1 files changed, 67 insertions, 4 deletions
diff --git a/python/notebooks/Allocation Reports.ipynb b/python/notebooks/Allocation Reports.ipynb
index 00ad7972..2b35e2bb 100644
--- a/python/notebooks/Allocation Reports.ipynb
+++ b/python/notebooks/Allocation Reports.ipynb
@@ -9,7 +9,8 @@
     "import datetime\n",
     "import pandas.tseries.offsets as off\n",
     "import globeop_reports as go\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -65,7 +66,7 @@
    "outputs": [],
    "source": [
     "#Number of bond positions by strategy by month - and copy to clipboard\n",
-    "go.num_bond_by_strat()"
+    "#go.num_bond_by_strat()"
    ]
   },
   {
@@ -75,7 +76,7 @@
    "outputs": [],
    "source": [
     "#Number of bond trades by direction by month - and copy to clipboard\n",
-    "go.num_bond_trades()"
+    "#go.num_bond_trades()"
    ]
   },
   {
@@ -108,7 +109,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df"
+    "df = go.get_rmbs_pos_df()"
    ]
   },
   {
@@ -116,6 +117,68 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "bond_dur, bond_yield = {}, {}\n",
+    "for d, g in df.groupby(pd.Grouper(freq='M')):\n",
+    "        bond_dur[d] = sum(g.curr_ntl * g[('moddur', 3)])/sum(g.curr_ntl)\n",
+    "        bond_yield[d] = sum(g.endlocalmv * g[('moddur', 3)] * g.b_yield) /sum(g.endlocalmv * g[('moddur', 3)])\n",
+    "a = pd.Series(bond_dur)\n",
+    "b = pd.Series(bond_yield)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax0 = fig.add_subplot(111)\n",
+    "\n",
+    "a.name = 'Yield-to-maturity'\n",
+    "ax1.set_xlabel('date')\n",
+    "a.plot(kind='line', color = 'r', ax=ax0, label = a.name)\n",
+    "ax0.set_ylabel('Duration')\n",
+    "ax0.legend(loc=0)\n",
+    "\n",
+    "ax1 = ax0.twinx()\n",
+    "b.name = 'Duration'\n",
+    "b.plot(kind='line', secondary_y=True, ax=ax1, label = b.name)\n",
+    "ax1.set_xlim([a.index.min(), a.index.max()])\n",
+    "ax1.set_ylabel('Yield-to-Maturity')\n",
+    "ax1.legend(loc=2)\n",
+    "#plt.legend(bbox_to_anchor=(0, -.1), loc=0, borderaxespad=0.)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#filter out First-Pay bonds. \n",
+    "df_1 = c[c.strat != 'MTG_FP']\n",
+    "bond_dur, bond_yield = {}, {}\n",
+    "for d, g in df_1.groupby(pd.Grouper(freq='M')):\n",
+    "        bond_dur[d] = sum(g.curr_ntl * g[('moddur', 3)])/sum(g.curr_ntl)\n",
+    "        bond_yield[d] = sum(g.endlocalmv * g[('moddur', 3)] * g.b_yield) /sum(g.endlocalmv * g[('moddur', 3)])\n",
+    "a_1 = pd.Series(bond_dur)\n",
+    "b_1 = pd.Series(bond_yield)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],