8499b4ee1b
1. Temporal pipeline in training forward (submit_forward_ops_main): - mamba2_step: temporal scan enriches h_s2 with history - compute_predictive_coding_loss: temporal smoothness - apply_regime_dropout: regime-conditioned dropout - launch_isv_temporal_route: per-feature temporal weights - risk_budget_forward: risk budget from h_s2 These were only in the monitoring path (reduce_current_q_stats), never in the actual training forward. The model trained without any temporal context. 2. ISV signal update after each adam step: update_isv_signals() called after mamba2 backward, reads pinned loss/grad_norm/Q-mean and updates the 12-element ISV vector. Was never called during training — ISV signals stayed at zero. 3. Backtest min_hold fixed: eval_min_hold was hardcoded 0 — no hold enforcement in validation. Now uses config.min_hold_bars. 4. Backtest ISV signals: Passes frozen ISV signals from last training step to validation backtest for adaptive hold enforcement. 5. aux_frequency parameter (default 4): IQL, IQN, attention, CQL run every 4th step instead of every step. ~4x faster epochs. graph_forward still runs every step. Co-Authored-By: Claude Opus 4.6 (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;