AI Model Framework

AIM’s trading system predominantly leverages Large Language Models (LLMs) augmented by a continuous feedback mechanism:

LLM Ensemble Approach

• Multiple LLMs: AIM blends insights from top tier models such as GPT, Claude, and others, pooling their strengths to form comprehensive market perspectives.
• Dynamic Model Selection: Depending on realtime market conditions and data availability, AIM can weight certain models more heavily to optimize creating decisions.

Investment Opinion Aggregation

• Diverse Model Outputs: Each LLM provides an independent set of predictions and trading signals based on its own training and prompts.
• Consensus Mechanism: AIM integrates these signals via a consensus algorithm. If a majority of models indicate a bullish trend, for example, AIM adjusts trading actions accordingly.

Continuous Self-Feedback Loop

• Performance Tracking: The system monitors how each LLM driven insight performs over various market conditions.
• Iterative Fine-Tuning: Models are retrained or revaluated based on historical outcomes, ensuring that the overall framework remains adaptive.

Execution Layer

• Order Optimization: Trades are split into smaller orders when necessary to minimize slippage and market impact.
• High Frequency Execution: AIM’s advanced High Frequency Trading interface processes trades within milliseconds, capitalizing on market opportunities before they vanish.