Python readWSDFile 예제들

예제 #1

파일: TestingSVM.py 프로젝트: mornydew/PyProj

baseDir = '/Users/eugene/Downloads/Data/'
# baseDir = '/Users/eugene/Downloads/marketQuotationData/'
# 沪深300 上证50 中证500
instruments = [
    '000300.SH', '000016.SH', '000905.SH', '002047.SZ', '600015.SH',
    '600674.SH', '000566.SZ'
]
instrument = instruments[6]
initCapital = 10000  #100000000.0 # 一亿
startYear = 2015
yearNum = 2
# startYear = 2014; yearNum = 3
# startYear = 2014; yearNum = 2
winK = 15

df = readWSDFile(baseDir, instrument, startYear, yearNum)
print 'Day count:', len(df)
# print df.head(5)
dfi = readWSDIndexFile(baseDir, instrument, startYear, yearNum)

X, y, actionDates = prepareData(df, dfi, win=winK)
print np.shape(X), np.shape(actionDates), np.shape(y)
print y
normalizer = preprocessing.Normalizer().fit(X)  # fit does nothing
X_norm = normalizer.transform(X)
gamma, C, score = optimizeSVM(X_norm, y, kFolds=10)
print 'gamma=', gamma, 'C=', C, 'score=', score
clf = svm.SVC(kernel='rbf', gamma=gamma, C=C)
# clf = svm.SVC(kernel='rbf', gamma=0.125, C=0.125)
# clf = svm.SVC(kernel='rbf', gamma=512, C=32768)
# clf = svm.SVC(kernel='rbf', gamma=2048, C=32768)

예제 #2

파일: TestingRF.py 프로젝트: Ernestyj/PyProj

'''
def annualizedReturnRatioSingle(portfolio, C=100000.0, T=250.0, D=250.0):
    import math
    return math.pow(portfolio/C, D/T) - 1


baseDir = '/Users/eugene/Downloads/Data/'
# baseDir = '/Users/eugene/Downloads/marketQuotationData/'
# 沪深300 上证50 中证500
instruments = ['000300.SH', '000016.SH', '000905.SH']
instrument = instruments[2]
initCapital = 100000000.0 # 一亿
startYear = 2015; yearNum = 1
# startYear = 2014; yearNum = 2

df = readWSDFile(baseDir, instrument, startYear, yearNum)
print 'Day count:', len(df)
# print df.head(5)
dfi = readWSDIndexFile(baseDir, instrument, startYear, yearNum)

X, y, actionDates = prepareData(df, dfi, win=16)
print np.shape(X), np.shape(actionDates), np.shape(y); print y
normalizer = preprocessing.Normalizer().fit(X)  # fit does nothing
X_norm = normalizer.transform(X)
# gamma, C, score = optimizeSVM(X_norm, y, kFolds=10); print 'gamma=',gamma, 'C=',C, 'score=',score
# clf = svm.SVC(kernel='rbf', gamma=0.125, C=0.125)
clf = svm.SVC(kernel='rbf', gamma=512, C=32768)
# clf = svm.SVC(kernel='rbf', gamma=2048, C=32768)
# clf = svm.SVC(kernel='rbf', gamma=2048, C=32768)
# clf = svm.SVC(kernel='rbf', gamma=0.125, C=0.125)
# clf = svm.SVC(kernel='rbf', gamma=0.125, C=0.125)