Information Asymmetry Costing Retail Traders Billions, Engineer Working To Close It

Hedge funds and institutional trading desks spend heavily on proprietary algorithms, premium market data, and execution infrastructure designed to extract small advantages at scale. Retail investors, trading their own savings, operate with fewer inputs and less tooling. The imbalance is longstanding and structural, and it has shaped debates about fairness, market quality, and who benefits from modern market microstructure.

Suresh Rajendran, a Bengaluru-based machine learning engineer and product leader, has spent much of his career working on systems that convert large amounts of data into decision support. He is co-founder and chief product officer of Alphashots.AI, a trading analytics platform focused on statistical candlestick pattern matching, a category of technical analysis that attempts to relate repeating price configurations to probabilistic outcomes.

In an interview, Rajendran framed the retail disadvantage as an information asymmetry problem. Institutions can afford faster and richer feeds, internal research, and specialized infrastructure, while individuals often rely on widely available charting tools and delayed data.

The pattern has been studied for decades. Academic and industry research has repeatedly shown that many retail traders underperform over time, even as retail participation has grown. One concept frequently cited in market microstructure is order-flow toxicity, the idea that some counterparties are systematically better informed, an advantage that can influence execution quality and realized returns. Rajendran points to that literature when explaining why he believes the gap is not primarily about trader discipline, but about tooling and access.

“This became personal for me in 2011,” Rajendran said, describing early losses that led him to focus on structural explanations and the mechanics of disadvantage.

Rajendran describes his work through a broader access lens, tools that take specialist capabilities and make them usable by non-specialists. Earlier in his career, he worked across multiple domains including education technology and enterprise software, and he has published applied research related to machine learning in markets.

The core technical claim behind many modern retail analytics products, including Alphashots.AI, is straightforward. Historical price action contains repeating configurations that can be cataloged and tested. The differentiator is how patterns are defined, how they are matched, and how results are presented.

“Human traders look at a chart and see a shape,” Rajendran said. “Our models look at the same chart and see a statistical distribution.”

In practice, machine learning driven pattern systems typically do three things. First, they encode a candle or multi-candle setup into measurable features. Second, they search historical datasets for similar configurations. Third, they estimate conditional outcomes, such as how often price moved up or down by a defined amount within a given time window. Rajendran argues that, whatever one thinks of candlestick analysis as a discipline, machine learning based pattern matching can make the evaluation process more explicit by surfacing sample sizes and outcome frequencies.

That emphasis on showing the work reflects a broader shift in retail fintech away from opaque trade calls and toward research-style tools. Rather than telling users what to buy or sell, these products aim to provide context, what historically happened after similar setups, how often, and under which conditions.

Still, the approach has limits. Critics of pattern-based prediction argue that markets are reflexive. As a behavior becomes widely known and traded, its edge can shrink or invert. Data-mining is another risk. With enough pattern definitions, some will look predictive by chance. Rajendran acknowledged the concern and said the intent is to support hypothesis testing rather than automate trading.

“We are not executing trades on behalf of users,” he said. “We are helping them evaluate whether a specific thesis holds up against historical data.”

Looking ahead, Rajendran said he expects more trading analytics to move toward agentic workflows, systems that can run multi-step research tasks automatically. Examples include scanning watchlists for emerging configurations, tracking pattern performance across changing regimes, and surfacing alerts with supporting historical comparisons. If that trend continues, the retail question may shift from whether individuals can access institutional-grade analysis to how transparent, auditable, and responsibly presented that analysis is.

For Rajendran, the motivation remains focused on narrowing a gap he sees as entrenched in modern markets, the difference between participants who can afford industrial-scale analytics and those who cannot. Whether pattern matching and machine learning based validation meaningfully improves outcomes for typical retail traders is likely to remain contested, but the direction of tooling is clear. More computation, more testing, and more attempts to quantify what used to be a largely qualitative reading of charts.