Expert view: In an interview with Mint, Shruti Jain, Chief Strategy Officer at Arihant Capital Markets, explains how different algorithmic trading is from traditional trading. She says new traders can start with ready-made strategies built by experienced traders, available on regulated platforms. She, however, emphasises that while the machine can be faster and unbiased, it still can’t be accountable. That stays with the person behind it. Edited excerpts:
How different is algo trading from traditional trading, and has it really become accessible to the average retail trader?
Traditional trading is completely human-driven. As a trader, you need to be glued to the screen and watch markets from 9:15am-3:30pm, analyse trends, decide what to buy or sell and execute trades manually with all the delay and emotion that can impact your outcome.
Algo trading, on the other hand, is completely automated. You still use your own strategy and know-how, but the computer program does the job of watching the market, executing trades in milliseconds when your conditions are met, keeping emotions out of the equation.
So it’s basically a computer program that uses predefined rules, written as code, watches the market and executes the moment its conditions are met, at a speed no human can match.
Now, in India, algo trading was formally introduced in 2008 through Direct Market Access (DMA). But for over a decade, only big institutions and large traders had access to it as it required knowledge of coding and a sophisticated infrastructure.
That’s completely changed today. You can trade with algos without the need to know any kind of algos. ArihantPlus has a no-code algo platform through which you can build, backtest, and deploy a strategy without writing a single line of Python code.
In fact, you also have access to pre-built strategies from experts that you can pick based on your risk level, budget and market view if you don’t have a strategy of your own.
Brokers also provide API access through which you can trade in algos. We provide free APIs to clients that are used to build algos and deploy them in their own environment. However, I believe “accessible” and “easy” aren’t the same thing. SEBI’s April 2026 framework – mandatory Algo IDs, static IP whitelisting, and registered strategies has formalised this space, and access has outpaced understanding.
The tool has become very accessible, but the discipline to use it well is still catching up. That’s exactly why education needs to be part of the conversation.
Without it, we’re seeing a lot of traders jumping on the algo trading bandwagon and burning themselves before they’ve understood what they’re actually running.
At ArihantPlus we recently started AI & Algo Trading conclaves, not just to give traders access to algo trading through our no-code platform, but to walk them through how to use it responsibly and what the real risks are.
If a beginner wants to enter algo trading today, where should they start, and what do they absolutely need to know before putting real money behind an algorithm?
Now it really depends on whether you are an experienced trader who’s new to algo trading, or someone who’s new to trading altogether and wants to start directly with algos. The starting point should be different for each.
If you’re already an experienced trader, you already know how you think about the market and have your own strategy – your entries, your risk, your conviction.
What you’re missing is just the ability to automate it so you don’t have to be constantly watching the screen and execute trades manually. A strategy builder is the right tool here, because you’re not learning to trade; you’re translating what you already know into rules.
We’ve taken this a step further at ArihantPlus, we have AI built into our algo strategy builder, so you can describe your strategy in a chat, and it builds it out for you. You still review it, backtest it, and decide whether to deploy it. The AI removes the coding step; the judgment and strategy are still yours.
Now, if you’re new to both trading and algos, building your own strategy from scratch is a much harder place to start. You don’t yet have a feel for how markets behave, so you have no real way to judge whether a rule you’ve written actually makes sense.
For this group, I’d suggest starting with ready-made strategies built by experienced traders, available on regulated platforms, and using that as a way to learn how a strategy is structured before attempting to build your own. However, it’s important to backtest and forwardtest even a ready-made strategy first and treat the early months as learning, not earning.
What are the biggest risks and common pitfalls retail algo traders should watch out for?
One of the biggest mistakes in algorithmic trading is assuming automation removes risk. It doesn’t. Algo trading just executes your risk faster. Without the right strategy for the right market direction, and without a stop-loss, a position limit, and a capital allocation rule built in, an algo will lose money at the same speed and precision it makes it.
We recently invited expert algo traders to our AI and Algo Trading Conclave, and two themes that were common from all their talks were – one of the biggest mistakes traders make when using algos is “overfitting”.
Essentially, building a strategy that looks perfect because it’s been tuned to fit exactly what already happened, not because it has a real, repeatable edge. It performs beautifully in backtest and falls apart the moment markets behave even slightly differently.
The second mistake is “confirmation bias masquerading as research.”
Most Indian traders approach backtesting with the mindset of “let me show that my strategy works” instead of “let me try to break my strategy,” and they inevitably fall into destructive testing habits.
They need to use backtesting to find the exact conditions where the strategy fails or suffers large drawdowns, so they don’t lose money when it goes live. One last one I’d like to highlight comes from the data that makes it hardest to ignore: SEBI’s FY26 study found that 87.7% of individual equity-derivatives traders lost money, with aggregate losses of about ₹91,685 crore.
Now, nearly 92% of these losses came from option trading, whereas roughly 99% of the profits earned by proprietary desks and FPIs came from algorithmic trading. That gap isn’t about who had access to an algo.
It’s about process, risk management, and understanding why a strategy should work, versus chasing a signal because it’s automated and feels objective.
What is the future of retail trading with AI and algo? How is AI helping automate the algo strategy with prompts and MCP?
AI is already weaving into algo-trading, and this shift will make algo trading even easier and more accessible for retail traders. Currently, there are no-code platforms where you use a rule-based form to create your strategy.
The next thing is, AI models take a strategy described in plain language and turn it into something testable and executable.
“Alert me if my portfolio’s sector concentration crosses 20%” or “backtest a mean-reversion strategy on Nifty” are becoming valid ways to interact with markets, not just chatbot prompts.
The piece making this genuinely useful, rather than just another chatbot, is something called MCP, Model Context Protocol. It’s an open standard that lets an AI assistant securely connect to your actual brokerage account and data, instead of just answering in a vacuum with no idea what you hold.
Several Indian brokers have already rolled out versions of this over the past year, so this isn’t a one-off experiment. It’s getting real, and it brings in unthinkable possibilities for traders.
Where this goes next: strategy creation gets democratised. You won’t need to be a programmer to build one. But I’d be careful not to conflate that with democratising edge.
AI can help you build and test a strategy faster. It still can’t tell you whether the underlying idea deserves your capital. So you still need to use your own research and judgement to have the edge. If making money with AI and algo was that simple, everyone would leave their jobs and jump into it.
If an algorithm can execute trades faster and without emotional bias, why should a trader retain any human intervention at all? Where does human judgment still matter?
Speed and getting rid of emotional bias are the real advantages of algo trading, but they live entirely at the execution layer. They answer – “how fast can this be done,” not “should this be done at all.”
An algorithm optimises against historical data. It has no way of knowing when the world underneath that data has changed – a regulatory shift, geopolitical crisis impacting the sector and industry, a macro shock, a company-specific event that’s never happened before and therefore can’t be backtested.
Recognising that a strategy’s original thesis has broken, and having the discipline to pull the plug, is a human call every time.
Also, planning the size of the trade and your exit, depending on how much risk you can actually absorb, not just what a formula says you can, is also fundamentally personal, not statistical.
And frankly, SEBI’s own regulatory design reflects this: brokers are made the principal, accountable party for every algorithmic order precisely because the responsibility doesn’t disappear just because the execution is automated. The machine can be faster and unbiased. It still can’t be accountable. That stays with the person behind it.
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Disclaimer: This article is for educational purposes only and does not constitute investment advice. The views and recommendations expressed are those of the expert, not Mint. We advise investors to consult with certified experts before making any investment decisions, as market conditions can change rapidly and circumstances may vary.
