diff options
Diffstat (limited to 'python/notebooks/VaR.ipynb')
| -rw-r--r-- | python/notebooks/VaR.ipynb | 71 |
1 files changed, 60 insertions, 11 deletions
diff --git a/python/notebooks/VaR.ipynb b/python/notebooks/VaR.ipynb index 3bb3e83d..178d72b3 100644 --- a/python/notebooks/VaR.ipynb +++ b/python/notebooks/VaR.ipynb @@ -10,13 +10,16 @@ "from analytics.index_data import get_index_quotes\n", "from analytics.scenarios import run_portfolio_scenarios\n", "from analytics import Swaption, BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio, ProbSurface, DualCorrTranche\n", - "from db import dbconn\n", + "from db import dbconn, dbengine\n", "\n", "import datetime\n", "import exploration.VaR as var\n", "import pandas as pd\n", + "import numpy as np\n", "\n", - "conn = dbconn('dawndb')" + "conn = dbconn('dawndb')\n", + "dawndb = dbengine('dawndb')\n", + "serenitasdb = dbengine('serenitasdb')" ] }, { @@ -25,7 +28,7 @@ "metadata": {}, "outputs": [], "source": [ - "date = (datetime.date.today() - pd.tseries.offsets.BDay(1)).date()\n", + "date = (datetime.date.today() - pd.tseries.offsets.BDay(3)).date()\n", "report_date = (date + pd.tseries.offsets.BMonthEnd(-1)).date()\n", "index_type = \"IG\"\n", "quantile = .025" @@ -63,7 +66,8 @@ "outputs": [], "source": [ "#Mortgage Hedge VaR - use IG spread relative move for VaR\n", - "df = var.get_pos(date, 'HEDGE_MBS')\n", + "df = pd.read_sql_query(\"SELECT * from list_cds_marks_by_strat(%s) where strategy ='HEDGE_MBS'\",\n", + " dawndb, params=(date,))\n", "portf = Portfolio([CreditIndex(row.p_index, row.p_series, row.tenor,\n", " report_date, -row.notional)\n", " for row in df[['p_index', 'tenor', 'p_series', 'notional']].\n", @@ -136,7 +140,6 @@ " \"FROM cds WHERE swap_type='CD_INDEX' AND termination_cp IS null \"\n", " \"AND folder = 'IGOPTDEL' OR folder = 'HYOPTDEL' \"\n", " \"AND trade_date <= %s\")\n", - "conn = dbconn('dawndb')\n", "with conn.cursor() as c:\n", " #Get Tranche Trade Ids\n", " c.execute(t_sql_string, (date,))\n", @@ -158,14 +161,18 @@ " portf.add_trade(CreditIndex.from_tradeid(index_id), 'index_' + str(index_id))\n", " \n", "#Update manually - positive notional = long risk\n", - "non_trancheSwap_risk_notional = 33763230\n", - "portf.add_trade(CreditIndex('HY', on_the_run('HY'), '5yr', value_date = date, notional = -non_trancheSwap_risk_notional), 'port')\n", + "non_trancheSwap_risk_notional = 49119912 \n", + "\n", + "portf.add_trade(CreditIndex('HY', on_the_run('HY'), '5yr', value_date = date, notional = -non_trancheSwap_risk_notional), 'bond')\n", " \n", "portf.value_date = date\n", - "portf.mark()\n", + "portf.mark(interp_method=\"bivariate_spline\")\n", "portf.reset_pv()\n", " \n", - "vs = BlackSwaptionVolSurface(portf.swaptions[0].index.index_type, portf.swaptions[0].index.series, value_date=date)\n", + "vs = BlackSwaptionVolSurface(portf.swaptions[0].index.index_type, \n", + " portf.swaptions[0].index.series, \n", + " value_date=date, \n", + " interp_method = \"bivariate_spline\")\n", "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", "vol_shock = [0]\n", "corr_shock = [0]\n", @@ -187,8 +194,50 @@ "metadata": {}, "outputs": [], "source": [ - "var.cleared_cds_margins(report_date)" + "spread_shock = np.arange(-.4, 2.2, .2)\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", + " vol_surface=vol_surface)\n", + "scens.sum(axis=1)\n", + "\n", + "risk_notional = [t.notional * t._index.duration for t in portf.indices]\n", + "portf.trades[0]._index.duration()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Calculate the margins for cleared CDS required for each strategy\n", + "df = pd.read_sql_query(\"SELECT * from list_cds_marks_by_strat(%s)\",\n", + " dawndb, params=(date,))\n", + "percentile = .95 #monthly 90%tile case.\n", + "shocks, widen, tighten, onTR_dur, onTR_spread = {}, {}, {}, {}, {}\n", + "for ind in ['IG', 'HY', 'EU']:\n", + " shocks[ind], onTR_spread[ind], onTR_dur[ind] = rel_spread_diff(date, index=ind)\n", + " widen[ind] = shocks[ind].quantile(percentile)\n", + " tighten[ind] = shocks[ind].quantile(1-percentile)\n", + "\n", + "df['onTR_notional'] = df.apply(lambda df:\n", + " df.notional * df.factor * df.duration / onTR_dur[df.p_index], axis=1)\n", + "df['widen'] = df.apply(lambda df:\n", + " df.onTR_notional * onTR_spread[df.p_index] * onTR_dur[df.p_index] * widen[df.p_index]/10000, axis=1)\n", + "df['tighten'] = df.apply(lambda df:\n", + " df.onTR_notional * onTR_spread[df.p_index] * onTR_dur[df.p_index] * tighten[df.p_index]/10000, axis=1)\n", + "delta_alloc = df.groupby('strategy').sum()\n", + "delta_alloc['total'] = delta_alloc.apply(lambda df: max(abs(df.widen), abs(df.tighten)), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -207,7 +256,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.1" } }, "nbformat": 4, |