ekam230/Python Matplotlib Tech Indicators

## Python Matplotlib Tech Indicators
# THIS VERSION IS FOR PYTHON 3 #

#https://pythonprogramming.net/advanced-matplotlib-graphing-charting-tutorial/
#
import urllib.request, urllib.error, urllib.parse
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_ohlc
import matplotlib
import pylab
matplotlib.rcParams.update({'font.size': 9})

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] # cause 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


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 bytespdate2num(fmt, encoding='utf-8'):
    strconverter = mdates.strpdate2num(fmt)
    def bytesconverter(b):
        s = b.decode(encoding)
        return strconverter(s)
    return bytesconverter

def graphData(stock,MA1,MA2):

    '''
        Use this to dynamically pull a stock:
    '''
    try:
        print('Currently Pulling',stock)
        urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
        stockFile =[]
        try:
            sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
            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 as e:
            print(str(e), 'failed to organize pulled data.')
    except Exception as e:
        print(str(e), 'failed to pull pricing data')

    try:
        date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile,delimiter=',', unpack=True,
                                                              converters={ 0: bytespdate2num('%Y%m%d')})
        x = 0
        y = len(date)
        newAr = []
        while x < y:
            appendLine = date[x],openp[x],highp[x],lowp[x],closep[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((6,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d')
        candlestick_ohlc(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((6,4), (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((6,4), (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'))
        for label in ax2.xaxis.get_ticklabels():
            label.set_rotation(45)

        plt.suptitle(stock.upper(),color='w')
        plt.setp(ax0.get_xticklabels(), visible=False)
        plt.setp(ax1.get_xticklabels(), visible=False)

        ax1.annotate('Big news!',(date[510],Av1[510]),
            xytext=(0.8, 0.9), textcoords='axes fraction',
            arrowprops=dict(facecolor='white', shrink=0.05),
            fontsize=14, color = 'w',
            horizontalalignment='right', verticalalignment='bottom')

        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 as e:
        print('main loop',str(e))

while True:
    stock = input('Stock to plot: ')
    graphData(stock,10,50)
	# THIS VERSION IS FOR PYTHON 3 #

	#https://pythonprogramming.net/advanced-matplotlib-graphing-charting-tutorial/
	#
	import urllib.request, urllib.error, urllib.parse
	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_ohlc
	import matplotlib
	import pylab
	matplotlib.rcParams.update({'font.size': 9})

	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] # cause 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


	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 bytespdate2num(fmt, encoding='utf-8'):
	strconverter = mdates.strpdate2num(fmt)
	def bytesconverter(b):
	s = b.decode(encoding)
	return strconverter(s)
	return bytesconverter

	def graphData(stock,MA1,MA2):

	'''
	Use this to dynamically pull a stock:
	'''
	try:
	print('Currently Pulling',stock)
	urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
	stockFile =[]
	try:
	sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
	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 as e:
	print(str(e), 'failed to organize pulled data.')
	except Exception as e:
	print(str(e), 'failed to pull pricing data')

	try:
	date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile,delimiter=',', unpack=True,
	converters={ 0: bytespdate2num('%Y%m%d')})
	x = 0
	y = len(date)
	newAr = []
	while x < y:
	appendLine = date[x],openp[x],highp[x],lowp[x],closep[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((6,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d')
	candlestick_ohlc(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((6,4), (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((6,4), (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'))
	for label in ax2.xaxis.get_ticklabels():
	label.set_rotation(45)

	plt.suptitle(stock.upper(),color='w')
	plt.setp(ax0.get_xticklabels(), visible=False)
	plt.setp(ax1.get_xticklabels(), visible=False)

	ax1.annotate('Big news!',(date[510],Av1[510]),
	xytext=(0.8, 0.9), textcoords='axes fraction',
	arrowprops=dict(facecolor='white', shrink=0.05),
	fontsize=14, color = 'w',
	horizontalalignment='right', verticalalignment='bottom')

	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 as e:
	print('main loop',str(e))

	while True:
	stock = input('Stock to plot: ')
	graphData(stock,10,50)