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import pandas as pd
from serenitas.utils.db import dbconn
import argparse
from serenitas.utils.exchange import ExchangeMessage
from exchangelib import FileAttachment
from io import StringIO
def difference(df):
if ("db_mv" in df.columns) and ("db_notional" in df.columns):
df["mv_difference"] = df["db_mv"] - df["admin_mv"]
df["notional_difference"] = df["db_notional"] - df["admin_notional"]
elif "db_mv" in df.columns:
df["mv_difference"] = df["db_mv"] - df["admin_mv"]
elif "db_notional" in df.columns:
df["notional_difference"] = df["db_notional"] - df["admin_notional"]
return df
def sums(df):
if ("db_mv" in df.columns) and ("db_notional" in df.columns):
return df[["db_mv", "admin_mv", "db_notional", "admin_notional"]].sum()
elif "db_mv" in df.columns:
return df[["db_mv", "admin_mv"]].sum()
elif "db_notional" in df.columns:
return df[["db_notional", "admin_notional"]].sum()
def recon(hierarchy_file, date):
df = pd.read_excel(hierarchy_file)
# security_balance = df[df["Asset Type"] == "FIXED INCOME SECURITIES"][
# "Base Market Value"
# ].sum()
bowd_bond_trades = df[df["CUSIP"].notnull()]
bond_asset_classes = ["Subprime", "CRT", "CLO"]
for asset in bond_asset_classes:
db_bond_trades = pd.read_sql_query(
f"select * from risk_positions(%s, %s, 'BOWDST')",
dawndb,
params=(date, asset),
)
bond_trades = 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",
]
]
bond_trades["db_notional"] = bond_trades["Shares/Par"] * bond_trades["factor"]
bond_trades.rename(
columns={
"usd_market_value": "db_mv",
"Shares/Par": "admin_notional",
"Base Market Value": "admin_mv",
},
inplace=True,
)
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 admin_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_mv, globeop_nav as admin_mv 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,),
)
kinds = [bond_trades, tranche_trades, cdx_trades, cdx_swaption_trades]
names = ["bond_trades", "tranche_trades", "cdx_trades", "cdx_swaption_trades"]
message = ""
em = ExchangeMessage()
attachments = []
for kind, name in zip(kinds, names):
# difference(kind).to_csv(f"/home/serenitas/flint/{name}_{date}.csv")
buf = StringIO()
difference(kind).to_csv(buf)
attachments.append(
FileAttachment(name=f"{name}_{date}.csv", content=buf.getvalue().encode())
)
pd.set_option("display.float_format", lambda x: "%.2f" % x)
message += f"\n{name}: {pd.DataFrame(sums(kind), columns=['sums'])}"
# print(f"{name}: {sums(kind)}"
em.send_email(
subject="Notional Totals",
body=message,
to_recipients=("fyu@lmcg.com",),
attach=attachments,
)
parser = argparse.ArgumentParser()
parser.add_argument("end_date")
args = parser.parse_args()
if __name__ == "__main__":
dawndb = dbconn("dawndb")
hierarchy_file = "/home/serenitas/flint/rec.xlsx"
date = args.end_date
recon(hierarchy_file, date)
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