This study examines the dynamic relationship of Algorithmic traders (ATs) with market quality, using data from the National Stock Exchange (NSE), India. It categorizes ATs into two categories, namely, High-Frequency Traders (HFTs) and Buy-side Algorithmic Traders (BATs) and investigates their differential impact on liquidity and, conversely, the differential impact of liquidity on HFTs’ and BATs’ participation in 1-minute intervals. BATs’ order placement narrows quoted spread, whereas HFTs’ and BATs’ cancellation widens the same. HFTs’ order placement increases price impact but reduces realized spread, whereas HFTs’ cancellation increases realized spread. When quoted spread increases, ATs increase their participation (both order placement and cancellation). When realized spread increases, HFTs’ cancellation decreases in the market. Contrarily, when price impact increases, HFTs’ participation and BATs’ cancellation increase. This study provides new evidence on crowding out among ATs, wherein the order placement of BATs crowds out that of HFTs, but not vice versa.

Furthermore, it shows that within-group and between-group commonalities exist in trading volumes, wherein the within-group commonality is higher for BATs than HFTs. The results also indicate that BATs and HFTs are contrarian traders because an increase in BATs’ (HFTs’) buy-side trades trigger a raise in HFTs’ (BATs’) sell-side trades. Additionally, it examines the differential impact of ATs (both HFTs and BATs) and Non-ATs (NATs) on volatility, and conversely, the differential impact of volatility shocks on traders using jump robust estimates developed by Andersen et al., (2012), namely, MedRV and MinRV. The results indicate that trading by NATs reduce volatility, whereas BATs and HFTs increase the same. Likewise, excessive directional trading by BATs and HFTs increase volatility, whereas the same activity by NATs decreases volatility marginally. Contrarily, all traders increase their trading one hour following a volatility shock, wherein ATs’ activity is the highest. During volatility shocks, BATs take extreme buy or sell positions, whereas HFTs tend to withdraw from the market.