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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"from pandas.tseries.offsets import BDay, MonthEnd\n",
"import globeop_reports as go\n",
"import pandas as pd\n",
"import analytics\n",
"import numpy as np\n",
"\n",
"from analytics.index_data import get_index_quotes\n",
"from analytics.scenarios import run_portfolio_scenarios\n",
"from analytics import BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio,DualCorrTranche\n",
"\n",
"from utils.db import dbconn, dbengine\n",
"\n",
"from risk.tranches import get_tranche_portfolio\n",
"from risk.swaptions import get_swaption_portfolio\n",
"from risk.bonds import subprime_risk, clo_risk, crt_risk\n",
"\n",
"dawn_engine = dbengine('dawndb')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"################################### Average Portfolio Sales Turnover - as of last monthend from today\n",
"#Actually: Rolling months sum of (total bond sales proceeds + paydown)/monthly NAV\n",
"fund='BRINKER'\n",
"sql_string = \"SELECT * FROM bonds WHERE buysell IS False and fund = %s order by trade_date desc\"\n",
"df = pd.read_sql_query(sql_string, dawn_engine,\n",
" parse_dates={'lastupdate':{'utc':True}, 'trade_date': {}, 'settle_date':{}},\n",
" params=[fund,],\n",
" index_col = 'trade_date')\n",
"df = df.groupby(pd.Grouper(freq='M')).sum()\n",
"\n",
"brinker_nav = pd.read_csv(\n",
" \"/home/serenitas/edwin/Python/brinker_nav.csv\",\n",
" parse_dates=[\"date\"],\n",
" index_col=[\"date\"])\n",
"\n",
"start_date = datetime.date(2019,3,18)\n",
"end_date = datetime.date.today()\n",
"cf = pd.read_sql_query(\"SELECT * FROM cashflow_history where date > %s and date <= %s\", dawn_engine,\n",
" params=[start_date, end_date],\n",
" parse_dates=['date'],\n",
" index_col=['date']).sort_index()\n",
"sql_string = \"SELECT description, identifier, notional, price, factor FROM risk_positions(%s, %s, 'BRINKER')\"\n",
"pos = {}\n",
"for d in cf.index.unique():\n",
" for ac in ['Subprime', 'CRT']:\n",
" pos[d, ac] = pd.read_sql_query(sql_string, dawn_engine, params=[d.date(), ac])\n",
"pos = pd.concat(pos, names=['date', 'asset_class'])\n",
"pos = pos.reset_index(level=[1,2])\n",
"\n",
"cf_1 = pd.merge_asof(cf, pos.sort_index(), left_index=True, right_index=True, by='identifier')\n",
"cf_1 = cf_1.dropna(subset=['notional'])\n",
"cf_1 = cf_1[(cf_1.principal_bal != 0) & (cf_1.principal != 0)]\n",
"cf_1['paydown'] = cf_1.apply(lambda df: df.notional * df.factor/df.principal_bal * df.principal, axis=1)\n",
"paydowns = cf_1.paydown.groupby(pd.Grouper(freq='M')).sum()\n",
"turnover = pd.concat([paydowns, df.principal_payment, df.accrued_payment], axis=1).fillna(0)\n",
"brinker_nav = brinker_nav.groupby(pd.Grouper(freq='M')).last()\n",
"turnover = (turnover.sum(axis=1)/brinker_nav.nav).rolling(min(13, len(turnover))-1).sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#PNL over different time frames\n",
"sql_string = \"SELECT * from bbh_val\"\n",
"df = pd.read_sql_query(sql_string, dawn_engine,\n",
" parse_dates=['accounting_date'],\n",
" index_col = 'accounting_date')\n",
"sql_string = \"SELECT * from subscription_and_fee where fund = 'BRINKER'\"\n",
"flow = pd.read_sql_query(sql_string, dawn_engine,\n",
" parse_dates=['date'],\n",
" index_col = 'date')\n",
"df = df.groupby('accounting_date').nth(-1)\n",
"df = df.merge(flow, how='left', left_index=True, right_index=True)\n",
"df.fillna(0, inplace=True)\n",
"df['beg_nav'] = df.total_net_assets.shift(1) + df.subscription.shift(1) - df.redemption\n",
"df.loc['2019-3-19','beg_nav'] = 110000000\n",
"df['ret'] = (df.total_net_assets - df.beg_nav)/df.beg_nav\n",
"cum_ret = (df.ret+1).cumprod()\n",
"\n",
"monthly= cum_ret.groupby(pd.Grouper(freq='M')).nth(-1)\n",
"quarterly = cum_ret.groupby(pd.Grouper(freq='Q')).nth(-1)\n",
"monthly.pct_change(), quarterly.pct_change()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#PNL breakdown\n",
"sql_string = \"SELECT * from bbh_pnl\"\n",
"pnl = pd.read_sql_query(sql_string, dawn_engine,\n",
" parse_dates=['accounting_date'])\n",
"sql_string = \"SELECT * from securities\"\n",
"bonds = pd.read_sql_query(sql_string, dawn_engine, index_col = 'cusip')\n",
"pnl = pnl.merge(bonds, how='left', left_on='security_id', right_on='cusip')\n",
"pnl.loc[(pnl.sub_security_type_code == 'CXT'),'asset_class'] = 'Tranches'\n",
"pnl.loc[(pnl.sub_security_type_code == 'CDX'),'asset_class'] = 'Tranches'\n",
"pnl.loc[(pnl.sub_security_type_code == 'SWP'),'asset_class'] = 'IR-Hedges'\n",
"pnl.asset_class.fillna('Others', inplace=True)\n",
"pnl.set_index(['accounting_date', 'asset_class'], inplace=True)\n",
"base_change = pnl['base_change_total'].groupby(['accounting_date', 'asset_class']).sum()\n",
"base_change.unstack()\n"
]
}
],
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|
