Basic Logic of the AI Decision Engine
The AI decision engine can be understood as the brain of the entire trading system. Its core task is to transform market data into concrete trading decisions, such as buying, selling, market making, or arbitrage. The decision engine typically combines multiple data sources, including price data, on-chain data, market sentiment, and risk parameters, and then generates trading signals through models or rules.
A complete AI decision-making process usually includes several steps:
- Data Input: Receiving various types of data from the market and on-chain
- Signal Generation: Using models or strategies to determine market direction or trading opportunities
- Risk Assessment: Assessing current position risk and market volatility risk
- Decision Output: Deciding whether to trade, trade direction, and trade size
- Execution Instructions: Sending decisions to the trade execution system
Therefore, AI decision-making is not simply price prediction but a comprehensive decision process that includes risk control and fund management.
Generation and Optimization of Trading Strategies
Trading strategies can be derived from various methods, such as statistical models, machine learning models, arbitrage logic, or market-making strategies. The advantage of AI is its ability to continuously backtest and optimize strategy parameters using historical data, allowing strategies to maintain a certain level of stability in different market environments.
During strategy generation, AI does not necessarily use a single strategy but can run multiple strategies simultaneously, such as trend strategies, mean reversion strategies, arbitrage strategies, etc., and then dynamically adjust the capital allocation for different strategies based on market conditions. This approach can reduce the risk of single-strategy failure.
Strategy optimization typically includes several aspects:
- Parameter optimization, such as moving average periods, stop-loss ratios, etc.
- Strategy switching under different market environments
- Multi-strategy combination and capital allocation
- Backtesting and simulated trading to evaluate strategy stability
Through continuous backtesting and optimization, AI can gradually improve the stability and risk-reward ratio of strategies, making the trading system more mature.
Automated Execution and Contract Interaction
Once a trading strategy has been generated and trading signals outputted, the next step is automated trade execution. In an on-chain trading environment, execution involves not only placing orders but also interacting with smart contracts—for example, swapping on DEXs, providing liquidity, lending, or executing arbitrage trades.
An automated execution system needs to address several issues, such as trade path selection, gas fee control, slippage control, and failed trade handling. For example, when trading on decentralized exchanges, AI needs to choose the optimal trade path to avoid significant slippage due to insufficient liquidity. At the same time, on-chain trading must also consider gas fees—if gas is too high, transaction costs may exceed arbitrage profits.
A complete automated execution system usually needs to have the following features:
- Automated order placement and cancellation
- DEX path selection and price comparison
- Gas fee optimization
- Slippage control and failed trade retries
- Automated interaction with smart contracts (swap, lending, market making, etc.)
The goal of an automated execution system is to ensure that trading strategies can be executed reliably, at low cost, and with high efficiency; otherwise, even correct strategies may fail to generate returns due to execution issues.
