double-quant 0.0.1a3

Quantum-inspired quantitative finance workflows and algorithms

Quantum Quantitative

Double Quant

Double Quant is a Python library for quantitative finance workflows built around quantum-inspired and quantum-backed algorithms.

Overview

Double Quant bridges quantum computing and quantitative finance with reusable data transforms, HHL-based solvers, Shapley-value algorithms, and higher-level portfolio and risk applications.

Key Features

• Quantum-aware algorithms for linear solving and Shapley attribution
• Layered package design across data, common utilities, algorithms, and applications
• Full type hints for stronger IDE support and safer integrations
• Portfolio and risk workflows built on top of reusable primitives
• Experiment-friendly components that can be composed outside the core package

Architecture

Double Quant is organized as a four-part package:

double_quant.data
double_quant.common
double_quant.algorithm
double_quant.application

Layer Responsibilities

• Data layer (double_quant.data): price sources and transforms from prices to returns, covariances, and expected returns
• Common layer (double_quant.common): shared models and metrics such as LinearSystem and risk helpers
• Algorithm layer (double_quant.algorithm): HHL solver variants plus classical and quantum Shapley calculators
• Application layer (double_quant.application): higher-level workflows such as portfolio optimization and risk attribution

Installation

• Python 3.11 or higher

Install from PyPI with uv

uv add double-quant

Install with pip

pip install double-quant

Install from source for development

git clone https://github.com/11D-Beyonder/double-quant.git
cd double-quant
uv sync

Quick Start

Example: Solving a Linear System

import numpy as np

from double_quant import HHLSolver

A = np.array([[2.0, 1.0], [1.0, 2.0]])
b = np.array([1.0, 1.0])

solution = HHLSolver.solve(A, b)
print(solution)

Example: Classical Risk Attribution

import pandas as pd

from double_quant import BinaryEnumerationCalculator, RiskAttributor

returns = pd.DataFrame(
    {
        "AAPL": [0.01, -0.03, 0.02, 0.015],
        "MSFT": [0.008, -0.01, 0.018, 0.012],
        "TLT": [0.002, 0.001, -0.002, 0.003],
    }
)

src = RiskAttributor(
    returns,
    BinaryEnumerationCalculator,
    alpha=0.95,
    mode="es",
).attribute()

print(src)

Core Components

HHL Solver

HHLSolver is the main entry point for solving Hermitian linear systems with the project's HHL-based workflow.

Shapley Calculators

Double Quant includes exact, Monte Carlo, and quantum Shapley-value calculators for attribution problems.

Risk Attribution

RiskAttributor supports both direct expected-shortfall attribution (mode="es") and the quantum-compatible risk-saving formulation (mode="rs").

Documentation

Project documentation lives in the docs/ directory, including:

• docs/application/risk.md
• docs/solver/shapley.md
• docs/experiments/risk.md

Development

Running Tests

uv run pytest

Building the Package

uv build

Code Style

This project uses ruff for linting and basedpyright for type checking.

Contributing

We welcome contributions. If you would like to contribute:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat(scope): add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Commit Message Format

We follow the Angular commit message convention:

<type>(<scope>): <description>

[optional body]

[optional footer]

Types: feat, fix, docs, style, refactor, test, chore

License

This project is licensed under the GPLv3. See LICENSE for details.

Acknowledgments

This project draws inspiration from:

• Qiskit
• Qiskit Finance