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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"import pandas.tseries.offsets as off\n",
"import globeop_reports as go\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"from db import dbengine\n",
"from yieldcurve import YC\n",
"from quantlib.termstructures.yield_term_structure import YieldTermStructure\n",
"\n",
"engine = dbengine('dawndb')\n",
"Sengine = dbengine('serenitasdb')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Plot this month's PNL\n",
"pnl_alloc_last_month = pnl_alloc.xs(report_date)\n",
"ax = pnl_alloc_last_month['strat_return'].plot(kind='bar', figsize = (6,6), width = .35)\n",
"ax.set_xlabel('Strategy')\n",
"ax.set_ylabel('Return (%)')\n",
"x_ticks = ax.get_xticks()\n",
"y_ticks = ax.get_yticks()\n",
"ax.set_yticklabels(['{:.2f}%'.format(y*100) for y in y_ticks])\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Pnl through time\n",
"#pnl_alloc_sum = pnl_alloc['mtdtotalbookpl']/ pnl_alloc['mtdtotalbookpl'].groupby(['date']).sum()\n",
"#pnl_alloc_sum['strat_return'].unstack().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Capital Allocation - Find the strategies that are not defined: undefined needs to be mapped in strat_map\n",
"port = go.get_portfolio().reset_index()\n",
"cap_alloc = port.reset_index().merge(strats, on=['strat', 'custacctname'], how='left')\n",
"undefined = cap_alloc[cap_alloc.pnl.isna()].groupby(['strat', 'custacctname']).last()\n",
"alloc1 = cap_alloc[cap_alloc.periodenddate == report_date].groupby(['capital']).sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# create piechart and add a circle at the center\n",
"alloc1['percentage'] = alloc1['endbooknav']/alloc1['endbooknav'].sum()\n",
"ax = alloc1[alloc1>0]['percentage'].plot(kind='pie', figsize=(8,4), autopct='%1.1f%%', pctdistance=1.25, labeldistance=1.5)\n",
"ax.add_artist(plt.Circle((0,0), 0.7, color='white'))\n",
"ax.axis('equal')\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Number of bond positions by strategy by month\n",
"df = go.get_portfolio()\n",
"df = df[(df.custacctname == 'V0NSCLMAMB') &\n",
" ~(df.invid.isin(['USD', 'CAD', 'EUR'])) & (df.endqty > 0)]\n",
"df = df.groupby(pd.Grouper(freq='M'), group_keys=False).apply(lambda df: df.loc[df.index[-1]])\n",
"num_bond_pos = df.groupby(['periodenddate', 'port']).identifier.nunique().unstack()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Number of bond trades by direction by month\n",
"sql_string = \"SELECT * FROM bonds\"\n",
"df = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['trade_date'],\n",
" index_col=['trade_date'])\n",
"df = df.groupby([pd.Grouper(freq='M'), 'buysell'], group_keys=False).identifier.count().unstack()\n",
"idx = pd.date_range(df.index[0], df.index[-1], freq = 'M')\n",
"num_bond_trades = df.reindex(idx, fill_value = 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#capital allocation across time\n",
"cap_alloc_time = cap_alloc.groupby(['periodenddate','capital']).sum()\n",
"cap_alloc_time = cap_alloc_time.reset_index('capital').groupby(pd.Grouper(freq='M'), group_keys=False).apply(lambda df: df.loc[df.index[-1]])\n",
"cap_alloc_time['perc'] = cap_alloc_time['endbooknav'].groupby('periodenddate').apply(lambda x: x/x.sum())\n",
"cap_alloc_time = cap_alloc_time.set_index('capital', append=True)['perc'].unstack()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = cap_alloc_time.plot.bar(stacked=True, legend=False, figsize=(10,6))\n",
"\n",
"#Format Y Axis\n",
"vals = ax.get_yticks()\n",
"ax.set_yticklabels(['{:3.0f}%'.format(x*100) for x in vals])\n",
"\n",
"#Format X Axis\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",
"ax.xaxis.set_major_formatter(plt.FixedFormatter(df.index.to_series().dt.strftime(\"%b %Y\")))\n",
"ax.xaxis.set_label_text(\"\")\n",
"lgd = ax.legend(loc='lower center', bbox_to_anchor=(0.50, -0.6), ncol=4)\n",
"plt.tight_layout()\n",
"ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/cap_alloc_1.png\", bbox_extra_artists=(lgd,), bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Calculate Interests allocation \n",
"sql_string = \"select periodenddate, strat, sum(endqty) as bal, counterparty \" \\\n",
" \"from valuation_reports where invid = 'USDLOAN' and \" \\\n",
" \"extract(month from periodenddate) = %s and \" \\\n",
" \"extract(year from periodenddate) = %s \" \\\n",
" \"group by periodenddate, strat, counterparty \" \\\n",
" \"order by periodenddate desc\"\n",
"df = pd.read_sql_query(sql_string, dbengine('dawndb'), \n",
" parse_dates=['periodenddate'],\n",
" index_col=['strat', 'counterparty'],\n",
" params=[report_date.month, report_date.year])\n",
"df['day_frac'] = -(df.groupby(level=['strat','counterparty'])['periodenddate'].transform(lambda s:\n",
" s.diff().astype('timedelta64[D]') / 360)).astype(float)\n",
"df = df.fillna(0)\n",
"r = {}\n",
"yc = YieldTermStructure()\n",
"for t in df['periodenddate'].unique():\n",
" yc.link_to(YC(evaluation_date=pd.Timestamp(t)))\n",
" r[pd.Timestamp(t)] = (float(yc.zero_rate(.083333)))\n",
"rates = pd.DataFrame.from_dict(r, orient='index')\n",
"df = df.reset_index().set_index('periodenddate', drop=False).join(rates)\n",
"df = df.rename(columns={0: 'rate'})\n",
"df = df.set_index(['strat','counterparty'], append=True)\n",
"df['interest'] = df['rate'] * df['day_frac'] * df['bal']\n",
"interests = df['interest'].groupby(level=['counterparty','strat']).sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"display_name": "Python 3",
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"file_extension": ".py",
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|
