Hypothesis to signal
Explain why a signal might exist, where it might fail, and what information is available at the time. Then define the rule.
Read the research framework ↗Quantitative discipline. AI-assisted inquiry.
Turn an idea into a testable hypothesis, connect signals with risk, and let the evidence shape your conviction.
A calculated teaching example, not actual market performance.
AI expands the space of questions. Quantitative methods put those questions to the test. We connect both in a research process with clear assumptions and boundaries.
Explain why a signal might exist, where it might fail, and what information is available at the time. Then define the rule.
Read the research framework ↗Use time-based validation, fair benchmarks, and sensitivity checks. Keep failed experiments so the process stays honest.
Explore AI-assisted research ↗Consider drawdown, turnover, trading friction, and position limits together. A result means more when its cost is visible.
Understand the risk framework ↗Switch strategies. Adjust exposure and trading costs. Every parameter changes the calculation, and every result can be exported for review.
Start an experiment ↗signal(t−1) × position(t) − trading cost
Lagged information. Explicit turnover costs.Reproducibility, costs, and validation belong before conviction.
Read the note ↗Give models a clear role, from organizing evidence to finding counterexamples.
Read the note ↗Use drawdown budgets, position limits, and stress scenarios to understand the boundary.
Read the note ↗