Portfolio Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: matplotlib in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (3.8.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (4.44.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (1.26.1)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (10.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: nbformat in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (5.9.2)\n",
      "Requirement already satisfied: fastjsonschema in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from nbformat) (2.19.0)\n",
      "Requirement already satisfied: jsonschema>=2.6 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from nbformat) (4.20.0)\n",
      "Requirement already satisfied: jupyter-core in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from nbformat) (5.5.0)\n",
      "Requirement already satisfied: traitlets>=5.1 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from nbformat) (5.13.0)\n",
      "Requirement already satisfied: attrs>=22.2.0 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jsonschema>=2.6->nbformat) (23.1.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jsonschema>=2.6->nbformat) (2023.11.2)\n",
      "Requirement already satisfied: referencing>=0.28.4 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jsonschema>=2.6->nbformat) (0.32.0)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jsonschema>=2.6->nbformat) (0.13.2)\n",
      "Requirement already satisfied: platformdirs>=2.5 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jupyter-core->nbformat) (3.11.0)\n",
      "Requirement already satisfied: pywin32>=300 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jupyter-core->nbformat) (306)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install matplotlib\n",
    "%pip install nbformat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: nbformat in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (5.9.2)\n",
      "Requirement already satisfied: fastjsonschema in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from nbformat) (2.19.0)\n",
      "Requirement already satisfied: jsonschema>=2.6 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from nbformat) (4.20.0)\n",
      "Requirement already satisfied: jupyter-core in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from nbformat) (5.5.0)\n",
      "Requirement already satisfied: traitlets>=5.1 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from nbformat) (5.13.0)\n",
      "Requirement already satisfied: attrs>=22.2.0 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jsonschema>=2.6->nbformat) (23.1.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jsonschema>=2.6->nbformat) (2023.11.2)\n",
      "Requirement already satisfied: referencing>=0.28.4 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jsonschema>=2.6->nbformat) (0.32.0)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jsonschema>=2.6->nbformat) (0.13.2)\n",
      "Requirement already satisfied: platformdirs>=2.5 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jupyter-core->nbformat) (3.11.0)\n",
      "Requirement already satisfied: pywin32>=300 in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (from jupyter-core->nbformat) (306)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: numpy in c:\\users\\james\\appdata\\roaming\\python\\python312\\site-packages (1.26.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install nbformat\n",
    "%run PortfolioBase.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.0\n",
      "[8.223159109219415, 14019450, 6805828, 99072980, 49694960]\n",
      "[1. 1. 1. 1. 1.]\n",
      "169593226.2231591\n",
      "1.982112614811289e-08\n",
      "0.05789425422673129\n",
      "1.019999980178874\n"
     ]
    }
   ],
   "source": [
    "#Define the assets\n",
    "assets = [Bond(\"Risk Free\",5, 1.01)]\n",
    "for i in range(4):\n",
    "    assets.append(BinomialStock(\"Stock \"+str(i),4, 2,-1, 0.5))\n",
    "\n",
    "market = Market(assets)\n",
    "\n",
    "startingAllocation = np.ones(5)\n",
    "\n",
    "def exampleTradingStrategy(history):\n",
    "    #Return a constant uniform allocation \n",
    "    return np.ones(5)\n",
    "\n",
    "examplePortfolio = Portfolio(exampleTradingStrategy, market, startingAllocation)\n",
    "#Initial value of the portfolio\n",
    "print(examplePortfolio.returnCurrentValue())\n",
    "for i in range(50):\n",
    "    examplePortfolio.stepTime()\n",
    "#Current market value of the assets\n",
    "print(examplePortfolio.market.returnAssetValues())\n",
    "#Current allocation of assets within the portfolio\n",
    "print(examplePortfolio.currentAllocation)\n",
    "#Current total value of the portfolio\n",
    "print(examplePortfolio.returnCurrentValue())\n",
    "\n",
    "print(examplePortfolio.market.calculateHistoricBeta(\"Risk Free\"))\n",
    "print(examplePortfolio.market.calculateHistoricBeta(\"Stock 1\"))\n",
    "\n",
    "betaSum = 0\n",
    "for i in range(0, 4):\n",
    "    betaSum += examplePortfolio.market.calculateHistoricBeta(f\"Stock {i}\")\n",
    "print(betaSum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the portfolio\n",
      "Mean after 1000 steps :3.4693082256956156e+158\n",
      "Variance after 1000 steps :inf\n",
      "Median after 1000 steps :5.423217459839253e+146\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Roaming\\Python\\Python312\\site-packages\\numpy\\core\\_methods.py:176: RuntimeWarning: overflow encountered in multiply\n",
      "  x = um.multiply(x, x, out=x)\n"
     ]
    }
   ],
   "source": [
    "#Variance of end value\n",
    "examplePortfolio.reset()\n",
    "finalValues = []\n",
    "for i in range(50):\n",
    "    for iterations in range(1000):\n",
    "        examplePortfolio.stepTime()\n",
    "    finalValues.append(examplePortfolio.returnCurrentValue())\n",
    "    examplePortfolio.reset()\n",
    "print(\"Value of the portfolio\")\n",
    "print(f\"Mean after 1000 steps :{np.mean(finalValues)}\")\n",
    "print(f\"Variance after 1000 steps :{np.var(finalValues)}\")\n",
    "print(f\"Median after 1000 steps :{np.median(finalValues)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'A graph to show the evolution of value of a portfolio over 50 realisations with 100 iterations')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Trajectory of the portfolio value\n",
    "import matplotlib.pyplot as plt\n",
    "examplePortfolio.reset()\n",
    "values = []\n",
    "\n",
    "runs = 50\n",
    "iterationsPerRun = 100\n",
    "\n",
    "for i in range(runs):\n",
    "    runValues = []\n",
    "    for iteration in range(iterationsPerRun):\n",
    "        examplePortfolio.evolve()\n",
    "        runValues.append(examplePortfolio.returnCurrentValue())\n",
    "    values.append(runValues)\n",
    "    examplePortfolio.reset()\n",
    "\n",
    "for runValues in values:\n",
    "    plt.plot(runValues)\n",
    "\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Portfolio Value\")\n",
    "plt.title(f\"A graph to show the evolution of value of a portfolio over {runs} realisations with {iterationsPerRun} iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'A graph to show the evolution of value of a portfolio over 50 realisations with 10 iterations')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "examplePortfolio.reset()\n",
    "values = []\n",
    "\n",
    "runs = 50\n",
    "iterationsPerRun = 10\n",
    "\n",
    "for i in range(runs):\n",
    "    runValues = []\n",
    "    for iteration in range(iterationsPerRun):\n",
    "        examplePortfolio.evolve()\n",
    "        runValues.append(examplePortfolio.returnCurrentValue())\n",
    "    values.append(runValues)\n",
    "    examplePortfolio.reset()\n",
    "\n",
    "for runValues in values:\n",
    "    plt.plot(runValues)\n",
    "\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Portfolio Value\")\n",
    "plt.title(f\"A graph to show the evolution of value of a portfolio over {runs} realisations with {iterationsPerRun} iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}