ZIWATECH · Contrarian research · Systematic execution
I’m an independent quantitative researcher focused on digital asset markets. I look for moments when most participants lean the same way, then build systematic strategies to trade against that crowd—with confirmation, explicit rules and controlled risk.
About my work
I study how groups of market participants behave under pressure—and what happens when the majority becomes crowded on one side. My work looks for disciplined opportunities to take the other side without relying on hindsight.
I prefer transparent rules to persuasive stories. Every idea should explain when it acts, how it can fail and how much risk it is allowed to take. If those answers are unclear, the research is not finished.
Research themes
Selected areas of ongoing research. The descriptions focus on process rather than publishing proprietary signals or parameters.
Market intelligence
I built a time-aligned research view that layers price, volatility, positioning and participant profitability. It helps me examine where market pressure is accumulating and how those conditions change through time.
Strategy research
I test whether an unusually one-sided market creates opportunity in the opposite direction. A crowded position alone is not enough: I wait for evidence that the dominant side is stretched, then enter progressively at more favourable prices while keeping exposure and exits predefined.
Execution & risk
I design entries, targets and protective exits to respond to volatility and accumulated exposure. The objective is not more activity—it is consistent decisions when conditions become uncertain.
How I work
Define the market behaviour, expected mechanism and conditions that would prove the idea wrong.
Collect and align data as it would have been known at the time, then inspect quality before modelling.
Separate development from evaluation and challenge the result across periods, parameters and assumptions.
Turn the research into explicit entry, execution and risk rules that can be logged and reviewed.
Compare expectations with observed outcomes and feed what changed into the next research cycle.
04 Point of view
“A good system is not one that predicts everything. It is one that knows exactly what to do when it is wrong.”
Open to thoughtful conversations
I’m interested in quantitative research, market structure, systematic strategy design and the engineering that makes it dependable.
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