def startUpdates(self):
"""Starts live feed from Bloomberg.
"""
# Analytics stream
self.blptsAnalytics = blpapiwrapper.BLPTS()
self.streamWatcherAnalytics = StreamWatcher(self,
BloombergQuery.ANALYTICS)
self.blptsAnalytics.register(self.streamWatcherAnalytics)
# Price only stream
self.blptsPriceOnly = blpapiwrapper.BLPTS()
self.streamWatcherPriceOnly = StreamWatcher(self,
BloombergQuery.PRICEONLY)
self.blptsPriceOnly.register(self.streamWatcherPriceOnly)
# Price change subscription
self.streamWatcherBID = StreamWatcher(self, BloombergQuery.BID)
self.bbgstreamBIDEM = blpapiwrapper.BLPStream(
list((self.embondsisins + BBGHand + ' Corp').astype(str)), 'BID',
0)
self.bbgstreamBIDEM.register(self.streamWatcherBID)
self.bbgstreamBIDEM.start()
# Risk free bonds: no streaming as too many updates - poll every 15 minutes
rfRequest = blpapiwrapper.BLPTS(
list((self.rfbondsisins + '@CBBT' + ' Corp').astype(str)),
self.bbgPriceRFQuery)
self.RFtimer = RFdata(900, rfRequest, self)
self.BDMdata = BDMdata(900, self) #15 MINUTES
self.BDMEODsave = BDMEODsave(self)
def firstPass(self, priorityBondList=[]):
"""Loads initial data upon start up. After downloading data on first pass, function will check for bonds
in SPECIALBONDS and will overwrite downloaded data with new set of data.
Keyword arguments:
priortyBondList : priorty bonds to be updated (defaults to [] if not specified)
"""
self.lastRefreshTime = datetime.datetime.now()
if priorityBondList == []:
emptyLines = list(self.df.index)
isins = self.embondsisins + BBGHand + ' Corp'
else:
emptyLines = priorityBondList
isins = self.df.loc[priorityBondList, 'ISIN'] + BBGHand + ' Corp'
isins = list(isins.astype(str))
blpts = blpapiwrapper.BLPTS(isins, self.bbgPriceLongQuery)
blptsStream = StreamWatcher(self, BloombergQuery.FIRSTPASS)
blpts.register(blptsStream)
blpts.get()
blpts.closeSession()
isins = self.rfbondsisins + ' @CBBT Corp'
isins = list(isins.astype(str))
blpts = blpapiwrapper.BLPTS(isins, self.bbgPriceRFQuery)
blptsStream = StreamWatcher(self, BloombergQuery.FIRSTPASS)
blpts.register(blptsStream)
blpts.get()
blpts.closeSession()
specialBondList = list(set(emptyLines) & set(self.SPECIALBONDS))
if len(specialBondList) > 0:
specialIsins = map(
lambda x: self.df.at[x, 'ISIN'] + BBGHand + ' Corp',
specialBondList)
blpts = blpapiwrapper.BLPTS(specialIsins,
self.bbgPriceLongSpecialQuery)
specialbondStream = StreamWatcher(self, BloombergQuery.FIRSTPASS)
blpts.register(specialbondStream)
blpts.get()
blpts.closeSession()
for bond in emptyLines:
self.updateStaticAnalytics(
bond
) # This will update benchmarks and fill grid. Has to be done here so all data for benchmarks is ready.
"""
import pandas as pd
import numpy as np
from matplotlib import pyplot
import blpapiwrapper as blpapi
import seaborn as sns
instruments_data = pd.read_excel('L:\\Rates & FX\\Quant Analysis\\pytest\\instruments.xlsx')
instruments = instruments_data['CUSIP'].dropna()
curvaPesos = pd.DataFrame(instruments_data['Gob CERO Pesos'])
curvaPesos.index = instruments_data['Plazo']
curvaUF = pd.DataFrame(instruments_data['Gob CERO UF'])
curvaUF.index = instruments_data['Plazo']
bbgData = blpapi.BLPTS(instruments,['MTY_YEARS','DUR_BID','BID_YIELD','TRADE_CRNCY','RTG_FITCH_NATIONAL_LT','NAME'])
bbgData.get()
table = bbgData.output
bbgData.closeSession()
table =pd.DataFrame(data = [table['NAME'],pd.to_numeric(table['MTY_YEARS']), pd.to_numeric(table['DUR_BID']),
pd.to_numeric(table['BID_YIELD']), table['TRADE_CRNCY'], table['RTG_FITCH_NATIONAL_LT']]).transpose()
gov_yield = []
i = 0
for maturity in table['MTY_YEARS']:
if table['TRADE_CRNCY'][i] == 'CLP':
gov_yield.append(np.interp(maturity, curvaPesos.index, curvaPesos['Gob CERO Pesos']))
elif table['TRADE_CRNCY'][i] == 'CLF':
gov_yield.append(np.interp(maturity, curvaUF.index, curvaUF['Gob CERO UF']))
i += 1
table['Govt Equivalent'] = gov_yield
def __init__(self, parent, mainframe=None):
"""
Keyword arguments:
parent : parent frame (Wx.Frame object)
mainframe : FlowTradingGUI > MainForm class instance (defaults to [] if not specified)
"""
self.parent = parent
self.mainframe = mainframe
self.th = None if mainframe is None else mainframe.th
self.dtToday = datetime.datetime.today().replace(hour=0,
minute=0,
second=0,
microsecond=0)
_offsets = (3, 1, 1, 1, 1, 1, 2)
self.dtYesterday = self.dtToday - datetime.timedelta(
days=_offsets[self.dtToday.weekday()])
self.dtLastWeek = self.dtToday - datetime.timedelta(days=7)
dtTemp = self.dtToday - datetime.timedelta(days=29)
self.dtLastMonth = dtTemp - datetime.timedelta(
days=_offsets[dtTemp.weekday()])
# Static columns either in bonduniverse or directly derived from it
colsDescription = [
'ISIN', 'BOND', 'SERIES', 'CRNCY', 'MATURITY', 'COUPON',
'AMT_OUTSTANDING', 'SECURITY_NAME', 'INDUSTRY_GROUP',
'CNTRY_OF_RISK', 'TICKER', 'MATURITYDT'
]
# these columns will feed from Bloomberg (or Front) and be regenerated only once
colsPriceHistory = [
'P1DFRT', 'P1D', 'P1W', 'P1M', 'Y1D', 'Y1W', 'Y1M', 'SAVG',
'ISP1D', 'ISP1W', 'ISP1M', 'PRINCIPAL_FACTOR'
] #'INTSWAP1D','INTSWAP1W','INTSWAP1M',
# these will feed from Bloomberg only once
colsRating = ['SNP', 'MDY', 'FTC']
colsAccrued = ['ACCRUED', 'D2CPN']
# these columns will feed from Bloomberg (or Front) and be regenerated all the time
colsPrice = [
'BID', 'ASK', 'MID', 'BID_SIZE', 'ASK_SIZE', 'BGN_MID', 'OLDBID',
'OLDASK'
]
colsAnalytics = [
'YLDB', 'YLDA', 'YLDM', 'ZB', 'ZA', 'ZM', 'RSI14', 'RISK_MID'
] #,'INTSWAP'
colsChanges = [
'DP1FRT', 'DP1D', 'DP1W', 'DP1M', 'DY1D', 'DY1W', 'DY1M', 'DISP1D',
'DISP1W', 'DISP1M'
]
colsPosition = ['POSITION', 'REGS', '144A', 'MV', 'RISK']
self.colsAll = colsDescription + colsPriceHistory + colsRating + colsAccrued + colsPrice + colsAnalytics + colsChanges + colsPosition # +colsPricingHierarchy+colsUpdate
self.df = pandas.DataFrame(columns=self.colsAll, index=bonds.index)
# self.df.drop(bonduniverseexclusionsList,inplace=True)
self.df['BOND'] = self.df.index
self.df['ISIN'] = bonds['REGS']
self.df['SERIES'] = 'REGS'
self.gridList = []
self.lock = threading.Lock()
for c in list(set(colsDescription) & set(bonds.columns)):
self.df[c] = bonds[c]
self.df.rename(columns={'AMT_OUTSTANDING': 'SIZE'}, inplace=True)
#self.df['SIZE'] = self.df['SIZE'].apply(lambda x: '{:,.0f}'.format(float(x) / 1000000) + 'm')
self.df['MATURITYDT'] = self.df['MATURITY'].apply(getMaturityDate)
self.df = self.df[self.df['MATURITYDT'] >= self.dtToday]
self.df['MATURITY'] = self.df['MATURITYDT'].apply(
lambda x: x.strftime('%d/%m/%y'))
self.df['POSITION'] = 0
pub.subscribe(self.updatePositions, "POSITION_UPDATE")
self.bondList = []
self.bbgPriceOnlyQuery = ['BID', 'ASK', 'BID_SIZE', 'ASK_SIZE']
self.bbgPriceQuery = ['YLD_CNV_BID', 'Z_SPRD_BID', 'RSI_14D']
self.bbgPriceSpecialQuery = [
'YLD_CNV_BID', 'OAS_SPREAD_BID', 'RSI_14D'
]
self.bbgPriceLongQuery = [
'BID', 'ASK', 'YLD_CNV_BID', 'Z_SPRD_BID', 'RSI_14D', 'BID_SIZE',
'ASK_SIZE'
]
self.bbgPriceLongSpecialQuery = [
'BID', 'ASK', 'YLD_CNV_BID', 'OAS_SPREAD_BID', 'RSI_14D',
'BID_SIZE', 'ASK_SIZE'
]
# self.bbgPriceQuery = ['YLD_CNV_BID', 'YLD_CNV_ASK', 'Z_SPRD_BID', 'Z_SPRD_ASK','RSI_14D']
# self.bbgPriceSpecialQuery = ['YLD_CNV_BID', 'YLD_CNV_ASK', 'OAS_SPREAD_BID', 'OAS_SPREAD_ASK','RSI_14D']
# self.bbgPriceLongQuery = ['BID', 'ASK', 'YLD_CNV_BID', 'YLD_CNV_ASK', 'Z_SPRD_BID', 'Z_SPRD_ASK','RSI_14D', 'BID_SIZE', 'ASK_SIZE']
# self.bbgPriceLongSpecialQuery = ['BID', 'ASK', 'YLD_CNV_BID', 'YLD_CNV_ASK', 'OAS_SPREAD_BID', 'OAS_SPREAD_ASK','RSI_14D', 'BID_SIZE', 'ASK_SIZE']
# self.bbgPriceSinkableQuery = ['BID', 'ASK', 'YLD_CNV_BID', 'YLD_CNV_ASK', 'RSI_14D', 'BID_SIZE', 'ASK_SIZE']
self.riskFreeIssuers = ['T', 'DBR', 'UKT', 'OBL']
self.bbgPriceRFQuery = ['BID', 'ASK', 'BID_YIELD', 'ASK_YIELD']
self.bbgSinkRequest = blpapiwrapper.BLPTS()
pass