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from db import dbengine
from pandas.tseries.offsets import DateOffset, MonthEnd
from yieldcurve import YC
from quantlib.time.api import Date, Months, Period
from quantlib.indexes.api import USDLibor
from quantlib.termstructures.yield_term_structure import YieldTermStructure
import pandas as pd
import numpy as np
import datetime
etengine = dbengine('etdb')
dawnengine = dbengine('dawndb')
def get_monthly_pnl(group_by=['identifier']):
sql_string = "SELECT * FROM pnl_reports"
df_pnl = pd.read_sql_query(sql_string, dawnengine,
parse_dates=['date'],
index_col=['date'])
df_pnl['identifier'] = df_pnl.invid.str.replace("_A$", "")
pnl_cols = ['bookunrealmtm', 'bookrealmtm', 'bookrealincome', 'bookunrealincome', 'totalbookpl']
monthend_pnl = df_pnl.groupby(pd.Grouper(freq='M'), group_keys=False).apply(lambda df: df.loc[df.index[-1]])
return monthend_pnl.groupby(['date'] + group_by)[['mtd' + col for col in pnl_cols]].sum()
def get_portfolio(report_date=None):
if report_date is not None:
sql_string = "SELECT * FROM valuation_reports where periodenddate = %s"
df = pd.read_sql_query(sql_string, dawnengine, parse_dates=['periodenddate'],
index_col=['periodenddate'], params=[report_date,])
else:
sql_string = "SELECT * FROM valuation_reports"
df = pd.read_sql_query(sql_string, dawnengine, parse_dates=['periodenddate'],
index_col=['periodenddate'])
df['identifier'] = df.invid.str.replace("_A$", "")
return df
def trade_performance():
sql_string = "SELECT * FROM bonds"
df_trades = pd.read_sql_query(sql_string, dawnengine,
parse_dates={'lastupdate': {'utc': True},
'trade_date': {},
'settle_date': {}})
df_trades = df_trades[df_trades['asset_class'] == 'Subprime']
df_pnl = get_monthly_pnl()
df_sell = df_trades[~df_trades.buysell].groupby('identifier').last().reset_index()
df_sell.identifier = df_sell.identifier.str[:9]
df_sell['trade_pnl_date'] = df_sell.trade_date + MonthEnd(0)
df_buy = df_trades[df_trades.buysell].groupby('identifier').last().reset_index()
df_all = df_sell.merge(df_pnl.groupby('identifier').sum().reset_index(), on=['identifier'])
df_all = df_all.merge(df_pnl.reset_index()[['date', 'identifier', 'mtdtotalbookpl']],
left_on=['trade_pnl_date', 'identifier'],
right_on=['date', 'identifier'],
suffixes=('', '_at_trade_month'))
df_all = df_all.drop(['date', 'trade_pnl_date'], axis=1)
#now build up the table
g = df_buy.groupby('identifier').sum()
init_inv = g.principal_payment + g.accrued_payment
init_inv.name = 'initialinvestment'
first_buy_date = df_buy.groupby('identifier').first().trade_date
first_buy_date.name = 'firstbuydate'
df_all = df_all.join(init_inv, on='identifier')
df_all = df_all.join(first_buy_date, on='identifier')
df_all['percent_gain'] = df_all.mtdtotalbookpl / df_all.initialinvestment
df_all['days_held'] = df_all.trade_date - df_all.firstbuydate
df_all = df_all.sort_values('trade_date', ascending=False)
return df_all
def get_net_navs():
sql_string = "SELECT * FROM valuation_reports"
df_val = pd.read_sql_query(sql_string, dawnengine, parse_dates=['periodenddate'])
nav = df_val[df_val.fund == 'SERCGMAST'].groupby('periodenddate')['endbooknav'].sum()
nav = nav.resample('M').last()
df = pd.read_csv('/home/serenitas/edwin/Python/subscription_fee_data.csv',
parse_dates=['date'],
index_col =['date'])
df.index = df.index.to_period('M').to_timestamp('M')
df = df.join(nav)
df['begbooknav'] = (df.endbooknav + df.net_flow).shift(1)
df.at[('2013-01-31', 'begbooknav')] = 12500000
return df
def shift_cash(date, amount, df, strat):
nav = get_net_navs()
df.loc[date, strat] = df.loc[date, strat] - amount/nav.loc[date].endbooknav
df.loc[date,'Cash'] = df.loc[date, 'Cash'] + amount/nav.loc[date].endbooknav
return df
def calc_trade_performance_stats():
df = trade_performance().set_index('trade_date')
df.days_held = df.days_held.dt.days
df['winners'] = df.percent_gain > 0
df['curr_face'] = df.principal_payment/(df.price/100)
index = ['All', '2017', '2016', '2015', '2014', '2013']
results = pd.DataFrame(index=index)
win_per = df.winners.mean()
loss_per = 1 - win_per
for x, df1 in df.groupby('winners'):
for y, df2 in df1.groupby(pd.Grouper(freq='A')):
y = y.date().year
results.loc[y] = df2[df2.days_held.notnull()].mean()[['curr_face', 'initialinvestment', 'days_held']]
df[df.days_held.notnull()]['days_held'].groupby(pd.Grouper(freq='A')).mean()
def hist_pos(date=None, asset_class = 'rmbs'):
end_date = pd.datetime.today() - MonthEnd(1)
dates = pd.date_range(datetime.date(2013,1,31), end_date, freq='M')
calc_df = pd.DataFrame()
for d in dates:
if asset_class == 'rmbs':
calc_df = calc_df.append(rmbs_pos(d))
else:
calc_df = calc_df.append(clo_pos(d), sort=True)
return calc_df
def rmbs_pos(date):
date = date.date() if isinstance(date, pd.Timestamp) else date
pos = get_portfolio(date)
pos = pos[(pos.port == 'MORTGAGES') &
(pos.endbookmv > 0) &
(pos.custacctname == 'V0NSCLMAMB') &
(pos['invid'].str.len() >= 9)]
pos = pos[['endbookmv', 'endlocalmarketprice', 'identifier']]
sql_string = ("SELECT distinct timestamp FROM priced where "
"normalization = 'current_notional' and "
"model_version = 1 and "
"date(timestamp) < %s and date(timestamp) > %s "
"order by timestamp desc")
timestamps = pd.read_sql_query(sql_string, dawnengine, parse_dates=['timestamp'],
params=[date, date - DateOffset(15, 'D')])
model_date = (timestamps.loc[0][0]).date()
yc = YieldTermStructure()
libor1m = USDLibor(Period(1, Months), yc)
yc.link_to(YC(evaluation_date=model_date))
libor = libor1m.fixing(libor1m.fixing_calendar.adjust(Date.from_datetime(date)))
sql_string = ("SELECT date(timestamp) as timestamp, cusip, model_version, "
"pv, moddur, delta_yield, delta_ir "
"FROM priced where date(timestamp) = %s "
"and model_version <> 2")
params_list = (model_date,)
if date > datetime.date(2017, 9, 30):
r = dawnengine.execute("SELECT latest_sim FROM latest_sim(%s)",
model_date)
model_id, = next(r)
sql_string += " AND model_id_sub = %s"
params_list += (model_id,)
model = pd.read_sql_query(sql_string, dawnengine, parse_dates=['timestamp'],
params=params_list)
model = model[model['pv'] != 0]
comb_g = pos.loc[date].groupby('identifier').agg({'endbookmv': 'sum',
'endlocalmarketprice': 'mean'})
model = pd.merge(comb_g, model, left_on='identifier', right_on='cusip')
positions = model.set_index(['cusip', 'model_version']).unstack(1).dropna()
v1 = positions.xs(1, level='model_version', axis=1)
v3 = positions.xs(3, level='model_version', axis=1)
v3 = v3.assign(curr_ntl = v3.endbookmv/v3.endlocalmarketprice *100)
v3 = v3.assign(b_yield = v3.moddur.apply(lambda x:
float(yc.zero_rate(x)) - libor))
v3.b_yield += np.minimum((v1.pv / v1.endlocalmarketprice * 100)
** (1/v1.moddur) - 1, 1).dropna()
v3.delta_yield *= v3.endbookmv / v3.pv
v3.delta_ir *= np.minimum(1, 1/v3.moddur) * \
(v3.endlocalmarketprice/100)/ v3.pv * v3.curr_ntl
return v3.reset_index().set_index('timestamp')
def clo_pos(date):
date = date.date() if isinstance(date, pd.Timestamp) else date
df = get_portfolio(date)
df = df[(df.port == 'CLO') &
(df.endbookmv > 0) &
(df.custacctname == 'V0NSCLMAMB') &
(df['invid'].str.len() >= 9)]
df = df[['endbookmv', 'endlocalmarketprice', 'identifier']]
if df.empty is True:
return df
else:
sql_string = "select distinct cusip, identifier from bonds where asset_class = 'CLO'"
cusip_map = {r['identifier']: r['cusip'] for r in dawnengine.execute(sql_string)}
df['cusip'] = df['identifier'].replace(cusip_map)
cusips = df.loc[[df.index[-1]], 'cusip']
placeholders = ",".join(["%s"] * (1 + len(cusips)))
sql_string = f"SELECT * FROM historical_cusip_risk({placeholders})"
model = pd.read_sql_query(sql_string, etengine, parse_dates=['pricingdate'],
params=(date, *cusips))
model.index = cusips
calc_df = df.loc[[df.index[-1]]].set_index('cusip').join(model)
calc_df['hy_equiv'] = calc_df.delta * calc_df.endbookmv
calc_df['date'] = date
return calc_df.set_index('date')
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