portfolio_optimization/portfolio_optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93785316-d761-43f1-b48c-e7bbec5af9a7",
   "metadata": {},
   "source": [
    "# Portfolio optimization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0fad25a-52ba-4705-810e-bc53681b3651",
   "metadata": {},
   "source": [
    "## Introduction \n",
    "\n",
    "We consider the portfolio optimization problem, where an investor aims to allocate capital among $N$ assets within a single period. Portfolio optimization is a fundamental concept in finance that focuses on balancing risk and return, The objective is to determine the optimal weights to assign to each asset in the portfolio.\n",
    "\n",
    "![Example](pfo.png \"Example\")\n",
    "\n",
    "This illustration shows the percentage of capital to invest in each asset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161db149-2a67-4633-9134-fc8174e10e5d",
   "metadata": {},
   "source": [
    "## Notebook installation requirements "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0014dc6d-27a2-4d24-8cb3-0da5a4ea905b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All packages have been successfully installed.\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "import sys\n",
    "\n",
    "def install_requirements():\n",
    "    try:\n",
    "        with open('requirements.txt') as f:\n",
    "            packages = f.read().splitlines()\n",
    "        subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])\n",
    "        print(\"All packages have been successfully installed.\")\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred while installing the packages: {e}\")\n",
    "\n",
    "install_requirements()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b061208-0c01-4878-888d-cce2d563d047",
   "metadata": {},
   "source": [
    "## Problem description\n",
    "\n",
    "There are multiple variants of portfolio optimization problems. Let's start with the most fundamental one - the Markovitz model - which minimizes the total variance under a fixed return.\n",
    "\n",
    "**Input**\n",
    "\n",
    "* $N \\in \\mathbb{N}$ assets; for each asset $j$, $j = 1, \\dots, N$, its expected return $M_j \\in \\mathbb{R}^+$\n",
    "* A desired return $R \\in \\mathbb{R}^+$\n",
    "* For each pair of assets $(i, j)$, $i = 1, \\dots, N$, $j = 1, \\dots, N$, its covariance $C_i^j \\in \\mathbb{R}^+$\n",
    "\n",
    "**Problem**: find for each asset, the proportion of the capital invested in it, such that the desired return is met.\n",
    "\n",
    "**Objective**: minimize the risk (total variance)\n",
    "$$\n",
    "w^T C w\n",
    "$$\n",
    "where $w$ represents the vector of allocations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "954cc8a3-3226-4097-b5bd-255c935504e0",
   "metadata": {},
   "source": [
    "## Mathematical model \n",
    "\n",
    "**Variables**:\n",
    "\n",
    "* $w_i \\in [0,1]$,  $i = 1, \\dots, N$: proportion of capital invested in asset $i$ (weights to assign to each asset in the portfolio)\n",
    "\n",
    "**Objective**:\n",
    "* Minimize the risk (total variance)\n",
    "$$\n",
    "\\sum_{i=1}^N{\\sum_{j=1}^N{C_i^jw_iw_j}}\n",
    "$$\n",
    "\n",
    "**Constraints**:\n",
    "\n",
    "* Expected return\n",
    "$$\n",
    "\\sum_{i=1}^N{w_iM_i}\\geq R\n",
    "$$\n",
    "\n",
    "* Sum of proportions equals to 1 \n",
    "$$\n",
    "\\sum_{i=1}^N{w_i}= 1\n",
    "$$\n",
    "\n",
    "The model has the following properties:\n",
    "\n",
    "* Continuous variables only\n",
    "* Quadratic objective\n",
    "* Convex (since a covariance matrix is positive semi-definite)\n",
    "* Therefore, it is a convex QP\n",
    "\n",
    "We solve the problem with Artelys Knitro using the Pyomo modeler."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154c2f8a-fad4-413c-ab7e-db9dfbec9732",
   "metadata": {},
   "source": [
    "## Input\n",
    "\n",
    "We retrieve realistic input data from http://www.nasdaq.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4700913-1d0d-4f7e-a24a-7331c0e71a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_assets = 10\n",
    "\n",
    "expected_returns = [8.68097, -6.08624, -66.84089, -69.02419, -0.61631, 10.46047, 7.63278, 20.49615, -0.257636, 19.25747]\n",
    "\n",
    "cov_matrix = [[1.75, 0.38, 0.54, 0.63, -0.14, 0.29, -0.15, -0.19, 0.06, 0.10],\n",
    "[0.38, 1.74, 0.75, 2.13, -0.14, 0.30, 0.86, 0.24, 0.28, 0.47],\n",
    "[0.54, 0.75, 6.85, 0.31, -0.01, 0.84, -0.14, 0.71, 0.50, 0.95],\n",
    "[0.63, 2.13, 0.31, 10.31, 0.48, 0.27, 0.23, 0.50, -0.02, 0.47],\n",
    "[-0.14, -0.14, -0.01, 0.48, 1.24, -0.02, 0.12, 0.15, -0.10, 0.46],\n",
    "[0.29, 0.30, 0.84, 0.27, -0.02, 1.08, 0.54, 0.38, 0.54, 0.75],\n",
    "[-0.15, 0.86, -0.14, 0.23, 0.12, 0.54, 0.97, 0.49, 0.44, 0.60],\n",
    "[-0.19, 0.24, 0.71, 0.50, 0.15, 0.38, 0.49, 2.35, 0.46, 0.86],\n",
    "[0.06, 0.28, 0.50, -0.02, -0.10, 0.54, 0.44, 0.46, 0.84, 0.37],\n",
    "[0.10, 0.47, 0.95, 0.47, 0.46, 0.75, 0.60, 0.86, 0.37, 3.16]]\n",
    "\n",
    "desired_return = 10\n",
    "\n",
    "assets_names = [\n",
    "    \"Facebook\",\n",
    "    \"Intel\",\n",
    "    \"Frontier\",\n",
    "    \"Micron\",\n",
    "    \"Apple\",\n",
    "    \"Qualcomm\",\n",
    "    \"Sirius\",\n",
    "    \"App. Mat.\",\n",
    "    \"Cisco\",\n",
    "    \"Yahoo\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c848d0b-a57b-4da6-876b-990387bd3e22",
   "metadata": {},
   "source": [
    "## Pyomo model\n",
    "\n",
    "Let's write the model with the modeler Pyomo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fcb475d0-7d97-4504-a1c0-b3c58dbcd153",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyomo.environ as pyo\n",
    "\n",
    "def build_model(nb_assets, expected_returns, cov_matrix, desired_return):\n",
    "    model = pyo.ConcreteModel()\n",
    "\n",
    "    ####################\n",
    "    # Create variables #\n",
    "    ####################\n",
    "\n",
    "    model.N = pyo.RangeSet(0, nb_assets - 1)\n",
    "    model.w = pyo.Var(model.N, within=pyo.NonNegativeReals)\n",
    "\n",
    "    #############\n",
    "    # Objective #\n",
    "    #############\n",
    "\n",
    "    model.objective = pyo.Objective(\n",
    "        expr=pyo.quicksum(\n",
    "            pyo.quicksum(cov_matrix[i][j] * model.w[i] * model.w[j] for j in model.N)\n",
    "            for i in model.N),\n",
    "        sense=pyo.minimize)\n",
    "\n",
    "    ###############\n",
    "    # Constraints #\n",
    "    ###############\n",
    "\n",
    "    def weight_constraint_rule(model):\n",
    "        return sum(model.w[i] for i in model.N) == 1\n",
    "    model.weight_constraint = pyo.Constraint(rule=weight_constraint_rule)\n",
    "\n",
    "    def link_constraint_rule(model, i):\n",
    "        return sum(model.w[i]*expected_returns[i] for i in model.N) >= desired_return\n",
    "    model.link_constraint = pyo.Constraint(model.N, rule=link_constraint_rule)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "502126d1-6293-497c-938b-01e7fc542dde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Artelys Knitro 14.1.0: \n",
      "=======================================\n",
      "          Commercial License\n",
      "         Artelys Knitro 14.1.0\n",
      "=======================================\n",
      "\n",
      "No start point provided -- Knitro computing one.\n",
      "\n",
      "Knitro presolve eliminated 0 variables and 9 constraints.\n",
      "\n",
      "concurrent_evals         0\n",
      "datacheck                0\n",
      "hessian_no_f             1\n",
      "hessopt                  1\n",
      "The problem is identified as a convex QP.\n",
      "Knitro running advanced initialization strategy specified by initpt_strategy.\n",
      "\n",
      "Problem Characteristics                                 (   Presolved)\n",
      "-----------------------\n",
      "Objective goal:  Minimize\n",
      "Objective type:  quadratic\n",
      "Number of variables:                                 10 (          10)\n",
      "    bounded below only:                              10 (          10)\n",
      "    bounded above only:                               0 (           0)\n",
      "    bounded below and above:                          0 (           0)\n",
      "    fixed:                                            0 (           0)\n",
      "    free:                                             0 (           0)\n",
      "Number of constraints:                               11 (           2)\n",
      "    linear equalities:                                1 (           1)\n",
      "    quadratic equalities:                             0 (           0)\n",
      "    gen. nonlinear equalities:                        0 (           0)\n",
      "    linear one-sided inequalities:                   10 (           1)\n",
      "    quadratic one-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear one-sided inequalities:            0 (           0)\n",
      "    linear two-sided inequalities:                    0 (           0)\n",
      "    quadratic two-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear two-sided inequalities:            0 (           0)\n",
      "Number of nonzeros in Jacobian:                     110 (          20)\n",
      "Number of nonzeros in Hessian:                       55 (          55)\n",
      "\n",
      "Knitro using the Interior-Point/Barrier Direct algorithm.\n",
      "\n",
      "  Iter      Objective      FeasError   OptError    ||Step||    CGits \n",
      "--------  --------------  ----------  ----------  ----------  -------\n",
      "       0    1.028275e+00   1.640e-01\n",
      "       6    4.289111e-01   1.110e-16   6.594e-08   6.656e-04        0\n",
      "\n",
      "EXIT: Optimal solution found.\n",
      "\n",
      "HINT: Knitro spent 100.0% of solution time (0.002000 secs) checking model\n",
      "      convexity. To skip the automatic convexity checker for QPs and QCQPs,\n",
      "      explicity set the user option convex=0 or convex=1.\n",
      "\n",
      "Final Statistics\n",
      "----------------\n",
      "Final objective value               =   4.28911054799611e-01\n",
      "Final feasibility error (abs / rel) =   1.11e-16 / 9.92e-19\n",
      "Final optimality error  (abs / rel) =   6.59e-08 / 5.21e-08\n",
      "# of iterations                     =          6 \n",
      "# of CG iterations                  =          0 \n",
      "# of function evaluations           =          0\n",
      "# of gradient evaluations           =          0\n",
      "# of Hessian evaluations            =          0\n",
      "Total program time (secs)           =       0.002 (     0.016 CPU time)\n",
      "Time spent in evaluations (secs)    =       0.000\n",
      "\n",
      "===============================================================================\n",
      "\n",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
     ]
    }
   ],
   "source": [
    "# Build the model\n",
    "model = build_model(nb_assets, expected_returns, cov_matrix, desired_return)\n",
    "\n",
    "# Solve the model\n",
    "solver = pyo.SolverFactory('knitroampl')  \n",
    "results = solver.solve(model, tee=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714db193-935f-4bab-89b1-386248f94de5",
   "metadata": {},
   "source": [
    "## Output visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3269abe-bfa5-4a0e-9a14-35ad448c6c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "def draw_combined_charts(model, desired_return, assets_names):\n",
    "    portfolio_weights_bar = [pyo.value(model.w[j]) for j in model.N]\n",
    "    portfolio_weights_pie = [pyo.value(model.w[j]) for j in model.N if pyo.value(model.w[j]) > 0.01]\n",
    "    selected_assets_bar = [assets_names[j] for j in model.N]\n",
    "    selected_assets_pie = [assets_names[j] for j in model.N if pyo.value(model.w[j]) > 0.01]\n",
    "\n",
    "    colors = ['#636EFA', '#EF553B', '#00CC96', '#AB63FA', '#FFA15A', '#19D3F3', '#FF6692', '#B6E880', '#FF97FF', '#FECB52']\n",
    "\n",
    "    asset_color_map = {asset: colors[i % len(colors)] for i, asset in enumerate(assets_names)}\n",
    "\n",
    "    bar_colors = [asset_color_map[asset] for asset in selected_assets_bar]\n",
    "    pie_colors = [asset_color_map[asset] for asset in selected_assets_pie]\n",
    "\n",
    "    fig = make_subplots(\n",
    "        rows=1, cols=2,\n",
    "        column_widths=[0.5, 0.5],\n",
    "        subplot_titles=(\"Bar chart\", \"Pie chart\"),\n",
    "        specs=[[{\"type\": \"xy\"}, {\"type\": \"domain\"}]]\n",
    "    )\n",
    "\n",
    "    # Bar chart\n",
    "    fig.add_trace(\n",
    "        go.Bar(\n",
    "            x=selected_assets_bar,\n",
    "            y=portfolio_weights_bar,\n",
    "            text=[f'{weight:.2f}' for weight in portfolio_weights_bar],\n",
    "            textposition='auto',\n",
    "            marker=dict(color=bar_colors),  \n",
    "            opacity=0.8,\n",
    "            name='Bar Chart',\n",
    "            showlegend=False\n",
    "        ),\n",
    "        row=1, col=1\n",
    "    )\n",
    "\n",
    "    # Pie chart\n",
    "    fig.add_trace(\n",
    "        go.Pie(\n",
    "            labels=selected_assets_pie,\n",
    "            values=portfolio_weights_pie,\n",
    "            textinfo='label+percent',\n",
    "            hoverinfo='label+value+percent',\n",
    "            marker=dict(colors=pie_colors, line=dict(color='#000000', width=2)),\n",
    "            opacity=0.8,\n",
    "            name='Pie Chart'\n",
    "        ),\n",
    "        row=1, col=2\n",
    "    )\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=f\"Knitro solution for a desired return of {desired_return}\",\n",
    "        xaxis_title='Assets',\n",
    "        yaxis_title='Weight',\n",
    "        height=600, \n",
    "        width=1000, \n",
    "    )\n",
    "\n",
    "    fig.show(\"png\")\n",
    "\n",
    "    print(f\"The total variance is {round(model.objective(), 2)} for a desired return of {desired_return}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "98103fe6-f89d-4319-a02f-54dfe09e7648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total variance is 0.43 for a desired return of 10\n"
     ]
    }
   ],
   "source": [
    "draw_combined_charts(model, desired_return, assets_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b382c8bf-f491-4e77-aac6-69f87e5dea6c",
   "metadata": {},
   "source": [
    "Here for a desired return of 10, it seems better to diversify to reduce the portfolio risk."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51e33cf-69f2-43eb-8ec5-c5434b944166",
   "metadata": {},
   "source": [
    "## Optimization considering various desired returns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7cad147-586e-4d2c-982f-3533dc57358c",
   "metadata": {},
   "source": [
    "In the previous section, we considered a single fixed return. Now let's consider the optimization for different levels of return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "471932ad-98d7-45be-ba66-e0e79a07a98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#List of desired returns \n",
    "desired_returns = [5, 7.5, 10, 15]\n",
    "\n",
    "models = []\n",
    "solver = pyo.SolverFactory('knitroampl')\n",
    "\n",
    "for desired_return in desired_returns:\n",
    "    model = build_model(nb_assets, expected_returns, cov_matrix, desired_return)\n",
    "    results = solver.solve(model)\n",
    "    models.append(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ab749c8-1d5a-4428-ba81-c35633a25b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Improve visualisation for various desired returns\n",
    "def draw_figures(models, desired_returns, asset_names, title_name):\n",
    "    fig = go.Figure()\n",
    "    colors = ['blue', 'green', 'red', 'purple', 'orange', 'brown', 'pink']  \n",
    "\n",
    "    for i, model in enumerate(models):\n",
    "        portfolio_weights = [pyo.value(model.w[j]) for j in model.N]\n",
    "        selected_assets = [asset_names[j] for j in model.N]\n",
    "        \n",
    "        fig.add_trace(go.Bar(\n",
    "            x=selected_assets,\n",
    "            y=portfolio_weights,\n",
    "            name=f'Desired return: {desired_returns[i]} and total variance: {round(model.objective(),2)}',\n",
    "            text=[f'{weight:.2f}' for weight in portfolio_weights],\n",
    "            textposition='auto',\n",
    "            marker_color=colors[i % len(colors)],\n",
    "            opacity=0.8,\n",
    "        ))\n",
    "\n",
    "    fig.update_layout(\n",
    "        title= title_name,\n",
    "        xaxis_title='Assets',\n",
    "        yaxis_title='Weight',\n",
    "        xaxis=dict(\n",
    "            tickmode='array',\n",
    "            tickvals=selected_assets,\n",
    "            ticktext=[asset_names[j] for j in model.N]  # Utiliser les noms des actifs pour les labels\n",
    "        ),\n",
    "        bargap=0.2,  \n",
    "        bargroupgap=0.1,\n",
    "        barmode='group', \n",
    "        legend_title_text='Desired returns and corresponding total variance',  \n",
    "    )\n",
    "\n",
    "    fig.update_layout(autosize=False, width=1024, height=600)\n",
    "    \n",
    "    fig.show(\"png\")\n",
    "\n",
    "draw_figures(models, desired_returns, assets_names, 'Knitro solution of portfolio optimization for varying desired returns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285c7278-d2b8-4f39-adc8-2b8b2908459a",
   "metadata": {},
   "source": [
    "Here, we can observe the different assets and their weights based on the desired return. We notice that for low returns, it is advantageous to diversify the assets in which to allocate parts of the portfolio to minimize variance and thus minimize risks. On the other hand, as we aim for higher returns (and consequently accept higher risks), we end up investing on the asset that yields the most. However, the risk increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb3f98a-851f-41ad-a49b-57c71461af8c",
   "metadata": {},
   "source": [
    "## Taking into account diversification "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b38fc77d-6c9a-4974-b2ca-e8016324a5ad",
   "metadata": {},
   "source": [
    "In portfolio optimization, diversification is a critical principle used to reduce risk by allocating investments among various financial assets, industries, and other categories. Diversification has multiple benefits:\n",
    "\n",
    "1. **Risk Reduction**: Diversification helps in spreading risk across different assets, reducing the impact of any single asset's poor performance on the overall portfolio.\n",
    "\n",
    "2. **Control Over Portfolio Composition**: By setting bounds on asset weights and limiting the number of assets, investors can ensure that the portfolio remains manageable and aligned with investment objectives.\n",
    "\n",
    "3. **Improved Returns**: A well-diversified portfolio can improve the risk-adjusted returns by taking advantage of the benefits of diversification without excessive exposure to any single asset.\n",
    "\n",
    "In this section, we exhibit two ways to take diversification into account.\n",
    "\n",
    "### Diversification constraints\n",
    "\n",
    "For each asset in the portfolio, we define a lower bound $L_i$ and an upper bound $U_i$ on the weight $w_i$:\n",
    "\n",
    "$$\n",
    "\\forall i = 1, \\dots, N \\quad L_i \\leq w_i \\leq U_i\n",
    "$$\n",
    "\n",
    "where:\n",
    "- $w_i$ is the weight of asset $i$ in the portfolio.\n",
    "- $L_i$ is the minimum allowable weight for asset $i$.\n",
    "- $U_i$ is the maximum allowable weight for asset $i$.\n",
    "\n",
    "This constraint ensures that the weight of each asset remains within a specified range, promoting diversification by preventing over-concentration in any single asset.\n",
    "\n",
    "### Cardinality constraints\n",
    "\n",
    "To further control the diversification, we introduce a cardinality constraint to ensure that exactly $K$ assets are selected in the portfolio. For this, we introduce a new set of binary variables:\n",
    "\n",
    "* $x_i \\in \\{0,1\\}$, $i = 1, \\dots, N$: $x_i = 1$ iff asset $i$ is selected in the portfolio.\n",
    "\n",
    "To link these variables with the $w_i$ variables, we need the following constraints:\n",
    "\n",
    "$$\n",
    "\\forall i = 1, \\dots, N \\qquad w_i \\leq x_i\n",
    "$$\n",
    "\n",
    "These constraints ensures that if $w_i$ is non-null, then $x_i$ takes value $1$.\n",
    "\n",
    "Then, the cardinality constraint can be expressed as:\n",
    "\n",
    "$$\n",
    "\\sum_{i=1}^N x_i = K\n",
    "$$\n",
    "\n",
    "where:\n",
    "- $x_i = 1$ if asset $i$ is included in the portfolio.\n",
    "- $x_i = 0$ if asset $i$ is not included in the portfolio.\n",
    "- $K$ is the desired number of assets in the portfolio.\n",
    "\n",
    "### Combining diversification and cardinality constraints\n",
    "\n",
    "The weight constraints and the cardinality constraint together ensure that the portfolio is not only diversified but also limited to a specific number of assets. If both constraints are used, the diversification constraints and the linking constraints between $x_i$ and $w_i$ can be merged into:\n",
    "\n",
    "$$\n",
    "\\forall i = 1, \\dots, N \\qquad L_i x_i \\leq w_i \\leq U_i x_i\n",
    "$$\n",
    "\n",
    "This ensures that if an asset $i$ is not selected $x_i = 0$, its weight $w_i$ will be zero. If an asset is selected $x_i = 1$, its weight will lie between $L_i$ and $U_i$.\n",
    "\n",
    "Now let's implement these constraints into the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3530b22c-df77-4a20-b77d-66573ee90ad4",
   "metadata": {},
   "source": [
    "### Input data\n",
    "\n",
    "We need to specify additional inputs for these extensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87666a7f-8585-46ce-8338-be539632560e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional inputs for diversification\n",
    "L = [0.05, 0.05, 0.06, 0.07, 0.06, 0.05, 0.06, 0.07, 0.05, 0.06]\n",
    "U =  [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n",
    "K = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e50ae480-09f1-453f-b893-5b51a08108d7",
   "metadata": {},
   "source": [
    "### Pyomo model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8425275d-555b-48a0-b866-d6aef96c39c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def diversification_extended_model(model,L,U,K):\n",
    "\n",
    "    # New variable\n",
    "    model.x = pyo.Var(model.N, within=pyo.Binary)\n",
    "\n",
    "    # New constraints\n",
    "\n",
    "    def diversification_lower_constraint_rule(model,i):\n",
    "        return L[i]*model.x[i] <= model.w[i]\n",
    "    model.diversification_lower_constraint = pyo.Constraint(model.N, rule=diversification_lower_constraint_rule)\n",
    "\n",
    "    def diversification_upper_constraint_rule(model,i):\n",
    "        return model.w[i] <= U[i]*model.x[i]\n",
    "    model.diversification_upper_constraint = pyo.Constraint(model.N, rule=diversification_upper_constraint_rule)\n",
    "\n",
    "    def cardinality_nb_assets_constraint_rule(model):\n",
    "        return sum(model.x[i] for i in model.N) == 3\n",
    "    model.cardinality_nb_assets_constraint = pyo.Constraint(rule=cardinality_nb_assets_constraint_rule)\n",
    "\n",
    "models_with_diversifications = []\n",
    "solver = pyo.SolverFactory('knitroampl')\n",
    "\n",
    "for desired_return in desired_returns:\n",
    "    model = build_model(nb_assets, expected_returns, cov_matrix, desired_return)\n",
    "    diversification_extended_model(model,L,U,K)\n",
    "    results = solver.solve(model)\n",
    "    models_with_diversifications.append(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b688a224-0806-4437-aaad-69ecf40f62a3",
   "metadata": {},
   "source": [
    "### Comparative analysis of models with and without diversification and cardinality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2fee98a5-9bc6-42d0-abc2-7d01661576cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_figures(models, desired_returns, assets_names, 'Portfolio optimization without diversifications')\n",
    "draw_figures(models_with_diversifications, desired_returns, assets_names, 'Portfolio optimization with diversifications and limited assets of 3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e63bb0-b2bd-4c69-80fa-68e59195b238",
   "metadata": {},
   "source": [
    "By incorporating extensions related to diversification and cardinality constraints, some assets may no longer be included in the portfolio, while others may see their weights increase. This occurs because such constraints can limit the number of assets selected and enforce a more concentrated allocation, thereby intensifying the focus on a smaller set of high-performing assets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5243a551-84d8-404e-9dab-75b083f2ac82",
   "metadata": {},
   "source": [
    "## Taking into account transaction costs "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdce927f-0401-48e2-ba3d-55d4c78697c4",
   "metadata": {},
   "source": [
    "Rebalancing a portfolio involves buying and selling securities to adjust its composition, leading to turnover. While the basic Markowitz model assumes there are no trading costs, in practice, turnover incurs expenses.  \n",
    "There are 2 kinds of transaction costs:  \n",
    "\n",
    "- **Fixed costs** which represent commissions or transfer fees\n",
    "- **Variable costs** which depend on the transaction volume, representing costs such as market impact or bid/ask spread, for example.\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7191035-73eb-4479-8b80-32ef7a264ea1",
   "metadata": {},
   "source": [
    "In the variant considered in this section, the objective is to maximize the return and the maximum variance is set as a constraint. To make the model more meaningful, we consider that a proportion of the capital is already invested in some assets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "407d080b-1c95-44c7-8c1c-4ea174302853",
   "metadata": {},
   "source": [
    "### Problem description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e630ea52-3167-4150-b6f9-d04c1913b4cd",
   "metadata": {},
   "source": [
    "**Input**:\n",
    "\n",
    "* $N \\in \\mathbb{N}$: number of assets \n",
    "* $W^0_i \\in \\mathbb{R}^+$, $i = 1, \\dots, N$: fraction of the portfolio value already invested in asset $i$.\n",
    "* $\\Gamma \\in \\mathbb{R}^+$: limited risk level\n",
    "* $M_i \\in \\mathbb{R}^+$, $i = 1, \\dots, N$: expected return of asset $i$\n",
    "* $F_i \\in \\mathbb{R}^+$, $i = 1, \\dots, N$: fixed cost if buying asset $i$\n",
    "* $V_i \\in \\mathbb{R}^+$, $i = 1, \\dots, N$: variable factor cost when buying asset $i$\n",
    "\n",
    "**Problem**: find for each asset, the proportion of the capital invested in it, such that the allowed risk level is not exceeded.\n",
    "\n",
    "**Objective**: maximum the total return\n",
    "\n",
    "### Mathematic model\n",
    "\n",
    "**Variables**:\n",
    "\n",
    "* $w_i \\in [0,1]$, $i = 1, \\dots, N$: proportion of the capital allocated to asset $i$\n",
    "* $x_i \\in \\{0,1\\}$, $i = 1, \\dots, N$: $x_i = 1$ iff asset $i$ is selected in the portfolio\n",
    "\n",
    "We need two additional sets of variables to model the absolute value $| w_i - {W_0}_i |$ representing the proportion of the  captial moved from asset $i$ (bought or sold):\n",
    "* $z^+_i \\in [0,1]$, $i = 1, \\dots, N$: positive part of $w_i - {W_0}_i$\n",
    "* $z^-_i \\in [0,1]$, $i = 1, \\dots, N$: negative part of $w_i - {W_0}_i$\n",
    "\n",
    "**Objective**:\n",
    "\n",
    "* Maximize the return\n",
    "\n",
    "$$\n",
    "\\sum_{i=1}^N{M_i w_i}\n",
    "$$\n",
    "\n",
    "**Constraints**:\n",
    "\n",
    "* Definition of $z^+_i$ and $z^-_i$:\n",
    "\n",
    "$$\n",
    "z^+_i - z^-_i = w_i - {W_0}_i\n",
    "$$\n",
    "\n",
    "* Total risk limit:\n",
    "\n",
    "$$\n",
    "\\sum_{i=1}^N{\\sum_{j=1}^N{C_i^j w_i w_j}} \\leq \\Gamma^2\n",
    "$$\n",
    "\n",
    "* Transaction costs:\n",
    "\n",
    "$$\n",
    "\\sum_{i=1}^N{w_i} + \\sum_{i=1}^N{C^f_i} = \\sum_{i=1}^N{W^0_i}\n",
    "$$\n",
    "Where $C^f_i = F_i x_i + V_i(z^+_i + z^-_i)$ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9171c51-c74e-4b1b-b99c-b3e07b3555bd",
   "metadata": {},
   "source": [
    "### Input data\n",
    "\n",
    "We keep the same dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "37489b47-583b-48c5-b988-813886167858",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional input for transaction costs extension\n",
    "GAMMA = 0.7\n",
    "W_0 = [0.1 for i in range(nb_assets)]\n",
    "F = [0.1 for i in range(nb_assets)]\n",
    "# We'll vary V, in order to see the difference of this factor \n",
    "V_list = [[1 for i in range(nb_assets)],[0.75 for i in range(nb_assets)],[0.5 for i in range(nb_assets)],[0.25 for i in range(nb_assets)],[0 for i in range(nb_assets)]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70af75d-1597-41cc-bed7-427461486f8b",
   "metadata": {},
   "source": [
    "### Pyomo model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19e1b913-37ba-42a9-a009-211f1026c899",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model_tc(nb_assets, expected_returns, cov_matrix, GAMMA, W_0, F, V):\n",
    "    model = pyo.ConcreteModel()\n",
    "\n",
    "    ####################\n",
    "    # Create variables #\n",
    "    ####################\n",
    "\n",
    "    model.N = pyo.RangeSet(0, nb_assets - 1)\n",
    "    model.w = pyo.Var(model.N, within=pyo.NonNegativeReals)\n",
    "    model.x = pyo.Var(model.N, within=pyo.Binary)\n",
    "    model.z_plus = pyo.Var(model.N, within=pyo.NonNegativeReals)\n",
    "    model.z_minus = pyo.Var(model.N, within=pyo.NonNegativeReals)\n",
    "\n",
    "    #############\n",
    "    # Objective #\n",
    "    #############\n",
    "\n",
    "    model.objective = pyo.Objective(\n",
    "        expr=pyo.quicksum(model.w[i]*expected_returns[i] for i in model.N),\n",
    "        sense=pyo.maximize)\n",
    "\n",
    "    ###############\n",
    "    # Constraints #\n",
    "    ###############\n",
    "\n",
    "    def absolute_rule(model,i):\n",
    "        return (model.z_plus[i] - model.z_minus[i] == model.w[i] - W_0[i])\n",
    "    model.absolute_rule_constraint = pyo.Constraint(model.N, rule=absolute_rule)\n",
    "    \n",
    "    def risk_constraint_rule(model):\n",
    "        total = 0\n",
    "        for i in model.N:\n",
    "            for j in model.N:\n",
    "                total += cov_matrix[i][j] * model.w[i] * model.w[j]\n",
    "        return (total <= GAMMA)\n",
    "    model.risk_constraint = pyo.Constraint(rule=risk_constraint_rule)\n",
    "\n",
    "    def transaction_costs_rule(model):\n",
    "        total = 0\n",
    "        for i in model.N:\n",
    "            transaction_cost = F[i]*model.x[i] +  V[i]*(model.z_plus[i] + model.z_minus[i])\n",
    "            total += transaction_cost + model.w[i]\n",
    "        return (total == 1)\n",
    "    model.transaction_costs_constraint = pyo.Constraint(rule=transaction_costs_rule)   \n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "604c2461-89aa-4282-9eb3-3aa7d4ee11d8",
   "metadata": {},
   "source": [
    "### Comparative analysis of models for various variable cost factor\n",
    "\n",
    "A high variable cost limits the changes we can make to our current portfolio. That's why it's important to vary this factor to observe the differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b9087bd-8dc0-4eb1-9bf9-d46c34959c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = pyo.SolverFactory('knitroampl')\n",
    "models_transaction_costs = [] \n",
    "\n",
    "for V in V_list:\n",
    "    # Build the model\n",
    "    model = build_model_tc(nb_assets, expected_returns, cov_matrix, GAMMA, W_0, F, V)\n",
    "    \n",
    "    # Solve the model  \n",
    "    results = solver.solve(model)\n",
    "    models_transaction_costs.append(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f769a9cf-09d0-4e29-a1b3-69cfd9504875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_figures_with_transactions(models, V_list, assets_names, title_name):\n",
    "    fig = go.Figure()\n",
    "    colors = ['blue', 'green', 'red', 'purple', 'orange', 'brown', 'pink']  \n",
    "\n",
    "    selected_assets = [assets_names[j] for j in range(nb_assets)]\n",
    "\n",
    "    initial_portfolio = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "            x=selected_assets,\n",
    "            y= initial_portfolio,\n",
    "            name='Initial portfolio and expected portfolio return: -7.63',\n",
    "            text=[f'{weight:.2f}' for weight in initial_portfolio],\n",
    "            textposition='auto',\n",
    "            marker_color=colors[0],\n",
    "            opacity=0.8,\n",
    "        ))\n",
    "\n",
    "    for i, model in enumerate(models):\n",
    "        portfolio_weights = [pyo.value(model.w[j]) for j in model.N]\n",
    "        \n",
    "        fig.add_trace(go.Bar(\n",
    "            x=selected_assets,\n",
    "            y=portfolio_weights,\n",
    "            name=f'Variable cost coefficient: {V_list[i][0]} and expected portfolio return: {round(model.objective(),2)}',\n",
    "            text=[f'{weight:.2f}' for weight in portfolio_weights],\n",
    "            textposition='auto',\n",
    "            marker_color=colors[(i+1) % len(colors)],\n",
    "            opacity=0.8,\n",
    "        ))\n",
    "\n",
    "    fig.update_layout(\n",
    "        title= title_name,\n",
    "        xaxis_title='Assets',\n",
    "        yaxis_title='Weight',\n",
    "        xaxis=dict(\n",
    "            tickmode='array',\n",
    "            tickvals=selected_assets,\n",
    "            ticktext=[assets_names[j] for j in model.N]  \n",
    "        ),\n",
    "        bargap=0.2,  \n",
    "        bargroupgap=0.1,\n",
    "        barmode='group', \n",
    "        legend_title_text='Various cost coeffcient and expected portfolio return',  \n",
    "    )\n",
    "\n",
    "    fig.update_layout(autosize=False, width=1024, height=600)    \n",
    "    fig.show(\"png\")\n",
    "\n",
    "draw_figures_with_transactions(models_transaction_costs, V_list, assets_names, \"Portfolio optimization with transaction costs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07630df-1b55-4465-9f92-27c1a4d13908",
   "metadata": {},
   "source": [
    "When transaction costs are taken into account, assets with negative expected returns are removed from the portfolio because the costs associated with trading prevent extensive diversification, making it impractical to include assets that do not contribute positively to the portfolio's overall return. However, if we lower $\\Gamma$ , these assets might still be used, as in our example where the variance limit remains high, allowing for their inclusion despite their negative expected returns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd8605f7-b934-42e7-a0a1-b43c472156b8",
   "metadata": {},
   "source": [
    "# Optimizing portfolios with a large number of assets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed3b098-8717-47a9-b577-a887908ee665",
   "metadata": {},
   "source": [
    "In this section, we will test our baseline model with a large number of assets. Despite the increased complexity, we expect to successfully solve the optimization problem using Knitro. By scaling up the number of assets, we aim to demonstrate the robustness and efficiency of our model in handling more extensive portfolios (data is taken from  the top 100 companies listed on the Australian Securities Exchange (ASX))."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22da86b-03f0-4675-919e-9f9acf33b5ad",
   "metadata": {},
   "source": [
    "## Input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f7f96da-9e43-44dc-b8fc-039bcb8ad733",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0         1         2         3         4         5         6  \\\n",
      "0   2.534536  0.047356  0.333075  0.471132  1.125881  0.178098  0.355739   \n",
      "1   0.047356  0.858942  0.510742  0.825108  0.458187  0.646647  0.352422   \n",
      "2   0.333075  0.510742  3.991906  1.778789  1.167146  1.545083  0.353567   \n",
      "3   0.471132  0.825108  1.778789  2.427180  1.437683  1.336803  0.876136   \n",
      "4   1.125881  0.458187  1.167146  1.437683  3.722347  1.044947  0.626953   \n",
      "..       ...       ...       ...       ...       ...       ...       ...   \n",
      "95  0.118763  0.956775  3.275828  2.440533  1.366173  1.810132  0.523715   \n",
      "96  0.234778  0.307864  0.047807  0.557954  0.457672  0.362358  0.252096   \n",
      "97  0.128372  0.773043  2.452065  1.905568  1.235017  1.493521  0.827747   \n",
      "98 -0.373046  0.567859  2.184141  1.309281  2.529939  1.386597  0.453257   \n",
      "99  0.278282  0.468650  1.890667  1.177265  1.009143  0.823746 -0.094318   \n",
      "\n",
      "           7         8         9  ...        90        91        92        93  \\\n",
      "0   0.371387  0.061000  0.103717  ...  0.712271  0.110855  0.340319 -0.002515   \n",
      "1   0.455319  0.389024  0.547975  ...  0.276990  0.903207  0.394965  0.516879   \n",
      "2   1.108423  1.634315  1.651680  ...  0.345831  2.292321  1.387456  1.336884   \n",
      "3   1.191104  0.972624  1.145486  ...  0.681949  1.629457  1.054428  0.921847   \n",
      "4   0.603336  0.869405  0.853699  ...  0.638905  0.915822  0.613417  0.623297   \n",
      "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
      "95  1.459429  1.769964  1.673468  ...  1.123193  2.960856  2.069482  1.429013   \n",
      "96  0.432723  0.224596  0.334573  ...  0.610308  0.609492  0.276193  0.373179   \n",
      "97  1.381025  1.204098  1.378932  ...  0.673395  1.997442  1.247179  1.172630   \n",
      "98 -0.508326  1.295372  0.643417  ...  1.119884  1.541162  0.535477  0.626703   \n",
      "99  0.784772  1.043259  0.626009  ...  0.608678  1.485579  1.114680  0.470371   \n",
      "\n",
      "          94        95        96        97        98        99  \n",
      "0   0.138123  0.118763  0.234778  0.128372 -0.373046  0.278282  \n",
      "1   0.468797  0.956775  0.307864  0.773043  0.567859  0.468650  \n",
      "2   1.100214  3.275828  0.047807  2.452065  2.184141  1.890667  \n",
      "3   0.773240  2.440533  0.557954  1.905568  1.309281  1.177265  \n",
      "4   0.803760  1.366173  0.457672  1.235017  2.529939  1.009143  \n",
      "..       ...       ...       ...       ...       ...       ...  \n",
      "95  1.139057  5.150562  0.403979  3.046236  2.498345  2.273416  \n",
      "96  0.399913  0.403979  0.845086  0.432317  0.821179  0.533661  \n",
      "97  0.970937  3.046236  0.432317  2.522697  1.853837  1.559979  \n",
      "98  0.745253  2.498345  0.821179  1.853837  8.443884  2.213008  \n",
      "99  0.853287  2.273416  0.533661  1.559979  2.213008  2.552560  \n",
      "\n",
      "[100 rows x 100 columns]\n",
      "    Mean Returns\n",
      "0       8.304699\n",
      "1      -2.337330\n",
      "2       1.605106\n",
      "3      -1.227668\n",
      "4       5.528455\n",
      "..           ...\n",
      "95     -7.154680\n",
      "96      3.794437\n",
      "97     -7.363839\n",
      "98      2.441982\n",
      "99      8.664054\n",
      "\n",
      "[100 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "nb_assets = 100\n",
    "\n",
    "data_cov_matrix = pd.read_csv('sigma_example.csv')\n",
    "print(data_cov_matrix)\n",
    "cov_matrix = data_cov_matrix.values.tolist()\n",
    "\n",
    "data_expected_returns = pd.read_csv('mu_example.csv')\n",
    "print(data_expected_returns)\n",
    "expected_returns = data_expected_returns.squeeze().tolist()\n",
    "\n",
    "# Like in the first section, we vary the desired return \n",
    "desired_returns = [5.0, 7.5, 10.0, 15.0]\n",
    "\n",
    "data_assets_name = pd.read_csv('names.csv')\n",
    "assets_names = data_assets_name.squeeze().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3e1346-6f64-4cd8-989d-860c4e73da8a",
   "metadata": {},
   "source": [
    "## Resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01464df1-3f9b-4981-be6b-b997e62a071f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Artelys Knitro 14.1.0: \n",
      "=======================================\n",
      "          Commercial License\n",
      "         Artelys Knitro 14.1.0\n",
      "=======================================\n",
      "\n",
      "No start point provided -- Knitro computing one.\n",
      "\n",
      "Knitro presolve eliminated 0 variables and 99 constraints.\n",
      "\n",
      "concurrent_evals         0\n",
      "datacheck                0\n",
      "hessian_no_f             1\n",
      "hessopt                  1\n",
      "The problem is identified as a convex QP.\n",
      "Knitro running advanced initialization strategy specified by initpt_strategy.\n",
      "\n",
      "Problem Characteristics                                 (   Presolved)\n",
      "-----------------------\n",
      "Objective goal:  Minimize\n",
      "Objective type:  quadratic\n",
      "Number of variables:                                100 (         100)\n",
      "    bounded below only:                             100 (         100)\n",
      "    bounded above only:                               0 (           0)\n",
      "    bounded below and above:                          0 (           0)\n",
      "    fixed:                                            0 (           0)\n",
      "    free:                                             0 (           0)\n",
      "Number of constraints:                              101 (           2)\n",
      "    linear equalities:                                1 (           1)\n",
      "    quadratic equalities:                             0 (           0)\n",
      "    gen. nonlinear equalities:                        0 (           0)\n",
      "    linear one-sided inequalities:                  100 (           1)\n",
      "    quadratic one-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear one-sided inequalities:            0 (           0)\n",
      "    linear two-sided inequalities:                    0 (           0)\n",
      "    quadratic two-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear two-sided inequalities:            0 (           0)\n",
      "Number of nonzeros in Jacobian:                   10100 (         200)\n",
      "Number of nonzeros in Hessian:                     5050 (        5050)\n",
      "\n",
      "Knitro using the Interior-Point/Barrier Direct algorithm.\n",
      "\n",
      "  Iter      Objective      FeasError   OptError    ||Step||    CGits \n",
      "--------  --------------  ----------  ----------  ----------  -------\n",
      "       0    3.043740e+00   9.146e-01\n",
      "      10    2.108024e-01   3.331e-16   5.501e-08   7.208e-04        0\n",
      "\n",
      "EXIT: Optimal solution found.\n",
      "\n",
      "HINT: Knitro spent  12.5% of solution time (0.004000 secs) checking model\n",
      "      convexity. To skip the automatic convexity checker for QPs and QCQPs,\n",
      "      explicity set the user option convex=0 or convex=1.\n",
      "\n",
      "Final Statistics\n",
      "----------------\n",
      "Final objective value               =   2.10802384746899e-01\n",
      "Final feasibility error (abs / rel) =   3.33e-16 / 2.18e-18\n",
      "Final optimality error  (abs / rel) =   5.50e-08 / 4.30e-08\n",
      "# of iterations                     =         10 \n",
      "# of CG iterations                  =          0 \n",
      "# of function evaluations           =          0\n",
      "# of gradient evaluations           =          0\n",
      "# of Hessian evaluations            =          0\n",
      "Total program time (secs)           =       0.032 (     0.031 CPU time)\n",
      "Time spent in evaluations (secs)    =       0.000\n",
      "\n",
      "===============================================================================\n",
      "\n",
      "\bArtelys Knitro 14.1.0: \n",
      "=======================================\n",
      "          Commercial License\n",
      "         Artelys Knitro 14.1.0\n",
      "=======================================\n",
      "\n",
      "No start point provided -- Knitro computing one.\n",
      "\n",
      "Knitro presolve eliminated 0 variables and 99 constraints.\n",
      "\n",
      "concurrent_evals         0\n",
      "datacheck                0\n",
      "hessian_no_f             1\n",
      "hessopt                  1\n",
      "The problem is identified as a convex QP.\n",
      "Knitro running advanced initialization strategy specified by initpt_strategy.\n",
      "\n",
      "Problem Characteristics                                 (   Presolved)\n",
      "-----------------------\n",
      "Objective goal:  Minimize\n",
      "Objective type:  quadratic\n",
      "Number of variables:                                100 (         100)\n",
      "    bounded below only:                             100 (         100)\n",
      "    bounded above only:                               0 (           0)\n",
      "    bounded below and above:                          0 (           0)\n",
      "    fixed:                                            0 (           0)\n",
      "    free:                                             0 (           0)\n",
      "Number of constraints:                              101 (           2)\n",
      "    linear equalities:                                1 (           1)\n",
      "    quadratic equalities:                             0 (           0)\n",
      "    gen. nonlinear equalities:                        0 (           0)\n",
      "    linear one-sided inequalities:                  100 (           1)\n",
      "    quadratic one-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear one-sided inequalities:            0 (           0)\n",
      "    linear two-sided inequalities:                    0 (           0)\n",
      "    quadratic two-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear two-sided inequalities:            0 (           0)\n",
      "Number of nonzeros in Jacobian:                   10100 (         200)\n",
      "Number of nonzeros in Hessian:                     5050 (        5050)\n",
      "\n",
      "Knitro using the Interior-Point/Barrier Direct algorithm.\n",
      "\n",
      "  Iter      Objective      FeasError   OptError    ||Step||    CGits \n",
      "--------  --------------  ----------  ----------  ----------  -------\n",
      "       0    2.964169e+00   1.831e+00\n",
      "       9    2.412572e-01   0.000e+00   1.539e-09   2.163e-04        0\n",
      "\n",
      "EXIT: Optimal solution found.\n",
      "\n",
      "HINT: Knitro spent  16.1% of solution time (0.005000 secs) checking model\n",
      "      convexity. To skip the automatic convexity checker for QPs and QCQPs,\n",
      "      explicity set the user option convex=0 or convex=1.\n",
      "\n",
      "Final Statistics\n",
      "----------------\n",
      "Final objective value               =   2.41257153008162e-01\n",
      "Final feasibility error (abs / rel) =   0.00e+00 / 0.00e+00\n",
      "Final optimality error  (abs / rel) =   1.54e-09 / 1.34e-09\n",
      "# of iterations                     =          9 \n",
      "# of CG iterations                  =          0 \n",
      "# of function evaluations           =          0\n",
      "# of gradient evaluations           =          0\n",
      "# of Hessian evaluations            =          0\n",
      "Total program time (secs)           =       0.031 (     0.031 CPU time)\n",
      "Time spent in evaluations (secs)    =       0.000\n",
      "\n",
      "===============================================================================\n",
      "\n",
      "\bArtelys Knitro 14.1.0: \n",
      "=======================================\n",
      "          Commercial License\n",
      "         Artelys Knitro 14.1.0\n",
      "=======================================\n",
      "\n",
      "No start point provided -- Knitro computing one.\n",
      "\n",
      "Knitro presolve eliminated 0 variables and 99 constraints.\n",
      "\n",
      "concurrent_evals         0\n",
      "datacheck                0\n",
      "hessian_no_f             1\n",
      "hessopt                  1\n",
      "The problem is identified as a convex QP.\n",
      "Knitro running advanced initialization strategy specified by initpt_strategy.\n",
      "\n",
      "Problem Characteristics                                 (   Presolved)\n",
      "-----------------------\n",
      "Objective goal:  Minimize\n",
      "Objective type:  quadratic\n",
      "Number of variables:                                100 (         100)\n",
      "    bounded below only:                             100 (         100)\n",
      "    bounded above only:                               0 (           0)\n",
      "    bounded below and above:                          0 (           0)\n",
      "    fixed:                                            0 (           0)\n",
      "    free:                                             0 (           0)\n",
      "Number of constraints:                              101 (           2)\n",
      "    linear equalities:                                1 (           1)\n",
      "    quadratic equalities:                             0 (           0)\n",
      "    gen. nonlinear equalities:                        0 (           0)\n",
      "    linear one-sided inequalities:                  100 (           1)\n",
      "    quadratic one-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear one-sided inequalities:            0 (           0)\n",
      "    linear two-sided inequalities:                    0 (           0)\n",
      "    quadratic two-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear two-sided inequalities:            0 (           0)\n",
      "Number of nonzeros in Jacobian:                   10100 (         200)\n",
      "Number of nonzeros in Hessian:                     5050 (        5050)\n",
      "\n",
      "Knitro using the Interior-Point/Barrier Direct algorithm.\n",
      "\n",
      "  Iter      Objective      FeasError   OptError    ||Step||    CGits \n",
      "--------  --------------  ----------  ----------  ----------  -------\n",
      "       0    2.944023e+00   3.850e+00\n",
      "       9    3.220799e-01   0.000e+00   8.584e-09   4.459e-04        0\n",
      "\n",
      "EXIT: Optimal solution found.\n",
      "\n",
      "HINT: Knitro spent   6.7% of solution time (0.002000 secs) checking model\n",
      "      convexity. To skip the automatic convexity checker for QPs and QCQPs,\n",
      "      explicity set the user option convex=0 or convex=1.\n",
      "\n",
      "Final Statistics\n",
      "----------------\n",
      "Final objective value               =   3.22079920265484e-01\n",
      "Final feasibility error (abs / rel) =   0.00e+00 / 0.00e+00\n",
      "Final optimality error  (abs / rel) =   8.58e-09 / 7.22e-09\n",
      "# of iterations                     =          9 \n",
      "# of CG iterations                  =          0 \n",
      "# of function evaluations           =          0\n",
      "# of gradient evaluations           =          0\n",
      "# of Hessian evaluations            =          0\n",
      "Total program time (secs)           =       0.030 (     0.031 CPU time)\n",
      "Time spent in evaluations (secs)    =       0.000\n",
      "\n",
      "===============================================================================\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\bArtelys Knitro 14.1.0: \n",
      "=======================================\n",
      "          Commercial License\n",
      "         Artelys Knitro 14.1.0\n",
      "=======================================\n",
      "\n",
      "No start point provided -- Knitro computing one.\n",
      "\n",
      "Knitro presolve eliminated 0 variables and 99 constraints.\n",
      "\n",
      "concurrent_evals         0\n",
      "datacheck                0\n",
      "hessian_no_f             1\n",
      "hessopt                  1\n",
      "The problem is identified as a convex QP.\n",
      "Knitro running advanced initialization strategy specified by initpt_strategy.\n",
      "\n",
      "Problem Characteristics                                 (   Presolved)\n",
      "-----------------------\n",
      "Objective goal:  Minimize\n",
      "Objective type:  quadratic\n",
      "Number of variables:                                100 (         100)\n",
      "    bounded below only:                             100 (         100)\n",
      "    bounded above only:                               0 (           0)\n",
      "    bounded below and above:                          0 (           0)\n",
      "    fixed:                                            0 (           0)\n",
      "    free:                                             0 (           0)\n",
      "Number of constraints:                              101 (           2)\n",
      "    linear equalities:                                1 (           1)\n",
      "    quadratic equalities:                             0 (           0)\n",
      "    gen. nonlinear equalities:                        0 (           0)\n",
      "    linear one-sided inequalities:                  100 (           1)\n",
      "    quadratic one-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear one-sided inequalities:            0 (           0)\n",
      "    linear two-sided inequalities:                    0 (           0)\n",
      "    quadratic two-sided inequalities:                 0 (           0)\n",
      "    gen. nonlinear two-sided inequalities:            0 (           0)\n",
      "Number of nonzeros in Jacobian:                   10100 (         200)\n",
      "Number of nonzeros in Hessian:                     5050 (        5050)\n",
      "\n",
      "Knitro using the Interior-Point/Barrier Direct algorithm.\n",
      "\n",
      "  Iter      Objective      FeasError   OptError    ||Step||    CGits \n",
      "--------  --------------  ----------  ----------  ----------  -------\n",
      "       0    2.517500e+00   5.136e+00\n",
      "       7    7.590161e-01   2.518e-11   4.613e-09   6.518e-04        0\n",
      "\n",
      "EXIT: Optimal solution found.\n",
      "\n",
      "HINT: Knitro spent  23.3% of solution time (0.007000 secs) checking model\n",
      "      convexity. To skip the automatic convexity checker for QPs and QCQPs,\n",
      "      explicity set the user option convex=0 or convex=1.\n",
      "\n",
      "Final Statistics\n",
      "----------------\n",
      "Final objective value               =   7.59016079636064e-01\n",
      "Final feasibility error (abs / rel) =   2.52e-11 / 1.65e-13\n",
      "Final optimality error  (abs / rel) =   4.61e-09 / 2.07e-09\n",
      "# of iterations                     =          7 \n",
      "# of CG iterations                  =          0 \n",
      "# of function evaluations           =          0\n",
      "# of gradient evaluations           =          0\n",
      "# of Hessian evaluations            =          0\n",
      "Total program time (secs)           =       0.030 (     0.016 CPU time)\n",
      "Time spent in evaluations (secs)    =       0.000\n",
      "\n",
      "===============================================================================\n",
      "\n",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
     ]
    }
   ],
   "source": [
    "# Resolution with new inputs \n",
    "models = []\n",
    "solver = pyo.SolverFactory('knitroampl')\n",
    "\n",
    "for desired_return in desired_returns:\n",
    "    model = build_model(nb_assets, expected_returns, cov_matrix, desired_return)\n",
    "    results = solver.solve(model, tee=True)\n",
    "    models.append(model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d73bb5-abf0-4a61-95cb-0d54b7839b44",
   "metadata": {},
   "source": [
    "## Visualization of chosen assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e7f9eac-0428-45e6-8105-de89742264d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_figures_ha(models, desired_returns, assets_names, title_name):\n",
    "    fig = go.Figure()\n",
    "    colors = ['blue', 'green', 'red', 'purple', 'orange', 'brown', 'pink'] \n",
    "\n",
    "    for i, model in enumerate(models):\n",
    "        portfolio_weights = [pyo.value(model.w[j]) for j in model.N if model.w[j]() > 0.05]\n",
    "        selected_assets = [assets_names[j] for j in model.N if model.w[j]() > 0.05]\n",
    "        \n",
    "        fig.add_trace(go.Bar(\n",
    "            x=selected_assets,\n",
    "            y=portfolio_weights,\n",
    "            name=f'Desired Return: {desired_returns[i]} and Total Variance: {round(model.objective(),2)}',\n",
    "            text=[f'{weight:.2f}' for weight in portfolio_weights],\n",
    "            textposition='auto',\n",
    "            marker_color=colors[i % len(colors)],\n",
    "            opacity=0.8,\n",
    "        ))\n",
    "\n",
    "    fig.update_layout(\n",
    "        title= title_name,\n",
    "        xaxis_title='Assets',\n",
    "        yaxis_title='Weight',\n",
    "        xaxis=dict(\n",
    "            tickmode='array',\n",
    "            tickvals=list(range(len(assets_names))),  \n",
    "            ticktext=selected_assets  \n",
    "        ),\n",
    "        bargap=0.2,  \n",
    "        bargroupgap=0.1,\n",
    "        barmode='group', \n",
    "        legend_title_text='Desired Returns and corresponding Total Variance ',  \n",
    "    )\n",
    "\n",
    "    fig.update_layout(autosize=False, width=1024, height=600)\n",
    "    \n",
    "    fig.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e9767f8-442e-434f-b684-1cf6f3cdf615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_figures_ha(models, desired_returns, assets_names, 'Selected assets among the 100 assets available')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d68be55-001f-4af8-917d-20e6baed54d8",
   "metadata": {},
   "source": [
    "As the figure shows, at most 10 assets are selected to minimize the variance for these desired returns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "256d828a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}