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from serenitas.utils.exchange import ExchangeMessage
import pandas as pd
from serenitas.utils.db2 import dbconn
from psycopg.errors import UniqueViolation
import datetime
import logging
import argparse
import re
from serenitas.ops.trade_dataclasses import CreditSwaptionDeal, desc_str
from serenitas.ops.dataclass_mapping import Fund
parser = argparse.ArgumentParser()
parser.add_argument(
"workdate",
nargs="?",
default=str(datetime.date.today()),
type=datetime.date.fromisoformat,
)
args = parser.parse_args()
logger = logging.getLogger(__name__)
columns = [
"option_recap",
"index_buyer",
"index_seller",
"trade_date",
"effective_date",
"execution_time",
"notional",
"price",
"mid_price",
"strike",
"expiry_date",
"option_type",
"exercise_clearing_house",
"premium_fee",
"premium_fee_pay_date",
"trade_id",
"venue_mic",
]
# Counterparty Ticket to Database
_baml_funds: dict[str, Fund] = {
"LMIL-Boston Patriot Bowdoin St LLC": "BOWDST",
"Iso Selene Inc.": "ISOSEL",
"Serenitas Credit Gamma Master Fund LP": "SERCGMAST",
"Lmcg Investments, Llc": "SERCGMAST",
"Reep-Ofc One Bowdoin Square Ma Llc Et Al (Cob)": "BOWDST",
}
def regex_baml_numbers(text: str):
return float(re.search(r"[\d,]+", text)[0].replace(",", ""))
def from_baml_email(d: dict, conn):
buysell = "Bank Of America" in d["Index Seller"]
index, series = re.search(r"(HY|IG)\.([\d]{2})", d["OPTION RECAP"]).groups()
security_desc = desc_str(index, series, "5")
trade_date = datetime.datetime.strptime(d["Trade Date"], "%d-%b-%Y")
price = float(d["Price"]) / 100
sql_str = (
"SELECT * FROM index_desc WHERE INDEX=%s AND series=%s "
"AND lastdate >= %s AND tenor='5yr'"
)
with conn.cursor() as c:
c.execute(sql_str, (index, series, trade_date))
row = c.fetchone()
return CreditSwaptionDeal(
buysell=buysell,
fund=_baml_funds[d[f"Index {'Buyer' if buysell else 'Seller'}"]],
cp_code="BAMSNY",
portfolio="OPTIONS",
folder=index + ("PAYER" if d["Option Type"] == "Payer" else "REC"),
trade_date=trade_date,
settle_date=datetime.datetime.strptime(d["Premium Fee Pay Date"], "%d-%b-%Y"),
expiration_date=datetime.datetime.strptime(d["Expiry Date"], "%d-%b-%Y"),
notional=regex_baml_numbers(d["Notional"]),
option_type=d["Option Type"].upper(),
strike=d["Strike"],
swap_type="CD_INDEX_OPTION",
fixed_rate=row.coupon / 100,
maturity=row.maturity,
security_desc=security_desc,
security_id=row.redindexcode,
currency="USD",
price=price,
initial_margin_percentage=(
regex_baml_numbers(d["Collateral"])
/ regex_baml_numbers(d["Premium Fee"])
* price
)
if d.get("Collateral")
else None,
)
if __name__ == "__main__":
dawndb = dbconn("dawndb")
em = ExchangeMessage()
start = datetime.datetime.combine(args.workdate, datetime.time.min).replace(
tzinfo=em._account.default_timezone
)
for msg in em.get_msgs(
path=["AutoBook", "BAML Swaption"], datetime_received__gte=start
):
dfs = pd.read_html(msg.body)
tickets = []
for ticket in dfs:
if len(ticket[0]) > 5:
ticket = ticket.set_index(0).to_dict()[1]
if "Block" in ticket or "Unwind Price" in ticket:
continue
tickets.append(ticket)
trade = from_baml_email(ticket, dawndb)
trade.stage()
df = pd.DataFrame.from_dict(tickets)
df.columns = df.columns.str.lower().str.replace(" ", "_")
if "collateral" in df.columns:
additional_columns = ["collateral"]
else:
additional_columns = []
df = df[columns + additional_columns]
place_holders = ",".join(["%s"] * len(df.columns))
try:
with dawndb.cursor() as c:
c.executemany(
f"INSERT INTO baml_swaption_ticket({','.join(df.columns)})"
f"VALUES ({place_holders})",
df.itertuples(index=False),
)
except UniqueViolation as e:
logger.warning(str(e))
CreditSwaptionDeal._insert_queue.clear()
else:
CreditSwaptionDeal.commit()
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