{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analytics.tranche_basket import DualCorrTranche, TrancheBasket\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ig29 = TrancheBasket(\"IG\", 29, \"5yr\")\n",
    "ig29.tweak()\n",
    "ig29.build_skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phoenix = DualCorrTranche(\"BS\", 4, \"3yr\",attach=15, detach=30, corr_attach=None, corr_detach=None, tranche_running=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phoenix.value_date=datetime.date.today()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phoenix.mark(skew=ig29.skew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phoenix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(phoenix.spread, phoenix.duration, phoenix._index.spread())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Phoenix\n",
    "import pandas as pd\n",
    "date_range = pd.bdate_range(end=datetime.date.today(), periods=20, freq = '3B')\n",
    "df = pd.DataFrame(index = date_range, columns = ['spread', 'duration', 'port_spread'])\n",
    "index = TrancheBasket(\"IG\", 29, \"5yr\")\n",
    "tranche = DualCorrTranche(\"BS\", 4, \"3yr\",attach=15, detach=30, corr_attach=None, corr_detach=None, tranche_running=100)\n",
    "for date in date_range:\n",
    "    index.value_date = date\n",
    "    index.tweak()\n",
    "    index.build_skew()\n",
    "    tranche.value_date = date\n",
    "    tranche.mark(skew=index.skew)\n",
    "    df.loc[date] = [tranche.spread, tranche.duration, tranche._index.spread()[0]/10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['spread', 'port_spread']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyisda.curve import Senior, MM14\n",
    "palma._index[(\"CMACGM\", Senior, MM14)].to_series()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jtd = palma.jump_to_default(ig29.skew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jtd.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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