Discovering Universal Technical Indicators with Alphaevolve

This paper reports an unexpected pattern that appeared repeatedly during evolutionary search over technical indicator architectures. Across unrelated markets (crypto, FX, index futures, equities), the search process converged toward similar structural motifs.

These motifs included multi-scale momentum, entropy-based filtering, volatility-adaptive scaling, and regime gating. They reappeared independently across runs, suggesting that certain indicator structures may function as “market invariants” under diverse microstructures.

The system does not use prior knowledge of finance; it only evaluates candidate architectures by out-of-sample performance and stability. The repeated convergence raises questions about whether modern markets impose structural constraints that shape successful technical signals.

From a market microstructure perspective, the repeated emergence of similar indicator components was the most interesting outcome.

Across markets with very different behavior — crypto (jump-prone), FX (mean-reverting), equity indices (regime-switching) — the evolved indicators tended to modulate position sizing based on: • local entropy (as a proxy for noise/chaos) • short + medium horizon trend consistency • volatility bursts

This is consistent with the idea that markets penalize signals most during entropy spikes, regardless of asset class.

Would be interested in perspectives from people studying microstructure-aware signal generation.