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
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
|
import datetime
import math
import logging
import subprocess
from bs4 import BeautifulSoup
import pandas as pd
from exchangelib import HTMLBody
from io import StringIO
from sqlalchemy.engine import Engine
logger = logging.getLogger(__name__)
CASH_STRATEGY_MAPPING = {
"COCSH": ["IGREC", "IGPAYER", "HYPAYER", "HYREC", "HYOPTDEL", "IGOPTDEL"],
"IRDEVCSH": ["DV01", "STEEP", "FLAT"],
"TCSH": [
"IGMEZ",
"IGSNR",
"IGEQY",
"HYMEZ",
"HYEQY",
"HYSNR",
"BSPK",
"XOMEZ",
"XOEQY",
"IGINX",
"HYINX",
"XOINX",
"EUMEZ",
"EUINX",
],
"MBSCDSCSH": ["HEDGE_MBS", "MBSCDS"],
"MACCDSCSH": ["HEDGE_MAC", "CASH_BASIS"],
"CVECSH": ["SER_ITRXCURVE", "SER_IGCURVE", "SER_HYCURVE", "XCURVE"],
"CLOCDSCSH": ["HEDGE_CLO", "M_CLO_BB20"],
"M_CSH_CASH": ["CRT_LD", "CRT_LD_JNR", "CRT_SD", "MTG_FP", "MTG_LMG", "M_MTG_FP"],
}
STRATEGY_CASH_MAPPING = {e: k for k, v in CASH_STRATEGY_MAPPING.items() for e in v}
def compare_notionals(
df: pd.DataFrame, positions: pd.DataFrame, fcm: str, fund: str
) -> None:
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.notional != 0.0)
]
if not diff_notionals.empty:
logger.error(f"Database and {fcm} FCM at {fund} know different notionals")
for t in diff_notionals.itertuples():
logger.error(
f"{t.Index[0]}\t{t.Index[1]:%Y-%m-%d}\t{t.notional}\t{t.NOTIONAL}"
)
def compare_notionals_rates(
df: pd.DataFrame, positions: pd.DataFrame, fcm: str
) -> None:
check_notionals = positions.join(df["NOTIONAL"], how="left")
diff_notionals = check_notionals[
(check_notionals.notional != check_notionals.NOTIONAL)
& (check_notionals.notional != 0.0)
]
if not diff_notionals.empty:
logger.error(f"Database and {fcm} FCM know different notionals")
for t in diff_notionals.itertuples():
if hasattr(t, "effective_date"):
msg = f"{t.Index[0]}\t{t.effective_date:%Y-%m-%d}\t{t.notional}\t{t.NOTIONAL}"
else:
msg = f"{t.Index[0]}\t{t.Index[1]:%Y-%m-%d}\t{t.notional}\t{t.NOTIONAL}"
logger.error(msg)
def get_bilateral_trades(d: datetime.date, fund: str, engine: Engine) -> pd.DataFrame:
df_cds = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin_percentage * abs(notional) / 100 as IA "
"FROM list_cds2(%s::date, %s) "
"WHERE orig_attach IS NOT NULL or cpty_id='6SIT0'", # that way we get all tranches + the ABS_CDS
engine,
params=(d, fund),
)
df_swaptions = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin_percentage * abs(notional) / 100 AS IA "
"FROM list_swaptions(%s::date, %s) ",
engine,
params=(d, fund),
)
df_caps = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin_percentage * amount / 100 AS IA "
"FROM capfloors "
"WHERE cpty_id IS NOT NULL "
"AND trade_date <= %s AND fund=%s",
engine,
params=(d, fund),
)
df_forwards = pd.read_sql_query(
"SELECT cpty_id, folder, ia FROM ("
" SELECT cpty_id, folder, initial_margin_percentage * buy_amount / 100 AS ia,"
" trade_date, settle_date, fund FROM spots"
" UNION"
" SELECT UNNEST(ARRAY[near_cpty_id, far_cpty_id]) AS cpty_id, folder, 0.0 AS ia,"
" trade_date, unnest(ARRAY[near_settle_date, far_settle_date]) AS settle_date,"
" fund FROM fx_swaps"
") a "
"WHERE cpty_id IS NOT NULL AND trade_date <=%s AND fund=%s AND settle_date >=%s",
engine,
params=(d, fund, d),
)
df_trs = pd.read_sql_query(
"SELECT cpty_id, folder, initial_margin_percentage * notional/100 as IA from trs where cpty_id is NOT NULL and trade_date <= %s and fund=%s",
engine,
params=(d, fund),
)
df = pd.concat([df_cds, df_swaptions, df_caps, df_forwards, df_trs])
df = df.replace({"folder": STRATEGY_CASH_MAPPING})
return df
def send_email(d: datetime.date, df: pd.DataFrame) -> None:
from serenitas.utils.exchange import ExchangeMessage
pd.set_option("display.float_format", "{:.2f}".format)
df = df.drop("date", axis=1).set_index("broker")
cp_mapping = {
"CITI": "Citi",
"MS": "Morgan Stanley",
"GS": "Goldman Sachs",
"BAML_FCM": "Baml FCM",
"BAML_ISDA": "Baml OTC",
"WELLS": "Wells Fargo",
"BNP": "BNP Paribas",
"CS": "Credit Suisse",
"JPM": "JP Morgan",
}
buf = StringIO()
buf.write("<html><body>\n")
for cp, df in df.groupby(level="broker"):
name = cp_mapping[cp]
buf.write(f"<h3> At {name}:</h3>\n")
try:
df.loc[cp].to_html(buf, index=False)
except AttributeError:
df.loc[cp].to_frame().T.to_html(buf, index=False)
buf.write("</body/></html>")
em = ExchangeMessage()
em.send_email(
f"IAM booking {d:%Y-%m-%d}",
HTMLBody(buf.getvalue()),
["serenitas.otc@sscinc.com"],
["nyops@lmcg.com"],
reply_to=("nyops@lmcg.com",),
)
def load_pdf(file_path, pages=False):
proc = subprocess.run(
["pdftohtml", "-xml", "-stdout", "-i", file_path.as_posix()],
capture_output=True,
)
soup = BeautifulSoup(proc.stdout, features="lxml")
if pages:
r = []
for page in soup.findAll("page"):
l = page.findAll("text")
r.append(sorted(l, key=lambda x: (int(x["top"]), int(x["left"]))))
return r
else:
l = soup.findAll("text")
l = sorted(l, key=lambda x: (int(x["top"]), int(x["left"])))
return l
def get_col(l, top, bottom, left, right, bbox=False):
actual_left, actual_right = math.inf, -math.inf
r = []
for c in l:
if (
int(c["left"]) >= left
and int(c["left"]) + int(c["width"]) < right
and int(c["top"]) >= top
and int(c["top"]) + int(c["height"]) < bottom
):
r.append(c.text)
actual_left = min(int(c["left"]), actual_left)
actual_right = max(int(c["left"]) + int(c["width"]), actual_right)
if bbox:
return r, (actual_left, actual_right)
else:
return r
def prev_business_day(d: datetime.date):
if (offset := d.weekday() - 4) > 0:
return d - datetime.timedelta(days=offset)
elif offset == -4:
return d - datetime.timedelta(days=3)
else:
return d - datetime.timedelta(days=1)
def next_business_day(d: datetime.date):
if (offset := 7 - d.weekday()) > 3:
return d + datetime.timedelta(days=1)
else:
return d + datetime.timedelta(days=offset)
def parse_num(s):
s = s.replace(",", "")
if s[0] == "(":
return -float(s[1:-1])
else:
return float(s)
|
