Retail traders in India are increasingly turning to algorithmic trading as a potential solution to significant financial losses in the derivatives market. Despite accounting for 57% of equity cash trades and 70% of derivatives trades, retail participation in algorithmic trading remains low at 13%, compared to 97% among foreign portfolio investors.
The accessibility of algorithmic trading tools has improved significantly, with APIs and AI-assisted platforms becoming available to retail traders. This shift is driven by the reduced cost of infrastructure and the formalization of retail algo frameworks by NSE in August 2025. The ease of access to these tools is seen as a major factor in the potential rise of retail algo adoption.
AI and automation are addressing several challenges faced by retail traders, such as emotional decision-making and lack of discipline. However, the primary barrier has been the inaccessibility of automation itself, which is now being overcome with the availability of low-code platforms and backtesting tools.
“Three things converged at once. First, infrastructure that used to sit exclusively with institutions and prop desks is now available over an API from any broker.”
Rakesh Pujara, Founder and Managing Partner, Compounding Wealth Advisors LLP
The opportunity for growth in retail algorithmic trading is substantial, given the current disparity between retail and institutional algo usage. With more than 90% of individual F&O traders losing money between FY22 and FY24, automation could provide a more stable trading environment.
Looking ahead, the retail algo share is expected to increase from its current base of 13%, potentially leading to a market where retail flow is predominantly algo-driven. This shift could result in a trading landscape where algorithms are primarily interacting with other algorithms.
Background
Algorithmic trading has long been dominated by institutions, but recent developments have made these tools more accessible to retail traders. The NSE's formalization of retail algo frameworks and the decline in infrastructure costs have contributed to this shift.
Looking ahead, the retail algo share is expected to increase from its current base of 13%, potentially leading to a market where retail flow is predominantly algo-driven. This shift could result in a trading landscape where algorithms are primarily interacting with other algorithms.
