| ---
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| license: mit
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| tags: [time-series, market-microstructure, randomness, econophysics, honest-odds]
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| ---
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| # No-Edge Detector
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| Part of **[Nexus — The Honest Odds Project](https://nexusfinancial.sbs)**.
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| Given a price series, this model classifies it as:
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| - **memoryless_no_edge** — statistically indistinguishable from a random walk (e.g. Deriv synthetic indices);
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| - **mean_reverting** — shows mean-reversion structure (e.g. real-underlying baskets);
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| - **trending** — shows momentum/trend structure.
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| It detects **statistical structure, not profit.** Structure is *necessary but not sufficient* for an edge —
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| it still has to survive spread/payout. On Deriv synthetics there is no structure at all; on real-underlying
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| baskets there is mean-reversion, but the payout eats it. **This is not financial advice and predicts no prices.**
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| ## Features (all causal)
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| acf1, acf2, acf3, acf4, acf5, absacf1, absacf2, absacf3, vr2, vr4, vr8, hurst, runs_z, er — autocorrelation (returns + |returns|), variance ratios, Hurst exponent,
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| runs-test z, efficiency ratio; computed on a window of 256 points.
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| ## Metrics
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| Held-out accuracy: **0.940** (3-class). See `metrics.json`.
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| ## Use
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| ```python
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| import numpy as np, common as C
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| from inference import predict
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| probs, labels = predict("no-edge-detector", C.features_from_prices(my_price_series))
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| ```
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| safetensors weights + numpy inference (no torch). License: MIT.
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