Financial Algorithmic Trading and Market Liquidity: A Comprehensive Analysis and Trading Strategies

Abstract

The paper seeks to determine how the algorithms in robot trading affect market liquidity usage through the data collection and comparison of studies. Four main trading algorithms: High-Frequency Trading (HFT), Statistical Arbitrage, Market Making as well as Momentum Trading, are chosen and analyzed by quantitative analysis method. The basic evaluation criteria are the indices that measure the profitability (or loss), risk-adjusted performance (quantified by the Sharpe ratio), maximum drawdown and increases in the bid-ask spread of each strategy. The results show there are difference in measureable magnitude of the effectiveness and market impact of every algorithm. Event trading F–is thought of as high trade volume and liquidity provision, usually with greater transaction costs. Statistical arbitrage produces moderate returns that promote price convergence, in effect, small discounts on prices reduce market volatility. It comes out that Market Making turnes out to be the reliable and constant liquidity provider while the bid-ask spread is kept a low with the volatility at the lower levels. The momentum trading method enables us high returns in times of trends but in times of reversals this method also brings us new risks. The comparison allows to identify the plusses and minuses of the algorithmic trading strategies. It is instrumental in making the right option and considering all of the imitative aspects of the process.