算法交易之市场微观结构

Group 1: Market Microstructure and Algorithmic Trading - Algorithmic trading is closely linked to market microstructure, which serves as the core logic for trading strategies and is influenced by the proliferation of algorithmic trading[1] - Key dimensions of market microstructure include liquidity, volatility, investor structure, and regulatory frameworks[2][5] Group 2: Liquidity Metrics - Liquidity is a critical factor affecting trading costs and is assessed through metrics such as TwSpread (relative spread), QuoteSize (market depth), and AccTurnover (transaction amount)[2][12] - TwSpread measures the relative price difference, with lower values indicating better liquidity and lower trading costs[14] - QuoteSize reflects the average number of buy and sell orders in the order book, with larger sizes indicating stronger liquidity[23] Group 3: Volatility Metrics - Volatility is an important parameter in algorithmic trading strategy design, assessed through TickPeriod (the average time between price changes) and ValidVolatility (effective price fluctuation)[3][39] - A smaller TickPeriod indicates higher volatility, while ValidVolatility increases with greater trading activity and price fluctuations[43][51] Group 4: Investor Structure - The structure of investors significantly impacts market microstructure, with metrics like AucVolRatioOpen and AucVolRatioClose indicating the proportion of trading volume during opening and closing auctions[4][62] - Higher auction volume ratios suggest greater participation from institutional investors, which can amplify market impacts during significant events[64] Group 5: Regulatory Impact - Regulatory frameworks play a crucial role in shaping market microstructure and must be accurately implemented in algorithmic trading systems[5][68] - Recent regulations have aimed to reduce transaction costs, such as the reduction of trading fees by 30% to 50% in 2023, which positively affects market activity[69]