Aaron’s Autonomous Alpha (AAA)

My responsibility

I designed Aaron’s Autonomous Alpha as a cloud-based, model-agnostic, multi-asset trading system for fully autonomous operation and continuous strategy and software improvement. It remains in active development. Development is focused on paper/testnet validation, with live trading disabled.

I define requirements and acceptance criteria and coordinate multiple AI assistants in implementation and review, arranging exchanges of work between them. My responsibility is to connect that work, select tools appropriate to the task, and decide what evidence is needed before relying on a result.

Why paper comes first

AAA is intended eventually to trade real capital. That makes extensive paper testing part of the work: the system has to produce evidence that its decisions, execution, and accounting hold together before I would consider entrusting it with real money. A convincing demonstration or a promising simulated return is not enough.

Live trading remains disabled. Paper results, readiness for live operation, and any decision to authorize it are separate questions; profitability and economic edge have not been established.

Evidence before confidence

I establish what evidence is needed before a consequential action or software change can proceed. The review process distinguishes proposed behavior from implemented work, executed tests, deployment, and observed operation. I challenge reviews that substitute a large test count for coverage of the behavior that matters.

Model agnosticism is a design requirement; portability across models remains to be demonstrated.

Reusable instructions

I authored reusable agent skills and a research plugin for project orientation, release review, research, and evidence audits. These artifacts capture recurring requirements in a form I can apply and improve across development work.

I designed the Toolset Reference Library (TRL) to make tool selection more deliberate, documenting relevant capabilities, access limits, evidence, and alternatives.

I specified Ultimate Auto Mode (UAM), an in-development approach to matching AI resources to the demands of a task. Implementation and test cases exist; the skill is currently disabled, and routine automatic or cloud operation has not been demonstrated.

Learning without endless preparation

I created Anti-Zeno’s Paradox, or AZP, to keep experimental preparation focused on what is necessary to learn responsibly. Its purpose is to distinguish essential safeguards from refinements that can wait for evidence. AAA’s completed work includes design, reusable tools, and source implementation within a larger unfinished system; continuous autonomous trading, profitability, and economic edge are not established outcomes.