1 files changed, 77 insertions, 2 deletions
diff --git a/python/notebooks/Curve Trades.ipynb b/python/notebooks/Curve Trades.ipynb
index a6ae0b7e..b2cde72e 100644
--- a/python/notebooks/Curve Trades.ipynb
+++ b/python/notebooks/Curve Trades.ipynb
@@ -16,6 +16,7 @@
     "\n",
     "from ipywidgets import widgets\n",
     "from analytics.scenarios import run_curve_scenarios\n",
+    "from scipy.optimize import brentq\n",
     "from db import dbengine"
    ]
   },
@@ -41,7 +42,7 @@
    "outputs": [],
    "source": [
     "index = w.value\n",
-    "report_date = (pd.datetime.today() - pd.offsets.BDay(2)).normalize()"
+    "report_date = (pd.datetime.today() - pd.offsets.BDay(2)).date()"
    ]
   },
   {
@@ -91,8 +92,82 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "rolling = 10\n",
+    "years = 5\n",
+    "ret = ct.curve_returns(index, rolling, years)\n",
+    "if index == 'IG':\n",
+    "    ret1 = ct.curve_returns('HY', rolling, years)\n",
+    "    suf = ' HY'\n",
+    "else:\n",
+    "    ret1 = ct.curve_returns('IG', rolling, years)\n",
+    "    suf = ' IG'\n",
+    "ret = ret.join(ret1['5yr long'], rsuffix=suf)\n",
+    "col_name = '5yr long' + suf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Strategy Cumulative Return\n",
+    "#Margin Requirement: 3% for IG Long only (33.3x), 25bps for curve (400x)\n",
+    "#Assume Margin Requirement of 10% for IG Long only (10x) and size the curve trade margin to\n",
+    "lev = 10\n",
+    "#1) have the same return volatility or \n",
+    "#curve_lev = ret['5yr long'].std()/ret['3-5-10'].std()\n",
+    "#2) have the same cumulative return\n",
+    "ret['5yr long lev'] = lev * ret['5yr long']\n",
+    "def aux(x, ret, col_a, col_b):\n",
+    "    ret[col_b + ' lev'] = x * ret[col_b]\n",
+    "    cum_ret = (ret+1).cumprod()\n",
+    "    return cum_ret[col_a][-1] - cum_ret[col_b + ' lev'][-1]\n",
+    "\n",
+    "curve_lev = brentq(aux, 0.01, 3 * lev, args=(ret, '5yr long lev', '3-5-10'))\n",
+    "other_lev = brentq(aux, 0.01, 3 * lev, args=(ret, '5yr long lev', col_name))\n",
+    "\n",
+    "ret['3-5-10 lev'] = curve_lev * ret['3-5-10']\n",
+    "ret[col_name + ' lev'] = other_lev * ret[col_name]\n",
+    "cum_ret = (ret+1).cumprod()\n",
+    "cum_ret_ax = cum_ret[['5yr long lev', '3-5-10 lev', col_name + ' lev']].plot()\n",
+    "cum_ret_ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/curve_trades_cum_return.png\", bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "curve_lev, other_lev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "#Curve Trade returns\n",
-    "ct.curve_returns()"
+    "ct.curve_returns_stats(ret)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#2016 scenario: max drawdown from the 2015 peak to 2016 trough\n",
+    "peak = cum_ret['2015'].max()\n",
+    "trough = cum_ret['2016'].min()\n",
+    "scenario_2016 = pd.DataFrame({'peak': peak,\n",
+    "                        'trough': trough,\n",
+    "                        'max_drawdown': (peak - trough)/peak,\n",
+    "                        'peak_dates': cum_ret['2015'].idxmax(),\n",
+    "                        'trough_dates': cum_ret['2016'].idxmin()})\n",
+    "scenario_2016"
    ]
   },
   {