d0c037a3d2
Decisive smoke train-xrkb7 @ ebc7144434: WR=0.4346 with full Phase 3
mechanism + enlarged aux head. Statistically identical to all baselines
(0.4338-0.4346). Hypothesis falsified.
Root cause investigation:
- Aux head's 70% accuracy was mostly majority-class prediction on
imbalanced data (fold 0: 88% down labels)
- Fold 1 with balanced labels: aux accuracy dropped to 52% (near-random)
- Aux head has minimal per-bar discriminative power
Architectural insight: aux head predicts next-bar direction but the
system makes MULTI-BAR TRADE decisions with target_bars / profit_target
/ stop_loss / conviction plan parameters. The decision horizons mismatch.
This commit:
1. Revert mechanism to dormant (W=0, beta=0) - hypothesis falsified
2. Preserve infrastructure (kernels, NaN guards, enlarged aux head)
3. Write vNext spec: trade-outcome aux head with plan_params concat
input + K=3 outputs {Profit, Stop, Timeout} + 12-weight W matrix
vNext reuses ~80% of current infrastructure. ~2-3 days focused work.
See docs/plans/2026-05-14-sp22-h6-vNext-trade-outcome-aux.md.
Cargo check clean.
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;