🟢AI Decision Core Design
Last updated
Last updated
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.
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.
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.
