Cryptographic Biorhythms

Mon, 28 Mar 2022 00:00:00 +0800

TL;DR

The piece starts from a hand-built, domain-specific sentiment model (previously used to read r/wallstreetbets slang) and asks whether it can establish a usable relationship between crypto sentiment and price.
A naive correlation between one day’s sentiment and the next day’s price turns out to be a trap: it only confirms the sentiment model reflects current conditions, and trading on raw sentiment signals loses money in practice.
Granger causality fails for crypto because sentiment and price move together with no clear first mover - causality runs in both directions.
Phase-space/attractor reconstruction (the Lotka-Volterra predator-prey approach) also fails, because crypto prices are too stochastic and unbounded to define a stable basin of attraction.
A 2018 Nature causal-decomposition method - splitting the data into intrinsic mode functions and checking which components’ removal most changes coherence between the two series - does work: price leads sentiment short-term, but sentiment leads price on a roughly 30-day horizon, and curve crossings anticipate inflection points 15-20 days out.
A simple stop-loss strategy built on this turned a hypothetical $10,000 into $27,140 backtesting on Ethereum over about a year, against $4,162 for buy-and-hold; the piece attributes the underlying cyclicality to “trader stamina” - narrative exhaustion rather than any external periodic trigger - and a postscript records real (if imperfect) live trading results and a later switch to a BERT-based sentiment model.

Key Takeaways

Cryptography on Eigenform Articles

Cryptographic Biorhythms