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
|
from . import DAILY_DIR, SftpClient
from .common import compare_notionals, compare_notionals_rates, STRATEGY_CASH_MAPPING
import pandas as pd
from sqlalchemy.exc import IntegrityError
def download_files(*args, fund="Serenitas", **kwargs):
if fund != "Serenitas":
return
sftp = SftpClient.from_creds("baml_fcm")
sftp.download_files("outgoing", DAILY_DIR / fund / "BAML_reports")
def load_csv(file_type, fund, d, **kwargs):
return pd.read_csv(
DAILY_DIR
/ fund
/ "BAML_reports"
/ f"OTC_Open_Positions_-_{file_type}_-_LMCG_{d:%Y%m%d}.CSV",
**kwargs,
)
def collateral(d, positions, *, engine, fund="Serenitas", positions_irs, **kwargs):
df = load_csv(
"Credit",
fund,
d,
usecols=[
"MTM",
"ACCRUEDCPN",
"VARMARGIN",
"REDCODE",
"NOTIONAL",
"EODSETTLEMENTPRICE",
"PERIOD",
"BUYSELL",
],
index_col=["REDCODE"],
)
df.PERIOD = pd.to_datetime(df.PERIOD.astype("str") + "20")
df = df.set_index("PERIOD", append=True)
df = df[df.EODSETTLEMENTPRICE.notnull()]
df["NOTIONAL"] = df.NOTIONAL.where(df.BUYSELL == "Buy", -df.NOTIONAL).astype(
"float"
)
df["DIRTYUPFRONT"] = (df.MTM + df.ACCRUEDCPN) / df.NOTIONAL
df.index.names = ["security_id", "maturity"]
# dropped old versions (trades should be offsetting
df = df.groupby(level=["security_id", "maturity"]).agg(
{"NOTIONAL": "sum", "DIRTYUPFRONT": "first"}
)
df = df[df.NOTIONAL != 0.0]
compare_notionals(df, positions, "BAML")
positions["dirtyupfront"] = df.reindex(positions.index)["DIRTYUPFRONT"]
positions["Amount"] = positions["notional"] * positions["dirtyupfront"]
positions.folder = positions.folder.replace(STRATEGY_CASH_MAPPING)
def aux(row):
if row.folder == "XCURVE":
return "SER_IGCVECSH" if row.currency == "USD" else "SER_ITRXCVCSH"
else:
return row.folder
positions.folder = positions.apply(aux, axis=1)
df = (
positions.groupby(["folder", "currency"])
.agg({"Amount": "sum"})
.reset_index(["folder", "currency"])
)
df = df.rename(columns={"folder": "Strategy", "currency": "Currency"})
df_rates = load_csv(
"Rates",
fund,
d,
usecols=[
"COBDATE",
"CLEAREDTRADEID",
"FIXEDRATE",
"NPV",
"DIRECTION",
"NOTIONAL",
"EFFDATE",
"MATDATE",
],
index_col=["CLEAREDTRADEID"],
parse_dates=["MATDATE", "EFFDATE"],
)
compare_notionals_rates(df_rates, positions_irs, "BAML")
positions_irs["Amount"] = df_rates.reindex(positions_irs.index)["NPV"]
positions_irs.folder.replace(STRATEGY_CASH_MAPPING, inplace=True)
df_rates = (
positions_irs.groupby(["folder", "currency"])
.agg({"Amount": "sum"})
.reset_index(["folder", "currency"])
)
df_rates = df_rates.rename(columns={"folder": "Strategy", "currency": "Currency"})
col_names = [
"Statement Date",
"AT",
"AT CCY",
"Beginning Balance",
"CDS Initial Coupon",
"CDS Trade Variation",
"Price Aligned Interest",
"Clearing Fee",
"Commission",
"Cash Amounts",
"Ending Balance",
"Account Value at Market",
"Net P&L",
"Initial Margin Requirement",
"Margin Excess/Deficit",
]
df_margin = pd.read_csv(
DAILY_DIR / fund / "BAML_reports" / f"OTC_Moneyline_{d:%Y%m%d}.CSV",
usecols=col_names,
parse_dates=["Statement Date"],
index_col=["Statement Date"],
)
df_margin.AT = df_margin.AT.replace(
{
"Q4": "EUR",
"F4": "EUR",
"Q1": "USD",
"F2": "USD",
"9Z": "ZZZZZ",
}
)
df_margin = df_margin.set_index("AT", append=True)
df_margin = df_margin.groupby(level=(0, 1)).sum()
df_margin = df_margin.reset_index()
col_names.pop(2)
try:
place_holders = ",".join(["%s"] * (len(col_names) - 1))
with engine.connect() as conn:
conn.execute(
f"INSERT INTO fcm_moneyline VALUES(%s, 'V0NSCLMFCM', {place_holders})",
list(df_margin[col_names].itertuples(index=False)),
)
except IntegrityError:
pass
df = pd.concat([df, df_rates])
df = (
df.groupby(["Strategy", "Currency"])
.agg({"Amount": "sum"})
.reset_index(["Strategy", "Currency"])
)
df["date"] = d
return df.set_index("Strategy")
|
