Paper_Portfolio_Group_Constraints.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figures & Statistics for the Paper:\n",
    "# Portfolio Group Constraints\n",
    "\n",
    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "/ [GitHub](https://github.com/Hvass-Labs/FinanceOps) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmlHaWuVxIA0pKL1yjryR0Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This Python Notebook produces the plots and statistics used in the paper entitled **\"Portfolio Group Constraints\"** which can be downloaded from [SSRN](https://ssrn.com/abstract=4033243) and [GitHub](https://github.com/Hvass-Labs/Finance-Papers).\n",
    "\n",
    "See the [GitHub repository](https://github.com/Hvass-Labs/FinanceOps) for instructions on how to install and run this Python Notebook. The Python source-code is well-documented so you can hopefully understand and modify it yourself, but there is otherwise a minimum of explanations in this Notebook because it is all explained in the paper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Imports\n",
    "\n",
    "This Jupyter Notebook is implemented in Python v. 3.8 and requires various packages for numerical computations and plotting. See the installation instructions in the README-file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports from Python packages.\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# InvestOps.\n",
    "import investops as iv\n",
    "from investops.group_constraints import GroupConstraints\n",
    "from investops.random import (rand_normal, rand_uniform,\n",
    "                              rand_where, rand_groups,\n",
    "                              gen_asset_names, gen_group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.3.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# InvestOps version.\n",
    "iv.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random number generator.\n",
    "# The seed makes the experiments repeatable.\n",
    "rng = np.random.default_rng(seed=80085)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create directory for plots if it does not exist already.\n",
    "path_plots = 'plots/group_constraints/'\n",
    "if not os.path.exists(path_plots):\n",
    "    os.makedirs(path_plots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seaborn set plotting style.\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot-sizes.\n",
    "figsize_small = (10, 4)\n",
    "figsize_mid = (10, 8)\n",
    "figsize_big = (10, 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Example - Positive Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The individual group-names.\n",
    "GROUP_A = 'Group A'\n",
    "GROUP_B = 'Group B'\n",
    "GROUP_C = 'Group C'\n",
    "\n",
    "# List of all the group-names.\n",
    "group_names = [GROUP_A, GROUP_B, GROUP_C]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The individual asset-names.\n",
    "ASSET_1 = 'Asset 1'\n",
    "ASSET_2 = 'Asset 2'\n",
    "ASSET_3 = 'Asset 3'\n",
    "ASSET_4 = 'Asset 4'\n",
    "\n",
    "# List of all the asset-names.\n",
    "asset_names = [ASSET_1, ASSET_2, ASSET_3, ASSET_4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This data-structure defines the association between assets\n",
    "# and groups. It is a dictionary of lists, so it enables us to\n",
    "# lookup the list of groups that are associated with an asset-name.\n",
    "asset_to_groups = \\\n",
    "{\n",
    "    # Groups that Asset 1 belongs to.\n",
    "    ASSET_1: [GROUP_A],\n",
    "    \n",
    "    # Groups that Asset 2 belongs to.\n",
    "    ASSET_2: [GROUP_A, GROUP_B],\n",
    "    \n",
    "    # Groups that Asset 3 belongs to.\n",
    "    ASSET_3: [GROUP_B, GROUP_C],\n",
    "    \n",
    "    # Groups that Asset 4 belongs to.\n",
    "    ASSET_4: [GROUP_C],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group-limits for the POSITIVE portfolio weights.\n",
    "# This must be a Pandas Series so the groups are named properly.\n",
    "group_lim_pos = {GROUP_A: 0.05, GROUP_B: 0.1, GROUP_C: 0.2}\n",
    "group_lim_pos = pd.Series(group_lim_pos)\n",
    "\n",
    "# We do not use group-limits for the NEGATIVE portfolio weights.\n",
    "group_lim_neg = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original portfolio weights found through some other process,\n",
    "# e.g. by estimating the future asset-returns and use them to\n",
    "# determine how much of the portfolio to invest in each asset.\n",
    "weights_org = {ASSET_1:0.05, ASSET_2:0.1, ASSET_3:0.15, ASSET_4:0.2}\n",
    "weights_org = pd.Series(weights_org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the solver for the Group Constraints.\n",
    "# This sets up internal data-structures for efficiently solving\n",
    "# the problem. If you later change the asset-names, group-names,\n",
    "# group-limits, or the mapping from assets to lists of groups,\n",
    "# then you must create a new instance of this solver.\n",
    "grp = GroupConstraints(asset_names=asset_names,\n",
    "                       group_names=group_names,\n",
    "                       asset_to_groups=asset_to_groups,\n",
    "                       group_lim_pos=group_lim_pos,\n",
    "                       group_lim_neg=group_lim_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Asset 1    0.016667\n",
       "Asset 2    0.033333\n",
       "Asset 3    0.065788\n",
       "Asset 4    0.134212\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the adjusted weights that satisfy the group constraints.\n",
    "weights_new = grp.constrain_weights(weights_org=weights_org)\n",
    "weights_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum of the adjusted portfolio weights.\n",
    "weights_new.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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>Asset 1</th>\n",
       "      <th>Asset 2</th>\n",
       "      <th>Asset 3</th>\n",
       "      <th>Asset 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.114286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.064286</td>\n",
       "      <td>0.131148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.065788</td>\n",
       "      <td>0.134212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.065788</td>\n",
       "      <td>0.134212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Asset 1   Asset 2   Asset 3   Asset 4\n",
       "0  0.050000  0.100000  0.150000  0.200000\n",
       "1  0.016667  0.033333  0.060000  0.114286\n",
       "2  0.016667  0.033333  0.064286  0.131148\n",
       "3  0.016667  0.033333  0.065788  0.134212\n",
       "4  0.016667  0.033333  0.065788  0.134212"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the adjusted weights that satisfy the group constraints.\n",
    "# This logs the adjusted weights for all iterations of the algorithm.\n",
    "# Note that it is significantly slower to log all results, so this\n",
    "# should only be done for testing / debugging purposes, and not for\n",
    "# an actual trading or back-testing-system.\n",
    "weights_new_log = grp.constrain_weights(weights_org=weights_org,\n",
    "                                        log=True)\n",
    "weights_new_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group A</th>\n",
       "      <th>Group B</th>\n",
       "      <th>Group C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.174286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.097619</td>\n",
       "      <td>0.195433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.099121</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.099121</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group A   Group B   Group C\n",
       "0     0.15  0.250000  0.350000\n",
       "1     0.05  0.093333  0.174286\n",
       "2     0.05  0.097619  0.195433\n",
       "3     0.05  0.099121  0.200000\n",
       "4     0.05  0.099121  0.200000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the group-sums.\n",
    "group_sums_pos, group_sums_neg = grp.group_sums(weights=weights_new_log)\n",
    "group_sums_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group A</th>\n",
       "      <th>Group B</th>\n",
       "      <th>Group C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "      <td>1.147541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.024390</td>\n",
       "      <td>1.023367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.008866</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.008866</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Group A   Group B   Group C\n",
       "0  0.333333  0.400000  0.571429\n",
       "1  1.000000  1.071429  1.147541\n",
       "2  1.000000  1.024390  1.023367\n",
       "3  1.000000  1.008866  1.000000\n",
       "4  1.000000  1.008866  1.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the group-ratios between the group-limits / group-sums.\n",
    "group_ratios_pos, group_ratios_neg = grp.group_ratios(weights=weights_new_log)\n",
    "group_ratios_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternative portfolio weights.\n",
    "weights_alt = {ASSET_1:0.05, ASSET_2:0.0, ASSET_3:0.1, ASSET_4:0.1}\n",
    "weights_alt = pd.Series(weights_alt)\n",
    "\n",
    "# Sum of the alternative weights.\n",
    "weights_alt.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Group A    0.05\n",
       " Group B    0.10\n",
       " Group C    0.20\n",
       " dtype: float64,\n",
       " Group A   -0.0\n",
       " Group B   -0.0\n",
       " Group C   -0.0\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group-sums for the alternative weights.\n",
    "grp.group_sums(weights=weights_alt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Group A    1.0\n",
       " Group B    1.0\n",
       " Group C    1.0\n",
       " dtype: float64,\n",
       " None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group-ratios for the alternative weights.\n",
    "grp.group_ratios(weights=weights_alt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Example - Positive & Negative Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group-limits for the POSITIVE portfolio weights.\n",
    "group_lim_pos = {GROUP_A: 0.05, GROUP_B: 0.1, GROUP_C: 0.2}\n",
    "group_lim_pos = pd.Series(group_lim_pos)\n",
    "\n",
    "# Group-limits for the NEGATIVE portfolio weights.\n",
    "group_lim_neg = -group_lim_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original portfolio weights.\n",
    "weights_org = {ASSET_1:-0.05, ASSET_2:-0.1, ASSET_3:0.15, ASSET_4:0.2}\n",
    "weights_org = pd.Series(weights_org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the solver for the Group Constraints.\n",
    "grp = GroupConstraints(asset_names=asset_names,\n",
    "                       group_names=group_names,\n",
    "                       asset_to_groups=asset_to_groups,\n",
    "                       group_lim_pos=group_lim_pos,\n",
    "                       group_lim_neg=group_lim_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Asset 1   -0.016667\n",
       "Asset 2   -0.033333\n",
       "Asset 3    0.085714\n",
       "Asset 4    0.114286\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the adjusted weights that satisfy the group constraints.\n",
    "weights_new = grp.constrain_weights(weights_org=weights_org)\n",
    "weights_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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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>Asset 1</th>\n",
       "      <th>Asset 2</th>\n",
       "      <th>Asset 3</th>\n",
       "      <th>Asset 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.050000</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.016667</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.114286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.016667</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.114286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Asset 1   Asset 2   Asset 3   Asset 4\n",
       "0 -0.050000 -0.100000  0.150000  0.200000\n",
       "1 -0.016667 -0.033333  0.085714  0.114286\n",
       "2 -0.016667 -0.033333  0.085714  0.114286"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the iterations of all the adjusted weights.\n",
    "weights_new_log = grp.constrain_weights(weights_org=weights_org,\n",
    "                                        log=True)\n",
    "weights_new_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group A</th>\n",
       "      <th>Group B</th>\n",
       "      <th>Group C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group A   Group B  Group C\n",
       "0      0.0  0.150000     0.35\n",
       "1      0.0  0.085714     0.20\n",
       "2      0.0  0.085714     0.20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group A</th>\n",
       "      <th>Group B</th>\n",
       "      <th>Group C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group A   Group B  Group C\n",
       "0    -0.15 -0.100000     -0.0\n",
       "1    -0.05 -0.033333     -0.0\n",
       "2    -0.05 -0.033333     -0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group-sums.\n",
    "group_sums_pos, group_sums_neg = grp.group_sums(weights=weights_new_log)\n",
    "display(group_sums_pos)\n",
    "display(group_sums_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group A</th>\n",
       "      <th>Group B</th>\n",
       "      <th>Group C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>inf</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>inf</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group A   Group B   Group C\n",
       "0      inf  0.666667  0.571429\n",
       "1      inf  1.166667  1.000000\n",
       "2      inf  1.166667  1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group A</th>\n",
       "      <th>Group B</th>\n",
       "      <th>Group C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Group A  Group B  Group C\n",
       "0  0.333333      1.0      inf\n",
       "1  1.000000      3.0      inf\n",
       "2  1.000000      3.0      inf"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group-ratios between the group-limits / group-sums.\n",
    "group_ratios_pos, group_ratios_neg = grp.group_ratios(weights=weights_new_log)\n",
    "display(group_ratios_pos)\n",
    "display(group_ratios_neg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rand_portfolio(num_assets, num_groups):\n",
    "    \"\"\"\n",
    "    Generate random portfolio weights and random associations\n",
    "    between groups and assets.\n",
    "    \n",
    "    :param num_assets: Int with number of assets.\n",
    "    :param num_groups: Int with number of groups.\n",
    "    :return: \n",
    "        - GroupConstraints object.\n",
    "        - Pandas Series with random portfolio weights.\n",
    "    \"\"\"\n",
    "    # Default asset and group names.\n",
    "    asset_names = gen_asset_names(num_assets=num_assets)\n",
    "    group_names = gen_group_names(num_groups=num_groups)\n",
    "\n",
    "    # Dict with random mappings from asset-names to lists of group-names.\n",
    "    asset_to_groups = \\\n",
    "        rand_groups(rng=rng, num_assets=num_assets, num_groups=num_groups,\n",
    "                    min_groups_per_asset=1,\n",
    "                    max_groups_per_asset=num_groups,\n",
    "                    asset_names=asset_names, group_names=group_names)\n",
    "\n",
    "    # Random positive group-limits. These should not be zero!\n",
    "    group_lim_pos = rand_uniform(rng=rng, index=group_names,\n",
    "                                 low=0.05, high=0.2)\n",
    "    \n",
    "    # Random negative group-limits. These should not be zero!\n",
    "    group_lim_neg = rand_uniform(rng=rng, index=group_names,\n",
    "                                 low=-0.2, high=-0.05)\n",
    "\n",
    "    # Randomly set some of the group-limits to infinity.\n",
    "    prob = 0.05\n",
    "    group_lim_pos = \\\n",
    "        rand_where(rng=rng, x=group_lim_pos, y=np.inf, prob=prob)\n",
    "    group_lim_neg = \\\n",
    "        rand_where(rng=rng, x=group_lim_neg, y=-np.inf, prob=prob)\n",
    "\n",
    "    # Create the solver for portfolio group constraints.\n",
    "    grp = GroupConstraints(asset_to_groups=asset_to_groups,\n",
    "                           group_lim_pos=group_lim_pos,\n",
    "                           group_lim_neg=group_lim_neg,\n",
    "                           asset_names=asset_names,\n",
    "                           group_names=group_names)\n",
    "\n",
    "    # Random portfolio weights.\n",
    "    weights_org = rand_normal(rng=rng, index=asset_names,\n",
    "                              low=-1.0, high=1.0,\n",
    "                              mean=0.0, std=0.005)\n",
    "\n",
    "    return grp, weights_org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Big Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of assets.\n",
    "num_assets = 1000\n",
    "\n",
    "# Number of groups.\n",
    "num_groups = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random portfolio.\n",
    "grp, weights_org = rand_portfolio(num_assets=num_assets, num_groups=num_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asset 0</th>\n",
       "      <th>Asset 1</th>\n",
       "      <th>Asset 2</th>\n",
       "      <th>Asset 3</th>\n",
       "      <th>Asset 4</th>\n",
       "      <th>Asset 5</th>\n",
       "      <th>Asset 6</th>\n",
       "      <th>Asset 7</th>\n",
       "      <th>Asset 8</th>\n",
       "      <th>Asset 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Asset 990</th>\n",
       "      <th>Asset 991</th>\n",
       "      <th>Asset 992</th>\n",
       "      <th>Asset 993</th>\n",
       "      <th>Asset 994</th>\n",
       "      <th>Asset 995</th>\n",
       "      <th>Asset 996</th>\n",
       "      <th>Asset 997</th>\n",
       "      <th>Asset 998</th>\n",
       "      <th>Asset 999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005447</td>\n",
       "      <td>-0.003987</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>5.660581e-06</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>-0.003270</td>\n",
       "      <td>0.008778</td>\n",
       "      <td>0.002518</td>\n",
       "      <td>0.005334</td>\n",
       "      <td>0.008926</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002840</td>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>0.005715</td>\n",
       "      <td>-0.004063</td>\n",
       "      <td>-0.001948</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.001340</td>\n",
       "      <td>0.005342</td>\n",
       "      <td>0.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000319</td>\n",
       "      <td>-0.000270</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>4.136658e-07</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>-0.000198</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000178</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>-0.000280</td>\n",
       "      <td>-0.000118</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000353</td>\n",
       "      <td>-0.000294</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>5.371166e-07</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>-0.000198</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>-0.000309</td>\n",
       "      <td>-0.000118</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000377</td>\n",
       "      <td>-0.000314</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>5.371166e-07</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>-0.000198</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000184</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>-0.000335</td>\n",
       "      <td>-0.000118</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000393</td>\n",
       "      <td>-0.000329</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>5.371166e-07</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>-0.000198</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000186</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>-0.000356</td>\n",
       "      <td>-0.000118</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Asset 0   Asset 1   Asset 2       Asset 3   Asset 4   Asset 5   Asset 6  \\\n",
       "0  0.005447 -0.003987  0.001680  5.660581e-06  0.000891 -0.003270  0.008778   \n",
       "1  0.000319 -0.000270  0.000084  4.136658e-07  0.000071 -0.000198  0.000642   \n",
       "2  0.000353 -0.000294  0.000084  5.371166e-07  0.000101 -0.000198  0.000833   \n",
       "3  0.000377 -0.000314  0.000084  5.371166e-07  0.000129 -0.000198  0.000986   \n",
       "4  0.000393 -0.000329  0.000084  5.371166e-07  0.000153 -0.000198  0.001092   \n",
       "\n",
       "    Asset 7   Asset 8   Asset 9  ...  Asset 990  Asset 991  Asset 992  \\\n",
       "0  0.002518  0.005334  0.008926  ...  -0.002840   0.006320   0.000939   \n",
       "1  0.000147  0.000268  0.000449  ...  -0.000178   0.000318   0.000047   \n",
       "2  0.000163  0.000268  0.000449  ...  -0.000182   0.000318   0.000047   \n",
       "3  0.000174  0.000268  0.000449  ...  -0.000184   0.000318   0.000047   \n",
       "4  0.000182  0.000268  0.000449  ...  -0.000186   0.000318   0.000047   \n",
       "\n",
       "   Asset 993  Asset 994  Asset 995  Asset 996  Asset 997  Asset 998  Asset 999  \n",
       "0   0.005715  -0.004063  -0.001948   0.000231   0.001340   0.005342   0.000153  \n",
       "1   0.000335  -0.000280  -0.000118   0.000041   0.000098   0.000269   0.000008  \n",
       "2   0.000371  -0.000309  -0.000118   0.000118   0.000127   0.000269   0.000008  \n",
       "3   0.000396  -0.000335  -0.000118   0.000231   0.000150   0.000269   0.000008  \n",
       "4   0.000412  -0.000356  -0.000118   0.000231   0.000167   0.000269   0.000008  \n",
       "\n",
       "[5 rows x 1000 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate and log the adjusted weights satisfying the constraints.\n",
    "weights_new_log = grp.constrain_weights(weights_org=weights_org, log=True, max_iter=20)\n",
    "weights_new_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with the log's weight-ratios and group-ratios.\n",
    "fig = grp.plot_log(weights_org=weights_org,\n",
    "                   weights_new_log=weights_new_log,\n",
    "                   all_xticks=True, rasterized=True)\n",
    "\n",
    "# Save the figure to a file.\n",
    "filename = 'Portfolio Group Constraints - Convergence Log.svg'\n",
    "filename = os.path.join(path_plots, filename)\n",
    "fig.savefig(filename, bbox_inches='tight')\n",
    "\n",
    "# Show the figure here.\n",
    "fig;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Usage\n",
    "\n",
    "Perform thousands of trials with random portfolios to measure the actual time-usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the functions once to compile with Numba Jit,\n",
    "# otherwise it could distort the timing tests below.\n",
    "\n",
    "# Generate a random portfolio.\n",
    "grp, weights_org = rand_portfolio(num_assets=100, num_groups=10)\n",
    "\n",
    "# Calculate the constrained portfolio weights.\n",
    "weights_new = grp.constrain_weights(weights_org=weights_org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Names used in log of results.\n",
    "NUM_ASSETS = 'Num Assets'\n",
    "NUM_GROUPS = 'Num Groups'\n",
    "TIME = 'Time / msec'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_append(num_assets, num_groups, start_time, end_time):\n",
    "    \"\"\"\n",
    "    Append the results to the log.\n",
    "\n",
    "    :param num_assets: Int with number of portfolio assets.\n",
    "    :param num_groups: Int with number of groups.\n",
    "    :param start_time: Float with start-time.\n",
    "    :param end_time: Float with end-time.\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    log_data = \\\n",
    "    {\n",
    "        NUM_ASSETS: num_assets,\n",
    "        NUM_GROUPS: num_groups,\n",
    "        TIME: (end_time - start_time) * 1000,\n",
    "    }\n",
    "    log.append(log_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of assets in each random portfolio.\n",
    "num_assets = [50, 100, 250, 500, 750, 1000, 1250, 1500, 1750, 2000]\n",
    "\n",
    "# Number of groups in each random portfolio.\n",
    "num_groups = [1, 10, 50, 100, 200, 300, 400, 500]\n",
    "\n",
    "# Min / max number of random trials for each portfolio config.\n",
    "min_trials = 10\n",
    "max_trials = 100\n",
    "\n",
    "# Max iterations for the group-constraint algorithm.\n",
    "max_iter = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14min 10s, sys: 3.46 s, total: 14min 14s\n",
      "Wall time: 14min 8s\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num Assets</th>\n",
       "      <th>Num Groups</th>\n",
       "      <th>Time / msec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2.515793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>3.270626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2.907753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>3.332376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2.494097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Num Assets  Num Groups  Time / msec\n",
       "0          50           1     2.515793\n",
       "1          50           1     3.270626\n",
       "2          50           1     2.907753\n",
       "3          50           1     3.332376\n",
       "4          50           1     2.494097"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Initialize log with results.\n",
    "log = []\n",
    "\n",
    "# For each number of assets in the portfolio.\n",
    "for n in num_assets:\n",
    "    # For each number of groups.\n",
    "    for g in num_groups:\n",
    "        # Initialize sum of time-usage.\n",
    "        sum_time = 0\n",
    "\n",
    "        # Initialize number of random trials performed.\n",
    "        num_trials = 0\n",
    "\n",
    "        # Repeat until enough random trials have been performed.\n",
    "        while num_trials < max_trials:\n",
    "            # Print status.\n",
    "            print(f'num_assets={n}, num_groups={g}      ', end='\\r')\n",
    "\n",
    "            # Generate random portfolio.\n",
    "            # We don't measure the time used to initialize the solver.\n",
    "            grp, weights_org = rand_portfolio(num_assets=n, num_groups=g)\n",
    "\n",
    "            # Time the group-constraint algorithm on this portfolio.\n",
    "            start_time = time()\n",
    "            weights_new = grp.constrain_weights(weights_org=weights_org, max_iter=max_iter)\n",
    "            end_time = time()\n",
    "            sum_time += end_time - start_time\n",
    "            log_append(num_assets=n, num_groups=g,\n",
    "                       start_time=start_time, end_time=end_time)\n",
    "\n",
    "            # Increase number of trials performed.\n",
    "            num_trials += 1\n",
    "\n",
    "            # Break out of for-loop if spent more than 1 second.\n",
    "            if num_trials > min_trials and sum_time > 1:\n",
    "                break\n",
    "\n",
    "# Convert log to Pandas DataFrame.\n",
    "df_log = pd.DataFrame(log)\n",
    "\n",
    "# Show the first rows of the log.\n",
    "df_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     80.000000\n",
       "mean      99.712500\n",
       "std        1.608472\n",
       "min       89.000000\n",
       "25%      100.000000\n",
       "50%      100.000000\n",
       "75%      100.000000\n",
       "max      100.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stats for num trials per combination of num_assets and num_groups.\n",
    "df_log.groupby([NUM_ASSETS, NUM_GROUPS]).apply(lambda df: len(df)).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot time-usage with num_assets on x-axis and num_groups as hue.\n",
    "\n",
    "# Create figure.\n",
    "fig, ax = plt.subplots(figsize=figsize_small)\n",
    "\n",
    "# Plot the data.\n",
    "sns.lineplot(data=df_log, x=NUM_ASSETS, y=TIME, hue=NUM_GROUPS,\n",
    "             ci=99, ax=ax);\n",
    "\n",
    "# Save plot to a file.\n",
    "filename = 'Time-Usage Comparison (x = Num Assets).svg'\n",
    "filename = os.path.join(path_plots, filename)\n",
    "fig.savefig(filename, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot time-usage with num_groups on x-axis and num_assets as hue.\n",
    "\n",
    "# Create figure.\n",
    "fig, ax = plt.subplots(figsize=figsize_small)\n",
    "\n",
    "# Plot the data.\n",
    "sns.lineplot(data=df_log, x=NUM_GROUPS, y=TIME, hue=NUM_ASSETS,\n",
    "             ci=99, ax=ax);\n",
    "\n",
    "# Save plot to a file.\n",
    "filename = 'Time-Usage Comparison (x = Num Groups).svg'\n",
    "filename = os.path.join(path_plots, filename)\n",
    "fig.savefig(filename, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License (MIT)\n",
    "\n",
    "Copyright (c) 2022 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}