\n",
 983 |        "    <tr>\n",
 984 |        "      <th>12</th>\n",
 985 |        "      <td>ATVI</td>\n",
 986 |        "      <td>83.82</td>\n",
 987 |        "      <td>0.710587</td>\n",
 988 |        "      <td>233</td>\n",
 989 |        "    </tr>\n",
 990 |        "    <tr>\n",
 991 |        "      <th>13</th>\n",
 992 |        "      <td>ALGN</td>\n",
 993 |        "      <td>313.73</td>\n",
 994 |        "      <td>0.709093</td>\n",
 995 |        "      <td>62</td>\n",
 996 |        "    </tr>\n",
 997 |        "    <tr>\n",
 998 |        "      <th>14</th>\n",
 999 |        "      <td>NEM</td>\n",
1000 |        "      <td>63.72</td>\n",
1001 |        "      <td>0.698936</td>\n",
1002 |        "      <td>307</td>\n",
1003 |        "    </tr>\n",
1004 |        "    <tr>\n",
1005 |        "      <th>15</th>\n",
1006 |        "      <td>ODFL</td>\n",
1007 |        "      <td>196.24</td>\n",
1008 |        "      <td>0.690263</td>\n",
1009 |        "      <td>99</td>\n",
1010 |        "    </tr>\n",
1011 |        "    <tr>\n",
1012 |        "      <th>16</th>\n",
1013 |        "      <td>ROL</td>\n",
1014 |        "      <td>55.90</td>\n",
1015 |        "      <td>0.676072</td>\n",
1016 |        "      <td>350</td>\n",
1017 |        "    </tr>\n",
1018 |        "    <tr>\n",
1019 |        "      <th>17</th>\n",
1020 |        "      <td>NOW</td>\n",
1021 |        "      <td>439.05</td>\n",
1022 |        "      <td>0.656782</td>\n",
1023 |        "      <td>44</td>\n",
1024 |        "    </tr>\n",
1025 |        "    <tr>\n",
1026 |        "      <th>18</th>\n",
1027 |        "      <td>LOW</td>\n",
1028 |        "      <td>159.19</td>\n",
1029 |        "      <td>0.646186</td>\n",
1030 |        "      <td>123</td>\n",
1031 |        "    </tr>\n",
1032 |        "    <tr>\n",
1033 |        "      <th>19</th>\n",
1034 |        "      <td>DPZ</td>\n",
1035 |        "      <td>405.67</td>\n",
1036 |        "      <td>0.644884</td>\n",
1037 |        "      <td>48</td>\n",
1038 |        "    </tr>\n",
1039 |        "    <tr>\n",
1040 |        "      <th>20</th>\n",
1041 |        "      <td>FBHS</td>\n",
1042 |        "      <td>85.93</td>\n",
1043 |        "      <td>0.635603</td>\n",
1044 |        "      <td>228</td>\n",
1045 |        "    </tr>\n",
1046 |        "    <tr>\n",
1047 |        "      <th>21</th>\n",
1048 |        "      <td>FAST</td>\n",
1049 |        "      <td>49.98</td>\n",
1050 |        "      <td>0.624303</td>\n",
1051 |        "      <td>392</td>\n",
1052 |        "    </tr>\n",
1053 |        "    <tr>\n",
1054 |        "      <th>22</th>\n",
1055 |        "      <td>TGT</td>\n",
1056 |        "      <td>136.10</td>\n",
1057 |        "      <td>0.616877</td>\n",
1058 |        "      <td>144</td>\n",
1059 |        "    </tr>\n",
1060 |        "    <tr>\n",
1061 |        "      <th>23</th>\n",
1062 |        "      <td>QCOM</td>\n",
1063 |        "      <td>119.34</td>\n",
1064 |        "      <td>0.609546</td>\n",
1065 |        "      <td>164</td>\n",
1066 |        "    </tr>\n",
1067 |        "    <tr>\n",
1068 |        "      <th>24</th>\n",
1069 |        "      <td>URI</td>\n",
1070 |        "      <td>181.40</td>\n",
1071 |        "      <td>0.590406</td>\n",
1072 |        "      <td>108</td>\n",
1073 |        "    </tr>\n",
1074 |        "    <tr>\n",
1075 |        "      <th>25</th>\n",
1076 |        "      <td>CHTR</td>\n",
1077 |        "      <td>602.51</td>\n",
1078 |        "      <td>0.586074</td>\n",
1079 |        "      <td>32</td>\n",
1080 |        "    </tr>\n",
1081 |        "    <tr>\n",
1082 |        "      <th>26</th>\n",
1083 |        "      <td>MSCI</td>\n",
1084 |        "      <td>369.93</td>\n",
1085 |        "      <td>0.583839</td>\n",
1086 |        "      <td>53</td>\n",
1087 |        "    </tr>\n",
1088 |        "    <tr>\n",
1089 |        "      <th>27</th>\n",
1090 |        "      <td>VAR</td>\n",
1091 |        "      <td>174.79</td>\n",
1092 |        "      <td>0.569642</td>\n",
1093 |        "      <td>112</td>\n",
1094 |        "    </tr>\n",
1095 |        "    <tr>\n",
1096 |        "      <th>28</th>\n",
1097 |        "      <td>ROK</td>\n",
1098 |        "      <td>247.30</td>\n",
1099 |        "      <td>0.560821</td>\n",
1100 |        "      <td>79</td>\n",
1101 |        "    </tr>\n",
1102 |        "    <tr>\n",
1103 |        "      <th>29</th>\n",
1104 |        "      <td>BIO</td>\n",
1105 |        "      <td>517.62</td>\n",
1106 |        "      <td>0.558503</td>\n",
1107 |        "      <td>37</td>\n",
1108 |        "    </tr>\n",
1109 |        "    <tr>\n",
1110 |        "      <th>30</th>\n",
1111 |        "      <td>KSU</td>\n",
1112 |        "      <td>193.03</td>\n",
1113 |        "      <td>0.548382</td>\n",
1114 |        "      <td>101</td>\n",
1115 |        "    </tr>\n",
1116 |        "    <tr>\n",
1117 |        "      <th>31</th>\n",
1118 |        "      <td>KLAC</td>\n",
1119 |        "      <td>216.00</td>\n",
1120 |        "      <td>0.547432</td>\n",
1121 |        "      <td>90</td>\n",
1122 |        "    </tr>\n",
1123 |        "    <tr>\n",
1124 |        "      <th>32</th>\n",
1125 |        "      <td>ADBE</td>\n",
1126 |        "      <td>449.01</td>\n",
1127 |        "      <td>0.540159</td>\n",
1128 |        "      <td>43</td>\n",
1129 |        "    </tr>\n",
1130 |        "    <tr>\n",
1131 |        "      <th>33</th>\n",
1132 |        "      <td>ABMD</td>\n",
1133 |        "      <td>307.07</td>\n",
1134 |        "      <td>0.537076</td>\n",
1135 |        "      <td>63</td>\n",
1136 |        "    </tr>\n",
1137 |        "    <tr>\n",
1138 |        "      <th>34</th>\n",
1139 |        "      <td>CDNS</td>\n",
1140 |        "      <td>111.60</td>\n",
1141 |        "      <td>0.532542</td>\n",
1142 |        "      <td>175</td>\n",
1143 |        "    </tr>\n",
1144 |        "    <tr>\n",
1145 |        "      <th>35</th>\n",
1146 |        "      <td>NFLX</td>\n",
1147 |        "      <td>486.12</td>\n",
1148 |        "      <td>0.531236</td>\n",
1149 |        "      <td>40</td>\n",
1150 |        "    </tr>\n",
1151 |        "    <tr>\n",
1152 |        "      <th>36</th>\n",
1153 |        "      <td>ADSK</td>\n",
1154 |        "      <td>240.90</td>\n",
1155 |        "      <td>0.526803</td>\n",
1156 |        "      <td>81</td>\n",
1157 |        "    </tr>\n",
1158 |        "    <tr>\n",
1159 |        "      <th>37</th>\n",
1160 |        "      <td>FTNT</td>\n",
1161 |        "      <td>132.50</td>\n",
1162 |        "      <td>0.522105</td>\n",
1163 |        "      <td>147</td>\n",
1164 |        "    </tr>\n",
1165 |        "    <tr>\n",
1166 |        "      <th>38</th>\n",
1167 |        "      <td>MSFT</td>\n",
1168 |        "      <td>215.73</td>\n",
1169 |        "      <td>0.518900</td>\n",
1170 |        "      <td>90</td>\n",
1171 |        "    </tr>\n",
1172 |        "    <tr>\n",
1173 |        "      <th>39</th>\n",
1174 |        "      <td>EA</td>\n",
1175 |        "      <td>145.30</td>\n",
1176 |        "      <td>0.517087</td>\n",
1177 |        "      <td>134</td>\n",
1178 |        "    </tr>\n",
1179 |        "    <tr>\n",
1180 |        "      <th>40</th>\n",
1181 |        "      <td>TMO</td>\n",
1182 |        "      <td>422.95</td>\n",
1183 |        "      <td>0.513970</td>\n",
1184 |        "      <td>46</td>\n",
1185 |        "    </tr>\n",
1186 |        "    <tr>\n",
1187 |        "      <th>41</th>\n",
1188 |        "      <td>KR</td>\n",
1189 |        "      <td>36.29</td>\n",
1190 |        "      <td>0.502626</td>\n",
1191 |        "      <td>540</td>\n",
1192 |        "    </tr>\n",
1193 |        "    <tr>\n",
1194 |        "      <th>42</th>\n",
1195 |        "      <td>DHI</td>\n",
1196 |        "      <td>73.64</td>\n",
1197 |        "      <td>0.499570</td>\n",
1198 |        "      <td>266</td>\n",
1199 |        "    </tr>\n",
1200 |        "    <tr>\n",
1201 |        "      <th>43</th>\n",
1202 |        "      <td>CTXS</td>\n",
1203 |        "      <td>141.53</td>\n",
1204 |        "      <td>0.496854</td>\n",
1205 |        "      <td>138</td>\n",
1206 |        "    </tr>\n",
1207 |        "    <tr>\n",
1208 |        "      <th>44</th>\n",
1209 |        "      <td>LEN</td>\n",
1210 |        "      <td>77.72</td>\n",
1211 |        "      <td>0.495581</td>\n",
1212 |        "      <td>252</td>\n",
1213 |        "    </tr>\n",
1214 |        "    <tr>\n",
1215 |        "      <th>45</th>\n",
1216 |        "      <td>MAS</td>\n",
1217 |        "      <td>61.01</td>\n",
1218 |        "      <td>0.493611</td>\n",
1219 |        "      <td>321</td>\n",
1220 |        "    </tr>\n",
1221 |        "    <tr>\n",
1222 |        "      <th>46</th>\n",
1223 |        "      <td>TMUS</td>\n",
1224 |        "      <td>119.48</td>\n",
1225 |        "      <td>0.490167</td>\n",
1226 |        "      <td>164</td>\n",
1227 |        "    </tr>\n",
1228 |        "    <tr>\n",
1229 |        "      <th>47</th>\n",
1230 |        "      <td>BBY</td>\n",
1231 |        "      <td>107.33</td>\n",
1232 |        "      <td>0.486775</td>\n",
1233 |        "      <td>182</td>\n",
1234 |        "    </tr>\n",
1235 |        "    <tr>\n",
1236 |        "      <th>48</th>\n",
1237 |        "      <td>VRTX</td>\n",
1238 |        "      <td>280.26</td>\n",
1239 |        "      <td>0.486553</td>\n",
1240 |        "      <td>69</td>\n",
1241 |        "    </tr>\n",
1242 |        "    <tr>\n",
1243 |        "      <th>49</th>\n",
1244 |        "      <td>PWR</td>\n",
1245 |        "      <td>50.22</td>\n",
1246 |        "      <td>0.482556</td>\n",
1247 |        "      <td>390</td>\n",
1248 |        "    </tr>\n",
1249 |        "    <tr>\n",
1250 |        "      <th>50</th>\n",
1251 |        "      <td>OTIS</td>\n",
1252 |        "      <td>67.90</td>\n",
1253 |        "      <td>0.481000</td>\n",
1254 |        "      <td>288</td>\n",
1255 |        "    </tr>\n",
1256 |        "  </tbody>\n",
1257 |        "</table>\n",
1258 |        "</div>"
1259 |       ],
1260 |       "text/plain": [
1261 |        "   Ticker    Price  One-Year Price Return  Number of Shares to Buy\n",
1262 |        "0    NVDA   458.14               2.015964                       42\n",
1263 |        "1    DXCM   441.29               1.721219                       44\n",
1264 |        "2     AMD    85.46               1.632817                      229\n",
1265 |        "3    CARR    29.94               1.489900                      654\n",
1266 |        "4    AAPL   453.87               1.172528                       43\n",
1267 |        "5    REGN   624.50               1.031728                       31\n",
1268 |        "6    SWKS   153.26               0.921570                      127\n",
1269 |        "7     WST   283.20               0.921000                       69\n",
1270 |        "8    LRCX   393.68               0.892740                       49\n",
1271 |        "9    QRVO   132.38               0.858187                      148\n",
1272 |        "10   PYPL   196.30               0.829338                       99\n",
1273 |        "11   AMZN  3192.88               0.759898                        6\n",
1274 |        "12   ATVI    83.82               0.710587                      233\n",
1275 |        "13   ALGN   313.73               0.709093                       62\n",
1276 |        "14    NEM    63.72               0.698936                      307\n",
1277 |        "15   ODFL   196.24               0.690263                       99\n",
1278 |        "16    ROL    55.90               0.676072                      350\n",
1279 |        "17    NOW   439.05               0.656782                       44\n",
1280 |        "18    LOW   159.19               0.646186                      123\n",
1281 |        "19    DPZ   405.67               0.644884                       48\n",
1282 |        "20   FBHS    85.93               0.635603                      228\n",
1283 |        "21   FAST    49.98               0.624303                      392\n",
1284 |        "22    TGT   136.10               0.616877                      144\n",
1285 |        "23   QCOM   119.34               0.609546                      164\n",
1286 |        "24    URI   181.40               0.590406                      108\n",
1287 |        "25   CHTR   602.51               0.586074                       32\n",
1288 |        "26   MSCI   369.93               0.583839                       53\n",
1289 |        "27    VAR   174.79               0.569642                      112\n",
1290 |        "28    ROK   247.30               0.560821                       79\n",
1291 |        "29    BIO   517.62               0.558503                       37\n",
1292 |        "30    KSU   193.03               0.548382                      101\n",
1293 |        "31   KLAC   216.00               0.547432                       90\n",
1294 |        "32   ADBE   449.01               0.540159                       43\n",
1295 |        "33   ABMD   307.07               0.537076                       63\n",
1296 |        "34   CDNS   111.60               0.532542                      175\n",
1297 |        "35   NFLX   486.12               0.531236                       40\n",
1298 |        "36   ADSK   240.90               0.526803                       81\n",
1299 |        "37   FTNT   132.50               0.522105                      147\n",
1300 |        "38   MSFT   215.73               0.518900                       90\n",
1301 |        "39     EA   145.30               0.517087                      134\n",
1302 |        "40    TMO   422.95               0.513970                       46\n",
1303 |        "41     KR    36.29               0.502626                      540\n",
1304 |        "42    DHI    73.64               0.499570                      266\n",
1305 |        "43   CTXS   141.53               0.496854                      138\n",
1306 |        "44    LEN    77.72               0.495581                      252\n",
1307 |        "45    MAS    61.01               0.493611                      321\n",
1308 |        "46   TMUS   119.48               0.490167                      164\n",
1309 |        "47    BBY   107.33               0.486775                      182\n",
1310 |        "48   VRTX   280.26               0.486553                       69\n",
1311 |        "49    PWR    50.22               0.482556                      390\n",
1312 |        "50   OTIS    67.90               0.481000                      288"
1313 |       ]
1314 |      },
1315 |      "execution_count": 9,
1316 |      "metadata": {},
1317 |      "output_type": "execute_result"
1318 |     }
1319 |    ],
1320 |    "source": [
1321 |     "position_size = float(portfolio_size) / len(final_dataframe.index)\n",
1322 |     "for i in range(0, len(final_dataframe['Ticker'])):\n",
1323 |     "    final_dataframe.loc[i, 'Number of Shares to Buy'] = math.floor(position_size / final_dataframe['Price'][i])\n",
1324 |     "final_dataframe"
1325 |    ]
1326 |   },
1327 |   {
1328 |    "cell_type": "markdown",
1329 |    "metadata": {},
1330 |    "source": [
1331 |     "## Building a Better (and More Realistic) Momentum Strategy\n",
1332 |     "\n",
1333 |     "Real-world quantitative investment firms differentiate between \"high quality\" and \"low quality\" momentum stocks:\n",
1334 |     "\n",
1335 |     "* High-quality momentum stocks show \"slow and steady\" outperformance over long periods of time\n",
1336 |     "* Low-quality momentum stocks might not show any momentum for a long time, and then surge upwards.\n",
1337 |     "\n",
1338 |     "The reason why high-quality momentum stocks are preferred is because low-quality momentum can often be cause by short-term news that is unlikely to be repeated in the future (such as an FDA approval for a biotechnology company).\n",
1339 |     "\n",
1340 |     "To identify high-quality momentum, we're going to build a strategy that selects stocks from the highest percentiles of: \n",
1341 |     "\n",
1342 |     "* 1-month price returns\n",
1343 |     "* 3-month price returns\n",
1344 |     "* 6-month price returns\n",
1345 |     "* 1-year price returns\n",
1346 |     "\n",
1347 |     "Let's start by building our DataFrame. You'll notice that I use the abbreviation `hqm` often. It stands for `high-quality momentum`."
1348 |    ]
1349 |   },
1350 |   {
1351 |    "cell_type": "code",
1352 |    "execution_count": 10,
1353 |    "metadata": {},
1354 |    "outputs": [
1355 |     {
1356 |      "data": {
1357 |       "text/plain": [
1358 |        "Index(['Ticker', 'Price', 'Number of Shares to Buy', 'One-Year Price Return',\n",
1359 |        "       'One-Year Return Percentile', 'Six-Month Price Return',\n",
1360 |        "       'Six-Month Return Percentile', 'Three-Month Price Return',\n",
1361 |        "       'Three-Month Return Percentile', 'One-Month Price Return',\n",
1362 |        "       'One-Month Return Percentile', 'HQM Score'],\n",
1363 |        "      dtype='object')"
1364 |       ]
1365 |      },
1366 |      "execution_count": 10,
1367 |      "metadata": {},
1368 |      "output_type": "execute_result"
1369 |     }
1370 |    ],
1371 |    "source": [
1372 |     "hqm_columns = [\n",
1373 |     "                'Ticker', \n",
1374 |     "                'Price', \n",
1375 |     "                'Number of Shares to Buy', \n",
1376 |     "                'One-Year Price Return', \n",
1377 |     "                'One-Year Return Percentile',\n",
1378 |     "                'Six-Month Price Return',\n",
1379 |     "                'Six-Month Return Percentile',\n",
1380 |     "                'Three-Month Price Return',\n",
1381 |     "                'Three-Month Return Percentile',\n",
1382 |     "                'One-Month Price Return',\n",
1383 |     "                'One-Month Return Percentile',\n",
1384 |     "                'HQM Score'\n",
1385 |     "                ]\n",
1386 |     "\n",
1387 |     "hqm_dataframe = pd.DataFrame(columns = hqm_columns)\n",
1388 |     "\n",
1389 |     "for symbol_string in symbol_strings:\n",
1390 |     "#     print(symbol_strings)\n",
1391 |     "    batch_api_call_url = f'https://sandbox.iexapis.com/stable/stock/market/batch/?types=stats,quote&symbols={symbol_string}&token={IEX_CLOUD_API_TOKEN}'\n",
1392 |     "    data = requests.get(batch_api_call_url).json()\n",
1393 |     "    for symbol in symbol_string.split(','):\n",
1394 |     "        hqm_dataframe = hqm_dataframe.append(\n",
1395 |     "                                        pd.Series([symbol, \n",
1396 |     "                                                   data[symbol]['quote']['latestPrice'],\n",
1397 |     "                                                   'N/A',\n",
1398 |     "                                                   data[symbol]['stats']['year1ChangePercent'],\n",
1399 |     "                                                   'N/A',\n",
1400 |     "                                                   data[symbol]['stats']['month6ChangePercent'],\n",
1401 |     "                                                   'N/A',\n",
1402 |     "                                                   data[symbol]['stats']['month3ChangePercent'],\n",
1403 |     "                                                   'N/A',\n",
1404 |     "                                                   data[symbol]['stats']['month1ChangePercent'],\n",
1405 |     "                                                   'N/A',\n",
1406 |     "                                                   'N/A'\n",
1407 |     "                                                   ], \n",
1408 |     "                                                  index = hqm_columns), \n",
1409 |     "                                        ignore_index = True)\n",
1410 |     "        \n",
1411 |     "hqm_dataframe.columns"
1412 |    ]
1413 |   },
1414 |   {
1415 |    "cell_type": "markdown",
1416 |    "metadata": {},
1417 |    "source": [
1418 |     "## Calculating Momentum Percentiles\n",
1419 |     "\n",
1420 |     "We now need to calculate momentum percentile scores for every stock in the universe. More specifically, we need to calculate percentile scores for the following metrics for every stock:\n",
1421 |     "\n",
1422 |     "* `One-Year Price Return`\n",
1423 |     "* `Six-Month Price Return`\n",
1424 |     "* `Three-Month Price Return`\n",
1425 |     "* `One-Month Price Return`\n",
1426 |     "\n",
1427 |     "Here's how we'll do this:"
1428 |    ]
1429 |   },
1430 |   {
1431 |    "cell_type": "code",
1432 |    "execution_count": 11,
1433 |    "metadata": {},
1434 |    "outputs": [
1435 |     {
1436 |      "name": "stdout",
1437 |      "output_type": "stream",
1438 |      "text": [
1439 |       "0       0.885149\n",
1440 |       "1      0.0237624\n",
1441 |       "2       0.578218\n",
1442 |       "3       0.992079\n",
1443 |       "4        0.89703\n",
1444 |       "         ...    \n",
1445 |       "500     0.211881\n",
1446 |       "501     0.457426\n",
1447 |       "502     0.843564\n",
1448 |       "503     0.255446\n",
1449 |       "504     0.772277\n",
1450 |       "Name: One-Year Return Percentile, Length: 505, dtype: object\n",
1451 |       "0       0.837624\n",
1452 |       "1      0.0158416\n",
1453 |       "2       0.839604\n",
1454 |       "3       0.968317\n",
1455 |       "4       0.629703\n",
1456 |       "         ...    \n",
1457 |       "500     0.405941\n",
1458 |       "501      0.39604\n",
1459 |       "502     0.906931\n",
1460 |       "503     0.227723\n",
1461 |       "504     0.776238\n",
1462 |       "Name: Six-Month Return Percentile, Length: 505, dtype: object\n",
1463 |       "0      0.473267\n",
1464 |       "1      0.908911\n",
1465 |       "2      0.643564\n",
1466 |       "3      0.887129\n",
1467 |       "4       0.19802\n",
1468 |       "         ...   \n",
1469 |       "500    0.374257\n",
1470 |       "501    0.544554\n",
1471 |       "502    0.611881\n",
1472 |       "503     0.70297\n",
1473 |       "504    0.665347\n",
1474 |       "Name: Three-Month Return Percentile, Length: 505, dtype: object\n",
1475 |       "0       0.530693\n",
1476 |       "1       0.827723\n",
1477 |       "2       0.742574\n",
1478 |       "3       0.879208\n",
1479 |       "4      0.0693069\n",
1480 |       "         ...    \n",
1481 |       "500     0.370297\n",
1482 |       "501     0.762376\n",
1483 |       "502     0.641584\n",
1484 |       "503     0.312871\n",
1485 |       "504     0.792079\n",
1486 |       "Name: One-Month Return Percentile, Length: 505, dtype: object\n"
1487 |      ]
1488 |     },
1489 |     {
1490 |      "data": {
1491 |       "text/html": [
1492 |        "<div>\n",
1493 |        "<style scoped>\n",
1494 |        "    .dataframe tbody tr th:only-of-type {\n",
1495 |        "        vertical-align: middle;\n",
1496 |        "    }\n",
1497 |        "\n",
1498 |        "    .dataframe tbody tr th {\n",
1499 |        "        vertical-align: top;\n",
1500 |        "    }\n",
1501 |        "\n",
1502 |        "    .dataframe thead th {\n",
1503 |        "        text-align: right;\n",
1504 |        "    }\n",
1505 |        "</style>\n",
1506 |        "<table border=\"1\" class=\"dataframe\">\n",
1507 |        "  <thead>\n",
1508 |        "    <tr style=\"text-align: right;\">\n",
1509 |        "      <th></th>\n",
1510 |        "      <th>Ticker</th>\n",
1511 |        "      <th>Price</th>\n",
1512 |        "      <th>Number of Shares to Buy</th>\n",
1513 |        "      <th>One-Year Price Return</th>\n",
1514 |        "      <th>One-Year Return Percentile</th>\n",
1515 |        "      <th>Six-Month Price Return</th>\n",
1516 |        "      <th>Six-Month Return Percentile</th>\n",
1517 |        "      <th>Three-Month Price Return</th>\n",
1518 |        "      <th>Three-Month Return Percentile</th>\n",
1519 |        "      <th>One-Month Price Return</th>\n",
1520 |        "      <th>One-Month Return Percentile</th>\n",
1521 |        "      <th>HQM Score</th>\n",
1522 |        "    </tr>\n",
1523 |        "  </thead>\n",
1524 |        "  <tbody>\n",
1525 |        "    <tr>\n",
1526 |        "      <th>0</th>\n",
1527 |        "      <td>A</td>\n",
1528 |        "      <td>98.19</td>\n",
1529 |        "      <td>N/A</td>\n",
1530 |        "      <td>0.444090</td>\n",
1531 |        "      <td>0.885149</td>\n",
1532 |        "      <td>0.147456</td>\n",
1533 |        "      <td>0.837624</td>\n",
1534 |        "      <td>0.221461</td>\n",
1535 |        "      <td>0.473267</td>\n",
1536 |        "      <td>0.093820</td>\n",
1537 |        "      <td>0.530693</td>\n",
1538 |        "      <td>N/A</td>\n",
1539 |        "    </tr>\n",
1540 |        "    <tr>\n",
1541 |        "      <th>1</th>\n",
1542 |        "      <td>AAL</td>\n",
1543 |        "      <td>13.89</td>\n",
1544 |        "      <td>N/A</td>\n",
1545 |        "      <td>-0.526494</td>\n",
1546 |        "      <td>0.0237624</td>\n",
1547 |        "      <td>-0.564540</td>\n",
1548 |        "      <td>0.0158416</td>\n",
1549 |        "      <td>0.509431</td>\n",
1550 |        "      <td>0.908911</td>\n",
1551 |        "      <td>0.172430</td>\n",
1552 |        "      <td>0.827723</td>\n",
1553 |        "      <td>N/A</td>\n",
1554 |        "    </tr>\n",
1555 |        "    <tr>\n",
1556 |        "      <th>2</th>\n",
1557 |        "      <td>AAP</td>\n",
1558 |        "      <td>161.13</td>\n",
1559 |        "      <td>N/A</td>\n",
1560 |        "      <td>0.088066</td>\n",
1561 |        "      <td>0.578218</td>\n",
1562 |        "      <td>0.148378</td>\n",
1563 |        "      <td>0.839604</td>\n",
1564 |        "      <td>0.295700</td>\n",
1565 |        "      <td>0.643564</td>\n",
1566 |        "      <td>0.144608</td>\n",
1567 |        "      <td>0.742574</td>\n",
1568 |        "      <td>N/A</td>\n",
1569 |        "    </tr>\n",
1570 |        "    <tr>\n",
1571 |        "      <th>3</th>\n",
1572 |        "      <td>AAPL</td>\n",
1573 |        "      <td>467.65</td>\n",
1574 |        "      <td>N/A</td>\n",
1575 |        "      <td>1.171724</td>\n",
1576 |        "      <td>0.992079</td>\n",
1577 |        "      <td>0.401695</td>\n",
1578 |        "      <td>0.968317</td>\n",
1579 |        "      <td>0.474900</td>\n",
1580 |        "      <td>0.887129</td>\n",
1581 |        "      <td>0.189840</td>\n",
1582 |        "      <td>0.879208</td>\n",
1583 |        "      <td>N/A</td>\n",
1584 |        "    </tr>\n",
1585 |        "    <tr>\n",
1586 |        "      <th>4</th>\n",
1587 |        "      <td>ABBV</td>\n",
1588 |        "      <td>97.29</td>\n",
1589 |        "      <td>N/A</td>\n",
1590 |        "      <td>0.478770</td>\n",
1591 |        "      <td>0.89703</td>\n",
1592 |        "      <td>0.001711</td>\n",
1593 |        "      <td>0.629703</td>\n",
1594 |        "      <td>0.077880</td>\n",
1595 |        "      <td>0.19802</td>\n",
1596 |        "      <td>-0.024533</td>\n",
1597 |        "      <td>0.0693069</td>\n",
1598 |        "      <td>N/A</td>\n",
1599 |        "    </tr>\n",
1600 |        "    <tr>\n",
1601 |        "      <th>...</th>\n",
1602 |        "      <td>...</td>\n",
1603 |        "      <td>...</td>\n",
1604 |        "      <td>...</td>\n",
1605 |        "      <td>...</td>\n",
1606 |        "      <td>...</td>\n",
1607 |        "      <td>...</td>\n",
1608 |        "      <td>...</td>\n",
1609 |        "      <td>...</td>\n",
1610 |        "      <td>...</td>\n",
1611 |        "      <td>...</td>\n",
1612 |        "      <td>...</td>\n",
1613 |        "      <td>...</td>\n",
1614 |        "    </tr>\n",
1615 |        "    <tr>\n",
1616 |        "      <th>500</th>\n",
1617 |        "      <td>YUM</td>\n",
1618 |        "      <td>93.51</td>\n",
1619 |        "      <td>N/A</td>\n",
1620 |        "      <td>-0.214524</td>\n",
1621 |        "      <td>0.211881</td>\n",
1622 |        "      <td>-0.116757</td>\n",
1623 |        "      <td>0.405941</td>\n",
1624 |        "      <td>0.161550</td>\n",
1625 |        "      <td>0.374257</td>\n",
1626 |        "      <td>0.068180</td>\n",
1627 |        "      <td>0.370297</td>\n",
1628 |        "      <td>N/A</td>\n",
1629 |        "    </tr>\n",
1630 |        "    <tr>\n",
1631 |        "      <th>501</th>\n",
1632 |        "      <td>ZBH</td>\n",
1633 |        "      <td>140.16</td>\n",
1634 |        "      <td>N/A</td>\n",
1635 |        "      <td>0.003007</td>\n",
1636 |        "      <td>0.457426</td>\n",
1637 |        "      <td>-0.127491</td>\n",
1638 |        "      <td>0.39604</td>\n",
1639 |        "      <td>0.250906</td>\n",
1640 |        "      <td>0.544554</td>\n",
1641 |        "      <td>0.151414</td>\n",
1642 |        "      <td>0.762376</td>\n",
1643 |        "      <td>N/A</td>\n",
1644 |        "    </tr>\n",
1645 |        "    <tr>\n",
1646 |        "      <th>502</th>\n",
1647 |        "      <td>ZBRA</td>\n",
1648 |        "      <td>285.97</td>\n",
1649 |        "      <td>N/A</td>\n",
1650 |        "      <td>0.373952</td>\n",
1651 |        "      <td>0.843564</td>\n",
1652 |        "      <td>0.223856</td>\n",
1653 |        "      <td>0.906931</td>\n",
1654 |        "      <td>0.283668</td>\n",
1655 |        "      <td>0.611881</td>\n",
1656 |        "      <td>0.115379</td>\n",
1657 |        "      <td>0.641584</td>\n",
1658 |        "      <td>N/A</td>\n",
1659 |        "    </tr>\n",
1660 |        "    <tr>\n",
1661 |        "      <th>503</th>\n",
1662 |        "      <td>ZION</td>\n",
1663 |        "      <td>34.95</td>\n",
1664 |        "      <td>N/A</td>\n",
1665 |        "      <td>-0.161814</td>\n",
1666 |        "      <td>0.255446</td>\n",
1667 |        "      <td>-0.255398</td>\n",
1668 |        "      <td>0.227723</td>\n",
1669 |        "      <td>0.328190</td>\n",
1670 |        "      <td>0.70297</td>\n",
1671 |        "      <td>0.055265</td>\n",
1672 |        "      <td>0.312871</td>\n",
1673 |        "      <td>N/A</td>\n",
1674 |        "    </tr>\n",
1675 |        "    <tr>\n",
1676 |        "      <th>504</th>\n",
1677 |        "      <td>ZTS</td>\n",
1678 |        "      <td>163.66</td>\n",
1679 |        "      <td>N/A</td>\n",
1680 |        "      <td>0.290969</td>\n",
1681 |        "      <td>0.772277</td>\n",
1682 |        "      <td>0.102747</td>\n",
1683 |        "      <td>0.776238</td>\n",
1684 |        "      <td>0.307237</td>\n",
1685 |        "      <td>0.665347</td>\n",
1686 |        "      <td>0.157549</td>\n",
1687 |        "      <td>0.792079</td>\n",
1688 |        "      <td>N/A</td>\n",
1689 |        "    </tr>\n",
1690 |        "  </tbody>\n",
1691 |        "</table>\n",
1692 |        "<p>505 rows × 12 columns</p>\n",
1693 |        "</div>"
1694 |       ],
1695 |       "text/plain": [
1696 |        "    Ticker   Price Number of Shares to Buy  One-Year Price Return  \\\n",
1697 |        "0        A   98.19                     N/A               0.444090   \n",
1698 |        "1      AAL   13.89                     N/A              -0.526494   \n",
1699 |        "2      AAP  161.13                     N/A               0.088066   \n",
1700 |        "3     AAPL  467.65                     N/A               1.171724   \n",
1701 |        "4     ABBV   97.29                     N/A               0.478770   \n",
1702 |        "..     ...     ...                     ...                    ...   \n",
1703 |        "500    YUM   93.51                     N/A              -0.214524   \n",
1704 |        "501    ZBH  140.16                     N/A               0.003007   \n",
1705 |        "502   ZBRA  285.97                     N/A               0.373952   \n",
1706 |        "503   ZION   34.95                     N/A              -0.161814   \n",
1707 |        "504    ZTS  163.66                     N/A               0.290969   \n",
1708 |        "\n",
1709 |        "    One-Year Return Percentile  Six-Month Price Return  \\\n",
1710 |        "0                     0.885149                0.147456   \n",
1711 |        "1                    0.0237624               -0.564540   \n",
1712 |        "2                     0.578218                0.148378   \n",
1713 |        "3                     0.992079                0.401695   \n",
1714 |        "4                      0.89703                0.001711   \n",
1715 |        "..                         ...                     ...   \n",
1716 |        "500                   0.211881               -0.116757   \n",
1717 |        "501                   0.457426               -0.127491   \n",
1718 |        "502                   0.843564                0.223856   \n",
1719 |        "503                   0.255446               -0.255398   \n",
1720 |        "504                   0.772277                0.102747   \n",
1721 |        "\n",
1722 |        "    Six-Month Return Percentile  Three-Month Price Return  \\\n",
1723 |        "0                      0.837624                  0.221461   \n",
1724 |        "1                     0.0158416                  0.509431   \n",
1725 |        "2                      0.839604                  0.295700   \n",
1726 |        "3                      0.968317                  0.474900   \n",
1727 |        "4                      0.629703                  0.077880   \n",
1728 |        "..                          ...                       ...   \n",
1729 |        "500                    0.405941                  0.161550   \n",
1730 |        "501                     0.39604                  0.250906   \n",
1731 |        "502                    0.906931                  0.283668   \n",
1732 |        "503                    0.227723                  0.328190   \n",
1733 |        "504                    0.776238                  0.307237   \n",
1734 |        "\n",
1735 |        "    Three-Month Return Percentile  One-Month Price Return  \\\n",
1736 |        "0                        0.473267                0.093820   \n",
1737 |        "1                        0.908911                0.172430   \n",
1738 |        "2                        0.643564                0.144608   \n",
1739 |        "3                        0.887129                0.189840   \n",
1740 |        "4                         0.19802               -0.024533   \n",
1741 |        "..                            ...                     ...   \n",
1742 |        "500                      0.374257                0.068180   \n",
1743 |        "501                      0.544554                0.151414   \n",
1744 |        "502                      0.611881                0.115379   \n",
1745 |        "503                       0.70297                0.055265   \n",
1746 |        "504                      0.665347                0.157549   \n",
1747 |        "\n",
1748 |        "    One-Month Return Percentile HQM Score  \n",
1749 |        "0                      0.530693       N/A  \n",
1750 |        "1                      0.827723       N/A  \n",
1751 |        "2                      0.742574       N/A  \n",
1752 |        "3                      0.879208       N/A  \n",
1753 |        "4                     0.0693069       N/A  \n",
1754 |        "..                          ...       ...  \n",
1755 |        "500                    0.370297       N/A  \n",
1756 |        "501                    0.762376       N/A  \n",
1757 |        "502                    0.641584       N/A  \n",
1758 |        "503                    0.312871       N/A  \n",
1759 |        "504                    0.792079       N/A  \n",
1760 |        "\n",
1761 |        "[505 rows x 12 columns]"
1762 |       ]
1763 |      },
1764 |      "execution_count": 11,
1765 |      "metadata": {},
1766 |      "output_type": "execute_result"
1767 |     }
1768 |    ],
1769 |    "source": [
1770 |     "time_periods = [\n",
1771 |     "                'One-Year',\n",
1772 |     "                'Six-Month',\n",
1773 |     "                'Three-Month',\n",
1774 |     "                'One-Month'\n",
1775 |     "                ]\n",
1776 |     "\n",
1777 |     "for row in hqm_dataframe.index:\n",
1778 |     "    for time_period in time_periods:\n",
1779 |     "        hqm_dataframe.loc[row, f'{time_period} Return Percentile'] = stats.percentileofscore(hqm_dataframe[f'{time_period} Price Return'], hqm_dataframe.loc[row, f'{time_period} Price Return'])/100\n",
1780 |     "\n",
1781 |     "# Print each percentile score to make sure it was calculated properly\n",
1782 |     "for time_period in time_periods:\n",
1783 |     "    print(hqm_dataframe[f'{time_period} Return Percentile'])\n",
1784 |     "\n",
1785 |     "#Print the entire DataFrame    \n",
1786 |     "hqm_dataframe"
1787 |    ]
1788 |   },
1789 |   {
1790 |    "cell_type": "markdown",
1791 |    "metadata": {},
1792 |    "source": [
1793 |     "## Calculating the HQM Score\n",
1794 |     "\n",
1795 |     "We'll now calculate our `HQM Score`, which is the high-quality momentum score that we'll use to filter for stocks in this investing strategy.\n",
1796 |     "\n",
1797 |     "The `HQM Score` will be the arithmetic mean of the 4 momentum percentile scores that we calculated in the last section.\n",
1798 |     "\n",
1799 |     "To calculate arithmetic mean, we will use the `mean` function from Python's built-in `statistics` module."
1800 |    ]
1801 |   },
1802 |   {
1803 |    "cell_type": "code",
1804 |    "execution_count": 12,
1805 |    "metadata": {},
1806 |    "outputs": [],
1807 |    "source": [
1808 |     "from statistics import mean\n",
1809 |     "\n",
1810 |     "for row in hqm_dataframe.index:\n",
1811 |     "    momentum_percentiles = []\n",
1812 |     "    for time_period in time_periods:\n",
1813 |     "        momentum_percentiles.append(hqm_dataframe.loc[row, f'{time_period} Return Percentile'])\n",
1814 |     "    hqm_dataframe.loc[row, 'HQM Score'] = mean(momentum_percentiles)"
1815 |    ]
1816 |   },
1817 |   {
1818 |    "cell_type": "markdown",
1819 |    "metadata": {},
1820 |    "source": [
1821 |     "## Selecting the 50 Best Momentum Stocks\n",
1822 |     "\n",
1823 |     "As before, we can identify the 50 best momentum stocks in our universe by sorting the DataFrame on the `HQM Score` column and dropping all but the top 50 entries."
1824 |    ]
1825 |   },
1826 |   {
1827 |    "cell_type": "code",
1828 |    "execution_count": 13,
1829 |    "metadata": {},
1830 |    "outputs": [],
1831 |    "source": [
1832 |     "hqm_dataframe.sort_values(by = 'HQM Score', ascending = False)\n",
1833 |     "hqm_dataframe = hqm_dataframe[:51]"
1834 |    ]
1835 |   },
1836 |   {
1837 |    "cell_type": "markdown",
1838 |    "metadata": {},
1839 |    "source": [
1840 |     "## Calculating the Number of Shares to Buy\n",
1841 |     "\n",
1842 |     "We'll use the `portfolio_input` function that we created earlier to accept our portfolio size. Then we will use similar logic in a `for` loop to calculate the number of shares to buy for each stock in our investment universe."
1843 |    ]
1844 |   },
1845 |   {
1846 |    "cell_type": "code",
1847 |    "execution_count": 14,
1848 |    "metadata": {},
1849 |    "outputs": [
1850 |     {
1851 |      "name": "stdout",
1852 |      "output_type": "stream",
1853 |      "text": [
1854 |       "Enter the value of your portfolio:1000000\n"
1855 |      ]
1856 |     }
1857 |    ],
1858 |    "source": [
1859 |     "portfolio_input()"
1860 |    ]
1861 |   },
1862 |   {
1863 |    "cell_type": "code",
1864 |    "execution_count": 15,
1865 |    "metadata": {},
1866 |    "outputs": [
1867 |     {
1868 |      "data": {
1869 |       "text/html": [
1870 |        "<div>\n",
1871 |        "<style scoped>\n",
1872 |        "    .dataframe tbody tr th:only-of-type {\n",
1873 |        "        vertical-align: middle;\n",
1874 |        "    }\n",
1875 |        "\n",
1876 |        "    .dataframe tbody tr th {\n",
1877 |        "        vertical-align: top;\n",
1878 |        "    }\n",
1879 |        "\n",
1880 |        "    .dataframe thead th {\n",
1881 |        "        text-align: right;\n",
1882 |        "    }\n",
1883 |        "</style>\n",
1884 |        "<table border=\"1\" class=\"dataframe\">\n",
1885 |        "  <thead>\n",
1886 |        "    <tr style=\"text-align: right;\">\n",
1887 |        "      <th></th>\n",
1888 |        "      <th>Ticker</th>\n",
1889 |        "      <th>Price</th>\n",
1890 |        "      <th>Number of Shares to Buy</th>\n",
1891 |        "      <th>One-Year Price Return</th>\n",
1892 |        "      <th>One-Year Return Percentile</th>\n",
1893 |        "      <th>Six-Month Price Return</th>\n",
1894 |        "      <th>Six-Month Return Percentile</th>\n",
1895 |        "      <th>Three-Month Price Return</th>\n",
1896 |        "      <th>Three-Month Return Percentile</th>\n",
1897 |        "      <th>One-Month Price Return</th>\n",
1898 |        "      <th>One-Month Return Percentile</th>\n",
1899 |        "      <th>HQM Score</th>\n",
1900 |        "    </tr>\n",
1901 |        "  </thead>\n",
1902 |        "  <tbody>\n",
1903 |        "    <tr>\n",
1904 |        "      <th>0</th>\n",
1905 |        "      <td>A</td>\n",
1906 |        "      <td>98.19</td>\n",
1907 |        "      <td>199</td>\n",
1908 |        "      <td>0.444090</td>\n",
1909 |        "      <td>0.885149</td>\n",
1910 |        "      <td>0.147456</td>\n",
1911 |        "      <td>0.837624</td>\n",
1912 |        "      <td>0.221461</td>\n",
1913 |        "      <td>0.473267</td>\n",
1914 |        "      <td>0.093820</td>\n",
1915 |        "      <td>0.530693</td>\n",
1916 |        "      <td>0.681683</td>\n",
1917 |        "    </tr>\n",
1918 |        "    <tr>\n",
1919 |        "      <th>1</th>\n",
1920 |        "      <td>AAL</td>\n",
1921 |        "      <td>13.89</td>\n",
1922 |        "      <td>1411</td>\n",
1923 |        "      <td>-0.526494</td>\n",
1924 |        "      <td>0.0237624</td>\n",
1925 |        "      <td>-0.564540</td>\n",
1926 |        "      <td>0.0158416</td>\n",
1927 |        "      <td>0.509431</td>\n",
1928 |        "      <td>0.908911</td>\n",
1929 |        "      <td>0.172430</td>\n",
1930 |        "      <td>0.827723</td>\n",
1931 |        "      <td>0.444059</td>\n",
1932 |        "    </tr>\n",
1933 |        "    <tr>\n",
1934 |        "      <th>2</th>\n",
1935 |        "      <td>AAP</td>\n",
1936 |        "      <td>161.13</td>\n",
1937 |        "      <td>121</td>\n",
1938 |        "      <td>0.088066</td>\n",
1939 |        "      <td>0.578218</td>\n",
1940 |        "      <td>0.148378</td>\n",
1941 |        "      <td>0.839604</td>\n",
1942 |        "      <td>0.295700</td>\n",
1943 |        "      <td>0.643564</td>\n",
1944 |        "      <td>0.144608</td>\n",
1945 |        "      <td>0.742574</td>\n",
1946 |        "      <td>0.70099</td>\n",
1947 |        "    </tr>\n",
1948 |        "    <tr>\n",
1949 |        "      <th>3</th>\n",
1950 |        "      <td>AAPL</td>\n",
1951 |        "      <td>467.65</td>\n",
1952 |        "      <td>41</td>\n",
1953 |        "      <td>1.171724</td>\n",
1954 |        "      <td>0.992079</td>\n",
1955 |        "      <td>0.401695</td>\n",
1956 |        "      <td>0.968317</td>\n",
1957 |        "      <td>0.474900</td>\n",
1958 |        "      <td>0.887129</td>\n",
1959 |        "      <td>0.189840</td>\n",
1960 |        "      <td>0.879208</td>\n",
1961 |        "      <td>0.931683</td>\n",
1962 |        "    </tr>\n",
1963 |        "    <tr>\n",
1964 |        "      <th>4</th>\n",
1965 |        "      <td>ABBV</td>\n",
1966 |        "      <td>97.29</td>\n",
1967 |        "      <td>201</td>\n",
1968 |        "      <td>0.478770</td>\n",
1969 |        "      <td>0.89703</td>\n",
1970 |        "      <td>0.001711</td>\n",
1971 |        "      <td>0.629703</td>\n",
1972 |        "      <td>0.077880</td>\n",
1973 |        "      <td>0.19802</td>\n",
1974 |        "      <td>-0.024533</td>\n",
1975 |        "      <td>0.0693069</td>\n",
1976 |        "      <td>0.448515</td>\n",
1977 |        "    </tr>\n",
1978 |        "    <tr>\n",
1979 |        "      <th>5</th>\n",
1980 |        "      <td>ABC</td>\n",
1981 |        "      <td>104.97</td>\n",
1982 |        "      <td>186</td>\n",
1983 |        "      <td>0.163705</td>\n",
1984 |        "      <td>0.651485</td>\n",
1985 |        "      <td>0.100112</td>\n",
1986 |        "      <td>0.774257</td>\n",
1987 |        "      <td>0.241867</td>\n",
1988 |        "      <td>0.522772</td>\n",
1989 |        "      <td>0.066990</td>\n",
1990 |        "      <td>0.364356</td>\n",
1991 |        "      <td>0.578218</td>\n",
1992 |        "    </tr>\n",
1993 |        "    <tr>\n",
1994 |        "      <th>6</th>\n",
1995 |        "      <td>ABMD</td>\n",
1996 |        "      <td>305.78</td>\n",
1997 |        "      <td>64</td>\n",
1998 |        "      <td>0.546138</td>\n",
1999 |        "      <td>0.942574</td>\n",
2000 |        "      <td>0.874783</td>\n",
2001 |        "      <td>0.99802</td>\n",
2002 |        "      <td>0.645795</td>\n",
2003 |        "      <td>0.954455</td>\n",
2004 |        "      <td>0.145153</td>\n",
2005 |        "      <td>0.746535</td>\n",
2006 |        "      <td>0.910396</td>\n",
2007 |        "    </tr>\n",
2008 |        "    <tr>\n",
2009 |        "      <th>7</th>\n",
2010 |        "      <td>ABT</td>\n",
2011 |        "      <td>102.94</td>\n",
2012 |        "      <td>190</td>\n",
2013 |        "      <td>0.161073</td>\n",
2014 |        "      <td>0.647525</td>\n",
2015 |        "      <td>0.139130</td>\n",
2016 |        "      <td>0.823762</td>\n",
2017 |        "      <td>0.093917</td>\n",
2018 |        "      <td>0.233663</td>\n",
2019 |        "      <td>0.081815</td>\n",
2020 |        "      <td>0.457426</td>\n",
2021 |        "      <td>0.540594</td>\n",
2022 |        "    </tr>\n",
2023 |        "    <tr>\n",
2024 |        "      <th>8</th>\n",
2025 |        "      <td>ACN</td>\n",
2026 |        "      <td>237.88</td>\n",
2027 |        "      <td>82</td>\n",
2028 |        "      <td>0.197484</td>\n",
2029 |        "      <td>0.69505</td>\n",
2030 |        "      <td>0.085519</td>\n",
2031 |        "      <td>0.762376</td>\n",
2032 |        "      <td>0.279570</td>\n",
2033 |        "      <td>0.605941</td>\n",
2034 |        "      <td>0.067045</td>\n",
2035 |        "      <td>0.366337</td>\n",
2036 |        "      <td>0.607426</td>\n",
2037 |        "    </tr>\n",
2038 |        "    <tr>\n",
2039 |        "      <th>9</th>\n",
2040 |        "      <td>ADBE</td>\n",
2041 |        "      <td>453.91</td>\n",
2042 |        "      <td>43</td>\n",
2043 |        "      <td>0.532839</td>\n",
2044 |        "      <td>0.934653</td>\n",
2045 |        "      <td>0.196705</td>\n",
2046 |        "      <td>0.879208</td>\n",
2047 |        "      <td>0.252382</td>\n",
2048 |        "      <td>0.550495</td>\n",
2049 |        "      <td>0.006829</td>\n",
2050 |        "      <td>0.150495</td>\n",
2051 |        "      <td>0.628713</td>\n",
2052 |        "    </tr>\n",
2053 |        "    <tr>\n",
2054 |        "      <th>10</th>\n",
2055 |        "      <td>ADI</td>\n",
2056 |        "      <td>120.52</td>\n",
2057 |        "      <td>162</td>\n",
2058 |        "      <td>0.056525</td>\n",
2059 |        "      <td>0.522772</td>\n",
2060 |        "      <td>0.002920</td>\n",
2061 |        "      <td>0.631683</td>\n",
2062 |        "      <td>0.149173</td>\n",
2063 |        "      <td>0.360396</td>\n",
2064 |        "      <td>0.017244</td>\n",
2065 |        "      <td>0.174257</td>\n",
2066 |        "      <td>0.422277</td>\n",
2067 |        "    </tr>\n",
2068 |        "    <tr>\n",
2069 |        "      <th>11</th>\n",
2070 |        "      <td>ADM</td>\n",
2071 |        "      <td>46.62</td>\n",
2072 |        "      <td>420</td>\n",
2073 |        "      <td>0.178916</td>\n",
2074 |        "      <td>0.679208</td>\n",
2075 |        "      <td>-0.018869</td>\n",
2076 |        "      <td>0.580198</td>\n",
2077 |        "      <td>0.328413</td>\n",
2078 |        "      <td>0.70495</td>\n",
2079 |        "      <td>0.123837</td>\n",
2080 |        "      <td>0.687129</td>\n",
2081 |        "      <td>0.662871</td>\n",
2082 |        "    </tr>\n",
2083 |        "    <tr>\n",
2084 |        "      <th>12</th>\n",
2085 |        "      <td>ADP</td>\n",
2086 |        "      <td>139.82</td>\n",
2087 |        "      <td>140</td>\n",
2088 |        "      <td>-0.174896</td>\n",
2089 |        "      <td>0.243564</td>\n",
2090 |        "      <td>-0.227657</td>\n",
2091 |        "      <td>0.265347</td>\n",
2092 |        "      <td>0.037317</td>\n",
2093 |        "      <td>0.134653</td>\n",
2094 |        "      <td>-0.042030</td>\n",
2095 |        "      <td>0.0435644</td>\n",
2096 |        "      <td>0.171782</td>\n",
2097 |        "    </tr>\n",
2098 |        "    <tr>\n",
2099 |        "      <th>13</th>\n",
2100 |        "      <td>ADSK</td>\n",
2101 |        "      <td>234.20</td>\n",
2102 |        "      <td>83</td>\n",
2103 |        "      <td>0.528696</td>\n",
2104 |        "      <td>0.928713</td>\n",
2105 |        "      <td>0.116990</td>\n",
2106 |        "      <td>0.794059</td>\n",
2107 |        "      <td>0.312257</td>\n",
2108 |        "      <td>0.677228</td>\n",
2109 |        "      <td>-0.001259</td>\n",
2110 |        "      <td>0.122772</td>\n",
2111 |        "      <td>0.630693</td>\n",
2112 |        "    </tr>\n",
2113 |        "    <tr>\n",
2114 |        "      <th>14</th>\n",
2115 |        "      <td>AEE</td>\n",
2116 |        "      <td>85.23</td>\n",
2117 |        "      <td>230</td>\n",
2118 |        "      <td>0.073499</td>\n",
2119 |        "      <td>0.556436</td>\n",
2120 |        "      <td>-0.044706</td>\n",
2121 |        "      <td>0.538614</td>\n",
2122 |        "      <td>0.202938</td>\n",
2123 |        "      <td>0.447525</td>\n",
2124 |        "      <td>0.086675</td>\n",
2125 |        "      <td>0.483168</td>\n",
2126 |        "      <td>0.506436</td>\n",
2127 |        "    </tr>\n",
2128 |        "    <tr>\n",
2129 |        "      <th>15</th>\n",
2130 |        "      <td>AEP</td>\n",
2131 |        "      <td>87.88</td>\n",
2132 |        "      <td>223</td>\n",
2133 |        "      <td>-0.069760</td>\n",
2134 |        "      <td>0.368317</td>\n",
2135 |        "      <td>-0.186159</td>\n",
2136 |        "      <td>0.326733</td>\n",
2137 |        "      <td>0.084255</td>\n",
2138 |        "      <td>0.217822</td>\n",
2139 |        "      <td>-0.007456</td>\n",
2140 |        "      <td>0.106931</td>\n",
2141 |        "      <td>0.25495</td>\n",
2142 |        "    </tr>\n",
2143 |        "    <tr>\n",
2144 |        "      <th>16</th>\n",
2145 |        "      <td>AES</td>\n",
2146 |        "      <td>17.54</td>\n",
2147 |        "      <td>1117</td>\n",
2148 |        "      <td>0.141966</td>\n",
2149 |        "      <td>0.635644</td>\n",
2150 |        "      <td>-0.177281</td>\n",
2151 |        "      <td>0.336634</td>\n",
2152 |        "      <td>0.510037</td>\n",
2153 |        "      <td>0.910891</td>\n",
2154 |        "      <td>0.186968</td>\n",
2155 |        "      <td>0.873267</td>\n",
2156 |        "      <td>0.689109</td>\n",
2157 |        "    </tr>\n",
2158 |        "    <tr>\n",
2159 |        "      <th>17</th>\n",
2160 |        "      <td>AFL</td>\n",
2161 |        "      <td>39.27</td>\n",
2162 |        "      <td>499</td>\n",
2163 |        "      <td>-0.298263</td>\n",
2164 |        "      <td>0.116832</td>\n",
2165 |        "      <td>-0.283044</td>\n",
2166 |        "      <td>0.190099</td>\n",
2167 |        "      <td>0.178662</td>\n",
2168 |        "      <td>0.4</td>\n",
2169 |        "      <td>0.082055</td>\n",
2170 |        "      <td>0.459406</td>\n",
2171 |        "      <td>0.291584</td>\n",
2172 |        "    </tr>\n",
2173 |        "    <tr>\n",
2174 |        "      <th>18</th>\n",
2175 |        "      <td>AIG</td>\n",
2176 |        "      <td>31.76</td>\n",
2177 |        "      <td>617</td>\n",
2178 |        "      <td>-0.468456</td>\n",
2179 |        "      <td>0.0356436</td>\n",
2180 |        "      <td>-0.398200</td>\n",
2181 |        "      <td>0.0673267</td>\n",
2182 |        "      <td>0.242767</td>\n",
2183 |        "      <td>0.528713</td>\n",
2184 |        "      <td>0.042150</td>\n",
2185 |        "      <td>0.271287</td>\n",
2186 |        "      <td>0.225743</td>\n",
2187 |        "    </tr>\n",
2188 |        "    <tr>\n",
2189 |        "      <th>19</th>\n",
2190 |        "      <td>AIV</td>\n",
2191 |        "      <td>39.00</td>\n",
2192 |        "      <td>502</td>\n",
2193 |        "      <td>-0.260956</td>\n",
2194 |        "      <td>0.168317</td>\n",
2195 |        "      <td>-0.323003</td>\n",
2196 |        "      <td>0.142574</td>\n",
2197 |        "      <td>0.096556</td>\n",
2198 |        "      <td>0.243564</td>\n",
2199 |        "      <td>0.004109</td>\n",
2200 |        "      <td>0.140594</td>\n",
2201 |        "      <td>0.173762</td>\n",
2202 |        "    </tr>\n",
2203 |        "    <tr>\n",
2204 |        "      <th>20</th>\n",
2205 |        "      <td>AIZ</td>\n",
2206 |        "      <td>129.93</td>\n",
2207 |        "      <td>150</td>\n",
2208 |        "      <td>0.016575</td>\n",
2209 |        "      <td>0.475248</td>\n",
2210 |        "      <td>-0.116835</td>\n",
2211 |        "      <td>0.40396</td>\n",
2212 |        "      <td>0.418900</td>\n",
2213 |        "      <td>0.839604</td>\n",
2214 |        "      <td>0.254327</td>\n",
2215 |        "      <td>0.948515</td>\n",
2216 |        "      <td>0.666832</td>\n",
2217 |        "    </tr>\n",
2218 |        "    <tr>\n",
2219 |        "      <th>21</th>\n",
2220 |        "      <td>AJG</td>\n",
2221 |        "      <td>111.00</td>\n",
2222 |        "      <td>176</td>\n",
2223 |        "      <td>0.195680</td>\n",
2224 |        "      <td>0.689109</td>\n",
2225 |        "      <td>-0.011750</td>\n",
2226 |        "      <td>0.6</td>\n",
2227 |        "      <td>0.253483</td>\n",
2228 |        "      <td>0.554455</td>\n",
2229 |        "      <td>0.092902</td>\n",
2230 |        "      <td>0.520792</td>\n",
2231 |        "      <td>0.591089</td>\n",
2232 |        "    </tr>\n",
2233 |        "    <tr>\n",
2234 |        "      <th>22</th>\n",
2235 |        "      <td>AKAM</td>\n",
2236 |        "      <td>110.00</td>\n",
2237 |        "      <td>178</td>\n",
2238 |        "      <td>0.206310</td>\n",
2239 |        "      <td>0.70495</td>\n",
2240 |        "      <td>0.075705</td>\n",
2241 |        "      <td>0.752475</td>\n",
2242 |        "      <td>0.125834</td>\n",
2243 |        "      <td>0.312871</td>\n",
2244 |        "      <td>-0.027015</td>\n",
2245 |        "      <td>0.0594059</td>\n",
2246 |        "      <td>0.457426</td>\n",
2247 |        "    </tr>\n",
2248 |        "    <tr>\n",
2249 |        "      <th>23</th>\n",
2250 |        "      <td>ALB</td>\n",
2251 |        "      <td>94.85</td>\n",
2252 |        "      <td>206</td>\n",
2253 |        "      <td>0.339750</td>\n",
2254 |        "      <td>0.823762</td>\n",
2255 |        "      <td>0.036265</td>\n",
2256 |        "      <td>0.691089</td>\n",
2257 |        "      <td>0.528245</td>\n",
2258 |        "      <td>0.918812</td>\n",
2259 |        "      <td>0.120477</td>\n",
2260 |        "      <td>0.673267</td>\n",
2261 |        "      <td>0.776733</td>\n",
2262 |        "    </tr>\n",
2263 |        "    <tr>\n",
2264 |        "      <th>24</th>\n",
2265 |        "      <td>ALGN</td>\n",
2266 |        "      <td>309.25</td>\n",
2267 |        "      <td>63</td>\n",
2268 |        "      <td>0.692338</td>\n",
2269 |        "      <td>0.970297</td>\n",
2270 |        "      <td>0.131757</td>\n",
2271 |        "      <td>0.815842</td>\n",
2272 |        "      <td>0.554489</td>\n",
2273 |        "      <td>0.928713</td>\n",
2274 |        "      <td>0.143933</td>\n",
2275 |        "      <td>0.738614</td>\n",
2276 |        "      <td>0.863366</td>\n",
2277 |        "    </tr>\n",
2278 |        "    <tr>\n",
2279 |        "      <th>25</th>\n",
2280 |        "      <td>ALK</td>\n",
2281 |        "      <td>38.63</td>\n",
2282 |        "      <td>507</td>\n",
2283 |        "      <td>-0.387440</td>\n",
2284 |        "      <td>0.0633663</td>\n",
2285 |        "      <td>-0.438559</td>\n",
2286 |        "      <td>0.0534653</td>\n",
2287 |        "      <td>0.519770</td>\n",
2288 |        "      <td>0.916832</td>\n",
2289 |        "      <td>0.103833</td>\n",
2290 |        "      <td>0.576238</td>\n",
2291 |        "      <td>0.402475</td>\n",
2292 |        "    </tr>\n",
2293 |        "    <tr>\n",
2294 |        "      <th>26</th>\n",
2295 |        "      <td>ALL</td>\n",
2296 |        "      <td>99.53</td>\n",
2297 |        "      <td>197</td>\n",
2298 |        "      <td>-0.079870</td>\n",
2299 |        "      <td>0.354455</td>\n",
2300 |        "      <td>-0.230788</td>\n",
2301 |        "      <td>0.261386</td>\n",
2302 |        "      <td>0.045750</td>\n",
2303 |        "      <td>0.146535</td>\n",
2304 |        "      <td>0.088925</td>\n",
2305 |        "      <td>0.49703</td>\n",
2306 |        "      <td>0.314851</td>\n",
2307 |        "    </tr>\n",
2308 |        "    <tr>\n",
2309 |        "      <th>27</th>\n",
2310 |        "      <td>ALLE</td>\n",
2311 |        "      <td>105.13</td>\n",
2312 |        "      <td>186</td>\n",
2313 |        "      <td>0.071934</td>\n",
2314 |        "      <td>0.542574</td>\n",
2315 |        "      <td>-0.246470</td>\n",
2316 |        "      <td>0.241584</td>\n",
2317 |        "      <td>0.112362</td>\n",
2318 |        "      <td>0.277228</td>\n",
2319 |        "      <td>0.024073</td>\n",
2320 |        "      <td>0.20396</td>\n",
2321 |        "      <td>0.316337</td>\n",
2322 |        "    </tr>\n",
2323 |        "    <tr>\n",
2324 |        "      <th>28</th>\n",
2325 |        "      <td>ALXN</td>\n",
2326 |        "      <td>107.60</td>\n",
2327 |        "      <td>182</td>\n",
2328 |        "      <td>-0.079570</td>\n",
2329 |        "      <td>0.356436</td>\n",
2330 |        "      <td>-0.006381</td>\n",
2331 |        "      <td>0.60396</td>\n",
2332 |        "      <td>0.003280</td>\n",
2333 |        "      <td>0.0792079</td>\n",
2334 |        "      <td>-0.040910</td>\n",
2335 |        "      <td>0.0455446</td>\n",
2336 |        "      <td>0.271287</td>\n",
2337 |        "    </tr>\n",
2338 |        "    <tr>\n",
2339 |        "      <th>29</th>\n",
2340 |        "      <td>AMAT</td>\n",
2341 |        "      <td>68.10</td>\n",
2342 |        "      <td>287</td>\n",
2343 |        "      <td>0.389577</td>\n",
2344 |        "      <td>0.855446</td>\n",
2345 |        "      <td>-0.013556</td>\n",
2346 |        "      <td>0.594059</td>\n",
2347 |        "      <td>0.301391</td>\n",
2348 |        "      <td>0.651485</td>\n",
2349 |        "      <td>0.082413</td>\n",
2350 |        "      <td>0.461386</td>\n",
2351 |        "      <td>0.640594</td>\n",
2352 |        "    </tr>\n",
2353 |        "    <tr>\n",
2354 |        "      <th>30</th>\n",
2355 |        "      <td>AMCR</td>\n",
2356 |        "      <td>11.52</td>\n",
2357 |        "      <td>1702</td>\n",
2358 |        "      <td>0.073093</td>\n",
2359 |        "      <td>0.552475</td>\n",
2360 |        "      <td>0.105582</td>\n",
2361 |        "      <td>0.778218</td>\n",
2362 |        "      <td>0.238793</td>\n",
2363 |        "      <td>0.510891</td>\n",
2364 |        "      <td>0.077579</td>\n",
2365 |        "      <td>0.433663</td>\n",
2366 |        "      <td>0.568812</td>\n",
2367 |        "    </tr>\n",
2368 |        "    <tr>\n",
2369 |        "      <th>31</th>\n",
2370 |        "      <td>AMD</td>\n",
2371 |        "      <td>85.23</td>\n",
2372 |        "      <td>230</td>\n",
2373 |        "      <td>1.590065</td>\n",
2374 |        "      <td>0.99604</td>\n",
2375 |        "      <td>0.532176</td>\n",
2376 |        "      <td>0.984158</td>\n",
2377 |        "      <td>0.597011</td>\n",
2378 |        "      <td>0.948515</td>\n",
2379 |        "      <td>0.560304</td>\n",
2380 |        "      <td>0.99802</td>\n",
2381 |        "      <td>0.981683</td>\n",
2382 |        "    </tr>\n",
2383 |        "    <tr>\n",
2384 |        "      <th>32</th>\n",
2385 |        "      <td>AME</td>\n",
2386 |        "      <td>104.38</td>\n",
2387 |        "      <td>187</td>\n",
2388 |        "      <td>0.177719</td>\n",
2389 |        "      <td>0.677228</td>\n",
2390 |        "      <td>-0.002080</td>\n",
2391 |        "      <td>0.615842</td>\n",
2392 |        "      <td>0.305470</td>\n",
2393 |        "      <td>0.659406</td>\n",
2394 |        "      <td>0.147190</td>\n",
2395 |        "      <td>0.752475</td>\n",
2396 |        "      <td>0.676238</td>\n",
2397 |        "    </tr>\n",
2398 |        "    <tr>\n",
2399 |        "      <th>33</th>\n",
2400 |        "      <td>AMGN</td>\n",
2401 |        "      <td>253.49</td>\n",
2402 |        "      <td>77</td>\n",
2403 |        "      <td>0.176726</td>\n",
2404 |        "      <td>0.671287</td>\n",
2405 |        "      <td>0.086140</td>\n",
2406 |        "      <td>0.764356</td>\n",
2407 |        "      <td>0.020895</td>\n",
2408 |        "      <td>0.112871</td>\n",
2409 |        "      <td>-0.034769</td>\n",
2410 |        "      <td>0.0534653</td>\n",
2411 |        "      <td>0.400495</td>\n",
2412 |        "    </tr>\n",
2413 |        "    <tr>\n",
2414 |        "      <th>34</th>\n",
2415 |        "      <td>AMP</td>\n",
2416 |        "      <td>166.96</td>\n",
2417 |        "      <td>117</td>\n",
2418 |        "      <td>0.254633</td>\n",
2419 |        "      <td>0.736634</td>\n",
2420 |        "      <td>-0.101617</td>\n",
2421 |        "      <td>0.429703</td>\n",
2422 |        "      <td>0.385995</td>\n",
2423 |        "      <td>0.80198</td>\n",
2424 |        "      <td>0.089628</td>\n",
2425 |        "      <td>0.50099</td>\n",
2426 |        "      <td>0.617327</td>\n",
2427 |        "    </tr>\n",
2428 |        "    <tr>\n",
2429 |        "      <th>35</th>\n",
2430 |        "      <td>AMT</td>\n",
2431 |        "      <td>252.70</td>\n",
2432 |        "      <td>77</td>\n",
2433 |        "      <td>0.137500</td>\n",
2434 |        "      <td>0.631683</td>\n",
2435 |        "      <td>-0.022213</td>\n",
2436 |        "      <td>0.574257</td>\n",
2437 |        "      <td>0.091099</td>\n",
2438 |        "      <td>0.231683</td>\n",
2439 |        "      <td>-0.018941</td>\n",
2440 |        "      <td>0.0871287</td>\n",
2441 |        "      <td>0.381188</td>\n",
2442 |        "    </tr>\n",
2443 |        "    <tr>\n",
2444 |        "      <th>36</th>\n",
2445 |        "      <td>AMZN</td>\n",
2446 |        "      <td>3267.82</td>\n",
2447 |        "      <td>6</td>\n",
2448 |        "      <td>0.753428</td>\n",
2449 |        "      <td>0.978218</td>\n",
2450 |        "      <td>0.471720</td>\n",
2451 |        "      <td>0.974257</td>\n",
2452 |        "      <td>0.339324</td>\n",
2453 |        "      <td>0.728713</td>\n",
2454 |        "      <td>0.018781</td>\n",
2455 |        "      <td>0.178218</td>\n",
2456 |        "      <td>0.714851</td>\n",
2457 |        "    </tr>\n",
2458 |        "    <tr>\n",
2459 |        "      <th>37</th>\n",
2460 |        "      <td>ANET</td>\n",
2461 |        "      <td>225.98</td>\n",
2462 |        "      <td>86</td>\n",
2463 |        "      <td>-0.065031</td>\n",
2464 |        "      <td>0.374257</td>\n",
2465 |        "      <td>-0.093866</td>\n",
2466 |        "      <td>0.449505</td>\n",
2467 |        "      <td>0.011798</td>\n",
2468 |        "      <td>0.0910891</td>\n",
2469 |        "      <td>0.024541</td>\n",
2470 |        "      <td>0.207921</td>\n",
2471 |        "      <td>0.280693</td>\n",
2472 |        "    </tr>\n",
2473 |        "    <tr>\n",
2474 |        "      <th>38</th>\n",
2475 |        "      <td>ANSS</td>\n",
2476 |        "      <td>321.00</td>\n",
2477 |        "      <td>61</td>\n",
2478 |        "      <td>0.451591</td>\n",
2479 |        "      <td>0.887129</td>\n",
2480 |        "      <td>0.058949</td>\n",
2481 |        "      <td>0.722772</td>\n",
2482 |        "      <td>0.229415</td>\n",
2483 |        "      <td>0.487129</td>\n",
2484 |        "      <td>0.044242</td>\n",
2485 |        "      <td>0.283168</td>\n",
2486 |        "      <td>0.59505</td>\n",
2487 |        "    </tr>\n",
2488 |        "    <tr>\n",
2489 |        "      <th>39</th>\n",
2490 |        "      <td>ANTM</td>\n",
2491 |        "      <td>293.84</td>\n",
2492 |        "      <td>66</td>\n",
2493 |        "      <td>-0.013031</td>\n",
2494 |        "      <td>0.439604</td>\n",
2495 |        "      <td>-0.053364</td>\n",
2496 |        "      <td>0.514851</td>\n",
2497 |        "      <td>0.058803</td>\n",
2498 |        "      <td>0.164356</td>\n",
2499 |        "      <td>0.104273</td>\n",
2500 |        "      <td>0.582178</td>\n",
2501 |        "      <td>0.425248</td>\n",
2502 |        "    </tr>\n",
2503 |        "    <tr>\n",
2504 |        "      <th>40</th>\n",
2505 |        "      <td>AON</td>\n",
2506 |        "      <td>196.84</td>\n",
2507 |        "      <td>99</td>\n",
2508 |        "      <td>0.006102</td>\n",
2509 |        "      <td>0.463366</td>\n",
2510 |        "      <td>-0.183042</td>\n",
2511 |        "      <td>0.332673</td>\n",
2512 |        "      <td>0.010224</td>\n",
2513 |        "      <td>0.0891089</td>\n",
2514 |        "      <td>-0.026247</td>\n",
2515 |        "      <td>0.0633663</td>\n",
2516 |        "      <td>0.237129</td>\n",
2517 |        "    </tr>\n",
2518 |        "    <tr>\n",
2519 |        "      <th>41</th>\n",
2520 |        "      <td>AOS</td>\n",
2521 |        "      <td>50.31</td>\n",
2522 |        "      <td>389</td>\n",
2523 |        "      <td>0.101690</td>\n",
2524 |        "      <td>0.59802</td>\n",
2525 |        "      <td>0.141115</td>\n",
2526 |        "      <td>0.825743</td>\n",
2527 |        "      <td>0.260257</td>\n",
2528 |        "      <td>0.564356</td>\n",
2529 |        "      <td>0.032405</td>\n",
2530 |        "      <td>0.231683</td>\n",
2531 |        "      <td>0.55495</td>\n",
2532 |        "    </tr>\n",
2533 |        "    <tr>\n",
2534 |        "      <th>42</th>\n",
2535 |        "      <td>APA</td>\n",
2536 |        "      <td>16.40</td>\n",
2537 |        "      <td>1195</td>\n",
2538 |        "      <td>-0.291451</td>\n",
2539 |        "      <td>0.124752</td>\n",
2540 |        "      <td>-0.441406</td>\n",
2541 |        "      <td>0.0514851</td>\n",
2542 |        "      <td>0.485613</td>\n",
2543 |        "      <td>0.893069</td>\n",
2544 |        "      <td>0.260429</td>\n",
2545 |        "      <td>0.952475</td>\n",
2546 |        "      <td>0.505446</td>\n",
2547 |        "    </tr>\n",
2548 |        "    <tr>\n",
2549 |        "      <th>43</th>\n",
2550 |        "      <td>APD</td>\n",
2551 |        "      <td>295.10</td>\n",
2552 |        "      <td>66</td>\n",
2553 |        "      <td>0.242925</td>\n",
2554 |        "      <td>0.728713</td>\n",
2555 |        "      <td>0.124499</td>\n",
2556 |        "      <td>0.807921</td>\n",
2557 |        "      <td>0.273219</td>\n",
2558 |        "      <td>0.592079</td>\n",
2559 |        "      <td>0.040389</td>\n",
2560 |        "      <td>0.265347</td>\n",
2561 |        "      <td>0.598515</td>\n",
2562 |        "    </tr>\n",
2563 |        "    <tr>\n",
2564 |        "      <th>44</th>\n",
2565 |        "      <td>APH</td>\n",
2566 |        "      <td>110.83</td>\n",
2567 |        "      <td>176</td>\n",
2568 |        "      <td>0.255236</td>\n",
2569 |        "      <td>0.738614</td>\n",
2570 |        "      <td>0.053364</td>\n",
2571 |        "      <td>0.714851</td>\n",
2572 |        "      <td>0.342563</td>\n",
2573 |        "      <td>0.740594</td>\n",
2574 |        "      <td>0.168114</td>\n",
2575 |        "      <td>0.819802</td>\n",
2576 |        "      <td>0.753465</td>\n",
2577 |        "    </tr>\n",
2578 |        "    <tr>\n",
2579 |        "      <th>45</th>\n",
2580 |        "      <td>APTV</td>\n",
2581 |        "      <td>91.40</td>\n",
2582 |        "      <td>214</td>\n",
2583 |        "      <td>0.072118</td>\n",
2584 |        "      <td>0.544554</td>\n",
2585 |        "      <td>-0.011731</td>\n",
2586 |        "      <td>0.60198</td>\n",
2587 |        "      <td>0.490744</td>\n",
2588 |        "      <td>0.89505</td>\n",
2589 |        "      <td>0.172564</td>\n",
2590 |        "      <td>0.829703</td>\n",
2591 |        "      <td>0.717822</td>\n",
2592 |        "    </tr>\n",
2593 |        "    <tr>\n",
2594 |        "      <th>46</th>\n",
2595 |        "      <td>ARE</td>\n",
2596 |        "      <td>174.20</td>\n",
2597 |        "      <td>112</td>\n",
2598 |        "      <td>0.208731</td>\n",
2599 |        "      <td>0.706931</td>\n",
2600 |        "      <td>-0.001613</td>\n",
2601 |        "      <td>0.619802</td>\n",
2602 |        "      <td>0.214849</td>\n",
2603 |        "      <td>0.465347</td>\n",
2604 |        "      <td>0.078630</td>\n",
2605 |        "      <td>0.441584</td>\n",
2606 |        "      <td>0.558416</td>\n",
2607 |        "    </tr>\n",
2608 |        "    <tr>\n",
2609 |        "      <th>47</th>\n",
2610 |        "      <td>ATO</td>\n",
2611 |        "      <td>108.98</td>\n",
2612 |        "      <td>179</td>\n",
2613 |        "      <td>-0.039357</td>\n",
2614 |        "      <td>0.415842</td>\n",
2615 |        "      <td>-0.125705</td>\n",
2616 |        "      <td>0.4</td>\n",
2617 |        "      <td>0.089492</td>\n",
2618 |        "      <td>0.229703</td>\n",
2619 |        "      <td>0.053203</td>\n",
2620 |        "      <td>0.306931</td>\n",
2621 |        "      <td>0.338119</td>\n",
2622 |        "    </tr>\n",
2623 |        "    <tr>\n",
2624 |        "      <th>48</th>\n",
2625 |        "      <td>ATVI</td>\n",
2626 |        "      <td>82.88</td>\n",
2627 |        "      <td>236</td>\n",
2628 |        "      <td>0.706749</td>\n",
2629 |        "      <td>0.976238</td>\n",
2630 |        "      <td>0.310780</td>\n",
2631 |        "      <td>0.950495</td>\n",
2632 |        "      <td>0.110910</td>\n",
2633 |        "      <td>0.273267</td>\n",
2634 |        "      <td>0.048812</td>\n",
2635 |        "      <td>0.289109</td>\n",
2636 |        "      <td>0.622277</td>\n",
2637 |        "    </tr>\n",
2638 |        "    <tr>\n",
2639 |        "      <th>49</th>\n",
2640 |        "      <td>AVB</td>\n",
2641 |        "      <td>156.62</td>\n",
2642 |        "      <td>125</td>\n",
2643 |        "      <td>-0.263600</td>\n",
2644 |        "      <td>0.162376</td>\n",
2645 |        "      <td>-0.336301</td>\n",
2646 |        "      <td>0.120792</td>\n",
2647 |        "      <td>0.012586</td>\n",
2648 |        "      <td>0.0930693</td>\n",
2649 |        "      <td>0.005162</td>\n",
2650 |        "      <td>0.142574</td>\n",
2651 |        "      <td>0.129703</td>\n",
2652 |        "    </tr>\n",
2653 |        "    <tr>\n",
2654 |        "      <th>50</th>\n",
2655 |        "      <td>AVGO</td>\n",
2656 |        "      <td>336.73</td>\n",
2657 |        "      <td>N/A</td>\n",
2658 |        "      <td>0.177460</td>\n",
2659 |        "      <td>0.675248</td>\n",
2660 |        "      <td>0.030386</td>\n",
2661 |        "      <td>0.679208</td>\n",
2662 |        "      <td>0.278870</td>\n",
2663 |        "      <td>0.60198</td>\n",
2664 |        "      <td>0.074210</td>\n",
2665 |        "      <td>0.405941</td>\n",
2666 |        "      <td>0.590594</td>\n",
2667 |        "    </tr>\n",
2668 |        "  </tbody>\n",
2669 |        "</table>\n",
2670 |        "</div>"
2671 |       ],
2672 |       "text/plain": [
2673 |        "   Ticker    Price Number of Shares to Buy  One-Year Price Return  \\\n",
2674 |        "0       A    98.19                     199               0.444090   \n",
2675 |        "1     AAL    13.89                    1411              -0.526494   \n",
2676 |        "2     AAP   161.13                     121               0.088066   \n",
2677 |        "3    AAPL   467.65                      41               1.171724   \n",
2678 |        "4    ABBV    97.29                     201               0.478770   \n",
2679 |        "5     ABC   104.97                     186               0.163705   \n",
2680 |        "6    ABMD   305.78                      64               0.546138   \n",
2681 |        "7     ABT   102.94                     190               0.161073   \n",
2682 |        "8     ACN   237.88                      82               0.197484   \n",
2683 |        "9    ADBE   453.91                      43               0.532839   \n",
2684 |        "10    ADI   120.52                     162               0.056525   \n",
2685 |        "11    ADM    46.62                     420               0.178916   \n",
2686 |        "12    ADP   139.82                     140              -0.174896   \n",
2687 |        "13   ADSK   234.20                      83               0.528696   \n",
2688 |        "14    AEE    85.23                     230               0.073499   \n",
2689 |        "15    AEP    87.88                     223              -0.069760   \n",
2690 |        "16    AES    17.54                    1117               0.141966   \n",
2691 |        "17    AFL    39.27                     499              -0.298263   \n",
2692 |        "18    AIG    31.76                     617              -0.468456   \n",
2693 |        "19    AIV    39.00                     502              -0.260956   \n",
2694 |        "20    AIZ   129.93                     150               0.016575   \n",
2695 |        "21    AJG   111.00                     176               0.195680   \n",
2696 |        "22   AKAM   110.00                     178               0.206310   \n",
2697 |        "23    ALB    94.85                     206               0.339750   \n",
2698 |        "24   ALGN   309.25                      63               0.692338   \n",
2699 |        "25    ALK    38.63                     507              -0.387440   \n",
2700 |        "26    ALL    99.53                     197              -0.079870   \n",
2701 |        "27   ALLE   105.13                     186               0.071934   \n",
2702 |        "28   ALXN   107.60                     182              -0.079570   \n",
2703 |        "29   AMAT    68.10                     287               0.389577   \n",
2704 |        "30   AMCR    11.52                    1702               0.073093   \n",
2705 |        "31    AMD    85.23                     230               1.590065   \n",
2706 |        "32    AME   104.38                     187               0.177719   \n",
2707 |        "33   AMGN   253.49                      77               0.176726   \n",
2708 |        "34    AMP   166.96                     117               0.254633   \n",
2709 |        "35    AMT   252.70                      77               0.137500   \n",
2710 |        "36   AMZN  3267.82                       6               0.753428   \n",
2711 |        "37   ANET   225.98                      86              -0.065031   \n",
2712 |        "38   ANSS   321.00                      61               0.451591   \n",
2713 |        "39   ANTM   293.84                      66              -0.013031   \n",
2714 |        "40    AON   196.84                      99               0.006102   \n",
2715 |        "41    AOS    50.31                     389               0.101690   \n",
2716 |        "42    APA    16.40                    1195              -0.291451   \n",
2717 |        "43    APD   295.10                      66               0.242925   \n",
2718 |        "44    APH   110.83                     176               0.255236   \n",
2719 |        "45   APTV    91.40                     214               0.072118   \n",
2720 |        "46    ARE   174.20                     112               0.208731   \n",
2721 |        "47    ATO   108.98                     179              -0.039357   \n",
2722 |        "48   ATVI    82.88                     236               0.706749   \n",
2723 |        "49    AVB   156.62                     125              -0.263600   \n",
2724 |        "50   AVGO   336.73                     N/A               0.177460   \n",
2725 |        "\n",
2726 |        "   One-Year Return Percentile  Six-Month Price Return  \\\n",
2727 |        "0                    0.885149                0.147456   \n",
2728 |        "1                   0.0237624               -0.564540   \n",
2729 |        "2                    0.578218                0.148378   \n",
2730 |        "3                    0.992079                0.401695   \n",
2731 |        "4                     0.89703                0.001711   \n",
2732 |        "5                    0.651485                0.100112   \n",
2733 |        "6                    0.942574                0.874783   \n",
2734 |        "7                    0.647525                0.139130   \n",
2735 |        "8                     0.69505                0.085519   \n",
2736 |        "9                    0.934653                0.196705   \n",
2737 |        "10                   0.522772                0.002920   \n",
2738 |        "11                   0.679208               -0.018869   \n",
2739 |        "12                   0.243564               -0.227657   \n",
2740 |        "13                   0.928713                0.116990   \n",
2741 |        "14                   0.556436               -0.044706   \n",
2742 |        "15                   0.368317               -0.186159   \n",
2743 |        "16                   0.635644               -0.177281   \n",
2744 |        "17                   0.116832               -0.283044   \n",
2745 |        "18                  0.0356436               -0.398200   \n",
2746 |        "19                   0.168317               -0.323003   \n",
2747 |        "20                   0.475248               -0.116835   \n",
2748 |        "21                   0.689109               -0.011750   \n",
2749 |        "22                    0.70495                0.075705   \n",
2750 |        "23                   0.823762                0.036265   \n",
2751 |        "24                   0.970297                0.131757   \n",
2752 |        "25                  0.0633663               -0.438559   \n",
2753 |        "26                   0.354455               -0.230788   \n",
2754 |        "27                   0.542574               -0.246470   \n",
2755 |        "28                   0.356436               -0.006381   \n",
2756 |        "29                   0.855446               -0.013556   \n",
2757 |        "30                   0.552475                0.105582   \n",
2758 |        "31                    0.99604                0.532176   \n",
2759 |        "32                   0.677228               -0.002080   \n",
2760 |        "33                   0.671287                0.086140   \n",
2761 |        "34                   0.736634               -0.101617   \n",
2762 |        "35                   0.631683               -0.022213   \n",
2763 |        "36                   0.978218                0.471720   \n",
2764 |        "37                   0.374257               -0.093866   \n",
2765 |        "38                   0.887129                0.058949   \n",
2766 |        "39                   0.439604               -0.053364   \n",
2767 |        "40                   0.463366               -0.183042   \n",
2768 |        "41                    0.59802                0.141115   \n",
2769 |        "42                   0.124752               -0.441406   \n",
2770 |        "43                   0.728713                0.124499   \n",
2771 |        "44                   0.738614                0.053364   \n",
2772 |        "45                   0.544554               -0.011731   \n",
2773 |        "46                   0.706931               -0.001613   \n",
2774 |        "47                   0.415842               -0.125705   \n",
2775 |        "48                   0.976238                0.310780   \n",
2776 |        "49                   0.162376               -0.336301   \n",
2777 |        "50                   0.675248                0.030386   \n",
2778 |        "\n",
2779 |        "   Six-Month Return Percentile  Three-Month Price Return  \\\n",
2780 |        "0                     0.837624                  0.221461   \n",
2781 |        "1                    0.0158416                  0.509431   \n",
2782 |        "2                     0.839604                  0.295700   \n",
2783 |        "3                     0.968317                  0.474900   \n",
2784 |        "4                     0.629703                  0.077880   \n",
2785 |        "5                     0.774257                  0.241867   \n",
2786 |        "6                      0.99802                  0.645795   \n",
2787 |        "7                     0.823762                  0.093917   \n",
2788 |        "8                     0.762376                  0.279570   \n",
2789 |        "9                     0.879208                  0.252382   \n",
2790 |        "10                    0.631683                  0.149173   \n",
2791 |        "11                    0.580198                  0.328413   \n",
2792 |        "12                    0.265347                  0.037317   \n",
2793 |        "13                    0.794059                  0.312257   \n",
2794 |        "14                    0.538614                  0.202938   \n",
2795 |        "15                    0.326733                  0.084255   \n",
2796 |        "16                    0.336634                  0.510037   \n",
2797 |        "17                    0.190099                  0.178662   \n",
2798 |        "18                   0.0673267                  0.242767   \n",
2799 |        "19                    0.142574                  0.096556   \n",
2800 |        "20                     0.40396                  0.418900   \n",
2801 |        "21                         0.6                  0.253483   \n",
2802 |        "22                    0.752475                  0.125834   \n",
2803 |        "23                    0.691089                  0.528245   \n",
2804 |        "24                    0.815842                  0.554489   \n",
2805 |        "25                   0.0534653                  0.519770   \n",
2806 |        "26                    0.261386                  0.045750   \n",
2807 |        "27                    0.241584                  0.112362   \n",
2808 |        "28                     0.60396                  0.003280   \n",
2809 |        "29                    0.594059                  0.301391   \n",
2810 |        "30                    0.778218                  0.238793   \n",
2811 |        "31                    0.984158                  0.597011   \n",
2812 |        "32                    0.615842                  0.305470   \n",
2813 |        "33                    0.764356                  0.020895   \n",
2814 |        "34                    0.429703                  0.385995   \n",
2815 |        "35                    0.574257                  0.091099   \n",
2816 |        "36                    0.974257                  0.339324   \n",
2817 |        "37                    0.449505                  0.011798   \n",
2818 |        "38                    0.722772                  0.229415   \n",
2819 |        "39                    0.514851                  0.058803   \n",
2820 |        "40                    0.332673                  0.010224   \n",
2821 |        "41                    0.825743                  0.260257   \n",
2822 |        "42                   0.0514851                  0.485613   \n",
2823 |        "43                    0.807921                  0.273219   \n",
2824 |        "44                    0.714851                  0.342563   \n",
2825 |        "45                     0.60198                  0.490744   \n",
2826 |        "46                    0.619802                  0.214849   \n",
2827 |        "47                         0.4                  0.089492   \n",
2828 |        "48                    0.950495                  0.110910   \n",
2829 |        "49                    0.120792                  0.012586   \n",
2830 |        "50                    0.679208                  0.278870   \n",
2831 |        "\n",
2832 |        "   Three-Month Return Percentile  One-Month Price Return  \\\n",
2833 |        "0                       0.473267                0.093820   \n",
2834 |        "1                       0.908911                0.172430   \n",
2835 |        "2                       0.643564                0.144608   \n",
2836 |        "3                       0.887129                0.189840   \n",
2837 |        "4                        0.19802               -0.024533   \n",
2838 |        "5                       0.522772                0.066990   \n",
2839 |        "6                       0.954455                0.145153   \n",
2840 |        "7                       0.233663                0.081815   \n",
2841 |        "8                       0.605941                0.067045   \n",
2842 |        "9                       0.550495                0.006829   \n",
2843 |        "10                      0.360396                0.017244   \n",
2844 |        "11                       0.70495                0.123837   \n",
2845 |        "12                      0.134653               -0.042030   \n",
2846 |        "13                      0.677228               -0.001259   \n",
2847 |        "14                      0.447525                0.086675   \n",
2848 |        "15                      0.217822               -0.007456   \n",
2849 |        "16                      0.910891                0.186968   \n",
2850 |        "17                           0.4                0.082055   \n",
2851 |        "18                      0.528713                0.042150   \n",
2852 |        "19                      0.243564                0.004109   \n",
2853 |        "20                      0.839604                0.254327   \n",
2854 |        "21                      0.554455                0.092902   \n",
2855 |        "22                      0.312871               -0.027015   \n",
2856 |        "23                      0.918812                0.120477   \n",
2857 |        "24                      0.928713                0.143933   \n",
2858 |        "25                      0.916832                0.103833   \n",
2859 |        "26                      0.146535                0.088925   \n",
2860 |        "27                      0.277228                0.024073   \n",
2861 |        "28                     0.0792079               -0.040910   \n",
2862 |        "29                      0.651485                0.082413   \n",
2863 |        "30                      0.510891                0.077579   \n",
2864 |        "31                      0.948515                0.560304   \n",
2865 |        "32                      0.659406                0.147190   \n",
2866 |        "33                      0.112871               -0.034769   \n",
2867 |        "34                       0.80198                0.089628   \n",
2868 |        "35                      0.231683               -0.018941   \n",
2869 |        "36                      0.728713                0.018781   \n",
2870 |        "37                     0.0910891                0.024541   \n",
2871 |        "38                      0.487129                0.044242   \n",
2872 |        "39                      0.164356                0.104273   \n",
2873 |        "40                     0.0891089               -0.026247   \n",
2874 |        "41                      0.564356                0.032405   \n",
2875 |        "42                      0.893069                0.260429   \n",
2876 |        "43                      0.592079                0.040389   \n",
2877 |        "44                      0.740594                0.168114   \n",
2878 |        "45                       0.89505                0.172564   \n",
2879 |        "46                      0.465347                0.078630   \n",
2880 |        "47                      0.229703                0.053203   \n",
2881 |        "48                      0.273267                0.048812   \n",
2882 |        "49                     0.0930693                0.005162   \n",
2883 |        "50                       0.60198                0.074210   \n",
2884 |        "\n",
2885 |        "   One-Month Return Percentile HQM Score  \n",
2886 |        "0                     0.530693  0.681683  \n",
2887 |        "1                     0.827723  0.444059  \n",
2888 |        "2                     0.742574   0.70099  \n",
2889 |        "3                     0.879208  0.931683  \n",
2890 |        "4                    0.0693069  0.448515  \n",
2891 |        "5                     0.364356  0.578218  \n",
2892 |        "6                     0.746535  0.910396  \n",
2893 |        "7                     0.457426  0.540594  \n",
2894 |        "8                     0.366337  0.607426  \n",
2895 |        "9                     0.150495  0.628713  \n",
2896 |        "10                    0.174257  0.422277  \n",
2897 |        "11                    0.687129  0.662871  \n",
2898 |        "12                   0.0435644  0.171782  \n",
2899 |        "13                    0.122772  0.630693  \n",
2900 |        "14                    0.483168  0.506436  \n",
2901 |        "15                    0.106931   0.25495  \n",
2902 |        "16                    0.873267  0.689109  \n",
2903 |        "17                    0.459406  0.291584  \n",
2904 |        "18                    0.271287  0.225743  \n",
2905 |        "19                    0.140594  0.173762  \n",
2906 |        "20                    0.948515  0.666832  \n",
2907 |        "21                    0.520792  0.591089  \n",
2908 |        "22                   0.0594059  0.457426  \n",
2909 |        "23                    0.673267  0.776733  \n",
2910 |        "24                    0.738614  0.863366  \n",
2911 |        "25                    0.576238  0.402475  \n",
2912 |        "26                     0.49703  0.314851  \n",
2913 |        "27                     0.20396  0.316337  \n",
2914 |        "28                   0.0455446  0.271287  \n",
2915 |        "29                    0.461386  0.640594  \n",
2916 |        "30                    0.433663  0.568812  \n",
2917 |        "31                     0.99802  0.981683  \n",
2918 |        "32                    0.752475  0.676238  \n",
2919 |        "33                   0.0534653  0.400495  \n",
2920 |        "34                     0.50099  0.617327  \n",
2921 |        "35                   0.0871287  0.381188  \n",
2922 |        "36                    0.178218  0.714851  \n",
2923 |        "37                    0.207921  0.280693  \n",
2924 |        "38                    0.283168   0.59505  \n",
2925 |        "39                    0.582178  0.425248  \n",
2926 |        "40                   0.0633663  0.237129  \n",
2927 |        "41                    0.231683   0.55495  \n",
2928 |        "42                    0.952475  0.505446  \n",
2929 |        "43                    0.265347  0.598515  \n",
2930 |        "44                    0.819802  0.753465  \n",
2931 |        "45                    0.829703  0.717822  \n",
2932 |        "46                    0.441584  0.558416  \n",
2933 |        "47                    0.306931  0.338119  \n",
2934 |        "48                    0.289109  0.622277  \n",
2935 |        "49                    0.142574  0.129703  \n",
2936 |        "50                    0.405941  0.590594  "
2937 |       ]
2938 |      },
2939 |      "execution_count": 15,
2940 |      "metadata": {},
2941 |      "output_type": "execute_result"
2942 |     }
2943 |    ],
2944 |    "source": [
2945 |     "position_size = float(portfolio_size) / len(hqm_dataframe.index)\n",
2946 |     "for i in range(0, len(hqm_dataframe['Ticker'])-1):\n",
2947 |     "    hqm_dataframe.loc[i, 'Number of Shares to Buy'] = math.floor(position_size / hqm_dataframe['Price'][i])\n",
2948 |     "hqm_dataframe"
2949 |    ]
2950 |   },
2951 |   {
2952 |    "cell_type": "markdown",
2953 |    "metadata": {},
2954 |    "source": [
2955 |     "## Formatting Our Excel Output\n",
2956 |     "\n",
2957 |     "We will be using the XlsxWriter library for Python to create nicely-formatted Excel files.\n",
2958 |     "\n",
2959 |     "XlsxWriter is an excellent package and offers tons of customization. However, the tradeoff for this is that the library can seem very complicated to new users. Accordingly, this section will be fairly long because I want to do a good job of explaining how XlsxWriter works."
2960 |    ]
2961 |   },
2962 |   {
2963 |    "cell_type": "code",
2964 |    "execution_count": 16,
2965 |    "metadata": {},
2966 |    "outputs": [],
2967 |    "source": [
2968 |     "writer = pd.ExcelWriter('momentum_strategy.xlsx', engine='xlsxwriter')\n",
2969 |     "hqm_dataframe.to_excel(writer, sheet_name='Momentum Strategy', index = False)"
2970 |    ]
2971 |   },
2972 |   {
2973 |    "cell_type": "markdown",
2974 |    "metadata": {},
2975 |    "source": [
2976 |     "## Creating the Formats We'll Need For Our .xlsx File\n",
2977 |     "\n",
2978 |     "You'll recall from our first project that formats include colors, fonts, and also symbols like % and $. We'll need four main formats for our Excel document:\n",
2979 |     "\n",
2980 |     "* String format for tickers\n",
2981 |     "* \\$XX.XX format for stock prices\n",
2982 |     "* \\$XX,XXX format for market capitalization\n",
2983 |     "* Integer format for the number of shares to purchase\n",
2984 |     "\n",
2985 |     "Since we already built our formats in the last section of this course, I've included them below for you. Run this code cell before proceeding."
2986 |    ]
2987 |   },
2988 |   {
2989 |    "cell_type": "code",
2990 |    "execution_count": 17,
2991 |    "metadata": {},
2992 |    "outputs": [],
2993 |    "source": [
2994 |     "background_color = '#0a0a23'\n",
2995 |     "font_color = '#ffffff'\n",
2996 |     "\n",
2997 |     "string_template = writer.book.add_format(\n",
2998 |     "        {\n",
2999 |     "            'font_color': font_color,\n",
3000 |     "            'bg_color': background_color,\n",
3001 |     "            'border': 1\n",
3002 |     "        }\n",
3003 |     "    )\n",
3004 |     "\n",
3005 |     "dollar_template = writer.book.add_format(\n",
3006 |     "        {\n",
3007 |     "            'num_format':'$0.00',\n",
3008 |     "            'font_color': font_color,\n",
3009 |     "            'bg_color': background_color,\n",
3010 |     "            'border': 1\n",
3011 |     "        }\n",
3012 |     "    )\n",
3013 |     "\n",
3014 |     "integer_template = writer.book.add_format(\n",
3015 |     "        {\n",
3016 |     "            'num_format':'0',\n",
3017 |     "            'font_color': font_color,\n",
3018 |     "            'bg_color': background_color,\n",
3019 |     "            'border': 1\n",
3020 |     "        }\n",
3021 |     "    )\n",
3022 |     "\n",
3023 |     "percent_template = writer.book.add_format(\n",
3024 |     "        {\n",
3025 |     "            'num_format':'0.0%',\n",
3026 |     "            'font_color': font_color,\n",
3027 |     "            'bg_color': background_color,\n",
3028 |     "            'border': 1\n",
3029 |     "        }\n",
3030 |     "    )"
3031 |    ]
3032 |   },
3033 |   {
3034 |    "cell_type": "code",
3035 |    "execution_count": 18,
3036 |    "metadata": {},
3037 |    "outputs": [],
3038 |    "source": [
3039 |     "column_formats = { \n",
3040 |     "                    'A': ['Ticker', string_template],\n",
3041 |     "                    'B': ['Price', dollar_template],\n",
3042 |     "                    'C': ['Number of Shares to Buy', integer_template],\n",
3043 |     "                    'D': ['One-Year Price Return', percent_template],\n",
3044 |     "                    'E': ['One-Year Return Percentile', percent_template],\n",
3045 |     "                    'F': ['Six-Month Price Return', percent_template],\n",
3046 |     "                    'G': ['Six-Month Return Percentile', percent_template],\n",
3047 |     "                    'H': ['Three-Month Price Return', percent_template],\n",
3048 |     "                    'I': ['Three-Month Return Percentile', percent_template],\n",
3049 |     "                    'J': ['One-Month Price Return', percent_template],\n",
3050 |     "                    'K': ['One-Month Return Percentile', percent_template],\n",
3051 |     "                    'L': ['HQM Score', integer_template]\n",
3052 |     "                    }\n",
3053 |     "\n",
3054 |     "for column in column_formats.keys():\n",
3055 |     "    writer.sheets['Momentum Strategy'].set_column(f'{column}:{column}', 20, column_formats[column][1])\n",
3056 |     "    writer.sheets['Momentum Strategy'].write(f'{column}1', column_formats[column][0], string_template)"
3057 |    ]
3058 |   },
3059 |   {
3060 |    "cell_type": "markdown",
3061 |    "metadata": {},
3062 |    "source": [
3063 |     "## Saving Our Excel Output\n",
3064 |     "\n",
3065 |     "As before, saving our Excel output is very easy:"
3066 |    ]
3067 |   },
3068 |   {
3069 |    "cell_type": "code",
3070 |    "execution_count": 19,
3071 |    "metadata": {},
3072 |    "outputs": [],
3073 |    "source": [
3074 |     "writer.save()"
3075 |    ]
3076 |   }
3077 |  ],
3078 |  "metadata": {
3079 |   "kernelspec": {
3080 |    "display_name": "Python 3",
3081 |    "language": "python",
3082 |    "name": "python3"
3083 |   },
3084 |   "language_info": {
3085 |    "codemirror_mode": {
3086 |     "name": "ipython",
3087 |     "version": 3
3088 |    },
3089 |    "file_extension": ".py",
3090 |    "mimetype": "text/x-python",
3091 |    "name": "python",
3092 |    "nbconvert_exporter": "python",
3093 |    "pygments_lexer": "ipython3",
3094 |    "version": "3.8.2"
3095 |   }
3096 |  },
3097 |  "nbformat": 4,
3098 |  "nbformat_minor": 4
3099 | }
3100 | 


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
 1 | jupyter==1.0.0
 2 | jupyter-client==6.1.3
 3 | jupyter-console==6.1.0
 4 | jupyter-core==4.6.3
 5 | numpy==1.17.4
 6 | pandas==0.25.3
 7 | requests==2.22.0
 8 | scipy==1.5.2
 9 | XlsxWriter==1.2.2
10 | 


--------------------------------------------------------------------------------
/starter_files/001_equal_weight_S&P_500.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Equal-Weight S&P 500 Index Fund\n",
  8 |     "\n",
  9 |     "## Introduction & Library Imports\n",
 10 |     "\n",
 11 |     "The S&P 500 is the world's most popular stock market index. The largest fund that is benchmarked to this index is the SPDR® S&P 500® ETF Trust. It has more than US$250 billion of assets under management.\n",
 12 |     "\n",
 13 |     "The goal of this section of the course is to create a Python script that will accept the value of your portfolio and tell you how many shares of each S&P 500 constituent you should purchase to get an equal-weight version of the index fund.\n",
 14 |     "\n",
 15 |     "## Library Imports\n",
 16 |     "\n",
 17 |     "The first thing we need to do is import the open-source software libraries that we'll be using in this tutorial."
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "code",
 22 |    "execution_count": null,
 23 |    "metadata": {},
 24 |    "outputs": [],
 25 |    "source": []
 26 |   },
 27 |   {
 28 |    "cell_type": "markdown",
 29 |    "metadata": {},
 30 |    "source": [
 31 |     "## Importing Our List of Stocks\n",
 32 |     "\n",
 33 |     "The next thing we need to do is import the constituents of the S&P 500.\n",
 34 |     "\n",
 35 |     "These constituents change over time, so in an ideal world you would connect directly to the index provider (Standard & Poor's) and pull their real-time constituents on a regular basis.\n",
 36 |     "\n",
 37 |     "Paying for access to the index provider's API is outside of the scope of this course. \n",
 38 |     "\n",
 39 |     "There's a static version of the S&P 500 constituents available here. [Click this link to download them now](https://drive.google.com/file/d/1ZJSpbY69DVckVZlO9cC6KkgfSufybcHN/view?usp=sharing). Move this file into the `starter-files` folder so it can be accessed by other files in that directory.\n",
 40 |     "\n",
 41 |     "Now it's time to import these stocks to our Jupyter Notebook file."
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": null,
 47 |    "metadata": {},
 48 |    "outputs": [],
 49 |    "source": []
 50 |   },
 51 |   {
 52 |    "cell_type": "markdown",
 53 |    "metadata": {},
 54 |    "source": [
 55 |     "## Acquiring an API Token\n",
 56 |     "\n",
 57 |     "Now it's time to import our IEX Cloud API token. This is the data provider that we will be using throughout this course.\n",
 58 |     "\n",
 59 |     "API tokens (and other sensitive information) should be stored in a `secrets.py` file that doesn't get pushed to your local Git repository. We'll be using a sandbox API token in this course, which means that the data we'll use is randomly-generated and (more importantly) has no cost associated with it.\n",
 60 |     "\n",
 61 |     "[Click here](http://nickmccullum.com/algorithmic-trading-python/secrets.py) to download your `secrets.py` file. Move the file into the same directory as this Jupyter Notebook before proceeding."
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": null,
 67 |    "metadata": {},
 68 |    "outputs": [],
 69 |    "source": []
 70 |   },
 71 |   {
 72 |    "cell_type": "markdown",
 73 |    "metadata": {},
 74 |    "source": [
 75 |     "## Making Our First API Call\n",
 76 |     "\n",
 77 |     "Now it's time to structure our API calls to IEX cloud. \n",
 78 |     "\n",
 79 |     "We need the following information from the API:\n",
 80 |     "\n",
 81 |     "* Market capitalization for each stock\n",
 82 |     "* Price of each stock\n",
 83 |     "\n"
 84 |    ]
 85 |   },
 86 |   {
 87 |    "cell_type": "code",
 88 |    "execution_count": null,
 89 |    "metadata": {},
 90 |    "outputs": [],
 91 |    "source": []
 92 |   },
 93 |   {
 94 |    "cell_type": "markdown",
 95 |    "metadata": {},
 96 |    "source": [
 97 |     "## Parsing Our API Call\n",
 98 |     "\n",
 99 |     "The API call that we executed in the last code block contains all of the information required to build our equal-weight S&P 500 strategy. \n",
100 |     "\n",
101 |     "With that said, the data isn't in a proper format yet. We need to parse it first."
102 |    ]
103 |   },
104 |   {
105 |    "cell_type": "code",
106 |    "execution_count": null,
107 |    "metadata": {},
108 |    "outputs": [],
109 |    "source": []
110 |   },
111 |   {
112 |    "cell_type": "markdown",
113 |    "metadata": {},
114 |    "source": [
115 |     "## Adding Our Stocks Data to a Pandas DataFrame\n",
116 |     "\n",
117 |     "The next thing we need to do is add our stock's price and market capitalization to a pandas DataFrame. Think of a DataFrame like the Python version of a spreadsheet. It stores tabular data."
118 |    ]
119 |   },
120 |   {
121 |    "cell_type": "code",
122 |    "execution_count": null,
123 |    "metadata": {},
124 |    "outputs": [],
125 |    "source": []
126 |   },
127 |   {
128 |    "cell_type": "code",
129 |    "execution_count": null,
130 |    "metadata": {},
131 |    "outputs": [],
132 |    "source": []
133 |   },
134 |   {
135 |    "cell_type": "markdown",
136 |    "metadata": {},
137 |    "source": [
138 |     "## Looping Through The Tickers in Our List of Stocks\n",
139 |     "\n",
140 |     "Using the same logic that we outlined above, we can pull data for all S&P 500 stocks and store their data in the DataFrame using a `for` loop."
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": null,
146 |    "metadata": {},
147 |    "outputs": [],
148 |    "source": []
149 |   },
150 |   {
151 |    "cell_type": "code",
152 |    "execution_count": null,
153 |    "metadata": {},
154 |    "outputs": [],
155 |    "source": []
156 |   },
157 |   {
158 |    "cell_type": "markdown",
159 |    "metadata": {},
160 |    "source": [
161 |     "## Using Batch API Calls to Improve Performance\n",
162 |     "\n",
163 |     "Batch API calls are one of the easiest ways to improve the performance of your code.\n",
164 |     "\n",
165 |     "This is because HTTP requests are typically one of the slowest components of a script.\n",
166 |     "\n",
167 |     "Also, API providers will often give you discounted rates for using batch API calls since they are easier for the API provider to respond to.\n",
168 |     "\n",
169 |     "IEX Cloud limits their batch API calls to 100 tickers per request. Still, this reduces the number of API calls we'll make in this section from 500 to 5 - huge improvement! In this section, we'll split our list of stocks into groups of 100 and then make a batch API call for each group."
170 |    ]
171 |   },
172 |   {
173 |    "cell_type": "code",
174 |    "execution_count": null,
175 |    "metadata": {},
176 |    "outputs": [],
177 |    "source": []
178 |   },
179 |   {
180 |    "cell_type": "code",
181 |    "execution_count": null,
182 |    "metadata": {},
183 |    "outputs": [],
184 |    "source": []
185 |   },
186 |   {
187 |    "cell_type": "markdown",
188 |    "metadata": {},
189 |    "source": [
190 |     "## Calculating the Number of Shares to Buy\n",
191 |     "\n",
192 |     "As you can see in the DataFrame above, we stil haven't calculated the number of shares of each stock to buy.\n",
193 |     "\n",
194 |     "We'll do that next."
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "code",
199 |    "execution_count": null,
200 |    "metadata": {},
201 |    "outputs": [],
202 |    "source": []
203 |   },
204 |   {
205 |    "cell_type": "code",
206 |    "execution_count": null,
207 |    "metadata": {},
208 |    "outputs": [],
209 |    "source": []
210 |   },
211 |   {
212 |    "cell_type": "markdown",
213 |    "metadata": {},
214 |    "source": [
215 |     "## Formatting Our Excel Output\n",
216 |     "\n",
217 |     "We will be using the XlsxWriter library for Python to create nicely-formatted Excel files.\n",
218 |     "\n",
219 |     "XlsxWriter is an excellent package and offers tons of customization. However, the tradeoff for this is that the library can seem very complicated to new users. Accordingly, this section will be fairly long because I want to do a good job of explaining how XlsxWriter works.\n",
220 |     "\n",
221 |     "### Initializing our XlsxWriter Object"
222 |    ]
223 |   },
224 |   {
225 |    "cell_type": "code",
226 |    "execution_count": null,
227 |    "metadata": {},
228 |    "outputs": [],
229 |    "source": []
230 |   },
231 |   {
232 |    "cell_type": "markdown",
233 |    "metadata": {},
234 |    "source": [
235 |     "### Creating the Formats We'll Need For Our `.xlsx` File\n",
236 |     "\n",
237 |     "Formats include colors, fonts, and also symbols like `%` and `$`. We'll need four main formats for our Excel document:\n",
238 |     "* String format for tickers\n",
239 |     "* \\\\$XX.XX format for stock prices\n",
240 |     "* \\\\$XX,XXX format for market capitalization\n",
241 |     "* Integer format for the number of shares to purchase"
242 |    ]
243 |   },
244 |   {
245 |    "cell_type": "code",
246 |    "execution_count": null,
247 |    "metadata": {},
248 |    "outputs": [],
249 |    "source": []
250 |   },
251 |   {
252 |    "cell_type": "markdown",
253 |    "metadata": {},
254 |    "source": [
255 |     "### Applying the Formats to the Columns of Our `.xlsx` File\n",
256 |     "\n",
257 |     "We can use the `set_column` method applied to the `writer.sheets['Recommended Trades']` object to apply formats to specific columns of our spreadsheets.\n",
258 |     "\n",
259 |     "Here's an example:\n",
260 |     "\n",
261 |     "```python\n",
262 |     "writer.sheets['Recommended Trades'].set_column('B:B', #This tells the method to apply the format to column B\n",
263 |     "                     18, #This tells the method to apply a column width of 18 pixels\n",
264 |     "                     string_template #This applies the format 'string_template' to the column\n",
265 |     "                    )\n",
266 |     "```"
267 |    ]
268 |   },
269 |   {
270 |    "cell_type": "code",
271 |    "execution_count": null,
272 |    "metadata": {},
273 |    "outputs": [],
274 |    "source": []
275 |   },
276 |   {
277 |    "cell_type": "markdown",
278 |    "metadata": {},
279 |    "source": [
280 |     "This code works, but it violates the software principle of \"Don't Repeat Yourself\". \n",
281 |     "\n",
282 |     "Let's simplify this by putting it in 2 loops:"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": null,
288 |    "metadata": {},
289 |    "outputs": [],
290 |    "source": []
291 |   },
292 |   {
293 |    "cell_type": "markdown",
294 |    "metadata": {},
295 |    "source": [
296 |     "## Saving Our Excel Output\n",
297 |     "\n",
298 |     "Saving our Excel file is very easy:"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "code",
303 |    "execution_count": null,
304 |    "metadata": {},
305 |    "outputs": [],
306 |    "source": []
307 |   }
308 |  ],
309 |  "metadata": {
310 |   "kernelspec": {
311 |    "display_name": "Python 3",
312 |    "language": "python",
313 |    "name": "python3"
314 |   },
315 |   "language_info": {
316 |    "codemirror_mode": {
317 |     "name": "ipython",
318 |     "version": 3
319 |    },
320 |    "file_extension": ".py",
321 |    "mimetype": "text/x-python",
322 |    "name": "python",
323 |    "nbconvert_exporter": "python",
324 |    "pygments_lexer": "ipython3",
325 |    "version": "3.8.2"
326 |   }
327 |  },
328 |  "nbformat": 4,
329 |  "nbformat_minor": 4
330 | }
331 | 


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/starter_files/002_quantitative_momentum_strategy.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Quantitative Momentum Strategy\n",
  8 |     "\n",
  9 |     "\"Momentum investing\" means investing in the stocks that have increased in price the most.\n",
 10 |     "\n",
 11 |     "For this project, we're going to build an investing strategy that selects the 50 stocks with the highest price momentum. From there, we will calculate recommended trades for an equal-weight portfolio of these 50 stocks.\n",
 12 |     "\n",
 13 |     "\n",
 14 |     "## Library Imports\n",
 15 |     "\n",
 16 |     "The first thing we need to do is import the open-source software libraries that we'll be using in this tutorial."
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "code",
 21 |    "execution_count": null,
 22 |    "metadata": {},
 23 |    "outputs": [],
 24 |    "source": []
 25 |   },
 26 |   {
 27 |    "cell_type": "markdown",
 28 |    "metadata": {},
 29 |    "source": [
 30 |     "## Importing Our List of Stocks\n",
 31 |     "\n",
 32 |     "As before, we'll need to import our list of stocks and our API token before proceeding. Make sure the `.csv` file is still in your working directory and import it with the following command:"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": null,
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": []
 41 |   },
 42 |   {
 43 |    "cell_type": "markdown",
 44 |    "metadata": {},
 45 |    "source": [
 46 |     "## Making Our First API Call\n",
 47 |     "\n",
 48 |     "It's now time to make the first version of our momentum screener!\n",
 49 |     "\n",
 50 |     "We need to get one-year price returns for each stock in the universe. Here's how."
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": null,
 56 |    "metadata": {},
 57 |    "outputs": [],
 58 |    "source": []
 59 |   },
 60 |   {
 61 |    "cell_type": "markdown",
 62 |    "metadata": {},
 63 |    "source": [
 64 |     "## Parsing Our API Call\n",
 65 |     "\n",
 66 |     "This API call has all the information we need. We can parse it using the same square-bracket notation as in the first project of this course. Here is an example."
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": null,
 72 |    "metadata": {},
 73 |    "outputs": [],
 74 |    "source": []
 75 |   },
 76 |   {
 77 |    "cell_type": "markdown",
 78 |    "metadata": {},
 79 |    "source": [
 80 |     "## Executing A Batch API Call & Building Our DataFrame\n",
 81 |     "\n",
 82 |     "Just like in our first project, it's now time to execute several batch API calls and add the information we need to our DataFrame.\n",
 83 |     "\n",
 84 |     "We'll start by running the following code cell, which contains some code we already built last time that we can re-use for this project. More specifically, it contains a function called `chunks` that we can use to divide our list of securities into groups of 100."
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "code",
 89 |    "execution_count": null,
 90 |    "metadata": {},
 91 |    "outputs": [],
 92 |    "source": [
 93 |     "# Function sourced from \n",
 94 |     "# https://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks\n",
 95 |     "def chunks(lst, n):\n",
 96 |     "    \"\"\"Yield successive n-sized chunks from lst.\"\"\"\n",
 97 |     "    for i in range(0, len(lst), n):\n",
 98 |     "        yield lst[i:i + n]   \n",
 99 |     "        \n",
100 |     "symbol_groups = list(chunks(stocks['Ticker'], 100))\n",
101 |     "symbol_strings = []\n",
102 |     "for i in range(0, len(symbol_groups)):\n",
103 |     "    symbol_strings.append(','.join(symbol_groups[i]))\n",
104 |     "#     print(symbol_strings[i])\n",
105 |     "\n",
106 |     "my_columns = ['Ticker', 'Price', 'One-Year Price Return', 'Number of Shares to Buy']"
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "markdown",
111 |    "metadata": {},
112 |    "source": [
113 |     "Now we need to create a blank DataFrame and add our data to the data frame one-by-one."
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": null,
119 |    "metadata": {},
120 |    "outputs": [],
121 |    "source": []
122 |   },
123 |   {
124 |    "cell_type": "markdown",
125 |    "metadata": {},
126 |    "source": [
127 |     "## Removing Low-Momentum Stocks\n",
128 |     "\n",
129 |     "The investment strategy that we're building seeks to identify the 50 highest-momentum stocks in the S&P 500.\n",
130 |     "\n",
131 |     "Because of this, the next thing we need to do is remove all the stocks in our DataFrame that fall below this momentum threshold. We'll sort the DataFrame by the stocks' one-year price return, and drop all stocks outside the top 50.\n"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": null,
137 |    "metadata": {},
138 |    "outputs": [],
139 |    "source": []
140 |   },
141 |   {
142 |    "cell_type": "markdown",
143 |    "metadata": {},
144 |    "source": [
145 |     "## Calculating the Number of Shares to Buy\n",
146 |     "\n",
147 |     "Just like in the last project, we now need to calculate the number of shares we need to buy. The one change we're going to make is wrapping this functionality inside a function, since we'll be using it again later in this Jupyter Notebook.\n",
148 |     "\n",
149 |     "Since we've already done most of the work on this, try to complete the following two code cells without watching me do it first!"
150 |    ]
151 |   },
152 |   {
153 |    "cell_type": "code",
154 |    "execution_count": null,
155 |    "metadata": {},
156 |    "outputs": [],
157 |    "source": []
158 |   },
159 |   {
160 |    "cell_type": "code",
161 |    "execution_count": null,
162 |    "metadata": {},
163 |    "outputs": [],
164 |    "source": []
165 |   },
166 |   {
167 |    "cell_type": "markdown",
168 |    "metadata": {},
169 |    "source": [
170 |     "## Building a Better (and More Realistic) Momentum Strategy\n",
171 |     "\n",
172 |     "Real-world quantitative investment firms differentiate between \"high quality\" and \"low quality\" momentum stocks:\n",
173 |     "\n",
174 |     "* High-quality momentum stocks show \"slow and steady\" outperformance over long periods of time\n",
175 |     "* Low-quality momentum stocks might not show any momentum for a long time, and then surge upwards.\n",
176 |     "\n",
177 |     "The reason why high-quality momentum stocks are preferred is because low-quality momentum can often be cause by short-term news that is unlikely to be repeated in the future (such as an FDA approval for a biotechnology company).\n",
178 |     "\n",
179 |     "To identify high-quality momentum, we're going to build a strategy that selects stocks from the highest percentiles of: \n",
180 |     "\n",
181 |     "* 1-month price returns\n",
182 |     "* 3-month price returns\n",
183 |     "* 6-month price returns\n",
184 |     "* 1-year price returns\n",
185 |     "\n",
186 |     "Let's start by building our DataFrame. You'll notice that I use the abbreviation `hqm` often. It stands for `high-quality momentum`."
187 |    ]
188 |   },
189 |   {
190 |    "cell_type": "code",
191 |    "execution_count": null,
192 |    "metadata": {},
193 |    "outputs": [],
194 |    "source": []
195 |   },
196 |   {
197 |    "cell_type": "markdown",
198 |    "metadata": {},
199 |    "source": [
200 |     "## Calculating Momentum Percentiles\n",
201 |     "\n",
202 |     "We now need to calculate momentum percentile scores for every stock in the universe. More specifically, we need to calculate percentile scores for the following metrics for every stock:\n",
203 |     "\n",
204 |     "* `One-Year Price Return`\n",
205 |     "* `Six-Month Price Return`\n",
206 |     "* `Three-Month Price Return`\n",
207 |     "* `One-Month Price Return`\n",
208 |     "\n",
209 |     "Here's how we'll do this:"
210 |    ]
211 |   },
212 |   {
213 |    "cell_type": "code",
214 |    "execution_count": null,
215 |    "metadata": {},
216 |    "outputs": [],
217 |    "source": []
218 |   },
219 |   {
220 |    "cell_type": "markdown",
221 |    "metadata": {},
222 |    "source": [
223 |     "## Calculating the HQM Score\n",
224 |     "\n",
225 |     "We'll now calculate our `HQM Score`, which is the high-quality momentum score that we'll use to filter for stocks in this investing strategy.\n",
226 |     "\n",
227 |     "The `HQM Score` will be the arithmetic mean of the 4 momentum percentile scores that we calculated in the last section.\n",
228 |     "\n",
229 |     "To calculate arithmetic mean, we will use the `mean` function from Python's built-in `statistics` module."
230 |    ]
231 |   },
232 |   {
233 |    "cell_type": "code",
234 |    "execution_count": null,
235 |    "metadata": {},
236 |    "outputs": [],
237 |    "source": []
238 |   },
239 |   {
240 |    "cell_type": "markdown",
241 |    "metadata": {},
242 |    "source": [
243 |     "## Selecting the 50 Best Momentum Stocks\n",
244 |     "\n",
245 |     "As before, we can identify the 50 best momentum stocks in our universe by sorting the DataFrame on the `HQM Score` column and dropping all but the top 50 entries."
246 |    ]
247 |   },
248 |   {
249 |    "cell_type": "code",
250 |    "execution_count": null,
251 |    "metadata": {},
252 |    "outputs": [],
253 |    "source": []
254 |   },
255 |   {
256 |    "cell_type": "markdown",
257 |    "metadata": {},
258 |    "source": [
259 |     "## Calculating the Number of Shares to Buy\n",
260 |     "\n",
261 |     "We'll use the `portfolio_input` function that we created earlier to accept our portfolio size. Then we will use similar logic in a `for` loop to calculate the number of shares to buy for each stock in our investment universe."
262 |    ]
263 |   },
264 |   {
265 |    "cell_type": "code",
266 |    "execution_count": null,
267 |    "metadata": {},
268 |    "outputs": [],
269 |    "source": []
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": null,
274 |    "metadata": {},
275 |    "outputs": [],
276 |    "source": []
277 |   },
278 |   {
279 |    "cell_type": "markdown",
280 |    "metadata": {},
281 |    "source": [
282 |     "## Formatting Our Excel Output\n",
283 |     "\n",
284 |     "We will be using the XlsxWriter library for Python to create nicely-formatted Excel files.\n",
285 |     "\n",
286 |     "XlsxWriter is an excellent package and offers tons of customization. However, the tradeoff for this is that the library can seem very complicated to new users. Accordingly, this section will be fairly long because I want to do a good job of explaining how XlsxWriter works."
287 |    ]
288 |   },
289 |   {
290 |    "cell_type": "code",
291 |    "execution_count": null,
292 |    "metadata": {},
293 |    "outputs": [],
294 |    "source": []
295 |   },
296 |   {
297 |    "cell_type": "markdown",
298 |    "metadata": {},
299 |    "source": [
300 |     "## Creating the Formats We'll Need For Our .xlsx File\n",
301 |     "\n",
302 |     "You'll recall from our first project that formats include colors, fonts, and also symbols like % and $. We'll need four main formats for our Excel document:\n",
303 |     "\n",
304 |     "* String format for tickers\n",
305 |     "* \\$XX.XX format for stock prices\n",
306 |     "* \\$XX,XXX format for market capitalization\n",
307 |     "* Integer format for the number of shares to purchase\n",
308 |     "\n",
309 |     "Since we already built our formats in the last section of this course, I've included them below for you. Run this code cell before proceeding."
310 |    ]
311 |   },
312 |   {
313 |    "cell_type": "code",
314 |    "execution_count": null,
315 |    "metadata": {},
316 |    "outputs": [],
317 |    "source": [
318 |     "background_color = '#0a0a23'\n",
319 |     "font_color = '#ffffff'\n",
320 |     "\n",
321 |     "string_template = writer.book.add_format(\n",
322 |     "        {\n",
323 |     "            'font_color': font_color,\n",
324 |     "            'bg_color': background_color,\n",
325 |     "            'border': 1\n",
326 |     "        }\n",
327 |     "    )\n",
328 |     "\n",
329 |     "dollar_template = writer.book.add_format(\n",
330 |     "        {\n",
331 |     "            'num_format':'$0.00',\n",
332 |     "            'font_color': font_color,\n",
333 |     "            'bg_color': background_color,\n",
334 |     "            'border': 1\n",
335 |     "        }\n",
336 |     "    )\n",
337 |     "\n",
338 |     "integer_template = writer.book.add_format(\n",
339 |     "        {\n",
340 |     "            'num_format':'0',\n",
341 |     "            'font_color': font_color,\n",
342 |     "            'bg_color': background_color,\n",
343 |     "            'border': 1\n",
344 |     "        }\n",
345 |     "    )\n",
346 |     "\n",
347 |     "percent_template = writer.book.add_format(\n",
348 |     "        {\n",
349 |     "            'num_format':'0.0%',\n",
350 |     "            'font_color': font_color,\n",
351 |     "            'bg_color': background_color,\n",
352 |     "            'border': 1\n",
353 |     "        }\n",
354 |     "    )"
355 |    ]
356 |   },
357 |   {
358 |    "cell_type": "code",
359 |    "execution_count": null,
360 |    "metadata": {},
361 |    "outputs": [],
362 |    "source": []
363 |   },
364 |   {
365 |    "cell_type": "markdown",
366 |    "metadata": {},
367 |    "source": [
368 |     "## Saving Our Excel Output\n",
369 |     "\n",
370 |     "As before, saving our Excel output is very easy:"
371 |    ]
372 |   },
373 |   {
374 |    "cell_type": "code",
375 |    "execution_count": null,
376 |    "metadata": {},
377 |    "outputs": [],
378 |    "source": []
379 |   }
380 |  ],
381 |  "metadata": {
382 |   "kernelspec": {
383 |    "display_name": "Python 3",
384 |    "language": "python",
385 |    "name": "python3"
386 |   },
387 |   "language_info": {
388 |    "codemirror_mode": {
389 |     "name": "ipython",
390 |     "version": 3
391 |    },
392 |    "file_extension": ".py",
393 |    "mimetype": "text/x-python",
394 |    "name": "python",
395 |    "nbconvert_exporter": "python",
396 |    "pygments_lexer": "ipython3",
397 |    "version": "3.8.5"
398 |   }
399 |  },
400 |  "nbformat": 4,
401 |  "nbformat_minor": 4
402 | }
403 | 


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