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import datetime
import pandas as pd
import re
from env import DAILY_DIR
from utils.db import dbconn
def gs_navs():
d = {}
for fname in (DAILY_DIR / "GS_reports").glob("Trade_Detail*.xls"):
try:
df = pd.read_excel(fname, skiprows=9, skipfooter=77, index_col="Trade Id")
except ValueError:
continue
df["Trade Date"] = pd.to_datetime(df["Trade Date"])
df = df[["Trade Date", "Buy/Sell", "Notional (USD)", "NPV (USD)"]]
df.columns = ["trade_date", "buy/sell", "notional", "nav"]
name = fname.name.replace("9972734", "")
m = re.match(r"[^\d]*(\d{2}_.{3}_\d{4})", name)
if m:
date_string, = m.groups()
date = datetime.datetime.strptime(date_string, "%d_%b_%Y")
d[date] = df
df = pd.concat(d)
# nav is from Goldman's point of view
df.nav *= -1.0
return df
def ms_navs():
d = {}
for fname in (DAILY_DIR / "MS_reports").glob("Trade_Detail*.xls"):
df = pd.read_excel(fname, index_col="trade_id")
df.trade_date = pd.to_datetime(df.trade_date)
df = df[
["trade_date", "pay_rec", "notional_in_trade_ccy", "exposure_in_rpt_ccy"]
]
df.columns = ["trade_date", "buy/sell", "notional", "nav"]
m = re.match(r"[^\d]*(\d{8})", fname.name)
if m:
date_string, = m.groups()
date = datetime.datetime.strptime(date_string, "%Y%m%d")
d[date] = df
return pd.concat(d)
def citi_navs():
l = []
for fname in (DAILY_DIR / "CITI_reports").glob("262966_Portfolio_*.xlsx"):
df = pd.read_excel(
fname, skiprows=6, skipfooter=2, parse_dates=["Trade Date", "Value Date"]
)
df = df.dropna(subset=["Operations File"]).set_index(
["Value Date", "Operations File"]
)
df = df[["Trade Date", "Party Position", "Notional", "Market Value"]]
df.columns = ["trade_date", "buy/sell", "notional", "nav"]
l.append(df)
df = pd.concat(l)
# nav is from Citi's point of view
df.nav *= -1.0
return df
def baml_navs():
d = {}
for fname in (DAILY_DIR / "BAML_ISDA_reports").glob(
"Interest Rates Trade Summary_*.xls"
):
date = datetime.datetime.strptime(fname.stem.split("_")[1], "%d-%b-%Y")
df = pd.read_excel(fname, skiprows=6, nrows=1)
df = df.set_index("Trade ID")
df = df[["Trade Date", "Flow Direction", "Notional", "MTM(USD)"]]
df.columns = ["trade_date", "buy/sell", "notional", "nav"]
d[date] = df
return pd.concat(d)
if __name__ == "__main__":
for cp in ["MS", "CITI", "GS", "BAML"]:
df = globals()[f"{cp.lower()}_navs"]()
with dbconn("dawndb") as conn:
with conn.cursor() as c:
for k, v in df[["nav"]].iterrows():
c.execute(
"INSERT INTO external_marks_deriv "
"VALUES(%s, %s, %s, %s) ON CONFLICT DO NOTHING",
(*k, float(v), cp),
)
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