64bbbe4181
Closes the fix gap left by the failing-test commit. The aggregation kernel body now reads `is_win_per_env[env]` (when non-NULL) in the `tc != 0` branch in place of the broken per-bar `step_ret > 0` predicate; the producer site at `experience_kernels.cu::segment_ complete` writes the segment-level `(segment_return > 0.0f) ? 1 : 0` flag at the same location as `alpha_per_env[i] = alpha`. Atomic changes: - sp20_aggregate_inputs_kernel.cu: when is_win_per_env != NULL, read the per-env i32 flag for wins_count; legacy `sr > 0` predicate preserved as NULL-tolerant fallback for oracle-test scaffolds. - experience_kernels.cu: new `int* is_win_per_env` kernel arg; segment_complete branch writes `(segment_return > 0.0f) ? 1 : 0` at the same site as `alpha_per_env[i] = alpha`. Race-free per-i write (mirrors the alpha producer pattern). NULL-tolerant. - gpu_experience_collector.rs: `is_win_per_env: CudaSlice<i32>` `[alloc_episodes]` field, alloc_zeros at construction, kernel-arg pass at experience_env_step launch site, raw_ptr() pass at sp20_aggregate_inputs launch site. - state_reset_registry.rs: FoldReset entry + assertion test (sp20_is_win_per_env_registered_fold_reset). The every_fold_and_soft_reset_entry_has_dispatch_arm test passing confirms the dispatch arm is present. - training_loop.rs: `"is_win_per_env" => memset_zeros` arm. - docs/dqn-wire-up-audit.md: 2026-05-10 fix-commit entry. Bug pattern for the memory pearl: per-bar mark-to-market step_return is NOT the trade's segment-level win/loss outcome at close bars because tx_cost (deducted via `*cash -= cost` in trade_physics.cuh::execute_trade) plus the per-bar Δprice tick swamp out segment-accumulated unrealized gains. The segment-level realized P&L (`(realized_pnl + unrealized_at_exit - trade_start_pnl) / prev_equity`) is the correct outcome scalar; the producer in segment_complete already had it (used by sp20_compute_event_reward's sign check for alpha), the aggregation kernel just wasn't reading the right thing. Bug latent since SP20 Phase 1.4 / 2.2 landed on 2026-05-09; caught 2026-05-10 by HEALTH_DIAG observation in a multi-seed L40S smoke (alpha_ema populated correctly while wr_ema pinned at 0.0000 across 17 epochs). Verification: - SQLX_OFFLINE=true cargo check -p ml --tests passes - 21 SP20 lib tests pass; 9 state_reset_registry tests pass - The new GPU oracle test wr_ema_uses_segment_pnl_via_is_win_per_env_predicate passes after this commit (would FAIL on prior commit's kernel-body) - The NULL-tolerance pin null_is_win_per_env_falls_back_to_legacy_step_ret_predicate preserves the pre-fix oracle-test contract 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;