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AI Design• 2025

Algo Trading Platform

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Overview

1. The Problem

Retail traders lack access to sophisticated automated trading infrastructure. Managing multiple broker connections, executing consistent strategies, and maintaining proper risk controls requires significant technical expertise and custom development that most individual traders cannot build themselves.

2. The Solution

A multi-broker automated trading platform that abstracts broker complexity behind a unified interface. Users can define trading strategies through templates while the system handles execution, position management, and risk enforcement. The platform supports both Interactive Brokers (IBKR) and Alpaca with isolated multi-tenant data.

3. Innovation and Features

  • Strategy/Risk Separation Architecture: Trading logic is decoupled from risk parameters, allowing users to adjust risk tolerance via UI without modifying code
  • Template-Based Strategy System: Three template types (Scanner, Indicator, Fixed Instrument) enable rapid strategy development
  • AI-Powered Optimization: 8 specialized AI agents (Strategy Advisor, Risk Manager, Portfolio Manager, etc.) provide governance and guidance
  • Multi-Source Sentiment Analysis: Aggregates data from 3 providers with weighted scoring and confidence metrics
  • Pipeline Visualizer: Interactive debugging dashboard with Mermaid diagrams showing the complete trading lifecycle

    • 4. Libraries and Frameworks

  • Backend: FastAPI, PostgreSQL 15, SQLAlchemy, ib_async, Pydantic, APScheduler
  • Frontend: Jinja2, Tailwind CSS 3.4+, DaisyUI 4.12+, Vanilla JavaScript
  • Infrastructure: Docker, Python 3.11

    • Design Process Summary

    The architecture evolved around core separation principles—keeping strategy logic independent from risk management to enable runtime configurability. A provider pattern was adopted for broker and sentiment integrations, allowing new providers to be added without changing core logic. The multi-tenant design was built from the ground up with JWT authentication and per-user data isolation. Documentation-driven development ensured AI agents could query validation rules via API, creating a 3-layer validation architecture (Backend enforcement → Human documentation → Agent access).

    Tech Stack

    Python
    FastAPI
    PostgreSQL
    SQLAlchemy
    Pydantic
    Docker
    Jinja2
    Tailwind CSS
    DaisyUI
    JavaScript
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