#AIBT The trading strategy of AIBT primarily revolves around its flagship AI quantitative trading strategy "Aladdin", which integrates various advanced technologies aimed at achieving efficient and low-risk trading. The following are the core characteristics of its trading strategy:
Multimodal Decision Engine
Real-time analysis of on-chain data, social sentiment, macroeconomic indicators, and other multidimensional information to build comprehensive market insights and provide data support for strategies. Deep Reinforcement Learning (DRL) Architecture
Dynamically optimizing trading paths through reinforcement learning algorithms, automatically adapting to market fluctuations, such as switching to hedging mode or arbitrage strategies during extreme market conditions (like a Bitcoin crash) to maintain stable returns. Emotional immune mechanism
Effectively avoid emotional trading actions (such as chasing highs and cutting losses), ensuring that trading decisions are based on objective data rather than subjective judgment. Smart Routing System
Dynamically optimize trading paths, reduce slippage loss, enhance trading efficiency through cross-chain liquidity pool scheduling and gas fee forecasting. Risk Control Matrix
The multi-layer risk control system integrates delta-neutral protection for perpetual contracts, on-chain insurance pools, AI circuit breaker mechanisms, etc., monitoring market risks in real-time and automatically intercepting abnormal trades (such as sandwich attacks), reducing the liquidation rate by more than 70% compared to traditional platforms.
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#AIBT The trading strategy of AIBT primarily revolves around its flagship AI quantitative trading strategy "Aladdin", which integrates various advanced technologies aimed at achieving efficient and low-risk trading. The following are the core characteristics of its trading strategy:
Multimodal Decision Engine
Real-time analysis of on-chain data, social sentiment, macroeconomic indicators, and other multidimensional information to build comprehensive market insights and provide data support for strategies.
Deep Reinforcement Learning (DRL) Architecture
Dynamically optimizing trading paths through reinforcement learning algorithms, automatically adapting to market fluctuations, such as switching to hedging mode or arbitrage strategies during extreme market conditions (like a Bitcoin crash) to maintain stable returns.
Emotional immune mechanism
Effectively avoid emotional trading actions (such as chasing highs and cutting losses), ensuring that trading decisions are based on objective data rather than subjective judgment.
Smart Routing System
Dynamically optimize trading paths, reduce slippage loss, enhance trading efficiency through cross-chain liquidity pool scheduling and gas fee forecasting.
Risk Control Matrix
The multi-layer risk control system integrates delta-neutral protection for perpetual contracts, on-chain insurance pools, AI circuit breaker mechanisms, etc., monitoring market risks in real-time and automatically intercepting abnormal trades (such as sandwich attacks), reducing the liquidation rate by more than 70% compared to traditional platforms.