{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Market simulation\n",
    "\n",
    "The purpose of this tutorial is to show a more in-depth guide on how to simulate a market using **Garpar**, along with all the available tweaks for the process.\n",
    "\n",
    "Currently, the system supports market simulation using one of the following distributions: normal, uniform, or Lévy stable. We will use the normal distribution for this tutorial.\n",
    "\n",
    "We can run our first simulation by doing the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='portfolio'>\n",
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\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>Stocks</th>\n",
       "      <th>S0[W 1.0, H 0.5]</th>\n",
       "      <th>S1[W 1.0, H 0.5]</th>\n",
       "      <th>S2[W 1.0, H 0.5]</th>\n",
       "      <th>S3[W 1.0, H 0.5]</th>\n",
       "      <th>S4[W 1.0, H 0.5]</th>\n",
       "      <th>S5[W 1.0, H 0.5]</th>\n",
       "      <th>S6[W 1.0, H 0.5]</th>\n",
       "      <th>S7[W 1.0, H 0.5]</th>\n",
       "      <th>S8[W 1.0, H 0.5]</th>\n",
       "      <th>S9[W 1.0, H 0.5]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Days</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.920895</td>\n",
       "      <td>100.977804</td>\n",
       "      <td>100.852994</td>\n",
       "      <td>100.826406</td>\n",
       "      <td>100.932308</td>\n",
       "      <td>100.669982</td>\n",
       "      <td>101.144285</td>\n",
       "      <td>100.609409</td>\n",
       "      <td>101.034065</td>\n",
       "      <td>99.131065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.737157</td>\n",
       "      <td>100.047650</td>\n",
       "      <td>102.133867</td>\n",
       "      <td>99.794720</td>\n",
       "      <td>102.073283</td>\n",
       "      <td>99.597965</td>\n",
       "      <td>102.487318</td>\n",
       "      <td>99.384048</td>\n",
       "      <td>102.063241</td>\n",
       "      <td>100.147446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98.763680</td>\n",
       "      <td>101.358082</td>\n",
       "      <td>100.744169</td>\n",
       "      <td>100.878668</td>\n",
       "      <td>102.866470</td>\n",
       "      <td>100.451176</td>\n",
       "      <td>103.731281</td>\n",
       "      <td>100.312369</td>\n",
       "      <td>100.952183</td>\n",
       "      <td>101.369123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.668174</td>\n",
       "      <td>102.252520</td>\n",
       "      <td>101.792928</td>\n",
       "      <td>100.161038</td>\n",
       "      <td>101.894385</td>\n",
       "      <td>99.596375</td>\n",
       "      <td>104.916545</td>\n",
       "      <td>99.330287</td>\n",
       "      <td>101.797925</td>\n",
       "      <td>102.292031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>76.875325</td>\n",
       "      <td>93.952644</td>\n",
       "      <td>100.549606</td>\n",
       "      <td>87.611397</td>\n",
       "      <td>112.544293</td>\n",
       "      <td>110.429192</td>\n",
       "      <td>102.198211</td>\n",
       "      <td>144.319422</td>\n",
       "      <td>116.985284</td>\n",
       "      <td>94.735930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>77.612021</td>\n",
       "      <td>94.650527</td>\n",
       "      <td>99.841613</td>\n",
       "      <td>88.421415</td>\n",
       "      <td>111.590615</td>\n",
       "      <td>109.173362</td>\n",
       "      <td>103.359683</td>\n",
       "      <td>145.449017</td>\n",
       "      <td>118.337632</td>\n",
       "      <td>95.648017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>76.815479</td>\n",
       "      <td>95.681774</td>\n",
       "      <td>98.860011</td>\n",
       "      <td>87.641372</td>\n",
       "      <td>110.641001</td>\n",
       "      <td>110.390700</td>\n",
       "      <td>104.475420</td>\n",
       "      <td>143.967196</td>\n",
       "      <td>119.704481</td>\n",
       "      <td>96.395229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>76.051003</td>\n",
       "      <td>94.824296</td>\n",
       "      <td>97.802788</td>\n",
       "      <td>86.922012</td>\n",
       "      <td>109.279200</td>\n",
       "      <td>108.982600</td>\n",
       "      <td>103.094116</td>\n",
       "      <td>142.692225</td>\n",
       "      <td>120.683205</td>\n",
       "      <td>95.596266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>76.597432</td>\n",
       "      <td>96.325666</td>\n",
       "      <td>98.649989</td>\n",
       "      <td>87.730029</td>\n",
       "      <td>110.371305</td>\n",
       "      <td>109.941169</td>\n",
       "      <td>104.306715</td>\n",
       "      <td>141.057011</td>\n",
       "      <td>119.776307</td>\n",
       "      <td>94.786646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><em class='portfolio-dim'>366 days x 10 stocks - W.Size 5</em>\n",
       "</div>"
      ],
      "text/plain": [
       "Stocks  S0[W 1.0, H 0.5]  S1[W 1.0, H 0.5]  S2[W 1.0, H 0.5]  \\\n",
       "Days                                                           \n",
       "0             100.000000        100.000000        100.000000   \n",
       "1              98.920895        100.977804        100.852994   \n",
       "2              99.737157        100.047650        102.133867   \n",
       "3              98.763680        101.358082        100.744169   \n",
       "4              99.668174        102.252520        101.792928   \n",
       "...                  ...               ...               ...   \n",
       "361            76.875325         93.952644        100.549606   \n",
       "362            77.612021         94.650527         99.841613   \n",
       "363            76.815479         95.681774         98.860011   \n",
       "364            76.051003         94.824296         97.802788   \n",
       "365            76.597432         96.325666         98.649989   \n",
       "\n",
       "Stocks  S3[W 1.0, H 0.5]  S4[W 1.0, H 0.5]  S5[W 1.0, H 0.5]  \\\n",
       "Days                                                           \n",
       "0             100.000000        100.000000        100.000000   \n",
       "1             100.826406        100.932308        100.669982   \n",
       "2              99.794720        102.073283         99.597965   \n",
       "3             100.878668        102.866470        100.451176   \n",
       "4             100.161038        101.894385         99.596375   \n",
       "...                  ...               ...               ...   \n",
       "361            87.611397        112.544293        110.429192   \n",
       "362            88.421415        111.590615        109.173362   \n",
       "363            87.641372        110.641001        110.390700   \n",
       "364            86.922012        109.279200        108.982600   \n",
       "365            87.730029        110.371305        109.941169   \n",
       "\n",
       "Stocks  S6[W 1.0, H 0.5]  S7[W 1.0, H 0.5]  S8[W 1.0, H 0.5]  S9[W 1.0, H 0.5]  \n",
       "Days                                                                            \n",
       "0             100.000000        100.000000        100.000000        100.000000  \n",
       "1             101.144285        100.609409        101.034065         99.131065  \n",
       "2             102.487318         99.384048        102.063241        100.147446  \n",
       "3             103.731281        100.312369        100.952183        101.369123  \n",
       "4             104.916545         99.330287        101.797925        102.292031  \n",
       "...                  ...               ...               ...               ...  \n",
       "361           102.198211        144.319422        116.985284         94.735930  \n",
       "362           103.359683        145.449017        118.337632         95.648017  \n",
       "363           104.475420        143.967196        119.704481         96.395229  \n",
       "364           103.094116        142.692225        120.683205         95.596266  \n",
       "365           104.306715        141.057011        119.776307         94.786646  \n",
       "StocksSet [366 days x 10 stocks - W.Size 5]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from garpar.datasets.risso import make_risso_normal\n",
    "\n",
    "make_risso_normal()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the simulation generates **10 stocks** with **365 days** of prices for each one. These prices follow the same distribution.\n",
    "\n",
    "For this tutorial, we will focus on three sections of the `StocksSet`:\n",
    "\n",
    "1. Entropy (`H`): Located in the top section, representing the entropy of the prices of each stock. By default, this value is `0.5`.\n",
    "2. Stock Prices: The section that displays the value of each stock for each day.\n",
    "3. Market Data: Found at the bottom, this section includes:\n",
    "   - Total days\n",
    "   - Number of stocks\n",
    "   - Window size (we will explain its significance as we progress).\n",
    "\n",
    "## Parameters of a Simulation\n",
    "\n",
    "There are several parameters that influence a simulation. This section will demonstrate how each parameter affects the results.\n",
    "\n",
    "Let's start with a simple example. Suppose we want to simulate **5 stocks** over **10 days**, each starting at a price of `110`. This can be achieved with the following function call:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='portfolio'>\n",
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stocks</th>\n",
       "      <th>S0[W 1.0, H 0.5]</th>\n",
       "      <th>S1[W 1.0, H 0.5]</th>\n",
       "      <th>S2[W 1.0, H 0.5]</th>\n",
       "      <th>S3[W 1.0, H 0.5]</th>\n",
       "      <th>S4[W 1.0, H 0.5]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Days</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111.168446</td>\n",
       "      <td>111.456351</td>\n",
       "      <td>108.637250</td>\n",
       "      <td>108.602480</td>\n",
       "      <td>109.120660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111.988896</td>\n",
       "      <td>112.660221</td>\n",
       "      <td>107.691671</td>\n",
       "      <td>107.776799</td>\n",
       "      <td>109.813859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>111.308701</td>\n",
       "      <td>113.787950</td>\n",
       "      <td>106.453914</td>\n",
       "      <td>107.095502</td>\n",
       "      <td>110.889846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110.389759</td>\n",
       "      <td>112.366001</td>\n",
       "      <td>105.768317</td>\n",
       "      <td>108.198458</td>\n",
       "      <td>112.004445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>109.425866</td>\n",
       "      <td>113.883860</td>\n",
       "      <td>107.072057</td>\n",
       "      <td>106.956759</td>\n",
       "      <td>110.984344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110.584283</td>\n",
       "      <td>113.220401</td>\n",
       "      <td>106.392518</td>\n",
       "      <td>107.621344</td>\n",
       "      <td>112.446833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>109.474168</td>\n",
       "      <td>114.262793</td>\n",
       "      <td>107.567329</td>\n",
       "      <td>108.428206</td>\n",
       "      <td>111.083256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>108.309656</td>\n",
       "      <td>115.667603</td>\n",
       "      <td>108.928582</td>\n",
       "      <td>109.556429</td>\n",
       "      <td>109.364552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>109.503205</td>\n",
       "      <td>114.872183</td>\n",
       "      <td>108.046792</td>\n",
       "      <td>111.019097</td>\n",
       "      <td>109.996908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110.668799</td>\n",
       "      <td>116.083915</td>\n",
       "      <td>107.279557</td>\n",
       "      <td>112.104631</td>\n",
       "      <td>111.150328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><em class='portfolio-dim'>11 days x 5 stocks - W.Size 5</em>\n",
       "</div>"
      ],
      "text/plain": [
       "Stocks  S0[W 1.0, H 0.5]  S1[W 1.0, H 0.5]  S2[W 1.0, H 0.5]  \\\n",
       "Days                                                           \n",
       "0             110.000000        110.000000        110.000000   \n",
       "1             111.168446        111.456351        108.637250   \n",
       "2             111.988896        112.660221        107.691671   \n",
       "3             111.308701        113.787950        106.453914   \n",
       "4             110.389759        112.366001        105.768317   \n",
       "5             109.425866        113.883860        107.072057   \n",
       "6             110.584283        113.220401        106.392518   \n",
       "7             109.474168        114.262793        107.567329   \n",
       "8             108.309656        115.667603        108.928582   \n",
       "9             109.503205        114.872183        108.046792   \n",
       "10            110.668799        116.083915        107.279557   \n",
       "\n",
       "Stocks  S3[W 1.0, H 0.5]  S4[W 1.0, H 0.5]  \n",
       "Days                                        \n",
       "0             110.000000        110.000000  \n",
       "1             108.602480        109.120660  \n",
       "2             107.776799        109.813859  \n",
       "3             107.095502        110.889846  \n",
       "4             108.198458        112.004445  \n",
       "5             106.956759        110.984344  \n",
       "6             107.621344        112.446833  \n",
       "7             108.428206        111.083256  \n",
       "8             109.556429        109.364552  \n",
       "9             111.019097        109.996908  \n",
       "10            112.104631        111.150328  \n",
       "StocksSet [11 days x 5 stocks - W.Size 5]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_risso_normal(days=10, stocks=5, price=110)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, we might want a more 'controlled' or even deterministic environment. We can achieve this by using a seed. The `random_state` parameter represents this seed, as shown in the following example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='portfolio'>\n",
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>Stocks</th>\n",
       "      <th>S0[W 1.0, H 0.5]</th>\n",
       "      <th>S1[W 1.0, H 0.5]</th>\n",
       "      <th>S2[W 1.0, H 0.5]</th>\n",
       "      <th>S3[W 1.0, H 0.5]</th>\n",
       "      <th>S4[W 1.0, H 0.5]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Days</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.805864</td>\n",
       "      <td>100.935769</td>\n",
       "      <td>99.326480</td>\n",
       "      <td>100.900464</td>\n",
       "      <td>100.649803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.723073</td>\n",
       "      <td>99.709073</td>\n",
       "      <td>98.300671</td>\n",
       "      <td>99.884634</td>\n",
       "      <td>101.809271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.481068</td>\n",
       "      <td>98.845525</td>\n",
       "      <td>99.570943</td>\n",
       "      <td>98.878556</td>\n",
       "      <td>100.721910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.870674</td>\n",
       "      <td>97.846148</td>\n",
       "      <td>100.461281</td>\n",
       "      <td>99.783308</td>\n",
       "      <td>99.528206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101.124337</td>\n",
       "      <td>96.897283</td>\n",
       "      <td>101.310699</td>\n",
       "      <td>98.637364</td>\n",
       "      <td>100.581171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>102.006936</td>\n",
       "      <td>95.770467</td>\n",
       "      <td>100.074504</td>\n",
       "      <td>99.746865</td>\n",
       "      <td>101.208367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>103.332098</td>\n",
       "      <td>94.844957</td>\n",
       "      <td>101.280570</td>\n",
       "      <td>100.450735</td>\n",
       "      <td>100.356336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>104.518001</td>\n",
       "      <td>94.306958</td>\n",
       "      <td>102.217987</td>\n",
       "      <td>99.473017</td>\n",
       "      <td>101.303750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>105.458859</td>\n",
       "      <td>93.176509</td>\n",
       "      <td>100.824345</td>\n",
       "      <td>100.715559</td>\n",
       "      <td>100.136298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>106.641517</td>\n",
       "      <td>92.403867</td>\n",
       "      <td>101.862962</td>\n",
       "      <td>99.497536</td>\n",
       "      <td>99.235579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><em class='portfolio-dim'>11 days x 5 stocks - W.Size 5</em>\n",
       "</div>"
      ],
      "text/plain": [
       "Stocks  S0[W 1.0, H 0.5]  S1[W 1.0, H 0.5]  S2[W 1.0, H 0.5]  \\\n",
       "Days                                                           \n",
       "0             100.000000        100.000000        100.000000   \n",
       "1              98.805864        100.935769         99.326480   \n",
       "2              99.723073         99.709073         98.300671   \n",
       "3             100.481068         98.845525         99.570943   \n",
       "4              99.870674         97.846148        100.461281   \n",
       "5             101.124337         96.897283        101.310699   \n",
       "6             102.006936         95.770467        100.074504   \n",
       "7             103.332098         94.844957        101.280570   \n",
       "8             104.518001         94.306958        102.217987   \n",
       "9             105.458859         93.176509        100.824345   \n",
       "10            106.641517         92.403867        101.862962   \n",
       "\n",
       "Stocks  S3[W 1.0, H 0.5]  S4[W 1.0, H 0.5]  \n",
       "Days                                        \n",
       "0             100.000000        100.000000  \n",
       "1             100.900464        100.649803  \n",
       "2              99.884634        101.809271  \n",
       "3              98.878556        100.721910  \n",
       "4              99.783308         99.528206  \n",
       "5              98.637364        100.581171  \n",
       "6              99.746865        101.208367  \n",
       "7             100.450735        100.356336  \n",
       "8              99.473017        101.303750  \n",
       "9             100.715559        100.136298  \n",
       "10             99.497536         99.235579  \n",
       "StocksSet [11 days x 5 stocks - W.Size 5]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_risso_normal(days=10, stocks=5, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to try this with different values of `random_state`.\n",
    "\n",
    "Now, let’s talk about the `H` value. This represents the entropy of a given stock, which can be thought of as the informational efficiency of the market. In simple terms, it describes how quickly information spreads throughout the market. Entropy is essentially a measure of unpredictability. When the entropy value is closer to 1.0, it means that the chances of winning or losing on any given day are nearly equal—making the stock's movements highly unpredictable. On the other hand, when entropy is closer to 0.0, it suggests that the chances of winning or losing are imbalanced, meaning the stock's behavior is more predictable or skewed in one direction.\n",
    "\n",
    "To test this, lets try using a high entropy value and a low entropy value. This will show how this concept is visualized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Price'}, xlabel='Days'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_risso_normal(stocks=1, entropy=1.0, random_state=702).plot.line(\n",
    "    palette=\"flare\"\n",
    ")\n",
    "make_risso_normal(stocks=1, entropy=0.0, random_state=702).plot.line(\n",
    "    palette=\"viridis\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the stock following the red line doesn't show a clear, sustained trend of either dropping or rising, while the one with the teal color tends to drop over time.\n",
    "\n",
    "> Note that we used a method `line` to create the graph, the attribute `plot` is known as an _accessor_ and we will explain them in one of the lasts sections of this tutorial.\n",
    "\n",
    "Another important parameter is the `window_size`, which represents how long an investor will hold a stock. By default, we've set it to 5 days. This value is combined with the entropy to determine the loss probability each day. So for example, lets assume the same entropy value and see how the stocks values diverge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Price'}, xlabel='Days'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_risso_normal(\n",
    "    stocks=1, entropy=0.5, window_size=5, random_state=702\n",
    ").plot.line(palette=\"flare\")\n",
    "make_risso_normal(\n",
    "    stocks=1, entropy=0.5, window_size=7, random_state=702\n",
    ").plot.line(palette=\"viridis\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the simulation produces different results based on the `window_size`.\n",
    "\n",
    "We’ve also included some validations. For example, if we try to simulate a market with fewer `days` than the `window_size`, the simulation will fail. In such cases, we issue a warning to inform the user about what's happening:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'window_size' must be in the interval (0, days]",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmake_risso_normal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdays\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstocks\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m42\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwindow_size\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m20\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/FAMAF/Tesis/garpar/garpar/datasets/risso.py:380\u001b[39m, in \u001b[36mmake_risso_normal\u001b[39m\u001b[34m(mu, sigma, entropy, random_state, n_jobs, verbose, **kwargs)\u001b[39m\n\u001b[32m    340\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Create a StocksSet instance using RissoNormal maker.\u001b[39;00m\n\u001b[32m    341\u001b[39m \n\u001b[32m    342\u001b[39m \u001b[33;03mThis function is intended to create a StocksSet instance using the\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    369\u001b[39m \u001b[33;03m    Generated stocks set instance.\u001b[39;00m\n\u001b[32m    370\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    371\u001b[39m maker = RissoNormal(\n\u001b[32m    372\u001b[39m     mu=mu,\n\u001b[32m    373\u001b[39m     sigma=sigma,\n\u001b[32m   (...)\u001b[39m\u001b[32m    377\u001b[39m     verbose=verbose,\n\u001b[32m    378\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m380\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmaker\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmake_stocks_set\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/FAMAF/Tesis/garpar/garpar/datasets/ds_base.py:345\u001b[39m, in \u001b[36mRandomEntropyStocksSetMakerABC.make_stocks_set\u001b[39m\u001b[34m(self, window_size, days, stocks, price, weights)\u001b[39m\n\u001b[32m    322\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Create a StocksSet instance with random prices.\u001b[39;00m\n\u001b[32m    323\u001b[39m \n\u001b[32m    324\u001b[39m \u001b[33;03mParameters\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    342\u001b[39m \n\u001b[32m    343\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    344\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m window_size <= \u001b[32m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m window_size > days:\n\u001b[32m--> \u001b[39m\u001b[32m345\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m\u001b[33mwindow_size\u001b[39m\u001b[33m'\u001b[39m\u001b[33m must be in the interval (0, days]\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m    347\u001b[39m initial_prices = \u001b[38;5;28mself\u001b[39m._coerce_price(stocks, price)\n\u001b[32m    349\u001b[39m loss_probability = \u001b[38;5;28mself\u001b[39m.get_window_loss_probability(\n\u001b[32m    350\u001b[39m     window_size, \u001b[38;5;28mself\u001b[39m.entropy\n\u001b[32m    351\u001b[39m )\n",
      "\u001b[31mValueError\u001b[39m: 'window_size' must be in the interval (0, days]"
     ]
    }
   ],
   "source": [
    "make_risso_normal(days=10, stocks=4, random_state=42, window_size=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we mentioned, there are other distributions that a market prices follow. Feel free to test them. In the current version we have the functions `make_risso_unifom` and `make_risso_levy_stable` appart from the `make_risso_normal` shown in the previous steps.\n",
    "\n",
    "There are other parameters that can be used in a simulation, such as `n_jobs`, which represents the number of concurrent 'workers' used for the simulation, or the `verbose` parameter, which, when set to `1`, shows details of such 'workers'.\n",
    "\n",
    "## Combining simulations\n",
    "\n",
    "We can use the `make_multisector` function to run the simulation with different distribution or simulation values at the same time. For example, if we want to see how the prices change with a particular seed and the three distributions, we can do something like the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='portfolio'>\n",
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stocks</th>\n",
       "      <th>rissonormal_S0[W 1.0, H 0.5]</th>\n",
       "      <th>rissouniform_S0[W 1.0, H 0.5]</th>\n",
       "      <th>rissolevystable_S0[W 1.0, H 0.5]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Days</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.805864</td>\n",
       "      <td>97.632493</td>\n",
       "      <td>99.783564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.723073</td>\n",
       "      <td>101.690331</td>\n",
       "      <td>100.449422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.481068</td>\n",
       "      <td>105.711307</td>\n",
       "      <td>101.042132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.870674</td>\n",
       "      <td>100.473104</td>\n",
       "      <td>100.711882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101.124337</td>\n",
       "      <td>104.818773</td>\n",
       "      <td>102.554182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>102.006936</td>\n",
       "      <td>108.857429</td>\n",
       "      <td>103.839387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>103.332098</td>\n",
       "      <td>114.204546</td>\n",
       "      <td>104.271329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>104.518001</td>\n",
       "      <td>118.185062</td>\n",
       "      <td>106.170553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>105.458859</td>\n",
       "      <td>120.886469</td>\n",
       "      <td>106.377469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>106.641517</td>\n",
       "      <td>122.287369</td>\n",
       "      <td>106.493258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><em class='portfolio-dim'>11 days x 3 stocks - W.Size 5</em>\n",
       "</div>"
      ],
      "text/plain": [
       "Stocks  rissonormal_S0[W 1.0, H 0.5]  rissouniform_S0[W 1.0, H 0.5]  \\\n",
       "Days                                                                  \n",
       "0                         100.000000                     100.000000   \n",
       "1                          98.805864                      97.632493   \n",
       "2                          99.723073                     101.690331   \n",
       "3                         100.481068                     105.711307   \n",
       "4                          99.870674                     100.473104   \n",
       "5                         101.124337                     104.818773   \n",
       "6                         102.006936                     108.857429   \n",
       "7                         103.332098                     114.204546   \n",
       "8                         104.518001                     118.185062   \n",
       "9                         105.458859                     120.886469   \n",
       "10                        106.641517                     122.287369   \n",
       "\n",
       "Stocks  rissolevystable_S0[W 1.0, H 0.5]  \n",
       "Days                                      \n",
       "0                             100.000000  \n",
       "1                              99.783564  \n",
       "2                             100.449422  \n",
       "3                             101.042132  \n",
       "4                             100.711882  \n",
       "5                             102.554182  \n",
       "6                             103.839387  \n",
       "7                             104.271329  \n",
       "8                             106.170553  \n",
       "9                             106.377469  \n",
       "10                            106.493258  \n",
       "StocksSet [11 days x 3 stocks - W.Size 5]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from garpar.datasets import (\n",
    "    RissoUniform,\n",
    "    RissoNormal,\n",
    "    RissoLevyStable,\n",
    "    make_multisector,\n",
    ")\n",
    "\n",
    "make_multisector(\n",
    "    RissoNormal(random_state=42),\n",
    "    RissoUniform(random_state=42),\n",
    "    RissoLevyStable(random_state=42),\n",
    "    days=10,\n",
    "    stocks=3,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classes RissoUniform, RissoNormal and RissoLevyStable are known as makers in our system. Objects of these classes are in charge of creating `StocksSet`, the difference between each one is the distribution a generated `StocksSet` prices follow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The accessors\n",
    "\n",
    "The accessors are properties that allow an object to be treated as a particular tipe. When we introduce the entropy value, we utilized the property `plot`, this property had an interface inside to make different plots of the `StocksSet`. We used only the line plot, but if we want to make a boxplot; we will simply make something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Price'}, xlabel='Stocks'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_risso_normal(\n",
    "    stocks=1, entropy=0.5, window_size=7, random_state=702\n",
    ").plot.box(palette=\"viridis\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many other accessors. Feel free to look for the Core subpackage in the [**API section**](../api/garpar.core.rst), where we published every function prototype alongside its documentation!\n",
    "\n",
    "Now that you know how the simulation works, we recommend you to follow [this tutorial](portfolio_optimization.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "garpar",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}