b6f6d4fd02
Adds an integration-style test exercising the TARGET_HOLD_PCT_INDEX =
514 controller through the full Phase 1.4 Path C chain (Stats →
Aggregate → EMAs → Controllers) at the two spec §4.2 called-out
operating points:
- Warmup: aux_conf_p50 = 0.05 → target_hold_pct = 0.725
- Confident: aux_conf_p50 = 0.40 → target_hold_pct = 0.20
Both exercise `clamp(0.8 - aux_p50 × 1.5, 0.1, 0.8)` at non-
saturating points. The existing
sp20_controllers_compute_test::target_hold_pct_inverse_relation unit
test covers endpoints (aux_p50 ∈ {0.0, 0.5, 0.2}); this test asserts
the formula holds end-to-end through all 4 kernels.
Engineering aux_conf:
K=2 logits [a, b] ⇒ peak_softmax = sigmoid(b-a) ⇒ aux_conf = peak-0.5
aux_conf=0.05 ⇒ Δlogit = ln(0.55/0.45) ≈ 0.20067
aux_conf=0.40 ⇒ Δlogit = ln(0.90/0.10) ≈ 2.19722
All envs given identical Δlogit so aux_conf_p50 lands exactly.
Per `pearl_tests_must_prove_not_lock_observations`: spec §4.2 names
these operating points as self-stabilizing-property invariants, so
the assertion is invariant-style not observed-value-style.
NO production code changes. TARGET_HOLD_PCT controller was wired in
Phase 1.3 (sp20_controllers_compute_kernel.cu:164-173) atomically
with the other 5 controllers. This commit is test-only, satisfying
Task 3.3's "verify the wire path is complete and add a behavioral
test" requirement.
See plan errata Gap 8 for the plan-vs-reality decision rationale.
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;