73fd49f4b5
Smoke (with mag_concat off-by-one fixed and fxcache regenerated):
- training MAG_DIST: Q=0.287 H=0.301 F=0.412 (healthy diversity)
- INTENT at eval: Q=0.045 H=0.045 F=0.911 (network learned Full)
- realised at eval: Q=0.592 H=0.408 F=0.000 (Full silenced)
Even though q_f >> q_h >> q_q (network strongly prefers Full), eval
realised Full = 0.000. Position-sizing pipeline composes four
multiplicative shrinks at experience_kernels.cu:1615-1672:
effective_max_pos = max_position
× cvar_scale (line 1621)
× q_gap_conviction (line 1628, clamped [0.25,1])
× kelly_f (line 1649, only if >20 trades)
× var_scale (line 1671, = 1/(1+sqrt(var_q)))
Each term well-bounded individually but composing them silences the
policy at validation when var_q persists high (var_q ≈ 30 → var_scale
≈ 0.15; even with conviction = 1.0, kelly_f = 1.0, the compound 0.15
falls in the Quarter bucket [0, 0.375)). Network INTENT reaches the
target_position kernel correctly; var_scale strips it back out.
Same family as val-Flat-collapse (warm-branch Kelly = 0 from balanced
priors) — fix is the same pearl
(`pearl_blend_formulas_must_have_permanent_floor.md`):
var_scale = max(var_scale, q_gap_conviction)
The q_gap-derived conviction is already an adaptive ISV-coupled signal
(line 1628), already clamped to [0.25, 1.0]. Using it as a permanent
floor on var_scale lets the policy's magnitude intent reach the
realised position when conviction is high, regardless of variance.
No tuned constants — feedback_adaptive_not_tuned + feedback_isv_for_adaptive_bounds
both honoured by reusing the existing adaptive bound rather than
introducing a new threshold.
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;