diff options
| -rw-r--r-- | python/notebooks/Reto Report.ipynb | 33 |
1 files changed, 22 insertions, 11 deletions
diff --git a/python/notebooks/Reto Report.ipynb b/python/notebooks/Reto Report.ipynb index c1170b16..12f3f3af 100644 --- a/python/notebooks/Reto Report.ipynb +++ b/python/notebooks/Reto Report.ipynb @@ -18,13 +18,11 @@ "from analytics import BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio,DualCorrTranche\n", "from copy import deepcopy\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')" + "from utils.db import dbconn, dbengine, serenitas_engine, dawn_engine" ] }, { @@ -208,7 +206,12 @@ "#min/max/mean cds trades by notional\n", "volume = g.sum()/nav.endbooknav\n", "volume.fillna(0, inplace=True)\n", - "volume.min(), volume.max()/4, volume.mean()/4" + "volume.min(), volume.max()/4, volume.mean()/4\n", + "\n", + "#Max trades per day - CDS trades only, bond trades only, combined bond/cds trades\n", + "cds_trades[cds_trades.cp_code != 'CONTRA'].groupby(pd.Grouper(freq='D')).count().max()\n", + "bond_trades.groupby(pd.Grouper(freq='D')).count().max()\n", + "cds_trades[cds_trades.cp_code != 'CONTRA'].id.append(bond_trades.id).groupby(pd.Grouper(freq='D')).count().max()" ] }, { @@ -334,6 +337,13 @@ "source": [ "################################## Historical Notioinals and HY Equiv\n", "dates = pd.date_range(datetime.date(2013, 1, 30), pd.datetime.today() - MonthEnd(1), freq=\"BM\")\n", + "#look for a day with HY quotes... we need that to construct HY Equiv\n", + "sql_string = 'select distinct(date) from index_quotes where index = %s order by date asc'\n", + "hy_dates = pd.read_sql_query(sql_string, serenitas_engine, parse_dates = 'date', params=['HY',])\n", + "def nearest(items, pivot):\n", + " return min(items, key=lambda x: abs(x - pivot))\n", + "hy_dates.apply(lambda x: nearest(dates, x))\n", + "pd.merge_asof(pd.DataFrame(dates), hy_dates, left_index=True, right_index=True)\n", "portfs = {}\n", "for d in dates:\n", " d = d.date()\n", @@ -346,8 +356,9 @@ "metadata": {}, "outputs": [], "source": [ - "##################################\n", + "#### Function to build portfolio\n", "def build_portf(position_date, spread_date=None):\n", + " analytics.init_ontr()\n", " if spread_date is None:\n", " spread_date=position_date\n", " conn = dawn_engine.raw_connection()\n", @@ -386,7 +397,7 @@ "\n", " #get bond risks:\n", " sql_string = (\"SELECT distinct timestamp::date FROM priced where normalization = 'current_notional' and model_version = 1 \"\n", - " \"and date(timestamp) < %s and date(timestamp) > %s order by timestamp desc\")\n", + " \"and date(timestamp) <= %s and date(timestamp) >= %s order by timestamp desc\")\n", " with dbconn('etdb') as etconn, dbconn('dawndb') as dawnconn:\n", " timestamps = pd.read_sql_query(sql_string, dawn_engine, parse_dates=[\"timestamp\"], \n", " params=[position_date, position_date - pd.tseries.offsets.DateOffset(15, \"D\")])\n", @@ -420,7 +431,7 @@ "################################### Calculate stress scenario \n", "position_date = (datetime.date.today() - BDay(1)).date()\n", "spread_date = position_date\n", - "#analytics.init_ontr(spread_date)" + "analytics.init_ontr(spread_date)" ] }, { @@ -461,7 +472,7 @@ " except:\n", " vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series + 1, \n", " value_date=spread_date, interp_method = \"bivariate_linear\")\n", - " vol_surface[(trade.index.index_type, trade.index.series, trade.option_type)] = vs[vs.list(source='GS', option_type=trade.option_type)[-1]]\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=[pd.Timestamp(spread_date)], params=[\"pnl\"],\n", " spread_shock=widen,\n", @@ -492,9 +503,9 @@ "source": [ "################################### Run set of scenario\n", "spread_shock = np.round(np.arange(-.2, 1, .05), 3)\n", - "scens = run_portfolio_scenarios(portf, date_range, params=['pnl', 'delta'],\n", + "scens = run_portfolio_scenarios(portf, date_range=[pd.Timestamp(spread_date)], params=['pnl', 'delta'],\n", " spread_shock=spread_shock,\n", - " vol_shock=vol_shock,\n", + " vol_shock=[0],\n", " corr_shock=[0],\n", " vol_surface=vol_surface)\n", "\n", @@ -548,7 +559,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.8.0" } }, "nbformat": 4, |