Amin is a data scientist by background and previously spent three years at Palantir. He started EarningsWatcher as a newsletter focused on making earnings-volatility research more accessible to individual options traders. The project grew organically into a platform and community; EarningsWatcher has since raised funding to expand the product and team.
In a March 2026 interview with All Day X-Ray (Substack), he described the through-line from Palantir to earnings research: alpha lives in connections between data, not in isolated numbers — implied moves, IV dynamics, historical distributions, and risk/reward need one clear view.
“My lightbulb moment was realizing that if you integrate them into one clear view, you stop guessing and start structuring trades like an operator, not a gambler.”
From Palantir to EarningsWatcher
At Palantir, Amin worked on integrating complex datasets so decision-makers could see the full picture. As a Palantir options holder he went deeper into options trading; earnings stood out because data, probabilities, and statistical distributions collide in a structured way — while many retail traders still treat the event like a coin flip on direction.
The first “needle” he looks for on a busy earnings calendar is the gap between the options-implied move and how the stock has actually moved historically — then how often and under what conditions that gap shows up. That framing is educational context for research, not a claim that any gap is a trade.
How he thinks about earnings volatility
From a quantitative perspective, earnings are less about guessing up or down and more about whether the move exceeds what is already priced in. The market embeds a distribution into implied volatility; comparing implied versus realized behavior is the core lens behind EarningsWatcher’s public education and product tools.
One structural pattern he emphasizes: into earnings, implied volatility tends to expand, then reset after the print — a supply-and-demand cycle that shows up across bull and bear regimes even as narratives change. Educational pages such as IV rush and IV crush explain that cycle for readers.
Community-built product ideas
Several platform tools came from watching how members already trade. A clear example is IV Rush: members were riding IV expansion into earnings and exiting before the release, but lacked structured data to model it. Studying that workflow led to the IV Rush Radar — turning a community tactic into a data-backed research workflow inside the app.
The harder product challenge, in Amin’s words, was translation: institutional-style models are dense; the work is surfacing the few metrics that drive a research decision without requiring a quant PhD.
Claims about platform methodology and public data studies are explained in the EarningsWatcher methodology. Educational pages distinguish historical observations from forecasts and include the limitations of the underlying measures.
Beyond the trades
Outside markets, Amin decompresses with music — guitar and drums — and has talked about collaborations with education communities such as AITradingCoach and The Trading Syndicate. Longer-form answers on product philosophy and the Palantir years are in the Beyond The Trades interview.
Expertise and role
Amin’s work focuses on earnings-options research: how options price scheduled uncertainty, how historical moves compare with implied moves, and how volatility behavior can affect an options position. He leads the platform’s research direction and contributes to educational material published in the EarningsWatcher wiki.
That experience does not make any outcome predictable. Earnings events can produce large, discontinuous moves; options involve material risk, and past observations do not guarantee future results.
Explore the research
- Read the methodology for definitions of peak moves, implied moves, beat rates, dates, and timestamps.
- Explore the public earnings expected-moves study for an example of historical implied-move comparisons.
- Open the interactive earnings calendar for this week’s expected-moves chart.
- Return to the EarningsWatcher wiki for educational guides and calculators.
Disclosure and educational purpose
EarningsWatcher publishes research and educational tools for people studying options around earnings. Content is not investment, legal, tax, or financial advice; it is not a recommendation to buy or sell a security or options strategy. Members should independently verify information, consider their own circumstances, and consult a qualified professional where appropriate.