diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/collateral_calc.py | 676 |
1 files changed, 410 insertions, 266 deletions
diff --git a/python/collateral_calc.py b/python/collateral_calc.py index e17b95d3..6cd1dc7a 100644 --- a/python/collateral_calc.py +++ b/python/collateral_calc.py @@ -5,6 +5,7 @@ import sys from utils import SerenitasFileHandler from utils.db import dawn_engine, dbconn + try: from env import DAILY_DIR, LOG_DIR except KeyError: @@ -20,35 +21,43 @@ from sqlalchemy.exc import IntegrityError from ssh2.session import Session from ssh2.utils import wait_socket from ssh2.error_codes import LIBSSH2_ERROR_EAGAIN -from ssh2.sftp import (LIBSSH2_FXF_READ, LIBSSH2_SFTP_S_IRUSR, - LIBSSH2_SFTP_S_IFDIR, LIBSSH2_SFTP_S_IFREG) +from ssh2.sftp import ( + LIBSSH2_FXF_READ, + LIBSSH2_SFTP_S_IRUSR, + LIBSSH2_SFTP_S_IFDIR, + LIBSSH2_SFTP_S_IFREG, +) def get_sftp_client(): - transport = Transport(('prmssp.amer.sgcib.com', 22)) - transport.connect(username='SerenitasGamma@USA', password='SSqrrLL99') + transport = Transport(("prmssp.amer.sgcib.com", 22)) + transport.connect(username="SerenitasGamma@USA", password="SSqrrLL99") return SFTPClient.from_transport(transport) + def get_baml_sftp_client(): - transport = Transport(('ftps.b2b.ml.com', 22)) + transport = Transport(("ftps.b2b.ml.com", 22)) pkey = RSAKey.from_private_key_file(os.path.expanduser("~/.ssh/id_rsa_lmcg")) - transport.connect(username='lmcginvs', pkey=pkey) + transport.connect(username="lmcginvs", pkey=pkey) return SFTPClient.from_transport(transport) + def get_wells_sftp_client(): - transport = Transport(('axst.wellsfargo.com', 10022)) - transport.connect(username='LMCHsWC6EP', password='HI2s2h19+') + transport = Transport(("axst.wellsfargo.com", 10022)) + transport.connect(username="LMCHsWC6EP", password="HI2s2h19+") return SFTPClient.from_transport(transport) + def get_wells_sftp_client2(): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - sock.connect(('axst.wellsfargo.com', 10022)) + sock.connect(("axst.wellsfargo.com", 10022)) session = Session() session.handshake(sock) - session.userauth_password('LMCHsWC6EP', '0lvK+7xL') + session.userauth_password("LMCHsWC6EP", "0lvK+7xL") sftp = session.sftp_init() return sftp + def download_wells_files2(d=None): DATA_DIR = DAILY_DIR / "Wells_reports" sftp = get_wells_sftp_client2() @@ -60,16 +69,17 @@ def download_wells_files2(d=None): for f in files: local_file = DATA_DIR / f if not local_file.exists(): - with sftp.open(f"/RECEIVE/339425_DATO2/{f}", - LIBSSH2_FXF_READ, LIBSSH2_SFTP_S_IRUSR) as remote_handle, \ - local_file.open("wb") as local_handle: + with sftp.open( + f"/RECEIVE/339425_DATO2/{f}", LIBSSH2_FXF_READ, LIBSSH2_SFTP_S_IRUSR + ) as remote_handle, local_file.open("wb") as local_handle: for size, data in remote_handle: local_handle.write(data) + def download_baml_files(d=None): DATA_DIR = DAILY_DIR / "BAML_reports" sftp = get_baml_sftp_client() - for f in sftp.listdir('outgoing'): + for f in sftp.listdir("outgoing"): local_file = DATA_DIR / f if not local_file.exists(): sftp.get(f"outgoing/{f}", localpath=DATA_DIR / f) @@ -84,17 +94,23 @@ def download_wells_files(d=None): sftp.get(f"{base_dir}/{f}", localpath=DATA_DIR / f) -def download_sftp_files(d=None, - report_types=["OTC_CASH_ACTIVITY", "OTC_POSITIONS", - "OTC_MARGIN", "OTC_MARGIN_EX_DEF", - "OTC_STATEMENT"], - retry_count=0): +def download_sftp_files( + d=None, + report_types=[ + "OTC_CASH_ACTIVITY", + "OTC_POSITIONS", + "OTC_MARGIN", + "OTC_MARGIN_EX_DEF", + "OTC_STATEMENT", + ], + retry_count=0, +): if retry_count > 20: return DATA_DIR = DAILY_DIR / "SG_reports" sftp = get_sftp_client() if d is None: - for f in sftp.listdir('OTC'): + for f in sftp.listdir("OTC"): if f.endswith("OTC_STATEMENT.xls"): print(f) sftp.get(f"OTC/{f}", localpath=DATA_DIR / f) @@ -107,7 +123,7 @@ def download_sftp_files(d=None, continue else: - file_list = sftp.listdir('OTC') + file_list = sftp.listdir("OTC") for report_type in report_types: if report_type == "OTC_STATEMENT": f = f"{d:%Y%m%d}_{report_type}.xls" @@ -123,20 +139,22 @@ def download_sftp_files(d=None, sftp.get(f"OTC/{f}", localpath=DATA_DIR / f) sftp.close() + def download_ms_emails_from_gmail(): DATA_DIR = DAILY_DIR / "MS_reports" from download_emails import GmailMessage + gm = GmailMessage() - for msg in gm.list_msg_ids('Globeop/Operations'): + for msg in gm.list_msg_ids("Globeop/Operations"): try: - message = gm.from_id(msg['id']) - subject = message['subject'] - if 'SERCX **Daily' in subject: + message = gm.from_id(msg["id"]) + subject = message["subject"] + if "SERCX **Daily" in subject: for attach in message.iter_attachments(): fname = attach.get_filename() - if 'NETSwaps' in fname: + if "NETSwaps" in fname: fname = "Trade_Detail_" + fname.split("_")[1] - elif 'NET_Collateral' in fname: + elif "NET_Collateral" in fname: fname = "Collateral_Detail_" + fname.rsplit("_", 1)[1] else: continue @@ -147,47 +165,51 @@ def download_ms_emails_from_gmail(): else: p.write_bytes(part.get_payload(decode=True)) except (KeyError, UnicodeDecodeError) as e: - logger.error("error decoding " + msg['id']) + logger.error("error decoding " + msg["id"]) continue + def download_ms_emails(count=20): em = ExchangeMessage() - emails = em.get_msgs(path=["NYops", "Margin calls MS"], - count=count, - subject__contains="SERCX **Daily") + emails = em.get_msgs( + path=["NYops", "Margin calls MS"], + count=count, + subject__contains="SERCX **Daily", + ) DATA_DIR = DAILY_DIR / "MS_reports" for msg in emails: for attach in msg.attachments: - if 'NETSwaps' in attach.name: + if "NETSwaps" in attach.name: fname = "Trade_Detail_" + attach.name.split("_")[1] - elif 'NET_Collateral' in attach.name: + elif "NET_Collateral" in attach.name: fname = "Collateral_Detail_" + attach.name.rsplit("_", 1)[1] else: continue - p = DATA_DIR / fname + p = DATA_DIR / fname if not p.exists(): p.write_bytes(attach.content) def download_gs_emails(count=20): em = ExchangeMessage() - emails = em.get_msgs(path=["NYops", "Margin calls"], - count=count, - subject__contains="Margin") + emails = em.get_msgs( + path=["NYops", "Margin calls"], count=count, subject__contains="Margin" + ) DATA_DIR = DAILY_DIR / "GS_reports" for msg in emails: for attach in msg.attachments: fname = attach.name - if fname.endswith('xls'): + if fname.endswith("xls"): p = DATA_DIR / fname if not p.exists(): p.write_bytes(attach.content) + def download_citi_emails(count=20): em = ExchangeMessage() - emails = em.get_msgs(path=["NYops", "Margin Calls Citi"], - count=count, - subject__startswith="262966") + emails = em.get_msgs( + path=["NYops", "Margin Calls Citi"], count=count, subject__startswith="262966" + ) DATA_DIR = DAILY_DIR / "CITI_reports" for msg in emails: for attach in msg.attachments: @@ -196,61 +218,90 @@ def download_citi_emails(count=20): if not p.exists(): p.write_bytes(attach.content) + def compare_notionals(df, positions, fcm: str): - 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 = ( + positions.groupby(level=["security_id", "maturity"])[["notional"]] + .sum() + .join(df["NOTIONAL"], how="left") + ) + diff_notionals = check_notionals[ + check_notionals.notional != check_notionals.NOTIONAL + ] if not diff_notionals.empty: logger.error(f"Database and {fcm} FCM know different notionals") for t in diff_notionals.itertuples(): - logger.error(f"{t.Index[0]}\t{t.Index[1].date()}\t{t.notional}\t{t.NOTIONAL}") + logger.error( + f"{t.Index[0]}\t{t.Index[1].date()}\t{t.notional}\t{t.NOTIONAL}" + ) + def baml_collateral(d): - df = pd.read_csv(DAILY_DIR / "BAML_reports" / - f"OTC_Open_Positions_-_Credit_-_LMCG_{d:%Y%m%d}.CSV", - usecols=['MTM', 'ACCRUEDCPN', 'VARMARGIN', 'REDCODE', - 'NOTIONAL', 'EODSETTLEMENTPRICE', 'PERIOD', 'BUYSELL'], - index_col=['REDCODE']) - df.PERIOD = pd.to_datetime(df.PERIOD.astype('str') + '20') + df = pd.read_csv( + DAILY_DIR + / "BAML_reports" + / f"OTC_Open_Positions_-_Credit_-_LMCG_{d:%Y%m%d}.CSV", + 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()] - positions = pd.read_sql_query("SELECT security_id, security_desc, maturity, " - "folder, notional, currency " - "FROM list_cds_positions_by_strat_fcm(%s, 'BAML')", - dawn_engine, params=(d.date(),), - index_col=['security_id', "maturity"]) - df["NOTIONAL"] = (df.NOTIONAL. - where(df.BUYSELL == 'Buy', -df.NOTIONAL). - astype("float")) - df["DIRTYUPFRONT"] = (df.MTM + df.ACCRUEDCPN ) / df.NOTIONAL + positions = pd.read_sql_query( + "SELECT security_id, security_desc, maturity, " + "folder, notional, currency " + "FROM list_cds_positions_by_strat_fcm(%s, 'BAML')", + dawn_engine, + params=(d.date(),), + index_col=["security_id", "maturity"], + ) + 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"] compare_notionals(df, positions, "BAML") positions["dirtyupfront"] = df.reindex(positions.index)["DIRTYUPFRONT"] - positions['amount'] = positions['notional'] * positions['dirtyupfront'] - positions.folder = positions.folder.map({'HEDGE_MBS': 'MBSCDSCSH', - 'SER_ITRXCURVE': 'SER_ITRXCVCSH', - 'SER_IGCURVE': 'SER_IGCVECSH', - 'HYOPTDEL': 'COCSH', - 'IGOPTDEL': 'COCSH', - 'IGINX': 'TCSH', - 'HYINX': 'TCSH'}) - df = (positions. - groupby('folder'). - agg({'amount': 'sum', 'currency': 'first'}). - reset_index('folder')) - df.columns = ['Strategy', 'Amount', 'Currency'] - df_margin = pd.read_csv(DAILY_DIR / "BAML_reports" / - f"OTC_Moneyline_{d:%Y%m%d}.CSV", - usecols=['Statement Date', 'AT CCY', 'Initial Margin Requirement'], - parse_dates=['Statement Date']) - df_margin.columns = ['date', 'currency', 'amount'] - df_margin['account'] = 'V0NSCLMFCM' + positions["amount"] = positions["notional"] * positions["dirtyupfront"] + positions.folder = positions.folder.map( + { + "HEDGE_MBS": "MBSCDSCSH", + "SER_ITRXCURVE": "SER_ITRXCVCSH", + "SER_IGCURVE": "SER_IGCVECSH", + "HYOPTDEL": "COCSH", + "IGOPTDEL": "COCSH", + "IGINX": "TCSH", + "HYINX": "TCSH", + } + ) + df = ( + positions.groupby("folder") + .agg({"amount": "sum", "currency": "first"}) + .reset_index("folder") + ) + df.columns = ["Strategy", "Amount", "Currency"] + df_margin = pd.read_csv( + DAILY_DIR / "BAML_reports" / f"OTC_Moneyline_{d:%Y%m%d}.CSV", + usecols=["Statement Date", "AT CCY", "Initial Margin Requirement"], + parse_dates=["Statement Date"], + ) + df_margin.columns = ["date", "currency", "amount"] + df_margin["account"] = "V0NSCLMFCM" try: - dawn_engine.execute("INSERT INTO fcm_im " - "VALUES(%(date)s, %(account)s, %(currency)s, %(amount)s)", - df_margin.iloc[-1].to_dict()) + dawn_engine.execute( + "INSERT INTO fcm_im " + "VALUES(%(date)s, %(account)s, %(currency)s, %(amount)s)", + df_margin.iloc[-1].to_dict(), + ) except IntegrityError: pass df["date"] = d @@ -259,78 +310,112 @@ def baml_collateral(d): def wells_collateral(d): account = "A5882186" - file_name = (DAILY_DIR / "Wells_reports" / - f"OTC_CDS_Position_Activity_{account}_{d:%m%d%Y}.csv") + file_name = ( + DAILY_DIR + / "Wells_reports" + / f"OTC_CDS_Position_Activity_{account}_{d:%m%d%Y}.csv" + ) try: - df = pd.read_csv(file_name, - usecols=["TENOR", "MARKET_VALUE_NPV", - "PAIR_CLIP", "BUY_SELL", "NOTIONAL", - "MATURITY_DATE"], - parse_dates=["MATURITY_DATE"], - index_col=["PAIR_CLIP", "MATURITY_DATE"]) + df = pd.read_csv( + file_name, + usecols=[ + "TENOR", + "MARKET_VALUE_NPV", + "PAIR_CLIP", + "BUY_SELL", + "NOTIONAL", + "MATURITY_DATE", + ], + parse_dates=["MATURITY_DATE"], + index_col=["PAIR_CLIP", "MATURITY_DATE"], + ) except ValueError: # backpopulated files have a different format... - df = pd.read_csv(file_name, - usecols=["Tenor", "NPV", "Reference_Entity_ID", - "Fixed_Rate_Notional_Buy", "Amount", - "Scheduled_Termination_Date"], - parse_dates=["Scheduled_Termination_Date"], - index_col=["Reference_Entity_ID", "Scheduled_Termination_Date"]) - df = df.rename(columns={"Tenor": "TENOR", - "NPV": "MARKET_VALUE_NPV", - "Amount": "NOTIONAL"}) + df = pd.read_csv( + file_name, + usecols=[ + "Tenor", + "NPV", + "Reference_Entity_ID", + "Fixed_Rate_Notional_Buy", + "Amount", + "Scheduled_Termination_Date", + ], + parse_dates=["Scheduled_Termination_Date"], + index_col=["Reference_Entity_ID", "Scheduled_Termination_Date"], + ) + df = df.rename( + columns={"Tenor": "TENOR", "NPV": "MARKET_VALUE_NPV", "Amount": "NOTIONAL"} + ) df["BUY_SELL"] = 1 df.loc[df.Fixed_Rate_Notional_Buy.isnull(), "BUY_SELL"] = 2 del df["Fixed_Rate_Notional_Buy"] - positions = pd.read_sql_query("SELECT security_id, security_desc, maturity, " - "folder, notional, currency " - "FROM list_cds_positions_by_strat_fcm(%s, 'WF')", - dawn_engine, params=(d.date(),), - index_col=["security_id", "maturity"]) - df["NOTIONAL"] = (df.NOTIONAL. - where(df.BUY_SELL == 1, -df.NOTIONAL). - astype("float")) + positions = pd.read_sql_query( + "SELECT security_id, security_desc, maturity, " + "folder, notional, currency " + "FROM list_cds_positions_by_strat_fcm(%s, 'WF')", + dawn_engine, + params=(d.date(),), + index_col=["security_id", "maturity"], + ) + df["NOTIONAL"] = df.NOTIONAL.where(df.BUY_SELL == 1, -df.NOTIONAL).astype("float") df["DIRTYUPFRONT"] = df.MARKET_VALUE_NPV / df.NOTIONAL df.index.names = ["security_id", "maturity"] compare_notionals(df, positions, "Wells") positions = positions.join(df, how="left") positions["amount"] = positions["notional"] * positions["DIRTYUPFRONT"] - positions.folder = positions.folder.map({'HEDGE_MBS': 'MBSCDSCSH', - 'SER_ITRXCURVE': 'SER_ITRXCVCSH', - 'SER_IGCURVE': 'SER_IGCVECSH', - "HYOPTDEL": 'COCSH', - "IGOPTDEL": 'COCSH', - "IGINX": "TCSH", - "HYINX": "TCSH"}) - df = (positions. - groupby('folder'). - agg({"amount": "sum", - "currency": "first"}). - reset_index("folder")) + positions.folder = positions.folder.map( + { + "HEDGE_MBS": "MBSCDSCSH", + "SER_ITRXCURVE": "SER_ITRXCVCSH", + "SER_IGCURVE": "SER_IGCVECSH", + "HYOPTDEL": "COCSH", + "IGOPTDEL": "COCSH", + "IGINX": "TCSH", + "HYINX": "TCSH", + } + ) + df = ( + positions.groupby("folder") + .agg({"amount": "sum", "currency": "first"}) + .reset_index("folder") + ) df.columns = ["Strategy", "Amount", "Currency"] - df_margin = pd.read_csv(DAILY_DIR / "Wells_reports" / - f"OTC_Moneyline_Activity_{account}_{d:%m%d%Y}.csv", - usecols=["CURRENCY_NAME", "CURRENT_IM", "VALUE_DATE"], - parse_dates=["VALUE_DATE"], - index_col=["CURRENCY_NAME"]) + df_margin = pd.read_csv( + DAILY_DIR + / "Wells_reports" + / f"OTC_Moneyline_Activity_{account}_{d:%m%d%Y}.csv", + usecols=["CURRENCY_NAME", "CURRENT_IM", "VALUE_DATE"], + parse_dates=["VALUE_DATE"], + index_col=["CURRENCY_NAME"], + ) try: - dawn_engine.execute("INSERT INTO fcm_im " - "VALUES(%s, 'WFNSCLMFCM', 'USD', %s)", - df_margin.loc["ZZZZZ", ["VALUE_DATE", "CURRENT_IM"]].tolist()) + dawn_engine.execute( + "INSERT INTO fcm_im " "VALUES(%s, 'WFNSCLMFCM', 'USD', %s)", + df_margin.loc["ZZZZZ", ["VALUE_DATE", "CURRENT_IM"]].tolist(), + ) except IntegrityError: pass df["date"] = d return df + def sg_collateral(d): - df_activity = pd.read_csv(DAILY_DIR / "SG_reports" / f"{d:%Y%m%d}_OTC_CASH_ACTIVITY.csv", - usecols=["Ticket Reference", "Record Type", "Currency", "Amount"]) - df_position = pd.read_csv(DAILY_DIR / "SG_reports" / f"{d:%Y%m%d}_OTC_POSITIONS.csv", - usecols=["Ticket Reference", "Reference Entity", "Mtm Value"]) - df_activity = (df_activity.loc[df_activity['Record Type'] == "VM"]. - set_index("Ticket Reference")) - df_margin = pd.read_csv(DAILY_DIR / "SG_reports" / f"{d:%Y%m%d}_OTC_MARGIN_EX_DEF.csv", - usecols=["Currency", "SG IMR"]) + df_activity = pd.read_csv( + DAILY_DIR / "SG_reports" / f"{d:%Y%m%d}_OTC_CASH_ACTIVITY.csv", + usecols=["Ticket Reference", "Record Type", "Currency", "Amount"], + ) + df_position = pd.read_csv( + DAILY_DIR / "SG_reports" / f"{d:%Y%m%d}_OTC_POSITIONS.csv", + usecols=["Ticket Reference", "Reference Entity", "Mtm Value"], + ) + df_activity = df_activity.loc[df_activity["Record Type"] == "VM"].set_index( + "Ticket Reference" + ) + df_margin = pd.read_csv( + DAILY_DIR / "SG_reports" / f"{d:%Y%m%d}_OTC_MARGIN_EX_DEF.csv", + usecols=["Currency", "SG IMR"], + ) df_position = df_position.set_index("Ticket Reference") # expired_trades # df_position = df_position.append( @@ -341,51 +426,57 @@ def sg_collateral(d): # df.loc['N201811090000A3K215946925849228U1', 'Mtm Value'] = 0. # df.loc['N201811090000A3K215946925849228U1', 'Reference Entity'] = 'CDX-NAIGS31V1-5Y' - df['Collateral'] = df['Mtm Value'] - df['Amount'] - ref_entity = df['Reference Entity'].str.split("-", expand=True) + df["Collateral"] = df["Mtm Value"] - df["Amount"] + ref_entity = df["Reference Entity"].str.split("-", expand=True) del ref_entity[0] - ref_entity.columns = ['to_split', 'tenor'] - ref_entity = ref_entity.join(ref_entity['to_split'].str. - extract("(IG|HY|EUROPE)S(\d+)V(\d+)$", - expand=True)) - del ref_entity['to_split'] - ref_entity.columns = ['tenor', 'index_type', 'series', 'version'] + ref_entity.columns = ["to_split", "tenor"] + ref_entity = ref_entity.join( + ref_entity["to_split"].str.extract("(IG|HY|EUROPE)S(\d+)V(\d+)$", expand=True) + ) + del ref_entity["to_split"] + ref_entity.columns = ["tenor", "index_type", "series", "version"] ref_entity.index_type[ref_entity.index_type == "EUROPE"] = "EU" df = df.join(ref_entity) - df = df.groupby(['index_type', 'series', 'tenor'])['Collateral'].sum() - positions = pd.read_sql_query("SELECT security_desc, folder, notional, currency " - "FROM list_cds_positions_by_strat(%s)", - dawn_engine, params=(d.date(),)) + df = df.groupby(["index_type", "series", "tenor"])["Collateral"].sum() + positions = pd.read_sql_query( + "SELECT security_desc, folder, notional, currency " + "FROM list_cds_positions_by_strat(%s)", + dawn_engine, + params=(d.date(),), + ) instruments = positions.security_desc.str.split(expand=True)[[1, 3, 4]] - instruments.columns = ['index_type', 'series', 'tenor'] + instruments.columns = ["index_type", "series", "tenor"] instruments.series = instruments.series.str.extract("S(\d+)") instruments.index_type[instruments.index_type == "EUR"] = "EU" positions = positions.join(instruments) - del positions['security_desc'] - positions = positions.set_index(['index_type', 'series', 'tenor']) + del positions["security_desc"] + positions = positions.set_index(["index_type", "series", "tenor"]) df = positions.join(df) def f(g): g.Collateral = g.Collateral * g.notional / g.notional.sum() return g - df = (df.groupby(level=['index_type', 'series', 'tenor']). - apply(f)) - df = df.groupby(['folder']).agg({'Collateral': 'sum', 'currency': 'first'}) - df = df.reset_index('folder') - df = df.rename(columns={'folder': 'Strategy', - 'currency': 'Currency', - 'Collateral': 'Amount'}) - df.Strategy = df.Strategy.map({'HEDGE_MBS': 'MBSCDSCSH', - 'SER_ITRXCURVE': 'SER_ITRXCVCSH', - 'SER_IGCURVE': 'SER_IGCVECSH', - 'HYOPTDEL': 'HYCDSCSH', - 'IGOPTDEL': 'IGCDSCSH'}) - df_margin['account'] = 'SGNSCLMASW' - df_margin = df_margin.rename(columns={'SG IMR': 'amount', - 'Currency': 'currency'}) - df_margin['date'] = d + + df = df.groupby(level=["index_type", "series", "tenor"]).apply(f) + df = df.groupby(["folder"]).agg({"Collateral": "sum", "currency": "first"}) + df = df.reset_index("folder") + df = df.rename( + columns={"folder": "Strategy", "currency": "Currency", "Collateral": "Amount"} + ) + df.Strategy = df.Strategy.map( + { + "HEDGE_MBS": "MBSCDSCSH", + "SER_ITRXCURVE": "SER_ITRXCVCSH", + "SER_IGCURVE": "SER_IGCVECSH", + "HYOPTDEL": "HYCDSCSH", + "IGOPTDEL": "IGCDSCSH", + } + ) + df_margin["account"] = "SGNSCLMASW" + df_margin = df_margin.rename(columns={"SG IMR": "amount", "Currency": "currency"}) + df_margin["date"] = d try: - df_margin.to_sql("fcm_im", dawn_engine, if_exists='append', index=False) + df_margin.to_sql("fcm_im", dawn_engine, if_exists="append", index=False) except IntegrityError: pass df["date"] = d @@ -394,33 +485,39 @@ def sg_collateral(d): def ms_collateral(d, dawn_trades): df = pd.read_excel(DAILY_DIR / "MS_reports" / f"Collateral_Detail_{d:%Y%m%d}.xls") - collat = df.loc[1, 'coll_val_ccy'].replace(",", "") + collat = df.loc[1, "coll_val_ccy"].replace(",", "") if "(" in collat: collat = collat[1:-1] - collat = - float(collat) + collat = -float(collat) else: collat = float(collat) df = pd.read_excel(DAILY_DIR / "MS_reports" / f"Trade_Detail_{d:%Y%m%d}.xls") - df = df.merge(dawn_trades, how='left', left_on='trade_id', right_on='cpty_id') + df = df.merge(dawn_trades, how="left", left_on="trade_id", right_on="cpty_id") missing_ids = df.loc[df.cpty_id.isnull(), "trade_id"] if not missing_ids.empty: raise ValueError(f"{missing_ids.tolist()} not in the database") - df = df.groupby('folder')[["collat_req_in_agr_ccy"]].sum() - df['Currency'] = "USD" + df = df.groupby("folder")[["collat_req_in_agr_ccy"]].sum() + df["Currency"] = "USD" df = df.reset_index() - col_names= ['Strategy', 'Amount', 'Currency'] + col_names = ["Strategy", "Amount", "Currency"] df.columns = col_names - df = df.append({"Strategy": "M_CSH_CASH", - "Amount": -collat - df.Amount.sum(), - "Currency": "USD"}, ignore_index=True) + df = df.append( + { + "Strategy": "M_CSH_CASH", + "Amount": -collat - df.Amount.sum(), + "Currency": "USD", + }, + ignore_index=True, + ) df["date"] = d return df def load_gs_file(d, pattern): try: - fname = next((DAILY_DIR / "GS_reports"). - glob(f"{pattern}*{d.strftime('%d_%b_%Y')}*")) + fname = next( + (DAILY_DIR / "GS_reports").glob(f"{pattern}*{d.strftime('%d_%b_%Y')}*") + ) except StopIteration: raise FileNotFoundError(f"GS {pattern} file not found for date {d}") return pd.read_excel(fname, skiprows=9, skipfooter=77) @@ -428,106 +525,141 @@ def load_gs_file(d, pattern): def load_citi_file(d): try: - fname = next((DAILY_DIR / "CITI_reports"). - glob(f"262966_Portfolio_{d.strftime('%Y%m%d')}*")) + fname = next( + (DAILY_DIR / "CITI_reports").glob( + f"262966_Portfolio_{d.strftime('%Y%m%d')}*" + ) + ) except StopIteration: raise FileNotFoundError(f"CITI file not found for date {d}") return pd.read_excel(fname, skiprows=6, skipfooter=2) def get_dawn_trades(d): - df_cds = pd.read_sql_query("SELECT cpty_id, folder FROM cds " - "WHERE cpty_id IS NOT NULL AND trade_date <= %s", - dawn_engine, - params=(d,)) - df_swaptions = pd.read_sql_query("SELECT cpty_id, folder FROM swaptions " - "WHERE cpty_id IS NOT NULL " - "AND trade_date <= %s", - dawn_engine, params=(d,)) - df_caps = pd.read_sql_query("SELECT cpty_id, folder FROM capfloors " - "WHERE cpty_id IS NOT NULL " - "AND trade_date <= %s", - dawn_engine, - params=(d,)) + df_cds = pd.read_sql_query( + "SELECT cpty_id, folder FROM cds " + "WHERE cpty_id IS NOT NULL AND trade_date <= %s", + dawn_engine, + params=(d,), + ) + df_swaptions = pd.read_sql_query( + "SELECT cpty_id, folder FROM swaptions " + "WHERE cpty_id IS NOT NULL " + "AND trade_date <= %s", + dawn_engine, + params=(d,), + ) + df_caps = pd.read_sql_query( + "SELECT cpty_id, folder FROM capfloors " + "WHERE cpty_id IS NOT NULL " + "AND trade_date <= %s", + dawn_engine, + params=(d,), + ) df = pd.concat([df_cds, df_swaptions, df_caps]) - df = df.replace({"folder": {'IGREC': 'COCSH', - 'IGPAYER': 'COCSH', - 'HYPAYER': 'COCSH', - 'HYREC': 'COCSH', - 'STEEP': 'IRDEVCSH', - 'FLAT': 'IRDEVCSH', - 'MBSCDS': 'MBSCDSCSH', - 'IGMEZ': 'TCSH', - 'IGSNR': 'TCSH', - 'IGEQY': 'TCSH', - 'HYMEZ': 'TCSH', - 'HYEQY': 'TCSH', - 'BSPK': 'TCSH'}}) + df = df.replace( + { + "folder": { + "IGREC": "COCSH", + "IGPAYER": "COCSH", + "HYPAYER": "COCSH", + "HYREC": "COCSH", + "STEEP": "IRDEVCSH", + "FLAT": "IRDEVCSH", + "MBSCDS": "MBSCDSCSH", + "IGMEZ": "TCSH", + "IGSNR": "TCSH", + "IGEQY": "TCSH", + "HYMEZ": "TCSH", + "HYEQY": "TCSH", + "BSPK": "TCSH", + } + } + ) return df + def gs_collateral(d, dawn_trades): df = load_gs_file(d, "Collateral_Detail") collateral = float(df.Quantity) df = load_gs_file(d, "Trade_Detail") - df = df[['Trade Id', 'Transaction Type', 'NPV (USD)', 'Initial Margin Required']] - df = df.merge(dawn_trades, how='left', left_on='Trade Id', right_on='cpty_id') + df = df[["Trade Id", "Transaction Type", "NPV (USD)", "Initial Margin Required"]] + df = df.merge(dawn_trades, how="left", left_on="Trade Id", right_on="cpty_id") missing_ids = df.loc[df.cpty_id.isnull(), "Trade Id"] if not missing_ids.empty: raise ValueError(f"{missing_ids.tolist()} not in the database") - df = df[['folder', 'NPV (USD)', 'Initial Margin Required']] - df = df.groupby('folder').sum() - df = df.sum(axis=1).to_frame(name='Amount') - df['Currency'] = 'USD' + df = df[["folder", "NPV (USD)", "Initial Margin Required"]] + df = df.groupby("folder").sum() + df = df.sum(axis=1).to_frame(name="Amount") + df["Currency"] = "USD" df = df.reset_index() - df.columns = ['Strategy', 'Amount', 'Currency'] + df.columns = ["Strategy", "Amount", "Currency"] df.Amount *= -1 - df = df.append({'Strategy': "M_CSH_CASH", - 'Amount': -collateral - df.Amount.sum(), - 'Currency': "USD"}, ignore_index=True) - df['date'] = d + df = df.append( + { + "Strategy": "M_CSH_CASH", + "Amount": -collateral - df.Amount.sum(), + "Currency": "USD", + }, + ignore_index=True, + ) + df["date"] = d return df def citi_collateral(d, dawn_trades): df = load_citi_file(d) collateral = get_citi_collateral(d - BDay()) - df = df[['Operations File', 'Market Value', 'BasicAmt']] - df = df.merge(dawn_trades, how='left', left_on='Operations File', right_on='cpty_id') + df = df[["Operations File", "Market Value", "BasicAmt"]] + df = df.merge( + dawn_trades, how="left", left_on="Operations File", right_on="cpty_id" + ) missing_ids = df.loc[df.cpty_id.isnull(), "Operations File"] if not missing_ids.empty: raise ValueError(f"{missing_ids.tolist()} not in the database") - df = df.groupby('folder').sum() - df = df.sum(axis=1).to_frame(name='Amount') - df['Currency'] = 'USD' + df = df.groupby("folder").sum() + df = df.sum(axis=1).to_frame(name="Amount") + df["Currency"] = "USD" df = df.reset_index() - df.columns = ['Strategy', 'Amount', 'Currency'] + df.columns = ["Strategy", "Amount", "Currency"] df.Amount *= -1 - df = df.append({"Strategy": "M_CSH_CASH", - "Amount": collateral - df.Amount.sum(), - "Currency": "USD"}, ignore_index=True) + df = df.append( + { + "Strategy": "M_CSH_CASH", + "Amount": collateral - df.Amount.sum(), + "Currency": "USD", + }, + ignore_index=True, + ) df["date"] = d - bus_day return df def send_email(d, dfs): - pd.set_option('display.float_format', '{:.2f}'.format) - content = HTMLBody('<html><body>' - '<h3>At Morgan Stanley:</h3>' - '{}' - '<h3>At Bank of America Merrill Lynch:</h3>' - '{}' - '<h3>At Goldman Sachs:</h3>' - '{}' - '<h3>At Citi:</h3>' - '{}' - '<h3>At Wells Fargo:</h3>' - '{}' - '</body><html>'.format(*(df.drop("date", axis=1). - to_html(index=False) for df in dfs))) + pd.set_option("display.float_format", "{:.2f}".format) + content = HTMLBody( + "<html><body>" + "<h3>At Morgan Stanley:</h3>" + "{}" + "<h3>At Bank of America Merrill Lynch:</h3>" + "{}" + "<h3>At Goldman Sachs:</h3>" + "{}" + "<h3>At Citi:</h3>" + "{}" + "<h3>At Wells Fargo:</h3>" + "{}" + "</body><html>".format( + *(df.drop("date", axis=1).to_html(index=False) for df in dfs) + ) + ) em = ExchangeMessage() - em.send_email(f"IAM booking {d:%Y-%m-%d}", content, - ['serenitas.otc@sscinc.com'], - ['nyops@lmcg.com']) + em.send_email( + f"IAM booking {d:%Y-%m-%d}", + content, + ["serenitas.otc@sscinc.com"], + ["nyops@lmcg.com"], + ) if __name__ == "__main__": @@ -535,19 +667,25 @@ if __name__ == "__main__": import logging from dates import bus_day from pandas.tseries.offsets import BDay + fh = SerenitasFileHandler("collateral_calc.log") logger = logging.getLogger("collateral_calc") logger.addHandler(fh) logger.setLevel(logging.WARNING) parser = argparse.ArgumentParser() - parser.add_argument('workdate', nargs='?', - type=lambda s: pd.datetime.strptime(s, "%Y-%m-%d").date(), - default=pd.Timestamp.today().normalize()) - parser.add_argument("-d", "--download", action="store_true", - help="download counterparty reports") - parser.add_argument("-s", "--send-email", action="store_true", - help="send email to Globeop") + parser.add_argument( + "workdate", + nargs="?", + type=lambda s: pd.datetime.strptime(s, "%Y-%m-%d").date(), + default=pd.Timestamp.today().normalize(), + ) + parser.add_argument( + "-d", "--download", action="store_true", help="download counterparty reports" + ) + parser.add_argument( + "-s", "--send-email", action="store_true", help="send email to Globeop" + ) args = parser.parse_args() if args.download: download_ms_emails() @@ -563,28 +701,34 @@ if __name__ == "__main__": df_ms = ms_collateral(args.workdate, dawn_trades) except FileNotFoundError as e: logger.info(e) - df_ms = ms_collateral(args.workdate -bus_day, dawn_trades) - #df_sg = sg_collateral(d) + df_ms = ms_collateral(args.workdate - bus_day, dawn_trades) + # df_sg = sg_collateral(d) df_baml = baml_collateral(args.workdate) try: df_gs = gs_collateral(args.workdate, dawn_trades) except FileNotFoundError as e: logger.info(e) - df_gs = gs_collateral(args.workdate-bus_day, dawn_trades) + df_gs = gs_collateral(args.workdate - bus_day, dawn_trades) df_wells = wells_collateral(args.workdate) - df = pd.concat([df_gs.set_index("Strategy"), - df_ms.set_index("Strategy"), - df_citi.set_index("Strategy"), - df_wells.set_index("Strategy"), - df_baml.set_index("Strategy")], - keys=["GS", "MS", "CITI", "WF", "BAML"], - names=["broker", "strategy"]).reset_index() + df = pd.concat( + [ + df_gs.set_index("Strategy"), + df_ms.set_index("Strategy"), + df_citi.set_index("Strategy"), + df_wells.set_index("Strategy"), + df_baml.set_index("Strategy"), + ], + keys=["GS", "MS", "CITI", "WF", "BAML"], + names=["broker", "strategy"], + ).reset_index() df.strategy = df.strategy.str.replace("^(M_|SER_)?", "", 1) df = df[["date", "broker", "strategy", "Amount", "Currency"]] - conn = dbconn('dawndb') - sql_str = ("INSERT INTO strategy_im VALUES(%s, %s, %s, %s, %s) " - "ON CONFLICT (date, strategy, broker) DO UPDATE " - "SET currency=EXCLUDED.currency, amount=EXCLUDED.amount") + conn = dbconn("dawndb") + sql_str = ( + "INSERT INTO strategy_im VALUES(%s, %s, %s, %s, %s) " + "ON CONFLICT (date, strategy, broker) DO UPDATE " + "SET currency=EXCLUDED.currency, amount=EXCLUDED.amount" + ) with conn.cursor() as c: for t in df.itertuples(index=False): c.execute(sql_str, t) |