{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![MOSEK ApS](https://www.mosek.com/static/images/branding/webgraphmoseklogocolor.png )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data-driven distributionally robust optimization ([Mohajerin Esfahani and Kuhn, 2018](https://rdcu.be/cbBoC))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook illustrates parameterization in MOSEK's [Fusion API for python](https://docs.mosek.com/9.2/pythonfusion/intro_info.html), by implementing the numerical experiments presented in a paper titled \"Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations\" ([Mohajerin Esfahani and Kuhn](https://rdcu.be/cbBoC) hereafter), authored by [Peyman Mohajerin Esfahani](https://scholar.google.co.in/citations?user=ZTan-7YAAAAJ&hl=en) and [Daniel Kuhn](https://scholar.google.com/citations?user=RqnXytkAAAAJ&hl=en). \n",
"\n",
"#### Contents:\n",
"1. [Data-driven stochastic programming (Paper summary)](#Data-driven-stochastic-programming-(Paper-summary)) \n",
" - [Choice of the ambiguity set](#Choice-of-the-ambiguity-set-$\\widehat{\\mathcal{P}}_N$)\n",
" - [Convex reduction of the worst-case expectation problem](#Convex-reduction-of-the-worst-case-expectation-problem)\n",
"\n",
"\n",
"2. [Mean risk portfolio optimization with Fusion API](#Mean-risk-Portfolio-Optimization-(Section-7.1-in-paper)) \n",
" - [Parameterized Fusion model](#Parameterized-Fusion-model)\n",
" - [Market setup](#Market-setup)\n",
"\n",
"\n",
"3. [Simulations and results](#Simulations-and-results)\n",
"\n",
" - [Impact of the Wasserstein radius](#Impact-of-the-Wasserstein-radius)\n",
" - [Portflios driven by out-of-sample performance](#Portfolios-driven-by-out-of-sample-performance)\n",
" - [Holdout method](#Holdout-method)\n",
" - [k-fold cross validation method](#k-fold-cross-validation-method)\n",
" - [Portfolios driven by reliability](#Portfolios-driven-by-reliability)\n",
" - [Impact of sample size on Wasserstein radius](#Impact-of-sample-size-on-Wasserstein-radius)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data-driven stochastic programming (Paper summary)\n",
"\n",
"Consider a loss function $h(x,\\xi)$ which depends on the decision variables $x$ and uncertain parameters $\\xi$. The goal is to minimize the expected value of $h(x,\\xi)$ for all $x\\in \\mathbb{X}$, where $\\mathbb{X} \\subset \\mathbb{R}^n$. The expected value is calculated over $\\mathbb{P}(\\xi)$, which is the probability distribution of the random vector $\\xi \\in \\mathbb{R}^m$. This distribution is supported on the uncertainty set $\\Xi$. The problem statement is written as in $\\ref{eq:main_prob}$.\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"J^\\star := \\underset{x\\in \\mathbb{X}}{\\inf} \\bigg\\{ \\mathbb{E}^{\\mathbb{P}}\\big[ h(x,\\xi) \\big] = \\int_{\\Xi} h(x,\\xi)\\mathbb{P}(d\\xi)\\bigg\\}\n",
"\\label{eq:main_prob} \\tag{1}\n",
"\\end{equation}\n",
"$$\n",
"\n",
"In practice, however, the probability distribution is often unknown. Instead, one usually has access to a set $\\widehat{\\Xi}_N := \\{\\widehat{\\xi}_i \\}_{i\\leq N}$, that contains $N$ historic realizations of the $\\xi$ vector. A data-driven solution $\\widehat{x}_N$ can be constructed by using some approximation of the true probability distribution. A natural choice for such an approximation is based on $\\widehat{\\Xi}_N$, and it is given by the discrete empirical probability distribution, as shown in $\\ref{eq:empirical_dist}$.\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\mathbb{\\widehat{P}}_N := \\frac{1}{N}\\sum^{N}_{i = 1} \\delta_{\\widehat{\\xi}_i}\n",
"\\label{eq:empirical_dist} \\tag{2}\n",
"\\end{equation}\n",
"$$\n",
"\n",
"The sample-average-approximation, which is obtained by replacing $\\mathbb{P}$ with $\\mathbb{\\widehat{P}}_N$ in $\\ref{eq:main_prob}$, is usually easier to solve but shows poor out-of-sample performance. Out-of-sample performance of a data-driven solution $\\widehat{x}_N$ is the expected loss for that decision under the true probability distribution. To obtain better out-of-sample performance, [Mohajerin Esfahani and Kuhn](https://rdcu.be/cbBoC) solve the robust optimization problem stated in $\\ref{eq:dist_rob}$.\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\widehat{J}_N := \\underset{x\\in \\mathbb{X}}{\\inf} \\underset{\\mathbb{Q}\\in \\mathcal{\\widehat{P}}_N}\\sup \\mathbb{E}^\\mathbb{Q}\\big[ h(x, \\xi)\\big]\n",
"\\label{eq:dist_rob} \\tag{3}\n",
"\\end{equation}\n",
"$$\n",
"\n",
"The distributionally robust problem stated in $\\ref{eq:dist_rob}$ is conceptually similar to $\\ref{eq:main_prob}$, but it compensates for the unknown $\\mathbb{P}$ by maximizing the expected value over all probability distributions $\\mathbb{Q}$ inside an ambiguity set $\\widehat{\\mathcal{P}}_N$. Maximization is done in order to obtain the worst-case estimate.\n",
"\n",
"If $\\widehat{x}_N$ is an optimal decision vector for the distributionally robust program, then the authors associate performance guarantees to $\\widehat{x}_N$ of the type shown in $\\ref{eq:perf_guarantee}$.\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\mathbb{P}^N \\bigg\\{ \\widehat{\\Xi}_N : \\mathbb{E}^\\mathbb{P}\\big[ h(\\widehat{x}_N, \\xi) \\big] \\leq \\widehat{J}_N \\bigg \\} \\geq 1 - \\beta\n",
"\\label{eq:perf_guarantee} \\tag{4}\n",
"\\end{equation}\n",
"$$\n",
"\n",
"Here, $\\mathbb{P}^N$ is a probability distribution that governs the realization of the set $\\widehat{\\Xi}_N$. The expression in $\\ref{eq:perf_guarantee}$ states that $\\widehat{J}_N$ provides an upper bound to the out-of-sample performance of $\\widehat{x}_N$ with at least $1-\\beta$ probability. Thus, $\\widehat{J}_N$ is a certificate of out-of-sample performance and the probability on the left side is the reliability of this certificate.\n",
"\n",
"### Choice of the ambiguity set $\\widehat{\\mathcal{P}}_N$\n",
"\n",
"The ambiguity set resides within a space of probability distributions and is chosen to be a ball of radius $\\epsilon$ centered at $\\widehat{\\mathbb{P}}_N$. The metric of choice is the [Wasserstein metric](http://nbviewer.jupyter.org/github/MOSEK/Tutorials/blob/master/wasserstein/wasserstein-bary.ipynb). Mathematically, the ambiguity set is defined as shown in $\\ref{eq:wass_ball}$.\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\mathbb{B}_\\epsilon(\\mathbb{\\widehat{P}}_N) := \\big\\{ \\mathbb{Q} \\in \\mathcal{M}(\\Xi) : d_W (\\mathbb{\\widehat{P}}_N, \\mathbb{Q}) \\leq \\epsilon \\big\\}\n",
"\\label{eq:wass_ball} \\tag{5}\n",
"\\end{equation}\n",
"$$\n",
"\n",
"As proved in section 3 of the paper, solving $\\ref{eq:dist_rob}$ with $\\mathcal{\\widehat{P}}_N$ set to $\\mathbb{B}_\\epsilon(\\mathbb{\\widehat{P}}_N)$ will provide finite sample performance guarantees of the type seen in $\\ref{eq:perf_guarantee}$. Furthermore, given certain assumptions (see paper), as $N\\to\\infty$, the certificate $\\widehat{J}_N$ approaches $J^\\star$ (optimal value of $\\ref{eq:main_prob}$) and the data-driven decision $\\widehat{x}_N$ approaches the true optimal decision $x^\\star$.\n",
"\n",
"### Convex reduction of the worst-case expectation problem\n",
"\n",
"The infinite dimensional maximization problem within $\\ref{eq:dist_rob}$ can be reduced to a computationally tractable finite-dimensional convex program, given the following requirements are met:\n",
"\n",
"1.) The uncertainty set $\\Xi$ is convex and closed.\n",
"\n",
"2.) The loss function is a point-wise maximum of more elementary functions, i.e. $l(\\xi) := \\underset{k\\leq K}{\\max} l_k(\\xi)$, where $-l_k(\\xi)$ are proper, convex, and lower semi-continuous for all $k\\leq K$. Note the suppression of the loss function's dependence on $x$.\n",
"\n",
"For problems that satisfy these requirements, the convex reduction is given in $\\ref{eq:convex_red}$.\n",
"$$\n",
"\\begin{align}\n",
"& \\underset{\\lambda, s_i, z_{ik}, \\nu_{ik}}{\\inf} & & \\lambda \\epsilon + \\frac{1}{N}\\sum_{i=1}^{N}s_i & \\nonumber \\\\\n",
"& \\text{s.t.} & &[-l_k]^*(z_{ik} - \\nu_{ik}) + \\sigma_{\\Xi}(\\nu_{ik}) - \\langle z_{ik},\\widehat{\\xi}_i\\rangle \\leq s_i & & \\forall i\\leq N , \\forall k\\leq K \\label{eq:convex_red} \\tag{6}\\ \\\\\n",
"& & &\\|z_{ik}\\|_* \\leq \\lambda & & \\forall i\\leq N, \\forall k\\leq K \\nonumber \\\\\n",
"\\end{align}\n",
"$$\n",
"\n",
"where $[-l_k]^*$ denotes the conjugate of the negative $k^{\\text{th}}$ component of the loss function, $\\|z_{ik}\\|_*$ is the dual norm corresponding to the norm used in defining the Wasserstein ball and $\\sigma_\\Xi$ is the support function for the uncertainty set.\n",
"\n",
"# Mean risk Portfolio Optimization (Section 7.1 in paper)\n",
"\n",
"Consider a market that forbids short-selling and has $m$ assets. Yearly returns of these assets are given by the random vector $\\xi = [\\xi_1, ..., \\xi_m]^T$. The percentage weights (of the total capital) invested in each asset are given by the decision vector $x = [x_1, ..., x_m]^T$ where $x$ belongs to $\\mathbb{X} = \\{x \\in \\mathbb{R}_+^m : \\sum^{m}_{i=1}x_i = 1\\}$. We have to solve the problem stated in $\\ref{eq:mean_risk_port}$.\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"J^\\star = \\underset{x\\in \\mathbb{X}}{\\inf} \\big\\{ \\mathbb{E}^{\\mathbb{P}}[-\\langle x, \\xi \\rangle] + \\rho \\mathbb{P}\\text{-CVaR}_\\alpha \\big(-\\langle x, \\xi\\rangle\\big)\\big\\}\n",
"\\label{eq:mean_risk_port} \\tag{7}\n",
"\\end{equation}\n",
"$$\n",
"\n",
"which amounts to minimizing a weighted sum of the expected value and the conditional value-at-risk (see also: [Fusion notebook on stochastic risk measures](https://nbviewer.jupyter.org/github/MOSEK/Tutorials/blob/master/stochastic-risk/stochastic-risk-measures.ipynb)) of the portfolio loss, $- \\langle x, \\xi \\rangle$. Using the definition of conditional value-at-risk ([Optimization of conditional value-at-risk](https://www.ise.ufl.edu/uryasev/files/2011/11/CVaR1_JOR.pdf) by R. T. Rockafellar and S. Uryasev), $\\ref{eq:mean_risk_port}$ can be written as a maximum of $K=2$ affine loss functions ($\\ref{eq:cvar_prob}$).\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"J^\\star = \\underset{x \\in \\mathbb{X}, \\tau \\in \\mathbb{R}}{\\inf} \\mathbb{E}^{\\mathbb{P}}\\big[\\underset{k\\leq K}{\\max} a_k \\langle x, \\xi \\rangle + b_k \\tau \\big]\n",
"\\label{eq:cvar_prob} \\tag{8}\n",
"\\end{equation}\n",
"$$\n",
"$$\n",
"\\text{where}\\,\\, a =\\big(-1, -1-\\frac{\\rho}{\\alpha}\\big)\\, ,\\,\\, b =\\big(\\rho, \\rho-\\frac{\\rho}{\\alpha}\\big) \n",
"$$\n",
"\n",
"If the uncertainty set is a polytope of the form $\\Xi = \\{ \\xi \\in \\mathbb{R}^m : C\\xi \\leq d \\}$, then the distributionally robust counterpart of the program in $\\ref{eq:cvar_prob}$ will have a convex reduction as shown in $\\ref{eq:convex_red_port}$. \n",
"\n",
"$$\n",
"\\begin{align}\n",
"& \\underset{x,\\tau,\\lambda,s_i, \\gamma_{ik}}{\\inf} & & \\lambda \\epsilon + \\frac{1}{N}\\sum^{N}_{i=1}s_i & \\nonumber \\\\\n",
"&\\text{s.t.} & & b_k \\tau + a_k \\langle x, \\widehat{\\xi}_i \\rangle + \\langle \\gamma_{ik}, d - C\\widehat{\\xi}_i\\rangle \\leq s_i & \\forall i\\leq N, \\forall k\\leq K \\label{eq:convex_red_port} \\tag{9}\\\\\n",
"& & &\\| C^T \\gamma_{ik} - a_k x\\|_* \\leq \\lambda & \\forall i\\leq N, \\forall k\\leq K &\\nonumber \\\\\n",
"& & & \\gamma_{ik} \\geq 0 & \\forall i \\leq N, \\forall k \\leq K & \\nonumber \\\\\n",
"& & & x \\in \\mathbb{X} & \\nonumber \\\\\n",
"\\end{align}\n",
"$$\n",
"\n",
"### Parameterized Fusion model\n",
"\n",
"Finally, we make a [Fusion](https://docs.mosek.com/9.2/pythonfusion/api-reference.html) model for the program in $\\ref{eq:convex_red_port}$. The following code cell defines `DistributionallyRobustPortfolio`, a wrapper class (around our fusion model) with some convenience methods. The key steps in the Fusion model implementation are (see `portfolio_model` method in class):\n",
"\n",
"- `M = Model('DistRobust_m{0}_N{1}'.format(m, N))`: instantiate a Fusion `Model` object.\n",
"\n",
"\n",
"- `dat = M.parameter('TrainData', [N, m])` and `eps = M.parameter('WasRadius')`: declare a Fusion `parameter` object named \"TrainData\" and a parameter for the Wasserstein Radius, called \"WasRadius\". \"TrainData\" represents the past `N` realizations of the `m` sized random vector, $\\xi$. \n",
"\n",
"\n",
"- `a_k = [-1, -51]` and `b_k = [10, -40]`: lists that are defined as in $\\ref{eq:cvar_prob}$. All experiments that follow have the same $a_k$ and $b_k$, therefore we use lists. However, with minor modifications these lists can be replaced with Fusion parameters. \n",
"\n",
"\n",
"- `x = M.variable('Weights', m, Domain.greaterThan(0.0))`: declare Fusion variables for all the optimization variables involved in $\\ref{eq:convex_red_port}$, except $\\gamma$, which is excluded as $\\Xi \\in \\mathbb{R}^m$ ([see the market setup below](#Market-setup)).\n",
"\n",
"\n",
"- `M.objective('J_N(e)', ObjectiveSense.Minimize, certificate)`: declare the objective sense and the expression of the objective. Here `certificate` is an `Expr` object that evaluates to the objective in $\\ref{eq:convex_red_port}$.\n",
"\n",
"\n",
"- `self.M.constraint('C1',Expr.add([e1, e2, Expr.neg(Expr.repeat(s, 2, 1))]), Domain.lessThan(0.0))`: defining the constraints. Here `e1` and `e2` are `Expr` objects that evaluate to the relevant terms in the first constraint in $\\ref{eq:convex_red_port}$. Constraints \"C2_pos\" and \"C2_neg\" correspond to the second constraint and finally \"C3\" ensures that $x \\in \\mathbb{X}$.\n",
"\n",
"\n",
"- `self.M.setSolverParam('optimizer','freeSimplex')`: use the [simplex optimizer](https://docs.mosek.com/9.2/pythonfusion/solving-linear.html#the-simplex-optimizer). By default, MOSEK will use its interior point optmizer. However, we plan to re-solve a linear model many times over and it is therefore imperative to exploit the warm-start capabilities of the simplex optimizer.\n",
"\n",
"Note: More details regarding the code can be found within comments."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"class DistributionallyRobustPortfolio:\n",
"\n",
" def __init__(self, m, N):\n",
" self.m, self.N = m, N\n",
" self.M = self.portfolio_model(m, N)\n",
" self.x = self.M.getVariable('Weights')\n",
" self.t = self.M.getVariable('Tau')\n",
" self.dat = self.M.getParameter('TrainData')\n",
" self.eps = self.M.getParameter('WasRadius')\n",
" self.sol_time = []\n",
"\n",
" def portfolio_model(self, m, N):\n",
" '''\n",
" Parameterized Fusion model for program in 9.\n",
" '''\n",
" M = Model('DistRobust_m{0}_N{1}'.format(m, N))\n",
" ##### PARAMETERS #####\n",
" dat = M.parameter('TrainData', [N, m])\n",
" eps = M.parameter('WasRadius')\n",
" a_k = [-1, -51] # Alternative: Fusion parameters.\n",
" b_k = [10, -40] # Alternative: Fusion parameters.\n",
" ##### VARIABLES #####\n",
" x = M.variable('Weights', m, Domain.greaterThan(0.0))\n",
" s = M.variable('s_i', N)\n",
" l = M.variable('Lambda')\n",
" t = M.variable('Tau')\n",
" ##### OBJECTIVE #####\n",
" # certificate = lamda*epsilon + sum(s)/N\n",
" certificate = Expr.add(Expr.mul(eps, l),\n",
" Expr.mul(1/N, Expr.sum(s)))\n",
" M.objective('J_N(e)', ObjectiveSense.Minimize, certificate)\n",
" ##### CONSTRAINTS #####\n",
" # b_k*t\n",
" e1 = Expr.transpose(Expr.repeat(Expr.mul(t, b_k), N, 1))\n",
" # a_k*\n",
" e2 = Expr.hstack([Expr.mul(a_k[i], Expr.mul(dat, x))\n",
" for i in range(2)])\n",
" # b_k*t + a_k* <= s\n",
" M.constraint('C1', Expr.add(\n",
" [e1, e2, Expr.neg(Expr.repeat(s, 2, 1))]), Domain.lessThan(0.0))\n",
" # a_k*x\n",
" e3 = Expr.hstack([Expr.mul(a_k[i], x) for i in range(2)])\n",
" e4 = Expr.repeat(Expr.repeat(l, m, 0), 2, 1)\n",
" # ||a_k*x||_infty <= lambda\n",
" M.constraint('C2_pos', Expr.sub(e3, e4), Domain.lessThan(0.0))\n",
" M.constraint('C2_neg', Expr.add(e3, e4), Domain.greaterThan(0.0))\n",
" # x \\in X\n",
" M.constraint('C3', Expr.sum(x), Domain.equalsTo(1.0))\n",
" # Use the simplex optimizer\n",
" M.setSolverParam('optimizer', 'freeSimplex')\n",
" return M\n",
"\n",
" def sample_average(self, x, t, data):\n",
" '''\n",
" Calculate the sample average approximation for given x and tau.\n",
" '''\n",
" l = np.matmul(data, x)\n",
" return np.mean(np.maximum(-l + 10*t, -51*l - 40*t))\n",
"\n",
" def iter_data(self, data_sets):\n",
" '''\n",
" Generator method for iterating through values for the \n",
" TrainData parameter.\n",
" '''\n",
" for data in data_sets:\n",
" yield self.simulate(data)\n",
"\n",
" def iter_radius(self, epsilon_range):\n",
" '''\n",
" Generator for iterating through values for the WasRadius\n",
" parameter.\n",
" '''\n",
" for epsilon in epsilon_range:\n",
" yield self.solve(epsilon)\n",
"\n",
" def simulate(self, data):\n",
" '''\n",
" Tentative goal of this method is to set a value for the\n",
" data parameter, and solve the model for the same.\n",
" \n",
" Define in child classes.\n",
" '''\n",
" pass\n",
"\n",
" def solve(self, epsilon):\n",
" '''\n",
" Tentative goal of this method is to set a value for the \n",
" radius parameter, and solve the model for the same.\n",
" \n",
" Define in child classes.\n",
" '''\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Market setup\n",
"\n",
"The simulation results presented in the paper are based on a market with $m = 10$ assets. Each asset's return is expressed as $\\xi_i = \\psi + \\zeta_i$, where $\\psi \\sim \\mathcal{N}(0, 0.02)$ is the systematic risk factor and $\\zeta_i \\sim \\mathcal{N}(0.03\\times i, 0.025\\times i)$ is the idiosyncratic risk factor. By construction, assets with higher indices have higher risk as well as higher returns. The uncertainty set is $\\Xi \\in \\mathbb{R}^m$, thus $C$ and $d$ are set to zero. $1$-norm is used to define the Wasserstein ball, therefore the dual norm will be the $\\infty$-norm. Lastly, $\\alpha = 20\\%$ and $\\rho = 10$ are used within the loss function. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Function to generate N samples of the m-shaped random vector xi.\n",
"def normal_returns(m, N):\n",
" R = np.vstack([np.random.normal(\n",
" i*0.03, np.sqrt((0.02**2+(i*0.025)**2)), N) for i in range(1, m+1)])\n",
" return (R.transpose())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simulations and results\n",
"\n",
"The concluding section of this notebook reproduces the results provided in sections 7.2 of [Mohajerin Esfahani and Kuhn](https://rdcu.be/cbBoC). We do not pursue details of the design of these simulations, instead we focus primarily on implementing them with the models constructed above. The reader will find details/hints about the code within the in-code comments.\n",
"\n",
"## Impact of the Wasserstein radius"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from mosek.fusion import Model, Expr, Domain, ObjectiveSense\n",
"import matplotlib.pyplot as plt\n",
"from scipy.special import erfinv\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.utils import resample"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Inherits from the portfolio class defined above.\n",
"class SimSet1(DistributionallyRobustPortfolio):\n",
" # Market setup\n",
" m = 10\n",
" mu = np.arange(1, 11)*0.03\n",
" var = 0.02 + (np.arange(1, 11)*0.025)\n",
"\n",
" # Constants for CVaR calculation.\n",
" beta, rho = 0.8, 10\n",
" c2_beta = 1/(np.sqrt(2*np.pi)*(np.exp(erfinv(2*beta - 1))**2)*(1-beta))\n",
"\n",
" def __init__(self, N, eps_range):\n",
" self.eps_range = eps_range\n",
" # Fusion model instance\n",
" super().__init__(SimSet1.m, N)\n",
"\n",
" def simulate(self, data):\n",
" '''\n",
" This method is called within the generator method called iter_data, which \n",
" was defined in the parent class.\n",
"\n",
" Returns\n",
" wts: optimal asset weights for each radius\n",
" perf: analytic out-of-sample performance for each radius\n",
" rel: reliability for each radius\n",
" '''\n",
" # Set TrainData parameter\n",
" self.dat.setValue(data)\n",
" # Iterate through range of Wasserstein radii (see solve method below)\n",
" wts, perf, rel = zip(*[(_w, _p, _r)\n",
" for _w, _p, _r in self.iter_radius(self.eps_range)])\n",
" return wts, perf, rel\n",
"\n",
" def solve(self, epsilon):\n",
" '''\n",
" This method is used within the generator method called iter_radius, which\n",
" was defined in the parent class.\n",
"\n",
" Returns:\n",
" x_sol: asset weights\n",
" out_perf: analytica out-of-sample performance\n",
" rel: reliability for the certificate\n",
" '''\n",
" # Set WasRadius parameter (TrainData is already set)\n",
" self.eps.setValue(epsilon)\n",
" # Solve the Fusion model\n",
" self.M.solve()\n",
" self.sol_time.append(self.M.getSolverDoubleInfo('optimizerTime'))\n",
" # Portfolio weights\n",
" x_sol = self.x.level()\n",
" # Analytical out-of-sample performance\n",
" out_perf = self.analytic_out_perf(x_sol)\n",
" return x_sol, out_perf, (out_perf <= self.M.primalObjValue())\n",
"\n",
" def analytic_out_perf(self, x_sol):\n",
" '''\n",
" Method to calculate the analytical value for the out-of-sample performance.\n",
" [see Rockafellar and Uryasev]\n",
" '''\n",
" mean_loss = -np.dot(x_sol, SimSet1.mu)\n",
" sd_loss = np.sqrt(np.dot(x_sol**2, SimSet1.var))\n",
" cVaR = mean_loss + (sd_loss*SimSet1.c2_beta)\n",
" return mean_loss + (SimSet1.rho*cVaR)\n",
"\n",
" def run_sim(self, data_sets):\n",
" '''\n",
" Method to iterate over several datasets and record the results.\n",
" '''\n",
" wts, perf, rel = zip(*self.iter_data(data_sets))\n",
" self.weights = np.mean(wts, axis=0)\n",
" self.perf_mu = np.mean(perf, axis=0)\n",
" self.perf_20 = np.quantile(perf, 0.2, axis=0)\n",
" self.perf_80 = np.quantile(perf, 0.8, axis=0)\n",
" self.reliability = np.mean(rel, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time taken in initial solve of model with N=300: 0.0064 s\n",
"Mean time to solve a model with N=300: 0.0018 s\n",
"Total time to solve 200 models with N=300: 13.0416 s\n"
]
}
],
"source": [
"# Cardinality\n",
"N = 300\n",
"# Number of simulations\n",
"num_sim = 200\n",
"\n",
"# Range of Wasserstein radii to consider\n",
"epsilon_range = np.append(np.concatenate(\n",
" [np.arange(1, 10)*10.0**(i) for i in range(-4, 0)]), 1.0)\n",
"\n",
"# Making 200 independent datasets\n",
"sim_data = [normal_returns(10, N) for i in range(num_sim)]\n",
"\n",
"# Instantiate SimSet1 class\n",
"sim1 = SimSet1(N, epsilon_range)\n",
"\n",
"# 200 simulations...\n",
"sim1.run_sim(sim_data)\n",
"\n",
"print(\"Time taken in initial solve of model with N={0}: {1:.4f} s\".format(\n",
" N, sim1.sol_time[0]))\n",
"print(\"Mean time to solve a model with N={0}: {1:.4f} s\".format(\n",
" N, np.mean(sim1.sol_time)))\n",
"print(\"Total time to solve 200 models with N={0}: {1:.4f} s\".format(\n",
" N, np.sum(sim1.sol_time)))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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