我对此感到挠头，因为我真的很困惑。
我正在尝试计算一个numpy数组的移动平均值。 numpy数组是从txt文件加载的。
我还尝试打印我的smas函数（我在加载的数据上计算的移动平均值），但没有这样做！
这是代码。
def backTest():
portfolio = 50000
tradeComm = 7.95
stance = 'none'
buyPrice = 0
sellPrice = 0
previousPrice = 0
totalProfit = 0
numberOfTrades = 0
startPrice = 0
startTime = 0
endTime = 0
totalInvestedTime = 0
overallStartTime = 0
overallEndTime = 0
unixConvertToWeeks = 7*24*60*60
unixConvertToDays = 24*60*60
date, closep, highp, lowp, openp, volume = np.genfromtxt('AAPL2.txt', delimiter=',', unpack=True,
converters={ 0: mdates.strpdate2num('%Y%m%d')})
window = 20
weights = np.repeat(1.0, window)/window
smas = np.convolve(closep, weights, 'valid')
prices = closep[19:]
for price in prices:
if stance == 'none':
if prices > smas:
print "buy triggered"
buyPrice = closep
print "bought stock for", buyPrice
stance = "holding"
startTime = unixStamp
print 'Enter Date:', time.strftime('%m/%d/%Y', time.localtime(startTime))
if numberOfTrades == 0:
startPrice = buyPrice
overallStartTime = unixStamp
numberOfTrades += 1
elif stance == 'holding':
if prices < smas:
print 'sell triggered'
sellPrice = closep
print 'finished trade, sold for:',sellPrice
stance = 'none'
tradeProfit = sellPrice - buyPrice
totalProfit += tradeProfit
print totalProfit
print 'Exit Date:', time.strftime('%m/%d/%Y', time.localtime(endTime))
endTime = unixStamp
timeInvested = endTime - startTime
totalInvestedTime += timeInvested
overallEndTime = endTime
numberOfTrades += 1
previousPrice = closep
这是错误：
Traceback (most recent call last):
File "C:\Users\antoniozeus\Desktop\backtester2.py", line 180, in <module>
backTest()
File "C:\Users\antoniozeus\Desktop\backtester2.py", line 106, in backTest
if prices > smas:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
最佳答案
如果您有一维一维的numpy数组，那么有一种非常巧妙的方式使用累积量（通过https://stackoverflow.com/a/14314054/1345536）进行移动平均：
def moving_average(a, n=3) :
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n - 1:] / n
您的代码段中有很多与手头问题无关的代码。
关于python - python错误-numpy数组上的移动平均值，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/22885899/