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
|
import pandas as pd
from functools import reduce
from position import get_list
from db import dbengine
from dates import bus_day, imm_dates
import numpy as np
from psycopg2.extensions import register_adapter, AsIs
register_adapter(np.int64, lambda x: AsIs(x))
def pnl_explain(identifier, start_date = None, end_date = None,
engine = dbengine("dawndb")):
""" if start_date is None, pnl since inception"""
trades = pd.read_sql_query("SELECT * FROM bonds where identifier=%s", engine,
params=(identifier,), parse_dates=['trade_date', 'settle_date'],
index_col=['trade_date'])
marks = pd.read_sql_query("SELECT * FROM marks where identifier=%s", engine,
params=(identifier,), parse_dates = ['date'], index_col='date')
factors = pd.read_sql_query("SELECT * FROM factors_history where identifier=%s", engine,
params=(identifier,), parse_dates = ['last_pay_date', 'prev_cpn_date'],
index_col=['last_pay_date'])
for key in ['faceamount', 'principal_payment', 'accrued_payment']:
trades.loc[~trades.buysell, key] = -trades[key][~trades.buysell]
df = (marks[['price']].join(factors, how='outer').
join(trades[['principal_payment', 'accrued_payment', 'faceamount']], how='outer'))
df.sort_index(inplace=True)
if start_date is None:
start_date = trades.index.min()
if end_date is None:
end_date = pd.datetime.today()
dates = pd.date_range(start_date, end_date, freq = bus_day)
keys1 = ['price','factor', 'coupon', 'prev_cpn_date']
df[keys1] = df[keys1].fillna(method='ffill')
keys2 = ['losses', 'principal','interest', 'faceamount','accrued_payment', 'principal_payment']
df[keys2] = df[keys2].fillna(value=0)
df.faceamount = df.faceamount.cumsum()
keys = keys1 + ['faceamount']
df1 = df.reindex(dates, keys, method='ffill')
keys = ['losses', 'principal','interest', 'accrued_payment', 'principal_payment']
df2 = df.reindex(dates, keys, fill_value=0)
daily = pd.concat([df1, df2], axis = 1)
daily['unrealized_pnl'] = daily.price.diff() * daily.factor.shift()/100 * daily.faceamount
daily['realized_pnl'] = (daily.price/100 * daily.factor.diff() + daily.principal/100) * \
daily.faceamount
daily['clean_nav'] = daily.price/100 * daily.factor * daily.faceamount
daily['realized_accrued'] = daily.interest/100 * daily.faceamount
days_accrued = daily.index - daily.prev_cpn_date
daily['accrued'] = days_accrued.dt.days/360*daily.coupon/100*daily.factor * \
daily.faceamount
extra_pnl = daily.clean_nav.diff() - daily.principal_payment
daily.loc[daily.principal_payment>0 , 'unrealized_pnl'] += extra_pnl[daily.principal_payment>0]
daily.loc[daily.principal_payment<0, 'realized_pnl'] += extra_pnl[daily.principal_payment<0]
daily['realized_accrued'] -= daily.accrued_payment
daily['unrealized_accrued'] = daily.accrued.diff() + daily.realized_accrued
return daily[['clean_nav', 'accrued', 'unrealized_pnl', 'realized_pnl', 'unrealized_accrued',
'realized_accrued']].iloc[1:,]
def pnl_explain_list(id_list, start_date = None, end_date = None, engine = dbengine("dawndb")):
return {identifier: pnl_explain(identifier, start_date, end_date, engine)
for identifier in id_list}
def cds_explain(index, series, tenor, attach = np.nan, detach = np.nan,
start_date = None, end_date = None, engine = dbengine('serenitasdb')):
if np.isnan(attach) or np.isnan(detach):
quotes = pd.read_sql_query("SELECT date, (100-closeprice)/100 AS upfront " \
"FROM index_quotes WHERE index=%s AND series=%s " \
"AND tenor=%s ORDER BY date",
engine, parse_dates=['date'],
index_col='date', params = (index, series, tenor))
sqlstring = "SELECT indexfactor/100 AS indexfactor, coupon, " \
"cumulativeloss/100 AS cumulativeloss, lastdate " \
"FROM index_desc WHERE index=%s AND series=%s AND tenor=%s " \
"ORDER BY lastdate"
factors = pd.read_sql_query(sqlstring, engine, parse_dates=['lastdate'],
index_col='lastdate',
params = (index, series, tenor))
else:
#we take the latest version available
sqlstring = "SELECT DISTINCT ON (quotedate) quotedate, upfront_mid AS upfront, " \
"tranche_spread FROM markit_tranche_quotes " \
"JOIN index_version USING (basketid) WHERE index=%s AND series=%s" \
"AND tenor=%s AND attach=%s AND detach=%s ORDER by quotedate, version desc"
quotes = pd.read_sql_query(sqlstring, engine, parse_dates=['quotedate'],
index_col='quotedate',
params = (index, series, tenor, int(attach), int(detach)))
sqlstring = "SELECT " \
"tranche_factor(%s::smallint, %s::smallint, indexfactor, cumulativeloss/100), " \
"indexfactor/100 AS indexfactor, cumulativeloss/100 AS cumulativeloss, " \
"lastdate FROM index_desc WHERE index=%s AND series=%s AND tenor=%s " \
"ORDER BY lastdate"
factors = pd.read_sql_query(sqlstring, engine, parse_dates=['lastdate'],
index_col='lastdate',
params = (attach, detach, index, series, tenor))
if start_date is None:
start_date = quotes.index.min()
if end_date is None:
end_date = pd.datetime.today()
#we use tranche_factor
if not np.isnan(attach):
factors['factor'] = factors.tranche_factor
else:
factors['factor'] = factors.indexfactor
dates = pd.date_range(start_date, end_date, freq = bus_day)
yearfrac = imm_dates(start_date, end_date)
yearfrac = yearfrac.to_series().reindex(dates, method='ffill')
yearfrac = yearfrac.index-yearfrac
yearfrac = (yearfrac.dt.days+1)/360
yearfrac.name = 'yearfrac'
quotes = quotes.reindex(dates, method='ffill')
recovery = -factors.indexfactor.diff()-factors.cumulativeloss.diff()
recovery.name = 'recovery'
recovery = recovery.shift(-1)
recovery = recovery.reindex(dates, fill_value=0).shift()
df = (quotes.
join(factors[['factor']], how='left').
join(recovery).join(yearfrac))
if not np.isnan(attach):
coupon = df.tranche_spread.iat[0]/10000
else:
coupon = factors.coupon.iat[0]/10000
df.factor = df.factor.bfill()
df.loc[df.factor.isnull(), 'factor'] = factors.factor.iat[-1]
df['clean_nav'] = df.upfront * df.factor
df['accrued'] = - df.yearfrac * coupon*df.factor
df['unrealized_accrued'] = df.accrued.diff()
df['realized_accrued'] = -df.unrealized_accrued.where(df.unrealized_accrued.isnull() |
(df.unrealized_accrued>0), 0)
df['unrealized_accrued'] = df.unrealized_accrued.where(df.unrealized_accrued.isnull()|
(df.unrealized_accrued<0), df.accrued)
df.loc[df.realized_accrued>0, 'realized_accrued'] += df.loc[df.realized_accrued>0, 'unrealized_accrued']
df['unrealized_pnl'] = df.upfront.diff() * df.factor.shift()/100
df['realized_pnl'] = df.upfront/100*df.factor.diff()+df.recovery
return df[['clean_nav', 'accrued', 'unrealized_accrued', 'realized_accrued',
'unrealized_pnl', 'realized_pnl']]
def convert_to_none(x):
return None if np.isnan(x) else x
def cds_explain_strat(strat, start_date, end_date, engine = dbengine("dawndb")):
cds_positions = pd.read_sql_table("orig_cds", engine,
parse_dates = ['trade_date', 'upfront_settle_date'],
index_col='dealid')
cds_positions = cds_positions.ix[(cds_positions.folder == strat) &
(cds_positions.upfront_settle_date<=pd.Timestamp(end_date))]
cds_positions.loc[cds_positions.protection=='Seller', "notional"] *= -1
df = {}
for r in cds_positions.itertuples():
key = (r.index, r.series, r.tenor)
trade_df = cds_explain(r.index, r.series, r.tenor, r.attach, r.detach,
max(r.trade_date, pd.Timestamp(start_date)), end_date,
engine)
trade_df = r.notional*trade_df
if pd.Timestamp(start_date) <= r.trade_date:
extra_pnl = trade_df.clean_nav.iat[0]+trade_df.accrued.iat[2] + r.upfront
trade_df.unrealized_pnl.iat[2] = extra_pnl
trade_df = trade_df.iloc[2:]
df[key] = df.get(key, 0) + trade_df
return pd.concat(df)
if __name__=="__main__":
workdate = pd.datetime.today()
engine = dbengine("dawndb")
clo_list = get_list(engine, workdate, 'CLO')
df = pnl_explain_list(clo_list.identifier.tolist(), '2015-10-30', '2015-11-30', engine)
df = pd.concat(df)
df_agg = df.groupby(level=1).sum()
cds_df = cds_explain_strat('SER_IGMEZ', '2014-09-18', '2015-12-08', engine)
#cds_df = cds_explain('HY', 21, '5yr', 25, 35, '2014-07-18')
|
