00_dx_quickstart.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": [
    "# 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": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ac624a8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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L6GhVF/6AzJZlcZf0PLblmVbuvCqd/iEfv/1rOUH58k5S/7i3AZPxWjQSfDknkX9emYHk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\nPh0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1096dabe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pdf.ix[:, :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": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10acbca20>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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35qQSkGXe7I5tUcwLtshO911JrVCwKS2B0UCQWntsf/maLYfqS8CXpnhr3xTX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0k3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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10adb5400>"
      ]
     },
     "metadata": {},
     "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": {
    "collapsed": true
   },
   "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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sabr = dx.sabr_stochastic_volatility('sabr', me_3)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "sabr = dx.geometric_brownian_motion('sabr', me_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The valuation object instantiation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "me_opt.add_constant('strike', me_3.get_constant('initial_value'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "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": {
    "collapsed": true
   },
   "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": [
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       "<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",
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      ],
      "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": [
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       "      <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",
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       "  </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_1\n",
      "0.8\n",
      "0.9\n",
      "1.0\n",
      "1.1\n",
      "1.2\n",
      "\n",
      "gbm_2\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": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>gbm_1_Delta</th>\n",
       "      <th>gbm_2_Delta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major</th>\n",
       "      <th>minor</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">0.8</th>\n",
       "      <th>factor</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
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       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>122.001955</td>\n",
       "      <td>61.767112</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>104.903955</td>\n",
       "      <td>75.421234</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>80.751955</td>\n",
       "      <td>109.161973</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>73.195955</td>\n",
       "      <td>128.739235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              gbm_1_Delta  gbm_2_Delta\n",
       "major minor                           \n",
       "0.8   factor    80.000000    80.000000\n",
       "      value    122.001955    61.767112\n",
       "0.9   factor    90.000000    90.000000\n",
       "      value    104.903955    75.421234\n",
       "1.0   factor   100.000000   100.000000\n",
       "      value     91.269955    91.269955\n",
       "1.1   factor   110.000000   110.000000\n",
       "      value     80.751955   109.161973\n",
       "1.2   factor   120.000000   120.000000\n",
       "      value     73.195955   128.739235"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "      <th>gbm_1_Delta</th>\n",
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       "      <td>30.732</td>\n",
       "      <td>-29.502843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <th>value</th>\n",
       "      <td>13.634</td>\n",
       "      <td>-15.848721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <th>value</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.1</th>\n",
       "      <th>value</th>\n",
       "      <td>-10.518</td>\n",
       "      <td>17.892018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <th>value</th>\n",
       "      <td>-18.074</td>\n",
       "      <td>37.469280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gbm_1_Delta  gbm_2_Delta\n",
       "major minor                          \n",
       "0.8   value       30.732   -29.502843\n",
       "0.9   value       13.634   -15.848721\n",
       "1.0   value        0.000     0.000000\n",
       "1.1   value      -10.518    17.892018\n",
       "1.2   value      -18.074    37.469280"
      ]
     },
     "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_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",
      "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",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vegas, benchvalue = port.get_port_risk(Greek='Vega', step=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>gbm_1_Vega</th>\n",
       "      <th>gbm_2_Vega</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major</th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"2\" valign=\"top\">0.80</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.400000</td>\n",
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       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>87.597955</td>\n",
       "      <td>77.334907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.85</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>88.473955</td>\n",
       "      <td>80.843542</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">0.90</th>\n",
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       "      <td>0.180000</td>\n",
       "      <td>0.450000</td>\n",
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       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>89.377955</td>\n",
       "      <td>84.336475</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.95</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.480000</td>\n",
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       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>90.351955</td>\n",
       "      <td>87.812380</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.00</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.500000</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>0.210000</td>\n",
       "      <td>0.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>92.143955</td>\n",
       "      <td>94.708426</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.10</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.550000</td>\n",
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       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>93.137955</td>\n",
       "      <td>98.125972</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.15</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>94.045955</td>\n",
       "      <td>101.519533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.20</th>\n",
       "      <th>factor</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>95.013955</td>\n",
       "      <td>104.887072</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              gbm_1_Vega  gbm_2_Vega\n",
       "major minor                         \n",
       "0.80  factor    0.160000    0.400000\n",
       "      value    87.597955   77.334907\n",
       "0.85  factor    0.170000    0.430000\n",
       "      value    88.473955   80.843542\n",
       "0.90  factor    0.180000    0.450000\n",
       "      value    89.377955   84.336475\n",
       "0.95  factor    0.190000    0.480000\n",
       "      value    90.351955   87.812380\n",
       "1.00  factor    0.200000    0.500000\n",
       "      value    91.269955   91.269955\n",
       "1.05  factor    0.210000    0.530000\n",
       "      value    92.143955   94.708426\n",
       "1.10  factor    0.220000    0.550000\n",
       "      value    93.137955   98.125972\n",
       "1.15  factor    0.230000    0.580000\n",
       "      value    94.045955  101.519533\n",
       "1.20  factor    0.240000    0.600000\n",
       "      value    95.013955  104.887072"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vegas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>gbm_1_Vega</th>\n",
       "      <th>gbm_2_Vega</th>\n",
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       "      <th>major</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.80</th>\n",
       "      <th>value</th>\n",
       "      <td>-3.672</td>\n",
       "      <td>-13.935048</td>\n",
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       "    <tr>\n",
       "      <th>0.85</th>\n",
       "      <th>value</th>\n",
       "      <td>-2.796</td>\n",
       "      <td>-10.426413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <th>value</th>\n",
       "      <td>-1.892</td>\n",
       "      <td>-6.933480</td>\n",
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       "    <tr>\n",
       "      <th>0.95</th>\n",
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       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <th>value</th>\n",
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       "      <th>1.05</th>\n",
       "      <th>value</th>\n",
       "      <td>0.874</td>\n",
       "      <td>3.438471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.10</th>\n",
       "      <th>value</th>\n",
       "      <td>1.868</td>\n",
       "      <td>6.856017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.15</th>\n",
       "      <th>value</th>\n",
       "      <td>2.776</td>\n",
       "      <td>10.249578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.20</th>\n",
       "      <th>value</th>\n",
       "      <td>3.744</td>\n",
       "      <td>13.617117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gbm_1_Vega  gbm_2_Vega\n",
       "major minor                        \n",
       "0.80  value      -3.672  -13.935048\n",
       "0.85  value      -2.796  -10.426413\n",
       "0.90  value      -1.892   -6.933480\n",
       "0.95  value      -0.918   -3.457575\n",
       "1.00  value       0.000    0.000000\n",
       "1.05  value       0.874    3.438471\n",
       "1.10  value       1.868    6.856017\n",
       "1.15  value       2.776   10.249578\n",
       "1.20  value       3.744   13.617117"
      ]
     },
     "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",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "DX Analytics (the \"dx library\") is licensed under the GNU Affero General Public License\n",
    "version 3 or later (see [http://www.gnu.org/licenses/](http://www.gnu.org/licenses/)).\n",
    "\n",
    "DX Analytics comes with no representations\n",
    "or warranties, to the extent permitted by applicable law.\n",
    "\n",
    "\n",
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "[http://tpq.io](http://tpq.io) | [team@tpq.io](mailto:team@tpq.io) | [http://twitter.com/dyjh](http://twitter.com/dyjh)\n",
    "\n",
    "**Quant Platform** |\n",
    "[http://quant-platform.com](http://quant-platform.com)\n",
    "\n",
    "**Derivatives Analytics with Python (Wiley Finance)** |\n",
    "[http://derivatives-analytics-with-python.com](http://derivatives-analytics-with-python.com)\n",
    "\n",
    "**Python for Finance (O'Reilly)** |\n",
    "[http://python-for-finance.com](http://python-for-finance.com)"
   ]
  }
 ],
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