MFDRO

MFDRO estimates reproducible, point-in-time disagreement between multivariate return distributions observed at multiple frequencies. It constructs empirical measures, places them on comparable scales, estimates a Wasserstein center, and returns a non-negative squared dispersion rho together with a complete audit trail.

Status: alpha research software. The numerical core is tested, but the public API is not yet frozen. Pin an exact version when preserving a research environment.

Why MFDRO

• Two or more user-configurable frequencies; three is only the default.
• Free-support or projected-quantile barycenter construction.
• Sliced or exact discrete transport dispersion where compatible.
• Strict point-in-time rolling windows, memberships, and optional calendars.
• Preflight diagnostics before expensive numerical work.
• Deterministic seeds, versioned configuration JSON, and portable result bundles with checksums.
• Typed public API, stable empty schemas, analytical tests, and a 95% branch coverage gate.

MFDRO does not acquire production data, infer investable universes, calibrate an ambiguity radius, optimize portfolios, or simulate trades. It is a focused signal component designed to work with whichever data, optimizer, and backtesting system a research project chooses.

Installation

Install the released package from PyPI:

python -m pip install mfdro

For a reproducible environment, pin the release:

python -m pip install "mfdro==0.1.0"

Clone the repository only when working with the bundled notebooks or source:

git clone https://github.com/jeyllani/mfdro.git
cd mfdro
python -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[notebooks]"

Quick start

import numpy as np
import pandas as pd

from mfdro import MultiFrequencySignal, SignalConfig

rng = np.random.default_rng(20250301)
dates = pd.bdate_range("2018-01-01", "2022-12-30")
returns = pd.DataFrame(
    rng.normal(0.0, 0.01, size=(len(dates), 6)),
    index=dates,
    columns=[f"asset_{index:02d}" for index in range(6)],
)

config = SignalConfig.projected(
    n_projections=100,
    n_quantiles=100,
    random_state=20250301,
)
engine = MultiFrequencySignal(config)

diagnostics = engine.validate_path_inputs(returns, lookback_months=36)
assert diagnostics.is_usable

path = engine.estimate_path(
    returns,
    lookback_months=36,
    on_insufficient="skip",
    seed_namespace="synthetic_example",
)

print(path.rho.tail())
print(path.audit[["date", "n_daily", "n_weekly", "n_monthly"]].tail())
print(path.skipped[["date", "reason"]].head())

config.write_json("artifacts/config.json")
path.save("artifacts/signal_path")

rho is a squared cross-frequency dispersion—not an expected return, trading direction, or automatically calibrated DRO radius. Any mapping into a portfolio policy is a separate point-in-time research decision.

More than three frequencies

from mfdro import FrequencySpec, SignalConfig

config = SignalConfig(
    frequency_specs=(
        FrequencySpec("daily", 1.0),
        FrequencySpec("weekly", 5.0, rule="W-FRI"),
        FrequencySpec("biweekly", 10.0, rule="2W-FRI"),
        FrequencySpec("monthly", 21.0, rule="ME"),
        FrequencySpec("quarterly", 63.0, rule="QE"),
    )
)

Horizons, resampling rules, boundary conventions, minimum observations, weights, and numerical settings all enter the configuration digest.

Documentation

The MkDocs site covers the data contract, configuration choices, output schemas, reproducibility, and backtesting integration. The notebook suite covers frequency construction, geometry, point-in-time controls, reproducible artifacts, and an optional Yahoo Finance case study.

Build the site locally:

python -m pip install -e ".[docs]"
python -m mkdocs serve

Then open http://127.0.0.1:8000.

Development

python -m pip install -e ".[test,docs,dev,notebooks]"
python -m pytest --cov=mfdro --cov-report=term-missing
python -m ruff check src tests examples
python -m ruff format --check src tests examples
python -m mypy src
python -m mkdocs build --strict
python -m build
python -m twine check dist/*

See CONTRIBUTING.md, SECURITY.md, and CHANGELOG.md before preparing a change or release.