perfattr

perfattr is a small, auditable portfolio performance-attribution calculation library built with pandas and NumPy.

The package provides a reusable Brinson-Fachler calculation core and a portable preparation layer for source-period weights and returns. Portfolio accounting, vendor schemas, and presentation remain outside the package boundary.

The calculation core accepts one or more prepared reporting periods and provides input validation, universe equalization, Brinson-Fachler allocation and selection, logarithmic contribution linking, Carino active-effect linking, cumulative and full-horizon results, and financial reconciliation. The completed initial roadmap is recorded in _extras/perfattr_roadmap_1.md. The completed portable preparation work is recorded in _extras/perfattr_roadmap_2.md, while later candidates are kept in the noncommitted _extras/perfattr_roadmap_3.md. Effective-dated classification was the first promoted candidate and its completed work is recorded in _extras/perfattr_roadmap_4.md, with its accepted contract in docs/effective_dated_classification_specification.md. The complete portable calculation contract is defined in docs/specification.md, and the accepted roadmap 2 preparation contract is in docs/preparation_specification.md.

import pandas as pd

from perfattr import calculate_attribution, prepare_attribution

portfolio = pd.DataFrame(
    [
        {
            "from_date": "2024-01-01",
            "thru_date": "2024-01-31",
            "identifier": "Equity",
            "weight": 0.60,
            "return": 0.04,
        },
        {
            "from_date": "2024-01-01",
            "thru_date": "2024-01-31",
            "identifier": "Bonds",
            "weight": 0.40,
            "return": 0.01,
        },
    ]
)
benchmark = pd.DataFrame(
    [
        {
            "from_date": "2024-01-01",
            "thru_date": "2024-01-31",
            "identifier": "Equity",
            "weight": 0.50,
            "return": 0.03,
        },
        {
            "from_date": "2024-01-01",
            "thru_date": "2024-01-31",
            "identifier": "Bonds",
            "weight": 0.50,
            "return": 0.015,
        },
    ]
)

prepared = prepare_attribution(portfolio, benchmark)
result = calculate_attribution(prepared.portfolio, prepared.benchmark)
print(result.period_detail)

Canonical CSV inputs can be loaded with read_performance_csv; optional mapping and classification readers are also available at the package root.

Mapping CSV files are headerless and use one uniform form per file. Existing static files remain two columns (identifier,classification_identifier). Effective-dated files use four columns in the order from_date,thru_date,identifier, classification_identifier. For example:

2024-01-01,2024-01-31,ASSET,Equity
2024-02-01,2024-12-31,ASSET,Fixed Income

The dates are closed and inclusive. A source period for a mapped identifier must be contained in exactly one assignment; perfattr does not split a source period at a classification boundary.

Cash, fees, and financing

Cash receives no special treatment: supply it as an ordinary identifier, or map it to a Cash classification, with the weight and return chosen by the host accounting system. perfattr never invents cash or hides a residual in it.

A fee or financing charge without exposure can be supplied as a zero-weight row with an authoritative nonzero contribution and a null return. The contribution is preserved and included in the ordinary attribution and linking calculations; perfattr does not infer the row from its name or calculate the charge. Financing with an explicit exposure and return can instead be represented as an ordinary identifier.

Development

Create and activate a virtual environment:

python3 -m venv .venv
source .venv/bin/activate

Install the package and development dependencies:

python -m pip install --editable ".[dev]"

Run the initial checks:

python -m pytest
python -m pylint src/perfattr tests scripts
python -m pyright

Run the four roadmap performance workloads:

python scripts/benchmark_core.py --samples 5
python scripts/benchmark_core.py --samples 5 --input-form authoritative
python scripts/benchmark_preparation.py --samples 5

Add --workload monthly_121260 --profile to inspect one workload's cumulative calculation-core call profile. The preparation benchmark compares static and effective-dated mappings through quarterly consolidation. The benchmark methodology and observations are recorded in docs/performance.md.

License

perfattr is distributed under the MIT License.