def examples():
"""
examples of how to get basic data from bbg
"""
# get some data for a single name
x = blp.bdp('BDEV LN Equity', 'px_last')
print(x)
print('the type of x', type(x))
print('the value of x:', x.iloc[0]['px_last'])
# get multiple data for a single name
y = blp.bdp('BDEV LN Equity', flds=['px_bid', 'px_ask'])
print(y)
# get multiple data for multiple names
z = blp.bdp(tickers=['BDEV LN Equity', 'BARC LN Equity'], flds=['px_bid', 'px_ask'])
print(z)
print('here is the bdev ask >>>', z.loc['BDEV LN Equity','px_ask'])
# get multiple data for multiple names
z = blp.bdp(tickers=['XS2011260705 Corp'], flds=['z_sprd_mid', 'z_sprd_mid'])
print(z)
print('here is the bdev ask >>>', z.loc['XS2011260705 Corp','z_sprd_mid'])
def get_data(ticker, fields, override, legend_labels, sort):
"""
Downloads data using blp.bpd
:param legend_labels: list[string], your name for fields
:param ticker: str/list, ticker(s) name(s)
:param fields: str/list, field(s) name(s)
:param override: dictionary, override values
:param sort: string, sort value if needed
:return: data to plot
"""
# print(override)
# print(fields, override[0], legend_labels)
data = pd.DataFrame()
for idx, field in enumerate(fields):
# print(idx, field)
try:
temp = blp.bdp(tickers=ticker, flds=field, **override[idx])
except Exception as e:
temp = blp.bdp(tickers=ticker, flds=field)
# print(temp)
data[legend_labels[idx]] = temp
if sort == 'a':
data.sort_values(by=data.columns[0], axis=0, inplace=True, ascending=True)
if sort == 'd':
data.sort_values(by=data.columns[0], axis=0, inplace=True, ascending=False)
return data
def examples():
"""
examples of how to get basic data from bbg
"""
# get some data for a single name
x = blp.bdp('BDEV LN Equity', 'px_last')
print(x)
print('the type of x', type(x))
print('the value of x:', x.iloc[0]['px_last'])
# get multiple data for a single name
y = blp.bdp('BDEV LN Equity', flds=['px_bid', 'px_ask'])
print(y)
# get multiple data for multiple names
z = blp.bdp(tickers=['BDEV LN Equity', 'BARC LN Equity'],
flds=['px_bid', 'px_ask'])
print(z)
print('here is the bdev ask >>>', z.loc['BDEV LN Equity', 'px_ask'])
# get history data for a single name
print('getting history...')
todaydate = datetime.datetime.today()
historicDate = todaydate - datetime.timedelta(3 * 365)
print(todaydate, historicDate)
x = blp.bdh('BDEV LN Equity',
flds='px_last',
start_date=historicDate,
end_date='today')
print(x.head())
print(x.tail())
def getListDataFromBbg(securtiyList, fieldList):
'''
gets data from bloomberg using a while loop until completion
inputs:
securtiyList - list of tickers to obtain
fieldList - the fields requested for each ticker
output:
resultsOfDataReq - resulting pandas array of the data that was requested
'''
# get the data (bdp returns a dataframe)
resultsOfDataReq = blp.bdp(tickers=securtiyList, flds=fieldList)
#pre-loop initialization
keepLooping = True
loopingCounter = 0
while keepLooping:
# check which items are missing
listError = list(
set(securtiyList) - set(resultsOfDataReq.index.values))
if len(listError) == 0:
# data collection process finished
keepLooping = False
break
else:
print(len(listError), 'of', len(securtiyList), 'remaining',
': loop', loopingCounter)
loopingCounter += 1
if loopingCounter > 10:
# more than 10 tries at data collection
keepLooping = False
break
# get and append more data
try:
nextChunk = blp.bdp(tickers=listError, flds=fieldList)
resultsOfDataReq = resultsOfDataReq.append(nextChunk)
except:
print('no data to add')
# remove duplicates
resultsOfDataReq = resultsOfDataReq.drop_duplicates()
return resultsOfDataReq
def get_data(ticker, fields, override):
"""
Downloads data using blp.bpd
:param ticker: str/list, ticker(s) name(s)
:param fields: str/list, field(s) name(s)
:param override: dictionary, override values
:return: data to plot
"""
data = blp.bdp(tickers=ticker, flds=fields, **override)
return data
def getListDataFromBbg(securtiyList, fieldList):
'''
gets data from bloomberg using a while loop
inputs:
securtiyList - list of tickers to obtain
fieldList - the fields requested for each ticker
output:
resultsOfDataReq - resulting pandas array of the data that was requested
'''
# create pandas dataframe
# resultsOfDataReq = pd.Dataframe()
# get the data
resultsOfDataReq = blp.bdp(tickers=securtiyList, flds=fieldList)
# check which items are missing
# listError = list(set(securtiyList)-set(resultsOfDataReq.index.values))
listError = list(set(securtiyList) - set(resultsOfDataReq.index.values))
pass
def settle_dt(cusips, fecha):
datos = blp.bdp(cusips, ['SETTLE_DT'], USER_LOCAL_TRADE_DATE=fecha)
return datos
def bdp(cusips):
datos = blp.bdp(cusips, ['SECURITY_NAME', 'MATURITY'])
return datos
def bloombergAPI_bigData(securtiyList, fieldList, rowChunk):
'''
if the number of tickers and fields are too large, then this function is used to break up the lists to allow for data connection
inputs:
securtiyList - list of tickers to obtain
fieldList - the fields requested for each ticker
rowChunk - how many rows to aquire in one batch
output:
resultsOfDataReq - resulting pandas array of the data that was requested
'''
# split the security list into batches of n rows
n = rowChunk
iterationsRequired = int(len(securtiyList) / n)
remainingBit = len(securtiyList) - iterationsRequired * n
print(
f'we will iterate {iterationsRequired} times and {remainingBit} will be left'
)
resultsOfDataReq = pd.DataFrame()
# bulk of data
for i in range(iterationsRequired):
print('processing: ', i * n, 'to', (i + 1) * n, 'from the list.')
nextChunk = blp.bdp(tickers=securtiyList[i * n:(i + 1) * n],
flds=fieldList)
resultsOfDataReq = resultsOfDataReq.append(nextChunk)
percentDone = round(
(len(resultsOfDataReq.index) / len(securtiyList)) * 100, 1)
print('items completed:', len(resultsOfDataReq.index), 'out of',
len(securtiyList), "=> precent aquired:", percentDone)
# if i > 2:
# print('breaking the loop for testing purpose')
# break
# end stub
if remainingBit == 0:
# there is no stub
pass
else:
# process the stub
print('processing last chuunk: ', iterationsRequired * n, 'to',
len(securtiyList))
nextChunk = blp.bdp(tickers=securtiyList[iterationsRequired *
n:len(securtiyList)],
flds=fieldList)
percentDone = round(
(len(resultsOfDataReq.index) / len(securtiyList)) * 100, 1)
print('items completed:', len(resultsOfDataReq.index), 'out of',
len(securtiyList), "=> precent aquired:", percentDone)
# compare rows of dataframe to securitiy list to find the missing items
#print('index column of the pandas list')]
#print(list(resultsOfDataReq.index.values))
print('missing items from the securities list')
listError = list(set(securtiyList) - set(resultsOfDataReq.index.values))
print(listError)
return resultsOfDataReq
def get_bonds():
"""
function to get bond data from bloomberg using tickers in an excel sheet.
return:
data - a pandas data set
"""
print('getting bond data...')
# securtiyList = ['US29265WAA62 Corp', 'XS1713463559 Corp', 'XS2000719992 Corp', 'XS0954675129 Corp', 'XS0954675129 Corp']
fieldList = [
'ticker', 'coupon', 'nxt_call_dt', 'final_maturity', 'mty_typ',
'px_mid', 'z_sprd_mid', 'yas_ispread', 'yas_bond_yld', 'yas_risk',
'crncy', 'payment_rank', 'industry_sector', 'rtg_moody', 'rtg_sp'
]
# the script fis here
dir_path = os.path.dirname(os.path.realpath(__file__))
# get items from sheet (in the same folder)
fileToGet = 'hybridSecurityList.xlsx'
# join file and correct folder
fileToGet = os.path.join(dir_path, fileToGet)
secListXlsx = pd.ExcelFile(fileToGet)
# the names of the available sheets
print('getting security list from:', secListXlsx.sheet_names)
df = pd.DataFrame(secListXlsx.parse('tickers'))
print('summary of the data')
print(df.info)
# put all isin's in a list
isin = df.iloc[:, 1].tolist()
securtiyList = [x + " Corp" for x in isin]
# make the lists unique (and keep the order)
securtiyList = list(collections.OrderedDict.fromkeys(securtiyList))
# get the data from bloomberg
print('getting data from bbg')
if len(securtiyList) > 49:
# there are lots of securities
print(f'lots of data requested: {len(securtiyList)} items requested')
reducedListSize = input(
f'reduce list size to value or press "x" to use {len(securtiyList)} >>>'
)
if reducedListSize == 'x':
# keep the same size
reducedListSize = len(securtiyList)
else:
# reeduce the list size
reducedListSize = int(reducedListSize)
# set the reduced list to size selected
reducedList = securtiyList[0:reducedListSize]
# run code on the smaller list
bondData = bloombergAPI_bigData(reducedList, fieldList, 10)
else:
bondData = blp.bdp(tickers=securtiyList, flds=fieldList)
#print('number of columns:', data.head())
print('data is fetched:')
print(bondData.info)
return bondData
con.start()
today = datetime.datetime.now()
s_date = '2011-01-01'
e_date = today
#read ticker list
names = ['Tickers']
tix = pd.read_csv("tickers.csv", names=names).values.tolist()
# create a global df for all prices. index needs to be created for begining of the month
global_index = pd.date_range(start=s_date, end=e_date, freq='W-FRI')
global_df = pd.DataFrame(index=global_index)
# %% bloomberg data requests
#create loop here for tickers in tix
for tt in tix:
test_temp = blp.bdh(tickers=tt,
flds=['last_price'],
start_date=s_date,
end_date=e_date,
Quote='G',
Per='W',
Fill='B',
Days='W')
#get the name for dataframe header
tick_name = blp.bdp(tickers=tt, flds=['Security_Name'])
header = str(tt).strip('[]')
test_temp.columns = [header]
#merge current df with global df
global_df = global_df.join(test_temp, how='outer')
global_df.to_csv('prices.csv')
def init_ref(self, ticker, fields):
self.tick = ticker
self.ref_data = blp.bdp(tickers=ticker, flds=fields)