Using Off-The-Shelf Cost Models

Usage

Using cost models is simple!

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

from investos.portfolio.strategy import YourDesiredStrategy

strategy = YourDesiredStrategy(
    actual_returns = actual_returns,
    ...,
    costs=[
        CostModelA(*args, **kwargs),
        CostModelB(*args, **kwargs)
    ],
)

and that's it!

Optional Arguments

All cost models take the following optional arguments:

• exclude_assets: [str]
• include_assets: [str]
  • Can't be used with exclude_assets
• gamma: float = 1
  • Gamma doesn't impact actual costs
  • Gamma only (linearly) increases estimated costs during convex optimization trade list generation
  • If you aren't using a convex optimization investment strategy, gamma does nothing

---

InvestOS provides the following cost models:

ShortHoldingCost

ShortHoldingCost calculates per period cost for holding short positions, given customizable short_rate.

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

• short_rates: pd.DataFrame | pd.Series | float,

TradingCost

TradingCost calculates per period cost for trades based on forecast spreads, standard deviations, volumes, and actual prices.

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

• forecast_volume: pd.DataFrame | pd.Series,
• forecaststddev: pd.DataFrame | pd.Series,
• actual_prices: pd.DataFrame,
• sensitivity_coeff: float = 1
  • For scaling transaction cost from market impact
  • 1 assumes trading 1 day's volume moves asset price by 1 forecast standard deviation in returns
• half_spread: pd.DataFrame | pd.Series | float
  • Half of forecast spread between bid and ask for each asset
  • This model assumes half_spread represents the cost of executing a trade

Next: The Choice Is Yours

Want to explore creating your own custom cost model? Check out Custom Cost Models.

Want to learn more about using constraint models? Check out Constraint Models.