This paper examines algorithmic trading's impact on market quality in emerging derivatives markets. Using high-frequency tick data from four major exchanges (2021-2023), analyze effects on liquidity, price discovery, and volatility through panel regression, fixed effects, and instrumental variable approaches addressing endogeneity. Results show algorithmic trading significantly reduces bid-ask spreads (18.7%) and increases market depth (24.3%) while accelerating price discovery. However, state-dependent effects emerge: volatility increases 31.5% during market stress periods. Cross-sectional analysis reveals liquidity improvements concentrate in highly liquid contracts, while thinly traded derivatives show minimal change. These findings illuminate market microstructure dynamics in emerging financial markets and inform regulatory frameworks balancing innovation promotion with systemic stability. Evidence indicates differential impacts across market conditions and contract liquidity levels, highlighting the nuanced relationship between algorithmic trading proliferation and market quality in emerging derivatives markets.