According to ClearTax CEO Archit Gupta, individual traders collectively lost ₹74,812 crore in FY24, while proprietary trading desks and foreign portfolio investors (FPIs) together booked over ₹61,000 crore in gross profits. As reported by Mint, the majority of institutional profits came through algorithmic trading strategies. The rapid rise in index options trading has intensified these structural disadvantages, with average daily premium turnover surging from ₹4,359 crore in FY20 to ₹64,881 crore in FY25. In addition to market competition, retail participants also have to bear securities transaction tax (STT), brokerage charges, exchange fees, GST and slab-rate taxation on their net trading profits.
A September 2024 SEBI study found that over 9 out of 10 individual traders in the equity F&O segment continue to incur significant losses, despite more than 75% of loss-making traders continuing to trade in F&O. According to Mint, a July 2025 SEBI study showed that 91% of the 9.6 million individual participants ended up with net losses. Over 75% of individual F&O traders in FY24 had declared an annual income of less than ₹5 lakh, a trend that persisted the following year. As per ClearTax CEO Archit Gupta, most of these losses come from easily observable habits: trading too much, trying to win back losses quickly, taking on positions too big for their account size, holding on to losing trades while selling winners too soon, ignoring trading costs, and trading without stop losses.
As reported by ClearTax, algorithmic systems operate in fractions of a second, place their servers inside exchange premises for faster execution, and pay significantly lower trading costs than retail investors. The biggest source of algorithmic returns comes from market making, where algorithms constantly offer to buy and sell active contracts, pocketing the tiny price difference on every trade. The second major driver is statistical arbitrage, where algorithms look for brief moments when related assets are priced incorrectly, such as slight mismatches between Nifty futures and the actual Nifty index. According to Gupta, these trading programs operate in fractions of a second because their computers are housed in the exact same building as the stock exchange, allowing algorithms to see price changes and place orders before a regular retail trader's screen can even refresh.
According to ClearTax, retail traders can maintain an edge by changing their time horizon to days or weeks where algorithms have less natural advantage, using defined-risk strategies like spreads and iron condors, and controlling trade sizes to risk only 2% to 5% of total capital instead of 20% to 30%. The company advises traders to generally trade less frequently and avoid rapid daily options trading, as high trading costs make rapid manual trading mathematically impossible for retail traders to sustain over time. Although a manual retail trader cannot beat a computer algorithm in speed, trading costs, or data processing, they can instead focus on the specific areas where algorithms are naturally weaker, such as longer-term strategies and fundamental analysis.
