{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import datetime\n",
    "import exploration.dispersion as disp\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import analytics.tranche_data as tdata\n",
    "\n",
    "from analytics.basket_index import MarkitBasketIndex\n",
    "from analytics import on_the_run\n",
    "from statsmodels.graphics.regressionplots import plot_fit\n",
    "from scipy.special import logit, expit\n",
    "from pygam import LinearGAM, s, f, GAM\n",
    "from utils.db import dbengine, dbconn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "value_date = (datetime.datetime.today() - pd.offsets.BDay(1)).date()\n",
    "start = (datetime.datetime.today() - pd.offsets.BDay(1) * 365 *4).date()\n",
    "#end = (start + pd.offsets.BDay(1) * 365).date()\n",
    "end = datetime.datetime.today()\n",
    "gini_model, gini_results = {}, {}\n",
    "conn = dbconn(\"serenitasdb\")\n",
    "conn.autocommit = True\n",
    "for index_type in ['HY', 'IG', 'EU', 'XO']:\n",
    "    risk = disp.get_tranche_data(dbconn(\"serenitasdb\"), index_type)\n",
    "    risk = risk[risk.index_duration > .5] #filter out the short duration ones\n",
    "    gini_results[index_type], gini_model[index_type] = disp.create_models_v2(conn, risk)\n",
    "    fitted = gini_model[index_type].fit()\n",
    "    w = 1/(expit(fitted.fittedvalues + fitted.resid) -expit(fitted.fittedvalues))**2\n",
    "    gini_results[index_type], gini_model[index_type] = disp.create_models_v2(conn, risk, w)\n",
    "gini_model['HY'].fit().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fieldlist = ['exp_percentage','dispersion','gini','tranche_loss_per','mispricing']\n",
    "for index_type in ['HY', 'IG', 'EU', 'XO']:\n",
    "    gini_results[index_type][fieldlist].to_csv('/home/serenitas/edwin/' + index_type + '_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run a particular gini scenario\n",
    "scenario = gini_results['HY'].loc(axis=0)[value_date,'HY',33,:,'5yr',0]\n",
    "scenario['gini'].iloc[0] = .7\n",
    "scenario_disp = expit(gini_model['HY'].fit().predict(scenario))\n",
    "mispricing = scenario['tranche_loss_per'] - scenario_disp\n",
    "mispricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the residuals\n",
    "fitted = gini_model['HY'].fit()\n",
    "plt.figure(figsize=(8,5))\n",
    "p=plt.scatter(x=expit(fitted.fittedvalues),y=expit(fitted.fittedvalues + fitted.resid) -expit(fitted.fittedvalues),edgecolor='k')\n",
    "xmin=min(expit(fitted.fittedvalues))\n",
    "xmax = max(expit(fitted.fittedvalues))\n",
    "plt.hlines(y=0,xmin=xmin*0.9,xmax=xmax*1.1,color='red',linestyle='--',lw=3)\n",
    "plt.xlabel(\"Fitted values\",fontsize=15)\n",
    "plt.ylabel(\"Residuals\",fontsize=15)\n",
    "plt.title(\"Fitted vs. residuals plot\",fontsize=18)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "value_date = (datetime.datetime.today() - pd.offsets.BDay(1)).date()\n",
    "start = (datetime.datetime.today() - pd.offsets.BDay(1) * 365 *4).date()\n",
    "#end = (start + pd.offsets.BDay(1) * 365).date()\n",
    "end = datetime.datetime.today()\n",
    "index_type = 'IG'\n",
    "serenitasconn = dbconn(\"serenitasdb\")\n",
    "serenitasconn.autocommit = True\n",
    "risk = disp.get_tranche_data(serenitasconn, index_type)\n",
    "train_data = risk[start: end]\n",
    "gini_calc, gini_model = disp.create_models(serenitasconn, train_data)\n",
    "gini_model.fit().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compare to realized delta-adjusted return\n",
    "tranche_returns = tdata.get_tranche_quotes(index=index_type)\n",
    "tranche_returns = tdata.tranche_returns(df=tranche_returns)\n",
    "attach = 0\n",
    "t = tranche_returns['delhedged_return'].reset_index(['index', 'tenor'], drop=True).xs(attach, level='attach')\n",
    "temp={}\n",
    "for i,g in t.groupby('series'):\n",
    "    temp[i] = (g.dropna()+1).cumprod()\n",
    "t = pd.concat(temp).reset_index(0, drop=True)\n",
    "t.unstack(level='series').plot()\n",
    "tranche_returns.to_csv('/home/serenitas/edwin/Python/temp3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "attach = 0\n",
    "\n",
    "returns = tranche_returns.xs(['HY', 29, '5yr', attach], level = ['index', 'series', 'tenor','attach'])['delhedged_return']\n",
    "model = gini_calc.xs(['HY', 29, '5yr', attach], level = ['index', 'series', 'tenor','attach'])['mispricing']\n",
    "returns = pd.merge(returns, model, left_index=True, right_index=True)\n",
    "model_verification = smf.ols(\"delhedged_return ~ mispricing \", data=returns).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tranche_returns.xs(29, level='series').unstack(level='attach').to_csv('/home/serenitas/edwin/Python/temp1.csv')"
   ]
  }
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