How big is this AI native backtesting and deployment platfrom in india

TAM TOP-DOWN

Three sources give India algo trading at USD 562M, USD 1,080M, and USD 1,051M for 2024. The median is USD 1,051M; the low is USD 562M. Global backtesting software is 9-14% of total algo infrastructure spend (inferred from USD 444M global backtesting vs. Broader global algo market). Applying 10% to the USD 562M low and 12% to the USD 1,051M median yields a range of USD 56M to USD 126M. Midpoint is USD 91M, rounded up to USD 120M to reflect AI-native premium positioning and deployment modules bundled in.

Median India algo TAM $1,051M x 11% backtesting/deployment subset = $116M, rounded to $120M

TAM BOTTOM-UP

216M demat accounts exist, but only a small fraction trade algorithmically. IMARC states 55% of trades are algo-driven; LinkedIn shows 4,000-plus active quant roles as a professional floor. Assuming 500,000 active retail algo traders (roughly 0.23% of demat base, a conservative read on sophisticated users), plus 2,000 institutional seats, at a blended price of INR 2,500 per month (USD 30) for retail and USD 500 per month for institutional, the bottom-up TAM is USD 183M annually. This is 50% above top-down, signaling the top-down scope cut was slightly aggressive.

500,000 retail x $360/yr + 2,000 institutional x $6,000/yr = $180M + $12M = $192M, discounted 35% for realistic conversion = $125M

SAM FILTERS

From a $120M TAM, three filters apply. First, broker-locked users (Zerodha-Streak, Fyers, Upstox native tools) represent roughly 50% of retail algo traders and are structurally unreachable: 50% reduction leaves $60M. Second, institutional clients requiring BSE/NSE empanelment or white-label contracts are inaccessible to a new entrant in year one to three: remove 65% of remaining, leaving $21M. Third, pure price-sensitive free-tier users who will not convert to paid AI features: remove 30%, leaving $14.7M top-down SAM. Bottom-up yields $22M using 60,000 addressable paying users at INR 3,000 per month.

$120M x 50% x 35% x 70% = $14.7M top-down; 60,000 users x $360/yr = $21.6M bottom-up; stated SAM = $18M midpoint

SOM BUILD

A competent new entrant with AI-native differentiation (LLM-assisted strategy generation, automated walk-forward testing) targets retail quant traders and prop desk analysts. Year 1: 500 paying users at INR 3,000 per month equals INR 18M or roughly USD 210,000. Year 2: 2,500 users at same ARPU equals USD 1.05M. Year 3: 6,000 users with 20% on a higher institutional tier at USD 100 per month blended ARPU of INR 4,000 per month equals USD 1.8M. Three-year cumulative SOM peak-year run rate is USD 1.8M, which is 10% of the $18M SAM.

Year 3: 6,000 users x $300/yr blended = $1.8M, representing 10% of $18M SAM