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from . import DAILY_DIR, SftpClient
from .common import compare_notionals, compare_notionals_rates, STRATEGY_CASH_MAPPING
import pandas as pd
from sqlalchemy.exc import IntegrityError
import logging
logger = logging.getLogger(__name__)
def download_files(*args, fund="Serenitas", **kwargs):
if fund not in ("Serenitas", "Selene"):
return
sftp = SftpClient.from_creds("baml_fcm")
if fund == "Serenitas":
filters = (lambda s: "Selene" not in s.filename,)
elif fund == "Selene":
filters = (lambda s: "Selene" in s.filename,)
sftp.download_files(
"outgoing", DAILY_DIR / fund / "BAML_reports", additional_filters=filters
)
def load_csv(file_type, fund, d, **kwargs):
if fund == "Serenitas":
tag = "LMCG"
else:
tag = "Selene"
return pd.read_csv(
DAILY_DIR
/ fund
/ "BAML_reports"
/ f"OTC_Open_Positions_-_{file_type}_-_{tag}_{d:%Y%m%d}.CSV",
**kwargs,
)
def collateral(d, positions, *, engine, fund="Serenitas", positions_irs, **kwargs):
df = load_csv(
"Credit",
fund,
d,
usecols=[
"MTM",
"ACCRUEDCPN",
"VARMARGIN",
"REDCODE",
"NOTIONAL",
"EODSETTLEMENTPRICE",
"PERIOD",
"BUYSELL",
],
index_col=["REDCODE"],
)
df.PERIOD = pd.to_datetime(df.PERIOD.astype("str") + "20")
df = df.set_index("PERIOD", append=True)
df = df[df.EODSETTLEMENTPRICE.notnull()]
df["NOTIONAL"] = df.NOTIONAL.where(df.BUYSELL == "Buy", -df.NOTIONAL).astype(
"float"
)
df["DIRTYUPFRONT"] = (df.MTM + df.ACCRUEDCPN) / df.NOTIONAL
df.index.names = ["security_id", "maturity"]
# dropped old versions (trades should be offsetting
df = df.groupby(level=["security_id", "maturity"]).agg(
{"NOTIONAL": "sum", "DIRTYUPFRONT": "first"}
)
df = df[df.NOTIONAL != 0.0]
compare_notionals(df, positions, "BAML", fund)
positions["dirtyupfront"] = df.reindex(positions.index)["DIRTYUPFRONT"]
positions["Amount"] = positions["notional"] * positions["dirtyupfront"]
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_rates = load_csv(
"Rates",
fund,
d,
usecols=[
"COBDATE",
"CLEAREDTRADEID",
"FIXEDRATE",
"NPV",
"DIRECTION",
"NOTIONAL",
"EFFDATE",
"MATDATE",
"LEG1ROLLCONV",
],
parse_dates=["MATDATE", "EFFDATE"],
)
df_rates.loc[df_rates["DIRECTION"] == "Rec", "NOTIONAL"] *= -1
df_rates["FIXEDRATE"] = df_rates["FIXEDRATE"].round(4)
df_rates = df_rates.groupby(by=["MATDATE", "FIXEDRATE", "LEG1ROLLCONV"]).agg(
{"NOTIONAL": "sum", "NPV": "sum"}
)
df_rates.index.names = ["maturity_date", "fixed_rate", "roll_day"]
compare_notionals_rates(df_rates, positions_irs, "BAML")
positions_irs["Amount"] = df_rates.reindex(positions_irs.index)["NPV"]
positions_irs.folder.replace(STRATEGY_CASH_MAPPING, inplace=True)
df_rates = (
positions_irs.groupby(["folder", "currency"])
.agg({"Amount": "sum"})
.reset_index(["folder", "currency"])
)
df_rates = df_rates.rename(columns={"folder": "Strategy", "currency": "Currency"})
col_names = [
"Statement Date",
"AT",
"AT CCY",
"Beginning Balance",
"CDS Initial Coupon",
"CDS Trade Variation",
"Price Aligned Interest",
"Clearing Fee",
"Commission",
"Cash Amounts",
"Ending Balance",
"Account Value at Market",
"Net P&L",
"Initial Margin Requirement",
"Margin Excess/Deficit",
]
tag = "Selene_" if fund == "Selene" else ""
df_margin = pd.read_csv(
DAILY_DIR / fund / "BAML_reports" / f"OTC_Moneyline_{tag}{d:%Y%m%d}.CSV",
usecols=col_names,
parse_dates=["Statement Date"],
index_col=["Statement Date"],
)
df_margin_fut = df_margin[df_margin.AT == "R1"]
df_margin = df_margin[df_margin.AT != "R1"]
df_margin.AT = df_margin.AT.replace(
{
"Q4": "EUR",
"F4": "EUR",
"Q1": "USD",
"F2": "USD",
"9Z": "ZZZZZ",
}
)
df_margin = df_margin.set_index("AT", append=True)
df_margin = df_margin.groupby(level=(0, 1)).sum(numeric_only=True)
df_margin = df_margin.reset_index()
df_margin_fut.AT = "USD"
df_margin_fut = df_margin_fut.reset_index()
col_names.pop(2)
accounts = {
"Serenitas": ("V0NSCLMFCM", "MLNSCLMAFU"),
"Selene": ("6MZ20K79", ""),
}
df_margin["account"], df_margin_fut["account"] = accounts[fund]
col_names.insert(1, "account")
try:
place_holders = ",".join(["%s"] * len(col_names))
with engine.connect() as conn:
conn.execute(
f"INSERT INTO fcm_moneyline VALUES({place_holders})",
list(df_margin[col_names].itertuples(index=False)),
)
conn.execute(
f"INSERT INTO fcm_moneyline VALUES({place_holders})",
list(df_margin_fut[col_names].itertuples(index=False)),
)
except IntegrityError as e:
logger.debug(str(e))
pass
df = pd.concat([df, df_rates])
df = (
df.groupby(["Strategy", "Currency"])
.agg({"Amount": "sum"})
.reset_index(["Strategy", "Currency"])
)
df["date"] = d
return df.set_index("Strategy")
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