1 files changed, 22 insertions, 21 deletions
diff --git a/python/notebooks/Valuation Backtest.ipynb b/python/notebooks/Valuation Backtest.ipynb
index 92549418..dfddbf86 100644
--- a/python/notebooks/Valuation Backtest.ipynb
+++ b/python/notebooks/Valuation Backtest.ipynb
@@ -23,7 +23,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "date = datetime.date.today() - pd.tseries.offsets.MonthEnd(1)"
+    "date = datetime.date.today() - pd.tseries.offsets.MonthEnd(1)\n",
+    "navs = ops.get_net_navs()\n",
+    "subprime = mark.get_mark_df('Subprime')\n",
+    "clo = mark.get_mark_df('CLO')\n",
+    "df = subprime.append(clo)"
    ]
   },
   {
@@ -86,17 +90,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Now Calculate alternate valuation methodologies\n",
-    "df = mark.get_mark_df()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mark.count_sources(df)"
+    "#source counts\n",
+    "g2 = df.set_index(\"source\", append=True).groupby(level=[\"date\", \"source\"])\n",
+    "g2[\"mark\"].count().unstack(-1).plot()"
    ]
   },
   {
@@ -106,8 +102,10 @@
    "outputs": [],
    "source": [
     "#difference by source\n",
-    "nav = ops.get_net_navs()['endbooknav']\n",
-    "difference = mark.diff_by_source_percentage(df)\n",
+    "difference = mark.diff_by_source(df)\n",
+    "difference = difference.join(navs['endbooknav'])\n",
+    "difference = difference.apply(lambda x: (x / x.endbooknav), axis=1)\n",
+    "del difference[\"endbooknav\"]\n",
     "#difference.to_clipboard()"
    ]
   },
@@ -141,7 +139,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = mark.alt_navs()"
+    "results = mark.alt_navs(df)"
    ]
   },
   {
@@ -189,7 +187,10 @@
    "outputs": [],
    "source": [
     "#Annual Return using different methodology\n",
-    "round(mark.annual_performance(results[1])*100,2)"
+    "perf = results[1].groupby(pd.Grouper(freq=\"A\")).last()\n",
+    "perf_ann = perf / perf.shift(1) - 1\n",
+    "perf_ann[\"2013\"] = perf[\"2013\"] / 100 - 1\n",
+    "round(perf_ann*100,2)"
    ]
   },
   {
@@ -209,8 +210,9 @@
    "outputs": [],
    "source": [
     "#A positive impact % means the alternative methodology results in a higher NAV than the fund's valuation policy.\n",
-    "df = mark.alt_nav_impact()\n",
-    "round(pd.DataFrame(df.iloc[-1]/df.iloc[-1]['endbooknav'])*100,2)"
+    "alt_nav_impact = mark.calc_mark_diff(df)\n",
+    "alt_nav_impact = alt_nav_impact.join(navs.endbooknav)\n",
+    "round(pd.DataFrame(alt_nav_impact.iloc[-1]/alt_nav_impact.iloc[-1]['endbooknav'])*100,2)"
    ]
   },
   {
@@ -220,12 +222,11 @@
    "outputs": [],
    "source": [
     "#% impact historical: positive % means the alternative methodology results in a higher NAV\n",
-    "nav_impact = df.divide(df.endbooknav, axis=0)\n",
+    "nav_impact = alt_nav_impact.divide(alt_nav_impact.endbooknav, axis=0)\n",
     "to_plot = ['mark_closest_all', 'mark_filtered_mean']\n",
     "nav_impact = nav_impact[to_plot].rename(columns={'mark_closest_all': 'mark to closest', \n",
     "                                    'mark_filtered_mean': 'mark to mean'})\n",
     "ax = nav_impact.plot()\n",
-    "ax.figure.set_figheight(7)\n",
     "ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/Valuation_3.png\", bbox_extra_artists=(lgd,), bbox_inches='tight')"
    ]
   },
@@ -298,7 +299,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.1"
   }
  },
  "nbformat": 4,