Deterministic, event-driven trading infrastructure for quantitative research and production deployment.

A modular open-source ecosystem focused on building realistic trading simulations, robust execution systems, and cloud-native research infrastructure — designed for serious financial engineering.

• Event-driven trading systems
• Deterministic backtesting & simulation
• Risk management & order lifecycle modeling
• Cloud-native research infrastructure
• Reproducible quantitative experimentation

trading-platform

Deterministic, event-driven trading engine for:

• Strategy research & simulation
• Realistic order lifecycle modeling
• Portfolio & risk management
• Transition from backtest to production

Status: active

Primary stack: Python, event-driven architecture

trading-infrastructure

Infrastructure automation for quantitative research and trading workloads:

• Kubernetes-based environments
• GitOps workflows
• Experiment tracking
• Secrets & configuration management

Includes: Argo CD, MLflow, Vault, cloud tooling

Goal: reproducible, production-grade research environments

oci-secrets-store-csi-driver-provider

Infrastructure component enabling secure secrets injection for containerized workloads (fork with multi-architecture support).

[ Market Data ]
       ↓
[ Strategy Engine ]
       ↓
[ Event Bus ]
       ↓
[ Order Management System ]
       ↓
[ Risk & Portfolio Layer ]
       ↓
[ Execution / Simulation ]
       ↓
[ Research & Infrastructure Stack ]

• Python
• Event-driven architecture
• Kubernetes
• GitOps (Argo CD)
• ML experiment tracking (MLflow)
• Secrets & config management (Vault / cloud providers)
• Cloud-native infrastructure

• Build realistic trading simulations that match production behavior
• Eliminate research / production divergence
• Enable reproducible quantitative experimentation
• Provide infrastructure patterns for serious trading systems
• Emphasize correctness, determinism, and system design

• Core event engine stabilization
• Portfolio & risk modeling layer
• Historical market replay system
• Strategy SDK
• Distributed simulation support
• Production execution connectors
• Improved monitoring & observability

Contributions, discussions, and architecture feedback are welcome.

Typical areas of interest:

• Trading system design
• Backtesting realism
• Performance & determinism
• Infrastructure automation
• Quant research workflows

Discussions are centralized in the docs repository. See individual repositories for contribution guidelines.

• Website: [TBA]
• Blog / Research notes: [TBA]
• Twitter/X: [TBA]
• Discord/Community: [TBA]

Each project specifies its own license. See individual repositories for details.