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from sqlalchemy import create_engine, MetaData, select, Column, func, bindparam
from collections import defaultdict
import pandas as pd
import numpy as np
from bbg_helpers import init_bbg_session, retrieve_data, BBG_IP
engine = create_engine("postgresql://et_user@debian/ET")
meta = MetaData(bind=engine)
meta.reflect(only=["bloomberg_corp_ref", "bloomberg_corp", "deal_indicative"])
deal_indicative = meta.tables["deal_indicative"]
bloomberg_corp_ref = meta.tables["bloomberg_corp_ref"]
bloomberg_corp = meta.tables["bloomberg_corp"]
s = (
select([Column("cusip"), Column("loanxid")])
.select_from(func.et_latestdealinfo(bindparam("dealname")))
.where(Column("cusip") != None)
)
# we build a dictionary with cusips as keys and values is a set of loanxids mapped to this cusip
result = select([deal_indicative.c.dealname]).execute()
d = defaultdict(set)
for r in result:
result2 = engine.execute(s, dealname=r.dealname)
for t in result2:
d[t.cusip].add(t.loanxid)
clean_mapping = ((cusip, loanxid - {None}) for cusip, loanxid in d.items())
def f(s):
if s:
return "{%s}" % ",".join(s)
else:
return None
clean_mapping = {cusip: f(loanxid) for cusip, loanxid in clean_mapping}
mapping = pd.DataFrame.from_dict(clean_mapping, orient="index")
mapping.index.name = "cusip"
mapping.columns = ["loanxid"]
currentdata = pd.read_sql_query(
"select id_bb_unique, cusip from bloomberg_corp_ref", engine, index_col="cusip"
)
mapping = mapping.ix[mapping.index.difference(currentdata.index)]
with init_bbg_session(BBG_IP) as session:
all_fields = [
"ISSUE_DT",
"LN_ISSUE_STATUS",
"ID_CUSIP",
"ID_BB_UNIQUE",
"SECURITY_TYP",
"AMT_OUTSTANDING",
"PX_LAST",
"LAST_UPDATE_DT",
"ISSUER",
"MATURITY",
"CPN",
"CPN_TYP",
"CPN_FREQ",
"FLT_SPREAD",
"LIBOR_FLOOR",
"LN_CURRENT_MARGIN",
"LN_TRANCHE_SIZE",
"AMT_ISSUED",
"LN_COVENANT_LITE",
"SECOND_LIEN_INDICATOR",
"DEFAULTED",
"DEFAULT_DATE",
"CALLED",
"CALLED_DT",
"PRICING_SOURCE",
"RESET_IDX",
]
securities = ["{0} Corp".format(cusip) for cusip in mapping.index]
df = retrieve_data(session, securities, all_fields)
df = pd.DataFrame.from_dict(df, orient="index")
df.index = df.index.str.slice(0, 9)
df.security = df.index.to_series()
df["loanxid"] = mapping.loanxid
df.dropna(subset=["ID_BB_UNIQUE"], inplace=True)
df.loc[df.LN_TRANCHE_SIZE.isnull(), "LN_TRANCHE_SIZE"] = df[
df.LN_TRANCHE_SIZE.isnull()
].AMT_ISSUED.values
df.drop_duplicates(subset="ID_BB_UNIQUE", inplace=True)
df.set_index("ID_BB_UNIQUE", inplace=True, drop=False)
currentdata.set_index("id_bb_unique", inplace=True)
df = df.ix[df.index.difference(currentdata.index)]
sql_colnames = [c.name for c in bloomberg_corp_ref.columns]
pd_colnames = [
"ID_BB_UNIQUE",
"ID_CUSIP",
"ISSUER",
"MATURITY",
"CPN",
"CPN_TYP",
"CPN_FREQ",
"FLT_SPREAD",
"LIBOR_FLOOR",
"LN_TRANCHE_SIZE",
"LN_COVENANT_LITE",
"SECOND_LIEN_INDICATOR",
"SECURITY_TYP",
"ISSUE_DT",
"DEFAULTED",
"DEFAULT_DATE",
"CALLED",
"CALLED_DT",
"LN_ISSUE_STATUS",
"loanxid",
]
to_insert = df.filter(pd_colnames)
to_insert.rename(
columns={k: v for k, v in zip(pd_colnames, sql_colnames)}, inplace=True
)
to_insert.to_sql("bloomberg_corp_ref", engine, if_exists="append", index=False)
pd_colnames = [
"ID_BB_UNIQUE",
"LAST_UPDATE_DT",
"PX_LAST",
"LN_CURRENT_MARGIN",
"AMT_OUTSTANDING",
"PRICING_SOURCE",
]
sql_colnames = [c.name for c in bloomberg_corp.columns]
to_insert2 = df.filter(pd_colnames)
to_insert2.rename(
columns={k: v for k, v in zip(pd_colnames, sql_colnames)}, inplace=True
)
to_insert2.dropna(subset=["pricingdate"], inplace=True)
to_insert2.set_index(["id_bb_unique", "pricingdate"], inplace=True)
to_insert2.to_sql("bloomberg_corp", engine, if_exists="append", index=True)
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