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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["mnn", "serve", "/code/app.ipynb", "--address", "0.0.0.0", "--port", "7860",  "--allow-websocket-origin", "*"]

app.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import manganite\n",
+    "%load_ext manganite"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Portfolio Selection Optimization\n",
+    "This model is an example of the classic [Markowitz portfolio selection optimization model](https://en.wikipedia.org/wiki/Markowitz_model). We want to find the fraction of the portfolio to invest among a set of stocks that balances risk and return. It is a Quadratic Programming (QP) model with vector and matrix data for returns and risk, respectively. This is best suited to a matrix formulation, so we use the Gurobi Python *matrix* interface. The basic model is fairly simple, so we also solve it parametrically to find the efficient frontier.\n",
+    "\n",
+    "**Download the Repository** <br /> \n",
+    "You can download the repository containing this and other examples by clicking [here](https://github.com/Gurobi/modeling-examples/archive/master.zip). \n",
+    "\n",
+    "**Gurobi License** <br /> \n",
+    "In order to run this Jupyter Notebook properly, you must have a Gurobi license. If you do not have one, you can request an [evaluation license](https://www.gurobi.com/downloads/request-an-evaluation-license/?utm_source=3PW&utm_medium=OT&utm_campaign=WW-MU-MUI-OR-O_LEA-PR_NO-Q3_FY20_WW_JPME_Lost_Luggage_Distribution_COM_EVAL_GitHub&utm_term=Lost%20Luggage%20Distribution&utm_content=C_JPM) as a *commercial user*, or download a [free license](https://www.gurobi.com/academia/academic-program-and-licenses/?utm_source=3PW&utm_medium=OT&utm_campaign=WW-MU-EDU-OR-O_LEA-PR_NO-Q3_FY20_WW_JPME_Lost_Luggage_Distribution_COM_EVAL_GitHub&utm_term=Lost%20Luggage%20Distribution&utm_content=C_JPM) as an *academic user*.\n",
+    "\n",
+    "\n",
+    "## Model Formulation\n",
+    "### Parameters\n",
+    "\n",
+    "We use the [Greek values](https://en.wikipedia.org/wiki/Greeks_\\(finance\\)) that are traditional in finance:\n",
+    "\n",
+    "- $$ \\delta $$: n-element vector measuring the change in price for each stock\n",
+    "- $$ \\sigma $$: n x n matrix measuring the covariance among stocks\n",
+    "\n",
+    "There is one additional parameter when solving the model parametrically:\n",
+    "\n",
+    "- r: target return\n",
+    "\n",
+    "\n",
+    "### Decision Variables\n",
+    "- $$ x \\ge 0 $$ : n-element vector where each element represents the fraction of the porfolio to invest in each stock\n",
+    "\n",
+    "### Objective Function\n",
+    "Minimize the total risk, a convex quadratic function:\n",
+    "<pre>\n",
+    "$$\n",
+    "\\begin{equation}\n",
+    "\\min x^t \\cdot \\sigma \\cdot x\n",
+    "\\end{equation}\n",
+    "$$\n",
+    "</pre>\n",
+    "\n",
+    "### Constraints\n",
+    "\n",
+    "Allocate the entire portfolio: the total investments should be 1.0 (100%), where $e$ is a unit vector (all 1's):\n",
+    "<pre>\n",
+    "$$\n",
+    "\\begin{equation}\n",
+    "e \\cdot x = 1\n",
+    "\\end{equation}\n",
+    "$$\n",
+    "</pre>\n",
+    "Return: When we solve the model parametrically for different return values $r$, we add a constraint on the target return:\n",
+    "<pre>\n",
+    "$$\n",
+    "\\begin{equation}\n",
+    "\\delta \\cdot x = r\n",
+    "\\end{equation}\n",
+    "$$\n",
+    "</pre>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python Implementation\n",
+    "### Stock data\n",
+    "Use [yfinance](https://pypi.org/project/yfinance/) library to get the latest 2 years of _actual stock data_ from the 20 most profitable US companies, [according to Wikipedia in April 2021](https://en.wikipedia.org/wiki/List_of_largest_companies_in_the_United_States_by_revenue#List_of_companies_by_profit).\n",
+    "### Dashboard\n",
+    "Use manganite package to create a beautiful dashoard from the jupyter notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import yfinance as yf\n",
+    "import numpy as np\n",
+    "\n",
+    "import gurobipy as gp\n",
+    "from gurobipy import GRB\n",
+    "from math import sqrt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "import plotly.io as pio\n",
+    "pio.templates.default = 'plotly_white'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%mnn widget --type text --tab \"Stock Selector\" --header \"Select your stocks\" --var stocks --position 0 0 3\n",
+    "stocks = 'AAPL, MSFT, JPM, GOOG, BAC, INTC, WFC, Meta'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the input string into a list using the column separator\n",
+    "tick_marks_list = stocks.split(',')\n",
+    "\n",
+    "# Remove unwanted characters and spaces from each element\n",
+    "clean_tick_marks = [tick.strip() for tick in tick_marks_list]\n",
+    "\n",
+    "data = yf.download(clean_tick_marks, period='2y')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%mnn widget --type plot --var fig_stocks --tab \"Stock Selector\" --position 1 0 4 --header \"Stock prices\"\n",
+    "\n",
+    "df_closing = data['Close']\n",
+    "fig_stocks = px.line(df_closing, x=df_closing.index, y=df_closing.columns,\n",
+    "            #   hover_data={\"date\": \"|%B %d, %Y\"},\n",
+    "              )\n",
+    "\n",
+    "# Update the layout to customize axis labels\n",
+    "fig_stocks.update_yaxes(title_text='Stock Price ($)')\n",
+    "\n",
+    "fig_stocks.update_xaxes(\n",
+    "  title_text='',\n",
+    "    dtick=\"M1\",\n",
+    "    tickformat=\"%b\\n%Y\",\n",
+    "    ticklabelmode=\"period\")\n",
+    "\n",
+    "fig_stocks.update_layout(legend_title=\"Stock\",)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%mnn execute --on button \"Optimize Portfolio\" --returns solution\n",
+    "\n",
+    "#calculating greeks\n",
+    "closes = np.transpose(np.array(data.Close)) # matrix of daily closing prices\n",
+    "absdiff = np.diff(closes)                   # change in closing price each day\n",
+    "reldiff = np.divide(absdiff, closes[:,:-1]) # relative change in daily closing price\n",
+    "delta = np.mean(reldiff, axis=1)            # mean price change\n",
+    "sigma = np.cov(reldiff)                     # covariance (standard deviations)\n",
+    "std = np.std(reldiff, axis=1)               # standard deviation\n",
+    "df_plot = pd.DataFrame({'std': std, 'delta': delta, 'stocks':clean_tick_marks})\n",
+    "print('solving QP model')\n",
+    "# Create an empty model\n",
+    "m = gp.Model('portfolio')\n",
+    "\n",
+    "# Add matrix variable for the stocks\n",
+    "x = m.addMVar(len(clean_tick_marks))\n",
+    "\n",
+    "# Objective is to minimize risk (squared).  This is modeled using the\n",
+    "# covariance matrix, which measures the historical correlation between stocks\n",
+    "portfolio_risk = x @ sigma @ x\n",
+    "m.setObjective(portfolio_risk, GRB.MINIMIZE)\n",
+    "\n",
+    "# Fix budget with a constraint\n",
+    "m.addConstr(x.sum() == 1, 'budget')\n",
+    "\n",
+    "# Verify model formulation\n",
+    "# m.write('portfolio_selection_optimization.lp')\n",
+    "\n",
+    "# Optimize model to find the minimum risk portfolio\n",
+    "m.optimize()\n",
+    "\n",
+    "minrisk_volatility = sqrt(m.ObjVal)\n",
+    "minrisk_return = delta @ x.X\n",
+    "\n",
+    "# Create an expression representing the expected return for the portfolio\n",
+    "portfolio_return = delta @ x\n",
+    "\n",
+    "target = m.addConstr(portfolio_return == minrisk_return, 'target')\n",
+    "\n",
+    "# solution = pd.DataFrame(data=np.append(x.X, [minrisk_volatility, minrisk_return]),\n",
+    "#              index=clean_tick_marks + ['Volatility', 'Expected Return'],\n",
+    "#              columns=['Minimum Risk Portfolio'])\n",
+    "\n",
+    "solution = pd.DataFrame(data=x.X,\n",
+    "             index=clean_tick_marks,\n",
+    "             columns=['Minimum Risk Portfolio'])\n",
+    "\n",
+    "exp_return = minrisk_return\n",
+    "exp_volatility = minrisk_volatility\n",
+    "\n",
+    "# Solve for efficient frontier by varying target return\n",
+    "frontier = np.empty((2,0))\n",
+    "for r in np.linspace(delta.min(), delta.max(), 25):\n",
+    "    target.rhs = r\n",
+    "    m.optimize()\n",
+    "    frontier = np.append(frontier, [[sqrt(m.ObjVal)],[r]], axis=1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%mnn widget --type plot --var fig_bar --tab \"Portfolio\" --position 0 0 3 --header \"Minimum Risk Portfolio\"\n",
+    "# Create a pie chart\n",
+    "fig_pie = px.pie(solution, values='Minimum Risk Portfolio', names=solution.index,\n",
+    "        title=f'Your Portfolio Distribution<br>Expected Return: {round(exp_return,6)}<br>Volatility: {round(exp_volatility,4)}')\n",
+    "fig_pie.update_traces(textposition='inside', textinfo='percent+label')\n",
+    "\n",
+    "# Create a bar chart\n",
+    "fig_bar = px.bar(solution, x=solution.index, y='Minimum Risk Portfolio',\n",
+    "title=f'Your Portfolio Distribution<br>Expected Return: {round(exp_return,6)}<br>Volatility: {round(exp_volatility,4)}',\n",
+    ")\n",
+    "\n",
+    "# Hide x-axis and y-axis labels\n",
+    "fig_bar.update_xaxes(title_text='')\n",
+    "fig_bar.update_yaxes(title_text='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%mnn widget --type plot --var fig --tab \"Portfolio\" --position 0 3 3 --header \"Efficient Frontier\"\n",
+    "\n",
+    "update = solution\n",
+    "# Plot volatility versus expected return for individual stocks\n",
+    "fig1 = px.scatter(df_plot, x=\"std\", y=\"delta\" ,\n",
+    "                                labels = { \"std\": \"Volatility (standard deviation)\", \"delta\": \"Expected Return\"}, text=\"stocks\" )\n",
+    "fig1.update_traces(textposition=\"bottom right\")\n",
+    "\n",
+    "# Plot volatility versus expected return for minimum risk portfolio\n",
+    "\n",
+    "fig2 = px.scatter(x=[minrisk_volatility], y=[minrisk_return],text = [\"Minimum Risk<br>Portfolio\"])\n",
+    "fig2.update_traces(textposition=\"bottom right\")\n",
+    "fig  = go.Figure(data=fig1.data + fig2.data)\n",
+    "\n",
+    "# Plot efficient frontier\n",
+    "\n",
+    "fig.add_trace(go.Scatter(x=frontier[0], y=frontier[1], mode='lines', name='Efficient Frontier'))\n",
+    "\n",
+    "# Set x and y labels using update_xaxes and update_yaxes\n",
+    "fig.update_xaxes(title_text=\"Volatility (standard deviation)\")\n",
+    "fig.update_yaxes(title_text=\"Expected Return\")\n",
+    "\n",
+    "fig.update_layout(legend=dict(\n",
+    "    yanchor=\"bottom\",\n",
+    "    y=0.01,\n",
+    "    xanchor=\"right\",\n",
+    "    x=0.99\n",
+    "))\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

requirements.txt

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+manganite
+yfinance
+numpy
+pandas
+plotly
+gurobipy
+scipy