coding – Sugata Ray

Python motivation – generating an efficient frontier

Date January 1, 2022

Posted by sugata

Why is it worth learning a programming language in Finance and how would one motivate students to make the push to do it? First, my firm belief is that high level coding (in a language like Python or VBA) is going to be as required for financial professionals in 5-10 years as Excel knowledge is today. Second, many investment banking analyst programs (and other analyst programs) already have a Python module – scraping the latest financials (or some other reporting) from Edgar need not be as painful as it used be 20-30 years ago. A few lines of code, executed quarterly, could save an analyst hours, if not days, of time. Finally, even if one does not plan to become a coder, it is worth knowing what coding can do for you – for example, when students move up to middle management, and have analysts of their own, they can potentially direct them to labor saving techniques using widely available programming languages.

So, if we start with the position that high level coding is useful in finance, what would be the best way to motivate a smart, finance student to learn some coding? (Note that I distinguish statistical packages like STATA or SAS from programming languages, although programming can be done in in STATA or SAS, I’m thinking traditional programming with loops and conditional statements)

One exercise I’ve found to work well in my class is generating an efficient frontier. Given a set of assets and historical returns, it is easy to use Excel to do mean variance optimization to generate individual points on the mean variance frontier. Using Excel’s “solver” tool to generate weights that maximize expected returns for a given level of risk, or minimize risk for a given expected return is quite easy and intuitive for most of my students. However, generating the entire curve is a bit beyond Excel’s usual bag of tricks. It would require running a bunch of solver optimizations for different levels of risk, and then plotting all the points. In class, I divide my students in N groups with each group doing the solver optimization for a given level of risk.

This sets up nicely to introduce a piece of Python coding which loops through a set of standard deviations and optimizes asset weights to generate max expected returns for each standard deviation – essentially what the different groups were doing in class! Students seem to understand this advantage of coding and it serves to tee up using Python for more complex analysis later in the class.

Wordclouds

Date December 5, 2020

Posted by sugata

As part of my adventures in natural language processing and learning Python, I wanted to try to learn how to make word clouds. We see these things all the time in powerpoint presentations.

They look fairly cool and the technology used to create them seems fairly straightforward. The computer counts the number of times a word appears in some text. Words that appear more frequently are bigger (ignoring common words like “the,” “of,” “a” and such) and words that appear sometimes but not as frequently are still shown, but take up less space.

This was a bit before the 2020 election and I wanted to see if different news sources were covering different topics and I wanted to be able to visualize these differences easily.

I wrote some code (some repurposed from https://www.datacamp.com/community/tutorials/wordcloud-python ) to scrape the RSS feeds of various news sources and generate word clouds. These were the clouds I got from CNN and BBC.

And this is the code I used to generate it. It’s currently set up for the CNN URLs, but you can put in what RSS feed URLs in and it should work (you will need to appropriately indent it to get it working, the indents don’t paste properly, unfortunately).


#import library
import requests
from bs4 import BeautifulSoup
#import pandas to create dataframe and CSV
import pandas as pd
import time
from wordcloud import WordCloud, STOPWORDS 
import matplotlib.pyplot as plt 


#enter URL
cnnurls = ["http://rss.cnn.com/rss/cnn_topstories.rss",
        "http://rss.cnn.com/rss/cnn_world.rss",
        "http://rss.cnn.com/rss/cnn_us.rss",
        "http://rss.cnn.com/rss/money_latest.rss",
        "http://rss.cnn.com/rss/cnn_allpolitics.rss",
        "http://rss.cnn.com/rss/cnn_tech.rss",
     #   "http://rss.cnn.com/rss/cnn_health.rss",
     #   "http://rss.cnn.com/rss/cnn_showbiz.rss",
     #   "http://rss.cnn.com/rss/cnn_travel.rss",
        "http://rss.cnn.com/rss/money_news_companies.rss",
        "http://rss.cnn.com/rss/money_news_international.rss",
        "http://rss.cnn.com/rss/money_news_economy.rss"
       ]
bbcurls = ["http://feeds.bbci.co.uk/news/rss.xml",
           "http://feeds.bbci.co.uk/news/world/rss.xml",
           "http://feeds.bbci.co.uk/news/uk/rss.xml",
           "http://feeds.bbci.co.uk/news/business/rss.xml",
           "http://feeds.bbci.co.uk/news/politics/rss.xml",
          # "http://feeds.bbci.co.uk/news/health/rss.xml",
           "http://feeds.bbci.co.uk/news/education/rss.xml",
           "http://feeds.bbci.co.uk/news/technology/rss.xml",
           "http://feeds.bbci.co.uk/news/entertainment_and_arts/rss.xml"
          ]
news_items = []
for url in cnnurls:
    resp = requests.get(url)

    soup = BeautifulSoup(resp.content, features="xml")

    items = soup.findAll('item')

    #print(len(items))
    
    #scarring HTML tags such as Title, Description, Links and Publication date
    for item in items:
        news_item = {}
        news_item['title'] = item.title.text
        news_item['description'] = item.description.text
       # news_item['link'] = item.link.text
       # news_item['pubDate'] = item.pubDate.text

        news_items.append(news_item)
    time.sleep(1)

df = pd.DataFrame(news_items,columns=['title','description'])
df.to_csv('CNNdata1.csv',index=False, encoding = 'utf-8')

df = pd.read_csv('CNNdata1.csv',encoding = 'utf-8') 
  
comment_words = '' 
stopwords = set(STOPWORDS) 
  
# iterate through the csv file 
for val in df.title: 
      
    # typecaste each val to string 
    val = str(val) 
  
    # split the value 
    tokens = val.split() 
      
    # Converts each token into lowercase 
    for i in range(len(tokens)): 
        tokens[i] = tokens[i].lower() 
      
    comment_words += " ".join(tokens)+" "
  
wordcloud = WordCloud(width = 800, height = 800, 
                background_color ='white', 
                stopwords = stopwords, 
                min_font_size = 10).generate(comment_words) 
  
# plot the WordCloud image                        
plt.figure(figsize = (8, 8), facecolor = None) 
plt.imshow(wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Looping and scraping

Date September 12, 2019

Posted by sugata

In the previous posts, I covered how to scrape some data (like a stock price) from a website. To get a workable dataset, we can write some code to continually loop, and collect that same data at a fixed interval.

The code below does this. A few points. (1) Python uses indentation as part of the syntax. After starting a loop (the while 1==1: statement below) or a conditional (the if XXX==YYY statement below), everything you want looping or conditionally done has to be indented. (2) the while 1==1 line simply says keep doing this … forever. Since 1 will always be equal to 1. and (3) the if statement below checks if the current minute is divisible by 5 and runs the scraping code if it is. You can change the interval by changing 5 to another number, or using the now.second or now.hour numbers.

from selenium import webdriver
import datetime
import time
from multiprocessing import Pool,TimeoutError
import urllib.request
import re
from urllib.error import URLError, HTTPError

while 1==1:
now = datetime.datetime.now()
if now.minute/5 == int(now.minute/5):
driverspy = webdriver.Chrome()
driverspy.get(‘https://finance.yahoo.com/quote/SPY?p=SPY’)
sourcespy = driverspy.page_source
now = datetime.datetime.now()
found = re.search(‘”52″>(\d+\.\d+)’, sourcespy).group(1)
print(“Time:”+str(now.hour)+”:”+str(now.minute)+”:”+str(now.second)+” Price:”+str(found))
time.sleep(75)
driverspy.quit()

While the code runs, you’ll get output that looks like the following. You can then either copy paste this to a CSV file or use Python code to export it in order to start building a dataset.

Time:12:15:20 Price:302.10
Time:12:20:8 Price:302.08
Time:12:25:19 Price:302.05
Time:12:30:20 Price:302.07
Time:12:35:9 Price:302.17
Time:12:40:9 Price:302.09
Time:12:45:28 Price:302.22
Time:12:50:28 Price:302.24
Time:12:55:16 Price:302.26
Time:13:0:8 Price:302.18
Time:13:5:9 Price:302.01
Time:13:10:8 Price:301.96
Time:13:15:28 Price:302.01
Time:13:20:29 Price:302.04
Time:13:25:8 Price:301.96
Time:13:30:20 Price:301.96
Time:13:35:19 Price:302.10
Time:13:40:28 Price:302.27
Time:13:45:20 Price:302.24
Time:13:50:8 Price:302.21
Time:13:55:8 Price:302.19
Time:14:0:8 Price:302.16

Webscraping with Python 2

Date September 11, 2019

Posted by sugata

After an interesting class of helping students install Jupyter Notebook and try to get some basic web automation up and running with selenium and chromedriver, I realized there were some common pitfalls with easy (or some not so easy fixes).

When you run code in Python, you will sometimes (in my case, often) get an error. Since Python is a package based language, the error will sometimes be long and complicated. The most important thing to look for is right at the end, which refers to the line of code that generates the error.

So, for example, if you try to copy and run the code in the first Webscraping tutorial, the first error you will receive is:

This is a result of the quotation marks on this website being much fancier than those Python can handle. Essentially, all quotation marks should be non-directional so ‘ and ” instead of ‘ and ” and ″ and “ and ”. Replace directional quotations with non-directional ones.

The next error you will likely receive is:

This simply says you need a package (or module) called selenium installed. On a Windows machine, this is done by opening the Anaconda Prompt (Start->Anacoda3->Anaconda Prompt) and typing in the following: pip install selenium <enter>

this should be followed by an installation taking place and some text indicating success. Something that looks like this.

If you use a Mac, you can do the same thing by opening up a terminal window and typing in the same thing.

The next error you might receive is one involving chromedriver. If might say Chromedriver is not in PATH or perhaps Chromedriver is not compatible with your version of chrome. On a PC, the first error is fixed by putting a copy of chromedriver.exe (not the zip file, and not a shortcut) in the same folder as your Python notebook. If you don’t know where your Python notebooks are in your directory structure, you can search for ipynb files in your computer. Jupyter notebook files have the extension *.ipynb so thye should be quite easy to find.

On a Mac the first error is fixed by adding the folder with Chromedriver to the system PATH (see instructions here and follow the 3rd set of instructions, adding a directory to PATH for all users, forever) . For more information on what PATH is, check out the delightful wiki on the subject.

Finally, the last error you will likely get will be:

This is a cryptic error and simply means that it could not find the snippet of text the re.search command was looking for. That’s because Yahoo often changes the source code and the tag number changes from 35 to something else. AS of the time of writing ,it is 52. With that final fix, the code should be able to run.

Notice the last line I added: print(found) – without this line, the code would run, but would not do anything. The final line generates feedback to indicate success! The price of SPY at the time of running was $299.35.

So… what can we do with this? Well, we can write a small loop to get the price of SPY every few minutes. More on that in a bit….

Webscraping with Python

Date August 16, 2018

Posted by sugata

This is some code I wrote to scrape stock prices with Python. I wrote it on Jupyter notebook.

First off you’ll need chromedriver (Google “download chromedriver” and get the file on the first link. Put it in the folder with your Jupyter notebook.

Next, you’ll need a bunch of libraries, some of which will need to be pip installed.

from selenium import webdriver
import datetime
import time
from multiprocessing import Pool,TimeoutError
import urllib.request
import re
from urllib.error import URLError, HTTPError

In the code below, you won’t need all of this, but I’m just copying the entire import section of my code.

Next, we’ll fire up a browser.

driverspy = webdriver.Chrome()
driverspy.get(‘https://finance.yahoo.com/quote/SPY?p=SPY’)

This should open a python controlled browser that surfs its way to Yahoo Finance and loads up the page for SPY (a popular S&P 500 ETF).

Finally, we’ll define a function to scrape the price and then scrape the price off this page.

sourcespy = driverspy.page_source
found = re.search(‘”35″>(\d+\.\d+)’, sourcespy).group(1)

If you look at the html code of the page_source of the Yahoo page with the SPY data, you’ll see it has, buried in it, something that looks like this:

283.82+1.72 (+0.61%)<div

We rely on the bolded part always being the same (“35”> … and encapsulating the bold+underlined price (283.82) to extract the price. The \d+.\d+ tells Python to look for a positive number, a period and another positive number.

Now, we have a basic scraper to get prices from Yahoo finance. If we set up a loop, we can get prices every few minutes and generate a time series dataset.

Python Basics

Date April 26, 2018

Posted by sugata

I recently learned and started using Python for some of my projects. Python is a high level programming language with a number of pre-programmed packages for a variety of useful tasks. Tasks I’ve used Python for include scraping the web for data (excellent!), machine learning (meh … but that’s more my fault than Python’s), OCR (super meh), and algorithmic name classification, such as gender determination (again, excellent!).

While I will not provide direct code to perform predictive analysis using Python, I will use this post to link to a variety of resources that I have used, along with how I use it.

First, how to get started with Python. I use Jupyter Notebook, along with Anaconda. Both of these are installed when you download and install the latest version of Anaconda – google “download jupyter notebook” and go to the first link. The actual download will be from the Anaconda website. As of posting, the latest version is Python 3.6. Click “Download,” run the file and choose all the default options and install Python and Jupyter Notebook.

Jupyter Notebook runs inside your browser. Open up Jupyter Notebook, create a folder for coding, and then create a new Notebook. Each Notebook has distinct cells for distinct blocks of code that can be run separately. Once you run the code in a cell, the output is produced right below. Here is an example:

As you can see, when you run each cell, it simply generates the output right below. One thing I wanted to point out is that variables and variable types are generated dynamically. the code “a=1” first defined a as an integer and then sets it to two. Printing (and other functions) can be applied to integers (e.g. “print(a)”) or strings (e.g. print(‘hello world’)) but not to a mix (see the error in the second cell).

The second thing (and I love this) is the indentation is part of the language.

if 3>2:

print(‘hi’)
print(‘there’)

will return

hi
there

if 3<2:

print(‘hi’)
print(‘there’)

will return nothing

but

if 3<2:

print(‘hi’)

print(‘there’)

will return

there

The indentation controls what is run in the “if” statement. This forces discipline in generating readable (and workable) code.

Once you’ve gotten Python up and running – you’ll need additional packages to do other code.

For webscraping, I’d recommend selenium and chromedriver.

For OCR, I’d recommend Tesseract (Google’s OCR).

For machine learning, I use (but don’t know enough to recommend) tensorflow.