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from . import DAILY_DIR
from bs4 import BeautifulSoup
from io import BytesIO
from pandas.tseries.offsets import BDay
import logging
import pandas as pd
import pathlib
import requests
import xlrd
import zipfile
from urllib.parse import urlsplit, parse_qs, urlunsplit, urljoin
from xlrd import open_workbook, xldate_as_tuple
logger = logging.getLogger(__name__)
def download_from_secure_id(
secure_id: str,
brand: str,
path: pathlib.Path,
base_url="https://secmail.bankofamerica.com",
):
password = {
"ghorel@lmcg.com": "v4vdMvH9Qe9t",
"nyops@lmcg.com": "a6lAkBfqDSHsrkGspYSS",
}
payload = {}
with requests.Session() as session:
r = session.get(
urljoin(base_url, "formpostdir/securereader"),
params={"id": secure_id, "brand": brand},
)
soup = BeautifulSoup(r.content, features="lxml")
form = soup.find(id="dialog")
if "messagenotfound" in form["action"]:
raise ValueError("message not found")
for inp in form.find_all("input"):
payload[inp["name"]] = inp["value"]
payload["dialog:password"] = password[payload["dialog:username"]]
r = session.post(base_url + form["action"], data=payload)
soup = BeautifulSoup(r.content, features="lxml")
form = soup.find(id="readTB")
payload = {
"readTB": "readTB",
"readTB:downloadZipButton": "readTB:downloadZipButton",
}
for inp in form.find_all("input"):
if "ViewState" in inp["name"]:
payload["javax.faces.ViewState"] = inp["value"]
r = session.post(urljoin(base_url, "securereader/read.jsf"), data=payload)
if r.headers["content-type"] == "application/octet-stream":
with zipfile.ZipFile(BytesIO(r.content)) as z:
for f in z.namelist():
if not f.endswith("html"):
z.extract(f, path=path)
def download_files(d=None, count=20):
from exchange import ExchangeMessage
DATA_DIR = DAILY_DIR / "BAML_ISDA_reports"
em = ExchangeMessage()
emails = em.get_msgs(path=["NYops", "Margin Calls Baml"], count=count)
for msg in emails:
if (
msg.sender.name == "us_otc_client_valuation@baml.com"
or msg.sender.name == "us_otc_client_valuation@bofa.com"
):
soup = BeautifulSoup(msg.body, features="lxml")
a = soup.find("a")
url = urlsplit(a["href"])
query = parse_qs(url.query)
base_url = urlunsplit(url[:2] + ("",) * 3)
try:
download_from_secure_id(
query["id"][0], query["brand"][0], DATA_DIR, base_url
)
except ValueError as e:
logger.error(e)
continue
if msg.sender.name == "bank_of_america_collateral_operations@bankofamerica.com":
for attach in msg.attachments:
if attach.name.endswith("xls"):
p = DATA_DIR / attach.name
if not p.exists():
p.write_bytes(attach.content)
def baml_load_excel(fname):
wb = open_workbook(fname)
s = wb.sheet_by_index(0)
headers = s.row_values(6, 0)
i = 7
rows = []
while s.cell(i, 0).value != "":
r = []
for e in s.row_slice(i, 0):
if e.ctype == xlrd.XL_CELL_DATE:
r.append(pd.Timestamp(*xldate_as_tuple(e.value, wb.datemode)))
else:
r.append(e.value)
rows.append(r)
i += 1
df = pd.DataFrame.from_records(rows, columns=headers)
for col in ["Original Notional", "Notional"]:
df[col] = pd.to_numeric(df[col].str.replace(",", ""))
return df
def collateral(d, dawn_trades, *args):
REPORTS_DIR = DAILY_DIR / "BAML_ISDA_reports"
try:
fname = next(REPORTS_DIR.glob(f"Collat_*{d:%m%d%Y}_*.xls"))
except StopIteration:
raise ValueError(f"no data for date {d}")
df = pd.read_excel(fname, skiprows=6, skipfooter=6)
df = df.drop(0, axis=0)
try:
collateral = float(df.Notional)
except TypeError:
collateral = df.Notional.sum()
d -= BDay()
fname = REPORTS_DIR / f"Interest Rates Trade Summary_{d:%d-%b-%Y}.xls"
# TODO: make more robust
df = baml_load_excel(fname)
df = df[["Trade ID", "MTM(USD)"]]
df["Trade ID"] = df["Trade ID"].astype("str")
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", "MTM(USD)", "ia"]]
df = df.groupby("folder").sum()
df = (df["ia"] - df["MTM(USD)"]).to_frame(name="Amount")
df["Currency"] = "USD"
df = df.reset_index()
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
return df.set_index("Strategy")
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