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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",
"round(results[0],1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Sale Difference\n",
"round(results[1],2)"
]
},
{
"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()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#plot\n",
"ax = difference.plot(kind = 'bar', legend = True, figsize = [10, 3.5])\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])\n",
"ax.set_xlabel('')\n",
"ax.grid(False, which='major',axis = 'x')\n",
"lgd = ax.legend(loc='upper center', bbox_to_anchor=(0.5, -.3), shadow=True, ncol=5)\n",
"ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/Valuation_2.png\", bbox_extra_artists=(lgd,), bbox_inches='tight')"
]
},
{
"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": [
"#Fund cumulative returns from the last 12 months\n",
"results[1]['mark_manager'][-12:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"round(mark.annual_performance(results[1])*100,2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#A positive impact % means the alternative methodology results in a higher NAV than the fund's valuation policy.\n",
"round(pd.DataFrame(mark.alt_nav_impact())*100,2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pnl_breakdown = ops.curr_port_PNL()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Unrealized MTM Gains/Loss\n",
"breakdown_summary = pd.DataFrame(index = {'unreal mark-to-market'}, columns={'Gains','Loss','Net'})\n",
"breakdown_summary['Gains'] = pnl_breakdown[pnl_breakdown['mtdbookunrealmtm']>=0].sum()['mtdbookunrealmtm']\n",
"breakdown_summary['Loss'] = pnl_breakdown[pnl_breakdown['mtdbookunrealmtm']<0].sum()['mtdbookunrealmtm']\n",
"breakdown_summary['Net'] = pnl_breakdown.sum()['mtdbookunrealmtm']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"breakdown_summary / nav[-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = ops.trade_performance()\n",
"df = df.set_index('trade_date')\n",
"df.days_held = df.days_held.dt.days\n",
"winner = df[df.percent_gain > 0]\n",
"df[df.days_held.notnull()].groupby(pd.Grouper(freq='A')).mean()\n",
"df"
]
}
],
"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
}
|
