{
 "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": [
    "# Quickstart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This brief first part illustrates---without much explanation---the usage of the DX Analytics library. It models two risk factors, two derivatives instruments and values these in a portfolio context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dx\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "from pylab import plt\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk Factor Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to define a **model for the risk-neutral discounting**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = dx.constant_short_rate('r', 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then define a **market environment** containing the major parameter specifications needed,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "me_1 = dx.market_environment('me', dt.datetime(2016, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "me_1.add_constant('initial_value', 100.)\n",
    "  # starting value of simulated processes\n",
    "me_1.add_constant('volatility', 0.2)\n",
    "  # volatiltiy factor\n",
    "me_1.add_constant('final_date', dt.datetime(2017, 6, 30))\n",
    "  # horizon for simulation\n",
    "me_1.add_constant('currency', 'EUR')\n",
    "  # currency of instrument\n",
    "me_1.add_constant('frequency', 'W')\n",
    "  # frequency for discretization\n",
    "me_1.add_constant('paths', 10000)\n",
    "  # number of paths\n",
    "me_1.add_curve('discount_curve', r)\n",
    "  # number of paths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the model object for the **first risk factor**, based on the geometric Brownian motion (Black-Scholes-Merton (1973) model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm_1 = dx.geometric_brownian_motion('gbm_1', me_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some paths visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf = pd.DataFrame(gbm_1.get_instrument_values(), index=gbm_1.time_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "%matplotlib inline\n",
    "pdf.iloc[:, :10].plot(legend=False, figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Second risk factor** with higher volatility. We overwrite the respective value in the market environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "me_2 = dx.market_environment('me_2', me_1.pricing_date)\n",
    "me_2.add_environment(me_1)  # add complete environment\n",
    "me_2.add_constant('volatility', 0.5)  # overwrite value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm_2 = dx.geometric_brownian_motion('gbm_2', me_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf = pd.DataFrame(gbm_2.get_instrument_values(), index=gbm_2.time_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "pdf.iloc[:, :10].plot(legend=False, figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valuation Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the risk factors, we can then define derivatives models for valuation. To this end, we need to add at least one (the `maturity`), in general two (`maturity` and `strike`), parameters to the market environments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "me_opt = dx.market_environment('me_opt', me_1.pricing_date)\n",
    "me_opt.add_environment(me_1)\n",
    "me_opt.add_constant('maturity', dt.datetime(2017, 6, 30))\n",
    "me_opt.add_constant('strike', 110.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first derivative is an **American put option** on the first risk factor `gbm_1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "am_put = dx.valuation_mcs_american_single(\n",
    "            name='am_put',\n",
    "            underlying=gbm_1,\n",
    "            mar_env=me_opt,\n",
    "            payoff_func='np.maximum(strike - instrument_values, 0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us calculate a **Monte Carlo present value estimate** and estimates for the option **Greeks**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.013"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "am_put.present_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5411"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "am_put.delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0082"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "am_put.gamma()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.1584"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "am_put.vega()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.7421"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "am_put.theta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-71.1355"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "am_put.rho()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second derivative is a **European call option** on the second risk factor `gbm_2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "eur_call = dx.valuation_mcs_european_single(\n",
    "            name='eur_call',\n",
    "            underlying=gbm_2,\n",
    "            mar_env=me_opt,\n",
    "            payoff_func='np.maximum(maturity_value - strike, 0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valuation and Greek estimation for this option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.641978"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eur_call.present_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8496"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eur_call.delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0057"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eur_call.gamma()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.2888"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eur_call.vega()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-8.7493"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eur_call.theta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.3995"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eur_call.rho()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excursion: SABR Model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate how general the approach of DX Analytics is, let us quickly analyze an option based on a SABR stochastic volatility process. In what follows herafter, the SABR model does not play a role.\n",
    "\n",
    "We need to define different parameters obviously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "me_3 = dx.market_environment('me_3', me_1.pricing_date)\n",
    "me_3.add_environment(me_1)  # add complete environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interest rate like parmeters\n",
    "me_3.add_constant('initial_value', 0.05)\n",
    "  # initial value\n",
    "me_3.add_constant('alpha', 0.1)\n",
    "  # initial variance\n",
    "me_3.add_constant('beta', 0.5)\n",
    "  # exponent\n",
    "me_3.add_constant('rho', 0.1)\n",
    "  # correlation factor\n",
    "me_3.add_constant('vol_vol', 0.5)\n",
    "  # volatility of volatility/variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model object instantiation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sabr = dx.sabr_stochastic_volatility('sabr', me_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The valuation object instantiation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "me_opt.add_constant('strike', me_3.get_constant('initial_value'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sabr_call = dx.valuation_mcs_european_single(\n",
    "            name='sabr_call',\n",
    "            underlying=sabr,\n",
    "            mar_env=me_opt,\n",
    "            payoff_func='np.maximum(maturity_value - strike, 0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some statistics --- same syntax/API even if the model is more complex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025849"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sabr_call.present_value(fixed_seed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8151"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sabr_call.delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0385"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sabr_call.rho()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resetting the option strike\n",
    "me_opt.add_constant('strike', 110.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Options Portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a portfolio context, we need to add **information about the model class(es)** to be used to the market environments of the risk factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "me_1.add_constant('model', 'gbm')\n",
    "me_2.add_constant('model', 'gbm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compose a portfolio consisting of our just defined options, we need to define **derivatives positions**. Note that this step is *independent* from the risk factor model and option model definitions. We only use the market environment data and some additional information needed (e.g. payoff functions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "put = dx.derivatives_position(\n",
    "            name='put',\n",
    "            quantity=2,\n",
    "            underlyings=['gbm_1'],\n",
    "            mar_env=me_opt,\n",
    "            otype='American single',\n",
    "            payoff_func='np.maximum(strike - instrument_values, 0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "call = dx.derivatives_position(\n",
    "            name='call',\n",
    "            quantity=3,\n",
    "            underlyings=['gbm_2'],\n",
    "            mar_env=me_opt,\n",
    "            otype='European single',\n",
    "            payoff_func='np.maximum(maturity_value - strike, 0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us define the **relevant market** by 2 Python dictionaries, the correlation between the two risk factors and a valuation environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_factors = {'gbm_1': me_1, 'gbm_2' : me_2}\n",
    "correlations = [['gbm_1', 'gbm_2', -0.4]]\n",
    "positions = {'put' : put, 'call' : call}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_env = dx.market_environment('general', dt.datetime(2016, 1, 1))\n",
    "val_env.add_constant('frequency', 'W')\n",
    "val_env.add_constant('paths', 10000)\n",
    "val_env.add_constant('starting_date', val_env.pricing_date)\n",
    "val_env.add_constant('final_date', val_env.pricing_date)\n",
    "val_env.add_curve('discount_curve', r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are used to define the **derivatives portfolio**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "port = dx.derivatives_portfolio(\n",
    "            name='portfolio',  # name \n",
    "            positions=positions,  # derivatives positions\n",
    "            val_env=val_env,  # valuation environment\n",
    "            risk_factors=risk_factors, # relevant risk factors\n",
    "            correlations=correlations, # correlation between risk factors\n",
    "            parallel=False)  # parallel valuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation and Valuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can get the **position values for the portfolio** via the `get_values` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total\n",
      " pos_value    91.269955\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>put</td>\n",
       "      <td>put</td>\n",
       "      <td>2</td>\n",
       "      <td>American single</td>\n",
       "      <td>[gbm_1]</td>\n",
       "      <td>15.029000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>30.058000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>call</td>\n",
       "      <td>call</td>\n",
       "      <td>3</td>\n",
       "      <td>European single</td>\n",
       "      <td>[gbm_2]</td>\n",
       "      <td>20.403985</td>\n",
       "      <td>EUR</td>\n",
       "      <td>61.211955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  position  name  quantity            otype risk_facts      value currency  \\\n",
       "0      put   put         2  American single    [gbm_1]  15.029000      EUR   \n",
       "1     call  call         3  European single    [gbm_2]  20.403985      EUR   \n",
       "\n",
       "   pos_value  \n",
       "0  30.058000  \n",
       "1  61.211955  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port.get_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Via the `get_statistics` methods delta and vega values are provided as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Totals\n",
      " pos_value     91.2700\n",
      "pos_delta      0.5310\n",
      "pos_vega     225.1372\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>put</td>\n",
       "      <td>put</td>\n",
       "      <td>2</td>\n",
       "      <td>American single</td>\n",
       "      <td>[gbm_1]</td>\n",
       "      <td>15.029</td>\n",
       "      <td>EUR</td>\n",
       "      <td>30.058</td>\n",
       "      <td>-1.1802</td>\n",
       "      <td>87.3484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>call</td>\n",
       "      <td>call</td>\n",
       "      <td>3</td>\n",
       "      <td>European single</td>\n",
       "      <td>[gbm_2]</td>\n",
       "      <td>20.404</td>\n",
       "      <td>EUR</td>\n",
       "      <td>61.212</td>\n",
       "      <td>1.7112</td>\n",
       "      <td>137.7888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  position  name  quantity            otype risk_facts   value currency  \\\n",
       "0      put   put         2  American single    [gbm_1]  15.029      EUR   \n",
       "1     call  call         3  European single    [gbm_2]  20.404      EUR   \n",
       "\n",
       "   pos_value  pos_delta  pos_vega  \n",
       "0     30.058    -1.1802   87.3484  \n",
       "1     61.212     1.7112  137.7888  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port.get_statistics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much more complex scenarios are possible with DX Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk Reports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having modeled the derivatives portfolio, **risk reports** are only two method calls away."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gbm_2\n",
      "0.8\n",
      "0.9\n",
      "1.0\n",
      "1.1\n",
      "1.2\n",
      "\n",
      "gbm_1\n",
      "0.8\n",
      "0.9\n",
      "1.0\n",
      "1.1\n",
      "1.2\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "deltas, benchvalue = port.get_port_risk(Greek='Delta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>gbm_2_Delta</th>\n",
       "      <th>gbm_1_Delta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major</th>\n",
       "      <th>minor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.8</th>\n",
       "      <th>factor</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>61.767112</td>\n",
       "      <td>122.001955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.9</th>\n",
       "      <th>factor</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>75.421234</td>\n",
       "      <td>104.903955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
       "      <th>factor</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>91.269955</td>\n",
       "      <td>91.269955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.1</th>\n",
       "      <th>factor</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>109.161973</td>\n",
       "      <td>80.751955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.2</th>\n",
       "      <th>factor</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>128.739235</td>\n",
       "      <td>73.195955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              gbm_2_Delta  gbm_1_Delta\n",
       "major minor                           \n",
       "0.8   factor    80.000000    80.000000\n",
       "      value     61.767112   122.001955\n",
       "0.9   factor    90.000000    90.000000\n",
       "      value     75.421234   104.903955\n",
       "1.0   factor   100.000000   100.000000\n",
       "      value     91.269955    91.269955\n",
       "1.1   factor   110.000000   110.000000\n",
       "      value    109.161973    80.751955\n",
       "1.2   factor   120.000000   120.000000\n",
       "      value    128.739235    73.195955"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>gbm_2_Delta</th>\n",
       "      <th>gbm_1_Delta</th>\n",
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       "    <tr>\n",
       "      <th>major</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <th>value</th>\n",
       "      <td>-29.502843</td>\n",
       "      <td>30.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <th>value</th>\n",
       "      <td>-15.848721</td>\n",
       "      <td>13.634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <th>value</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.1</th>\n",
       "      <th>value</th>\n",
       "      <td>17.892018</td>\n",
       "      <td>-10.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <th>value</th>\n",
       "      <td>37.469280</td>\n",
       "      <td>-18.074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gbm_2_Delta  gbm_1_Delta\n",
       "major minor                          \n",
       "0.8   value   -29.502843       30.732\n",
       "0.9   value   -15.848721       13.634\n",
       "1.0   value     0.000000        0.000\n",
       "1.1   value    17.892018      -10.518\n",
       "1.2   value    37.469280      -18.074"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltas.loc(axis=0)[:, 'value'] - benchvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gbm_2\n",
      "0.8\n",
      "0.8500000000000001\n",
      "0.9000000000000001\n",
      "0.9500000000000002\n",
      "1.0000000000000002\n",
      "1.0500000000000003\n",
      "1.1000000000000003\n",
      "1.1500000000000004\n",
      "1.2000000000000004\n",
      "\n",
      "gbm_1\n",
      "0.8\n",
      "0.8500000000000001\n",
      "0.9000000000000001\n",
      "0.9500000000000002\n",
      "1.0000000000000002\n",
      "1.0500000000000003\n",
      "1.1000000000000003\n",
      "1.1500000000000004\n",
      "1.2000000000000004\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vegas, benchvalue = port.get_port_risk(Greek='Vega', step=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>gbm_1_Vega</th>\n",
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       "      <th>major</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">0.80</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.160000</td>\n",
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       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>77.334907</td>\n",
       "      <td>87.597955</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.85</th>\n",
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       "      <td>0.430000</td>\n",
       "      <td>0.170000</td>\n",
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       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>80.843542</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.90</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>84.336475</td>\n",
       "      <td>89.377955</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.95</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.190000</td>\n",
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       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>87.812380</td>\n",
       "      <td>90.351955</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.00</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>91.269955</td>\n",
       "      <td>91.269955</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.05</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>94.708426</td>\n",
       "      <td>92.143955</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.10</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>98.125972</td>\n",
       "      <td>93.137955</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.15</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>101.519533</td>\n",
       "      <td>94.045955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.20</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>104.887072</td>\n",
       "      <td>95.013955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              gbm_2_Vega  gbm_1_Vega\n",
       "major minor                         \n",
       "0.80  factor    0.400000    0.160000\n",
       "      value    77.334907   87.597955\n",
       "0.85  factor    0.430000    0.170000\n",
       "      value    80.843542   88.473955\n",
       "0.90  factor    0.450000    0.180000\n",
       "      value    84.336475   89.377955\n",
       "0.95  factor    0.480000    0.190000\n",
       "      value    87.812380   90.351955\n",
       "1.00  factor    0.500000    0.200000\n",
       "      value    91.269955   91.269955\n",
       "1.05  factor    0.530000    0.210000\n",
       "      value    94.708426   92.143955\n",
       "1.10  factor    0.550000    0.220000\n",
       "      value    98.125972   93.137955\n",
       "1.15  factor    0.580000    0.230000\n",
       "      value   101.519533   94.045955\n",
       "1.20  factor    0.600000    0.240000\n",
       "      value   104.887072   95.013955"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vegas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0.85</th>\n",
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       "      <th>0.90</th>\n",
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       "      <th>1.00</th>\n",
       "      <th>value</th>\n",
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       "      <th>1.05</th>\n",
       "      <th>value</th>\n",
       "      <td>3.438471</td>\n",
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       "      <th>1.10</th>\n",
       "      <th>value</th>\n",
       "      <td>6.856017</td>\n",
       "      <td>1.868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.15</th>\n",
       "      <th>value</th>\n",
       "      <td>10.249578</td>\n",
       "      <td>2.776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.20</th>\n",
       "      <th>value</th>\n",
       "      <td>13.617117</td>\n",
       "      <td>3.744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gbm_2_Vega  gbm_1_Vega\n",
       "major minor                        \n",
       "0.80  value  -13.935048      -3.672\n",
       "0.85  value  -10.426413      -2.796\n",
       "0.90  value   -6.933480      -1.892\n",
       "0.95  value   -3.457575      -0.918\n",
       "1.00  value    0.000000       0.000\n",
       "1.05  value    3.438471       0.874\n",
       "1.10  value    6.856017       1.868\n",
       "1.15  value   10.249578       2.776\n",
       "1.20  value   13.617117       3.744"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vegas.loc(axis=0)[:, 'value'] - benchvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Copyright, License & Disclaimer**\n",
    "\n",
    "© Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "DX Analytics (the \"dx library\" or \"dx package\") is licensed under the GNU Affero General\n",
    "Public License version 3 or later (see http://www.gnu.org/licenses/).\n",
    "\n",
    "DX Analytics comes with no representations or warranties, to the extent\n",
    "permitted by applicable law.\n",
    "\n",
    "http://tpq.io | [dx@tpq.io](mailto:team@tpq.io) |\n",
    "http://twitter.com/dyjh\n",
    "\n",
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "**Quant Platform** | http://pqp.io\n",
    "\n",
    "**Python for Finance Training** | http://training.tpq.io\n",
    "\n",
    "**Certificate in Computational Finance** | http://compfinance.tpq.io\n",
    "\n",
    "**Derivatives Analytics with Python (Wiley Finance)** |\n",
    "http://dawp.tpq.io\n",
    "\n",
    "**Python for Finance (2nd ed., O'Reilly)** |\n",
    "http://py4fi.tpq.io"
   ]
  }
 ],
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