AARM - Autonomous Agentic Risk Management

An autonomous prediction-market intelligence system that monitors live prediction markets like polymarket along with ingesting news in order to execute the paper trades that it believes will be best.

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AARM — AI-Assisted Autonomous Reasoning for Markets

Inspiration

We wanted to build a system that could reason about market conditions in real time instead of simply reacting to static indicators. Most retail trading tools either overwhelm users with raw data or reduce complex market behavior into oversimplified signals.

Our inspiration came from the idea of combining:

  • real-time market information,
  • autonomous reasoning,
  • AI-generated analysis,
  • and modular signal pipelines

into one lightweight backend system.

The goal was to create an architecture where AI could act less like a chatbot and more like a reasoning engine for financial analysis.

What We Learned

This project taught us a lot about building production-style AI systems beyond just calling an LLM API.

Some of the biggest lessons included:

FastAPI Backend Architecture

We learned how to structure a scalable backend using:

  • connectors/ for external integrations
  • reasoning/ for AI analysis logic
  • signals/ for event and trade generation
  • shared/ for reusable utilities

This modular design made debugging and iteration significantly easier.

API Integration Challenges

Initially, the system was designed around OpenAI-compatible interfaces, but we later migrated to NVIDIA’s inference APIs. That required us to rethink:

  • authentication handling,
  • environment variable management,
  • request formatting,
  • and model abstraction layers.

We also learned how important environment configuration is in real backend systems.

Local Development & Deployment

We encountered several issues while configuring the development environment:

  • missing Python dependencies
  • environment variable loading failures
  • API import mismatches

Debugging these problems taught us how backend infrastructure actually behaves under the hood.

How We Built It

The project was built using:

  • Python
  • FastAPI
  • Uvicorn
  • NVIDIA AI APIs
  • REST API architecture

The backend exposes endpoints like:

GET /health
GET /trades
GET /analyze/{market_id}

The analysis pipeline works roughly like this:

  1. Market data enters through connectors
  2. Signals are generated and normalized
  3. The reasoning engine analyzes context
  4. NVIDIA-hosted models generate insights
  5. Structured trade or market responses are returned

Conceptually, the reasoning system attempts to estimate:

[ Decision = f(MarketSignals, Context, Risk, Momentum) ]

where multiple dynamic signals contribute to the final analysis.

Challenges We Faced

Environment & Dependency Management

One of the hardest parts was getting the backend environment configured correctly across local development.

We ran into issues like:

ModuleNotFoundError: No module named 'fastapi'

and later:

ModuleNotFoundError: No module named 'openai'

These errors forced us to better understand Python package management, virtual environments, and dependency isolation.

API Migration

Migrating from OpenAI-based tooling to NVIDIA APIs introduced additional complexity because we had to redesign portions of the inference layer while preserving compatibility with the rest of the backend.

Debugging Runtime Issues

Even after the server booted successfully, we still had to verify:

  • endpoint functionality,
  • inference responses,
  • API key loading,
  • and route-level behavior.

Accomplishments We’re Proud Of

  • Successfully built and ran a modular AI backend locally
  • Integrated external AI inference APIs
  • Structured the codebase for scalability
  • Created functional API endpoints with FastAPI
  • Solved authentication, dependency, and runtime issues end-to-end

Most importantly, we transformed a non-working prototype into a functioning AI reasoning system.

What’s Next

Next, we want to:

  • improve reasoning accuracy,
  • add real-time streaming market data,
  • implement persistent storage,
  • deploy the backend publicly,
  • and build a frontend dashboard for visualization.

We also plan to experiment with multi-agent reasoning workflows and risk-adjusted signal generation.

Final Thoughts

AARM became much more than a simple API project. It evolved into an exploration of how AI systems can reason over dynamic environments while maintaining modular, production-oriented architecture.

Building it taught us that the hardest part of AI systems is often not the model itself — it’s engineering the infrastructure around it.

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