{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import graphics as g\n",
    "import globeop_reports as go\n",
    "import analytics.curve_trades as ct\n",
    "import datetime\n",
    "\n",
    "from ipywidgets import widgets\n",
    "from pandas.tseries.offsets import BDay\n",
    "from analytics.scenarios import run_curve_scenarios\n",
    "from analytics.curve_trades import curve_spread_diff, spreads_diff_table, theta_matrix_by_series\n",
    "from scipy.optimize import brentq\n",
    "from utils.db import dbengine\n",
    "from analytics import CreditIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = widgets.Dropdown(\n",
    "    options=['IG', 'EU'],\n",
    "    value='IG',\n",
    "    description='Index:',\n",
    "    disabled=False,\n",
    ")\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = w.value\n",
    "report_date = (datetime.date.today() - BDay(1)).date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = w.value\n",
    "series = 34 if index == 'IG' else 32\n",
    "model = ct.curve_model('5yr', '10yr', index=index, max_series=series)\n",
    "model_results = ct.curve_model_results(model[0], model[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_results['diff'] = model_results['predicted'] - model_results['close_spread']\n",
    "model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model[1].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#HY curve trade re-ref calculator - sell protection index_1, buy protection index_2 \n",
    "date = datetime.date.today()\n",
    "index_type = 'HY'\n",
    "series_1 = '29'\n",
    "series_2 = '34'\n",
    "index_1_trade_price = 104.75\n",
    "index_2_trade_price = 104.8\n",
    "reref_2 = 104.71\n",
    "\n",
    "#-----------------------------\n",
    "index_1 = CreditIndex(index_type, series_1, '5yr', date)\n",
    "index_2 = CreditIndex(index_type, series_2, '5yr', date)\n",
    "index_1.price = index_1_trade_price\n",
    "index_2.price = index_2_trade_price\n",
    "index_2.notional = index_1.notional * index_1.risky_annuity/index_2.risky_annuity\n",
    "index_1.direction = 'Seller'\n",
    "index_2.direction = 'Buyer'\n",
    "past_pv = index_2.pv\n",
    "index_2.price = reref_2 \n",
    "index_1.pv = -index_1.pv + (index_2.pv - past_pv)\n",
    " \n",
    "pd.options.display.float_format = '{:,.4f}'.format\n",
    "d = {index_type + series_1: [index_1_trade_price, index_1.price , index_1.notional], index_type + series_2: [index_2_trade_price, reref_2, index_2.notional]}\n",
    "pd.DataFrame(d, index=['original ref', 're-ref', 'notionals'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#On the run spread differences\n",
    "spreads_diff = curve_spread_diff(index, 6)\n",
    "spreads_diff.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Table of Spread Differences, and Z-score of current spread differences\n",
    "spreads_diff_table(spreads_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Theta per unit duration\n",
    "ct.theta_matrix_by_series(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#on the run theta\n",
    "ct.on_the_run_theta(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling = 20\n",
    "years = 5\n",
    "ret = ct.curve_returns(index, rolling, years)\n",
    "if index == 'IG':\n",
    "    ret1 = ct.curve_returns('HY', rolling, years)\n",
    "    suf = ' HY'\n",
    "else:\n",
    "    ret1 = ct.curve_returns('IG', rolling, years)\n",
    "    suf = ' IG'\n",
    "ret = ret.join(ret1['5yr long'], rsuffix=suf)\n",
    "col_name = '5yr long' + suf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Strategy Cumulative Return\n",
    "#Margin Requirement: 3% for IG Long only (33.3x), 25bps for curve (400x)\n",
    "#Assume Margin Requirement of 10% for IG Long only (10x) and size the curve trade margin to\n",
    "lev = 10\n",
    "#1) have the same return volatility or \n",
    "#curve_lev = ret['5yr long'].std()/ret['3-5-10'].std()\n",
    "#2) have the same cumulative return\n",
    "ret['5yr long lev'] = lev * ret['5yr long']\n",
    "def aux(x, ret, col_a, col_b):\n",
    "    ret[col_b + ' lev'] = x * ret[col_b]\n",
    "    cum_ret = (ret+1).cumprod()\n",
    "    return cum_ret[col_a][-1] - cum_ret[col_b + ' lev'][-1]\n",
    "\n",
    "curve_lev = brentq(aux, 0.01, 3 * lev, args=(ret, '5yr long lev', '5-10'))\n",
    "other_lev = brentq(aux, 0.01, 3 * lev, args=(ret, '5yr long lev', col_name))\n",
    "\n",
    "ret['5-10 lev'] = curve_lev * ret['5-10']\n",
    "ret[col_name + ' lev'] = other_lev * ret[col_name]\n",
    "cum_ret = (ret+1).cumprod()\n",
    "cum_ret_ax = cum_ret[['5yr long lev', '5-10 lev', col_name + ' lev']].plot()\n",
    "cum_ret_ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/curve_trades_cum_return.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curve_lev, other_lev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Curve Trade returns\n",
    "ct.curve_returns_stats(ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2016 scenario: max drawdown from the 2015 peak to 2016 trough\n",
    "peak = cum_ret['2015'].max()\n",
    "trough = cum_ret['2016'].min()\n",
    "scenario_2016 = pd.DataFrame({'peak': peak,\n",
    "                        'trough': trough,\n",
    "                        'max_drawdown': (peak - trough)/peak,\n",
    "                        'peak_dates': cum_ret['2015'].idxmax(),\n",
    "                        'trough_dates': cum_ret['2016'].idxmin()})\n",
    "scenario_2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct.cross_series_curve(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Theta with 3-5-10 Strategy\n",
    "df = ct.ratio_within_series(param='duration')\n",
    "s = - df.theta['3yr'] / df.duration_ratio_to_5yr['3yr'] \\\n",
    "    + 2 * df.theta['5yr'] \\\n",
    "    - df.theta['10yr'] / df.duration_ratio_to_5yr['10yr']\n",
    "s.dropna().unstack(-1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Theta with 5-10 Strategy: buy sell 5y, buy 10y\n",
    "s = df.theta['5yr'] - df.theta['10yr'] / df.duration_ratio_to_5yr['10yr']\n",
    "s.dropna().unstack(-1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Relative Spread Difference\n",
    "spread_ratio = ct.ratio_within_series(param = 'close_spread')\n",
    "spread_ratio.groupby(level = ['date']).last()['close_spread_ratio_to_5yr'].plot()"
   ]
  },
  {
   "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": [
    "#Do the same regression for Itrxx\n",
    "model = ct.curve_model('5yr', '10yr', index='EU')\n",
    "model_results = ct.curve_model_results(model[0], model[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_results['diff'] = model_results['predicted'] - model_results['close_spread']\n",
    "model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ct.forward_spread(report_date, index)\n",
    "df.plot()\n",
    "plt.ylabel('spread')\n",
    "plt.xlabel('forward spread start date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ct.spot_forward(index)\n",
    "df = df.rename(columns={'1yr': 'Spot Spread - 1 Year Forward', 'current': 'Spot Spread - Today'})\n",
    "ax = df.plot(title = 'Credit Curve Roll Down')\n",
    "plt.ylabel('spread (bps)')\n",
    "ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/curve_trades_roll_down.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "portf = ct.curve_pos(report_date, index)\n",
    "shock_min = -.5\n",
    "shock_max = .8\n",
    "spread_shock = np.arange(shock_min, shock_max, 0.05)\n",
    "sql_string = \"SELECT closespread FROM index_quotes where index = %s and series = %s and tenor = %s and date = %s\"\n",
    "spread_df = pd.read_sql_query(sql_string, dbengine('serenitasdb'),\n",
    "                              params=[index, ct.on_the_run(index), '5yr', report_date])\n",
    "spread_range = np.round((1+ spread_shock) * spread_df.iloc[0][0], 2)\n",
    "closest_mat = min([t.end_date for t in portf.trades])\n",
    "date_range = pd.bdate_range(report_date, min(closest_mat, (report_date + 180* pd.offsets.DateOffset()).date()), freq='5B')\n",
    "curve_per = np.arange(.01, .99, .1)\n",
    "\n",
    "df = run_curve_scenarios(portf, spread_range, date_range, curve_per)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot steepness scenario at current spread\n",
    "#df_plot = df.set_index(['spread', 'curve_per'], append=True)\n",
    "#df_plot = df_plot.xs(round(spread_df.iloc[0][0], 2), level = 'spread')\n",
    "#df_plot.name = 'pnl'\n",
    "#g.plot_color_map(df_plot, spread_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the shape of the scenario that was run above\n",
    "ct.plot_curve_shape(report_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scenario Anslysis on current position\n",
    "#curve_positions = ct.curve_pos(report_date, index)\n",
    "#origpv = curve_positions.pv\n",
    "#flat_curve = ct.curve_shape(report_date, index, percentile = .05)\n",
    "#for ind in curve_positions.indices:\n",
    "#    ind.spread = flat_curve((pd.to_datetime(ind.end_date) - report_date).days/365)\n",
    "#PNL in flattening to a 5% case\n",
    "#curve_positions.pv - origpv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curve_positions = ct.curve_pos(report_date, index)\n",
    "df = ct.pos_pnl_abs(curve_positions, report_date)\n",
    "navs = go.get_net_navs()\n",
    "df_plot = df.pnl/navs.iloc[-1].endbooknav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df_plot.index, df_plot.values)\n",
    "ax.set(xlabel='date', ylabel='% of NAV',\n",
    "       title='PNL impact from spread curve scenario')\n",
    "plt.xticks(rotation=90)\n",
    "y_ticks = ax.get_yticks()\n",
    "ax.set_yticklabels(['{:.2f}%'.format(y*100) for y in y_ticks])\n",
    "plt.tight_layout()\n",
    "#ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/curve_trades.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Historical PNL in a 5% case\n",
    "df.pnl.quantile(.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scen_table = ct.curve_scen_table(curve_positions)\n",
    "scen_table.pnl = scen_table.pnl/navs.iloc[-1].endbooknav *100\n",
    "scen_table.pivot(index='tighter', columns='wider')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from exploration.VaR import hist_var\n",
    "IG = ct.curve_pos(report_date, 'IG')\n",
    "ITRX = ct.curve_pos(report_date, 'EU')\n",
    "VaR = hist_var(IG, 'IG') + hist_var(ITRX, 'EU')"
   ]
  },
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