metadata
license: mit
tags:
- benchmark
- negative-result
- time-series
- binary-options
- honest-odds
NoEdge-Bench — Baseline
Part of Nexus — The Honest Odds Project.
A deliberately honest null result. The best simple model we can train to predict the next tick of a memoryless synthetic series, from strictly-causal features, scores:
- AUC = 0.5031, accuracy = 0.5024 (permutation control AUC = 0.5004).
That is chance level — no better than a coin. This is the whole point: there is no player edge on synthetic indices. The edge is in the payout, not the market.
The look-ahead lesson
An earlier version scored a fake ~0.74 because a centered rolling feature peeked at the future. Fixing it to strictly causal features collapsed it to ~0.50. If a "signal" only works with look-ahead, it is not a signal. This baseline exists so any submitted model can be measured against the coin.
Use
from inference import predict
p, _ = predict("noedge-bench-baseline", X) # p ~ 0.5 regardless
safetensors weights + numpy inference (no torch). Not financial advice. License: MIT.