Quantum_Method_for_Portfolio_Optimizationipynb.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP8HbaGssP7BeFRacRrOcaZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UmarIgan/Machine-Learning/blob/master/Quantum_Method_for_Portfolio_Optimizationipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Installing the libraries"
      ],
      "metadata": {
        "id": "2HOUIYnJQkC-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Quantum computing for porfolio optimization"
      ],
      "metadata": {
        "id": "zfw8TaZxRi6v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install qiskit-finance\n",
        "!pip install riskfolio-lib"
      ],
      "metadata": {
        "id": "B6Ie-Jfjhm3l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c354b8f-a450-4fef-9f24-a6bfce14eca5"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**- Import Libraries**\n",
        "\n",
        "Import necessary packages:"
      ],
      "metadata": {
        "id": "o2c0tN1MSUvR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "from qiskit_finance import QiskitFinanceError\n",
        "from qiskit_finance.data_providers import YahooDataProvider\n",
        "from qiskit_finance.applications.optimization import PortfolioOptimization\n",
        "from qiskit.algorithms import QAOA, NumPyMinimumEigensolver\n",
        "from qiskit.utils import algorithm_globals, QuantumInstance\n",
        "from qiskit import Aer, execute\n",
        "from qiskit.algorithms.optimizers import COBYLA\n",
        "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
        "from qiskit_optimization.converters import QuadraticProgramToQubo\n",
        "from qiskit_optimization.converters import LinearEqualityToPenalty\n",
        "from qiskit.opflow import PauliExpectation, CVaRExpectation\n",
        "import datetime, matplotlib.pyplot as plt, numpy as np"
      ],
      "metadata": {
        "id": "YTzMg_xsNdVB"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Retrieve stock data from Yahoo Finance API:\n",
        "\n",
        "> Indented block\n",
        "\n"
      ],
      "metadata": {
        "id": "1cy8e8hvRzcE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stocks = [\"THYAO.IS\", \"YUNSA.IS\", \"GARAN.IS\", \"SASA.IS\", \"YKBNK.IS\", \"AKBNK.IS\", \"ENJSA.IS\",\"SAHOL.IS\", \"SISE.IS\", \"VAKBN.IS\", \"HALKB.IS\"]\n",
        "try:\n",
        "    data = YahooDataProvider(\n",
        "                 tickers = stocks,\n",
        "                 start=datetime.datetime(2020, 1, 1),\n",
        "                 end=datetime.datetime(2023, 4, 22))\n",
        "    \n",
        "    data.run()\n",
        "    for (cnt, s) in enumerate(data._tickers):\n",
        "        plt.plot(data._data[cnt], label=s)\n",
        "    plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=3)\n",
        "    plt.xticks(rotation=90)\n",
        "    plt.show()\n",
        "except QiskitFinanceError as ex: \n",
        "    data = None\n",
        "    print(ex)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "FahybjHR9jc5",
        "outputId": "edf2f65a-cf82-460b-c669-7df170a946f3"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def return_qb(data):\n",
        "    mu = data.get_period_return_mean_vector()\n",
        "    sigma = data.get_period_return_covariance_matrix()\n",
        "    q = 0.5                   # set risk factor\n",
        "    budget = 1  # set budget\n",
        "    penalty = len(stocks)      # set parameter to scale the budget penalty term\n",
        "\n",
        "    portfolio = PortfolioOptimization(expected_returns=mu, covariances=sigma, risk_factor=q, budget=budget)\n",
        "    qp = portfolio.to_quadratic_program()\n",
        "    return qp\n",
        "def index_to_selection(i, num_assets):\n",
        "    s = \"{0:b}\".format(i).rjust(num_assets)\n",
        "    x = np.array([1 if s[i]=='1' else 0 for i in reversed(range(num_assets))])\n",
        "    return x\n",
        "\n",
        "\n",
        "def print_result(result):\n",
        "    selection = result.x\n",
        "    value = result.fval\n",
        "    total_weight = sum(selection)\n",
        "    print('Optimal: selection {}, value {:.4f}'.format(selection, value))\n",
        "    print('\\n----------------- Asset weights ---------------------')\n",
        "    for i in range(len(stocks)):\n",
        "        if selection[i] > 0.5:\n",
        "            weight = selection[i] / total_weight\n",
        "            print('{}: {:.4f}'.format(stocks[i], weight))\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "\n",
        "qp = return_qb(data)\n",
        "exact_mes = NumPyMinimumEigensolver()\n",
        "exact_eigensolver = MinimumEigenOptimizer(exact_mes)\n",
        "result = exact_eigensolver.solve(qp)\n",
        "end_time = time.time()\n",
        "print(\"Time taken for quantum method: \", end_time - start_time, \"seconds\") "
      ],
      "metadata": {
        "id": "0YDrowA49x81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8263111d-66fb-4d81-da2e-d55d86f8a9a7"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken for quantum method:  0.3290722370147705 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- probability of each possible selection of assets based on the solution vector obtained from the optimization algorithm"
      ],
      "metadata": {
        "id": "sZLY9NnBUilD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dict(zip(stocks, list(result)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uTUqeCIeT4m1",
        "outputId": "20df419a-49da-4e2a-9bdb-9189d8cfa47d"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'THYAO.IS': 0.0,\n",
              " 'YUNSA.IS': 0.0,\n",
              " 'GARAN.IS': 0.0,\n",
              " 'SASA.IS': 1.0,\n",
              " 'YKBNK.IS': 0.0,\n",
              " 'AKBNK.IS': 0.0,\n",
              " 'ENJSA.IS': 0.0,\n",
              " 'SAHOL.IS': 0.0,\n",
              " 'SISE.IS': 0.0,\n",
              " 'VAKBN.IS': 0.0,\n",
              " 'HALKB.IS': 0.0}"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Using classical method "
      ],
      "metadata": {
        "id": "pnnPBiHdYGv6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import riskfolio as rp\n",
        "import yfinance as yf\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "tcEOp5zdW6F3"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_yf = yf.download(tickers=stocks, \n",
        "                      start=datetime.datetime(2020, 1, 1),\n",
        "                 end=datetime.datetime(2023, 4, 22))\n",
        "close_data = pd.DataFrame(data_yf['Close'])\n",
        "close_data.columns = stocks\n",
        "close_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "xi5RiHlGWyWH",
        "outputId": "78f0fa8d-a5ed-478c-e780-280a8b5a6f1c"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  11 of 11 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            THYAO.IS  YUNSA.IS  GARAN.IS  SASA.IS  YKBNK.IS  AKBNK.IS  \\\n",
              "Date                                                                    \n",
              "2020-01-02      8.18      7.56     11.26     6.13      9.88  2.923528   \n",
              "2020-01-03      7.96      7.31     10.95     6.02      9.64  2.875358   \n",
              "2020-01-06      7.71      7.13     10.60     6.03      9.41  2.790135   \n",
              "2020-01-07      7.79      7.27     10.59     6.03      9.45  2.853126   \n",
              "2020-01-08      7.96      7.31     11.08     6.10      9.63  2.797545   \n",
              "\n",
              "            ENJSA.IS  SAHOL.IS  SISE.IS  VAKBN.IS  HALKB.IS  \n",
              "Date                                                         \n",
              "2020-01-02      5.33     14.83     5.56      2.52     10.57  \n",
              "2020-01-03      5.27     14.27     5.37      2.45     10.20  \n",
              "2020-01-06      5.12     13.57     5.16      2.40      9.83  \n",
              "2020-01-07      5.12     13.54     5.25      2.40      9.95  \n",
              "2020-01-08      5.09     13.50     5.36      2.42      9.70  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e3e36d1c-b800-48e9-bcd7-2db2305c49cb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>THYAO.IS</th>\n",
              "      <th>YUNSA.IS</th>\n",
              "      <th>GARAN.IS</th>\n",
              "      <th>SASA.IS</th>\n",
              "      <th>YKBNK.IS</th>\n",
              "      <th>AKBNK.IS</th>\n",
              "      <th>ENJSA.IS</th>\n",
              "      <th>SAHOL.IS</th>\n",
              "      <th>SISE.IS</th>\n",
              "      <th>VAKBN.IS</th>\n",
              "      <th>HALKB.IS</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>8.18</td>\n",
              "      <td>7.56</td>\n",
              "      <td>11.26</td>\n",
              "      <td>6.13</td>\n",
              "      <td>9.88</td>\n",
              "      <td>2.923528</td>\n",
              "      <td>5.33</td>\n",
              "      <td>14.83</td>\n",
              "      <td>5.56</td>\n",
              "      <td>2.52</td>\n",
              "      <td>10.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>7.96</td>\n",
              "      <td>7.31</td>\n",
              "      <td>10.95</td>\n",
              "      <td>6.02</td>\n",
              "      <td>9.64</td>\n",
              "      <td>2.875358</td>\n",
              "      <td>5.27</td>\n",
              "      <td>14.27</td>\n",
              "      <td>5.37</td>\n",
              "      <td>2.45</td>\n",
              "      <td>10.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>7.71</td>\n",
              "      <td>7.13</td>\n",
              "      <td>10.60</td>\n",
              "      <td>6.03</td>\n",
              "      <td>9.41</td>\n",
              "      <td>2.790135</td>\n",
              "      <td>5.12</td>\n",
              "      <td>13.57</td>\n",
              "      <td>5.16</td>\n",
              "      <td>2.40</td>\n",
              "      <td>9.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>7.79</td>\n",
              "      <td>7.27</td>\n",
              "      <td>10.59</td>\n",
              "      <td>6.03</td>\n",
              "      <td>9.45</td>\n",
              "      <td>2.853126</td>\n",
              "      <td>5.12</td>\n",
              "      <td>13.54</td>\n",
              "      <td>5.25</td>\n",
              "      <td>2.40</td>\n",
              "      <td>9.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>7.96</td>\n",
              "      <td>7.31</td>\n",
              "      <td>11.08</td>\n",
              "      <td>6.10</td>\n",
              "      <td>9.63</td>\n",
              "      <td>2.797545</td>\n",
              "      <td>5.09</td>\n",
              "      <td>13.50</td>\n",
              "      <td>5.36</td>\n",
              "      <td>2.42</td>\n",
              "      <td>9.70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import riskfolio as rp\n",
        "def hierarchical_risk_parity(Y):\n",
        "    port = rp.HCPortfolio(returns=Y)\n",
        "\n",
        "    # Estimate optimal portfolio:\n",
        "\n",
        "    model = 'HRP'  # Could be HRP or HERC\n",
        "    codependence = 'pearson'  # Correlation matrix used to group static in clusters\n",
        "    rm = 'MV'  # Risk measure used, this time will be variance\n",
        "    rf = 0  # Risk free rate\n",
        "    linkage = 'single'  # Linkage method used to build clusters\n",
        "    max_k = 10  # Max number of clusters used in two difference gap statistic\n",
        "    leaf_order = True  # Consider optimal order of leafs in dendrogram\n",
        "\n",
        "    w = port.optimization(model=model,codependence=codependence,rm=rm,rf=rf,linkage=linkage,max_k=max_k,leaf_order=leaf_order)\n",
        "    return w\n",
        "\n",
        "def black_litterman(Y):\n",
        "    port = rp.Portfolio(returns=Y)\n",
        "    # Estimate optimal portfolio:\n",
        "    method_mu='hist' # Method to estimate expected returns based on historical data.\n",
        "    method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
        "    port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
        "\n",
        "    # Estimate optimal portfolio:\n",
        "\n",
        "    model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)\n",
        "    rm = 'EDaR' # Risk measure used, this time will be variance\n",
        "    obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
        "    hist = True # Use historical scenarios for risk measures that depend on scenarios\n",
        "    rf = 0 # Risk free rate\n",
        "    l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
        "    w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
        "    return w\n",
        "\n",
        "def hierarchical_equal_risk_contribution(Y):\n",
        "    port = rp.HCPortfolio(returns=Y)\n",
        "    # Estimate optimal portfolio:\n",
        "\n",
        "    model = 'HERC'  # Could be HRP or HERC\n",
        "    codependence = 'pearson'  # Correlation matrix used to group static in clusters\n",
        "    rm = 'MV'  # Risk measure used, this time will be variance\n",
        "    rf = 0  # Risk free rate\n",
        "    linkage = 'ward'  # Linkage method used to build clusters\n",
        "    max_k = 10  # Max number of clusters used in two difference gap statistic\n",
        "    leaf_order = True  # Consider optimal order of leafs in dendrogram\n",
        "\n",
        "    w = port.optimization(model=model,codependence=codependence,rm=rm,rf=rf,linkage=linkage,max_k=max_k,leaf_order=leaf_order).T\n",
        "\n",
        "    data2 = w.T.reset_index()\n",
        "    data2.columns = ['symbol', 'hierarchical equal risk contribution']\n",
        "\n",
        "    return data2"
      ],
      "metadata": {
        "id": "dGYkG14SWmrb"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "start_time = time.time()\n",
        "result1 = hierarchical_risk_parity(close_data)\n",
        "end_time = time.time()\n",
        "\n",
        "print(\"Time taken for hierarchical_risk_parity: \", end_time - start_time, \"seconds\")\n",
        "print(result1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qf6aASluXrPE",
        "outputId": "0d9a6b31-4db8-47c3-b1ca-555ea6bf6295"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken for hierarchical_risk_parity:  0.08768820762634277 seconds\n",
            "           weights\n",
            "THYAO.IS  0.075655\n",
            "YUNSA.IS  0.033677\n",
            "GARAN.IS  0.042885\n",
            "SASA.IS   0.328657\n",
            "YKBNK.IS  0.009502\n",
            "AKBNK.IS  0.000682\n",
            "ENJSA.IS  0.016170\n",
            "SAHOL.IS  0.000655\n",
            "SISE.IS   0.332015\n",
            "VAKBN.IS  0.158738\n",
            "HALKB.IS  0.001366\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = time.time()\n",
        "result2 = black_litterman(close_data)\n",
        "end_time = time.time()\n",
        "\n",
        "print(\"Time taken for black_litterman: \", end_time - start_time, \"seconds\")\n",
        "print(result2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZejTU3oiX5dc",
        "outputId": "80583ec9-107d-45f9-f588-007596776589"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken for black_litterman:  0.209367036819458 seconds\n",
            "           weights\n",
            "THYAO.IS  0.012292\n",
            "YUNSA.IS  0.005744\n",
            "GARAN.IS  0.004495\n",
            "SASA.IS   0.023140\n",
            "YKBNK.IS  0.004342\n",
            "AKBNK.IS  0.218107\n",
            "ENJSA.IS  0.013451\n",
            "SAHOL.IS  0.697816\n",
            "SISE.IS   0.009953\n",
            "VAKBN.IS  0.007129\n",
            "HALKB.IS  0.003532\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = time.time()\n",
        "result3 = hierarchical_equal_risk_contribution(close_data).set_index('symbol')\n",
        "end_time = time.time()\n",
        "print(\"Time taken for hierarchical_equal_risk_contribution: \", end_time - start_time, \"seconds\")\n",
        "print(result3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mydgt1vmX8m_",
        "outputId": "84f82eca-4dd8-4bfc-8642-ff59ff075d05"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken for hierarchical_equal_risk_contribution:  0.14191269874572754 seconds\n",
            "          hierarchical equal risk contribution\n",
            "symbol                                        \n",
            "THYAO.IS                          1.457838e-03\n",
            "YUNSA.IS                          3.452973e-05\n",
            "GARAN.IS                          5.798792e-04\n",
            "SASA.IS                           4.948897e-01\n",
            "YKBNK.IS                          1.447931e-05\n",
            "AKBNK.IS                          2.043205e-06\n",
            "ENJSA.IS                          1.138816e-05\n",
            "SAHOL.IS                          9.980930e-07\n",
            "SISE.IS                           4.999462e-01\n",
            "VAKBN.IS                          3.058813e-03\n",
            "HALKB.IS                          4.095768e-06\n"
          ]
        }
      ]
    }
  ]
}