1 files changed, 0 insertions, 58 deletions
diff --git a/python/notebooks/swaption_risk.ipynb b/python/notebooks/swaption_risk.ipynb
index 8c38ecf8..00b5db55 100644
--- a/python/notebooks/swaption_risk.ipynb
+++ b/python/notebooks/swaption_risk.ipynb
@@ -84,64 +84,6 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from analytics.scenarios import run_portfolio_scenarios\n",
-    "from analytics import BlackSwaptionVolSurface, CreditIndex\n",
-    "import analytics\n",
-    "import datetime\n",
-    "import numpy as np\n",
-    "\n",
-    "today = datetime.datetime.now()\n",
-    "yesterday = datetime.date.today() - pd.offsets.BDay()\n",
-    "\n",
-    "portf = get_swaption_portfolio(yesterday, conn, source_list=['GS'])\n",
-    "for i, amt in hedges.iteritems():\n",
-    "    portf.add_trade(CreditIndex(i[:2], i[2:4], '5yr', value_date=yesterday, notional=amt), ('delta', i))\n",
-    "\n",
-    "vol_surface = {}\n",
-    "for trade in portf.swaptions:\n",
-    "    vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
-    "                                 value_date=today.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",
-    "\n",
-    "#Set original_pv as of yesterday's EOD levels, don't reset PV after this time\n",
-    "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
-    "portf.reset_pv()\n",
-    "\n",
-    "#set ref to today's levels\n",
-    "portf.value_date = today\n",
-    "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
-    "\n",
-    "spread_shock = np.round(np.arange(-.1, .1, .01), 4)\n",
-    "scens = run_portfolio_scenarios(portf, [today], params=['pnl', 'hy_equiv', 'sigma'],\n",
-    "                                spread_shock=spread_shock,\n",
-    "                                vol_shock=[0],\n",
-    "                                corr_shock=[0],\n",
-    "                                vol_surface=vol_surface)\n",
-    "pnl = scens.xs('pnl', level = 2, axis=1).sum(axis=1)\n",
-    "hy_equiv = scens.xs('hy_equiv', level = 2, axis=1).sum(axis=1)\n",
-    "\n",
-    "ig = CreditIndex('IG', 32, '5yr', value_date = today)\n",
-    "ig.mark()\n",
-    "\n",
-    "pnl.index = pnl.index.set_levels((1+pnl.index.get_level_values('spread_shock')) * ig.spread, level = 'spread_shock')\n",
-    "hy_equiv.index = pnl.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pnl, hy_equiv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],