def __init__(self, strategy, index_code=None):
"""
:param strategy: 策略
:param index_code: index基准利率代码应该同tushare保持一致
sh 上证指数 sz 深圳成指 hs300 沪深300指数 sz50 上证50指数 zxb 中小板指数 cyb 创业板指数
:return:
"""
self.__baseRet_code = index_code
self.__baseRet_data = None
self.__currBaseRet = []
self.__last_baseRet = 0.0
self.__baseRet = dataseries.SequenceDataSeries()
self.__betaRet = dataseries.SequenceDataSeries()
self.__alphaRet = dataseries.SequenceDataSeries()
self.__sharpeRet = dataseries.SequenceDataSeries()
self.__LongestDrawDown = dataseries.SequenceDataSeries()
self.__MaxDrawDown = dataseries.SequenceDataSeries()
self.__ret_analyzer = returns.Returns()
self.__sharpe_ratio = sharpe.SharpeRatio(False)
self.__draw_down = drawdown.DrawDown()
self.__beta = Beta()
strategy.attachAnalyzer(self.__ret_analyzer)
strategy.attachAnalyzer(self.__sharpe_ratio)
strategy.attachAnalyzer(self.__draw_down)
strategy.attachAnalyzer(self.__beta)
strategy.attachAnalyzer(self)
from gamma import Gamma
from inv_gamma import Inverse_gamma
from student_t import Student_t
from uniform import Uniform
from numpy import array
x = array([1.2, 1.5, 2.1, 5.4])
x2 = array([.1, .3, .4, .7])
mu, sigma, alpha, beta, k, gamma, d1, d2, kk, theta = [
1.2, 2.3, .4, .5, 4, 3, 4, 6, 1.6, .7
]
lnorm = Lognorm(mu, sigma)
norm = Normal(mu, sigma)
beta = Beta(alpha, beta)
chi = Chi(k)
chi2 = Chi_square(k)
exp = Exponential(gamma)
f = F_dist(d1, d2)
gamma = Gamma(kk, theta)
invgamma = Inverse_gamma(mu, sigma)
t = Student_t(d2)
uniform = Uniform(alpha, d2)
print 'lnorm.cdf(x): ', lnorm.cdf(x)
print 'norm.cdf(x): ', norm.cdf(x)
print 'beta.cdf(x2): ', beta.cdf(x2)
print 'chi.cdf(x): ', chi.cdf(x)
print 'chi2.cdf(x): ', chi2.cdf(x)
print 'exp.cdf(x): ', exp.cdf(x)
