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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()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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|
