Aggregating Crypto Trader Frameworks into an IC-Weighted Consensus Signal
Summary
This research prototype turns trading frameworks attributed to 99 crypto traders into a daily Bitcoin consensus signal. It builds market features from price, technical indicators, and macro series, maps those features to trader-specific capability profiles, then aggregates individual directional signals using historical information coefficient (IC) weights. Traders with negative IC may contribute contrarian signals. The system describes threshold-based long and short entries, exits when the signal weakens or reverses, and risk controls including stops, profit targets, and emergency position reduction.
The document presents a design and operating parameters, not evidence of profitability. It characterizes trader profiles and IC weights as mostly static and says the system models decision frameworks rather than current trader opinions. Macro inputs depend on external data availability, and the BTC daily focus makes it unsuitable for high-frequency use. Leverage, slippage, drawdowns, and unvalidated signal stability remain practical concerns; the text recommends extended paper trading before live use.
Key ideas
- The method maps market features to individual trader capability profiles rather than simply counting bullish and bearish opinions.
- Historical IC weights aggregate each trader's signal, with negative-IC contributors potentially used contrarily.
- The feature set combines Bitcoin price indicators with macroeconomic market data.
- Thresholds govern directional entries and exits, alongside stop, target, and emergency-reduction controls.
- Static profiles, external-data dependence, and the absence of profitability evidence limit conclusions about live performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.