🟒AI Decision Core Design

Agent Noah decision core design

The AI Decision Core is built on several advanced models that work together to process market data and generate optimized trading decisions:

  • Transformer-based Market State Encoder: Encodes the current state of the market by processing vast amounts of real-time data.

  • LSTM Networks for Temporal Pattern Recognition: Captures long-term dependencies and identifies temporal patterns in price movements.

  • Reinforcement Learning for Position Management: Dynamically adjusts positions based on ongoing market feedback to maximize long-term returns.

  • Bayesian Networks for Risk Assessment: Assesses the probability of various market outcomes, enabling risk-informed decision-making.

  • Graph Neural Networks for Market Structure Analysis: Analyzes complex market structures and relationships between different assets, improving decision accuracy.

Decision Making Process

The AI Decision Core follows a structured process to ensure accurate and timely decision-making:

  • Market State Encoding:

    • Order Book State Embedding: Captures real-time liquidity and order book dynamics.

    • Temporal Pattern Embedding: Identifies recurring patterns in historical price data.

    • Market Structure Embedding: Analyzes broader market context to understand interrelationships between assets.

  • Signal Generation:

    • Multi-Model Consensus Mechanism: Combines outputs from different models to generate a unified trade signal.

    • Confidence Score Computation: Assigns a confidence level to each trade signal, indicating its probability of success.

    • Position Sizing Optimization: Dynamically adjusts trade size based on signal strength and market conditions.

    • Entry/Exit Timing Optimization: Refines the timing of trades for optimal execution and profitability.

  • Execution Planning:

    • Slippage Prediction: Anticipates potential slippage to minimize its impact on trade execution.

    • Order Routing Optimization: Selects the most efficient exchange or liquidity provider for executing orders.

    • Timing Optimization: Determines the best moment to enter or exit a trade for maximum effectiveness.

    • Fee Minimization: Minimizes transaction costs, accounting for exchange fees, slippage, and other costs.

Model Training

Continuous training ensures that the AI remains effective and adaptive to changing market conditions:

  • Adversarial Training for Robustness: Exposes the model to a variety of extreme market scenarios to improve its resilience.

  • Multi-Objective Optimization: Balances multiple goals (e.g., profit, risk, liquidity) to find the best possible trade-off.

  • Online Learning Components: The model updates in real-time based on new data, ensuring it adapts to current market conditions.

  • Regular Model Retraining: Periodic retraining using fresh data ensures the model remains aligned with the latest market trends.

  • Performance-Based Model Selection: Evaluates model performance and selects the best-performing strategies for real-time execution.

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