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import argparse
import datetime
import logging
import pandas as pd
from serenitas.utils.env import DAILY_DIR
from serenitas.analytics.dates import prev_business_day
from serenitas.utils.db import dawn_engine
from serenitas.utils.db import dbconn
from collateral.baml_isda import load_excel as load_excel_baml
from collateral.gs import load_file as load_excel_gs
from report_ops.enums import FundEnum
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
FX_REPORT_COLUMNS = [
"cpty_id",
"trade_date",
"settle_date",
"buy_currency",
"buy_amount",
"sell_currency",
"sell_amount",
]
def get_tag(fund):
if fund == "Serenitas":
return "TSLP"
elif fund == "BowdSt":
return "TLLC"
elif fund == "Selene":
return "INC"
else:
return ""
def read_BAMSNY(fund, workdate):
REPORTS_DIR = DAILY_DIR / fund / "BoA_reports"
try:
fname = next(
REPORTS_DIR.glob(
f"301__LMCG_INVESTMEN{get_tag(fund)}_CSA_{workdate:%m%d%Y}_*"
)
)
except StopIteration:
raise FileNotFoundError
df = load_excel_baml(fname)
df = df[df["ProductID"] == "FX_Fwd"]
df = df[
[
"Back Office Number",
"Trade Date",
"Maturity Date",
"Ccy1",
"Notional 1",
"Ccy2",
"Notional 2",
]
]
df.columns = FX_REPORT_COLUMNS
for date_column in (
"trade_date",
"settle_date",
):
df[date_column] = pd.to_datetime(df[date_column], infer_datetime_format=True)
df[["buy_amount", "sell_amount"]] = df[["buy_amount", "sell_amount"]].apply(
pd.to_numeric, errors="coerce"
)
yield from df.itertuples()
def read_MSCSNY(fund, workdate):
df = pd.read_excel(
DAILY_DIR
/ fund
/ "MS_reports"
/ f"Trade_Detail_FX_{prev_business_day(workdate):%Y%m%d}.xls"
)
df = df[
[
"trade_id",
"trade_date",
"settlement_date",
"buy_ccy",
"amt_buy_ccy",
"sell_ccy",
"amt_sell_ccy",
]
]
df.columns = FX_REPORT_COLUMNS
for date_column in (
"trade_date",
"settle_date",
):
df[date_column] = pd.to_datetime(
df[date_column], infer_datetime_format=True
).dt.date
df[["buy_amount", "sell_amount"]] = df[["buy_amount", "sell_amount"]].apply(
pd.to_numeric, errors="coerce"
)
yield from df.itertuples()
def extract_amounts_and_currencies(row):
cpty_id = row["Trade Id"]
trade_date = datetime.datetime.strptime(row["Trade Date"], "%d-%b-%Y").date()
settle_date = datetime.datetime.strptime(row["Maturity Date"], "%d-%b-%Y").date()
if row["Notional(1)"] < 0: # From perspective of GSIL
buy_amount = abs(row["Notional(1)"])
buy_currency = row["Not1Ccy"]
sell_amount = abs(row["Notional(2)"])
sell_currency = row["Not2Ccy"]
else:
buy_amount = abs(row["Notional(2)"])
buy_currency = row["Not2Ccy"]
sell_amount = abs(row["Notional(1)"])
sell_currency = row["Not1Ccy"]
return {
"cpty_id": cpty_id,
"trade_date": trade_date,
"settle_date": settle_date,
"buy_amount": buy_amount,
"buy_currency": buy_currency,
"sell_amount": sell_amount,
"sell_currency": sell_currency,
}
def read_GOLDNY(fund, workdate):
df = load_excel_gs(prev_business_day(workdate), fund, "Trade_Detail")
df = df[df["Transaction Type"] == "FX"]
result = df.apply(extract_amounts_and_currencies, axis=1)
yield from pd.DataFrame.from_records(result).itertuples()
def get_forwards(cob):
df = pd.read_sql_query("SELECT * FROM forward_trades", con=dawn_engine)
df[["buy_amount", "sell_amount"]] = df[["buy_amount", "sell_amount"]].apply(
pd.to_numeric, errors="coerce"
)
return df
def process_rows(fund, counterparty, workdate, dawn_trades, conn):
read_fun = globals()[f"read_{counterparty}"]
for row in read_fun(fund.name, workdate):
if row.cpty_id not in dawn_trades.cpty_id.values:
matching_candidates = dawn_trades.loc[
(dawn_trades["cp_code"] == counterparty)
& (dawn_trades["fund"] == fund.value)
]
filters = {
"trade_date": row.trade_date,
"settle_date": row.settle_date,
"buy_currency": row.buy_currency,
"sell_currency": row.sell_currency,
"buy_amount": row.buy_amount,
"sell_amount": row.sell_amount,
}
matching_candidates = matching_candidates.loc[
(matching_candidates[list(filters.keys())] == pd.Series(filters)).all(
axis=1
)
]
if not matching_candidates.empty:
matched_candidate = matching_candidates.iloc[0, :]
with conn.cursor() as c:
if matched_candidate.fx_type == "SPOT":
table = "spots"
column = "cpty_id"
elif matched_candidate.fx_type in ("NEAR", "FAR"):
table = "fx_swaps"
column = f"{matched_candidate.fx_type.lower()}_cpty_id"
c.execute(
f"UPDATE {table} SET {column}=%s WHERE id=%s",
(
row.cpty_id,
matched_candidate.id,
),
)
logger.info(
f"MATCHED {matched_candidate.id}: {table} to {row.cpty_id}"
)
conn.commit()
else:
raise ValueError(f"This is an unknown trade from us. {row.cpty_id}")
else:
pass # Will later start verifying that our trades are matched right
def main(workdate):
conn = dbconn("dawndb")
dawn_trades = get_forwards(workdate)
for fund in FundEnum:
for counterparty in (
"MSCSNY",
"BAMSNY",
"GOLDNY",
):
try:
process_rows(fund, counterparty, workdate, dawn_trades, conn)
except FileNotFoundError as e:
logger.warning(f"FileNotFound for {fund}: {counterparty} on {workdate}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"workdate",
type=datetime.date.fromisoformat,
nargs="?",
default=datetime.date.today(),
help="Workdate (YYYY-MM-DD)",
)
args = parser.parse_args()
main(args.workdate)
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