def __init__(self,
startDate,
endDate,
start_time='9:00',
end_time='16:00',
holidays=[],
weekmask='Mon Tue Wed Thu Fri',
calendar=None,
frequency='H',
sample='1'):
self.__startDate = startDate
self.__endDate = endDate
self.__sample = sample
acceptable_freq = ['D', 'M', 'H', 'S']
if frequency not in acceptable_freq:
raise ValueError(
'Frequency Value Not acceptable. Specify D, M, H, S')
self.__frequency = frequency
if (calendar != None):
self.__bday = CustomBusinessDay(calendar=calendar)
self.__bhour = CustomBusinessHour(start=start_time,
end=end_time,
calendar=calendar)
else:
self.__bday = CustomBusinessDay(holidays=holidays,
weekmask=weekmask)
self.__bhour = CustomBusinessHour(start=start_time,
end=end_time,
holidays=holidays,
weekmask=weekmask)
def create_bussiness_hour(start='00:00',
end='23:00',
week_mask='Mon Tue Wed Thu Fri'):
"""
This function creates custom business hour for using in date ranges creation.
Parameters
----------
start : str
The business hours start.
end : str
The business hours end.
week_mask : str
The mask that specify work weekdays.
Returns
-------
object
The business hour object.
"""
bhour = CustomBusinessHour(start='08:00',
end='23:00',
weekmask=week_mask)
def test_custom_businesshour_weekmask_and_holidays(weekmask, expected_time, mult):
# GH 23542
holidays = ["2018-11-09"]
bh = CustomBusinessHour(
start="08:00", end="17:00", weekmask=weekmask, holidays=holidays
)
result = Timestamp("2018-11-08 08:00") + mult * bh
expected = Timestamp(expected_time)
assert result == expected
def __init__(self,
startDate,
endDate,
startTime='9:00',
endTime='16:00',
holidays=[],
weekmask='Mon Tue Wed Thu Fri',
calendar=None,
frequency='H',
sample='1'):
self.__startDate = startDate
self.__endDate = endDate
self.__sample = sample
acceptable_freq = [
'B', 'C', 'D', 'W', 'M', 'SM', 'BM', 'CBM', 'MS', 'SMS', 'BMS',
'CBMS', 'Q', 'BQ', 'QS', 'BQS', 'A', 'Y', 'BA', 'BY', 'AS', 'YS',
'BAS', 'BYS', 'BH', 'H', 'T', 'S', 'L', 'U', 'N'
]
if frequency not in acceptable_freq:
raise ValueError(
'Frequency Value Not acceptable. Specify D, M, H, S')
self.__frequency = frequency
start = datetime.strptime(startTime, '%H:%M')
self.startMinuteDelta = start.hour * 60 + start.minute
end = datetime.strptime(endTime, '%H:%M')
self.endMinuteDelta = end.hour * 60 + end.minute
if (calendar != None):
self.__bday = CustomBusinessDay(calendar=calendar)
self.__bhour = CustomBusinessHour(start=startTime,
end=endTime,
calendar=calendar)
else:
self.__bday = CustomBusinessDay(holidays=holidays,
weekmask=weekmask)
self.__bhour = CustomBusinessHour(start=startTime,
end=endTime,
holidays=holidays,
weekmask=weekmask)
def __init__(self, startDate, endDate, startTime='9:00', endTime='16:00', holidays = [], weekmask = 'Mon Tue Wed Thu Fri', calendar = None, frequency='H', sample='1'):
self.__startDate = startDate
self.__endDate = endDate
self.__sample = sample
acceptable_freq = ['D', 'M', 'H', 'S', 'm']
if frequency not in acceptable_freq:
raise ValueError('Frequency Value Not acceptable. Specify D(day), M(minute), H(hour), S(second), m(month)')
self.__frequency = frequency
start = datetime.strptime(startTime, '%H:%M')
self.startMinuteDelta = start.hour * 60 + start.minute
end = datetime.strptime(endTime, '%H:%M')
self.endMinuteDelta = end.hour * 60 + end.minute
if(calendar != None):
self.__bday = CustomBusinessDay(calendar = calendar)
self.__bhour = CustomBusinessHour(start = startTime, end = endTime, calendar = calendar)
else:
self.__bday = CustomBusinessDay(holidays = holidays, weekmask = weekmask)
self.__bhour = CustomBusinessHour(start = startTime, end = endTime, holidays = holidays, weekmask = weekmask)
def get_startdate(self):
# today = pd.datetime.today()
# print(today)
# print( today - BDay(4))
# return self.date_pickup - BDay(self.duration.days())
#print(calendar.holidays(start='2020-01-01', end='2020-12-31'))
start_after_calculation = self.date_pickup - CustomBusinessDay(
self.duration.days, calendar=calendar)
if self.duration.seconds > 0:
start_after_calculation = start_after_calculation - CustomBusinessHour(
self.duration.seconds // 3600,
calendar=calendar,
start='06:59',
end='14:59'
) # - datetime.timedelta(seconds=self.duration.seconds);
return start_after_calculation
from pandas.tseries.holiday import USFederalHolidayCalendar
from pandas.tseries.offsets import CustomBusinessHour
import pandas as pd
import numpy as np
from sklearn import metrics
FX_LIST = ['EURUSD', 'USDJPY', 'GBPUSD', 'AUDUSD', 'EURJPY', 'EURGBP']
PREX = '../data/fx/prediction'
CROSS_PREX = '../data/fx/prediction/cross'
MON_PREX = '../data/fx/prediction/24'
DAY_PREX = '../data/fx/prediction/1'
CUSTOM_BH = CustomBusinessHour(calendar=USFederalHolidayCalendar(),
start='00:00',
end="01:00")
RANGE_TIME = pd.DatetimeIndex(start='20150106', end='20160531', freq=CUSTOM_BH)
def to_summary(fx):
# LOAD DATA
cro_cnn = pd.read_pickle('%s/NN/%sprediction.pkl' %
(CROSS_PREX, fx)).reset_index()
cro_knn = pd.read_pickle('%s/k-NN/%sprediction.pkl' %
(CROSS_PREX, fx)).reset_index()
cro_svm = pd.read_pickle('%s/SVM/%sprediction.pkl' %
(CROSS_PREX, fx)).reset_index()
mon_ann = pd.read_pickle('%s/NN/%sprediction.pkl' %
(MON_PREX, fx)).reset_index()
mon_knn = pd.read_pickle('%s/k-NN/%sprediction.pkl' %
(MON_PREX, fx)).reset_index()
mon_svm = pd.read_pickle('%s/SVM/%sprediction.pkl' %
(MON_PREX, fx)).reset_index()
USThanksgivingDay,
Holiday('Christmas', month=12, day=25, observance=nearest_workday)
]
# US business day
us_bd = CustomBusinessDay(calendar=USFederalHolidayCalendar())
# US financial business day
fi_bd = CustomBusinessDay(calendar=USFinancialHolidayCalendar())
fi_holidays = USFinancialHolidayCalendar().holidays
_ = fi_holidays('19500101',
'20500101') # Activate the holiday for better efficiency
# US Exchange bussiness hour
fi_bh = CustomBusinessHour(calendar=USFederalHolidayCalendar(),
start='9:30',
end='16:30')
def weekday_distance(t1, t2, convention='forward'):
""" Number of weekdays between t1 and t2: t2 - t1
Rolling forward convention: from 0 am+ to 0 am+
example: Friday to Saturday is 1
Sunday to Monday is 0
Rolling back convention: from 0 am- to 0 am -
example: Friday to Saturday is 0
Sunday to Monday is 1
Parameters
-------------
t1: datetime.date
t2: datetime.date
def calculatePricePrediction(self):
# Market opens at 9:30, closes at 16:00
# Closed on weekends and national holidays
# Code has to calculate and return from 1 up to 4 evenly spaced values (data points) to give a buy/sell Recommendation
# The returned data points are based on the regression line's data for future prices
# Because we will avoid non-trading hours, checks must be conducted for EOD and non trading days
mktCloseBase = datetime(year=2018,
month=1,
day=1,
hour=16,
minute=0,
second=0)
mktCloseOffset = timedelta(minutes=(self.timeInterval - 1))
adjustedMktClose = (mktCloseBase - mktCloseOffset).time()
mktClose = mktCloseBase.time() # 16:00 Eastern Standard Time (U.S.A.)
predictBeginIndex = 100
nextBusinessHour = CustomBusinessHour(
start='8:30', end='16:00', calendar=USFederalHolidayCalendar())
nextBusinessDay = CustomBusinessDay(
calendar=USFederalHolidayCalendar())
validFutureTimePriceSet = []
currentTimeStamp = self.timeStampList[predictBeginIndex - 1:]
# Creates a list of future time stamps and prices ignoring closing hours, weekends, and holidays
for timeStampIterator in range(0, 121):
if self.apiLookupFunction == 'TIME_SERIES_INTRADAY':
currentTimeStamp += PdMinute(self.timeInterval)
if currentTimeStamp.time > mktClose:
currentTimeStamp += nextBusinessHour
else:
currentTimeStamp += nextBusinessDay
pricePrediction = self.linearModel.predict(
predictBeginIndex +
timeStampIterator)[0][0] # 2D array with one value
validFutureTimePriceSet.append(
[currentTimeStamp[0].to_pydatetime(), pricePrediction])
# Defines modulo value for intraday and index positions for daily api data
if self.apiLookupFunction == 'TIME_SERIES_INTRADAY':
moduloValue = 0
if self.timeInterval == 1:
moduloValue = 30 # Every 30 minutes or next day
elif self.timeInterval == 5:
moduloValue = 24 # Every 2 hours or next day
else:
moduloValue = 18 # Every 3 hours of next day
else:
presetIndexList = [0, 5, 10, 21
] # Next day, next week, in two weeks, 30 days
# Adds pre determined values to a dictionary based on the API interval and function selected
presetTimeStampDict = {}
timeFormat = '%a, %b %d %I:%M%p'
for index, timeStamp in enumerate(validFutureTimePriceSet):
currentTimeStamp = validFutureTimePriceSet[index][0]
currentPrice = validFutureTimePriceSet[index][1]
if self.apiLookupFunction == 'TIME_SERIES_INTRADAY':
if currentTimeStamp.time() < adjustedMktClose:
if ((index % moduloValue) == 0 and (index != 0)):
presetTimeStampDict[currentTimeStamp.strftime(
timeFormat)] = currentPrice
else:
currentTimeStamp = validFutureTimePriceSet[index + 1][0]
presetTimeStampDict[currentTimeStamp.strftime(
timeFormat)] = currentPrice
break
else:
for presetIndexValue in presetIndexList:
if presetIndexValue == index:
presetTimeStampDict[currentTimeStamp.strftime(
timeFormat)] = currentPrice
return presetTimeStampDict
from pandas.tseries.holiday import (AbstractHolidayCalendar, Holiday,
nearest_workday, USMartinLutherKingJr,
USPresidentsDay, GoodFriday, USMemorialDay,
USLaborDay, USThanksgivingDay)
from pandas.tseries.offsets import CustomBusinessHour, CustomBusinessDay
# TODO: Check for behavior on Dec 31
class USTradingCalendar(AbstractHolidayCalendar):
rules = [
Holiday('NewYearsDay', month=1, day=1, observance=nearest_workday),
USMartinLutherKingJr, USPresidentsDay, GoodFriday, USMemorialDay,
Holiday('USIndependenceDay',
month=7,
day=4,
observance=nearest_workday), USLaborDay, USThanksgivingDay,
Holiday('Christmas', month=12, day=25, observance=nearest_workday)
]
trading_hours_us = CustomBusinessHour(start='09:30',
end='16:30',
calendar=USTradingCalendar())
trading_day_us = CustomBusinessDay(calendar=USTradingCalendar())
def getCustomTimeGroup(self): from pandas.tseries.offsets import CustomBusinessMonthBegin, CustomBusinessHour bh = CustomBusinessHour(start='16:00',weekmask='Sun Mon Tue Wed Thu Fri') #http://stackoverflow.com/questions/13019719/get-business-days-between-start-and-end-date-using-pandas #http://pandas.pydata.org/pandas-docs/stable/timeseries.html pass