Trading System Architecture Overview¶

Overview¶

A production-grade algorithmic trading system designed for local deployment, focusing on equities trading through Alpaca with paper trading capabilities. The system uses a microservices architecture with Prefect orchestration, Python-based services, and a modern web interface.

Author: Nishant Nayar
Email: nishant.nayar@hotmail.com
Repository: https://github.com/nishantnayar/trading-system
Documentation: https://nishantnayar.github.io/trading-system
Last Updated: December 2025
Status: ✅ Core Features Implemented (v1.0.0) | 🚧 Enhanced Features In Progress (v1.1.0)

System Requirements¶

Asset Class: Equities trading via Alpaca API
Data Frequency: Hourly data ingestion
Trading Mode: Paper trading initially, live trading later
Deployment: Local machine (Windows 10)
Architecture: Microservices with orchestration
Data Growth: Designed to handle growing datasets with Polars

Technology Stack¶

Core Technologies¶

Language: Python 3.11+
Environment: Anaconda
Database: PostgreSQL (metadata, transactions, logs)
Cache/Queue: Redis (caching, pub/sub)
Data Processing: Polars (analytics, large datasets)
Orchestration: Prefect (workflow management)
Validation: Pydantic (data models, API validation)

Frontend¶

Backend: FastAPI
Frontend: Streamlit + Plotly + Custom CSS
Charts: Plotly for interactive financial visualizations
Updates: Real-time updates via Streamlit's reactive framework

Development & Quality¶

Linting: Flake8 + Black + isort
Type Checking: mypy
Documentation: MkDocs
Logging: Loguru (consolidated logging)
Testing: pytest + coverage

Architecture Components¶

The system is organized into the following architectural components:

Services Architecture - Detailed breakdown of all microservices
Database Architecture - Database design and connectivity
UI Architecture - Frontend and user interface design
Prefect Architecture - Workflow orchestration and deployment
Deployment Architecture - System deployment strategy
Timezone Architecture - Timezone handling strategy

Communication Patterns¶

Service Communication¶

Synchronous: REST APIs for real-time requests
Asynchronous: Redis pub/sub for events
Batch Processing: Prefect flows for scheduled tasks
Data Synchronization: Event-driven updates between services

Message Flow¶

Data Ingestion → Strategy Engine → Risk Management → Execution
     ↓                ↓                ↓              ↓
Analytics Service ← Notification Service ← Redis ← PostgreSQL

Prefect Flow Orchestration¶

Market Data Flow → Strategy Flow → Risk Flow → Execution Flow
       ↓               ↓            ↓           ↓
   Analytics Flow ← Notification Flow ← Monitoring Flow

Security Architecture¶

API Security¶

Alpaca API keys stored in environment variables
Rate limiting on all API endpoints
Input validation with Pydantic models
SQL injection prevention with ORM

Data Security¶

Database connection encryption
Secure credential storage
Audit logging for all trades
Backup and recovery procedures

Monitoring & Observability¶

Logging Strategy¶

Loguru: Consolidated logging across all services
Structured Logging: JSON format for analysis
Log Levels: DEBUG, INFO, WARNING, ERROR, CRITICAL
Log Rotation: Daily rotation, 30-day retention

📝 Detailed Logging Analysis: For comprehensive logging architecture, structured logging patterns, and implementation strategies, see Logging Architecture Detailed Review.

Monitoring¶

System Health: Service status, database connections
Trading Metrics: P&L, trade count, execution time
Performance: Memory usage, CPU utilization
Alerts: Email notifications for critical events

Dashboard¶

Real-time Portfolio: Current positions and P&L
Strategy Performance: Returns, Sharpe ratio, drawdown
System Status: Service health, error rates
Trade History: Recent trades and orders

Development Workflow¶

Environment Setup¶

# Create conda environment
conda create -n trading-system python=3.11
conda activate trading-system

# Install dependencies
conda install -c conda-forge postgresql redis
pip install -r requirements.txt

# Setup databases
createdb trading_system
redis-server

Code Quality¶

# Pre-commit hooks
pre-commit install

# Code formatting
black .
isort .

# Linting
flake8 .

# Type checking
mypy .

Testing Strategy¶

Unit Tests: Individual service functions
Integration Tests: Service interactions
End-to-End Tests: Complete trading workflows
Strategy Tests: Backtesting validation

Configuration Management¶

Environment Configuration¶

# src/config/settings.py
from pydantic_settings import BaseSettings

class Settings(BaseSettings):
    # Database
    postgres_url: str
    redis_url: str

    # Alpaca API
    alpaca_api_key: str
    alpaca_secret_key: str
    alpaca_base_url: str = "https://paper-api.alpaca.markets"

    # Prefect
    prefect_api_url: str = "http://localhost:4200"

    # Logging
    log_level: str = "INFO"
    log_retention_days: int = 30

    class Config:
        env_file = ".env"

Strategy Configuration¶

# config/strategies.yaml
strategies:
  - name: "momentum_strategy"
    enabled: true
    parameters:
      lookback_period: 20
      threshold: 0.02
      max_position_size: 0.1
    risk_limits:
      max_drawdown: 0.05
      max_daily_loss: 0.02

Performance Considerations¶

Data Processing¶

Polars: Optimized for large datasets
Batch Processing: Efficient data pipeline
Caching: Redis for frequently accessed data
Indexing: Database indexes for fast queries

Scalability¶

Horizontal Scaling: Multiple service instances
Database Optimization: Query optimization, connection pooling
Memory Management: Efficient data structures
Async Processing: Non-blocking operations

Future Enhancements¶

Phase 1 (Current)¶

Paper trading with single strategy
Basic monitoring and alerts
Simple web interface

Phase 2¶

Live trading capabilities
Multiple strategy support
Advanced analytics
Mobile-responsive interface

Phase 3¶

Machine learning integration
Advanced risk management
Multi-asset support
Cloud deployment options

Getting Started¶

Setup Environment: Install Anaconda, PostgreSQL, Redis
Clone Repository: Get the codebase
Install Dependencies: Create conda environment
Configure: Set up API keys and database
Run Services: Start all microservices
Access Dashboard: Open web interface
Deploy Strategy: Configure and start trading

This architecture provides a solid foundation for a production-grade trading system that can scale with your needs while maintaining simplicity for local deployment.