## Python Screener for PEG Ratio

Now that we've brought in the price to book ratio, let's bring another metric to screen with. So we're starting now only with stocks with a PBR that is less than 0.7, now, of those companies, let's look for a (P/e) / growth of interest. We want a ratio that is less than 1. The idea of the PEG ratio is that a perfect 1 represents a "fair value" in accordance with price to earnings to growth. The reason we dont use a price to earnings ratio only to compare companies, as it excludes the expected "growth" factor in companies, which is obviously very important.

Not only do we want less than 1 for the PEG ratio, but I think we should also exclude any negative PE ratio.

The way you get a negative PE ratio is to have negative growth, so people are paying 10 times earnings, but your growth is -5% lets say, giving you a -2 PEG ratio. Also, earnings could decline and give a negativer PEG. Personally, I think we should find something with positive PEG, as we're looking for the long term. A case could be made, however, for a negative PEG. IF the reason for it is something like a company recognizing that it is stretched too thin, and wants to bring it in a bit to become more efficient, that's a possibility too. For now, I will require PEG to be above 0, but you can use your own discretion for it.

Keep in mind that we're still no where near to getting good picks just based on these 2 factors. Generally, just one of the stats might be relatively useless. They are meant to be used together.

You may also want to compare PEGs that you find to related companies, since growth in sectors can still vary greatly.

```import time
import urllib2

sp500 = ['a', 'aa', 'aapl', 'abbv', 'abc', 'abt', 'ace', 'aci', 'acn', 'act', 'adbe', 'adi', 'adm', 'adp', 'adsk', 'adt', 'aee', 'aeo', 'aep', 'aes', 'aet', 'afl', 'agn', 'aig', 'aiv', 'aiz', 'akam', 'all', 'altr', 'alxn', 'amat', 'amd', 'amgn', 'amp', 'amt', 'amzn', 'an', 'anf', 'ann', 'aon', 'apa', 'apc', 'apd', 'aph', 'apol', 'arg', 'arna', 'aro', 'ati', 'atvi', 'avb', 'avp', 'avy', 'axp', 'azo', 'ba', 'bac', 'bax', 'bbby', 'bbry', 'bbt', 'bby', 'bcr', 'bdx', 'beam', 'ben', 'bf-b', 'bhi', 'big', 'biib', 'bk', 'bks', 'blk', 'bll', 'bmc', 'bms', 'bmy', 'brcm', 'brk-b', 'bsx', 'btu', 'bwa', 'bxp', 'c', 'ca', 'cab', 'cag', 'cah', 'cam', 'cat', 'cb', 'cbg', 'cbs', 'cce', 'cci', 'ccl', 'celg', 'cern', 'cf', 'cfn', 'chk', 'chrw', 'ci', 'cim', 'cinf', 'cl', 'clf', 'clx', 'cma', 'cmcsa', 'cme', 'cmg', 'cmi', 'cms', 'cnp', 'cnx', 'cof', 'cog', 'coh', 'col', 'cop', 'cost', 'cov', 'cpb', 'crm', 'csc', 'csco', 'csx', 'ctas', 'ctl', 'ctsh', 'ctxs', 'cvc', 'cvs', 'cvx', 'd', 'dal', 'dd', 'dds', 'de', 'dell', 'df', 'dfs', 'dg', 'dgx', 'dhi', 'dhr', 'dis', 'disca', 'dks', 'dlph', 'dltr', 'dlx', 'dnb', 'dnr', 'do', 'dov', 'dow', 'dps', 'dri', 'dsw', 'dte', 'dtv', 'duk', 'dva', 'dvn', 'ea', 'ebay', 'ecl', 'ed', 'efx', 'eix', 'el', 'emc', 'emn', 'emr', 'eog', 'eqr', 'eqt', 'esrx', 'esv', 'etfc', 'etn', 'etr', 'ew', 'exc', 'expd', 'expe', 'expr', 'f', 'fast', 'fb', 'fcx', 'fdo', 'fdx', 'fe', 'ffiv', 'fhn', 'fis', 'fisv', 'fitb', 'fl', 'flir', 'flr', 'fls', 'flws', 'fmc', 'fosl', 'frx', 'fslr', 'fti', 'ftr', 'gas', 'gci', 'gd', 'ge', 'ges', 'gild', 'gis', 'glw', 'gm', 'gmcr', 'gme', 'gnw', 'goog', 'gpc', 'gps', 'grmn', 'grpn', 'gs', 'gt', 'gww', 'hal', 'har', 'has', 'hban', 'hcbk', 'hcn', 'hcp', 'hd', 'hes', 'hig', 'hog', 'hon', 'hot', 'hov', 'hp', 'hpq', 'hrb', 'hrl', 'hrs', 'hsp', 'hst', 'hsy', 'hum', 'ibm', 'ice', 'iff', 'igt', 'intc', 'intu', 'ip', 'ipg', 'ir', 'irm', 'isrg', 'itw', 'ivz', 'jbl', 'jci', 'jcp', 'jdsu', 'jec', 'jnj', 'jnpr', 'josb', 'joy', 'jpm', 'jwn', 'k', 'key', 'kim', 'klac', 'kmb', 'kmi', 'kmx', 'ko', 'kr', 'krft', 'kss', 'ksu', 'l', 'leg', 'len', 'lh', 'life', 'lll', 'lltc', 'lly', 'lm', 'lmt', 'lnc', 'lo', 'low', 'lrcx', 'lsi', 'ltd', 'luk', 'luv', 'lyb', 'm', 'ma', 'mac', 'mar', 'mas', 'mat', 'mcd', 'mchp', 'mck', 'mco', 'mcp', 'mdlz', 'mdt', 'met', 'mgm', 'mhfi', 'mjn', 'mkc', 'mmc', 'mmm', 'mnst', 'mo', 'molx', 'mon', 'mos', 'mpc', 'mrk', 'mro', 'ms', 'msft', 'msi', 'mtb', 'mu', 'mur', 'mwv', 'myl', 'nbl', 'nbr', 'ndaq', 'ne', 'nee', 'nem', 'nflx', 'nfx', 'ni', 'nile', 'nke', 'nly', 'noc', 'nok', 'nov', 'nrg', 'nsc', 'ntap', 'ntri', 'ntrs', 'nu', 'nue', 'nvda', 'nwl', 'nwsa', 'nyx', 'oi', 'oke', 'omc', 'orcl', 'orly', 'oxy', 'p', 'payx', 'pbct', 'pbi', 'pcar', 'pcg', 'pcl', 'pcln', 'pcp', 'pdco', 'peg', 'pep', 'petm', 'pets', 'pfe', 'pfg', 'pg', 'pgr', 'ph', 'phm', 'pki', 'pld', 'pll', 'pm', 'pnc', 'pnr', 'pnw', 'pom', 'ppg', 'ppl', 'prgo', 'pru', 'psa', 'psx', 'pwr', 'px', 'pxd', 'qcom', 'qep', 'r', 'rai', 'rdc', 'rf', 'rhi', 'rht', 'rl', 'rok', 'rop', 'rost', 'rrc', 'rsg', 'rsh', 'rtn', 's', 'sai', 'sbux', 'scg', 'schl', 'schw', 'sd', 'se', 'see', 'sfly', 'shld', 'shw', 'sial', 'siri', 'sjm', 'sks', 'slb', 'slm', 'sna', 'sndk', 'sne', 'sni', 'so', 'spg', 'spls', 'srcl', 'sre', 'sti', 'stj', 'stt', 'stx', 'stz', 'swk', 'swn', 'swy', 'syk', 'symc', 'syy', 't', 'tap', 'tdc', 'te', 'teg', 'tel', 'ter', 'tgt', 'thc', 'tibx', 'tif', 'tjx', 'tm', 'tmk', 'tmo', 'trip', 'trow', 'trv', 'tsla', 'tsn', 'tso', 'tss', 'twc', 'twx', 'txn', 'txt', 'tyc', 'ua', 'unh', 'unm', 'unp', 'ups', 'urbn', 'usb', 'utx', 'v', 'vale', 'var', 'vfc', 'viab', 'vitc', 'vlo', 'vmc', 'vno', 'vprt', 'vrsn', 'vtr', 'vz', 'wag', 'wat', 'wdc', 'wec', 'wfc', 'wfm', 'whr', 'win', 'wlp', 'wm', 'wmb', 'wmt', 'wpo', 'wpx', 'wtw', 'wu', 'wy', 'wyn', 'wynn', 'x', 'xel', 'xl', 'xlnx', 'xom', 'xray', 'xrx', 'xyl', 'yhoo', 'yum', 'zion', 'zlc', 'zmh', 'znga', 'camp', 'cldx', 'ecyt', 'gtn', 'htz', 'nus', 'pvtb', 'qdel', 'snts', 'wgo', 'wwww']

def yahooKeyStats(stock):
try:
pbr = sourceCode.split('Price/Book (mrq):</td><td class="yfnc_tabledata1">')[1].split('</td>')[0]

if float(pbr) < .70:
print 'price to book ratio:',stock,pbr

PEG5 = sourceCode.split('PEG Ratio (5 yr expected)<font size="-1"><sup>1</sup></font>:</td><td class="yfnc_tabledata1">')[1].split('</td>')[0]

print PEG5

except Exception,e:
print 'failed in the main loop',str(e)

for eachStock in sp500short:
yahooKeyStats(eachStock)
time.sleep(1)
```

The next tutorial:

• Programming for Fundamental Investing

• Getting Company Data

• Price to Book ratio example

• Python Stock Screener for Price to Book

• Python Screener for PEG Ratio

• Getting all Russell 3000 stock tickers

• Getting all Russell 3000 stock tickers part 2

• More stock Screening

• Completing Basic Stock Screener

• Connecting with Quandl for Annual Earnings Data

• Organizing Earnings Data

• Graphing Finance Data

• Finishing the Graphing

• Adding the Graphing to the Screener

• Preparing figure to Accept Finance Data

• Adding Historical Earnings to Stock Screener Chart Data

• Completing the Fundamental Investing Stock Screeners