import pandas as pd from serenitas.utils.db import dbconn import argparse from serenitas.utils.exchange import ExchangeMessage from exchangelib import FileAttachment from io import StringIO from serenitas.utils.env import DAILY_DIR from serenitas.utils.db import dbconn, dawn_engine import datetime from pandas.tseries.offsets import BDay import numpy as np import pandas_market_calendars as mcal def get_dir(date): p = DAILY_DIR / "BOWD_recon" / f"{date:%Y_%m}" return p def clear_date(date, conn): with conn.cursor() as c: c.execute("DELETE FROM bowdst_val bv WHERE as_of_date = %s;", (date,)) conn.commit() def load_val_report(date, conn): p = get_dir(date) / f"Asset_Detail.csv" df = pd.read_csv( p, thousands=",", parse_dates=["As Of Date", "Maturity Date", "Report Run Date"] ) df = df.drop( [ "Reporting Account Number", "Reporting Account Name", "Source Account Name", "Xref Security ID", "Country Name", "Country Code", "Local Currency Name", "Acct Base Currency Name", "Acct Base Currency Code", "CINS", "Issuer ID", "SEDOL", "Valoren", "Sicovam", "WPK", "Quick", "Underlying Sec ID", "Loan ID", "Manager", "Book Yield Value", "Counterparty", "Ticker with Exchange Code", "Ticker with Yellow Key", "Accounting Status", "Primary GSP Account", "Extended GSP Account Number", "Percent Of Total", ], axis=1, ) if "Acctg Status Update (EDT)" in df: del df["Acctg Status Update (EDT)"] elif "Acctg Status Update (EST)" in df: del df["Acctg Status Update (EST)"] df["Source Account Number"] = df["Source Account Number"].str[-4:].astype("int") df.columns = df.columns.str.replace(" ", "_").str.lower() df = df.rename( columns={ "shares/par": "current_notional", "local_unrealized_gain/loss": "local_unrealized_pnl", "base_unrealized_gain/loss": "base_unrealized_pnl", } ) for col in [ "current_notional", "local_price", "base_price", "local_cost", "base_cost", "local_market_value", "base_market_value", "local_unrealized_pnl", "base_unrealized_pnl", "local_notional_cost", "base_notional_cost", "local_notional_value", "base_notional_value", ]: if df[col].dtype != "float64": df[col] = df[col].apply(lambda s: "-" + s[1:-1] if s.startswith("(") else s) df[col] = pd.to_numeric(df[col].str.replace(",", "")) df["row"] = df.index clear_date(date, conn) df.to_sql("bowdst_val", dawn_engine, if_exists="append", index=False) def difference(df): if ("db_mv" in df.columns) and ("db_notional" in df.columns): df["mv_difference"] = df["db_mv"] - df["admin_mv"] df["notional_difference"] = df["db_notional"] - df["admin_notional"] elif "db_mv" in df.columns: df["mv_difference"] = df["db_mv"] - df["admin_mv"] elif "db_notional" in df.columns: df["notional_difference"] = df["db_notional"] - df["admin_notional"] return df def sums(df): if ("db_mv" in df.columns) and ("db_notional" in df.columns): return df[["db_mv", "admin_mv", "db_notional", "admin_notional"]].sum() elif "db_mv" in df.columns: return df[["db_mv", "admin_mv"]].sum() elif "db_notional" in df.columns: return df[["db_notional", "admin_notional"]].sum() def recon(hierarchy_file, date): df = pd.read_excel(hierarchy_file) bowd_bond_trades = df[df["CUSIP"].notnull()] bond_asset_classes = ["Subprime", "CRT", "CLO"] bond_trades_combined = [] for asset in bond_asset_classes: db_bond_trades = pd.read_sql_query( f"select * from risk_positions(%s, %s, 'BOWDST')", dawndb, params=(date, asset), ) bond_trades = bowd_bond_trades.merge( db_bond_trades, left_on="Mellon Security ID", right_on="identifier", how="right", )[ [ "description", "identifier", "notional", "factor", "Shares/Par", "Base Market Value", "usd_market_value", ] ] bond_trades["db_notional"] = bond_trades["notional"] * bond_trades["factor"] bond_trades.rename( columns={ "usd_market_value": "db_mv", "Shares/Par": "admin_notional", "Base Market Value": "admin_mv", }, inplace=True, ) bond_trades_combined.append(bond_trades) bond_trades_combined = pd.concat(bond_trades_combined) tranche_trades = pd.read_sql_query( f"select security_desc, maturity, orig_attach, orig_detach, sum(notional * tranche_factor) as db_notional, sum(admin_notional) as admin_notional, sum(serenitas_clean_nav) as db_mv, sum(admin_clean_nav) as admin_mv from tranche_risk_bowdst where date=%s group by security_desc, maturity, orig_attach, orig_detach ;", dawndb, params=(date,), ) cdx_trades = pd.read_sql_query( f"select security_id, security_desc, index, series, version, maturity, globeop_notional as admin_notional, notional * factor as db_notional, clean_nav as db_mv, globeop_nav as admin_mv from list_cds_marks(%s, null, 'BOWDST')", dawndb, params=(date,), ) cdx_swaption_trades = pd.read_sql_query( f"select security_id, option_type, strike, expiration_date, sum(serenitas_nav) as db_mv, sum(globeop_nav) as admin_mv from list_swaption_positions_and_risks(%s, 'BOWDST') group by security_id, option_type, strike, expiration_date;", dawndb, params=(date,), ) ir_swaption_trades = pd.read_sql_query( "SELECT deal_id, option_type, strike, SECURITY_Id, expiration_date, notional AS db_notional, current_notional AS admin_notional, nav AS db_mv, base_market_value AS admin_mv FROM list_ir_swaption_positions(%s, 'BOWDST') LEFT JOIN bowdst_val ON deal_id=link_ref WHERE as_of_date=%s;", dawndb, params=(last_bus_day(date), date), ) kinds = [ bond_trades_combined, tranche_trades, cdx_trades, cdx_swaption_trades, ir_swaption_trades, ] names = [ "bond_trades", "tranche_trades", "cdx_trades", "cdx_swaption_trades", "ir_swaption_trades", ] message = "" em = ExchangeMessage() attachments = [] for kind, name in zip(kinds, names): buf = StringIO() difference(kind.round(decimals=2)).to_csv(buf) attachments.append( FileAttachment(name=f"{name}_{date}.csv", content=buf.getvalue().encode()) ) pd.set_option("display.float_format", lambda x: "%.2f" % x) message += f"\n{name}: {pd.DataFrame(sums(kind), columns=['sums'])}" em.send_email( subject=f"Notional Totals {date}", body=message, to_recipients=("fyu@lmcg.com",), attach=attachments, ) def last_bus_day(date): holidays = mcal.get_calendar("NYSE").holidays().holidays if date in holidays: return (date - BDay(1)).date() elif not np.is_busday(date): return (date - BDay(1)).date() else: return date parser = argparse.ArgumentParser() parser.add_argument("end_date", type=datetime.date.fromisoformat) args = parser.parse_args() if __name__ == "__main__": dawndb = dbconn("dawndb") date = args.end_date load_val_report(date, dawndb) hierarchy_file = get_dir(date) / "hierarchy.xls" recon(hierarchy_file, date)