Distilling Crypto Trader Frameworks into a Weighted Consensus Signal
Summary
The document describes a prototype that turns crypto traders’ stated methods into a computable consensus process. It first converts BTC daily market data and macro inputs into structured states, including trend, momentum, volatility, recent price ranges, dollar strength, and equity risk appetite. It then applies rule-based capability factors for patterns and frameworks such as trend pullbacks, volatility squeeze breakouts, and macro conditions, mapping triggered factors to trader profiles and aggregating their judgments into a weighted signal.
The examples clarify how human trading descriptions can be represented as explicit conditions, but they are illustrative rules rather than validated predictors. The implementation uses daily BTC bars, static trader profiles and information coefficients, and does not yet adapt those weights online. The article presents no evidence of stable returns; it frames the work as an early research prototype and identifies future recalibration as an open problem.
Key ideas
- Market data and macro context are translated into structured state variables before signal decisions are made.
- Rule-based capability factors encode how different trading frameworks respond to market states.
- Trader profiles link those capabilities to individual judgments, which are aggregated into a weighted consensus.
- The prototype uses daily BTC data and static trader weights, limiting its frequency and adaptability.
- The document describes a research pipeline and does not establish profitability.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.