Lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LAB1 - Trabajando con las estadisticas de las opciones sobre el VIX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Hablaremos largo y tendido sobre opciones, no tengais la menor duda, pero para empezar, con un articulo, que nos incie en el uso de pandas, el uso de series temporales, y algunas pinceladas sobre finanzas, me parece un buen aperitivo, que marcara la linea que se pretende conseguir en el blog."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**¿Que es una opcion?** Es un contrato, donde el comprador adquiere un DERECHO a cambio de PAGAR un precio , y el vendedor adquiere una OBLIGACION a cambio de PERCIBIR un precio.    \n",
    "**¿Que tipo de opciones existen?** Existen las put y las call, cada una de ellas, para su ejecucion, necesita un comprador y un vendedor. Y es donde entran en juego los market makers, y la negociacion OTC, cosas que dejaremos para mas adelante.    \n",
    "**¿Que formatos de opciones existen?** Para mi, solo unas, las americanas, pero en realidad, hay muchisimos tipos, desde las Europeas, Bermuda, las Power, las Barreda, y luego tenemos aberraciones como los warrants o las binarias( donde en <a href='http://unespeculador.com/opciones-binarias-lo-que-la-verdad-esconde/'>este articulo</a> , <a href='https://telegram.me/joinchat/DP9o50A6SYeUKYdmtLJxaQ'>EnricECP</a> habla sobre la esperanza matematica negativa de las opciones binarias)    \n",
    "**¿Que es el VIX? :** Es el indice que mide la volatilidad de las opciones PUT del S&P 500.  \n",
    "**¿Que es una opcion CALL? :** Es un derivado financiero, el cuall el comprador de la opcion, sale beneficiado con las subidas, y el vendedor de la opcion, sale beneficiado con las caidas.   \n",
    "**¿Que es una opcion PUT? :**  Es un derivado financiero, el cuall el comprador de la opcion, sale beneficiado con las caidas, y el vendedor de la opcion, sale beneficiado con las subidas, o los escenarios laterales (tema que trataremos en otro estudio)  "
   ]
  },
  {
   "attachments": {
    "options-buyers-sellers-want.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ![options-buyers-sellers-want.png](attachment:options-buyers-sellers-want.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cargamos todas las librerias necesarias "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pylab/pythonlabs_01/pythonlabs/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd # esperemos que pronto sea import MODIN as pd.\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import scipy \n",
    "import datetime \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "pylab.rcParams['figure.figsize'] = 12, 12 \n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cargamos nuestro archivo pickle, con los datos previamente cargados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_pickle('/home/pylab/pythonlabs_01/pythonlabs/datasets/pickle/VIX_Stats.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Realizamos un analisis exploratorio de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(304, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape ## ( filas,columnas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-02', '2018-05-16', '2018-01-19', '2018-04-10',\n",
       "               '2019-01-30', '2018-05-10', '2018-10-10', '2018-02-01',\n",
       "               '2018-11-21', '2018-06-04',\n",
       "               ...\n",
       "               '2018-10-08', '2018-01-04', '2019-03-14', '2019-01-07',\n",
       "               '2019-01-29', '2018-10-18', '2018-11-14', '2018-08-06',\n",
       "               '2018-05-02', '2018-11-29'],\n",
       "              dtype='datetime64[ns]', name='quotedate', length=304, freq=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index # indice del dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symbol', 'calliv', 'calloi', 'callvol', 'putiv', 'putoi', 'putvol',\n",
       "       'meaniv'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns # columnas del dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>symbol</th>\n",
       "      <th>calliv</th>\n",
       "      <th>calloi</th>\n",
       "      <th>callvol</th>\n",
       "      <th>putiv</th>\n",
       "      <th>putoi</th>\n",
       "      <th>putvol</th>\n",
       "      <th>meaniv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quotedate</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>2018-01-02</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.7990</td>\n",
       "      <td>7911657.0</td>\n",
       "      <td>474007.0</td>\n",
       "      <td>0.1765</td>\n",
       "      <td>1973447.0</td>\n",
       "      <td>134049.0</td>\n",
       "      <td>0.9878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-16</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.2146</td>\n",
       "      <td>6510396.0</td>\n",
       "      <td>351323.0</td>\n",
       "      <td>0.4793</td>\n",
       "      <td>2390787.0</td>\n",
       "      <td>100156.0</td>\n",
       "      <td>0.8470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.4848</td>\n",
       "      <td>8290013.0</td>\n",
       "      <td>320794.0</td>\n",
       "      <td>0.5489</td>\n",
       "      <td>1816758.0</td>\n",
       "      <td>225070.0</td>\n",
       "      <td>1.0168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-10</th>\n",
       "      <td>VIX</td>\n",
       "      <td>0.7967</td>\n",
       "      <td>5599924.0</td>\n",
       "      <td>501640.0</td>\n",
       "      <td>1.2191</td>\n",
       "      <td>2522041.0</td>\n",
       "      <td>141925.0</td>\n",
       "      <td>1.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-30</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.1035</td>\n",
       "      <td>5792940.0</td>\n",
       "      <td>158592.0</td>\n",
       "      <td>0.7216</td>\n",
       "      <td>1643864.0</td>\n",
       "      <td>175943.0</td>\n",
       "      <td>0.9125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  calliv     calloi   callvol   putiv      putoi    putvol  \\\n",
       "quotedate                                                                     \n",
       "2018-01-02    VIX  1.7990  7911657.0  474007.0  0.1765  1973447.0  134049.0   \n",
       "2018-05-16    VIX  1.2146  6510396.0  351323.0  0.4793  2390787.0  100156.0   \n",
       "2018-01-19    VIX  1.4848  8290013.0  320794.0  0.5489  1816758.0  225070.0   \n",
       "2018-04-10    VIX  0.7967  5599924.0  501640.0  1.2191  2522041.0  141925.0   \n",
       "2019-01-30    VIX  1.1035  5792940.0  158592.0  0.7216  1643864.0  175943.0   \n",
       "\n",
       "            meaniv  \n",
       "quotedate           \n",
       "2018-01-02  0.9878  \n",
       "2018-05-16  0.8470  \n",
       "2018-01-19  1.0168  \n",
       "2018-04-10  1.0079  \n",
       "2019-01-30  0.9125  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5) # comprobamos integridad de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## El indice no es correcto, pues las fechas no estan ordenadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.sort_values(by='quotedate', ascending=True) ## Comprobamos siempre la intregridad de los datos, en este caso, al ser extraido de una base mayor, no estaba ordenado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>symbol</th>\n",
       "      <th>calliv</th>\n",
       "      <th>calloi</th>\n",
       "      <th>callvol</th>\n",
       "      <th>putiv</th>\n",
       "      <th>putoi</th>\n",
       "      <th>putvol</th>\n",
       "      <th>meaniv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quotedate</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>2018-01-02</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.7990</td>\n",
       "      <td>7911657.0</td>\n",
       "      <td>474007.0</td>\n",
       "      <td>0.1765</td>\n",
       "      <td>1973447.0</td>\n",
       "      <td>134049.0</td>\n",
       "      <td>0.9878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>VIX</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>32282656.0</td>\n",
       "      <td>1775860.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8011824.0</td>\n",
       "      <td>1175408.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>VIX</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>32778204.0</td>\n",
       "      <td>1935276.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8362104.0</td>\n",
       "      <td>1577264.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>VIX</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>34095000.0</td>\n",
       "      <td>2288688.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9026580.0</td>\n",
       "      <td>908536.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>VIX</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>35199628.0</td>\n",
       "      <td>4845204.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9507436.0</td>\n",
       "      <td>445232.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.5394</td>\n",
       "      <td>9825711.0</td>\n",
       "      <td>476051.0</td>\n",
       "      <td>0.3580</td>\n",
       "      <td>2411212.0</td>\n",
       "      <td>90019.0</td>\n",
       "      <td>0.9487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.7448</td>\n",
       "      <td>9692405.0</td>\n",
       "      <td>2194289.0</td>\n",
       "      <td>0.3122</td>\n",
       "      <td>2415311.0</td>\n",
       "      <td>678124.0</td>\n",
       "      <td>1.0285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.8422</td>\n",
       "      <td>9681042.0</td>\n",
       "      <td>794356.0</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>2421078.0</td>\n",
       "      <td>172117.0</td>\n",
       "      <td>1.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.6882</td>\n",
       "      <td>10345706.0</td>\n",
       "      <td>953906.0</td>\n",
       "      <td>0.2745</td>\n",
       "      <td>2522063.0</td>\n",
       "      <td>262989.0</td>\n",
       "      <td>0.9814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>VIX</td>\n",
       "      <td>1.3447</td>\n",
       "      <td>10890333.0</td>\n",
       "      <td>1387476.0</td>\n",
       "      <td>0.6105</td>\n",
       "      <td>2612823.0</td>\n",
       "      <td>491002.0</td>\n",
       "      <td>0.9776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  calliv      calloi    callvol   putiv      putoi  \\\n",
       "quotedate                                                             \n",
       "2018-01-02    VIX  1.7990   7911657.0   474007.0  0.1765  1973447.0   \n",
       "2018-01-03    VIX  0.0000  32282656.0  1775860.0  0.0000  8011824.0   \n",
       "2018-01-04    VIX  0.0000  32778204.0  1935276.0  0.0000  8362104.0   \n",
       "2018-01-05    VIX  0.0000  34095000.0  2288688.0  0.0000  9026580.0   \n",
       "2018-01-08    VIX  0.0000  35199628.0  4845204.0  0.0000  9507436.0   \n",
       "2018-01-09    VIX  1.5394   9825711.0   476051.0  0.3580  2411212.0   \n",
       "2018-01-10    VIX  1.7448   9692405.0  2194289.0  0.3122  2415311.0   \n",
       "2018-01-11    VIX  1.8422   9681042.0   794356.0  0.1790  2421078.0   \n",
       "2018-01-12    VIX  1.6882  10345706.0   953906.0  0.2745  2522063.0   \n",
       "2018-01-16    VIX  1.3447  10890333.0  1387476.0  0.6105  2612823.0   \n",
       "\n",
       "               putvol  meaniv  \n",
       "quotedate                      \n",
       "2018-01-02   134049.0  0.9878  \n",
       "2018-01-03  1175408.0  0.0000  \n",
       "2018-01-04  1577264.0  0.0000  \n",
       "2018-01-05   908536.0  0.0000  \n",
       "2018-01-08   445232.0  0.0000  \n",
       "2018-01-09    90019.0  0.9487  \n",
       "2018-01-10   678124.0  1.0285  \n",
       "2018-01-11   172117.0  1.0106  \n",
       "2018-01-12   262989.0  0.9814  \n",
       "2018-01-16   491002.0  0.9776  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**¿Que es la IV?** : La una prediccion realizada por consenso de mercado sobre el grado que el activo subyacente fluctuara en el futuro. Se estima en tiempo real, y la mas certera suele ser la que te proporciona el propio broker que operas    \n",
    "**¿Que es el OI?** Interes en abrir contratos, se utiliza para intuir las posiciones de los participantes, y dependiendo de la cola, deberiamos poder predecir, direccion del activo.   \n",
    "**¿Que es el VOL?** Cuando una order se ejecuta , forma parte del volumen. Es como la medida de los contratos negociados por unidad de tiempo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el proximo ariticulo, añadiremos la serie mas impoortante a nuestro data frame, y seguiremos adaptando el data frame a nuestras necesidades."
   ]
  }
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