PyPI 官网下载 | BloombergData-0.4.tar.gz

import datetime as dt import blpapi import logging from .BbgRefDataService import BbgRefDataService import pandas as pd import numpy as np from . import BbgLogger import pytz from tzlocal import get_localzone logger = BbgLogger.logger SECURITY_DATA = blpapi.Name("securityData") SECURITY = blpapi.Name("security") FIELD_DATA = blpapi.Name("fieldData") FIELD_EXCEPTIONS = blpapi.Name("fieldExceptions") FIELD_ID = blpapi.Name("fieldId") ERROR_INFO = blpapi.Name("errorInfo") BAR_DATA = blpapi.Name("barData") BAR_TICK_DATA = blpapi.Name("barTickData") OPEN = blpapi.Name("open") HIGH = blpapi.Name("high") LOW = blpapi.Name("low") CLOSE = blpapi.Name("close") VOLUME = blpapi.Name("volume") NUM_EVENTS = blpapi.Name("numEvents") TIME = blpapi.Name("time") class BbgIntradayBar(BbgRefDataService): def __init__(self, securities, startTime, endTime, event = "TRADE", barInterval = 60, timeZone = str(get_localzone()), gapFillInitialBar = False, adjustmentSplit = True, adjustmentAbnormal = False, adjustmentNormal = False, adjustmentFollowDPDF = True): ''' Bloomberg Intraday Bar query object. Allows user to input a list of securities retrieval over a specified time period subject to the usual constraints that apply to Bloomberg Intraday Bar data retrieval. Parameters ---------- fields : tuple, list, or ndarray The list of fields to be retrieved, field names and data types can be determined by typing FLDS <GO> and using the search box. securities : tuple, list, or ndarray List of Bloomberg tickers to retrieve data for. If one item is passed this can be input as a string, otherwise inputs must be passed as a list or array-like. startTime : datetime.datetime The start date and time at which to retrieving data from. Must be passed as a datetime. endTime : datetime.datetime The end date and time at which to retrieving data from. Must be passed as a datetime. event : string Defines the market event supplied for an intraday request. Could be TRADE, BID or ASK. If no event is passed, will default to TRADE. barInterval : integer Sets the length of each time-bar in the response. Entered as a whole number (between 1 and 1,440 minutes). If omitted, the request will default to 60 minutes. One minute is the lowest possible granularity. timeZone : string Timezone for the request based on the pytz package timezone names. If no timezone is passed, will default to current system timezone. gapFillInitialBar : bool Adjust historical pricing to reflect: Special Cash, Liquidation, Capital Gains, Long-Term Capital Gains, Short-Term Capital Gains, Memorial, Return of Capital, Rights Redemption, Miscellaneous, Return Premium, Preferred Rights Redemption, Proceeds/Rights, Proceeds/Shares, Proceeds/Warrants adjustmentSplit : bool Adjust historical pricing and/or volume to reflect: Spin-Offs, Stock Splits/Consolidations, Stock Dividend/Bonus, Rights Offerings/Entitlement. If not set, will be set to True. adjustmentAbnormal : bool Adjust historical pricing to reflect: Special Cash, Liquidation, Capital Gains, Long-Term Capital Gains, Short-Term Capital Gains, Memorial, Return of Capital, Rights Redemption, Miscellaneous, Return Premium, Preferred Rights Redemption, Proceeds/Rights, Proceeds/Shares, Proceeds/Warrants. If not set, will be set to False. adjustmentNormal : bool Adjust historical pricing to reflect: Regular Cash, Interim, 1st Interim, 2nd Interim, 3rd Interim, 4th Interim, 5th Interim, Income, Estimated, Partnership Distribution, Final, Interest on Capital, Distribution, Prorated. If not set, will be set to False. adjustmentFollowDPDF : bool Setting to True will follow the DPDF <GO> Terminal function. True is the default setting for this option. If not set, will be set to True. See Also -------- BbgIntradayBar.constructDf : Constructor method, retrieves data associated with a BbgDataPoint query object and generates a dataframe from it. BbgDataPoint : Retrieve single point static, calculated or other reference data. BbgIntradayTick : Retrieve historic tick-level data for a given security. BbgIntradayBar : Retrieve historic bar level data for a given security (open, high, low and close) for a specified time interval given in minutes. Examples -------- Retrieve open, high, low, close, volume, number of events and value data for a basket of securities between two datetimes. >>> import datetime as dt >>> import pandas as pd >>> import BloombergDataModule as bbg >>> futHist = bbg.BbgIntradayBar(securities = ["YMH0 Comdty", "XMH0 Comdty"], startTime = dt.datetime(2020, 1, 31, 9, 0, 0), endTime = dt.datetime(2020, 1, 31, 12, 0, 0), barInterval = 5) >>> futHist.constructDf().head() Field open high low close volume numEvents value Security time YMH0 Comdty 2020-01-31 09:10:00+11:00 99.37 99.375 99.37 99.375 149 3 14806.3 2020-01-31 09:15:00+11:00 99.375 99.38 99.375 99.38 1749 13 173807 2020-01-31 09:20:00+11:00 99.38 99.38 99.38 99.38 6 6 596.28 2020-01-31 09:25:00+11:00 99.38 99.38 99.375 99.38 2170 35 215655 2020-01-31 09:30:00+11:00 99.38 99.38 99.375 99.38 93 3 9241.89 ''' self.securities = list(securities) if type(securities) is not list else securities self.startTime = startTime self.endTime = endTime self.event = event self.barInterval = barInterval self.timeZone = timeZone self.gapFillInitialBar = gapFillInitialBar self.adjustmentSplit = adjustmentSplit self.adjustmentAbnormal = adjustmentAbnormal self.adjustmentNormal = adjustmentNormal self.adjustmentFollowDPDF = adjustmentFollowDPDF def constructDf(self): ''' The constructDf method retrieves data associated with a BbgIntradayBar query object and generates a dataframe from it. Parameters ---------- None Returns ------- table : DataFrame Raises ------ ValueError: Blah blah blah See Also -------- BbgDataHistory.constructDf : retrieves static history data and constructs a DataFrame from it. It has more customisability with respect to overrides BbgIntradayTick.constructDf: retrieves intraday (or multi-day) tick level data and constructs a dataframe from it. It has applications in more data intensive and granular analysis BbgDataPoint.constructDf: retrieves intraday (or multi-day) bar level (open-high-low-close) data and constructs a dataframe from it. It is for use in more data intensive and granular analysis.constructDf. The bar interval frequency can be specified in minutes to optimise for efficiency and speed. Notes ----- Blah blah blah Examples -------- Retrieve open, high, low, close, volume, number of events and value data for a basket of securities between two datetimes. >>> import datetime as dt >>> import pandas as pd >>> import BloombergDataModule as bbg >>> futHist = bbg.BbgIntradayBar(securities = ["YMH0 Comdty", "XMH0 Comdty"], startTime = dt.datetime(2020, 1, 31, 9, 0, 0), endTime = dt.datetime(2020, 1, 31, 12, 0, 0), barInterval = 5) >>> futHist.constructDf().head() Field open high low close volume numEvent