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-rw-r--r--python/globeop_reports.py14
-rw-r--r--python/mark_backtest_backfill.py2
-rw-r--r--python/mark_backtest_underpar.py4
-rw-r--r--python/notebooks/Valuation Backtest.ipynb796
4 files changed, 768 insertions, 48 deletions
diff --git a/python/globeop_reports.py b/python/globeop_reports.py
index e44641b9..bb2c01db 100644
--- a/python/globeop_reports.py
+++ b/python/globeop_reports.py
@@ -16,7 +16,7 @@ def get_monthly_pnl(group_by = ['identifier']):
index_col=['date'])
df_pnl['identifier'] = df_pnl.invid.str.replace("_A$", "")
pnl_cols = ['bookunrealmtm', 'bookrealmtm', 'bookrealincome', 'bookunrealincome', 'totalbookpl']
- monthend_pnl = df_pnl.groupby(pd.TimeGrouper('M')).apply(lambda df: df.loc[df.index[-1]])
+ monthend_pnl = df_pnl.groupby(pd.Grouper(freq='M')).apply(lambda df: df.loc[df.index[-1]])
return monthend_pnl.groupby(['date'] + group_by)[['mtd' + col for col in pnl_cols]].sum()
def get_portfolio(report_date = False):
@@ -41,8 +41,8 @@ def curr_port_PNL(date = datetime.date.today(), asset_class='Subprime'):
def trade_performance():
sql_string = "SELECT * FROM bonds"
- df_trades = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['lastupdate', 'trade_date', 'settle_date'])
- df_trades = df_trades[df_trades.asset_class == 'Subprime']
+ df_trades = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates={'lastupdate': {'utc': True}, 'trade_date': {}, 'settle_date': {}})
+ df_trades = df_trades[df_trades['asset_class'] == 'Subprime']
df_pnl = get_monthly_pnl()
df_sell = df_trades[df_trades.buysell == False].groupby('identifier').last().reset_index()
@@ -167,14 +167,14 @@ def num_bond_by_strat():
df = get_portfolio()
df = df[(df.custacctname == 'V0NSCLMAMB') &
~(df.invid.isin(['USD', 'CAD', 'EUR'])) & (df.endqty > 0)]
- df = df.groupby(pd.TimeGrouper('M')).apply(lambda df: df.loc[df.index[-1]])
+ df = df.groupby(pd.Grouper(freq='M')).apply(lambda df: df.loc[df.index[-1]])
return df.groupby(['periodenddate', 'port']).identifier.nunique().unstack()
def num_bond_trades():
sql_string = "SELECT * FROM bonds"
df = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['trade_date'],
index_col=['trade_date'])
- df = df.groupby([pd.TimeGrouper('M'), 'buysell']).identifier.count().unstack()
+ df = df.groupby([pd.Grouper(freq='M'), 'buysell']).identifier.count().unstack()
idx = pd.date_range(df.index[0], df.index[-1], freq = 'M')
return df.reindex(idx, fill_value = 0)
@@ -217,12 +217,12 @@ def calc_trade_performance_stats():
temp = {}
temp1 = {}
for x, df1 in df.groupby('winners'):
- for y, df2 in df1.groupby(pd.TimeGrouper(freq='A')):
+ for y, df2 in df1.groupby(pd.Grouper(freq='A')):
import pdb; pdb.set_trace()
y = y.date().year
results.loc[y] = df2[df2.days_held.notnull()].mean()[['curr_face','initialinvestment', 'days_held']]
#results.loc[] = len(df2[df2.winners == x].index)/len(df)
- df[df.days_held.notnull()]['days_held'].groupby(pd.TimeGrouper(freq='A')).mean()
+ df[df.days_held.notnull()]['days_held'].groupby(pd.Grouper(freq='A')).mean()
diff --git a/python/mark_backtest_backfill.py b/python/mark_backtest_backfill.py
index 37b2aae0..b204f97a 100644
--- a/python/mark_backtest_backfill.py
+++ b/python/mark_backtest_backfill.py
@@ -97,6 +97,8 @@ def get_globs():
return chain.from_iterable(globs)
settings = {
+ 'ReviewedPack.bseeman.SERENITAS.SERCGMAST.20171201.20171231.Draft.xlsx': ("JA:JX", "Securities Valuation Details", "Y"),
+ 'ReviewedPack.bseeman.SERENITAS.SERCGMAST.20171101.20171130.Draft.xlsx': ("JA:JX", "Securities Valuation Details", "Y"),
'ReviewedPack.bseeman.SERENITAS.SERCGMAST.20171001.20171031.Draft.xlsx': ("JA:JX", "Securities Valuation Details", "Y"),
'ReviewedPack.bseeman.SERENITAS.SERCGMAST.20170901.20170930.Draft.xlsx': ("JA:JX", "Securities Valuation Details", "Y"),
'ReviewedPack.bseeman.SERENITAS.SERCGMAST.20170801.20170831.Draft.xlsx': ("JA:JX", "Securities Valuation Details", "Y"),
diff --git a/python/mark_backtest_underpar.py b/python/mark_backtest_underpar.py
index d34c7a4d..56e1a2b7 100644
--- a/python/mark_backtest_underpar.py
+++ b/python/mark_backtest_underpar.py
@@ -50,7 +50,7 @@ def calc_mark_diff(df, sources= ['PRICESERVE', 'PRICINGDIRECT','BVAL','MARKIT','
def closest(x):
if x.mark.count() > 1:
- x.dist = abs(x.mark - x.mark[x.source == 'MANAGER'])
+ x['dist'] = abs(x.mark - x.mark[x.source == 'MANAGER'])
return x.mark[x.dist == x.dist[x.dist>0].min()].iloc[0]
else:
return x.mark[0]
@@ -98,7 +98,7 @@ def alt_navs():
return returns, nav_100
def annual_performance(nav_100):
- perf = nav_100.groupby(pd.TimeGrouper(freq = 'A')).last()
+ perf = nav_100.groupby(pd.Grouper(freq = 'A')).last()
perf_ann = perf/perf.shift(1) - 1
perf_ann['2013'] = perf['2013']/100-1
return perf_ann
diff --git a/python/notebooks/Valuation Backtest.ipynb b/python/notebooks/Valuation Backtest.ipynb
index cc5d1501..d557a08a 100644
--- a/python/notebooks/Valuation Backtest.ipynb
+++ b/python/notebooks/Valuation Backtest.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
@@ -17,8 +17,10 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
+ "execution_count": 47,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"#exclude sell price that are over 200\n",
@@ -27,9 +29,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 48,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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YhnGBnThJyzjzke3XK294vDKFyDQzA0+iJKx2b4CywrI3AhbJ9CTay1N56EEajSpSZWhH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\niEhU+SeqL4G7290bOgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f5dbfa36a20>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"#%matplotlib nbagg\n",
"%matplotlib inline\n",
@@ -39,8 +52,10 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
+ "execution_count": 49,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"#exclude trades that are over 5x mark for purpose of regression\n",
@@ -50,9 +65,114 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 50,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Intercept</th>\n",
+ " <th>mark</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>source</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>BROKER</th>\n",
+ " <td>7.9</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>BVAL</th>\n",
+ " <td>11.1</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>IDC</th>\n",
+ " <td>9.6</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>LMCG</th>\n",
+ " <td>8.3</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>MARKIT</th>\n",
+ " <td>16.2</td>\n",
+ " <td>0.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>PB</th>\n",
+ " <td>10.7</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>PRICESERVE</th>\n",
+ " <td>15.0</td>\n",
+ " <td>0.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>PRICINGDIRECT</th>\n",
+ " <td>9.9</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>REUTERS</th>\n",
+ " <td>5.9</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>S&amp;P</th>\n",
+ " <td>8.8</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Intercept mark\n",
+ "source \n",
+ "BROKER 7.9 0.9\n",
+ "BVAL 11.1 0.9\n",
+ "IDC 9.6 0.9\n",
+ "LMCG 8.3 1.0\n",
+ "MARKIT 16.2 0.8\n",
+ "PB 10.7 0.9\n",
+ "PRICESERVE 15.0 0.8\n",
+ "PRICINGDIRECT 9.9 0.9\n",
+ "REUTERS 5.9 0.9\n",
+ "S&P 8.8 0.9"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"#Regression Intercept\n",
"round(results[0],1)"
@@ -60,18 +180,99 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 51,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th>source</th>\n",
+ " <th>BROKER</th>\n",
+ " <th>BVAL</th>\n",
+ " <th>IDC</th>\n",
+ " <th>LMCG</th>\n",
+ " <th>MARKIT</th>\n",
+ " <th>PB</th>\n",
+ " <th>PRICESERVE</th>\n",
+ " <th>PRICINGDIRECT</th>\n",
+ " <th>REUTERS</th>\n",
+ " <th>S&amp;P</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>average</th>\n",
+ " <td>0.263</td>\n",
+ " <td>0.280</td>\n",
+ " <td>0.354</td>\n",
+ " <td>0.321</td>\n",
+ " <td>0.347</td>\n",
+ " <td>0.331</td>\n",
+ " <td>0.524</td>\n",
+ " <td>0.244</td>\n",
+ " <td>0.451</td>\n",
+ " <td>0.296</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>standard deviation</th>\n",
+ " <td>0.670</td>\n",
+ " <td>0.737</td>\n",
+ " <td>0.916</td>\n",
+ " <td>0.657</td>\n",
+ " <td>0.851</td>\n",
+ " <td>0.845</td>\n",
+ " <td>1.092</td>\n",
+ " <td>0.877</td>\n",
+ " <td>0.925</td>\n",
+ " <td>0.833</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "source BROKER BVAL IDC LMCG MARKIT PB PRICESERVE \\\n",
+ "average 0.263 0.280 0.354 0.321 0.347 0.331 0.524 \n",
+ "standard deviation 0.670 0.737 0.916 0.657 0.851 0.845 1.092 \n",
+ "\n",
+ "source PRICINGDIRECT REUTERS S&P \n",
+ "average 0.244 0.451 0.296 \n",
+ "standard deviation 0.877 0.925 0.833 "
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"#Sale Difference\n",
- "round(results[1],2)"
+ "round(results[1],3)"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
+ "execution_count": 52,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"#Now Calculate alternate valuation methodologies\n",
@@ -80,9 +281,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 53,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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rVtkh00mwCclBk+bAjcNujIxKQFHnDOP7+fLWE5VTNnFR3GxsVwxFggTbfBMZ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\nSUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f5dc24ee5f8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"%matplotlib inline\n",
"mark.count_sources(df)"
@@ -90,8 +302,10 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
+ "execution_count": 54,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"#difference by source\n",
@@ -102,9 +316,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 55,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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3OwazbhMR74iI3ohYrN2xmHW70s2zFhGrAScAM4G9gR8AWwMPArtI+lsbwxu1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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f5dbfe3b7b8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"#plot\n",
"ax = difference.plot(kind = 'bar', legend = True, figsize = [10, 3.5])\n",
@@ -126,7 +351,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
@@ -135,9 +360,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 57,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+bz1l798Jnmgqk+bovbCf9Hq1T09ISCOAVOAbwAzgBWNMFqHvoBs8XbCVadOm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\n+wzWAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f5db60d70f0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"#%matplotlib nbagg\n",
"to_plot = ['mark_closest_all', 'mark_mean_all']\n",
@@ -161,9 +397,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 58,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "date\n",
+ "2017-01-31 155.659\n",
+ "2017-02-28 161.233\n",
+ "2017-03-31 163.364\n",
+ "2017-04-30 163.298\n",
+ "2017-05-31 166.464\n",
+ "2017-06-30 169.261\n",
+ "2017-07-31 170.79\n",
+ "2017-08-31 176.764\n",
+ "2017-09-30 178.941\n",
+ "2017-10-31 182.531\n",
+ "2017-11-30 184.944\n",
+ "2017-12-31 184.296\n",
+ "Name: mark_manager, dtype: object"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"#Fund cumulative returns from the last 12 months\n",
"results[1]['mark_manager'][-12:]"
@@ -171,18 +431,214 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 59,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>mark_mean_all</th>\n",
+ " <th>mark_manager</th>\n",
+ " <th>mark_median_all</th>\n",
+ " <th>mark_closest_all</th>\n",
+ " <th>mark_filtered_mean</th>\n",
+ " <th>mark_filtered_median</th>\n",
+ " <th>mark_filtered_no_max_min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>date</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2013-12-31</th>\n",
+ " <td>22.34</td>\n",
+ " <td>21.11</td>\n",
+ " <td>21.78</td>\n",
+ " <td>20.76</td>\n",
+ " <td>22.48</td>\n",
+ " <td>22.34</td>\n",
+ " <td>22.22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2014-12-31</th>\n",
+ " <td>11.39</td>\n",
+ " <td>12.44</td>\n",
+ " <td>11.79</td>\n",
+ " <td>12.77</td>\n",
+ " <td>11.12</td>\n",
+ " <td>10.87</td>\n",
+ " <td>11.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015-12-31</th>\n",
+ " <td>3.13</td>\n",
+ " <td>2.95</td>\n",
+ " <td>0.69</td>\n",
+ " <td>2.45</td>\n",
+ " <td>3.15</td>\n",
+ " <td>0.42</td>\n",
+ " <td>1.93</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2016-12-31</th>\n",
+ " <td>9.34</td>\n",
+ " <td>10.65</td>\n",
+ " <td>9.58</td>\n",
+ " <td>11.17</td>\n",
+ " <td>9.18</td>\n",
+ " <td>9.66</td>\n",
+ " <td>8.94</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-12-31</th>\n",
+ " <td>25.07</td>\n",
+ " <td>18.82</td>\n",
+ " <td>25.21</td>\n",
+ " <td>18.61</td>\n",
+ " <td>25.73</td>\n",
+ " <td>25.99</td>\n",
+ " <td>26.08</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " mark_mean_all mark_manager mark_median_all mark_closest_all \\\n",
+ "date \n",
+ "2013-12-31 22.34 21.11 21.78 20.76 \n",
+ "2014-12-31 11.39 12.44 11.79 12.77 \n",
+ "2015-12-31 3.13 2.95 0.69 2.45 \n",
+ "2016-12-31 9.34 10.65 9.58 11.17 \n",
+ "2017-12-31 25.07 18.82 25.21 18.61 \n",
+ "\n",
+ " mark_filtered_mean mark_filtered_median mark_filtered_no_max_min \n",
+ "date \n",
+ "2013-12-31 22.48 22.34 22.22 \n",
+ "2014-12-31 11.12 10.87 11.27 \n",
+ "2015-12-31 3.15 0.42 1.93 \n",
+ "2016-12-31 9.18 9.66 8.94 \n",
+ "2017-12-31 25.73 25.99 26.08 "
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"round(mark.annual_performance(results[1])*100,2)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 60,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>2017-12-31 00:00:00</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>mark_mean_all</th>\n",
+ " <td>4.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mark_manager</th>\n",
+ " <td>0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mark_median_all</th>\n",
+ " <td>2.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mark_closest_all</th>\n",
+ " <td>0.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mark_filtered_mean</th>\n",
+ " <td>4.67</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mark_filtered_median</th>\n",
+ " <td>2.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mark_filtered_no_max_min</th>\n",
+ " <td>3.42</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>endbooknav</th>\n",
+ " <td>100.00</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 2017-12-31\n",
+ "mark_mean_all 4.23\n",
+ "mark_manager 0.00\n",
+ "mark_median_all 2.10\n",
+ "mark_closest_all 0.08\n",
+ "mark_filtered_mean 4.67\n",
+ "mark_filtered_median 2.23\n",
+ "mark_filtered_no_max_min 3.42\n",
+ "endbooknav 100.00"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"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)"
@@ -190,7 +646,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
@@ -199,8 +655,10 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
+ "execution_count": 63,
+ "metadata": {
+ "collapsed": true
+ },
"outputs": [],
"source": [
"#Unrealized MTM Gains/Loss\n",
@@ -212,26 +670,286 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 64,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Loss</th>\n",
+ " <th>Net</th>\n",
+ " <th>Gains</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>unreal mark-to-market</th>\n",
+ " <td>-0.008971</td>\n",
+ " <td>0.024987</td>\n",
+ " <td>0.033958</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Loss Net Gains\n",
+ "unreal mark-to-market -0.008971 0.024987 0.033958"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"breakdown_summary / nav[-1]"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 69,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>id</th>\n",
+ " <th>buysell</th>\n",
+ " <th>faceamount</th>\n",
+ " <th>price</th>\n",
+ " <th>accrued</th>\n",
+ " <th>principal_payment</th>\n",
+ " <th>accrued_payment</th>\n",
+ " <th>mtdbookunrealmtm</th>\n",
+ " <th>mtdbookrealmtm</th>\n",
+ " <th>mtdbookrealincome</th>\n",
+ " <th>mtdbookunrealincome</th>\n",
+ " <th>mtdtotalbookpl</th>\n",
+ " <th>mtdtotalbookpl_at_trade_month</th>\n",
+ " <th>initialinvestment</th>\n",
+ " <th>percent_gain</th>\n",
+ " <th>days_held</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>trade_date</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
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+ " <th></th>\n",
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+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2013-12-31</th>\n",
+ " <td>1.000010e+07</td>\n",
+ " <td>False</td>\n",
+ " <td>3.076243e+06</td>\n",
+ " <td>40.911075</td>\n",
+ " <td>0.059134</td>\n",
+ " <td>296393.020434</td>\n",
+ " <td>594.013248</td>\n",
+ " <td>-1.723066e-13</td>\n",
+ " <td>50699.311944</td>\n",
+ " <td>2374.714583</td>\n",
+ " <td>-131.913472</td>\n",
+ " <td>52942.113056</td>\n",
+ " <td>64936.786389</td>\n",
+ " <td>226686.285580</td>\n",
+ " <td>0.429660</td>\n",
+ " <td>59.458333</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2014-12-31</th>\n",
+ " <td>1.000023e+07</td>\n",
+ " <td>False</td>\n",
+ " <td>3.484545e+06</td>\n",
+ " <td>69.922735</td>\n",
+ " <td>0.068171</td>\n",
+ " <td>451702.362327</td>\n",
+ " <td>381.613812</td>\n",
+ " <td>-1.960784e-04</td>\n",
+ " <td>50983.447255</td>\n",
+ " <td>5650.365294</td>\n",
+ " <td>-115.727059</td>\n",
+ " <td>56518.085294</td>\n",
+ " <td>56557.663529</td>\n",
+ " <td>357199.758132</td>\n",
+ " <td>0.520216</td>\n",
+ " <td>144.294118</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015-12-31</th>\n",
+ " <td>1.042055e+06</td>\n",
+ " <td>False</td>\n",
+ " <td>3.798581e+06</td>\n",
+ " <td>69.527018</td>\n",
+ " <td>0.090903</td>\n",
+ " <td>614305.438948</td>\n",
+ " <td>645.517821</td>\n",
+ " <td>-1.041667e+03</td>\n",
+ " <td>6082.929583</td>\n",
+ " <td>4530.700000</td>\n",
+ " <td>-23.972708</td>\n",
+ " <td>9547.990208</td>\n",
+ " <td>13957.903958</td>\n",
+ " <td>527300.141933</td>\n",
+ " <td>0.299134</td>\n",
+ " <td>129.312500</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2016-12-31</th>\n",
+ " <td>5.408600e+02</td>\n",
+ " <td>False</td>\n",
+ " <td>8.123439e+06</td>\n",
+ " <td>60.971575</td>\n",
+ " <td>0.170445</td>\n",
+ " <td>420749.234186</td>\n",
+ " <td>1646.176886</td>\n",
+ " <td>1.563194e-13</td>\n",
+ " <td>54458.352000</td>\n",
+ " <td>16168.748200</td>\n",
+ " <td>159.319200</td>\n",
+ " <td>70786.419400</td>\n",
+ " <td>44229.832000</td>\n",
+ " <td>394671.938197</td>\n",
+ " <td>0.646151</td>\n",
+ " <td>253.780000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2017-12-31</th>\n",
+ " <td>6.421852e+02</td>\n",
+ " <td>False</td>\n",
+ " <td>6.382841e+06</td>\n",
+ " <td>98.668843</td>\n",
+ " <td>0.117950</td>\n",
+ " <td>742056.024711</td>\n",
+ " <td>1234.290701</td>\n",
+ " <td>-2.196644e+02</td>\n",
+ " <td>226385.825556</td>\n",
+ " <td>16805.782593</td>\n",
+ " <td>404.795556</td>\n",
+ " <td>243376.739259</td>\n",
+ " <td>209233.875556</td>\n",
+ " <td>578843.406367</td>\n",
+ " <td>1.363980</td>\n",
+ " <td>323.777778</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " id buysell faceamount price accrued \\\n",
+ "trade_date \n",
+ "2013-12-31 1.000010e+07 False 3.076243e+06 40.911075 0.059134 \n",
+ "2014-12-31 1.000023e+07 False 3.484545e+06 69.922735 0.068171 \n",
+ "2015-12-31 1.042055e+06 False 3.798581e+06 69.527018 0.090903 \n",
+ "2016-12-31 5.408600e+02 False 8.123439e+06 60.971575 0.170445 \n",
+ "2017-12-31 6.421852e+02 False 6.382841e+06 98.668843 0.117950 \n",
+ "\n",
+ " principal_payment accrued_payment mtdbookunrealmtm \\\n",
+ "trade_date \n",
+ "2013-12-31 296393.020434 594.013248 -1.723066e-13 \n",
+ "2014-12-31 451702.362327 381.613812 -1.960784e-04 \n",
+ "2015-12-31 614305.438948 645.517821 -1.041667e+03 \n",
+ "2016-12-31 420749.234186 1646.176886 1.563194e-13 \n",
+ "2017-12-31 742056.024711 1234.290701 -2.196644e+02 \n",
+ "\n",
+ " mtdbookrealmtm mtdbookrealincome mtdbookunrealincome \\\n",
+ "trade_date \n",
+ "2013-12-31 50699.311944 2374.714583 -131.913472 \n",
+ "2014-12-31 50983.447255 5650.365294 -115.727059 \n",
+ "2015-12-31 6082.929583 4530.700000 -23.972708 \n",
+ "2016-12-31 54458.352000 16168.748200 159.319200 \n",
+ "2017-12-31 226385.825556 16805.782593 404.795556 \n",
+ "\n",
+ " mtdtotalbookpl mtdtotalbookpl_at_trade_month initialinvestment \\\n",
+ "trade_date \n",
+ "2013-12-31 52942.113056 64936.786389 226686.285580 \n",
+ "2014-12-31 56518.085294 56557.663529 357199.758132 \n",
+ "2015-12-31 9547.990208 13957.903958 527300.141933 \n",
+ "2016-12-31 70786.419400 44229.832000 394671.938197 \n",
+ "2017-12-31 243376.739259 209233.875556 578843.406367 \n",
+ "\n",
+ " percent_gain days_held \n",
+ "trade_date \n",
+ "2013-12-31 0.429660 59.458333 \n",
+ "2014-12-31 0.520216 144.294118 \n",
+ "2015-12-31 0.299134 129.312500 \n",
+ "2016-12-31 0.646151 253.780000 \n",
+ "2017-12-31 1.363980 323.777778 "
+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"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"
+ "df[df.days_held.notnull()].groupby(pd.Grouper(freq='A')).mean()"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -250,7 +968,7 @@
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"pygments_lexer": "ipython3",
- "version": "3.6.1"
+ "version": "3.6.4"
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