AlphaGrep has entered the mutual fund space with the AlphaGrep Multi Asset Allocation Fund (AGMAAF). The fund uses quantitative models to rebalance investments weekly across equities, debt, and commodities. While the firm highlights strong backtested results, investors should note that simulated performance does not guarantee future returns.
What Happened
AlphaGrep, a quantitative proprietary trading firm, has launched its first mutual fund in India, the AlphaGrep Multi Asset Allocation Fund (AGMAAF). The fund aims to bring the firm’s algorithmic, model-based trading approach to retail investors. Unlike traditional funds that may rely on fund manager discretion, this scheme uses statistical models to manage the portfolio. The fund opened for investment on July 6, 2026.
How The Quantitative Strategy Works
The AGMAAF strategy is built on weekly rebalancing. Instead of a manager making manual calls, a quantitative model determines the asset allocation. The portfolio is diversified across multiple asset classes: equities, fixed income, gold, silver, copper, and crude oil. It also plans to invest in Real Estate Investment Trusts (REITs) and Infrastructure Investment Trusts (InvITs).
The allocation is flexible. Equities and fixed income can each range from 10% to 60%, while commodities can make up 10% to 40% of the portfolio. According to the firm, as of late June, the model had allocated approximately 34% to equity and 21% to commodities, with the remaining portion in fixed income.
The Backtesting Reality Check
The firm has shared backtested data to highlight the model’s potential. CEO Bhautik Ambani noted that the AGMAAF model projected a 14% compound annual growth rate (CAGR) with an annualized volatility of 7.5% and a peak drawdown of 13%. For comparison, the firm cited a static 60% equity allocation over 20 years, which reportedly yielded 11-11.5% CAGR with a 60% peak drawdown.
Investors must distinguish between backtested performance and actual market returns. Backtesting uses historical data to simulate how a strategy would have performed. Real-world market conditions, such as liquidity issues, sudden price gaps, and execution costs, can cause live returns to differ significantly from historical simulations. Past performance—whether simulated or real—is never a guarantee of future outcomes.
Business Ambitions And Distribution
AlphaGrep is aiming for an ambitious Assets Under Management (AUM) target of ₹20,000 to ₹30,000 crore within three to five years. To reach this, the firm plans to partner with mutual fund distributors (MFDs) and national distributors. They also plan to support their partners with an AI-based research tool.
The firm brings experience from managing a Category III Alternative Investment Fund (AIF) and a Portfolio Management Service (PMS). According to the firm, their AIF long-short strategy has yielded 13-13.5% gross returns over four years, and their PMS returned 17% post-fee over three years as of May 2026.
What Investors Should Monitor
For investors, the key monitorable will be how the model performs in live market conditions. Algorithmic strategies often face 'model risk,' where a strategy that worked perfectly in historical simulations may struggle when market dynamics change.
Investors should also track how the fund handles the 'distribution challenge' mentioned by the management. Educating retail investors about complex, model-driven multi-asset products is different from selling traditional schemes. Additionally, prospective investors should look for consistent updates on the fund's actual performance against its benchmark and its ability to manage volatility during market corrections, rather than relying solely on projected backtested data.