Skip to content

Instantly share code, notes, and snippets.

Show Gist options
  • Star 1 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save aarongilman/808bf352a795c5503c2cebfaae679555 to your computer and use it in GitHub Desktop.
Save aarongilman/808bf352a795c5503c2cebfaae679555 to your computer and use it in GitHub Desktop.
Capital Market Assumptions Optimizer with Interactive.ipynb
Display the source blob
Display the rendered blob
Raw
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Capital Market Assumptions Optimizer with Interactive.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/aarongilman/808bf352a795c5503c2cebfaae679555/capital-market-assumptions-optimizer-with-interactive.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "lG85PS_s-roE",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# IFP Asset Management Assumptions from the JPMorgan Capital Market Assumptions\n",
"\n",
"by Aaron Gilman, CFA and Pete Nunley, CFA\n",
"\n",
"JPMorgan releases capital market assumptions annually and are always transparent and consistent in terms of sharing how they arrive at each number and the robust process followed year after year. In the past, we typically use these numbers as one input into our strategic asset allocation process and it takes some massaging to transform into something investable/palatable for the average retail client. This year, we decided to take the capital market assumptions and experiment with portfolio optimizations at different levels of required returns to help set expectations going forward for what may be a suppressed return environment for risky assets. You can find the full version of their capital market assumptions with their own conclusions here: [2019 Long-Term Capital Market Assumptions (LTCMA)](https://am.jpmorgan.com/gb/en/asset-management/gim/adv/insights/ltcma-2019)\n",
"\n",
"I got inspiration for this from the guys at Newfound Research LLC. They have a cool web page that illustrates what they call \"The Weird Portfolio\", and it is an unconstrained portfolio optimization using similar asset classes to JPMorgan's Capital Market Assumptions that shows how weird a 60/40 portfolio looks in todays environment. You can check it out [here](https://www.theweirdportfolio.com)\n",
"\n",
"We are constraining our optimizations below, with a maximum of 20% in an asset class and long-only (no shorting/negative allocations).\n",
"\n",
"Full disclosure, this is by no means an attempt to predict which portfolio allocation is best over the next decade, it is simply a thought experiment to understand the implications published by a very well respected source in our industry and what it could mean for investors and portfolios. Our team uses this as a way to understand one possible path for the future and we realize it may or may not be realized.\n",
"\n",
"The spreadsheet we use for the optimization below can also be found [here](https://www.dropbox.com/s/5ipl1s0e0jmi40e/capital%20market%20assumptions.xlsx?dl=1) if you want to use it for your own research."
]
},
{
"metadata": {
"id": "k9v3ROYFaUQV",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Install required packages for analysis"
]
},
{
"metadata": {
"id": "HYXuYLsb-oMz",
"colab_type": "code",
"outputId": "a91a6381-a598-4eca-d1d3-90029dd3cf2f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
}
},
"cell_type": "code",
"source": [
"!pip install PyPortfolioOpt xlrd"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting PyPortfolioOpt\n",
" Downloading https://files.pythonhosted.org/packages/12/d4/6d616ceb238c16d863d84cb24a45d76914242a21d09b32bd9ce785f06b78/PyPortfolioOpt-0.2.0-py3-none-any.whl\n",
"Requirement already satisfied: xlrd in /usr/local/lib/python3.6/dist-packages (1.1.0)\n",
"Collecting noisyopt (from PyPortfolioOpt)\n",
" Downloading https://files.pythonhosted.org/packages/ed/e1/9fef3bf87c80ddb5e88df2654c8351bef9d027f3f4d485ad3c31b4f7ed50/noisyopt-0.2.2-py2.py3-none-any.whl\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from PyPortfolioOpt) (0.20.2)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from PyPortfolioOpt) (1.1.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from PyPortfolioOpt) (1.14.6)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from PyPortfolioOpt) (0.22.0)\n",
"Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->PyPortfolioOpt) (2018.9)\n",
"Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas->PyPortfolioOpt) (2.5.3)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2->pandas->PyPortfolioOpt) (1.11.0)\n",
"Installing collected packages: noisyopt, PyPortfolioOpt\n",
"Successfully installed PyPortfolioOpt-0.2.0 noisyopt-0.2.2\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "eznbjEM3aZ7N",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Import modules needed for code to execute"
]
},
{
"metadata": {
"id": "2cWM9rsAnok-",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"from pypfopt.efficient_frontier import EfficientFrontier\n",
"from pypfopt import risk_models\n",
"from pypfopt import expected_returns"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "jhT41YQcahOu",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Here we import the capital market assumptions spreadsheet to leverage for expected returns and covariances."
]
},
{
"metadata": {
"id": "lWqdWlK6ntym",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"expected_return = pd.read_excel('https://www.dropbox.com/s/5ipl1s0e0jmi40e/capital%20market%20assumptions.xlsx?dl=1', index_col=0)\n",
"expected_cov = pd.read_excel('https://www.dropbox.com/s/5ipl1s0e0jmi40e/capital%20market%20assumptions.xlsx?dl=1', sheet_name='covariance', index_col=0)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "DO5K4HR6n0J1",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"mu = expected_return['arithmetic_return']\n",
"S = expected_cov"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "cdaSUjd7Qq51",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Interactive Optimizer\n",
"\n",
"Try playing around with the parameters in the form below to see the results instantly. Please note that this needs to be opened and ran inside Google Colab in order for it to work properly."
]
},
{
"metadata": {
"id": "bIQ4kpz2L8Qr",
"colab_type": "code",
"outputId": "1e8d46dc-682a-402f-94ee-160c4c9a7864",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 431
}
},
"cell_type": "code",
"source": [
"#@title Fill in desired parameters for optimization. { display-mode: \"form\", run: \"auto\" }\n",
"\n",
"maximum_allocation = 0.2 #@param {type:\"slider\", min:0, max:1, step:0.01}\n",
"return_target = 0.07 #@param {type:\"slider\", min:0, max:0.20, step:0.01}\n",
"\n",
"ef = EfficientFrontier(mu, S, weight_bounds=(0, maximum_allocation))\n",
"ef.efficient_return(target_return=return_target, market_neutral=False)\n",
"weights = ef.clean_weights()\n",
"performance = ef.portfolio_performance(verbose=True)\n",
"results = pd.DataFrame.from_dict(weights, orient='index')\n",
"results = results[(results.T != 0).any()]\n",
"formatted_name = str(round(return_target * 100))\n",
"chart_name = \"Target Return {}% Allocations\".format(str(formatted_name))\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.style as style\n",
"style.use('fivethirtyeight')\n",
"\n",
"labels = results.index\n",
"sections = list(results.values)\n",
"\n",
"plt.pie(sections, labels=labels,\n",
" startangle=90,\n",
" autopct = '%1.2f%%')\n",
"\n",
"plt.axis('equal') # Try commenting this out.\n",
"plt.title(chart_name)\n",
"plt.show()"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"Expected annual return: 7.0%\n",
"Annual volatility: 7.8%\n",
"Sharpe Ratio: 0.64\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAFpCAYAAADKh88IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAADAIMQbAACAQb4AKZTPHbjwEGUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment