Completing the Fundamental Investing Stock Screeners

import urllib2
import time
import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
from matplotlib.finance import candlestick
import matplotlib
import pylab
matplotlib.rcParams.update({'font.size': 9})

evenBetter = ['AWH', 'AFAM', 'ACAS', 'ASI', 'NLY', 'ANH', 'AIZ', 'AXS', 'BHLB', 'COF', 'CMO', 'DYN', 'FDEF', 'HMN', 'IM', 'IRDM', 'KMPR', 'LNC', 'LMIA', 'MANT', 'MCGC', 'MRH', 'MVC', 'NAVG', 'OVTI', 'PRE', 'PMC', 'PJC', 'PTP', 'BPOP', 'PL', 'RF', 'SKYW', 'STI', 'SPN', 'SYA', 'TECD', 'TSYS', 'TITN', 'TPC', 'UNM', 'VOXX', 'XL']


def rsiFunc(prices, n=14):
    deltas = np.diff(prices)
    seed = deltas[:n+1]
    up = seed[seed>=0].sum()/n
    down = -seed[seed<0].sum()/n
    rs = up/down
    rsi = np.zeros_like(prices)
    rsi[:n] = 100. - 100./(1.+rs)

    for i in range(n, len(prices)):
        delta = deltas[i-1] # the diff is 1 shorter

        if delta>0:
            upval = delta
            downval = 0.
        else:
            upval = 0.
            downval = -delta

        up = (up*(n-1) + upval)/n
        down = (down*(n-1) + downval)/n

        rs = up/down
        rsi[i] = 100. - 100./(1.+rs)

    return rsi

def movingaverage(values,window):
    weigths = np.repeat(1.0, window)/window
    smas = np.convolve(values, weigths, 'valid')
    return smas # as a numpy array

########EMA CALC ADDED############
def ExpMovingAverage(values, window):
    weights = np.exp(np.linspace(-1., 0., window))
    weights /= weights.sum()
    a =  np.convolve(values, weights, mode='full')[:len(values)]
    a[:window] = a[window]
    return a


def computeMACD(x, slow=26, fast=12):
    """
    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
    return value is emaslow, emafast, macd which are len(x) arrays
    """
    emaslow = ExpMovingAverage(x, slow)
    emafast = ExpMovingAverage(x, fast)
    return emaslow, emafast, emafast - emaslow

###############################
def graphData(stock,MA1,MA2):
    #######################################
    #######################################
    '''
        Use this to dynamically pull a stock:
    '''
    try:
        print 'Currently Pulling',stock


        netIncomeAr = []
        revAr = []
        ROCAr = []

        
        endLink = 'sort_order=asc&auth_token=a3fpXxHfsiN7AF4gjakQ'
        try:
            netIncome = urllib2.urlopen('http://www.quandl.com/api/v1/datasets/OFDP/DMDRN_'+stock.upper()+'_NET_INC.csv?&'+endLink).read()
            revenue = urllib2.urlopen('http://www.quandl.com/api/v1/datasets/OFDP/DMDRN_'+stock.upper()+'_REV_LAST.csv?&'+endLink).read()
            ROC = urllib2.urlopen('http://www.quandl.com/api/v1/datasets/OFDP/DMDRN_'+stock.upper()+'_ROC.csv?&'+endLink).read()

            splitNI = netIncome.split('\n')
            print 'Net Income:'
            for eachNI in splitNI[1:-1]:
                print eachNI
                netIncomeAr.append(eachNI)

            print '_________'
            splitRev = revenue.split('\n')
            print 'Revenue:'
            for eachRev in splitRev[1:-1]:
                print eachRev
                revAr.append(eachRev)

            
            print '_________'
            splitROC = ROC.split('\n')
            print 'Return on Capital:'
            for eachROC in splitROC[1:-1]:
                print eachROC
                ROCAr.append(eachROC)


            incomeDate, income = np.loadtxt(netIncomeAr, delimiter=',',unpack=True,
                                            converters={ 0: mdates.strpdate2num('%Y-%m-%d')})

            revDate, revenue = np.loadtxt(revAr, delimiter=',',unpack=True,
                                            converters={ 0: mdates.strpdate2num('%Y-%m-%d')})

            rocDate, ROC = np.loadtxt(ROCAr, delimiter=',',unpack=True,
                                            converters={ 0: mdates.strpdate2num('%Y-%m-%d')})

        except Exception, e:
            print 'failed in the quandl grab'
            print str(e)
            time.sleep(555)




        
        
        print str(datetime.datetime.fromtimestamp(int(time.time())).strftime('%Y-%m-%d %H:%M:%S'))
        #Keep in mind this is close high low open, lol. 
        urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
        stockFile =[]
        try:
            sourceCode = urllib2.urlopen(urlToVisit).read()
            splitSource = sourceCode.split('\n')
            for eachLine in splitSource:
                splitLine = eachLine.split(',')
                if len(splitLine)==6:
                    if 'values' not in eachLine:
                        stockFile.append(eachLine)
        except Exception, e:
            print str(e), 'failed to organize pulled data.'
    except Exception,e:
        print str(e), 'failed to pull pricing data'
    #######################################
    #######################################
    try:   
        date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile,delimiter=',', unpack=True,
                                                              converters={ 0: mdates.strpdate2num('%Y%m%d')})
        x = 0
        y = len(date)
        newAr = []
        while x < y:
            appendLine = date[x],openp[x],closep[x],highp[x],lowp[x],volume[x]
            newAr.append(appendLine)
            x+=1
            
        Av1 = movingaverage(closep, MA1)
        Av2 = movingaverage(closep, MA2)

        SP = len(date[MA2-1:])
            
        fig = plt.figure(facecolor='#07000d')

        ax1 = plt.subplot2grid((9,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d')
        candlestick(ax1, newAr[-SP:], width=.6, colorup='#53c156', colordown='#ff1717')

        Label1 = str(MA1)+' SMA'
        Label2 = str(MA2)+' SMA'

        ax1.plot(date[-SP:],Av1[-SP:],'#e1edf9',label=Label1, linewidth=1.5)
        ax1.plot(date[-SP:],Av2[-SP:],'#4ee6fd',label=Label2, linewidth=1.5)
        
        ax1.grid(True, color='w')
        ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
        ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
        ax1.yaxis.label.set_color("w")
        ax1.spines['bottom'].set_color("#5998ff")
        ax1.spines['top'].set_color("#5998ff")
        ax1.spines['left'].set_color("#5998ff")
        ax1.spines['right'].set_color("#5998ff")
        ax1.tick_params(axis='y', colors='w')
        plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
        ax1.tick_params(axis='x', colors='w')
        plt.ylabel('Stock price and Volume')

        maLeg = plt.legend(loc=9, ncol=2, prop={'size':7},
                   fancybox=True, borderaxespad=0.)
        maLeg.get_frame().set_alpha(0.4)
        textEd = pylab.gca().get_legend().get_texts()
        pylab.setp(textEd[0:5], color = 'w')

        volumeMin = 0
        
        ax0 = plt.subplot2grid((9,4), (0,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
        rsi = rsiFunc(closep)
        rsiCol = '#c1f9f7'
        posCol = '#386d13'
        negCol = '#8f2020'
        
        ax0.plot(date[-SP:], rsi[-SP:], rsiCol, linewidth=1.5)
        ax0.axhline(70, color=negCol)
        ax0.axhline(30, color=posCol)
        ax0.fill_between(date[-SP:], rsi[-SP:], 70, where=(rsi[-SP:]>=70), facecolor=negCol, edgecolor=negCol, alpha=0.5)
        ax0.fill_between(date[-SP:], rsi[-SP:], 30, where=(rsi[-SP:]<=30), facecolor=posCol, edgecolor=posCol, alpha=0.5)
        ax0.set_yticks([30,70])
        ax0.yaxis.label.set_color("w")
        ax0.spines['bottom'].set_color("#5998ff")
        ax0.spines['top'].set_color("#5998ff")
        ax0.spines['left'].set_color("#5998ff")
        ax0.spines['right'].set_color("#5998ff")
        ax0.tick_params(axis='y', colors='w')
        ax0.tick_params(axis='x', colors='w')
        plt.ylabel('RSI')

        ax1v = ax1.twinx()
        ax1v.fill_between(date[-SP:],volumeMin, volume[-SP:], facecolor='#00ffe8', alpha=.4)
        ax1v.axes.yaxis.set_ticklabels([])
        ax1v.grid(False)
        ###Edit this to 3, so it's a bit larger
        ax1v.set_ylim(0, 3*volume.max())
        ax1v.spines['bottom'].set_color("#5998ff")
        ax1v.spines['top'].set_color("#5998ff")
        ax1v.spines['left'].set_color("#5998ff")
        ax1v.spines['right'].set_color("#5998ff")
        ax1v.tick_params(axis='x', colors='w')
        ax1v.tick_params(axis='y', colors='w')
        ax2 = plt.subplot2grid((9,4), (5,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
        fillcolor = '#00ffe8'
        nslow = 26
        nfast = 12
        nema = 9
        emaslow, emafast, macd = computeMACD(closep)
        ema9 = ExpMovingAverage(macd, nema)
        ax2.plot(date[-SP:], macd[-SP:], color='#4ee6fd', lw=2)
        ax2.plot(date[-SP:], ema9[-SP:], color='#e1edf9', lw=1)
        ax2.fill_between(date[-SP:], macd[-SP:]-ema9[-SP:], 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)

        plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
        ax2.spines['bottom'].set_color("#5998ff")
        ax2.spines['top'].set_color("#5998ff")
        ax2.spines['left'].set_color("#5998ff")
        ax2.spines['right'].set_color("#5998ff")
        ax2.tick_params(axis='x', colors='w')
        ax2.tick_params(axis='y', colors='w')
        plt.ylabel('MACD', color='w')
        ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper'))

        ######################################
        ######################################
        ax3 = plt.subplot2grid((9,4), (6,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
        ax3.plot(incomeDate,income,'#4ee6fd')
        ax3.spines['bottom'].set_color("#5998ff")
        ax3.spines['top'].set_color("#5998ff")
        ax3.spines['left'].set_color("#5998ff")
        ax3.spines['right'].set_color("#5998ff")
        ax3.tick_params(axis='x', colors='w')
        ax3.tick_params(axis='y', colors='w')
        ax3.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='upper'))
        plt.ylabel('NI', color='w')

        ax4 = plt.subplot2grid((9,4), (7,0),sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
        ax4.plot(revDate, revenue,'#4ee6fd')
        ax4.spines['bottom'].set_color("#5998ff")
        ax4.spines['top'].set_color("#5998ff")
        ax4.spines['left'].set_color("#5998ff")
        ax4.spines['right'].set_color("#5998ff")
        ax4.tick_params(axis='x', colors='w')
        ax4.tick_params(axis='y', colors='w')
        ax4.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='upper'))
        plt.ylabel('Rev', color='w')

        ax5 = plt.subplot2grid((9,4), (8,0), rowspan=1, sharex=ax1, colspan=4, axisbg='#07000d')
        ax5.plot(rocDate, ROC,'#4ee6fd')
        ax5.spines['bottom'].set_color("#5998ff")
        ax5.spines['top'].set_color("#5998ff")
        ax5.spines['left'].set_color("#5998ff")
        ax5.spines['right'].set_color("#5998ff")
        ax5.tick_params(axis='x', colors='w')
        ax5.tick_params(axis='y', colors='w')
        ax5.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='upper'))
        plt.ylabel('ROC', color='w')



        
        for label in ax5.xaxis.get_ticklabels():
            label.set_rotation(45)

        plt.suptitle(stock,color='w')

        plt.setp(ax0.get_xticklabels(), visible=False)
        ### add this ####
        plt.setp(ax1.get_xticklabels(), visible=False)
        plt.setp(ax2.get_xticklabels(), visible=False)
        plt.setp(ax3.get_xticklabels(), visible=False)
        plt.setp(ax4.get_xticklabels(), visible=False)
        
        

        plt.subplots_adjust(left=.09, bottom=.14, right=.94, top=.95, wspace=.20, hspace=0)
        plt.show()
        fig.savefig('example.png',facecolor=fig.get_facecolor())
           
    except Exception,e:
        print 'main loop',str(e)









def screener(stock):
    try:
        #print 'doing',stock
        sourceCode = urllib2.urlopen('http://finance.yahoo.com/q/ks?s='+stock).read()
        pbr = sourceCode.split('Price/Book (mrq):</td><td class="yfnc_tabledata1">')[1].split('</td>')[0]
        #print 'price to book ratio:',stock,pbr

        if float(pbr) < 1:
            #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]
            if 0 < float(PEG5) < 2:
                #print 'PEG forward 5 years',PEG5
                
                
                DE = sourceCode.split('Total Debt/Equity (mrq):</td><td class="yfnc_tabledata1">')[1].split('</td>')[0]
                #print 'Debt to Equity:',DE
                #if 0 < float(DE) < 2:
                PE12 = sourceCode.split('Trailing P/E (ttm, intraday):</td><td class="yfnc_tabledata1">')[1].split('</td>')[0]
                #print 'Trailing PE (12mo):',PE12
                if float(PE12) < 15:
                    # Your own SCREENED array.... 
                    #evenBetter.append(stock)
                    
                    print '______________________________________'
                    print ''
                    print stock,'meets requirements'
                    print 'price to book:',pbr
                    print 'PEG forward 5 years',PEG5
                    print 'Trailing PE (12mo):',PE12
                    print 'Debt to Equity:',DE
                    print '______________________________________'

                    if showCharts.lower() == 'y':

                        try:
                            graphData(stock,25,50)

                        except Exception, e:
                            print 'failed the main quandl loop for reason of',str(e)


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

showCharts = raw_input('Would you like to show the financial data (Quandl) charts? (Y/N): ')

if showCharts.lower()=='y':
    print 'okay, charts will be shown'

elif showCharts.lwoer()=='n':
    print 'okay, charts will NOT be shown.'

else:
    print 'invalid input, charts will NOT be shown.'




        

for eachStock in evenBetter:
    screener(eachStock)

		

That's all for this series. For more tutorials, head to the Home Page