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PythonCHBPythonCHB
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Merge pull request UWPCE-PythonCert#91 from voite1/master
Submitting Adding Final Assignment
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‎Students/A.Kramer/Final_Project/Chart.py‎

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'''
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Created on Dec 3, 2014
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@author: Aleksey Kramer
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'''
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mticker
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import matplotlib.dates as mdates
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from matplotlib.finance import candlestick
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import pylab
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# adjusted font size for the plots
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matplotlib.rcParams.update({"font.size": 10})
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# define RSI function
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def rsiFunc(prices, n=14):
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deltas = np.diff(prices)
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seed = deltas[:n+1]
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up = seed[seed >= 0].sum() / n
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down = -seed[seed<0].sum() / n
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rs = up/down
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rsi = np.zeros_like(prices)
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rsi[:n] = 100. - 100./(1. + rs)
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for i in range(n, len(prices)):
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delta = deltas[i-1]
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if delta > 0:
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upval = delta
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downval = 0.
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else:
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upval = 0.
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downval = -delta
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up = (up * (n-1) +upval) / n
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down = (down*(n-1) + downval) / n
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rs = up/down
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rsi[i] = 100. - 100. / (1. + rs)
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return rsi
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# define moving average function
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def movingaverage(values, window):
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weights = np.repeat(1.0, window) / window
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smas = np.convolve(values, weights, 'valid')
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return smas # numpy array
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def graphData(stock, MA1=12, MA2=26):
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try:
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# define the data file
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stockFile = "./data/" + stock + ".txt"
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# Load data into numpy arrays
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date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile, delimiter=",",
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unpack=True, converters = {0: mdates.strpdate2num("%Y%m%d")})
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#-----------------------------------------------------------------------------------------------
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# building data for drawing plotting candlestick chart. Basically, a an array of comma separated
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# values. The order of elements is very specific, so check the documentation for candlestick
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x = 0
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y = len(date)
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candleArray = []
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while x < y:
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# order of elements matters!!! - used for candlestick charting
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appendLine = date[x], openp[x], closep[x], highp[x], lowp[x], volume[x]
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candleArray.append(appendLine)
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x += 1
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# moving averages for 12 and 26 days
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Av1 = movingaverage(closep, MA1)
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Av2 = movingaverage(closep, MA2)
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# Starting point for graphs
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SP = len(date[MA2-1:])
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# Creating Moving Average labels
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label1=str(MA1) + " SMA"
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label2=str(MA2) + " SMA"
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# changing the face color of the graphics
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fig = plt.figure(facecolor="#07000D")
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# create room and plot candlestick chart
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ax1 = plt.subplot2grid((5,4), (1,0), rowspan=4, colspan=4, axisbg="#07000D")
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candlestick(ax1, candleArray[-SP:], width=0.75, colorup="#9EFF15", colordown="#FF1717")
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# plot moving averages
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ax1.plot(date[-SP:], Av1[-SP:], "#5998FF", label=label1, linewidth=1.5)
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ax1.plot(date[-SP:], Av2[-SP:], "#E1EDF9", label=label2, linewidth=1.5)
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# Set grid color to white
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ax1.grid(True, color="white")
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# Set number of tickers on x-axis
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ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
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# Format date for presentation
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ax1.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d"))
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# Change label and border colors
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ax1.yaxis.label.set_color("white")
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ax1.spines["bottom"].set_color("#5998FF")
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ax1.spines["top"].set_color("#5998FF")
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ax1.spines["left"].set_color("#5998FF")
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ax1.spines["right"].set_color("#5998FF")
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# Change tick color to white
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ax1.tick_params(axis="y", colors="white")
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ax1.tick_params(axis="x", colors="white")
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plt.ylabel("Stock Price and Volume")
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# display legend and set size of font to 7
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maLeg = plt.legend(loc=9, ncol=2, prop={"size":7}, fancybox=True)
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# update label transparency
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maLeg.get_frame().set_alpha(0.4)
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textEd = pylab.gca().get_legend().get_texts()
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# set label color
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pylab.setp(textEd[0:5], color = "white")
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# Tilt the labels to 45 degrees
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for label in ax1.xaxis.get_ticklabels():
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label.set_rotation(45)
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# set up RSI area
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ax0 = plt.subplot2grid((5,4), (0,0), sharex=ax1, rowspan=1, colspan=4, axisbg="#07000d")
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#plot RSI
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rsi = rsiFunc(closep)
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rsiCol = "#00FFE8"
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ax0.plot(date[-SP:],rsi[-SP:], rsiCol, linewidth=1.5)
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ax0.axhline(70, color = rsiCol)
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ax0.axhline(30, color = rsiCol)
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# color the spaces between the horizontal lines and the RSI line
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ax0.fill_between(date[-SP:],rsi[-SP:], 70, where=(rsi[-SP:] >= 70), facecolor=rsiCol, edgecolor=rsiCol)
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ax0.fill_between(date[-SP:],rsi[-SP:], 30, where=(rsi[-SP:] <= 30), facecolor=rsiCol, edgecolor=rsiCol)
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# Change label and border colors
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ax0.spines["bottom"].set_color("#5998FF")
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ax0.spines["top"].set_color("#5998FF")
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ax0.spines["left"].set_color("#5998FF")
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ax0.spines["right"].set_color("#5998FF")
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# Change tick color to white
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ax0.tick_params(axis="x", colors="white")
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ax0.tick_params(axis="y", colors="white")
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ax0.set_yticks([30,70])
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ax0.yaxis.label.set_color("white")
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plt.ylabel("RSI")
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# Plot volume on the same range as ax1
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volumeMin = 0
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ax1v = ax1.twinx()
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# subtract moving average calculations
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ax1v.fill_between(date[-SP:], volumeMin, volume[-SP:], facecolor="#00FFE8", alpha=.5)
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ax1v.axes.yaxis.set_ticklabels([])
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# hide grid
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ax1v.grid(False)
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# Change label and border colors
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ax1v.spines["bottom"].set_color("#5998FF")
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ax1v.spines["top"].set_color("#5998FF")
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ax1v.spines["left"].set_color("#5998FF")
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ax1v.spines["right"].set_color("#5998FF")
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# update the height of the volume part
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ax1v.set_ylim(0,3*volume.max())
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# Change axis color
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ax1v.tick_params(axis="x", colors="white")
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ax1v.tick_params(axis="y", colors="white")
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# Setting up the overall appearance of the plot
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plt.subplots_adjust(left=.08, bottom=.14, right=.95, top=.95, wspace=.20, hspace=0)
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plt.suptitle(stock, color="white")
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plt.setp(ax0.get_xticklabels(), visible=False)
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plt.show()
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fig.savefig("./data/" + stock + ".png", facecolor=fig.get_facecolor())
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except Exception, e:
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print "main loop", str(e)
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if __name__ == "__main__":
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# testing the calls
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# a list of stocks to process (for testing)
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stocksToPull = 'AAPL', 'GOOG', 'AMZN', 'EBAY', 'CMG', 'MSFT', 'C', 'BA', 'TSLA'
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graphData(stocksToPull[3])
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‎Students/A.Kramer/Final_Project/DataFactory.py‎

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'''
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Created on Nov 28, 2014
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@author: Aleksey Kramer
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'''
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import os
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import time
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import urllib2
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import datetime
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def pullStockData(stock):
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# check if data directory exists, if not, create
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if not os.path.exists("./data"):
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os.makedirs("./data")
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# print out the pull time
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print "Pulling stock", stock, "\t",
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print str(datetime.datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d %H:%M:%S"))
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# define url and file
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urlToVisit="http://chartapi.finance.yahoo.com/instrument/1.0/" + stock + "/chartdata;type=quote;range=1y/csv"
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saveFileLine = stock + ".txt"
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# Determine the time of the last entry in the file, if file exists
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# set the last Unix time to 0, if file dies not exist
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try:
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existingData = open("./data/" + saveFileLine, "r")
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allLines = existingData.read();
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splitExisting = allLines.split("\n")
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lastLine = splitExisting[-2]
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lastUnix = int(lastLine.split(",")[0])
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existingData.close()
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except Exception, e:
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print "Pulling data: Determining last date", e
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lastUnix = 0
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# Obtain data for the ticket from Yahoo finance and split the file by new line
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sourceCode = urllib2.urlopen(urlToVisit).read()
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splitSource = sourceCode.split("\n")
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# Open file for writing and filter out the data to ensure that
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# each line contains 6 entries separated by comma
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# no 'visited' in the line
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# and the timing in the entries for each record is greater than the last one already
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# stored in existing file
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saveFile = open("./data/" + saveFileLine, "a")
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for eachLine in splitSource:
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if "values" not in eachLine:
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splitLine = eachLine.split(",")
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if len(splitLine) == 6:
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# assure we are only getting new data in the file
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if int(splitLine[0]) > lastUnix:
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lineToWrite = eachLine + "\n"
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saveFile.write(lineToWrite)
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saveFile.close()
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# Print out logging info and make program sleep for 1 second
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print "Pulled Stock", stock
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print "Sleeping....."
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print str(datetime.datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d %H:%M:%S"))
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time.sleep(3)
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# Pull the data for selected stocks
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if __name__ == "__main__":
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# Testing data pull
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stocksToPull = 'AAPL', 'GOOG', 'AMZN', 'EBAY', 'CMG', 'MSFT', 'C', 'BA', 'TSLA'
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pullStockData(stocksToPull[3])
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‎Students/A.Kramer/Final_Project/README.txt‎

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1. Libraries used in the code:
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os
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time
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datetime
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urllib2
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matplotlib
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numpy
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pylab
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2. To run the code, run RunFinal.py from command line.
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Once prompted for an input, input the valid stock on the
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prompt and hit ENTER key. Wait for several seconds. The
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code is getting data from Yahoo Finance web services,
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creates a 'data' directory in the directory from which
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the script is run, saves the data file with the name of the
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stock in the 'data' directory.
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After the data is saved, the code is plotting the data
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to the screen.
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After the code is done with plotting, it saves the image
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of the plot in the data directory with the name of the
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stock in .png format.
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The logs and warnings displayed are just for logging and
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verification of the output.

‎Students/A.Kramer/Final_Project/RunFinal.py‎

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'''
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Created on Dec 3, 2014
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The code pulls one year worth of data for the stock of your choice
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and graphs the data using matplotlib, displaying candlestick chart,
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simple moving averages for 12 and 26 days (default), and a Relative
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Strength Index (RSI) for selected stock.
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I found an excellent video tutorial on financial graphing with python
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that shows how to use matplotlib, change parameters of the plots
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and to do math calculation to derive moving averages and RSI indexes.
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The tutorial can be found on this web site: http://sentdex.com/
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This is excellent source to learn charting with matplotlib
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@author: Aleksey Kramer
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'''
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from Chart import graphData
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from DataFactory import pullStockData
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def runCode():
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''' Execute code for final project '''
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answer = raw_input("Enter The stock to graph: ")
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pullStockData(answer.upper().strip())
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graphData(answer.upper().strip())
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# run main program
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if __name__ == "__main__":
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runCode()
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‎Students/A.Kramer/session08/generator_solution.py‎

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def __is_prime(n):
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if n < 2:
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return False
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if n == 2:
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return True
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# checking only odd numbers
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for x in range(3, int(n**0.5)+1, 2):
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for x in range(2, int(n**0.5)+1, 2):
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if n % x == 0:
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return False
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return True

‎Students/A.Kramer/session09/The Absolute Minimum Every About Unicode and Character Sets.pdf‎

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