AlphaLab

Institutional-Grade Quantitative Research & Algorithmic Trading Framework

Deterministic • Event-Driven • Immutable • Fully Typed • Production-Oriented

A modular Python framework for quantitative research, systematic strategy development, portfolio optimization, market simulation, broker integration, and production deployment.

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What is AlphaLab?

AlphaLab is an open-source Python framework for building deterministic quantitative research and algorithmic trading systems.

Instead of providing isolated utilities for market data, research, optimization, or execution, AlphaLab organizes the complete research-to-production workflow into independent but interoperable engines built around immutable state, deterministic execution, and event-driven architecture.

The framework is designed for researchers, quantitative developers, students, and engineering teams building reproducible trading infrastructure.

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Release Status

Current Release: v1.0.0

Metric	Status
Python	3.12+
Version	1.0.0
Tests	583 Passing
Static Typing	Strict MyPy
Linting	Ruff Clean
Package Build	✅ Passing
Wheel Validation	✅ Passing
Source Distribution	✅ Passing
License	MIT

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Core Principles

AlphaLab is built around a consistent engineering philosophy.

• Immutable domain models
• Deterministic execution
• Event-driven architecture
• Pure functional engine APIs
• Strict static typing
• Modular package boundaries
• Production-oriented design
• Reproducible research workflows

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Architecture

                          AlphaLab Workbench
                                  │
                                  ▼
                          Strategy Studio
                                  │
      ┌───────────────────────────┼───────────────────────────┐
      ▼                           ▼                           ▼
Universal Data Engine      Research Engine      Portfolio Optimizer
      │                           │                           │
      └───────────────────────────┼───────────────────────────┘
                                  ▼
                          Strategy Runtime
                                  │
                                  ▼
                       Broker Integrations
                                  │
                                  ▼
                         Production Runtime
                                  │
                                  ▼
                            Live Markets

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Framework Modules

Module	Purpose
Universal Data Engine	Market data ingestion, normalization and validation
Research Engine	Quantitative research and signal evaluation
Strategy Runtime	Deterministic strategy execution
Replay Engine	Historical event replay
Portfolio Optimizer	Portfolio construction and optimization
Broker Integrations	Unified broker abstraction layer
Production Runtime	Runtime supervision and monitoring
Strategy Studio	Research projects, experiments and pipelines
Workbench	Unified research-to-production workflow

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Getting Started

Installation

git clone https://github.com/VarunSingh022/AlphaLab.git

cd AlphaLab

python -m venv .venv

source .venv/bin/activate

pip install -e ".[dev]"

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Learn AlphaLab

The recommended way to learn the framework is through the curated examples.

Example	Description
01	Research Engine
02	Strategy Runtime
03	Historical Replay
04	Market Data
05	Broker Integrations
06	Portfolio Optimizer
07	Universal Data Engine
08	Strategy Studio
09	Workbench
10	Complete End-to-End Pipeline

Run any example:

python examples/01_research.py

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Documentation

The complete documentation is available in the docs/ directory.

Document	Description
Getting Started	Installation and first steps
Architecture	Framework architecture
ADR	Architectural Decision Records
Examples	Example walkthroughs
Engineering	Engineering guidelines
Roadmap	Future development

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Repository

alphalab/
├── common/
├── core/
├── data/
├── research/
├── strategy/
├── replay/
├── portfolio_optimizer/
├── integrations/
├── production/
├── studio/
├── workbench/
└── ...

Additional directories:

docs/          Documentation

examples/      Runnable examples

benchmarks/    Performance benchmarks

tests/         Automated test suite

configs/       Reference configuration files

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Quality Assurance

AlphaLab is continuously validated through automated tooling.

• ✅ 583 passing unit tests
• ✅ Strict MyPy type checking
• ✅ Ruff linting
• ✅ Source distribution validation
• ✅ Wheel validation
• ✅ Python packaging verification

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Roadmap

v1.0.0

• Universal Data Engine
• Research Engine
• Strategy Runtime
• Replay Engine
• Portfolio Optimizer
• Broker Integrations
• Production Runtime
• Strategy Studio
• AlphaLab Workbench

Future

• Feature Store
• Factor Library
• Options Engine
• Futures Engine
• Machine Learning
• Experiment Tracking
• Model Registry
• Distributed Research
• AlphaLab Cloud

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Contributing

Contributions are welcome.

Please read:

• CONTRIBUTING.md
• CODE_OF_CONDUCT.md
• SECURITY.md

before submitting issues or pull requests.

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License

Released under the MIT License.

See LICENSE for details.

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AlphaLab v1.0.0

Building deterministic infrastructure for quantitative research.