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import datetime
import logging
import subprocess
from bs4 import BeautifulSoup
import pandas as pd
from exchangelib import HTMLBody
from sqlalchemy.engine import Engine
logger = logging.getLogger(__name__)
CASH_STRATEGY_MAPPING = {
"COCSH": ["IGREC", "IGPAYER", "HYPAYER", "HYREC", "HYOPTDEL", "IGOPTDEL"],
"IRDEVCSH": ["STEEP", "FLAT"],
"TCSH": [
"IGMEZ",
"IGSNR",
"IGEQY",
"HYMEZ",
"HYEQY",
"BSPK",
"XOMEZ",
"IGINX",
"HYINX",
"XOINX",
"EUMEZ",
"EUINX",
],
"MBSCDSCSH": ["HEDGE_MBS", "MBSCDS"],
"MACCDSCSH": ["HEDGE_MAC"],
"SER_ITRXCVCSH": ["SER_ITRXCURVE"],
"SER_IGCVECSH": ["SER_IGCURVE"],
"CLOCDSCSH": ["HEDGE_CLO"],
}
STRATEGY_CASH_MAPPING = {e: k for k, v in CASH_STRATEGY_MAPPING.items() for e in v}
def compare_notionals(df: pd.DataFrame, positions: pd.DataFrame, fcm: str) -> None:
check_notionals = (
positions.groupby(level=["security_id", "maturity"])[["notional"]]
.sum()
.join(df["NOTIONAL"], how="left")
)
diff_notionals = check_notionals[
(check_notionals.notional != check_notionals.NOTIONAL)
& (check_notionals.notional != 0.0)
]
if not diff_notionals.empty:
logger.error(f"Database and {fcm} FCM know different notionals")
for t in diff_notionals.itertuples():
logger.error(
f"{t.Index[0]}\t{t.Index[1].date()}\t{t.notional}\t{t.NOTIONAL}"
)
def get_bilateral_trades(d: datetime.date, fund: str, engine: Engine) -> pd.DataFrame:
df_cds = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin_percentage * abs(notional) / 100 as IA "
"FROM list_cds(%s::date, %s) "
"WHERE cpty_id IS NOT NULL",
engine,
params=(d, fund),
)
df_swaptions = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin_percentage * notional / 100 AS IA "
"FROM swaptions "
"WHERE cpty_id IS NOT NULL "
"AND trade_date <= %s AND fund=%s",
engine,
params=(d, fund),
)
df_caps = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin_percentage * amount / 100 AS IA "
"FROM capfloors "
"WHERE cpty_id IS NOT NULL "
"AND trade_date <= %s AND fund=%s",
engine,
params=(d, fund),
)
df = pd.concat([df_cds, df_swaptions, df_caps])
df = df.replace({"folder": STRATEGY_CASH_MAPPING})
return df
def send_email(d: datetime.date, df: pd.DataFrame) -> None:
from exchange import ExchangeMessage
pd.set_option("display.float_format", "{:.2f}".format)
df = df.drop("date", axis=1).set_index("broker")
cp_mapping = {
"CITI": "Citi",
"MS": "Morgan Stanley",
"GS": "Goldman Sachs",
"BAML_FCM": "Baml FCM",
"BAML_ISDA": "Baml OTC",
"WELLS": "Wells Fargo",
"BNP": "BNP Paribas",
"CS": "Credit Suisse",
}
html = "<html><body>"
for cp, df in df.groupby(level="broker"):
name = cp_mapping[cp]
html += f"<h3> At {name}:</h3>\n{df.loc[cp].to_html(index=False)}"
em = ExchangeMessage()
em.send_email(
f"IAM booking {d:%Y-%m-%d}",
HTMLBody(html),
["serenitas.otc@sscinc.com"],
["nyops@lmcg.com"],
)
def load_pdf(file_path):
proc = subprocess.run(
["pdftohtml", "-xml", "-stdout", "-i", file_path.as_posix()],
capture_output=True,
)
soup = BeautifulSoup(proc.stdout, features="lxml")
l = soup.findAll("text")
l = sorted(l, key=lambda x: (int(x["top"]), int(x["left"])))
return l
def get_col(l, top, bottom, left, right):
return [
c.text
for c in l
if int(c["left"]) >= left
and int(c["left"]) < right
and int(c["top"]) >= top
and int(c["top"]) < bottom
]
def prev_business_day(d: datetime.date):
if (offset := d.weekday() - 4) > 0:
return d - datetime.timedelta(days=offset)
elif offset == -4:
return d - datetime.timedelta(days=3)
else:
return d - datetime.timedelta(days=1)
def next_business_day(d: datetime.date):
if (offset := 7 - d.weekday()) > 3:
return d + datetime.timedelta(days=1)
else:
return d + datetime.timedelta(days=offset)
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