Preparing figure to Accept Finance Data
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})
eachStock = 'EBAY','TSLA','AAPL'
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
########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
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(figsize=(10,8),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')
ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper'))
plt.ylabel('MACD', color='w')
#################
ax3 = plt.subplot2grid((9,4), (6,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
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'))
ax3.grid(True)
ax4 = plt.subplot2grid((9,4), (7,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
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'))
ax4.grid(True)
ax5 = plt.subplot2grid((9,4), (8,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
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'))
ax5.grid(True)
for label in ax5.xaxis.get_ticklabels():
label.set_rotation(45)
#################
plt.suptitle(stock,color='w')
plt.setp(ax0.get_xticklabels(), visible=False)
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)
while True:
stock = raw_input('Stock to plot: ')
graphData(stock,25,50)
The next tutorial:
