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-rw-r--r--python/notebooks/Reto Report.ipynb92
1 files changed, 71 insertions, 21 deletions
diff --git a/python/notebooks/Reto Report.ipynb b/python/notebooks/Reto Report.ipynb
index f266cd63..6769a123 100644
--- a/python/notebooks/Reto Report.ipynb
+++ b/python/notebooks/Reto Report.ipynb
@@ -71,9 +71,11 @@
"#(total Bond Sales Proceeds + paydown)/average starting 12 months NAV\n",
"#Actually: Rolling 12 months sum of (total bond sales proceeds + paydown)/monthly NAV\n",
"nav = go.get_net_navs()\n",
- "sql_string = \"SELECT * FROM bonds WHERE buysell IS False\"\n",
+ "fund='SERCGMAST'\n",
+ "sql_string = \"SELECT * FROM bonds WHERE buysell IS False and fund = %s\"\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",
"#Average traded volume (Bonds only)\n",
@@ -81,15 +83,14 @@
"#Now get portfolio paydown per month\n",
"portfolio = go.get_portfolio()\n",
"portfolio = portfolio[(portfolio.custacctname == 'V0NSCLMAMB') &\n",
- " (portfolio.port == 'MORTGAGES') &\n",
" (portfolio.identifier != 'USD') &\n",
" (portfolio.endqty != 0)]\n",
- "portfolio = portfolio.set_index('identifier', append=True)\n",
- "portfolio = portfolio['endqty'].groupby(['identifier', 'periodenddate']).sum()\n",
- "portfolio = portfolio.reset_index('identifier')\n",
"cf = pd.read_sql_query(\"SELECT * FROM cashflow_history\", dawn_engine,\n",
" parse_dates=['date'],\n",
" index_col=['date']).sort_index()\n",
+ "portfolio = portfolio.set_index('identifier', append=True)\n",
+ "portfolio = portfolio['endqty'].groupby(['identifier', 'periodenddate']).sum()\n",
+ "portfolio = portfolio.reset_index('identifier')\n",
"df_1 = pd.merge_asof(cf, portfolio.sort_index(), left_index=True, right_index=True, by='identifier')\n",
"df_1 = df_1.dropna(subset=['endqty'])\n",
"df_1 = df_1[(df_1.principal_bal != 0) & (df_1.principal != 0)]\n",
@@ -97,7 +98,6 @@
"paydowns = df_1.paydown.groupby(pd.Grouper(freq='M')).sum()\n",
"temp = pd.concat([paydowns, df.principal_payment, df.accrued_payment], axis=1).fillna(0)\n",
"turnover = (temp.sum(axis=1)/nav.begbooknav).rolling(12).sum()\n",
- "#turnover = temp.rolling(12).sum().sum(axis=1)/ nav.begbooknav.rolling(12).mean()\n",
"turnover[12:].plot()\n",
"turnover[-1]"
]
@@ -108,6 +108,36 @@
"metadata": {},
"outputs": [],
"source": [
+ "################################### Average Portfolio Sales Turnover - as of last monthend from today\n",
+ "#(total Bond Sales Proceeds + paydown)/average starting 12 months NAV\n",
+ "#Actually: Rolling 12 months sum of (total bond sales proceeds + paydown)/monthly NAV\n",
+ "nav = go.get_net_navs()\n",
+ "fund='SERCGMAST'\n",
+ "sql_string = \"SELECT * FROM bonds WHERE buysell IS False and fund = %s\"\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",
+ "cf = pd.read_sql_query(\"SELECT * FROM cashflow_history\", dawn_engine,\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])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"################################### Average Monthly Traded Volume\n",
"nav = go.get_net_navs()\n",
"sql_string = \"SELECT * FROM bonds\"\n",
@@ -153,6 +183,7 @@
"outputs": [],
"source": [
"################################## Calculate Historical Bond Duration/Yield\n",
+ "analytics.init_ontr()\n",
"mysql_engine = dbengine('rmbs_model')\n",
"end_date = pd.datetime.today() - MonthEnd(1)\n",
"dates = pd.date_range(datetime.date(2013, 1, 30), end_date, freq=\"M\")\n",
@@ -246,7 +277,10 @@
" rmbs_pos = subprime_risk(position_date, dawnconn, mysql_engine)\n",
" clo_pos = clo_risk(position_date, dawnconn, etconn)\n",
" crt_pos = crt_risk(position_date, dawnconn, mysqlcrt_engine)\n",
- "notional = rmbs_pos['hy_equiv'].sum() + clo_pos['hy_equiv'].sum() + crt_pos['hy_equiv'].sum()\n",
+ "if clo_pos is None:\n",
+ " notional = rmbs_pos['hy_equiv'].sum() + crt_pos['hy_equiv'].sum()\n",
+ "else:\n",
+ " notional = rmbs_pos['hy_equiv'].sum() + clo_pos['hy_equiv'].sum() + crt_pos['hy_equiv'].sum()\n",
"portf.add_trade(CreditIndex('HY', on_the_run('HY', spread_date), '5yr', \n",
" value_date = spread_date, \n",
" notional = -notional), ('bonds', ''))\n",
@@ -259,25 +293,27 @@
"for trade in portf.swaptions:\n",
" vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
" value_date=spread_date, interp_method = \"bivariate_linear\")\n",
- " vol_surface[(trade.index.index_type, trade.index.series)] = vs[vs.list(option_type='payer')[-1]]\n",
- "vol_shock = [0]\n",
- "corr_shock = [0, -.1]\n",
- "spread_shock = tighten + [0] + widen\n",
- "date_range = [pd.Timestamp(shock_date)]\n",
+ " vol_surface[(trade.index.index_type, trade.index.series, trade.option_type)] = vs[vs.list(source='MS', option_type=trade.option_type)[-1]]\n",
"\n",
- "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n",
- " spread_shock=spread_shock,\n",
- " vol_shock=vol_shock,\n",
- " corr_shock=corr_shock,\n",
+ "scens = run_portfolio_scenarios(portf, date_range=[pd.Timestamp(shock_date)], params=[\"pnl\"],\n",
+ " spread_shock=widen,\n",
+ " vol_shock=[0],\n",
+ " corr_shock = [0],\n",
" vol_surface=vol_surface)\n",
"\n",
"attrib = (scens.\n",
" reset_index(level=['date'], drop=True).\n",
" groupby(level=0, axis=1).sum())\n",
- "attrib.columns.name = 'strategy'\n",
- "results = attrib.xs((widen[2], 0.), level=['spread_shock', 'corr_shock']).unstack('strategy')\n",
- "results.name = 'pnl'\n",
- "#results.to_clipboard(header=True)"
+ "results = attrib.xs((widen[2], 0., 0.), level=['spread_shock', 'corr_shock', 'vol_shock']).T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results.to_clipboard(header=True)"
]
},
{
@@ -312,6 +348,20 @@
"nav = go.get_net_navs()\n",
"(synthetic/nav.endbooknav[-1]).plot()"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -330,7 +380,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
generated by cgit v1.2.3-70-g09d2 (git 2.52.0) at 2025-12-22 04:44:57 +0000