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AIM Investment Process

core

AIM takes four key steps that seamlessly combine realtime data collection, LLM-based analytics, and automated trading decisions to continually enhance its feedback loop.

Data Collection

  • Crypto News: Realtime updates on major economic events, industry trends, and project announcements
  • Market Analysis: Exchange price fluctuations, trading volume, order book data, and volatility indicators
  • Previous Decisions: Historical records of past trades and their outcomes for ongoing learning
  • Fear and Greed Index: A measure of market sentiment to gauge crowd psychology
  • Current Investment State: Existing portfolio positions, transaction history, and internal analytics

All these inputs create a comprehensive data environment for subsequent LLM-based evaluations.

Request Investment Options

  • GPT O1, Claude 3.5, and Deepseek R1 each analyze the aggregated data independently.
  • Recommendations can include Buy, Sell, Hold, or other strategic insights.
  • Different models bring diverse perspectives, reducing the likelihood of tunnel vision in making decisions.

Final Decision

  • AIM operates multiple AI-driven portfolio strategies, each optimized for distinct trading objectives (e.g., momentum capture, volatility hedging, arbitrage).
  • Each strategy independently analyzes the LLM-generated signals and makes investment decisions based on its specific mandate.
  • The system then consolidates these distributed decisions into a single, weighted investment directive that reflects the collective portfolio logic.

Trade Execution

  • The unified directive is executed through optimized trade placement across supported exchanges.
  • Each portfolio strategy submits its trade instructions, which are batched and optimized to reduce market impact and maximize capital efficiency.
  • All transactions and their results are logged and analyzed, feeding directly into the Data Collection phase to improve future decision-making and strategy performance.

Continuous Feedback and Growth

By repeating these steps, AIM ensures that previous outcomes constantly refine its models and strategies. As new data accumulates, the system adapts to evolving market conditions, aiming for higher performance and sustained value creation over time.