Creating Custom Risk Models

Extending BaseRisk

The BaseRisk class provides a foundational structure for creating custom risk models.

Below is a step-by-step guide for extending BaseRisk.

Import Required Modules:

First, ensure you have the necessary modules imported:

import cvxpy as cvx
import numpy as np

import investos.util as util
from investos.portfolio.risk_model import BaseRisk

Define the Custom Risk Class:

Subclass BaseRisk to implement your desired risk model.

class CustomRisk(BaseRisk):

Initialize Custom Attributes (Optional):

You may want to add additional attributes specific to your risk model. Override the __init__ method:

def __init__(self, *args, custom_param=None, **kwargs):
    super().__init__(*args, **kwargs)
    self.custom_param = custom_param

Implement the _estimated_cost_for_optimization Method:

_estimated_cost_for_optimization returns a utility cost expression for optimization that penalizes risk.

Given a datetime t, a numpy-like array of holding weights w_plus, a numpy-like array of trade weights z, and portfolio value value, return a two item tuple containing a CVXPY expression and a (possibly empty) list of constraints.

See FactorRisk for inspiration:

def _estimated_cost_for_optimization(self, t, w_plus, z, value):
        """Optimization (non-cash) cost penalty for assuming associated asset risk.

        Used by optimization strategy to determine trades.
        factor_covar = util.values_in_time(
            self.factor_covariance, t, lookback_for_closest=True
        factor_load = util.values_in_time(
            self.factor_loadings, t, lookback_for_closest=True
        idiosync_var = util.values_in_time(
            self.idiosyncratic_variance, t, lookback_for_closest=True

        self.expression = cvx.sum_squares(cvx.multiply(np.sqrt(idiosync_var), w_plus))

        risk_from_factors = factor_load.T @ factor_covar @ factor_load

        self.expression += w_plus @ risk_from_factors @ w_plus.T

        return self.expression, []

Implement Helper Methods (Optional):

You can add custom helper methods to factor in specific logic or utilities that help in constructing your risk model (and help in keeping your logic understandable).

Test Your Risk Model:

You can test that your custom risk model generates a utility penalty for risk as expected for a specific datetime period:

actual_returns = pd.DataFrame(...)  # Add your data here. Each asset should be a column, and it should be indexed by datetime
initial_holdings = pd.Series(...)  # Holding values, indexed by asset

strategy = SPO(

trade_list = strategy.generate_trade_list(

You can also plug your custom risk model into BacktestController (through your investment strategy) to run a full backtest!

backtest_controller = inv.portfolio.BacktestController(