ATOM OKX 4H Partial-Information Kalman Drift Filter: Trade the Filtered Ornstein-Uhlenbeck Trend with Log-Optimal (Half-Kelly) Sizing Set at Entry, Flat When the Filtered Drift Is Inside One Posterior Std, Long-Short (ATOMUSDT.OKX 4H, pure OHLCV)

Distilled from Bel Hadj Ayed et al., 'Performance analysis of the optimal strategy under partial information' (arXiv:1510.03596, 2015). The paper models log price as dS/S = mu_t dt + sigma dW, with an UNOBSERVABLE drift mu_t following an Ornstein-Uhlenbeck process d mu = -kappa mu dt + sigma_mu dB. Under partial information the investor only sees prices, so the optimal log-utility (Kelly/Merton) strategy holds the fraction f_t = mu_hat_t / sigma^2 of wealth, where mu_hat_t is the Kalman-Bucy filter estimate of the drift. The abstract states that the strategy exists and that its asymptotic Sharpe depends only on the signal-to-noise ratio sigma_mu/sigma and on kappa. It does NOT give numbers, and it does not say whether that Sharpe beats trading costs. That is what this hypothesis tests. Mechanism in one sentence, with instrument and timeframe removed: 'estimate a hidden mean-reverting drift with a steady-state Kalman filter on log returns and hold a position of sign and size mu_hat/sigma^2, flat when the estimate is not distinguishable from zero.' Honest classification: the steady-state Kalman-Bucy filter for an OU drift is mathematically an exponentially weighted return average with decay rate lambda = sqrt(kappa^2 + (sigma_mu/sigma)^2). So this IS a time-series-momentum variant. What is new is where the lookback comes from: the decay is derived from the model (kappa and SNR) rather than tuned. The position is Kelly-scaled by the inverse of variance rather than fixed. And the flat band is the filter's own posterior std rather than a hand-fitted threshold. INSTRUMENT AND VENUE SELECTION, stated as selection choices (L122): OKX is at 0.3% of the corpus, against a >=5% target. Binance USD-M is at 67%, so it is excluded. BTC and ETH are excluded because of corpus concentration, and ETH already has an OKX slot in the pipeline. ATOMUSDT.OKX was picked from the handful of OKX USDT swaps with 4H history back to 2020-05, about 6.4 years or ~14,000 bars, checked in the catalog on 2026-09-26. Many OKX symbols (SOL, XRP, LTC) currently have only ~2 days of 4H data. The mechanism is pure-price, so per L160 there is no supplementary feed on OKX. The 4H timeframe was chosen for sample size: daily bars give too few flips (L128), and hourly bars put the filter's sign flips inside fee range. DEVIATIONS FROM THE PAPER, declared: (1) The paper rebalances continuously to f_t. We size ONCE at entry to f_t and hold that notional until exit, because continuous rebalancing on a 0.10% round-trip venue is the fee graveyard (L150). (2) We use half-Kelly with a 1.5x equity cap, because full Kelly with an estimated drift is known to overbet. (3) We add a flat band of |mu_hat| < 1.0 * sqrt(P_ss) (the posterior std of the filter), because the paper's strategy is never flat (L150). WHAT THE FULL PAPER MUST CONFIRM: (a) the closed-form asymptotic Sharpe under partial information as a function of SNR and kappa. At our pre-set SNR (below), this Sharpe should be computed before coding. If it is below ~0.5 gross, the hypothesis should be withdrawn. (b) Whether the paper's formulas assume known kappa and sigma_mu. We fix them a priori rather than estimating them, and we do not optimize them in walk-forward beyond +-20% sensitivity. IMPLEMENTATION NOTE (recent verification deaths were sandbox timeouts): every update is recursive and O(1) per bar. That covers the EWMA variance, the steady-state gain computed once in __init__, and one scalar filter update per bar. There are no history rescans, and no lists sorted or rebuilt inside on_bar.