13_dx_quite_complex_portfolios.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"45%\" align=\"right\" border=\"4\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quite Complex Portfolios "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This part illustrates that you can **model, value and risk manage quite complex derivatives portfolios** with DX Analytics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dx import *\n",
    "import time\n",
    "from pylab import plt\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline\n",
    "np.random.seed(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Risk Factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example is based on a **multiple, correlated risk factors**, all (for the ease of exposition) `geometric_brownian_motion` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mer = market_environment(name='me', pricing_date=dt.datetime(2015, 1, 1))\n",
    "mer.add_constant('initial_value', 0.01)\n",
    "mer.add_constant('volatility', 0.1)\n",
    "mer.add_constant('kappa', 2.0)\n",
    "mer.add_constant('theta', 0.05)\n",
    "mer.add_constant('paths', 100) # dummy\n",
    "mer.add_constant('frequency', 'M') # dummy\n",
    "mer.add_constant('starting_date', mer.pricing_date)\n",
    "mer.add_constant('final_date', dt.datetime(2015, 12, 31)) # dummy\n",
    "ssr = stochastic_short_rate('ssr', mer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(ssr.process.time_grid, ssr.process.get_instrument_values()[:, :10]);\n",
    "plt.gcf().autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# market environments\n",
    "me = market_environment('gbm', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# geometric Brownian motion\n",
    "me.add_constant('initial_value', 36.)\n",
    "me.add_constant('volatility', 0.2) \n",
    "me.add_constant('currency', 'EUR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# jump diffusion\n",
    "me.add_constant('lambda', 0.4)\n",
    "me.add_constant('mu', -0.4) \n",
    "me.add_constant('delta', 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stochastic volatility\n",
    "me.add_constant('kappa', 2.0)\n",
    "me.add_constant('theta', 0.3) \n",
    "me.add_constant('vol_vol', 0.5)\n",
    "me.add_constant('rho', -0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using 2,500 paths and monthly discretization for the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# valuation environment\n",
    "val_env = market_environment('val_env', dt.datetime(2015, 1, 1))\n",
    "val_env.add_constant('paths', 1000)\n",
    "val_env.add_constant('frequency', 'M')\n",
    "val_env.add_curve('discount_curve', ssr)\n",
    "val_env.add_constant('starting_date', dt.datetime(2015, 1, 1))\n",
    "val_env.add_constant('final_date', dt.datetime(2016, 12, 31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add valuation environment to market environments\n",
    "me.add_environment(val_env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "no = 50  # 50 different risk factors in total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_factors = {}\n",
    "for rf in range(no):\n",
    "    # random model choice\n",
    "    sm = np.random.choice(['gbm', 'jd', 'sv'])\n",
    "    key = '%3d_%s' % (rf + 1, sm)\n",
    "    risk_factors[key] = market_environment(key, me.pricing_date)\n",
    "    risk_factors[key].add_environment(me)\n",
    "    # random initial_value\n",
    "    risk_factors[key].add_constant('initial_value',\n",
    "                                    np.random.random() * 40. + 20.)\n",
    "    # radnom volatility\n",
    "    risk_factors[key].add_constant('volatility',\n",
    "                                    np.random.random() * 0.6 + 0.05)\n",
    "    # the simulation model to choose\n",
    "    risk_factors[key].add_constant('model', sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['  1_sv', '  2_gbm', 0.1],\n",
       " ['  1_sv', '  3_gbm', -0.1],\n",
       " ['  1_sv', '  4_gbm', -0.1]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations = []\n",
    "keys = sorted(risk_factors.keys())\n",
    "for key in keys[1:]:\n",
    "    correlations.append([keys[0], key, np.random.choice([-0.1, 0.0, 0.1])])\n",
    "correlations[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Options Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We model a certain number of **derivative instruments** with the following major assumptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "me_option = market_environment('option', me.pricing_date)\n",
    "# choose from a set of maturity dates (month ends)\n",
    "maturities = pd.date_range(start=me.pricing_date,\n",
    "                           end=val_env.get_constant('final_date'),\n",
    "                           freq='M').to_pydatetime()\n",
    "me_option.add_constant('maturity', np.random.choice(maturities))\n",
    "me_option.add_constant('currency', 'EUR')\n",
    "me_option.add_environment(val_env)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Portfolio Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `derivatives_portfolio` object we compose consists of **multiple derivatives positions**. Each option differs with respect to the strike and the risk factor it is dependent on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5 times the number of risk factors\n",
    "# as portfolio positions/instruments\n",
    "pos = 5 * no              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "positions = {}\n",
    "for i in range(pos):\n",
    "    ot = np.random.choice(['am_put', 'eur_call'])\n",
    "    if ot == 'am_put':\n",
    "        otype = 'American single'\n",
    "        payoff_func = 'np.maximum(%5.3f - instrument_values, 0)'\n",
    "    else:\n",
    "        otype = 'European single'\n",
    "        payoff_func = 'np.maximum(maturity_value - %5.3f, 0)'\n",
    "    # random strike\n",
    "    strike = np.random.randint(36, 40)\n",
    "    underlying = sorted(risk_factors.keys())[(i + no) % no]\n",
    "    name = '%d_option_pos_%d' % (i, strike)\n",
    "    positions[name] = derivatives_position(\n",
    "                        name=name,\n",
    "                        quantity=np.random.randint(1, 10),\n",
    "                        underlyings=[underlying],\n",
    "                        mar_env=me_option,\n",
    "                        otype=otype,\n",
    "        payoff_func=payoff_func % strike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of derivatives positions\n",
    "len(positions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Portfolio Valuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, the derivatives portfolio with **sequential valuation**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "port = derivatives_portfolio(\n",
    "                name='portfolio',\n",
    "                positions=positions,\n",
    "                val_env=val_env,\n",
    "                risk_factors=risk_factors,\n",
    "                correlations=correlations,\n",
    "                parallel=False)  # sequential calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.1       ,  0.99498744,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.1       ,  0.01005038,  0.99493668, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [-0.1       ,  0.01005038, -0.01015242, ...,  0.99239533,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         1.        ,  0.        ],\n",
       "       [ 0.1       , -0.01005038,  0.01015242, ...,  0.01526762,\n",
       "         0.        ,  0.99227788]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port.val_env.get_list('cholesky_matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The call of the `get_values` method to **value all instruments**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Totals\n",
      " pos_value    10400.2920\n",
      "pos_delta      116.0584\n",
      "pos_vega      8477.9800\n",
      "dtype: float64\n",
      "CPU times: user 47.7 s, sys: 890 ms, total: 48.6 s\n",
      "Wall time: 12.4 s\n"
     ]
    }
   ],
   "source": [
    "%time res = port.get_statistics(fixed_seed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>otype</th>\n",
       "      <th>risk_facts</th>\n",
       "      <th>value</th>\n",
       "      <th>currency</th>\n",
       "      <th>pos_value</th>\n",
       "      <th>pos_delta</th>\n",
       "      <th>pos_vega</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>position</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0_option_pos_38</th>\n",
       "      <td>0_option_pos_38</td>\n",
       "      <td>6</td>\n",
       "      <td>European single</td>\n",
       "      <td>[  1_sv]</td>\n",
       "      <td>7.263</td>\n",
       "      <td>EUR</td>\n",
       "      <td>43.578</td>\n",
       "      <td>3.6036</td>\n",
       "      <td>22.7046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_option_pos_39</th>\n",
       "      <td>1_option_pos_39</td>\n",
       "      <td>8</td>\n",
       "      <td>American single</td>\n",
       "      <td>[  2_gbm]</td>\n",
       "      <td>5.398</td>\n",
       "      <td>EUR</td>\n",
       "      <td>43.184</td>\n",
       "      <td>-3.9168</td>\n",
       "      <td>124.5688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_option_pos_37</th>\n",
       "      <td>2_option_pos_37</td>\n",
       "      <td>3</td>\n",
       "      <td>European single</td>\n",
       "      <td>[  3_gbm]</td>\n",
       "      <td>3.306</td>\n",
       "      <td>EUR</td>\n",
       "      <td>9.918</td>\n",
       "      <td>1.9935</td>\n",
       "      <td>42.8949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3_option_pos_37</th>\n",
       "      <td>3_option_pos_37</td>\n",
       "      <td>8</td>\n",
       "      <td>American single</td>\n",
       "      <td>[  4_gbm]</td>\n",
       "      <td>9.097</td>\n",
       "      <td>EUR</td>\n",
       "      <td>72.776</td>\n",
       "      <td>-8.0000</td>\n",
       "      <td>5.3336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_option_pos_39</th>\n",
       "      <td>4_option_pos_39</td>\n",
       "      <td>1</td>\n",
       "      <td>European single</td>\n",
       "      <td>[  5_gbm]</td>\n",
       "      <td>17.664</td>\n",
       "      <td>EUR</td>\n",
       "      <td>17.664</td>\n",
       "      <td>0.7531</td>\n",
       "      <td>13.4445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_option_pos_37</th>\n",
       "      <td>5_option_pos_37</td>\n",
       "      <td>3</td>\n",
       "      <td>American single</td>\n",
       "      <td>[  6_jd]</td>\n",
       "      <td>13.354</td>\n",
       "      <td>EUR</td>\n",
       "      <td>40.062</td>\n",
       "      <td>-3.0000</td>\n",
       "      <td>21.9111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6_option_pos_36</th>\n",
       "      <td>6_option_pos_36</td>\n",
       "      <td>9</td>\n",
       "      <td>European single</td>\n",
       "      <td>[  7_jd]</td>\n",
       "      <td>17.651</td>\n",
       "      <td>EUR</td>\n",
       "      <td>158.859</td>\n",
       "      <td>7.9128</td>\n",
       "      <td>41.3010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7_option_pos_39</th>\n",
       "      <td>7_option_pos_39</td>\n",
       "      <td>3</td>\n",
       "      <td>American single</td>\n",
       "      <td>[  8_gbm]</td>\n",
       "      <td>9.538</td>\n",
       "      <td>EUR</td>\n",
       "      <td>28.614</td>\n",
       "      <td>-2.9169</td>\n",
       "      <td>-4.6260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8_option_pos_36</th>\n",
       "      <td>8_option_pos_36</td>\n",
       "      <td>4</td>\n",
       "      <td>American single</td>\n",
       "      <td>[  9_sv]</td>\n",
       "      <td>2.584</td>\n",
       "      <td>EUR</td>\n",
       "      <td>10.336</td>\n",
       "      <td>-0.5512</td>\n",
       "      <td>5.1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9_option_pos_39</th>\n",
       "      <td>9_option_pos_39</td>\n",
       "      <td>7</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 10_sv]</td>\n",
       "      <td>9.945</td>\n",
       "      <td>EUR</td>\n",
       "      <td>69.615</td>\n",
       "      <td>-3.2571</td>\n",
       "      <td>-1.2845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_option_pos_37</th>\n",
       "      <td>10_option_pos_37</td>\n",
       "      <td>9</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 11_jd]</td>\n",
       "      <td>13.708</td>\n",
       "      <td>EUR</td>\n",
       "      <td>123.372</td>\n",
       "      <td>7.4493</td>\n",
       "      <td>70.9308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11_option_pos_38</th>\n",
       "      <td>11_option_pos_38</td>\n",
       "      <td>1</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 12_jd]</td>\n",
       "      <td>1.247</td>\n",
       "      <td>EUR</td>\n",
       "      <td>1.247</td>\n",
       "      <td>0.2810</td>\n",
       "      <td>8.4409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12_option_pos_38</th>\n",
       "      <td>12_option_pos_38</td>\n",
       "      <td>2</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 13_sv]</td>\n",
       "      <td>12.707</td>\n",
       "      <td>EUR</td>\n",
       "      <td>25.414</td>\n",
       "      <td>-1.5630</td>\n",
       "      <td>-11.0008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13_option_pos_37</th>\n",
       "      <td>13_option_pos_37</td>\n",
       "      <td>9</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 14_jd]</td>\n",
       "      <td>21.096</td>\n",
       "      <td>EUR</td>\n",
       "      <td>189.864</td>\n",
       "      <td>7.2999</td>\n",
       "      <td>71.2782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14_option_pos_36</th>\n",
       "      <td>14_option_pos_36</td>\n",
       "      <td>3</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 15_gbm]</td>\n",
       "      <td>7.734</td>\n",
       "      <td>EUR</td>\n",
       "      <td>23.202</td>\n",
       "      <td>-1.1520</td>\n",
       "      <td>41.3682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15_option_pos_37</th>\n",
       "      <td>15_option_pos_37</td>\n",
       "      <td>5</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 16_gbm]</td>\n",
       "      <td>1.636</td>\n",
       "      <td>EUR</td>\n",
       "      <td>8.180</td>\n",
       "      <td>1.5570</td>\n",
       "      <td>51.8430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16_option_pos_38</th>\n",
       "      <td>16_option_pos_38</td>\n",
       "      <td>9</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 17_jd]</td>\n",
       "      <td>9.175</td>\n",
       "      <td>EUR</td>\n",
       "      <td>82.575</td>\n",
       "      <td>5.5710</td>\n",
       "      <td>125.4699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17_option_pos_37</th>\n",
       "      <td>17_option_pos_37</td>\n",
       "      <td>3</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 18_sv]</td>\n",
       "      <td>14.931</td>\n",
       "      <td>EUR</td>\n",
       "      <td>44.793</td>\n",
       "      <td>2.3937</td>\n",
       "      <td>2.6664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18_option_pos_39</th>\n",
       "      <td>18_option_pos_39</td>\n",
       "      <td>6</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 19_gbm]</td>\n",
       "      <td>15.695</td>\n",
       "      <td>EUR</td>\n",
       "      <td>94.170</td>\n",
       "      <td>4.2756</td>\n",
       "      <td>87.2814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19_option_pos_36</th>\n",
       "      <td>19_option_pos_36</td>\n",
       "      <td>8</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 20_jd]</td>\n",
       "      <td>12.461</td>\n",
       "      <td>EUR</td>\n",
       "      <td>99.688</td>\n",
       "      <td>-8.0000</td>\n",
       "      <td>-12.2504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20_option_pos_39</th>\n",
       "      <td>20_option_pos_39</td>\n",
       "      <td>8</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 21_sv]</td>\n",
       "      <td>5.136</td>\n",
       "      <td>EUR</td>\n",
       "      <td>41.088</td>\n",
       "      <td>-2.0192</td>\n",
       "      <td>61.7368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21_option_pos_37</th>\n",
       "      <td>21_option_pos_37</td>\n",
       "      <td>2</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 22_sv]</td>\n",
       "      <td>6.541</td>\n",
       "      <td>EUR</td>\n",
       "      <td>13.082</td>\n",
       "      <td>-0.9288</td>\n",
       "      <td>2.7770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22_option_pos_38</th>\n",
       "      <td>22_option_pos_38</td>\n",
       "      <td>8</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 23_jd]</td>\n",
       "      <td>1.238</td>\n",
       "      <td>EUR</td>\n",
       "      <td>9.904</td>\n",
       "      <td>-0.3840</td>\n",
       "      <td>62.1928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23_option_pos_39</th>\n",
       "      <td>23_option_pos_39</td>\n",
       "      <td>6</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 24_jd]</td>\n",
       "      <td>17.719</td>\n",
       "      <td>EUR</td>\n",
       "      <td>106.314</td>\n",
       "      <td>-4.3524</td>\n",
       "      <td>58.4406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24_option_pos_38</th>\n",
       "      <td>24_option_pos_38</td>\n",
       "      <td>4</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 25_jd]</td>\n",
       "      <td>11.286</td>\n",
       "      <td>EUR</td>\n",
       "      <td>45.144</td>\n",
       "      <td>3.2148</td>\n",
       "      <td>22.3364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_option_pos_36</th>\n",
       "      <td>25_option_pos_36</td>\n",
       "      <td>1</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 26_gbm]</td>\n",
       "      <td>25.986</td>\n",
       "      <td>EUR</td>\n",
       "      <td>25.986</td>\n",
       "      <td>0.8555</td>\n",
       "      <td>11.0891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26_option_pos_36</th>\n",
       "      <td>26_option_pos_36</td>\n",
       "      <td>4</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 27_jd]</td>\n",
       "      <td>7.186</td>\n",
       "      <td>EUR</td>\n",
       "      <td>28.744</td>\n",
       "      <td>3.0756</td>\n",
       "      <td>17.4980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27_option_pos_38</th>\n",
       "      <td>27_option_pos_38</td>\n",
       "      <td>7</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 28_sv]</td>\n",
       "      <td>0.907</td>\n",
       "      <td>EUR</td>\n",
       "      <td>6.349</td>\n",
       "      <td>1.5309</td>\n",
       "      <td>13.2580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28_option_pos_37</th>\n",
       "      <td>28_option_pos_37</td>\n",
       "      <td>8</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 29_jd]</td>\n",
       "      <td>25.429</td>\n",
       "      <td>EUR</td>\n",
       "      <td>203.432</td>\n",
       "      <td>6.8888</td>\n",
       "      <td>103.1016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29_option_pos_37</th>\n",
       "      <td>29_option_pos_37</td>\n",
       "      <td>5</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 30_gbm]</td>\n",
       "      <td>12.805</td>\n",
       "      <td>EUR</td>\n",
       "      <td>64.025</td>\n",
       "      <td>3.4515</td>\n",
       "      <td>70.5780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220_option_pos_38</th>\n",
       "      <td>220_option_pos_38</td>\n",
       "      <td>8</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 21_sv]</td>\n",
       "      <td>15.606</td>\n",
       "      <td>EUR</td>\n",
       "      <td>124.848</td>\n",
       "      <td>6.3080</td>\n",
       "      <td>27.5824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221_option_pos_39</th>\n",
       "      <td>221_option_pos_39</td>\n",
       "      <td>4</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 22_sv]</td>\n",
       "      <td>7.654</td>\n",
       "      <td>EUR</td>\n",
       "      <td>30.616</td>\n",
       "      <td>-1.6732</td>\n",
       "      <td>18.2884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222_option_pos_38</th>\n",
       "      <td>222_option_pos_38</td>\n",
       "      <td>4</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 23_jd]</td>\n",
       "      <td>23.064</td>\n",
       "      <td>EUR</td>\n",
       "      <td>92.256</td>\n",
       "      <td>3.5816</td>\n",
       "      <td>10.1348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223_option_pos_37</th>\n",
       "      <td>223_option_pos_37</td>\n",
       "      <td>5</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 24_jd]</td>\n",
       "      <td>15.969</td>\n",
       "      <td>EUR</td>\n",
       "      <td>79.845</td>\n",
       "      <td>-3.1480</td>\n",
       "      <td>55.6190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224_option_pos_37</th>\n",
       "      <td>224_option_pos_37</td>\n",
       "      <td>3</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 25_jd]</td>\n",
       "      <td>2.427</td>\n",
       "      <td>EUR</td>\n",
       "      <td>7.281</td>\n",
       "      <td>-0.4377</td>\n",
       "      <td>23.4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225_option_pos_39</th>\n",
       "      <td>225_option_pos_39</td>\n",
       "      <td>2</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 26_gbm]</td>\n",
       "      <td>23.994</td>\n",
       "      <td>EUR</td>\n",
       "      <td>47.988</td>\n",
       "      <td>1.6648</td>\n",
       "      <td>25.6842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226_option_pos_39</th>\n",
       "      <td>226_option_pos_39</td>\n",
       "      <td>5</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 27_jd]</td>\n",
       "      <td>5.369</td>\n",
       "      <td>EUR</td>\n",
       "      <td>26.845</td>\n",
       "      <td>3.5640</td>\n",
       "      <td>32.4455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227_option_pos_37</th>\n",
       "      <td>227_option_pos_37</td>\n",
       "      <td>1</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 28_sv]</td>\n",
       "      <td>16.571</td>\n",
       "      <td>EUR</td>\n",
       "      <td>16.571</td>\n",
       "      <td>-0.8666</td>\n",
       "      <td>-8.6498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228_option_pos_38</th>\n",
       "      <td>228_option_pos_38</td>\n",
       "      <td>3</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 29_jd]</td>\n",
       "      <td>24.760</td>\n",
       "      <td>EUR</td>\n",
       "      <td>74.280</td>\n",
       "      <td>2.5620</td>\n",
       "      <td>40.4946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229_option_pos_38</th>\n",
       "      <td>229_option_pos_38</td>\n",
       "      <td>6</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 30_gbm]</td>\n",
       "      <td>12.399</td>\n",
       "      <td>EUR</td>\n",
       "      <td>74.394</td>\n",
       "      <td>4.0752</td>\n",
       "      <td>86.0244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230_option_pos_36</th>\n",
       "      <td>230_option_pos_36</td>\n",
       "      <td>7</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 31_sv]</td>\n",
       "      <td>13.602</td>\n",
       "      <td>EUR</td>\n",
       "      <td>95.214</td>\n",
       "      <td>-5.3767</td>\n",
       "      <td>26.1534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231_option_pos_38</th>\n",
       "      <td>231_option_pos_38</td>\n",
       "      <td>1</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 32_jd]</td>\n",
       "      <td>9.795</td>\n",
       "      <td>EUR</td>\n",
       "      <td>9.795</td>\n",
       "      <td>0.7411</td>\n",
       "      <td>7.7550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232_option_pos_39</th>\n",
       "      <td>232_option_pos_39</td>\n",
       "      <td>1</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 33_sv]</td>\n",
       "      <td>8.961</td>\n",
       "      <td>EUR</td>\n",
       "      <td>8.961</td>\n",
       "      <td>-0.3033</td>\n",
       "      <td>10.3389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233_option_pos_37</th>\n",
       "      <td>233_option_pos_37</td>\n",
       "      <td>9</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 34_jd]</td>\n",
       "      <td>6.886</td>\n",
       "      <td>EUR</td>\n",
       "      <td>61.974</td>\n",
       "      <td>-2.0682</td>\n",
       "      <td>132.4161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234_option_pos_37</th>\n",
       "      <td>234_option_pos_37</td>\n",
       "      <td>6</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 35_jd]</td>\n",
       "      <td>2.778</td>\n",
       "      <td>EUR</td>\n",
       "      <td>16.668</td>\n",
       "      <td>-0.6306</td>\n",
       "      <td>65.8488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235_option_pos_37</th>\n",
       "      <td>235_option_pos_37</td>\n",
       "      <td>4</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 36_gbm]</td>\n",
       "      <td>0.813</td>\n",
       "      <td>EUR</td>\n",
       "      <td>3.252</td>\n",
       "      <td>-0.2988</td>\n",
       "      <td>32.4640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236_option_pos_39</th>\n",
       "      <td>236_option_pos_39</td>\n",
       "      <td>4</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 37_jd]</td>\n",
       "      <td>0.273</td>\n",
       "      <td>EUR</td>\n",
       "      <td>1.092</td>\n",
       "      <td>0.8576</td>\n",
       "      <td>30.1120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237_option_pos_37</th>\n",
       "      <td>237_option_pos_37</td>\n",
       "      <td>9</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 38_gbm]</td>\n",
       "      <td>15.832</td>\n",
       "      <td>EUR</td>\n",
       "      <td>142.488</td>\n",
       "      <td>-8.5698</td>\n",
       "      <td>22.3146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238_option_pos_38</th>\n",
       "      <td>238_option_pos_38</td>\n",
       "      <td>7</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 39_sv]</td>\n",
       "      <td>9.088</td>\n",
       "      <td>EUR</td>\n",
       "      <td>63.616</td>\n",
       "      <td>-3.9452</td>\n",
       "      <td>-3.6386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239_option_pos_39</th>\n",
       "      <td>239_option_pos_39</td>\n",
       "      <td>6</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 40_jd]</td>\n",
       "      <td>14.955</td>\n",
       "      <td>EUR</td>\n",
       "      <td>89.730</td>\n",
       "      <td>4.1862</td>\n",
       "      <td>72.1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240_option_pos_36</th>\n",
       "      <td>240_option_pos_36</td>\n",
       "      <td>2</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 41_jd]</td>\n",
       "      <td>5.380</td>\n",
       "      <td>EUR</td>\n",
       "      <td>10.760</td>\n",
       "      <td>-0.8410</td>\n",
       "      <td>16.4666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241_option_pos_36</th>\n",
       "      <td>241_option_pos_36</td>\n",
       "      <td>7</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 42_sv]</td>\n",
       "      <td>6.254</td>\n",
       "      <td>EUR</td>\n",
       "      <td>43.778</td>\n",
       "      <td>4.3939</td>\n",
       "      <td>5.6280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242_option_pos_38</th>\n",
       "      <td>242_option_pos_38</td>\n",
       "      <td>1</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 43_jd]</td>\n",
       "      <td>8.108</td>\n",
       "      <td>EUR</td>\n",
       "      <td>8.108</td>\n",
       "      <td>-0.2186</td>\n",
       "      <td>16.6002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243_option_pos_38</th>\n",
       "      <td>243_option_pos_38</td>\n",
       "      <td>5</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 44_jd]</td>\n",
       "      <td>4.440</td>\n",
       "      <td>EUR</td>\n",
       "      <td>22.200</td>\n",
       "      <td>-1.0505</td>\n",
       "      <td>70.7775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244_option_pos_37</th>\n",
       "      <td>244_option_pos_37</td>\n",
       "      <td>7</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 45_sv]</td>\n",
       "      <td>12.133</td>\n",
       "      <td>EUR</td>\n",
       "      <td>84.931</td>\n",
       "      <td>5.1870</td>\n",
       "      <td>22.6198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245_option_pos_36</th>\n",
       "      <td>245_option_pos_36</td>\n",
       "      <td>5</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 46_gbm]</td>\n",
       "      <td>22.709</td>\n",
       "      <td>EUR</td>\n",
       "      <td>113.545</td>\n",
       "      <td>4.7900</td>\n",
       "      <td>-2.6650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246_option_pos_37</th>\n",
       "      <td>246_option_pos_37</td>\n",
       "      <td>4</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 47_sv]</td>\n",
       "      <td>2.635</td>\n",
       "      <td>EUR</td>\n",
       "      <td>10.540</td>\n",
       "      <td>1.5900</td>\n",
       "      <td>3.7216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247_option_pos_39</th>\n",
       "      <td>247_option_pos_39</td>\n",
       "      <td>3</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 48_sv]</td>\n",
       "      <td>11.420</td>\n",
       "      <td>EUR</td>\n",
       "      <td>34.260</td>\n",
       "      <td>-1.4394</td>\n",
       "      <td>6.6159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248_option_pos_36</th>\n",
       "      <td>248_option_pos_36</td>\n",
       "      <td>6</td>\n",
       "      <td>European single</td>\n",
       "      <td>[ 49_gbm]</td>\n",
       "      <td>0.246</td>\n",
       "      <td>EUR</td>\n",
       "      <td>1.476</td>\n",
       "      <td>0.6678</td>\n",
       "      <td>32.0346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249_option_pos_36</th>\n",
       "      <td>249_option_pos_36</td>\n",
       "      <td>5</td>\n",
       "      <td>American single</td>\n",
       "      <td>[ 50_gbm]</td>\n",
       "      <td>4.128</td>\n",
       "      <td>EUR</td>\n",
       "      <td>20.640</td>\n",
       "      <td>-1.6095</td>\n",
       "      <td>67.1685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name  quantity            otype risk_facts  \\\n",
       "position                                                                     \n",
       "0_option_pos_38      0_option_pos_38         6  European single   [  1_sv]   \n",
       "1_option_pos_39      1_option_pos_39         8  American single  [  2_gbm]   \n",
       "2_option_pos_37      2_option_pos_37         3  European single  [  3_gbm]   \n",
       "3_option_pos_37      3_option_pos_37         8  American single  [  4_gbm]   \n",
       "4_option_pos_39      4_option_pos_39         1  European single  [  5_gbm]   \n",
       "...                              ...       ...              ...        ...   \n",
       "245_option_pos_36  245_option_pos_36         5  European single  [ 46_gbm]   \n",
       "246_option_pos_37  246_option_pos_37         4  European single   [ 47_sv]   \n",
       "247_option_pos_39  247_option_pos_39         3  American single   [ 48_sv]   \n",
       "248_option_pos_36  248_option_pos_36         6  European single  [ 49_gbm]   \n",
       "249_option_pos_36  249_option_pos_36         5  American single  [ 50_gbm]   \n",
       "\n",
       "                    value currency  pos_value  pos_delta  pos_vega  \n",
       "position                                                            \n",
       "0_option_pos_38     7.263      EUR     43.578     3.6036   22.7046  \n",
       "1_option_pos_39     5.398      EUR     43.184    -3.9168  124.5688  \n",
       "2_option_pos_37     3.306      EUR      9.918     1.9935   42.8949  \n",
       "3_option_pos_37     9.097      EUR     72.776    -8.0000    5.3336  \n",
       "4_option_pos_39    17.664      EUR     17.664     0.7531   13.4445  \n",
       "...                   ...      ...        ...        ...       ...  \n",
       "245_option_pos_36  22.709      EUR    113.545     4.7900   -2.6650  \n",
       "246_option_pos_37   2.635      EUR     10.540     1.5900    3.7216  \n",
       "247_option_pos_39  11.420      EUR     34.260    -1.4394    6.6159  \n",
       "248_option_pos_36   0.246      EUR      1.476     0.6678   32.0346  \n",
       "249_option_pos_36   4.128      EUR     20.640    -1.6095   67.1685  \n",
       "\n",
       "[250 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.set_index('position', inplace=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Full distribution of portfolio present values** illustrated via histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.5 s, sys: 246 ms, total: 11.8 s\n",
      "Wall time: 2.98 s\n"
     ]
    }
   ],
   "source": [
    "%time pvs = port.get_present_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(pvs, bins=30);\n",
    "plt.xlabel('portfolio present values')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some **statistics** via pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10385.491824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1891.255451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6045.025829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9059.919260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10189.486685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11436.385373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20446.938649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "count   1000.000000\n",
       "mean   10385.491824\n",
       "std     1891.255451\n",
       "min     6045.025829\n",
       "25%     9059.919260\n",
       "50%    10189.486685\n",
       "75%    11436.385373\n",
       "max    20446.938649"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf = pd.DataFrame(pvs)\n",
    "pdf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **delta** risk report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  1_sv\n",
      "0.8\n",
      "1.0\n",
      "1.2\n",
      "\n",
      "  2_gbm\n",
      "0.8\n",
      "1.0\n",
      "1.2\n",
      "\n",
      "  3_gbm\n",
      "0.8\n",
      "1.0\n",
      "1.2\n",
      "\n",
      "  4_gbm\n",
      "0.8\n",
      "1.0\n",
      "1.2\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  1_sv_Delta\n",
      "dim_0  dim_1 \n",
      "0.8    factor       28.73\n",
      "       value     10318.88\n",
      "1.0    factor       35.91\n",
      "       value     10385.48\n",
      "1.2    factor       43.09\n",
      "       value     10490.80\n",
      "Name:   1_sv_Delta, dtype: float64\n",
      "\n",
      "  2_gbm_Delta\n",
      "dim_0  dim_1 \n",
      "0.8    factor       30.36\n",
      "       value     10410.14\n",
      "1.0    factor       37.95\n",
      "       value     10385.48\n",
      "1.2    factor       45.54\n",
      "       value     10391.22\n",
      "Name:   2_gbm_Delta, dtype: float64\n",
      "\n",
      "  3_gbm_Delta\n",
      "dim_0  dim_1 \n",
      "0.8    factor       30.04\n",
      "       value     10440.04\n",
      "1.0    factor       37.55\n",
      "       value     10385.48\n",
      "1.2    factor       45.06\n",
      "       value     10421.06\n",
      "Name:   3_gbm_Delta, dtype: float64\n",
      "\n",
      "  4_gbm_Delta\n",
      "dim_0  dim_1 \n",
      "0.8    factor       22.29\n",
      "       value     10429.74\n",
      "1.0    factor       27.87\n",
      "       value     10385.48\n",
      "1.2    factor       33.44\n",
      "       value     10351.02\n",
      "Name:   4_gbm_Delta, dtype: float64\n",
      "CPU times: user 14.7 s, sys: 325 ms, total: 15 s\n",
      "Wall time: 3.82 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "deltas, benchmark = port.get_port_risk(Greek='Delta', fixed_seed=True, step=0.2,\n",
    "                                       risk_factors=list(risk_factors.keys())[:4])\n",
    "risk_report(deltas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **vega** risk report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  1_sv\n",
      "0.8\n",
      "1.0\n",
      "1.2\n",
      "\n",
      "  2_gbm\n",
      "0.8\n",
      "1.0\n",
      "1.2\n",
      "\n",
      "  3_gbm\n",
      "0.8\n",
      "1.0\n",
      "1.2\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  1_sv_Vega\n",
      "dim_0  dim_1 \n",
      "0.8    factor        0.42\n",
      "       value     10377.30\n",
      "1.0    factor        0.52\n",
      "       value     10384.95\n",
      "1.2    factor        0.62\n",
      "       value     10402.19\n",
      "Name:   1_sv_Vega, dtype: float64\n",
      "\n",
      "  2_gbm_Vega\n",
      "dim_0  dim_1 \n",
      "0.8    factor        0.27\n",
      "       value     10365.33\n",
      "1.0    factor        0.34\n",
      "       value     10384.95\n",
      "1.2    factor        0.41\n",
      "       value     10404.53\n",
      "Name:   2_gbm_Vega, dtype: float64\n",
      "\n",
      "  3_gbm_Vega\n",
      "dim_0  dim_1 \n",
      "0.8    factor        0.12\n",
      "       value     10374.04\n",
      "1.0    factor        0.14\n",
      "       value     10384.95\n",
      "1.2    factor        0.17\n",
      "       value     10395.83\n",
      "Name:   3_gbm_Vega, dtype: float64\n",
      "CPU times: user 13.2 s, sys: 200 ms, total: 13.4 s\n",
      "Wall time: 3.38 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "vegas, benchmark = port.get_port_risk(Greek='Vega', fixed_seed=True, step=0.2,\n",
    "                                      risk_factors=list(risk_factors.keys())[:3])\n",
    "risk_report(vegas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selected **results visualized**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res[['pos_value', 'pos_delta', 'pos_vega']].hist(bins=30, figsize=(9, 6))\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sample paths** for three underlyings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths_0 = list(port.underlying_objects.values())[0]\n",
    "paths_0.generate_paths()\n",
    "paths_1 = list(port.underlying_objects.values())[1]\n",
    "paths_1.generate_paths()\n",
    "paths_2 = list(port.underlying_objects.values())[2]\n",
    "paths_2.generate_paths()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paths for  32_jd (blue)\n",
      "Paths for  10_sv (red)\n",
      "Paths for   3_gbm (green)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pa = 5\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(port.time_grid, paths_0.instrument_values[:, :pa], 'b');\n",
    "print('Paths for %s (blue)' % paths_0.name)\n",
    "plt.plot(port.time_grid, paths_1.instrument_values[:, :pa], 'r.-');\n",
    "print ('Paths for %s (red)' % paths_1.name)\n",
    "plt.plot(port.time_grid, paths_2.instrument_values[:, :pa], 'g-.', lw=2.5);\n",
    "print('Paths for %s (green)' % paths_2.name)\n",
    "plt.ylabel('risk factor level')\n",
    "plt.gcf().autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Copyright, License & Disclaimer**\n",
    "\n",
    "© Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
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    "**Python for Finance (2nd ed., O'Reilly)** |\n",
    "http://py4fi.tpq.io"
   ]
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