Python MIT Open Courseware Stock Market Modeling Not Really?

Question

I simply copied this code from the MIT video lecture published online: (Lec 23 | MIT 6.00 Introduction to Computer Science and Programming, Autumn 2008). Since I had to copy it from a video lecture, I'm not sure I got the full program. It does not work as it is, I could use some recommendations.

Thank.

import pylab, random

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)
        if mo:
            self.price = self.price + random.gauss(.5, .5)*self.lastChange
        if self.price < 0.01:
            self.price = 0.0
        self.history.append(self.price)
        self.lastChange = oldPrice - self.price

    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):
            for a 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 = False
        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, mo)

    unitTestStock()
    pylab.show()
    assert False

class Market(object):
    def __init__(self):
        self.stks = []
        self.bias = 0.0

Answer 1

In addition to the errors of the s variable and the lack of a middle purpose, you also have a problem with indentation.

unitTestStock() Stock. , , , unitTestStock . , , unitTestStock() 3 .

:

class Stock(object):
    <...>

    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 = False
    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, mo)

unitTestStock()
pylab.show()
assert False

Answer 2

, mean = 0.0 a s:

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)

PS. , pdf, .