Algo trading by retail investors: Trust back-tested data, not hype

Invest a small amount, observe performance, then scale up gradually

The Securities and Exchange Board of India’s (Sebi) new norms for retail participation in algorithmic (algo) trading will take effect from August 1. Investors looking to adopt this automated approach must first assess the pros, cons and risks of this trading method.

 

Mixed views 

Reactions to the new norms are divided. Some believe they will improve transparency and security. “These norms promote accountability and reduce risk for retail investors,” says Rajesh Ganesh, founder and chief executive officer, TripleInt Trading Systems.

 

Others fear increased entry barriers. “The static IP requirement (a stable internet address) is impractical, especially for traders who use basic setups or travel. Compliance with these norms will require additional infrastructure like cloud servers, which will raise costs,” says Ramakrishnan Selvaraj, co-founder, coinhunt.bot.

 

Understanding algos 

Algo trading is the use of pre-programmed computer algos to place and execute trades automatically. “It offers speed. Automation removes emotion from decision-making. Trades only get executed when certain preset conditions are met, which reinforces discipline. Algos can also handle a high volume of trades,” says Ganesh.

 

Investors need to understand the difference between white-box and black-box algos. “In the case of white-box algos, the logic behind trade decisions is visible and understood by users. In the case of black-box algos, the reason why a buy or sell signal was generated is not disclosed,” says Ganesh.

 

White-box models are more transparent, but most providers offer black-box versions to protect their proprietary strategies. Sebi’s norms now permit only registered research analysts to offer black-box algos, thus providing a layer of oversight.

 

Selecting the right algo 

Ganesh suggests reviewing back-tested performance across various market conditions to assess the robustness of a strategy. He adds that drawdowns should ideally not exceed 20–30 per cent. Investors should also check risk-adjusted return ratios like Sharpe and Sortino.

 

Trading frequency also matters. “More trades can lead to higher costs and hence lower net returns,” says Ganesh.

 

Warning signs 

Investors must seek at least one year of back-tested data. “Short-term, cherry-picked performance data may hide significant losses,” says Selvaraj.

 

Algo providers must disclose net returns after factoring in taxes and transaction costs, and must incorporate a realistic trade failure rate in the results they show.

 

“Before selecting an algo, confirm that orders are actually being placed on the exchange through it. This will help you avoid fraudulent platforms,” says Vikas Singhania, chief executive officer, TradeSmart.

 

Mistakes to avoid 

First-time investors invest small amounts without understanding the strategy. When they lose that money, they invest more to try and recover the original amount. “Begin with a small investment, observe performance, then scale up gradually,” says Selvaraj.

 

Singhania warns that overleveraging can result in massive losses.

 

Many investors do not check back-tested data and rely on an algo’s popularity. “This tendency to follow the herd without doing due diligence often leads to losses,” says Selvaraj.

 

Not monitoring an algo is another common mistake. “Even automated systems require human supervision. Sometimes, technical issues can arise that may require manual intervention,” says Singhania.

 

Selvaraj advises using brokers with reliable application programming interfaces (APIs).

 

Algo trading suits those who favour disciplined, rule-based decisions. “Users need to have a fundamental understanding of how logical conditions work,” says Trivesh D, chief operating officer, Tradejini. He adds that algo trading is capital-intensive, hence investors must have a minimum ~10 lakh, plus some buffer.

 

Trivesh suggests that those who prefer a hands-off approach, get anxious in volatile markets, or are overwhelmed by system setup, back-testing and trade analysis should avoid algo trading.

 

Tips for controlling risk

 

•         Define maximum exposure per trade and total capital allocated to the algo

•         Use a kill switch to halt all trades instantly if required

•         Implement throttling to restrict order volume per second or minute

•         Set stop-losses and circuit breakers at both individual trade and portfolio levels

•         Define a daily loss cap to trigger automatic shutdown after a certain amount of loss