https://github.com/kgeon1002/quant-fund/releases

Quant Fund: Open-Source Quant Finance Toolkit and Research Hub

Welcome to Quant Fund, a robust open-source project built for researchers, developers, and practitioners in the field of quantitative finance. This project is part of the 量化开源基金会, a community dedicated to transparent, reproducible, and collaborative finance research. The goal is simple: provide a clear, usable, and scalable toolkit that helps you design, test, validate, and deploy quantitative strategies. This README walks you through what the project is, how it works, how to contribute, and how to get the most from the toolkit. The content here uses plain language, practical examples, and a focus on real-world workflows.

If you are looking for the Releases page to grab installers or prebuilt assets, you can navigate to the Releases section of this repository. The assets on that page are designed to make setup quick and predictable across major operating systems. The Release page also serves as a central place to review version history, fixes, and new features. See the link above for the latest assets and notes. For quick access, you will find two visible references to the same URL in this document: the first at the very top, and the second as a badge that links to the same page.

About Quant Fund

Quant Fund is a modular software stack for quantitative research, backtesting, risk analysis, and light live trading. It blends data ingestion, feature engineering, model evaluation, execution interfaces, and reporting into a coherent platform. The project emphasizes transparency and reproducibility. Every result should be traceable to data, code, and configuration. The framework aims to be accessible to students, researchers, and professionals, while remaining flexible enough for advanced users to extend.

量化开源基金会 is the guiding principle behind this work. The organization centers on open science, collaboration, and responsible development in quantitative finance. The project welcomes contributions from researchers, developers, students, and practitioners. We believe that sharing research, code, and data strengthens the field and accelerates progress. This README reflects that ethos with careful attention to clarity, reproducibility, and practical value.

Core Concepts and Philosophy

• Reproducibility first. Every model, backtest, and result should be reproducible given data, code, and configuration. We encourage deterministic seeds, versioned data, and clean dependency management.
• Modularity. The system divides into clear modules for data, models, backtesting, risk checks, and reporting. Each module is replaceable without breaking the rest of the system.
• Extensibility. Users can add new data adapters, new model classes, and new visualization methods without touching core code. Plugins and adapters follow a simple contract.
• Safety and governance. We provide audit trails for backtests and live runs. Users can export reports to share with stakeholders without exposing sensitive data.
• Accessibility. The toolkit targets a broad audience. It ships with sensible defaults, robust error messages, and comprehensive documentation.

What you can build with Quant Fund

• Strategy research: test ideas quickly against historical data, visualize outcomes, and compare strategies on standardized metrics.
• Portfolio construction: explore risk-based allocations, factor models, and optimization routines.
• Risk management: monitor drawdown, volatility, and exposure; build safety checks for live deployments.
• Data engineering: integrate datasets from multiple sources, align them by timestamps, and keep provenance intact.
• Research notebooks and reports: generate reproducible reports that combine code, results, and commentary.

Architecture Overview

Quant Fund is designed with a layered architecture. Each layer has a well-defined API and can be replaced or extended independently.

• Data Layer: Adapters that fetch, clean, and align data from various sources. Supports historical market data, fundamentals, macro indicators, and synthetic datasets for testing.
• Feature Layer: Tooling to derive signals, indicators, and engineered features. Includes rolling statistics, time-series transforms, and domain-specific features.
• Modeling Layer: A collection of model templates for backtesting and evaluation. Supports traditional factors, machine learning models, and custom strategies.
• Backtesting Layer: A deterministic engine that simulates trades over historical data with calendar-aware logic and slippage modeling.
• Risk & Compliance Layer: Modules for risk checks, exposure limits, and performance attribution. Provides charts and summaries for governance reviews.
• Execution & Live Layer: Interfaces to order management, brokers, and simulated markets. Supports dry-run, paper trading, and live trading with safeguards.
• Reporting Layer: Dashboards, notebooks, and export options. Generates performance reports, dashboards, and publication-ready figures.
• Orchestration Layer: A CLI and API to connect modules, configure experiments, and manage experiments across runs.

Getting Started

• Prerequisites: Python 3.9 or later, a modern package manager (pip, poetry, or conda), and a reasonable amount of disk space for data.
• Installation: Clone the repository, create a virtual environment, and install dependencies. Use a clean environment to avoid conflicts with other Python packages.
• Quick test: Load a small sample dataset and run a basic backtest to confirm the setup works on your machine.

Note: For the latest installers and prebuilt assets, refer to the Releases page. The assets are designed to simplify installation across Windows, macOS, and Linux. The file names typically reflect the platform and version, and you should choose the asset that matches your OS. After downloading, follow the platform-specific installation steps. The release page aggregates versioned assets, making it easy to pick the right tool for your environment.

Download and Install From Releases

The Releases page hosts prebuilt installers and packaged archives. Since the link includes a path, it points to a page where you can download specific assets. Typical assets you may encounter include:

• quant-fund-1.0.0-linux-x86_64.tar.gz — Linux tarball, extract and run the included executable.
• quant-fund-1.0.0-macos.dmg — macOS installer; drag and drop to Applications.
• quant-fund-1.0.0-windows-x86_64.exe — Windows installer; follow the on-screen prompts.
• quant-fund-1.0.0-standalone.zip — A portable bundle with Python and dependencies.

To install, download the asset that matches your system, then follow the platform-specific steps. On Linux, a typical flow is to extract the tarball and run the quant-fund binary or script included in the package. On Windows, run the installer and complete the setup wizard. On macOS, mount the disk image and drag the application to your Applications folder. After installation, you should be able to run quant-fund from your command line or via a graphically guided launcher.

If you use a package manager, you might also be able to install Quant Fund as a Python package or a container image. The Releases page often includes alternate distribution formats to fit development or production workflows. Always verify checksums if they are provided and keep your installation isolated from other projects to avoid version conflicts.

What’s in the Repository

• data_adapters/: A collection of connectors to fetch data from public sources and synthetic datasets for testing.
• features/: A library of indicators, transformations, and feature engineering utilities.
• models/: A suite of model templates, scoring, and backtest-ready constructs.
• backtester/: The engine that runs simulations, handles cash management, and records results.
• risk/: Tools for risk metrics, exposure checks, and performance attribution.
• execution/: Interfaces for simulated and live trading, with safeguards and logging.
• docs/: Documentation, tutorials, and design notes.
• notebooks/: Example notebooks for rapid experimentation and learning.
• tests/: Unit tests and integration tests to ensure quality across releases.

Usage Scenarios

• Research and learning: Use ready-made examples to understand backtesting concepts, risk metrics, and portfolio construction.
• Strategy development: Build new signals, combine them into strategies, and evaluate performance across multiple markets and periods.
• Reproducible experiments: Save configurations, data versions, and results in a structured format that makes replication easy.
• Collaboration: Share notebooks, strategies, and experiment results with teammates through a clean, version-controlled workflow.

Getting Hands-On: Example Workflows

Workflow 1: Quick Backtest of a Simple Moving Average Crossover

• Load daily price data for a set of instruments.
• Compute short and long moving averages as features.
• Define a simple crossover rule: go long when the short MA crosses above the long MA; exit when it crosses below.
• Run a backtest over a defined period.
• Generate performance metrics and a visualization.

Workflow 2: Risk-Aware Portfolio Allocation

• Build a universe of assets.
• Compute risk parity or minimum-variance allocations.
• Backtest with realistic transaction costs and slippage.
• Assess risk-adjusted performance using Sharpe ratio, Sortino, and maximum drawdown.
• Validate robustness with walk-forward testing.

Workflow 3: Data-Driven Signals with Simple ML

• Prepare features from price data and fundamentals.
• Train a baseline model to predict next-period returns.
• Use a risk-aware selection mechanism to build a portfolio.
• Backtest with out-of-sample validation and model monitoring.

Notes for Beginners

• Start small. Use a small data set and a simple strategy to learn the workflow.
• Keep everything versioned. Track your configuration, data, and results together.
• Focus on reproducibility. Ensure that anyone can reproduce your backtests with the same seeds and data.

Advanced Topics

• Factor modeling: Build multi-factor strategies, test factor tilts, and explore risk budgeting.
• Portfolio optimization: Explore constraint handling, turnover control, and transaction costs.
• Live risk monitoring: Connect to a live data feed and set automated risk checks to pause trading when risk exceeds thresholds.
• Explainability: Explain model decisions and feature contributions to help audit trails and governance.

Architecture Details and Design Rationale

• Data Layer: Adapters implement a common interface. They translate raw sources into a standardized internal format. Each adapter records metadata: source, timestamp, version, and quality flags.
• Feature Layer: Features are stateless transformations that can be composed. A registry helps you locate features by name or category.
• Modeling Layer: Models implement a common scoring interface. You can use rule-based models or trainable models. All models expose a standard fit, predict, and score API.
• Backtesting Layer: The engine uses a deterministic clock and discrete events. It handles order fills, position sizing, and cash management. It can simulate different market conditions by toggling slippage and commissions.
• Risk Layer: Provides a suite of metrics: drawdown, trailing drawdown, volatility, beta, and beta-adjusted returns. It can generate risk dashboards and export reports.
• Execution Layer: Abstracts the trade interface. It supports simulated trading, paper trading, and live trading if configured. All trades are logged with timing and slippage data.
• Reporting Layer: Visualizes performance and risk. It exports plots, tables, and markdown reports suitable for review meetings or publication.

Development and Contribution

We follow a lean, collaborative approach. The project welcomes contributions from the community. If you plan to contribute, start by reading the contribution guide in docs/. You will find instructions for code style, testing, and how to submit pull requests.

Code style and quality

• Pythonic code: Prefer simple, readable code. Use explicit variable names and short functions when possible.
• Documentation: Add docstrings and inline comments for complex logic.
• Testing: Write tests for new features. Run the test suite before submitting a pull request.
• Dependency management: Pin versions to avoid drift. Use a virtual environment for development and testing.

How to Contribute

• File a feature request or bug report via issues.
• Propose a pull request with a clear title and a short description.
• Include minimal reproducer data and steps to verify the change.
• Update related tests and documentation.

Community and Governance

• We maintain a code of conduct to ensure respectful collaboration.
• Governance follows a transparent process with community input for major changes.
• We encourage knowledge sharing through tutorials, notebooks, and posters explaining concepts.

Tutorials and Examples

• Hello, Quant Fund: A beginner-friendly walkthrough that creates a small strategy and runs a backtest.
• Data Wrangling 101: A guide to cleaning and aligning data from multiple sources.
• Signal Studio: A hands-on workshop for building and testing technical indicators.
• Risk and Return: An explanation of common risk metrics and how to interpret them in backtests.

Notebooks and Demonstrations

• Notebooks in the notebooks/ directory illustrate end-to-end workflows. They combine code, data, and narrative to help you learn quickly.
• Visualization notebooks show performance charts, drawdown graphs, and equity curves.

Data Policy and Safety

• Data provenance: Every dataset has a source tag and a version. This helps you reproduce results precisely.
• Privacy and ethics: Do not use personal or sensitive data in public experiments. When publishing results, anonymize data where required.
• Security: Treat live trading connections as sensitive. Use environment variables and secure credentials management.

Documentation Strategy

• User guide: How to use the toolkit for common tasks.
• API reference: Details of public classes, methods, and configuration options.
• Architecture docs: Design decisions and module responsibilities.
• Tutorials: Step-by-step guides for specific workflows.
• Release notes: Summary of changes, bug fixes, and new features for each version.

Testing and Quality Assurance

• Unit tests cover core utilities, data adapters, and model components.
• Integration tests verify end-to-end workflows with sample data.
• CI pipelines ensure the code builds and tests pass on multiple platforms.
• Regular code reviews keep quality high and logic clear.

Releases, Versioning, and Roadmap

• Versioning follows semantic versioning where practical.
• Each release includes a changelog, notes on breaking changes, and migration guidance.
• Roadmap outlines planned features and improvements for the next releases.

Architecture Diagrams and Visuals

• Architecture Diagram: A high-level view of modules and their interactions. This helps new contributors understand how data flows from ingestion to reporting.
• Data Flow Chart: A step-by-step depiction of how signals propagate through features, models, and backtests.
• UI Mockups: Simple visuals demonstrating how users interact with the CLI and dashboards.
• Governance and Audit Visuals: Charts showing how results are tracked and reviewed for governance.

Usage Tips and Best Practices

• Start with a clear hypothesis. Define the question before you begin.
• Keep data aligned. Ensure timestamps are synchronized across data sources.
• Track experiments. Record configurations, seeds, data versions, and random states.
• Use small data during learning. Move to large data when you are ready to scale.
• Validate results with out-of-sample tests to avoid look-ahead bias.

Security and Responsible Use

• Do not bypass data licenses or terms of use. Respect data rights and licensing.
• When sharing results, include enough context so others can reproduce them legally.
• Keep credentials out of code. Use secure vaults or environment management for live systems.

FAQ

• Q: What is Quant Fund best used for? A: It helps researchers, students, and practitioners design, test, and evaluate strategies in a transparent, reproducible way.

• Q: Can I use Quant Fund for live trading? A: Yes. The framework includes an execution layer that can connect to brokers or simulate live trading with safeguards.

• Q: How do I contribute? A: Start with the contribution guide in docs/. Open issues or propose features through pull requests.

• Q: Is there a recommended data source? A: The data layer supports multiple sources. Choose sources that fit your use case and license terms.

• Q: How do I run tests? A: Use the test suite in tests/ with your preferred test runner. Run tests before submitting changes.

Images and Visual Elements

• Banner and header images show finance and code themes. These visuals help users quickly grasp the project focus.
• Emojis are used to punctuate sections and steps, helping readability.

License and Governance

• License: MIT or equivalent approved license. See LICENSE for details.
• Contributions: All code becomes part of the open-source project under the license. Community contributions follow the project’s governance policy.

Documentation and Help

• docs/ contains user guides, API references, and tutorials.
• Notebooks illustrate practical workflows and serve as learning resources.
• The project aims to keep docs up to date with code changes to minimize confusion.

Performance and Optimization

• The backtesting engine is designed to be deterministic and fast for reasonable data sizes.
• Features and signals are kept lightweight and composable to reduce overhead.
• Data adapters are optimized for common data formats to minimize conversion costs.

Compatibility

• Cross-platform support for Linux, macOS, and Windows.
• Containerized options available for consistent environments.
• API and module interfaces are designed to remain stable to minimize disruption for downstream users.

Community and Support

• We encourage questions, clarifications, and constructive feedback.
• Community members help each other with debugging, improvements, and learning.
• Official channels include issues, discussion threads, and periodic community calls.

Contributing Checklist

• Run tests locally to verify changes.
• Add or update tests for new features or fixes.
• Update documentation to reflect changes.
• Ensure code style aligns with project guidelines.
• Include a minimal reproducer if you fix a bug.

Sustainability and Future Work

• The project aims to remain sustainable through community contributions and governance.
• Future work includes expanding data adapters, improving model templates, and adding more robust live trading connectors.
• We plan to broaden educational content, including more tutorials and governance materials.

Notes on Data and Reproducibility

• Reproducibility is a central pillar. We emphasize data versioning, environment snapshots, and deterministic seeds.
• When sharing results, document the exact data versions and code paths used to generate them.
• Publishing results should include a link to the exact notebook or script that produced them.

Notable Design Decisions

• Simplicity first: A simple API with clear semantics helps beginners learn quickly.
• Composability: Modules are designed to be combined in many ways, enabling flexible experimentation.
• Transparency: All steps, data, and configurations are accessible and auditable.
• Extensibility: The architecture supports easy addition of new data sources, models, and execution interfaces.

Advanced Usage Scenarios

• Multi-market research: Run experiments across multiple markets with synchronized data and consistent metrics.
• Factor-driven portfolios: Explore factor definitions, portfolio tilts, and risk balancing.
• Benchmarking suite: Compare strategies against standard benchmarks to evaluate performance.

Security and Incident Response

• If you suspect a security issue, report it through the project’s issue tracker with detailed reproduction steps.
• We respond to security issues promptly and coordinate fixes in a responsible manner.

Release Notes and Version History

• Each release includes a summary of changes, known issues, and migration tips.
• Version notes help users decide when to upgrade and what to adjust in their workflows.

Roadmap Highlights

• Expand data ecosystems with more sources.
• Improve live trading simulations with better slippage modeling.
• Add more visualization options for performance dashboards.
• Enhance notebook templates for faster onboarding.

Endnotes

• This README emphasizes practicality, clarity, and reproducibility.
• It is designed to be approachable for newcomers while still offering depth for advanced users.
• The project remains focused on open collaboration, responsible research, and high-quality software.

If you want to explore more, head to the Releases page for the latest assets and notes. The assets are designed to get you started quickly, and they reflect the ongoing work of the Quant Fund community. The link above is your gateway to the current release set and the associated documentation, tests, and example work.