Enterprise-Grade AI Trading Intelligence Platform
A next-generation, AI-native trading operating system that combines multi-agent AI reasoning, quantitative analysis, autonomous market research, and institutional-grade trading infrastructure.
ArrowEra Trade is a production-grade, modular AI trading platform inspired by concepts from advanced trading intelligence systems but completely redesigned and rewritten with:
- Modern Architecture: Microservices-based, event-driven, cloud-native
- AI-Native Design: Multi-agent orchestration with LangGraph-style workflows
- Institutional Quality: Bloomberg Terminal meets AI copilot
- Developer Experience: Clean APIs, TypeScript-first, comprehensive SDKs
- Scalability: Kubernetes-ready, horizontal scaling, distributed processing
┌─────────────────────────────────────────────────────────────────┐
│ Frontend Layer │
│ Next.js 15 • React • TypeScript • TailwindCSS • shadcn/ui │
│ Real-time Dashboards • Workflow Builder • AI IDE • Chat │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ API Gateway Layer │
│ FastAPI • WebSocket • gRPC • Rate Limiting • Auth │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ AI Agent Orchestration │
│ LangGraph DAG • Event Bus • Memory System • Tool Calling │
│ ┌──────────┬──────────┬──────────┬──────────┬─────────────┐ │
│ │ Market │ Quant │ News │ Risk │ Portfolio │ │
│ │ Analyst │ Research │ Intel │ Mgmt │ Agent │ │
│ └──────────┴──────────┴──────────┴──────────┴─────────────┘ │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Backend Services │
│ User Service • Market Data • Signals • Portfolio • Backtest │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Quant Engine │
│ Strategies • Factors • Signals • Optimization • Risk Models │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Data Layer │
│ PostgreSQL • TimescaleDB • Redis • Vector DB • Kafka/NATS │
└─────────────────────────────────────────────────────────────────┘
arrowera-trade/
├── apps/
│ ├── web/ # Next.js frontend application
│ └── docs/ # Documentation site
├── services/
│ ├── api/ # Main FastAPI backend
│ ├── gateway/ # API Gateway service
│ ├── worker/ # Celery/RQ background workers
│ └── scheduler/ # Cron job scheduler
├── packages/
│ ├── ui/ # Shared UI components
│ ├── config/ # Shared configuration
│ ├── types/ # TypeScript type definitions
│ ├── utils/ # Shared utilities
│ └── constants/ # Shared constants
├── agents/
│ ├── market-analyst/ # Market analysis agent
│ ├── quant-research/ # Quantitative research agent
│ ├── news-intelligence/ # News & sentiment agent
│ ├── risk-management/ # Risk management agent
│ ├── portfolio/ # Portfolio optimization agent
│ ├── macro-economics/ # Macro economics agent
│ ├── technical-analysis/ # Technical analysis agent
│ ├── strategy-builder/ # Strategy construction agent
│ ├── execution-simulation/ # Execution simulation agent
│ ├── backtesting/ # Backtesting agent
│ ├── sentiment-analysis/ # Sentiment analysis agent
│ ├── code-generation/ # Code generation agent
│ ├── data-cleaning/ # Data cleaning agent
│ └── memory-summarization/ # Memory summarization agent
├── workflows/
│ ├── templates/ # Workflow templates
│ └── pipelines/ # Data processing pipelines
├── ai-core/
│ ├── orchestrator/ # Agent orchestration engine
│ ├── memory/ # Agent memory system
│ ├── tools/ # AI tool definitions
│ └── llm/ # LLM providers & abstraction
├── frontend/
│ ├── components/ # React components
│ ├── hooks/ # Custom React hooks
│ ├── stores/ # Zustand state stores
│ ├── services/ # API client services
│ ├── layouts/ # Page layouts
│ └── pages/ # Application pages
├── backend/
│ ├── api/ # API route handlers
│ ├── routers/ # FastAPI routers
│ ├── services/ # Business logic services
│ ├── models/ # Database models
│ ├── schemas/ # Pydantic schemas
│ ├── db/ # Database configuration
│ ├── auth/ # Authentication module
│ └── middleware/ # Middleware components
├── quant-engine/
│ ├── strategies/ # Trading strategies
│ ├── factors/ # Factor models
│ ├── signals/ # Signal generation
│ ├── portfolio/ # Portfolio optimization
│ ├── risk/ # Risk analysis
│ └── backtest/ # Backtesting framework
├── data-pipelines/
│ ├── ingestion/ # Data ingestion modules
│ ├── processors/ # Data processors
│ ├── storage/ # Storage adapters
│ └── streams/ # Stream processing
├── infrastructure/
│ ├── docker/ # Docker configurations
│ ├── k8s/ # Kubernetes manifests
│ └── terraform/ # Infrastructure as code
├── monitoring/
│ ├── prometheus/ # Prometheus configs
│ ├── grafana/ # Grafana dashboards
│ └── otel/ # OpenTelemetry setup
├── deployment/
│ ├── dev/ # Development configs
│ ├── staging/ # Staging configs
│ └── prod/ # Production configs
├── scripts/ # Utility scripts
└── docs/ # Documentation
- Framework: Next.js 15 (App Router)
- Language: TypeScript 5.x
- Styling: TailwindCSS + shadcn/ui
- State Management: Zustand
- Data Fetching: TanStack Query
- Charts: Recharts + Apache ECharts
- Code Editor: Monaco Editor
- Animations: Framer Motion + GSAP
- Real-time: WebSocket
- Framework: FastAPI
- Language: Python 3.11+
- Async: asyncio + asyncpg
- Task Queue: Celery + Redis
- API: REST + GraphQL + gRPC
- Real-time: WebSocket + Server-Sent Events
- Orchestration: LangGraph / Custom DAG
- LLM Providers: OpenAI, Anthropic, Gemini, DeepSeek, Ollama
- Vector DB: Pinecone / Weaviate / pgvector
- Memory: Redis + Vector Store
- Tools: Custom tool calling framework
- Relational: PostgreSQL 16
- Time-Series: TimescaleDB
- Cache: Redis 7
- Event Streaming: Kafka / NATS
- Object Storage: S3-compatible
- Core: pandas, polars, numpy, scipy
- ML: PyTorch, scikit-learn
- Backtesting: vectorbt, backtrader
- Portfolio: pyfolio, cvxpy
- Containerization: Docker
- Orchestration: Kubernetes
- CI/CD: GitHub Actions
- Monitoring: Prometheus + Grafana
- Tracing: OpenTelemetry
- Logging: Structured logging with ELK
- 14+ specialized AI agents
- LangGraph-style DAG orchestration
- Event-driven communication
- Shared memory bus
- Human-in-the-loop approvals
- Autonomous task delegation
- Real-time market analysis
- Technical & fundamental analysis
- News & sentiment processing
- Signal generation
- Risk assessment
- Portfolio optimization
- Visual drag-drop builder
- Agent chaining
- Custom tool connectors
- Pre-built templates
- Backtesting pipelines
- Strategy automation
- Integrated code editor
- Strategy development
- Backtesting environment
- Live debugging
- Notebook support
- AI code assistance
- Multi-panel workspace
- Live market heatmaps
- Portfolio analytics
- Agent activity monitoring
- Signal visualization
- Performance metrics
- Node.js 20+
- Python 3.11+
- Docker & Docker Compose
- PostgreSQL 16
- Redis 7
# Clone the repository
git clone https://github.com/your-org/arrowera-trade.git
cd arrowera-trade
# Install frontend dependencies
cd apps/web
npm install
# Install backend dependencies
cd ../../backend
pip install -r requirements.txt
# Setup environment variables
cp .env.example .env
# Start services with Docker
docker-compose up -d
# Run migrations
python -m backend.db.migrate
# Start development servers
npm run dev # Frontend
python -m uvicorn backend.api.main:app --reload # Backend| Agent | Purpose | Tools |
|---|---|---|
| Market Analyst | Market trend analysis | Price data, indicators |
| Quant Research | Alpha generation | Statistical models |
| News Intelligence | News processing | NLP, sentiment |
| Risk Management | Risk assessment | VaR, stress tests |
| Portfolio | Portfolio optimization | MVO, Black-Litterman |
| Macro Economics | Macro analysis | Economic indicators |
| Technical Analysis | Chart patterns | TA indicators |
| Strategy Builder | Strategy creation | Backtesting |
| Execution Simulation | Order simulation | Market microstructure |
| Backtesting | Historical testing | Event-driven backtester |
| Sentiment Analysis | Social sentiment | Twitter, Reddit |
| Code Generation | Code assistance | AST, transpilers |
| Data Cleaning | Data quality | Validation, imputation |
| Memory Summarization | Context management | Summarization |
- JWT authentication
- OAuth 2.0 integration
- API key management
- Role-based access control (RBAC)
- Audit logging
- Encryption at rest & in transit
- Sandbox execution environment
- Secret vault integration
- Horizontal pod autoscaling
- Distributed task queues
- Database sharding ready
- CDN for static assets
- Edge caching
- Connection pooling
- Async I/O throughout
# Unit tests
npm test # Frontend
pytest # Backend
# Integration tests
npm run test:integration
pytest tests/integration
# E2E tests
npm run test:e2eFull documentation available at /docs or visit our Documentation Site.
We welcome contributions! Please see our Contributing Guide for details.
MIT License - see LICENSE for details.
- Core architecture
- Agent framework
- Basic UI components
- Market data pipelines
- Full agent implementation
- Workflow builder
- Backtesting engine
- Portfolio analytics
- Advanced AI features
- Mobile app
- Plugin marketplace
- Enterprise features
- GitHub Issues: Report bugs
- Discussions: Community forum
- Email: support@arrowera.trade
Built with ❤️ by the ArrowEra Team
Institutional-grade AI trading intelligence for the modern era.