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import datetime
import math
import logging
import subprocess
from bs4 import BeautifulSoup
import pandas as pd
from exchangelib import HTMLBody
from io import StringIO
from sqlalchemy.engine import Engine
logger = logging.getLogger(__name__)
CASH_STRATEGY_MAPPING = {
"COCSH": ["IGREC", "IGPAYER", "HYPAYER", "HYREC", "HYOPTDEL", "IGOPTDEL", "VOLRV"],
"IRDEVCSH": ["DV01", "STEEP", "FLAT"],
"TCSH": [
"IGMEZ",
"IGSNR",
"IGEQY",
"HYMEZ",
"HYEQY",
"HYSNR",
"BSPK",
"XOMEZ",
"XOEQY",
"IGINX",
"HYINX",
"XOINX",
"EUMEZ",
"EUINX",
],
"MBSCDSCSH": ["HEDGE_MBS", "MBSCDS"],
"MACCDSCSH": ["HEDGE_MAC", "CASH_BASIS", "SOFR"],
"CVECSH": ["SER_ITRXCURVE", "SER_IGCURVE", "SER_HYCURVE", "XCURVE"],
"CLOCDSCSH": ["HEDGE_CLO", "M_CLO_BB20"],
"M_CSH_CASH": [
"CRT_LD",
"CRT_LD_JNR",
"CRT_SD",
"MTG_FP",
"MTG_LMG",
"M_MTG_FP",
"M_MTG_LMG",
],
}
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, fund: 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 at {fund} know different notionals")
for t in diff_notionals.itertuples():
logger.error(
f"{t.Index[0]}\t{t.Index[1]:%Y-%m-%d}\t{t.notional}\t{t.NOTIONAL}"
)
def compare_notionals_rates(
df: pd.DataFrame, positions: pd.DataFrame, fcm: str
) -> None:
check_notionals = positions.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():
if hasattr(t, "effective_date"):
msg = f"{t.Index[0]}\t{t.effective_date:%Y-%m-%d}\t{t.notional}\t{t.NOTIONAL}"
else:
msg = f"{t.Index[0]}\t{t.Index[1]:%Y-%m-%d}\t{t.notional}\t{t.NOTIONAL}"
logger.error(msg)
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_cds2(%s::date, %s) "
"WHERE orig_attach IS NOT NULL or cpty_id='6SIT0'", # that way we get all tranches + the ABS_CDS
engine,
params=(d, fund),
)
df_swaptions = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin_percentage * abs(notional) / 100 AS IA "
"FROM list_swaptions(%s::date, %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_forwards = pd.read_sql_query(
"SELECT cpty_id, folder, ia FROM ("
" SELECT cpty_id, folder, initial_margin_percentage * buy_amount / 100 AS ia,"
" trade_date, settle_date, fund FROM spots"
" UNION"
" SELECT UNNEST(ARRAY[near_cpty_id, far_cpty_id]) AS cpty_id, folder, 0.0 AS ia,"
" trade_date, unnest(ARRAY[near_settle_date, far_settle_date]) AS settle_date,"
" fund FROM fx_swaps"
") a "
"WHERE cpty_id IS NOT NULL AND trade_date <=%s AND fund=%s AND settle_date >=%s",
engine,
params=(d, fund, d),
)
df_trs = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin_percentage * notional/100 AS ia FROM trs "
"WHERE cpty_id IS NOT NULL AND trade_date <= %s AND fund=%s",
engine,
params=(d, fund),
)
df_options = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin AS ia FROM equityoptions "
"WHERE cpty_id IS NOT NULL AND trade_date <= %s AND fund=%s",
engine,
params=(d, fund),
)
df_terminations = pd.read_sql_query(
"SELECT cpty_id, folder, 0 as ia FROM termination_collateral_mapping "
"WHERE termination_date <= %s AND fund=%s",
engine,
params=(d, fund),
)
df = pd.concat(
[
df_cds,
df_swaptions,
df_caps,
df_forwards,
df_trs,
df_options,
df_terminations,
]
)
df = df.replace({"folder": STRATEGY_CASH_MAPPING})
return df
def send_email(d: datetime.date, df: pd.DataFrame) -> None:
from serenitas.utils.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",
"JPM": "JP Morgan",
"BARCLAYS": "Barclays",
}
buf = StringIO()
buf.write("<html><body>\n")
for cp, df in df.groupby(level="broker"):
name = cp_mapping[cp]
buf.write(f"<h3> At {name}:</h3>\n")
try:
df.loc[cp].to_html(buf, index=False)
except AttributeError:
df.loc[cp].to_frame().T.to_html(buf, index=False)
buf.write("</body/></html>")
em = ExchangeMessage()
em.send_email(
f"IAM booking {d:%Y-%m-%d}",
HTMLBody(buf.getvalue()),
["serenitas.otc@sscinc.com"],
["nyops@lmcg.com"],
reply_to=("nyops@lmcg.com",),
)
def load_pdf(file_path, pages=False):
proc = subprocess.run(
["pdftohtml", "-xml", "-stdout", "-i", file_path.as_posix()],
capture_output=True,
)
soup = BeautifulSoup(proc.stdout, features="lxml")
if pages:
r = []
for page in soup.findAll("page"):
l = page.findAll("text")
r.append(sorted(l, key=lambda x: (int(x["top"]), int(x["left"]))))
return r
else:
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, bbox=False):
actual_left, actual_right = math.inf, -math.inf
r = []
for c in l:
if (
int(c["left"]) >= left
and int(c["left"]) + int(c["width"]) < right
and int(c["top"]) >= top
and int(c["top"]) + int(c["height"]) < bottom
):
r.append(c.text)
actual_left = min(int(c["left"]), actual_left)
actual_right = max(int(c["left"]) + int(c["width"]), actual_right)
if bbox:
return r, (actual_left, actual_right)
else:
return r
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)
def parse_num(s):
s = s.replace(",", "")
if s[0] == "(":
return -float(s[1:-1])
else:
return float(s)
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