daily update

to_explore/pyquantnews/54_BlackLitterman.ipynb

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+{
+ "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}