Using Off-The-Shelf Risk Models

Usage

Using risk models is simple!

You simply pass instantiated risk model instances into your desired investment strategy:

import investos as inv
from investos.portfolio.risk_model import *

risk_model = FactorRisk(
    factor_covariance=df_factor_covar,
    factor_loadings=df_loadings,
    idiosyncratic_variance=df_idio,
    exclude_assets=["cash"]
)

strategy = inv.portfolio.strategy.SPO(
    actual_returns=df_actual_returns,
    ...
    risk_model=risk_model,
)

and that's it!

Note: some simple investment strategies do not support risk models. If you pass a risk model to one of these strategies, it will have no effect.

Optional Arguments

All risk models take the following optional arguments:

• exclude_assets: [str]
• include_assets: [str]
  • Can't be used with exclude_assets
• gamma: float = 1
  • Linearly increases utility penalty during convex optimization trade list generation
  • Increase to penalize risk more, decrease to penalize risk less

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InvestOS provides the following risk models:

FactorRisk

FactorRisk is a multi-factor risk model.

To instantiate FactorRisk you will need to set the following arguments:

• factor_covariance: pd.DataFrame
  • Columns and index keys should be risk factors
  • Values are covariances
  • Optionally: with date as the first key in multi-index dataframe; to allow risk estimates to change throughout time. By default, will look for risk date equal to or less than trade date
• factor_loadings: pd.DataFrame
  • Columns should be unique asset IDs
  • Index keys should be risk factors
  • Values are loadings
  • Optionally: with date as the first key in multi-index dataframe; to allow risk estimates to change throughout time. By default, will look for risk date equal to or less than trade date
• idiosyncratic_variance: pd.DataFrame | pd.Series
  • Columns should be unique asset IDs
  • Values are idiosyncratic risks (residuals to factor risk)
  • Optionally: with date as index in dataframe; to allow risk estimates to change throughout time. By default, will look for risk date equal to or less than trade date

StatFactorRisk

StatFactorRisk creates a PCA-factor based risk model from actual_returns. To use this model, there must be more periods in actual_returns than assets in your investment strategy.

To instantiate StatFactorRisk you will need to set the following arguments:

• actual_returns: pd.DataFrame

You may optionally set the following arguments:

• n_factors: integer = 5
• start_date: datetime = actual_returns.index[0]
• end_date: datetime = actual_returns.index[-1]
• recalc_each_i_periods: integer|boolean = False
• timedelta: pd.Timedelta = pd.Timedelta("730 days")
  • Lookback period for calculating risk from actual_returns

Next: The Choice Is Yours

Want to explore an end-to-end example? Check out Single Period Optimization.