---
license: mit
tags: [time-series, market-microstructure, randomness, econophysics, honest-odds]
---

# No-Edge Detector

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

Given a price series, this model classifies it as:
- **memoryless_no_edge** — statistically indistinguishable from a random walk (e.g. Deriv synthetic indices);
- **mean_reverting** — shows mean-reversion structure (e.g. real-underlying baskets);
- **trending** — shows momentum/trend structure.

It detects **statistical structure, not profit.** Structure is *necessary but not sufficient* for an edge —
it still has to survive spread/payout. On Deriv synthetics there is no structure at all; on real-underlying
baskets there is mean-reversion, but the payout eats it. **This is not financial advice and predicts no prices.**

## Features (all causal)
acf1, acf2, acf3, acf4, acf5, absacf1, absacf2, absacf3, vr2, vr4, vr8, hurst, runs_z, er — autocorrelation (returns + |returns|), variance ratios, Hurst exponent,
runs-test z, efficiency ratio; computed on a window of 256 points.

## Metrics
Held-out accuracy: **0.940** (3-class). See `metrics.json`.

## Use
```python
import numpy as np, common as C
from inference import predict
probs, labels = predict("no-edge-detector", C.features_from_prices(my_price_series))
```
safetensors weights + numpy inference (no torch). License: MIT.