# Mean-Variance Optimization¶

Mathematical optimization is a very difficult problem in general, particularly when we are dealing with complex objectives and constraints. However, convex optimization problems are a well-understood class of problems, which happen to be incredibly useful for finance. A convex problem has the following form:

\begin{split}\begin{equation*} \begin{aligned} & \underset{\mathbf{x}}{\text{minimise}} & & f(\mathbf{x}) \\ & \text{subject to} & & g_i(\mathbf{x}) \leq 0, i = 1, \ldots, m\\ &&& A\mathbf{x} = b,\\ \end{aligned} \end{equation*}\end{split}

where $$\mathbf{x} \in \mathbb{R}^n$$, and $$f(\mathbf{x}), g_i(\mathbf{x})$$ are convex functions. [1]

Fortunately, portfolio optimization problems (with standard objectives and constraints) are convex. This allows us to immediately apply the vast body of theory as well as the refined solving routines – accordingly, the main difficulty is inputting our specific problem into a solver.

PyPortfolioOpt aims to do the hard work for you, allowing for one-liners like ef.min_volatility() to generate a portfolio that minimises the volatility, while at the same time allowing for more complex problems to be built up from modular units. This is all possible thanks to cvxpy, the fantastic python-embedded modelling language for convex optimization upon which PyPortfolioOpt’s efficient frontier functionality lies.

Tip

You can find complete examples in the relevant cookbook recipe.

## Structure¶

As shown in the definition of a convex problem, there are essentially two things we need to specify: the optimization objective, and the optimization constraints. For example, the classic portfolio optimization problem is to minimise risk subject to a return constraint (i.e the portfolio must return more than a certain amount). From an implementation perspective, however, there is not much difference between an objective and a constraint. Consider a similar problem, which is to maximize return subject to a risk constraint – now, the role of risk and return have swapped.

To that end, PyPortfolioOpt defines an objective_functions module that contains objective functions (which can also act as constraints, as we have just seen). The actual optimization occurs in the efficient_frontier.EfficientFrontier class. This class provides straightforward methods for optimising different objectives (all documented below).

However, PyPortfolioOpt was designed so that you can easily add new constraints or objective terms to an existing problem. For example, adding a regularisation objective (explained below) to a minimum volatility objective is as simple as:

ef = EfficientFrontier(expected_returns, cov_matrix)  # setup
ef.min_volatility()  # find the portfolio that minimises volatility and L2_reg

Tip

If you would like to plot the efficient frontier, take a look at the Plotting module.

## Basic Usage¶

The efficient_frontier module houses the EfficientFrontier class and its descendants, which generate optimal portfolios for various possible objective functions and parameters.

class pypfopt.efficient_frontier.EfficientFrontier(expected_returns, cov_matrix, weight_bounds=(0, 1), solver=None, verbose=False, solver_options=None)[source]

An EfficientFrontier object (inheriting from BaseConvexOptimizer) contains multiple optimization methods that can be called (corresponding to different objective functions) with various parameters. Note: a new EfficientFrontier object should be instantiated if you want to make any change to objectives/constraints/bounds/parameters.

Instance variables:

• Inputs:

• n_assets - int
• tickers - str list
• bounds - float tuple OR (float tuple) list
• cov_matrix - np.ndarray
• expected_returns - np.ndarray
• solver - str
• solver_options - {str: str} dict
• Output: weights - np.ndarray

Public methods:

• min_volatility() optimizes for minimum volatility
• max_sharpe() optimizes for maximal Sharpe ratio (a.k.a the tangency portfolio)
• efficient_risk() maximises return for a given target risk
• efficient_return() minimises risk for a given target return
• convex_objective() solves for a generic convex objective with linear constraints
• portfolio_performance() calculates the expected return, volatility and Sharpe ratio for the optimized portfolio.
• set_weights() creates self.weights (np.ndarray) from a weights dict
• clean_weights() rounds the weights and clips near-zeros.
• save_weights_to_file() saves the weights to csv, json, or txt.
__init__(expected_returns, cov_matrix, weight_bounds=(0, 1), solver=None, verbose=False, solver_options=None)[source]
Parameters: expected_returns (pd.Series, list, np.ndarray) – expected returns for each asset. Can be None if optimising for volatility only (but not recommended). cov_matrix (pd.DataFrame or np.array) – covariance of returns for each asset. This must be positive semidefinite, otherwise optimization will fail. weight_bounds (tuple OR tuple list, optional) – minimum and maximum weight of each asset OR single min/max pair if all identical, defaults to (0, 1). Must be changed to (-1, 1) for portfolios with shorting. solver (str) – name of solver. list available solvers with: cvxpy.installed_solvers() verbose (bool, optional) – whether performance and debugging info should be printed, defaults to False solver_options (dict, optional) – parameters for the given solver TypeError – if expected_returns is not a series, list or array TypeError – if cov_matrix is not a dataframe or array

Note

As of v0.5.0, you can pass a collection (list or tuple) of (min, max) pairs representing different bounds for different assets.

Tip

If you want to generate short-only portfolios, there is a quick hack. Multiply your expected returns by -1, then optimize a long-only portfolio.

min_volatility()[source]

Minimise volatility.

Returns: asset weights for the volatility-minimising portfolio OrderedDict
max_sharpe(risk_free_rate=0.02)[source]

Maximise the Sharpe Ratio. The result is also referred to as the tangency portfolio, as it is the portfolio for which the capital market line is tangent to the efficient frontier.

This is a convex optimization problem after making a certain variable substitution. See Cornuejols and Tutuncu (2006) for more.

Parameters: risk_free_rate (float, optional) – risk-free rate of borrowing/lending, defaults to 0.02. The period of the risk-free rate should correspond to the frequency of expected returns. ValueError – if risk_free_rate is non-numeric asset weights for the Sharpe-maximising portfolio OrderedDict

Caution

Because max_sharpe() makes a variable substitution, additional objectives may not work as intended.

Maximise the given quadratic utility, i.e:

$\max_w w^T \mu - \frac \delta 2 w^T \Sigma w$
Parameters: risk_aversion (positive float) – risk aversion parameter (must be greater than 0), defaults to 1 market_neutral – whether the portfolio should be market neutral (weights sum to zero), defaults to False. Requires negative lower weight bound. market_neutral – bool, optional asset weights for the maximum-utility portfolio OrderedDict

Note

pypfopt.black_litterman provides a method for calculating the market-implied risk-aversion parameter, which gives a useful estimate in the absence of other information!

efficient_risk(target_volatility, market_neutral=False)[source]

Maximise return for a target risk. The resulting portfolio will have a volatility less than the target (but not guaranteed to be equal).

Parameters: target_volatility (float) – the desired maximum volatility of the resulting portfolio. market_neutral – whether the portfolio should be market neutral (weights sum to zero), defaults to False. Requires negative lower weight bound. market_neutral – bool, optional ValueError – if target_volatility is not a positive float ValueError – if no portfolio can be found with volatility equal to target_volatility ValueError – if risk_free_rate is non-numeric asset weights for the efficient risk portfolio OrderedDict

Caution

If you pass an unreasonable target into efficient_risk() or efficient_return(), the optimizer will fail silently and return weird weights. Caveat emptor applies!

efficient_return(target_return, market_neutral=False)[source]

Calculate the ‘Markowitz portfolio’, minimising volatility for a given target return.

Parameters: target_return (float) – the desired return of the resulting portfolio. market_neutral (bool, optional) – whether the portfolio should be market neutral (weights sum to zero), defaults to False. Requires negative lower weight bound. ValueError – if target_return is not a positive float ValueError – if no portfolio can be found with return equal to target_return asset weights for the Markowitz portfolio OrderedDict
portfolio_performance(verbose=False, risk_free_rate=0.02)[source]

After optimising, calculate (and optionally print) the performance of the optimal portfolio. Currently calculates expected return, volatility, and the Sharpe ratio.

Parameters: verbose (bool, optional) – whether performance should be printed, defaults to False risk_free_rate (float, optional) – risk-free rate of borrowing/lending, defaults to 0.02. The period of the risk-free rate should correspond to the frequency of expected returns. ValueError – if weights have not been calcualted yet expected return, volatility, Sharpe ratio. (float, float, float)

Tip

If you would like to use the portfolio_performance function independently of any optimizer (e.g for debugging purposes), you can use:

from pypfopt import base_optimizer

base_optimizer.portfolio_performance(
weights, expected_returns, cov_matrix, verbose=True, risk_free_rate=0.02
)

Note

PyPortfolioOpt defers to cvxpy’s default choice of solver. If you would like to explicitly choose the solver, simply pass the optional solver = "ECOS" kwarg to the constructor. You can choose from any of the supported solvers, and pass in solver params via solver_options (a dict).

EfficientFrontier inherits from the BaseConvexOptimizer class. In particular, the functions to add constraints and objectives are documented below:

class pypfopt.base_optimizer.BaseConvexOptimizer

Add a new constraint to the optimization problem. This constraint must satisfy DCP rules, i.e be either a linear equality constraint or convex inequality constraint.

Examples:

ef.add_constraint(lambda x : x[0] == 0.02)
ef.add_constraint(lambda x : x >= 0.01)
ef.add_constraint(lambda x: x <= np.array([0.01, 0.08, ..., 0.5]))
Parameters: new_constraint – the constraint to be added

Adds constraints on the sum of weights of different groups of assets. Most commonly, these will be sector constraints e.g portfolio’s exposure to tech must be less than x%:

sector_mapper = {
"GOOG": "tech",
"FB": "tech",,
"XOM": "Oil/Gas",
"RRC": "Oil/Gas",
"MA": "Financials",
"JPM": "Financials",
}

sector_lower = {"tech": 0.1}  # at least 10% to tech
sector_upper = {
"tech": 0.4, # less than 40% tech
"Oil/Gas": 0.1 # less than 10% oil and gas
}
Parameters: sector_mapper ({str: str} dict) – dict that maps tickers to sectors sector_lower ({str: float} dict) – lower bounds for each sector sector_upper ({str:float} dict) – upper bounds for each sector

Add a new term into the objective function. This term must be convex, and built from cvxpy atomic functions.

Example:

def L1_norm(w, k=1):
return k * cp.norm(w, 1)

Parameters: new_objective (cp.Expression (i.e function of cp.Variable)) – the objective to be added

## Objective functions¶

The objective_functions module provides optimization objectives, including the actual objective functions called by the EfficientFrontier object’s optimization methods. These methods are primarily designed for internal use during optimization and each requires a different signature (which is why they have not been factored into a class). For obvious reasons, any objective function must accept weights as an argument, and must also have at least one of expected_returns or cov_matrix.

The objective functions either compute the objective given a numpy array of weights, or they return a cvxpy expression when weights are a cp.Variable. In this way, the same objective function can be used both internally for optimization and externally for computing the objective given weights. _objective_value() automatically chooses between the two behaviours.

objective_functions defaults to objectives for minimisation. In the cases of objectives that clearly should be maximised (e.g Sharpe Ratio, portfolio return), the objective function actually returns the negative quantity, since minimising the negative is equivalent to maximising the positive. This behaviour is controlled by the negative=True optional argument.

Currently implemented:

• Portfolio variance (i.e square of volatility)
• Portfolio return
• Sharpe ratio
• L2 regularisation (minimising this reduces nonzero weights)
• Transaction cost model (a simple one)
• Ex-ante (squared) tracking error
• Ex-post (squared) tracking error
pypfopt.objective_functions.L2_reg(w, gamma=1)[source]

L2 regularisation, i.e $$\gamma ||w||^2$$, to increase the number of nonzero weights.

Example:

ef = EfficientFrontier(mu, S)
ef.min_volatility()
Parameters: w (np.ndarray OR cp.Variable) – asset weights in the portfolio gamma (float, optional) – L2 regularisation parameter, defaults to 1. Increase if you want more non-negligible weights value of the objective function OR objective function expression float OR cp.Expression
pypfopt.objective_functions.ex_ante_tracking_error(w, cov_matrix, benchmark_weights)[source]

Calculate the (square of) the ex-ante Tracking Error, i.e $$(w - w_b)^T \Sigma (w-w_b)$$.

Parameters: w (np.ndarray OR cp.Variable) – asset weights in the portfolio cov_matrix (np.ndarray) – covariance matrix benchmark_weights (np.ndarray) – asset weights in the benchmark value of the objective function OR objective function expression float OR cp.Expression
pypfopt.objective_functions.ex_post_tracking_error(w, historic_returns, benchmark_returns)[source]

Calculate the (square of) the ex-post Tracking Error, i.e $$Var(r - r_b)$$.

Parameters: w (np.ndarray OR cp.Variable) – asset weights in the portfolio historic_returns (np.ndarray) – historic asset returns benchmark_returns (pd.Series or np.ndarray) – historic benchmark returns value of the objective function OR objective function expression float OR cp.Expression
pypfopt.objective_functions.portfolio_return(w, expected_returns, negative=True)[source]

Calculate the (negative) mean return of a portfolio

Parameters: w (np.ndarray OR cp.Variable) – asset weights in the portfolio expected_returns (np.ndarray) – expected return of each asset negative (boolean) – whether quantity should be made negative (so we can minimise) negative mean return float
pypfopt.objective_functions.portfolio_variance(w, cov_matrix)[source]

Calculate the total portfolio variance (i.e square volatility).

Parameters: w (np.ndarray OR cp.Variable) – asset weights in the portfolio cov_matrix (np.ndarray) – covariance matrix value of the objective function OR objective function expression float OR cp.Expression

Quadratic utility function, i.e $$\mu - \frac 1 2 \delta w^T \Sigma w$$.

Parameters: w (np.ndarray OR cp.Variable) – asset weights in the portfolio expected_returns (np.ndarray) – expected return of each asset cov_matrix (np.ndarray) – covariance matrix risk_aversion (float) – risk aversion coefficient. Increase to reduce risk. negative (boolean) – whether quantity should be made negative (so we can minimise). value of the objective function OR objective function expression float OR cp.Expression
pypfopt.objective_functions.sharpe_ratio(w, expected_returns, cov_matrix, risk_free_rate=0.02, negative=True)[source]

Calculate the (negative) Sharpe ratio of a portfolio

Parameters: w (np.ndarray OR cp.Variable) – asset weights in the portfolio expected_returns (np.ndarray) – expected return of each asset cov_matrix (np.ndarray) – covariance matrix risk_free_rate (float, optional) – risk-free rate of borrowing/lending, defaults to 0.02. The period of the risk-free rate should correspond to the frequency of expected returns. negative (boolean) – whether quantity should be made negative (so we can minimise) (negative) Sharpe ratio float
pypfopt.objective_functions.transaction_cost(w, w_prev, k=0.001)[source]

A very simple transaction cost model: sum all the weight changes and multiply by a given fraction (default to 10bps). This simulates a fixed percentage commission from your broker.

Parameters: w (np.ndarray OR cp.Variable) – asset weights in the portfolio w_prev (np.ndarray) – previous weights k (float) – fractional cost per unit weight exchanged value of the objective function OR objective function expression float OR cp.Expression

## More on L2 Regularisation¶

As has been discussed in the User Guide, mean-variance optimization often results in many weights being negligible, i.e the efficient portfolio does not end up including most of the assets. This is expected behaviour, but it may be undesirable if you need a certain number of assets in your portfolio.

In order to coerce the mean-variance optimizer to produce more non-negligible weights, we add what can be thought of as a “small weights penalty” to all of the objective functions, parameterised by $$\gamma$$ (gamma). Considering the minimum variance objective for instance, we have:

$\underset{w}{\text{minimise}} ~ \left\{w^T \Sigma w \right\} ~~~ \longrightarrow ~~~ \underset{w}{\text{minimise}} ~ \left\{w^T \Sigma w + \gamma w^T w \right\}$

Note that $$w^T w$$ is the same as the sum of squared weights (I didn’t write this explicitly to reduce confusion caused by $$\Sigma$$ denoting both the covariance matrix and the summation operator). This term reduces the number of negligible weights, because it has a minimum value when all weights are equally distributed, and maximum value in the limiting case where the entire portfolio is allocated to one asset. I refer to it as L2 regularisation because it has exactly the same form as the L2 regularisation term in machine learning, though a slightly different purpose (in ML it is used to keep weights small while here it is used to make them larger).

Note

In practice, $$\gamma$$ must be tuned to achieve the level of regularisation that you want. However, if the universe of assets is small (less than 20 assets), then gamma=1 is a good starting point. For larger universes, or if you want more non-negligible weights in the final portfolio, increase gamma.

## References¶

 [1] Boyd, S.; Vandenberghe, L. (2004). Convex Optimization.