Amin trained as an engineer at École Centrale Paris and began his career in ad-tech, building algorithms for real-time bidding. From 2017 to 2020 he worked at Palantir as a deployment strategist on the commercial side — embedded with client teams, sitting between the technical and the business problem, turning operational workflows into working systems inside Foundry.
He started EarningsWatcher as a newsletter for individual options traders who wanted to study earnings volatility properly rather than guess at it. It grew by word of mouth into a platform and a community, now used by more than a thousand traders. He is based in Paris and leads the company’s research and product direction.
“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
The lesson he took from Palantir was about connection rather than collection: value appears when siloed datasets are integrated and layered so a decision-maker can see the whole picture at once. As he put it in a 2026 interview, “alpha lives in the connections — not in isolated data points.” Earnings research has the same shape: implied moves, IV dynamics, historical distributions and risk/reward mean little apart, and quite a lot together.
Holding Palantir stock is what pulled him into options in the first place. Earnings stood out because it is one of the few moments where a date, a probability and a priced distribution collide in a structured way — and where many retail traders still treat the event as 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.
In his own words
- Beyond The Trades: interview with EarningsWatcher CEO Amin Khribi — the Palantir years, why he left, and the thinking behind the platform (All Day X-Ray, March 2026).
- I worked at Palantir for 3 years — here’s what people get wrong — his own account of the deployment-strategist role and how Palantir actually delivers.
- Case study: modelling earnings volatility for retail options traders — the options and equities data the platform’s research is built on (Massive, 2026).
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.