1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
|
import pandas as pd
from io import BytesIO
from pikepdf import Pdf
from . import DAILY_DIR
from .common import load_pdf, get_col, parse_num
paths = {
"Serenitas": ["NYops", "Margin Calls JPM"],
"BowdSt": ["BowdoinOps", "Margin JPM"],
}
accounts = {
"BowdSt": "909271",
"Serenitas": "923550",
}
passwords = {"BowdSt": "tm64EO", "Serenitas": "JV3RJu"}
def load_file(d, fund):
try:
fname = next(
(DAILY_DIR / fund / "JPM_reports").glob(
f"CSCFTCSTMT-*-{d:%y%m%d}-{accounts[fund]}_2.pdf"
)
)
except StopIteration:
raise FileNotFoundError(f"JPM file not found for date {d}")
return fname
def get_collateral(collat_page):
return float(get_col(collat_page, 200, 300, 1000, 1100)[0].replace(",", ""))
def load_positions(positions_page):
anchor = next(c for c in positions_page if c.text.startswith("Total Product Group"))
bottom = int(anchor["top"]) - 30
widths = (10, 160, 300, 350, 450, 500, 550, 600, 650, 750, 850, 950, 1000, 1200)
cols = [
get_col(positions_page, 200, bottom, l, r) for l, r in zip(widths, widths[1:])
]
def combine(l):
return [f"{l[0]} {l[1]}", *l[2:]]
cols = [combine(c) if len(c) == (len(cols[0]) + 1) else c for c in cols]
df = pd.DataFrame({c[0]: c[1:] for c in cols})
for col in ["Pay Notional", "Rec Notional", "MTM Amount", "IM Amount"]:
df[col] = df[col].apply(parse_num)
for col in ["Trade Date", "Maturity Date"]:
df[col] = pd.to_datetime(df[col], format="%d-%b-%y")
df["Deal ID"] = "810RI" + df["Deal ID"].str.extract(r"([^-]*)-.*")
return df
def download_files(em, count=20, *, fund="BowdSt", **kwargs):
if fund not in paths:
return
emails = em.get_msgs(
path=paths[fund], count=count, subject__contains=accounts[fund]
)
DATA_DIR = DAILY_DIR / fund / "JPM_reports"
for msg in emails:
for attach in msg.attachments:
fname = attach.name
p = DATA_DIR / fname
if not p.exists():
stream = BytesIO(attach.content)
pdf = Pdf.open(stream, password=passwords[fund])
pdf.save(p)
def collateral(d, dawn_trades, *, fund="BowdSt", **kwargs):
pdf_file = load_file(d, fund)
pages = load_pdf(pdf_file, pages=True)
try:
collat = get_collateral(pages[3])
except IndexError:
collat = 0.0
df = load_positions(pages[4])
df = df.merge(dawn_trades, how="left", left_on="Deal ID", right_on="cpty_id")
missing_ids = df.loc[df.cpty_id.isnull(), "Deal ID"]
if not missing_ids.empty:
raise ValueError(f"{missing_ids.tolist()} not in the database")
df = df[["folder", "MTM Amount", "IM Amount"]]
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 = df.append(
{
"Strategy": "M_CSH_CASH",
"Amount": -collat - df.Amount.sum(),
"Currency": "USD",
},
ignore_index=True,
)
df["date"] = d
return df.set_index("Strategy")
|
