def build_episodes(episodesParams):
fromDt = date.from_yyyymmdd(episodesParams['fromDate'])
toDt = date.from_yyyymmdd(episodesParams['toDate'])
testDays = episodesParams['testDays']
validateRatio = episodesParams['validateRatio']
trainRatio = episodesParams['trainRatio']
validateDays = testDays * validateRatio
trainDays = testDays * trainRatio
episodeDays = trainDays + validateDays + testDays
diffDays = date.days_between(fromDt, toDt)
numEpisodes = int(diffDays / float(testDays))
subArrays = np.array_split(np.array(range(diffDays)), numEpisodes)
episodeFrom_ = [int(subArray[0]) for subArray in subArrays[:-(trainRatio + validateRatio)]]
trainFrom_ = [date.add_days(fromDt, episodeFrom) for episodeFrom in episodeFrom_]
trainTo_ = [date.add_days(fromDt, episodeFrom + trainDays) for episodeFrom in episodeFrom_]
validateFrom_ = [date.add_days(fromDt, episodeFrom + trainDays + 1) for episodeFrom in episodeFrom_]
validateTo_ = [date.add_days(fromDt, episodeFrom + trainDays + validateDays) for episodeFrom in episodeFrom_]
testFrom_ = [date.add_days(fromDt, episodeFrom + trainDays + validateDays + 1) for episodeFrom in episodeFrom_]
testTo_ = [date.add_days(fromDt, episodeFrom + trainDays + validateDays + testDays) for episodeFrom in episodeFrom_]
trainFromTo_ = zip(trainFrom_, trainTo_)
validateFromTo_ = zip(validateFrom_, validateTo_)
testFromTo_ = zip(testFrom_, testTo_)
episodes = {'fromDate' : fromDt, 'toDate' : toDt, 'num' : len(episodeFrom_), 'train' : trainFromTo_, 'validate' : validateFromTo_, 'test' : testFromTo_}
return episodes
def __init__(self, portfolio):
fromDt = date.from_yyyymmdd(portfolio['fromDate'])
toDt = date.from_yyyymmdd(portfolio['toDate'])
elements = portfolio['elements']
self.iMax = len(elements)
tradable_ = [Feature(element['tradable'], fromDt, toDt) for element in elements]
observable__ = [[Feature(observable, fromDt, toDt) for observable in element['observables']] for element in elements]
self.c_ = np.array([element['cost'] for element in elements], float)
self.jLen_ = np.array([len(observable_) + 2 for observable_ in observable__])
self.jLen = np.sum(self.jLen_)
jTo_ = np.cumsum(self.jLen_)
jFrom_ = np.roll(jTo_, 1)
jFrom_[0] = 0
self.jFromTo_ = zip(jFrom_, jTo_)
feature_ = tradable_ + [item for sublist in observable__ for item in sublist]
commonDate_ = Feature.common_dates(feature_)
for feature in feature_:
feature.filter_dates(commonDate_)
self.date_Orig = commonDate_
self.observation__ = [[observable.value_ for observable in observable_] for observable_ in observable__]
price__ = np.swapaxes(np.array([tradable.value_ for tradable in tradable_], float), 0, 1)
self.r__Orig = price__ - np.roll(price__, 1, 0)
self.r__Orig[0] = 0
def __init__(self, key, fromDt, toDt):
path = util.featurePath("%s.feature" % key)
yyyymmdd_, self.value_ = np.loadtxt(open(path, 'r'), delimiter = ',', unpack = True)
yyyymmdd_ = yyyymmdd_.astype(int)
self.date_ = [date.from_yyyymmdd(yyyymmdd) for yyyymmdd in yyyymmdd_]
self.range_dates(fromDt, toDt)
