c90553953c
Closes the remaining SP21 Tier 1 hardcoded-constant items.
T1.2 — enrichment fed real metrics, not placeholders:
- was: extract_eval_trades_from_metrics(_, 60000.0, 0.0, 0.5, ...)
with hardcoded trade_count=60000, total_pnl=0.0, win_rate=0.5
- now: reads from self.last_val_metrics: Option<[f32; 14]> populated
by val backtest pass at metrics.rs:868. Layout [2]=win_rate,
[4]=total_trades, [7]=total_pnl. Cold-start fallback (None)
is (0.0, 0.0, 0.0) — preferable to fabricated 60000-trade
signal that biased E2/gamma/ensemble from epoch 0.
- Per feedback_no_todo_fixme + feedback_no_stubs.
T1.4 — backtracking thresholds signal-driven:
- Three hardcoded thresholds in run_backtracking_epoch_end replaced
with sigma = sqrt(ISV[VAL_SHARPE_VAR_EMA_INDEX=351]) derivatives:
a) Save trigger (improvement_rate > 0.01) → > 0.5σ.
The 0.01 fired on every epoch (any tiny change > 0.01);
0.5σ requires a meaningful move (typical sigma O(1-10)).
b) Plateau-detection frozen check (abs(delta) < 0.01) → < 0.5σ.
The 0.01 ~never fired; 0.5σ correctly identifies stagnation.
c) Route acceptance (>= min_improvement_rate=0.1) → >= 1.5σ.
Stricter than save-trigger as designed.
- BacktrackingState::min_improvement_rate field deleted — replaced
by per-call signal-driven computation. Floor 0.5 covers cold-start
before var_ema bootstraps from sentinel per
pearl_blend_formulas_must_have_permanent_floor.
- Per feedback_isv_for_adaptive_bounds + feedback_adaptive_not_tuned.
Affected files:
- crates/ml/src/trainers/dqn/trainer/training_loop.rs:1510-1530
(T1.2 enrichment) + :7458-7530 (T1.4 sigma + 3 threshold sites)
- crates/ml/src/trainers/dqn/trainer/mod.rs:108,142
(T1.4 min_improvement_rate field deletion)
Verification:
- cargo check -p ml --tests: passes (warnings only)
- cargo test -p ml --lib early_stopping: 8/8 pass
Cumulative SP21 Tier 1 status: T1.1a ✓, T1.1b ✓, T1.2 ✓, T1.4 ✓,
T2.3 ✓ — Tier 1 closed. Tier 2 (check_early_stopping(avg_q_value)
deletion + enrichment.rs constants soup) is next.
Plan reference: docs/plans/2026-05-10-sp21-train-eval-coherence-isv-defrost.md
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;