{
 "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 utils.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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