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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FuncFormatter \n",
+    "from datetime import datetime\n",
+    "import pandas as pd\n",
+    "\n",
+    "import mark_backtest_underpar as mark\n",
+    "import globeop_reports as ops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#exclude sell price that are over 200\n",
+    "df_long = mark.back_test('2013-01-01', '2018-01-01', sell_price_threshold = 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%matplotlib nbagg\n",
+    "%matplotlib inline\n",
+    "mark.pretty_plot(df_long)\n",
+    "#file saved in serenitas shared drive/edwin/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#exclude trades that are over 5x mark for purpose of regression\n",
+    "diff_threshold = 5\n",
+    "results = mark.stats(df_long, diff_threshold)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Regression Intercept\n",
+    "results[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Sale Difference\n",
+    "results[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Now Calculate alternate valuation methodologies\n",
+    "df = mark.get_mark_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "mark.count_sources(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#difference by source\n",
+    "nav = ops.get_net_navs()['endbooknav']\n",
+    "difference = mark.diff_by_source_percentage(df)\n",
+    "#difference.to_clipboard()\n",
+    "\n",
+    "#plot\n",
+    "ax = difference.plot(kind = 'bar', legend = True)\n",
+    "\n",
+    "visible = ax.xaxis.get_ticklabels()[::6]\n",
+    "for label in ax.xaxis.get_ticklabels():\n",
+    "    if label not in visible:\n",
+    "        label.set_visible(False)\n",
+    "        \n",
+    "ax.xaxis.set_major_formatter(plt.FixedFormatter(difference.index.to_series().dt.strftime(\"%b %Y\")))\n",
+    "ax.set_ylabel('NAV Impact vs. Fund Policy (%)')\n",
+    "vals = ax.get_yticks()\n",
+    "ax.set_yticklabels(['{:3.0f}%'.format(x*100) for x in vals])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = mark.alt_navs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%matplotlib nbagg\n",
+    "to_plot = ['mark_closest_all', 'mark_mean_all']\n",
+    "to_plot1 = ['mark_manager']\n",
+    "plot_df0 = results[1][to_plot]\n",
+    "plot_df1 = results[1][to_plot1]\n",
+    "\n",
+    "plot_df0 = plot_df0.rename(columns = {'mark_closest_all': 'Third-pary mark closest to LMCG valuation', \\\n",
+    "                                    'mark_mean_all': 'Average of all third-party marks'})\n",
+    "plot_df1 = plot_df1.rename(columns = {'mark_manager': 'Marks per fund valuation policy'})\n",
+    "\n",
+    "ax = plot_df0.plot(figsize = [10, 3.5])\n",
+    "ax = plot_df1.plot(marker = 'o', ax = ax)\n",
+    "plt.rcParams[\"font.family\"] = \"sans-serif\"\n",
+    "ax.set_xlabel('')\n",
+    "ax.set_ylabel('NAV', weight = 'bold')\n",
+    "ax.set_title('Fund Return Using Different Valuation Methods', weight = 'bold')\n",
+    "lgd = ax.legend(loc='upper center', bbox_to_anchor=(0.5, -.1),  shadow=True, ncol=3)\n",
+    "ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/Valuation_1.png\", bbox_extra_artists=(lgd,), bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mark.annual_performance(results[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame(mark.alt_nav_impact())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}