Protifolio_Optimization_via_PyPortfolioOpt.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TheAmirHK/Portfolio-analysis-and-optimization/blob/main/Protifolio_Optimization_via_PyPortfolioOpt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PyPortfolioOpt is a library that implements portfolio optimization methods, including classical efficient frontier techniques and Black-Litterman allocation,\n",
        "as well as more recent developments in the field like shrinkage and Hierarchical Risk Parity,\n",
        "along with some novel experimental features like exponentially-weighted covariance matrices. Ref = https://pyportfolioopt.readthedocs.io/en/latest/"
      ],
      "metadata": {
        "id": "Nl01d3XvJtRp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0V4W58Is0lK",
        "outputId": "32f7fd8b-e1a8-45bb-95db-e6fe89158c6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: PyPortfolioOpt in /usr/local/lib/python3.10/dist-packages (1.5.5)\n",
            "Requirement already satisfied: cvxpy<2.0.0,>=1.1.19 in /usr/local/lib/python3.10/dist-packages (from PyPortfolioOpt) (1.3.2)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from PyPortfolioOpt) (1.23.5)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.10/dist-packages (from PyPortfolioOpt) (1.5.3)\n",
            "Requirement already satisfied: scipy<2.0,>=1.3 in /usr/local/lib/python3.10/dist-packages (from PyPortfolioOpt) (1.11.3)\n",
            "Requirement already satisfied: osqp>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.19->PyPortfolioOpt) (0.6.2.post8)\n",
            "Requirement already satisfied: ecos>=2 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.19->PyPortfolioOpt) (2.0.12)\n",
            "Requirement already satisfied: scs>=1.1.6 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.19->PyPortfolioOpt) (3.2.4)\n",
            "Requirement already satisfied: setuptools>65.5.1 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.19->PyPortfolioOpt) (67.7.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2023.3.post1)\n",
            "Requirement already satisfied: qdldl in /usr/local/lib/python3.10/dist-packages (from osqp>=0.4.1->cvxpy<2.0.0,>=1.1.19->PyPortfolioOpt) (0.1.7.post0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.19->PyPortfolioOpt) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install PyPortfolioOpt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UyDhSMORq9Da"
      },
      "outputs": [],
      "source": [
        "import pandas_datareader.data as web\n",
        "import datetime\n",
        "from datetime import datetime, date, timedelta\n",
        "import yfinance as yf\n",
        "yf.pdr_override() # <== that's all it takes :-)\n",
        "from pandas_datareader import data as pdr\n",
        "import pandas as pd\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', None)\n",
        "import numpy as np\n",
        "from scipy.optimize import minimize\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# For warnings suppression\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5i7zzR74rIMD"
      },
      "outputs": [],
      "source": [
        "from pypfopt.expected_returns import mean_historical_return\n",
        "from pypfopt.risk_models import CovarianceShrinkage\n",
        "from pypfopt.efficient_frontier import EfficientFrontier\n",
        "from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices\n",
        "from pypfopt import HRPOpt\n",
        "from pypfopt.efficient_frontier import EfficientCVaR\n",
        "import cvxpy as cp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uddfGJclreh4"
      },
      "outputs": [],
      "source": [
        "# A function to download stocks' adjusted close price\n",
        "def receive_data(tickers, start, end):\n",
        "    adj_close_df = pd.DataFrame()\n",
        "    for ticker in tickers:\n",
        "        data = pdr.get_data_yahoo(ticker, start, end, progress=False)\n",
        "        adj_close_df[ticker] = data[\"Adj Close\"]\n",
        "    return adj_close_df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A function to normalize prices\n",
        "def daily_return(data):\n",
        "    log_returns = data.pct_change().dropna()\n",
        "    return log_returns"
      ],
      "metadata": {
        "id": "Utb9qArR85hm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# One portfolio test and evaluation\n",
        "def evaluation  (data, Rf = 0.02, weights = None):\n",
        "    result = pd.DataFrame()\n",
        "    log_return = daily_return(data)\n",
        "    if weights is None:\n",
        "        result[\"ExpRet\"] = log_return.mean()*252\n",
        "        result[\"ExpVol\"] = log_return.std() *np.sqrt(252)\n",
        "        result[\"ExpSha\"] = (result[\"ExpRet\"] - Rf) / result[\"ExpVol\"]\n",
        "    else:\n",
        "        result[\"ExpRet\"] = [np.sum(log_return.mean()*weights)*252]\n",
        "        result[\"ExpVol\"] = [np.sqrt(weights.T @ log_return.cov() @ weights) * np.sqrt(252)]\n",
        "        result[\"ExpSha\"] = (result[\"ExpRet\"] - Rf) / result[\"ExpVol\"]\n",
        "    return result"
      ],
      "metadata": {
        "id": "fPPgrRN09CyV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpyQxj76sZjp",
        "outputId": "1a07e674-0d9c-4b55-9b6b-ea4bd720329c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start date:2018-11-29 15:14:04.730982tr   End date:2023-11-28 15:14:04.730982tr\n"
          ]
        }
      ],
      "source": [
        "# Eahc individual stocks to be analyzed !\n",
        "stocks = [\"AAPL\", \"MSFT\", \"GOOG\", \"AMZN\",   # Technology stocks\n",
        "          \"TSLA\", \"TM\", \"MBGYY\", \"VWAGY\", \"VLVLY\", \"GM\",  # Motor manufacturing stocks\n",
        "          \"LLY\", \"JNJ\", \"AZN\"] # Health market stocks\n",
        "\n",
        "# define the number of years to be studied\n",
        "NYears = 5\n",
        "\n",
        "# Risk-free asset rate\n",
        "Rf = 0.02\n",
        "\n",
        "# Wealth value (Budget)\n",
        "wealth = 5000\n",
        "\n",
        "# Define the time period of study\n",
        "end = datetime.today()\n",
        "start = end - timedelta(days = NYears*365)\n",
        "print(\"Start date:%str   End date:%str\" %(start, end))\n",
        "\n",
        "portfolio = receive_data(stocks, start, end)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NbSWwiBbvApL"
      },
      "outputs": [],
      "source": [
        "# Calculate optimal share volumes using the obtained weights from simulation.\n",
        "def share_volume(weights, portfolio, wealth):\n",
        "    evaluation = pd.DataFrame()\n",
        "    evaluation['Price'] = portfolio.iloc[-2]\n",
        "    da = DiscreteAllocation(weights, evaluation['Price'], total_portfolio_value = wealth)\n",
        "    allocation, leftover = da.greedy_portfolio()\n",
        "    Optimal_weights = pd.DataFrame.from_dict([weights])\n",
        "    evaluation['Optimal Weights'] = Optimal_weights.T\n",
        "    evaluation['Optimal Volume'] = pd.DataFrame.from_dict([allocation]).T\n",
        "    evaluation['Total pay'] = evaluation['Optimal Volume'] * evaluation['Price']\n",
        "    print(\"Not allocated budegt: %0.2f $ out of %0.2f $ \"%(leftover,wealth ))\n",
        "    print(evaluation.replace(np.nan, 0))\n",
        "    return\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sharpe ratio\n",
        "Sharpe ratio is a measure used in finance to evaluate the performance of an investment or a trading strategy. It was developed by Nobel laureate William F. Sharpe. The ratio is calculated by subtracting the risk-free rate of return from the investment's return and then dividing the result by the investment's standard deviation.\n",
        "The Sharpe ratio essentially measures the risk-adjusted return of an investment. It provides insight into how well the return of an investment compensates the investor for the risk taken. A higher Sharpe ratio indicates better risk-adjusted performance, as it means the investment has generated more return for the amount of risk taken.\n",
        "The Sharpe ratio is widely used by investors and portfolio managers to compare the risk-adjusted returns of different investments or strategies. It helps in evaluating whether the excess return of an investment is sufficient given the level of risk involved, and it can be a valuable tool for making informed investment decisions."
      ],
      "metadata": {
        "id": "A6C7MuPCIe6p"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w7Qqk3Slre3B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "f6af730f-55d4-4902-b365-c6476652d2c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected annual return: 43.0%\n",
            "Annual volatility: 27.6%\n",
            "Sharpe Ratio: 1.49\n",
            "Not allocated budegt: 149.08 $ out of 5000.00 $ \n",
            "            Price  Optimal Weights  Optimal Volume    Total pay\n",
            "AAPL   189.789993          0.19052             5.0   948.949966\n",
            "MSFT   378.609985          0.00000             0.0     0.000000\n",
            "GOOG   138.050003          0.00000             0.0     0.000000\n",
            "AMZN   147.729996          0.00000             0.0     0.000000\n",
            "TSLA   236.080002          0.17788             4.0   944.320007\n",
            "TM     186.610001          0.00000             0.0     0.000000\n",
            "MBGYY   15.930000          0.00000             0.0     0.000000\n",
            "VWAGY   12.890000          0.00000             0.0     0.000000\n",
            "VLVLY   22.889999          0.00000             0.0     0.000000\n",
            "GM      28.500000          0.00000             0.0     0.000000\n",
            "LLY    591.530029          0.63159             5.0  2957.650146\n",
            "JNJ    151.279999          0.00000             0.0     0.000000\n",
            "AZN     63.660000          0.00000             0.0     0.000000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def MaxSharpe(data,wealth):\n",
        "    mu = mean_historical_return(data)\n",
        "    S = CovarianceShrinkage(data).ledoit_wolf()\n",
        "\n",
        "    ef = EfficientFrontier(mu, S)\n",
        "    ef.max_sharpe(risk_free_rate=0.02)\n",
        "\n",
        "    weight_arr = ef.weights\n",
        "    weights_max_sharpe = ef.clean_weights()\n",
        "    ef.portfolio_performance(verbose=True)\n",
        "    share_volume(weights_max_sharpe,data, wealth )\n",
        "    Optimal_weights = pd.DataFrame.from_dict([weights_max_sharpe])\n",
        "    Optimal_weights.iloc[0].plot(kind='pie',\n",
        "                                 autopct='%1.1f%%',\n",
        "                                 colors=sns.color_palette('Set2'),\n",
        "                                 textprops={'fontsize':8}, ylabel = \"Optimal weights\")\n",
        "    results = evaluation (data, weights = Optimal_weights.iloc[0].T)\n",
        "    return results\n",
        "MaxSharpe_results = MaxSharpe(portfolio,wealth)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hierarchical Risk Parity (HRP)\n",
        "Hierarchical Risk Parity (HRP) is a portfolio allocation technique in finance that aims to optimize the risk-return profile of a portfolio. It is based on the idea of diversification and risk parity, which means that each asset in the portfolio should contribute equally to the overall risk of the portfolio.\n",
        "In HRP, the assets are grouped into clusters based on their correlation with each other. The algorithm then calculates the risk contribution of each cluster and allocates capital to the clusters in a way that equalizes their risk contributions. This approach aims to create a more balanced and diversified portfolio, potentially reducing the overall risk of the portfolio while maintaining or improving its expected return."
      ],
      "metadata": {
        "id": "RB0084_JIqLa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "jPKEuLIfCrTS",
        "outputId": "da27e35e-5714-4444-ecf7-b7e7aa964ace"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected annual return: 19.9%\n",
            "Annual volatility: 19.9%\n",
            "Sharpe Ratio: 0.90\n",
            "Not allocated budegt: 4.09 $ out of 5000.00 $ \n",
            "            Price  Optimal Weights  Optimal Volume   Total pay\n",
            "AAPL   189.789993         0.081588               2  379.579987\n",
            "MSFT   378.609985         0.063267               1  378.609985\n",
            "GOOG   138.050003         0.080056               2  276.100006\n",
            "AMZN   147.729996         0.063402               2  295.459991\n",
            "TSLA   236.080002         0.032189               1  236.080002\n",
            "TM     186.610001         0.107955               3  559.830002\n",
            "MBGYY   15.930000         0.026927               8  127.440002\n",
            "VWAGY   12.890000         0.027566              11  141.790004\n",
            "VLVLY   22.889999         0.035067               7  160.229996\n",
            "GM      28.500000         0.027767               4  114.000000\n",
            "LLY    591.530029         0.085591               1  591.530029\n",
            "JNJ    151.279999         0.197110               6  907.679993\n",
            "AZN     63.660000         0.171516              13  827.579998\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def Hierarchical_Risk_Parity (data,wealth):\n",
        "    returns = data.pct_change().dropna()\n",
        "    hrp = HRPOpt(returns)\n",
        "    hrp_weights = hrp.optimize()\n",
        "    results = hrp.portfolio_performance(verbose=True)\n",
        "    share_volume(hrp_weights,data, 5000 )\n",
        "    Optimal_weights = pd.DataFrame.from_dict([hrp_weights])\n",
        "    Optimal_weights.iloc[0].plot(kind='pie',\n",
        "                                 autopct='%1.1f%%',\n",
        "                                 colors=sns.color_palette('Set2'),\n",
        "                                 textprops={'fontsize':8}, ylabel = \"Optimal weights\")\n",
        "    results = evaluation (data, weights = Optimal_weights.iloc[0].T)\n",
        "    return results\n",
        "HRP_results = Hierarchical_Risk_Parity(portfolio, wealth)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Efficient Conditional Value at Risk (CVaR)\n",
        "Efficient Conditional Value at Risk (CVaR) is a concept in finance that relates to the measurement of risk in an investment portfolio. CVaR is a risk measure that quantifies the potential losses in the tail of the distribution of possible returns. It provides a more comprehensive view of risk compared to traditional risk measures such as standard deviation or Value at Risk (VaR).\n",
        "Efficient CVaR, also known as mean-CVaR optimization, aims to construct an investment portfolio that maximizes the expected return while minimizing the Conditional Value at Risk. This approach considers not only the expected return and the volatility of the portfolio, but also the potential severity of losses beyond a certain threshold.\n",
        "By optimizing for Efficient CVaR, investors seek to build portfolios that are more resilient to extreme market events and tail risks. This can be particularly important for risk-averse investors or for those operating in volatile markets where the potential for large losses is a significant concern."
      ],
      "metadata": {
        "id": "_CpMFyIqJGLs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JrWCGIdJsKxa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "d44def20-397c-4778-802a-ca979f6593f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not allocated budegt: 413.18 $ out of 5000.00 $ \n",
            "            Price  Optimal Weights  Optimal Volume    Total pay\n",
            "AAPL   189.789993          0.00000             0.0     0.000000\n",
            "MSFT   378.609985          0.00000             0.0     0.000000\n",
            "GOOG   138.050003          0.00000             0.0     0.000000\n",
            "AMZN   147.729996          0.00000             0.0     0.000000\n",
            "TSLA   236.080002          0.00000             0.0     0.000000\n",
            "TM     186.610001          0.00000             0.0     0.000000\n",
            "MBGYY   15.930000          0.48020           150.0  2389.500046\n",
            "VWAGY   12.890000          0.00000             0.0     0.000000\n",
            "VLVLY   22.889999          0.00000             0.0     0.000000\n",
            "GM      28.500000          0.00000             0.0     0.000000\n",
            "LLY    591.530029          0.05865             0.0     0.000000\n",
            "JNJ    151.279999          0.38964            12.0  1815.359985\n",
            "AZN     63.660000          0.07151             6.0   381.959999\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def Efficient_CVaR (data, wealth):\n",
        "    mu = mean_historical_return(data)\n",
        "    S = CovarianceShrinkage(data).ledoit_wolf()\n",
        "\n",
        "    ef_cvar = EfficientCVaR(mu, S, solver=cp.ECOS)\n",
        "    cvar_weights = ef_cvar.min_cvar()\n",
        "    cleaned_weights = ef_cvar.clean_weights()\n",
        "    Optimal_weights = pd.DataFrame.from_dict([cleaned_weights])\n",
        "    Optimal_weights.iloc[0].plot(kind='pie',\n",
        "                                 autopct='%1.1f%%',\n",
        "                                 colors=sns.color_palette('Set2'),\n",
        "                                 textprops={'fontsize':8}, ylabel = \"Optimal weights\")\n",
        "    share_volume(cleaned_weights,data, 5000 )\n",
        "    results = evaluation (data, weights = Optimal_weights.iloc[0].T)\n",
        "    return results\n",
        "CVaR_results = Efficient_CVaR(portfolio, wealth)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({\n",
        "'Criteria': ['ExpRet', 'ExpVol', 'ExpSha'],\n",
        "'MaxSharpe_results': MaxSharpe_results.iloc[0].values.tolist(),\n",
        "'HRP_results': (HRP_results.iloc[0]).values.tolist(),\n",
        "'CVaR_results': (CVaR_results.iloc[0]).values.tolist()\n",
        "})\n",
        "\n",
        "# plotting graph\n",
        "df.plot(x=\"Criteria\", y=[\"MaxSharpe_results\", \"HRP_results\",\"CVaR_results\" ], kind=\"bar\", grid=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "8QWFUgX3Ymo1",
        "outputId": "e8a1855b-daa8-42ad-f67f-8834bf39fbbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPyKASQCuLDNgHHCTia4tPz",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}