AINFT: TESTING AI TRADING AGENTS WITH TRANSPARENCY AND ACCOUNTABILITY Building an AI trading agent is one thing. Proving that it can make decisions under different market conditions while managing risk is another. AINFT is addressing this challenge with an environment designed to stress-test AI trading agents using reproducible BTC and ETH paper markets. The platform focuses on more than just trading returns. It evaluates how agents make decisions, manage exposure and respond to market volatility. Here are some of its key features: ▪️ Reproducible Market Testing: AI agents are tested in BTC and ETH paper-trading environments, making their performance easier to review under defined conditions. ▪️ Locked Trading Rules: Agents operate within predefined parameters, helping ensure that evaluations follow consistent rules. ▪️ Risk-Adjusted Scoring: Performance is assessed using metrics such as returns, maximum drawdown and the Sharpe ratio, rather than looking at profits alone. ▪️ Decision Tracking: The Trace feature makes trading decisions available for review, providing greater visibility into how an agent responds to market conditions. ▪️ Guardrails: Developers can define limits such as maximum drawdown, trading modes and evaluation windows to assess how agents behave within specific risk boundaries. The screenshot also demonstrates a capital-preservation test with an 8% maximum drawdown limit and a 14-day forward evaluation window. Its example results show: ▪️ Return: +6.4% ▪️ Maximum drawdown: -4.1% ▪️ Sharpe ratio: 1.31 ▪️ Evidence score: 84.2 These are displayed test results, not a guarantee of future trading performance. The bigger idea is to make AI trading agent evaluation more transparent, measurable and reproducible. For developers, this creates an environment where trading strategies can be tested, compared and reviewed before being considered for real-world deployment. AINFT is bringing AI development and verifiable evaluation closer together. @justinsuntron @AINFTcom #TRONEcoStar