---
license: mit
tags: [benchmark, negative-result, time-series, binary-options, honest-odds]
---

# NoEdge-Bench — Baseline

Part of **[Nexus — The Honest Odds Project](https://nexusfinancial.sbs)**.

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
```python
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.