manchero-method 0.1.3

Menchero multiperiod performance attribution calculations at a sector and stock level

Menchero Method Performance Attribution

Menchero Method Performance Attribution is a Python package for performing single- and multi-period performance attribution using the Menchero multiperiod smoothing method, with support for both sector-level and stock-level breakdowns. Stock level attributions are calculated using our own internally developed adjustments descibed here

This library provides:

• Menchero smoothing for accurate multi-period attribution of returns.
• Quality assurance checks to compare calculated attributions vs. actual active returns.
• PnL (Profit & Loss) handling at both sector and stock levels.
• Flexible, pandas DataFrame-based interface for integration with Python analytics workflows.

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Table of Contents

• Features
• Installation
• Quickstart Example
• Usage
  • Sector-Level Attributions
  • Stock-Level Attributions
• How It Works
• Validation and Testing
• Contributing
• License

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Features

• Menchero multiperiod smoothing approach to handle multi-day or multi-month performance attribution.
• Sector-level vs. stock-level breakdowns:
  • Sector-level: See which sectors drove active performance over time.
  • Stock-level: Drill down to individual stock attributions, with optional re-allocation logic.
• PnL inclusion: Decide whether to include profit-and-loss percentages in your allocation effect.
• Flexible QA checks: Compare the summed selection/allocation to the final cumulative active return.
• Verbose logging mode for debugging and demonstration.

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Installation

You can install this package via pip:

pip install menchero-method

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Quickstart Example

import pandas as pd
from menchero_multiperiod_attribution import sectorAttributions, stockAttributions

# Example: create or load your data as a pandas DataFrame
data = pd.DataFrame({
    'date': [...],         # Python 'date' objects or Timestamps
    'sector': [...],       # Sector names (str)
    'stock': [...],        # Stock names (str), optional for sector-level
    'portfolio_weight': [...],
    'benchmark_weight': [...],
    'stock_return': [...],
    'pnl_pct': [...]
})

# Sector-level attributions
sector_output = sectorAttributions(
    raw=data, 
    pnl=True,      # include PnL in calculations
    check=False,   # skip QA checks for brevity
    verbose=False  # no debug printing
)

# Stock-level attributions
stock_output = stockAttributions(
    raw=data, 
    pnl=True,
    check=True,    # perform QA checks 
    verbose=True   # print debug info
)

print(sector_output.head())
print(stock_output[0].head())  # if check=True, returns (stock_output, qa_checks)

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Usage

Sector-Level Attributions

from menchero_multiperiod_attribution import sectorAttributions

# Example DataFrame 'df' with columns: 
#   date, sector, portfolio_weight, benchmark_weight, stock_return, pnl_pct
sector_result = sectorAttributions(
    raw=df,
    pnl=True,       # include or exclude PnL in your calculation
    check=False,    # set True to return QA checks
    verbose=True    # optional logging
)

• If check=True, returns a tuple (sector_result, qa_checks).
• The returned DataFrame (or first element of the tuple) is in a “long” format, with columns like [date, sector, variable, variable_value].

Stock-Level Attributions

from menchero_multiperiod_attribution import stockAttributions

# Example DataFrame 'df' with columns: 
#   date, sector, stock, portfolio_weight, benchmark_weight, stock_return, pnl_pct
stock_result = stockAttributions(
    raw=df,
    pnl=False,     # do not include PnL
    check=True,
    verbose=False
)

• If check=True, returns (stock_output, qa_checks). Otherwise, returns only stock_output.
• The DataFrame(s) have columns like [date, sector, stock, variable, variable_value].
• Internally, stockAttributions calculates sector-level attributions first, then modifies them at the stock level via _attributionModify.

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How It Works

1. Input Validation – The library checks that your input DataFrame has the required columns, correct data types, and that weights sum to approximately 1.
2. Menchero Method – The core _menchero function calculates:
   • Allocation, Selection, Interaction effects.
   • Cumulative returns and active return.
   • Menchero smoothing via multiplicative and corrective factors to properly handle compounding.
3. QA Checks (optional) – Sums allocation/selection across dates and compares to the final cumulative active return to ensure correctness.
4. Stock-Level Adjustments – For stock-level attributions, we apply _attributionModify to adjust the final allocation/selection by sector-level results.

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Validation and Testing

We maintain a comprehensive test suite that includes:

• Unit tests for internal functions (_menchero, _attributionCheck, _attributionModify, _validateInputData).
• Integration tests for sectorAttributions and ```stockAttributions.
• Golden tests with small, hand-verified datasets to confirm exact numeric results match expected Menchero smoothing outcomes. To run the tests locally, clone this repository and run:

pytest

(You may need to install development dependencies like pytest.)

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Contributing

Contributions, bug reports, and feature requests are welcome! Please:

1. Open an issue describing the problem or suggestion.
2. Fork the repo and create a new branch for your contribution.
3. Submit a pull request with your changes and relevant tests. We’ll review your pull request as soon as we can.

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License

This project is released under the MIT License. Feel free to use it in commercial and private projects. See the LICENSE file for details.

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Thank you for using the Menchero Method Performance Attribution Package! If you have any questions or feedback, please open an issue or submit a pull request. Contributions are always welcome.