{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "63831470",
   "metadata": {},
   "source": [
    "<div style=\"background-color:#000;\"><img src=\"pqn.png\"></img></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "535538d4",
   "metadata": {},
   "source": [
    "This code utilizes the Black-Litterman model to incorporate subjective views into the portfolio optimization process. It fetches price data for selected assets, constructs a covariance matrix, and sets up absolute views on asset returns. The Black-Litterman model is then used to compute new expected returns, followed by the construction of an efficient frontier. This approach helps in creating a more informed and optimized portfolio by blending market equilibrium with investor views."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30dc6f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c1d4af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypfopt.black_litterman import BlackLittermanModel\n",
    "from pypfopt.efficient_frontier import EfficientFrontier\n",
    "from pypfopt import risk_models, plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "293a45be",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb_terminal.sdk import openbb\n",
    "import seaborn as sns\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60664575",
   "metadata": {},
   "source": [
    "Fetch price data for selected assets from the OpenBB terminal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02c6c09c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = openbb.economy.index([\"AAPL\", \"BBY\", \"BAC\", \"SBUX\", \"T\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9081783b",
   "metadata": {},
   "source": [
    "Define absolute views on the expected returns for specific assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d9cb1ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "viewdict = {\n",
    "    \"AAPL\": 0.20, \n",
    "    \"BBY\": 0.30,\n",
    "    \"BAC\": 0.10,\n",
    "    \"SBUX\": 0.2,\n",
    "    \"T\": 0.15\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a6f94a",
   "metadata": {},
   "source": [
    "Construct the sample covariance matrix using historical price data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d0e2e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "cov_matrix = risk_models.sample_cov(prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500bebeb",
   "metadata": {},
   "source": [
    "Initialize the Black-Litterman model with equal weight priors and absolute views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6db6f3b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bl = BlackLittermanModel(\n",
    "    cov_matrix, \n",
    "    absolute_views=viewdict,\n",
    "    pi=\"equal\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ac9e80",
   "metadata": {},
   "source": [
    "Compute the implied expected returns using the Black-Litterman model and initialize Efficient Frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "024947c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "rets = bl.bl_returns()\n",
    "ef = EfficientFrontier(rets, cov_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "644d3dec",
   "metadata": {},
   "source": [
    "Plot the efficient frontier showing the possible portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86ea1335",
   "metadata": {},
   "outputs": [],
   "source": [
    "plotting.plot_efficient_frontier(ef, show_tickers=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aba68864",
   "metadata": {},
   "source": [
    "Calculate and display the optimal weights for the portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef1c2d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "bl.bl_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbae896",
   "metadata": {},
   "source": [
    "<a href=\"https://pyquantnews.com/\">PyQuant News</a> is where finance practitioners level up with Python for quant finance, algorithmic trading, and market data analysis. Looking to get started? Check out the fastest growing, top-selling course to <a href=\"https://gettingstartedwithpythonforquantfinance.com/\">get started with Python for quant finance</a>. For educational purposes. Not investment advise. Use at your own risk."
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
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