5ebebc564a
Plan 3 Task 6b. Portfolio-state tail-append: - PS_REGIME_SHIFT_BAR = 40 (hold_time of first detected regime shift, 0 if none) - PS_STRIDE 40 → 41 - All 6 hardcoded-stride sites migrated in lockstep Detector (experience_kernels.cu): - Adaptive threshold = clamp(0.25 × |clamp(sharpe, -2, 2)|, 0.05, 0.5) - Fires first bar where |regime_now - PS_PLAN_ENTRY_REGIME| > threshold - First-shift-only (short-circuits on non-zero PS_REGIME_SHIFT_BAR) - Uses new ISV_SHARPE_EMA_IDX = 22 macro in state_layout.cuh Consumer (segment_complete block): - bars_late_frac = clamp(bars_late / hold_time, 0, 1) - penalty = shaping × conviction × bars_late_frac × |reward| - All multiplicands except |reward| in [0,1]; max penalty = |reward| - reward -= penalty; rc[5] -= penalty (cancels with B.2/C.4/D.4a at other (i,t) slots; ISV[68] REWARD_BONUS_EMA shows net) - Consumer resets PS_REGIME_SHIFT_BAR after use **Iteration history.** First pass multiplied by ISV[Q_DIR_ABS_REF] (~5–50, an absolute Q-magnitude) AND |reward| — produced penalties 5–50× the reward, destabilising training (smoke: Return swings ±300–900%, Sharpe oscillating wildly). Root cause: Q_DIR_ABS_REF is an absolute magnitude, not a [0,1] coefficient; B.1 uses it as a DENOMINATOR to normalize q_range, not as a multiplier on an already-unbounded signal. Fix: drop q_scale, keep |reward| as the only unbounded factor. Smoke now passes cleanly with fold-2 best Sharpe 117.92 (up from T6a's 100.10). No new ISV slot. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;