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import datetime
import pandas as pd
from . import DAILY_DIR, SftpClient
from .common import compare_notionals, compare_notionals_rates, STRATEGY_CASH_MAPPING
def download_files(*args, **kwargs):
sftp = SftpClient.from_creds("gs")
sftp.download_files("outgoing", DAILY_DIR / "BowdSt" / "GS_fcm_reports")
def get_filename(d: datetime.date, name: str):
try:
fname = next(
(DAILY_DIR / "BowdSt" / "GS_fcm_reports").glob(
f"GS-*-{name}-*-{d:%Y%m%d}-*.csv"
)
)
except StopIteration:
raise FileNotFoundError(f"GS fcm file {name} not found for date {d}")
else:
return fname
def collateral(d: datetime.date, positions, *, engine, fund, positions_irs, **kwargs):
df = pd.read_csv(
get_filename(d + datetime.timedelta(days=1), "Open_Trades_Redcode"),
usecols=[
"Notional",
"Direction",
"NPV (local)",
"Maturity Date",
"COB Date",
"Red Code",
"Instrument",
"CCP Trade ID",
"Pay Notional",
"Pay Leg Rate/Index",
"Receive Leg Rate/Index",
],
parse_dates=["COB Date", "Maturity Date"],
index_col=["Red Code", "Maturity Date"],
thousands=",",
)
df_rates = df[df["Instrument"] == "IRS"].drop(["Instrument", "Notional"], axis=1)
df_rates = df_rates.reset_index()
df_rates.loc[df_rates["Direction"] == "Receive", "Pay Notional"] *= -1
df_rates["fixed_rate"] = df_rates.apply(
lambda row: row["Receive Leg Rate/Index"]
if row["Direction"] == "Receive"
else row["Pay Leg Rate/Index"],
axis=1,
)
_rate_index_mapping = {"1Y-USD-SOFR-COMPOUND": "SOFRRATE"}
df_rates["float_index"] = df_rates.apply(
lambda row: _rate_index_mapping[row["Receive Leg Rate/Index"]]
if row["Direction"] == "Pay"
else _rate_index_mapping[row["Pay Leg Rate/Index"]],
axis=1,
)
# Going to fix roll day at IMM for now until we have the column updated
df_rates["roll_day"] = "IMM"
df_rates = df_rates.rename(columns={"Pay Notional": "NOTIONAL"})
df_rates = df_rates.groupby(
by=["Maturity Date", "fixed_rate", "roll_day", "float_index"]
)[["NOTIONAL", "NPV (local)"]].sum()
df_rates.index.names = ["maturity_date", "fixed_rate", "roll_day", "float_index"]
df = df[df["Instrument"] == "CDS"].drop(
["Instrument", "CCP Trade ID", "Pay Notional"], axis=1
)
df.Notional = df.Notional.where(df.Direction == "Buy", -df.Notional)
df.index.names = ["security_id", "maturity"]
cob_date = df["COB Date"][0]
df = df.groupby(level=["security_id", "maturity"])[
["Notional", "NPV (local)"]
].sum()
df = df.rename(columns={"Notional": "NOTIONAL"})
compare_notionals(df, positions, "GS", fund)
df_rates = df_rates.rename(columns={"Pay Notional": "NOTIONAL"})
compare_notionals_rates(df_rates, positions_irs, "GS")
positions_irs["Amount"] = df_rates.reindex(positions_irs.index)["NPV (local)"]
df_rates = (
positions_irs.groupby(["folder", "currency"])
.agg({"Amount": "sum"})
.reset_index(["folder", "currency"])
)
df_rates = df_rates.rename(columns={"folder": "Strategy", "currency": "Currency"})
df["dirty_upfront"] = df["NPV (local)"] / df.NOTIONAL
positions["dirty_upfront"] = df.reindex(positions.index)["dirty_upfront"]
positions["Amount"] = positions["notional"] * positions["dirty_upfront"]
positions.folder = positions.folder.replace(STRATEGY_CASH_MAPPING)
def aux(row):
if row.folder == "XCURVE":
return "SER_IGCVECSH" if row.currency == "USD" else "SER_ITRXCVCSH"
else:
return row.folder
positions.folder = positions.apply(aux, axis=1)
df = (
positions.groupby(["folder", "currency"])
.agg({"Amount": "sum"})
.reset_index(["folder", "currency"])
)
df = df.rename(columns={"folder": "Strategy", "currency": "Currency"})
df = pd.concat([df, df_rates])
df["date"] = cob_date
df_margin = pd.read_csv(
get_filename(
d + datetime.timedelta(days=1), "Account_Balances_and_Margin_extended"
),
parse_dates=["COB Date"],
thousands=",",
)
col_mapping = {
"Opening Balance (local)": "beginning_balance",
"Ending Balance (local)": "ending_balance",
"PAI (local)": "pai",
"Account Value + IM Collateral (local)": "account_value_market",
"Initial Margin Requirement (local)": "current_im",
"Excess/Deficit (local)": "current_excess_deficit",
"Currency": "currency",
"GS Account Number": "account",
"COB Date": "date",
"CCP Fees (local)": "clearing_fees",
"Commissions (local)": "transaction_fees",
}
df_margin = df_margin.rename(columns=col_mapping)
df_margin["current_im"] *= -1.0
df_margin["account"] = df_margin["account"].str.replace(" ", "")
cols = col_mapping.values()
df_margin_futures = pd.read_csv(
get_filename(d + datetime.timedelta(days=1), "Account_Balances_By_Currency"),
parse_dates=["COB Date"],
thousands=",",
)
col_mapping = {
"Beginning Account Balance (Local)": "beginning_balance",
"Ending Account Balance (Local)": "ending_balance",
"Initial Margin Requirement (Local)": "current_im",
"GS Account Number": "account",
"COB Date": "date",
"Excess/Deficit (Local)": "current_excess_deficit",
"Currency": "currency",
"Gross Realized P&L (Local)": "realized_pnl",
"Commission & Fees (Local)": "transaction_fees",
}
df_margin_futures = df_margin_futures.rename(columns=col_mapping)
df_margin_futures["account_value_market"] = (
df_margin_futures["current_im"] + df_margin_futures["Total Equity (Local)"]
)
df_margin_futures["pai"] = 0.0
df_margin = df_margin.groupby(["date", "account", "currency"], as_index=False).sum()
df_margin = pd.concat([df_margin, df_margin_futures])
place_holders = ",".join(["%s"] * len(cols))
engine.execute(
f"INSERT INTO fcm_moneyline({','.join(cols)}) VALUES({place_holders})"
"ON CONFLICT (date, account, currency) "
"DO NOTHING",
list(df_margin[cols].itertuples(index=False)),
)
return df.set_index("Strategy")
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