MIT OpenCourseWare http://ocw.mit.edu6.00 Introduction to Computer Science and ProgrammingFall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.6.00 Handout, Lecture 23 (Not intended to make sense outside of lecture) class Stock(object): def __init__(self, price, distribution): self.price = price self.history = [price] self.distribution = distribution self.lastChange = 0 def setPrice(self, price): self.price = price self.history.append(price) def getPrice(self): return self.price def makeMove(self, mktBias, mo): oldPrice = self.price baseMove = self.distribution() + mktBias self.price = self.price * (1.0 + baseMove) self.price += mo*random.gauss(.25,.25)*self.lastChange if self.price < 0.01: self.price = 0.0 self.history.append(self.price) self.lastChange = self.price - oldPrice def showHistory(self, figNum): pylab.figure(figNum) pylab.plot(self.history) pylab.title('Closing Price, Test ' + str(figNum)) pylab.xlabel('Day') pylab.ylabel('Price') def unitTestStock(): def runSim(stks, fig, mo): mean = 0.0 for s in stks: for d in range(numDays): s.makeMove(bias, mo) s.showHistory(fig) mean += s.getPrice() mean = mean/float(numStks) pylab.axhline(mean) numStks = 20 numDays = 200 stks1 = [] stks2 = [] bias = 0.0 mo = 0 for i in range(numStks): volatility = random.uniform(0, 0.2) d1 = lambda: random.uniform(-volatility, volatility) d2 = lambda: random.gauss(0.0, volatility/2.0) stks1.append(Stock(100.0, d1)) stks2.append(Stock(100.0, d2)) runSim(stks1, 1, mo) runSim(stks2, 2,
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