Data Scraping the Toronto Stock Exchange: Extracting 3,660 companies' data

One of the tasks that I've always wanted to make more efficient in my stock trading is the work of scanning for stocks to trade. One look at my trading strategy posts and you'll see that I have devised many stock scanning systems in the past few years. The most recent system that I've used is one that uses options data to filter stocks. However, it is not automated. So it takes a lot of time to gather and analyze the data. Furthermore, the set of tools that I use is limited to U.S. stocks. Now that I have taken an interest in the Canadian stock market, I can't seem to find any public tool that I like. Thus, I am biting the bullet now and taking my time to develop a custom system once and for all. Before we can analyze stock data, we need to extract them first. Where better else for that than go straight to the source at, the parent company of Toronto Stock Exchange (TSX) and TSX Venture Exchange (TSXV). provide a list of publicly traded companies in ten Excel files. The files are divided by sectors. Each contain a number of fundamental company data, such as market capitalization and outstanding shares. So step 1 is to extract those data. This is where I am at now. I attached the source code for an alpha/developmental release below for anyone interested. It is a working program to scrape the data from's files. But it's still a work-in-progress. That's why I am calling it a version 0.1. The next milestone is to program a Stocks class to hold, organize, and manage all three thousand, six hundred, and sixty companies' data. This is an easy task to do by extending the built-in dictionary class in Python. However, I haven't gotten to that chapter yet in my scientific programming with Python learning book. I stopped at chapter 8 to work on this project. Chapter 9 is the inheritence and hierarchical material. The goal of this project is to build an automated data scraping program for TSX and TSXV data from various sources into my computer. Once I have my data, that's when the real fun starts. Regarding the code below, I know that source code is useless for most people. Once the project is complete, I will compile the code into a standalone application and post it on this site. Subscribe to my RSS feed so that you can keep up-to-date with the progress of this project and my other ramblings on trading. [python] # # version: 0.1 alpha release # revision date: March, 2010 # by Paul, """A data scraping module to extract company listing excel files from TMX.COM""" import xlrd # to read Excel file #import sys from finClasses import Stock # custom Stock class def _verify(): """Verification function for a rundown of the module""" pass # copy test block here when finished def findCol(sheet, key): """Find the column corresponding to header string 'key'""" firstRow = sheet.row_values(0) for col in range(len(firstRow)): if key in firstRow[col]: return col # return first sighting else: # not found raise ValueError("%s is not found!" % key) def scrapeXLS(book): """Data scraping function for TMX Excel file""" listingDict = {} # dict of ('ticker': market cap) for index in range(book.nsheets): sh = book.sheet_by_index(index) mcCol = findCol(sh, "Market Value") assert type(mcCol) is int, "mcCol is a %s" % type(mcCol) osCol = findCol(sh, "O/S Shares") assert type(osCol) is int, "osCol is a %s" % type(osCol) secCol = findCol(sh, "Sector") # multiple matches but taking first assert type(secCol) is int, "secCol is a %s" % type(secCol) hqCol = findCol(sh, "HQ\nRegion") assert type(hqCol) is int, "hqCol is a %s" % type(hqCol) for rx in range(1, sh.nrows): sym = str(sh.cell_value(rowx=rx, colx=4)) # symbol s = sh.cell_value(rowx=rx, colx=2) # exchange col. if s == "TSX": exch = "T" elif s == "TSXV": exch = "V" else: raise TypeError("Unknown exchange value") mc = sh.cell_value(rowx=rx, colx=mcCol) # market cap # check for empty market cap cell mc = int(mc) if type(mc) is float else 0 os = int(sh.cell_value(rowx=rx, colx=osCol)) # O/S shares sec = str(sh.cell_value(rowx=rx, colx=secCol)) # sector hq = str(sh.cell_value(rowx=rx, colx=hqCol)) # HQ region listingDict[sym] = Stock(symbol=sym,exchange=exch, mktCap=mc,osShares=os, sector=sec,hqRegion=hq) return listingDict def fetchFiles(fname): infile = open(fname, 'r') # text file of XLS file names listing = {} for line in infile: # 1 file name per line if line[0] == '#': continue # skip commented lines line = line.strip() # strip trailing \n print "Reading '%s' ..." % line xlsFile = "TMX/" + line # in TMX directory book = xlrd.open_workbook(xlsFile) # import Excel file listing.update(scrapeXLS(book)) # append scraped the data to dict return listing #if __name__ == '__main__': # verify block # if len(sys.argv) == 2 and sys.argv[1] == 'verify': # _verify() if __name__ == '__main__': # test block listing = fetchFiles('TMX/TMXfiles.txt') [/python]