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import pandas as pd
from serenitas.utils.db import dbconn
dawndb = dbconn("dawndb")
df = pd.read_excel("/home/serenitas/flint/rec.xlsx")
security_balance = df[df["Asset Type"] == "FIXED INCOME SECURITIES"][
"Base Market Value"
].sum()
print(f"The current security balance is {security_balance}")
bowd_bond_trades = df[df["CUSIP"].notnull()]
asset_classes = ["Subprime", "CRT", "CLO"]
for asset in asset_classes:
db_bond_trades = pd.read_sql_query(
f"select * from risk_positions('2021-02-26', %s, 'BOWDST')",
dawndb,
params=(asset,),
)
new_df = bowd_bond_trades.merge(
db_bond_trades, left_on="Mellon Security ID", right_on="identifier", how="right"
)[
[
"description",
"identifier",
"notional",
"factor",
"Shares/Par",
"Base Market Value",
"usd_market_value",
]
]
new_df["db_notional"] = new_df["Shares/Par"] * new_df["factor"]
# print(new_df)
date = "2021-02-28"
tranche_trades = pd.read_sql_query(
f"select security_desc, maturity, orig_attach, orig_detach, sum(notional * tranche_factor) as db_notional, sum(admin_notional) as admin_notional, sum(serenitas_clean_nav) as db_mv, sum(admin_clean_nav) as bowd_mv from tranche_risk_bowdst where date=%s group by security_desc, maturity, orig_attach, orig_detach ;",
dawndb,
params=(date,),
)
cdx_trades = pd.read_sql_query(
f"select security_id, security_desc, index, series, version, maturity, globeop_notional as admin_notional, notional * factor as db_notional, clean_nav as db_nav, globeop_nav as admin_nav from list_cds_marks(%s, null, 'BOWDST')",
dawndb,
params=(date,),
)
cdx_swaption_trades = pd.read_sql_query(
f"select security_id, option_type, strike, expiration_date, sum(serenitas_nav) as db_mv, sum(globeop_nav) as admin_mv from list_swaption_positions_and_risks(%s, 'BOWDST') group by security_id, option_type, strike, expiration_date;",
dawndb,
params=(date,),
)
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