Python MIT 开放课件股市模拟不完整?
我刚刚从在线发布的麻省理工学院视频讲座中复制了这段代码:(Lec 23 | MIT 6.00 计算机科学与编程简介,2008 年秋季)。由于我必须从视频讲座中复制它,因此我不确定我是否获得了完整的程序。它不能按原样工作,我可以使用一些指导。
谢谢。
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
I just copied this code from the MIT video lecture that is posted online: (Lec 23 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008). Since I had to copy it from a video lecture, I'm not sure I got the complete program. It is not working as is, I could use some guidance.
Thanks.
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
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除了错误输入变量 s 和缺少平均分配之外,您还存在缩进问题。
目前,您当前已将 unitTestStock() 定义为 Stock 类的属性。这不是您想要的,尤其是 unitTestStock 没有 self 参数。要解决您的问题,请合并上述更改,然后缩进函数 unitTestStock() 的整个主体及其后面的 3 行。
代码应该如下所示:
In addition to mistyping the variable s and missing the mean assignment, you also have an indentation problem.
As it stands, you've currently defined unitTestStock() as an attribute of the Stock class. This is not what you want, especially as unitTestStock has no self parameter. To fix your problem, incorporate the above changes, and then dedent the entire body of the function unitTestStock() and the 3 lines following it.
The code should look like this:
您似乎缺少
mean = 0.0
并且需要将a
更改为s
:PS。我认为大部分代码都在 此 pdf,可以在 此页面。
You seem to be missing
mean = 0.0
and need to change ana
to ans
:PS. I think most of this code is in this pdf, which can be found on this page.