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import datetime
import os
import os.path
from . import DAILY_DIR
from ftplib import FTP
import gnupg
from . import config
import re
import logging
import sys
sys.path.append("..")
import load_globeop_report
logger = logging.getLogger(__name__)
try:
import pandas as pd
from pandas.tseries.offsets import BDay
except ImportError:
pass
def get_ped(s):
if m := re.search("PED=([^.]+)", s):
PED = datetime.date.fromisoformat(m.group(1))
elif m := re.search("([^.]+)", s):
PED = pd.to_datetime(m.group(1), format="%Y%m%d") - BDay(1)
PED = PED.date()
else:
raise ValueError
return PED
def key_fun(s):
PED = get_ped(s)
if m := re.search("KD=([^.]+)", s):
KD = datetime.datetime.strptime(m.group(1), "%Y-%m-%d-%H-%M-%S")
elif m := re.search(r"([^.]+\.[^.]+)", s):
KD = datetime.datetime.strptime(m.group(1), "%Y%m%d.%H%M%S")
else:
raise ValueError
return (PED, KD)
def run_date(s):
if "SWO" in s:
date_string = s.split("_", 5)[4]
else:
date_string = s.split("_", 3)[2]
return datetime.datetime.strptime(date_string, "%Y%m%d.%H%M%S")
def get_ftp(folder):
ftp = FTP("ftp.globeop.com")
ftp.login("srntsftp", config.ftp_password)
ftp.cwd(folder)
return ftp
def get_gpg():
if os.name == "nt":
gpg = gnupg.GPG(
gpgbinary=r'"c:\\Program Files (x86)\\GNU\\GnuPG\\gpg2.exe"',
gnupghome=os.path.join(os.getenv("APPDATA"), "gnupg"),
)
elif os.name == "posix":
gpg = gnupg.GPG(gnupghome=os.path.join(os.environ["HOME"], ".gnupg"))
gpg.encoding = "utf8"
return gpg
def convert_to_csv(f):
mapping = (("Credit Default Swap", "CDS"), ("Swaption", "Swaption"), ("ALL", "All"))
if f.exists():
for sheet, name in mapping:
df = pd.read_excel(f, sheet_name=sheet, skiprows=[0, 1, 2, 3])
df.to_csv(f.parent / f"{name}_Report.csv", index=False)
f.unlink()
def download_data(engine, workdate: datetime.date):
ftp = get_ftp("outgoing")
files = ftp.nlst()
pnlfiles = [
filename
for filename in files
if filename.endswith("csv.asc") and "Profit" in filename
if get_ped(filename) < workdate
]
valuationfiles = [
filename
for filename in files
if filename.endswith("csv.asc") and "Valuation_TradeID" in filename
if get_ped(filename) < workdate
]
cdsfiles = [
filename
for filename in files
if "TradeSearch" in filename
if run_date(filename).date() <= workdate
]
available_files = []
if pnlfiles:
available_files.append(max(pnlfiles, key=key_fun))
if valuationfiles:
available_files.append(max(valuationfiles, key=key_fun))
if cdsfiles:
available_files.append(max(cdsfiles, key=run_date))
if not available_files:
logger.error("no file available for date: %s" % str(workdate))
return
reports_dir = DAILY_DIR / str(workdate) / "Reports"
if not reports_dir.exists():
reports_dir.mkdir(parents=True)
for filename in available_files:
with (reports_dir / filename).open("wb") as fh:
ftp.retrbinary("RETR " + filename, fh.write)
logger.info(f"downloaded {filename}")
gpg = get_gpg()
for filename in available_files:
if "Profit" in filename:
newfilename = "Pnl_Report.csv"
elif "Valuation" in filename:
newfilename = "Valuation_Report.csv"
else:
newfilename = "CDS_Report.xls"
with (reports_dir / filename).open("rb") as fh:
dec = gpg.decrypt_file(
fh,
output=(reports_dir / newfilename).as_posix(),
passphrase=config.key_password,
always_trust=True,
)
logger.info(f"{filename}: {dec.status}")
(reports_dir / filename).unlink()
# convert xls to csv
convert_to_csv(reports_dir / "CDS_Report.xls")
insert_todb(engine, workdate)
def insert_todb(engine, workdate: datetime.date):
reports_dir = DAILY_DIR / str(workdate) / "Reports"
if not reports_dir.exists():
reports_dir = (
DAILY_DIR
/ f"{workdate:%Y}"
/ f"{workdate:%Y_%m}"
/ str(workdate)
/ "Reports"
)
for report in ("Valuation", "Pnl", "CDS"):
fun = getattr(load_globeop_report, f"read_{report.lower()}_report")
table = f"{report.lower()}_reports"
report_file = reports_dir / f"{report}_Report.csv"
if not report_file.exists():
continue
df = fun(report_file)
if report == "Valuation":
period_end_date = pd.Timestamp(df.periodenddate[0])
sql_str = "DELETE FROM valuation_reports WHERE periodenddate=%s"
else:
df["date"] = period_end_date
sql_str = f"DELETE FROM {table} WHERE date=%s"
df["row"] = df.index
engine.execute(sql_str, (period_end_date,))
df.to_sql(table, engine, if_exists="append", index=False)
def upload_bond_marks(engine, workdate: datetime.date):
df = pd.read_sql_query(
"SELECT identifier, price from list_marks(%s) "
"RIGHT JOIN list_positions(%s, NULL, False) "
"USING (identifier)",
engine,
params=(workdate, workdate),
)
df.rename(columns={"identifier": "IDENTIFIER", "price": "Price"}, inplace=True)
fullpath = DAILY_DIR / str(workdate) / f"securitiesNpv{workdate:%Y%m%d_%H%M%S}.csv"
df.to_csv(fullpath, index=False)
ftp = get_ftp("incoming")
with fullpath.open("rb") as fh:
ftp.storbinary("STOR " + fullpath.name, fh)
logger.info("upload bond marks done")
def upload_cds_marks(engine, workdate: datetime.date):
df = pd.read_sql_query(
"""SELECT cds.dealid AS "DealID", 'CREDIT_SWAP' AS "Instrument Type",
(a.clean_nav+a.accrued) AS "NPV" from list_abscds_marks(%s) a
JOIN cds USING (security_id)""",
engine,
params=(workdate,),
)
fullpath = DAILY_DIR / str(workdate) / f"otcNpv{workdate:%Y%m%d}.csv"
df.to_csv(fullpath, index=False)
ftp = get_ftp("incoming")
with fullpath.open("rb") as fh:
ftp.storbinary("STOR " + fullpath.name, fh)
logger.info("upload cds marks done")
def upload_data(engine, workdate: datetime.datetime):
upload_bond_marks(engine, workdate)
upload_cds_marks(engine, workdate)
def back_fill(start_date="2017-07-20"):
date_rng = pd.date_range(start=start_date, end=pd.Timestamp.today(), freq="B")
for date in date_rng:
insert_todb(date.date())
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