{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib nbagg\n",
    "\n",
    "import os\n",
    "import sys\n",
    "sys.path.append(os.path.join(os.environ['CODE_DIR'], 'python', 'exploration'))\n",
    "sys.path.append(os.path.join(os.environ['CODE_DIR'], 'python'))\n",
    "import curve_trades as ct\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = 'IG'\n",
    "on_the_run = 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#On the run spread differences\n",
    "spreads_diff = ct.curve_spread_diff(index, on_the_run)\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",
    "ct.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, on_the_run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#on the run theta\n",
    "ct.on_the_run_theta(index, on_the_run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Curve Trade returns\n",
    "ct.curve_returns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct.cross_series_curve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Theta with 3-5-10 Strategy\n",
    "theta_ratio_within_series = ct.ratio_within_series(param = 'duration')\n",
    "ct.curve_3_5_10(theta_ratio_within_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Theta with 5-10 Strategy\n",
    "ct.curve_5_10(theta_ratio_within_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Relative Spread Difference\n",
    "spread_ratio = ct.ratio_within_series(param = 'closespread')\n",
    "spread_ratio.groupby(level = ['date']).last()['closespread_ratio_to_5yr'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ct.curve_model()\n",
    "model_results = ct.curve_model_results(model[0], model[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model[1].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "ax = model_results[['ratio','predicted']].plot()\n",
    "ax.plot(model_results.index, model_results.up_2_stdev, 'r--')\n",
    "ax.plot(model_results.index, model_results.down_2_stdev, 'b--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_results.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Var on current position\n",
    "results = ct.curve_var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results\n",
    "#first number: at 5% flatness\n",
    "#second number: at predicted curve shape\n",
    "#third number: at 5% flatness given duration and spread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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