Market Assumptions¶

MarketAssumptions defines the asset universe, expected returns, volatilities, correlations, and inflation model.

Basic Setup¶

from monteplan.config.schema import MarketAssumptions, AssetClass

market = MarketAssumptions(
    assets=[
        AssetClass(name="US Stocks", weight=0.7),
        AssetClass(name="US Bonds", weight=0.3),
    ],
    expected_annual_returns=[0.07, 0.03],
    annual_volatilities=[0.16, 0.06],
    correlation_matrix=[
        [1.0, 0.0],
        [0.0, 1.0],
    ],
    inflation_mean=0.03,
    inflation_vol=0.01,
)

Assets and Allocation¶

Each AssetClass has a name and a weight (target allocation). Weights must sum to 1.0:

assets = [
    AssetClass(name="US Stocks", weight=0.6),
    AssetClass(name="Intl Stocks", weight=0.2),
    AssetClass(name="US Bonds", weight=0.2),
]

The expected_annual_returns, annual_volatilities, and correlation_matrix arrays must have the same number of elements as assets.

Correlation Matrix¶

The correlation matrix controls co-movement between assets. It must be:

Square (n_assets x n_assets)
Symmetric
Have 1.0 on the diagonal

Example for 3 assets:

correlation_matrix = [
    [1.0,  0.3, -0.1],
    [0.3,  1.0,  0.2],
    [-0.1, 0.2,  1.0],
]

Return Model Selection¶

Choose how returns are generated with return_model:

Model	Value	Best For
Multivariate Normal	`"mvn"` (default)	Standard analysis, quick runs
Student-t	`"student_t"`	Fat-tailed scenarios
Block Bootstrap	`"bootstrap"`	When you have historical data
Regime Switching	`"regime_switching"`	Bull/bear market dynamics

# Fat-tailed returns
market = MarketAssumptions(
    ...,
    return_model="student_t",
    degrees_of_freedom=5.0,
)

See Return Models for detailed guidance on each model.

Inflation¶

Inflation follows an Ornstein-Uhlenbeck mean-reverting process:

Field	Default	Description
`inflation_mean`	0.03	Long-run annual inflation rate
`inflation_vol`	0.01	Annual inflation volatility

See Inflation Math for the underlying model.

Glide Paths¶

Shift asset allocation over time (e.g., more bonds as you age):

from monteplan.config.schema import GlidePath

market = MarketAssumptions(
    ...,
    glide_path=GlidePath(
        start_age=30,
        start_weights=[0.9, 0.1],    # 90/10 stocks/bonds at age 30
        end_age=70,
        end_weights=[0.4, 0.6],      # 40/60 stocks/bonds at age 70
    ),
)

The engine linearly interpolates weights between start_age and end_age. Beyond end_age, the end_weights are held constant.

Investment Fees¶

Three fee types, applied as monthly drag on portfolio value:

Field	Default	Description
`expense_ratio`	0.0	Annual fund expense ratio (e.g., 0.001 = 10 bps)
`aum_fee`	0.0	Annual AUM/platform fee
`advisory_fee`	0.0	Annual financial advisor fee

market = MarketAssumptions(
    ...,
    expense_ratio=0.001,    # 10 bps fund expenses
    advisory_fee=0.01,      # 1% advisor fee
)

The total annual fee drag is expense_ratio + aum_fee + advisory_fee, applied monthly as (1 - annual_fee / 12) on positions.