Retail algo trading explained: Risks, costs and what investors should check

Algo trading allows investors to execute trades automatically using predefined rules. While no-code platforms and ready-made strategies have made these tools more accessible, their performance depends on the underlying strategy, execution costs and changing market conditions.

Experts say investors should assess not just potential returns but also the possibility of losses, the reliability of back-tested results and the safeguards available to stop automated trades.

Who should consider algo trading?

Algo trading can help traders execute an existing strategy with greater speed and consistency. However, it does not replace the need to understand market behaviour, entry and exit conditions, or the risks involved.

“An algorithm only carries out a set of instructions. If the logic behind those instructions is weak, automation just helps you lose money faster and more consistently,” said Arindam Banerjee, Professor and Program Director, Master of Applied Finance and Wealth Management, SP Jain School of Global Management.

Shruti Jain, Chief Strategy Officer at Arihant Capital Markets, an Indian financial services and stockbroking company, said beginners can explore ready-made strategies, but should treat their initial months as a learning exercise rather than expect them to generate income. The ability to access an algorithm, she noted, does not establish whether its strategy is suitable.

Eshaan Lazarus, Founder and CEO of 021 Trade, an Indian stock-broking and fintech platform, said investors must understand what triggers trades, how much capital is at risk and how to stop the system.

He cited the 2012 Knight Capital incident, when a software deployment problem led to substantial trading losses within 45 minutes, as an example of the operational risks involved.

How reliable are back-tested returns?

Backtests simulate how a strategy would have performed using historical market data. They do not guarantee similar results in live trading.

Investors should examine maximum drawdown, the largest fall from a peak to a subsequent low, and the time taken to recover. They should also check whether the strategy has been tested across rising, falling, sideways and volatile markets.

“Treat it as a claim to be tested, not a result to be trusted,” Banerjee said, adding that investors should look for unrealistic assumptions about execution prices and transaction costs.

Lazarus recommended checking performance on data not used to develop or fine-tune the strategy. Repeatedly adjusting a strategy to fit historical data can make its back-tested returns less reliable as an indicator of future performance. Investors should also ask for live-trading results after costs, where available.

What costs can eat into returns?

The expenses extend beyond brokerage and platform fees to statutory charges, bid-ask spreads, slippage, funding costs and data fees.

Slippage is the difference between the expected price of a trade and its actual execution price. It can increase during volatile markets or when trading less-liquid stocks and options.

Frequent trading can compound these costs, potentially wiping out the small gains on which some strategies depend.

Jain said investors should account for the cumulative impact of transaction expenses rather than focus only on the subscription fee.

Citing a SEBI study, Lazarus said nearly 88% of individual futures and options (F&O) traders lost money in FY26, with transaction costs accounting for about 35% of the losses incurred by those who lost money.

The figures underline why investors should assess expected returns after all trading costs, particularly for strategies involving repeated transactions.

What if market conditions change?

A strategy designed for a range-bound market may struggle when prices begin trending sharply. Similarly, one built around low volatility or a particular level of liquidity may perform differently when those conditions change.

Operational risks can compound the problem. Connectivity failures, broker-server issues, rejected orders and coding errors can disrupt execution or leave positions exposed.

Banerjee recommended establishing a maximum drawdown limit and predefined rules for pausing or stopping a strategy, rather than making those decisions under pressure. Lazarus advised checking whether the platform has a kill switch to halt trading if an algorithm behaves unexpectedly.

Jain also stressed that position sizing, stop-losses and capital limits should be built into the strategy rather than treated as afterthoughts.

What should investors check before starting?

Before deploying capital, investors should establish:

• Strategy logic: What triggers entry and exit, and under what conditions might the strategy fail?
• Potential losses: What is the historical maximum drawdown, and how much capital could be at risk?
• Performance evidence: Are returns based only on backtests, or are live results after costs available?
• Total expenses: Do the calculations account for brokerage, statutory charges, spreads and slippage?
• Risk controls: Can investors set loss limits, monitor orders and stop the algorithm quickly?

Investors should also verify the provider’s credentials and applicable regulatory requirements. Starting with a small allocation and comparing live results with back-tested performance can help assess whether a strategy is working as expected.