- Query CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN at runtime
instead of name-based heuristics for shared memory tile sizing
- Cap shmem at 48KB on consumer GPUs (< 164KB hardware max) — A100/H100
with 164KB+ use full opt-in, RTX 3050/4090 use safe 48KB default
- Dynamic C51 atom scaling: <8GB→11, <16GB→21, >=16GB→51 atoms
- Dynamic CUDA stack: 16KB for 11 atoms, 32KB for 21, 64KB for 51
- VRAM-aware GPU experience episodes: 16 episodes on <8GB (was 256)
- Bypass CUDA Graph when shmem > 48KB (direct kernel launch fallback)
- Remove .max(51) on num_atoms_max — use actual configured atom count
- Shared query_max_shmem_bytes() used by both trainer and collector
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The fused DQN training kernel allocates DIST_SIZE(HIDDEN)*NUM_ATOMS
per-thread arrays on CUDA stack. With 51 atoms this needs ~52KB/thread
which exceeds stack limits on RTX 3050 (4GB). Scale atoms dynamically:
<8GB→11, <16GB→21, >=16GB→51 (matches smoke_test_real_data pattern).
Note: experience collector still compiles with atoms_max=51 hardcoded
(separate issue — needs collector kernel dim parameterization).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Final cleanup:
- 61 test files + 5 example files: candle imports replaced
- 8 testing/integration files: migrated to cudarc/ml-core types
- 3 services/trading_service test files: migrated
- Root Cargo.toml: candle-core, candle-nn removed from [workspace.dependencies]
- crates/ml/Cargo.toml: candle-nn dependency removed
- testing/e2e/Cargo.toml: candle-core dependency removed
Zero active candle_core/candle_nn/candle_optimisers code references remain.
Zero candle dependency declarations in any Cargo.toml.
Remaining "candle" strings are exclusively in doc comments.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Update all callers to match the new pure-cudarc APIs introduced by the
hive agent CudaSlice migration. Key changes:
- GpuTrainingGuard::new() now takes Arc<CudaStream>; callers use from_device()
- check_and_accumulate/qvalue_stats/qvalue_divergence take &CudaSlice<f32>
instead of &Tensor; callers convert via tensor_to_cuda_slice_f32()
- accumulate_q_value takes f32 scalar, returns () (no Result)
- GpuReplayBuffer::insert_batch gains batch_size arg, takes CudaSlice params
- signal_adapter functions take &Arc<CudaStream> (cudarc 0.17 Arc requirement)
- Add tensor_to_cuda_slice_u32() and cuda_f32_to_tensor() utility functions
- Replace CudaView usage with owned CudaSlice via tensor_to_cuda_slice_f32()
- Fix CudaStorage.device field access (was method call in older API)
- Fix borrow-after-move in copy_actions_out via scoped DtoD copy
Zero errors, zero warnings across lib + tests + examples + full workspace.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Eliminate all 26 Candle Tensor references from signal_adapter.rs.
Three functions (ppo_to_exposure_scores, signal_to_action_scores,
tft_quantile_to_signal) now take CudaSlice<f32> + CudaStream and
return CudaSlice<f32>, backed by three fused CUDA kernels in
signal_adapter_kernel.cu. Dead code evaluate_supervised_gpu_backtest
removed (zero callers). Added cuda_f32_to_tensor helper to
gpu_action_selector for DtoD copy back to Candle Tensor at API
boundaries (PPO hyperopt adapter, evaluate_baseline example).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: shmem_max_in_dim only included trunk dims (state_dim,
shared_h1, shared_h2) but not head dims (value_h, adv_h). When
hidden_dim_base=32 made the trunk narrow while heads stayed at 128,
the BF16 weight tile for branch output (255×128=32640 BF16 elements)
overflowed the shared memory region (12288 BF16 elements). On H100
the overflow landed in unused-but-mapped hardware shmem (silent
corruption). On RTX 3050 (48KB physical shmem) it hit unmapped
memory → CUDA_ERROR_ILLEGAL_ADDRESS.
Changes:
- gpu_dqn_trainer.rs: shmem_max_in_dim includes value_h/adv_h
- Remove all #[ignore] from smoke tests (feature_coverage,
training_stability, gpu_residency)
- Smoke tests use real .dbn data from test_data/ (hard error if missing)
- Remove synthetic_data() fallback — no fake data in tests
- GPU-direct DtoD training path (train_step_gpu, FusedTrainScalars)
- GPU-native PER priority update kernel (zero CPU readback)
- IQN dual-head integration (gpu_iqn_head.rs)
- BF16 dtype fixes across 6 model adapters
- Hyperopt 30D→31D (iqn_lambda)
- portfolio_transformer: unconditional BF16 (remove dead CPU branches)
- liquid/adapter: all tests use Cuda(0) directly
- Fix pre-existing gpu_kernel_parity_test.rs (stale args)
- Fix pre-existing evaluate_baseline.rs (removed fields)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Remove ALL #[cfg(feature = "cuda")] guards (~400+ occurrences)
- Remove ALL #[cfg_attr(not(feature = "cuda"), ignore)] test annotations (~250)
- Make cuda default feature in 9 ML crates (ml, ml-core, ml-dqn, ml-ppo, etc.)
- Convert nvrtc JIT compilation to precompiled nvcc (searchsorted, prefix_sum)
- Move compile_ptx_for_device() to ml-core for shared access
- Delete dead CPU code: multi_step.rs, self_supervised_pretraining.rs,
training_guard_gpu_tests.rs, CPU PER buffer paths, CPU Q-diagnostics
- Replace unwrap_or(Device::Cpu) with hard errors everywhere
- Remove dead is_cuda() else branches in DQN/PPO/hyperopt trainers
- Change config defaults from "cpu" to "cuda" (rainbow, tlob, pipeline)
- Port IQL value network to GPU kernel (5 CUDA entry points)
- Port HER goal relabeling to GPU kernel (warp-per-sample)
- Wire DSR GPU-to-CPU sync in training loop
- cfg!(feature = "cuda") → true in inference_validator
Zero warnings, zero errors across entire workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Delete every CPU fallback block across 14 files (-286 lines):
- dqn.rs: CPU replay buffer, tensor construction, PER fallback, gradient paths
- hyperopt/adapters/ppo.rs: CPU curiosity modules, trajectory generation
- hyperopt/adapters/dqn.rs: CPU backtest fallback, sync no-op
- trainers/ppo.rs: CPU training error stub
- l2_cache.rs: CPU stub functions (gate callers behind cuda too)
- replay_buffer_type.rs: CPU is_gpu_prioritized fallback
- validation/harness.rs: CPU regime breakdown fallback
- build.rs: cpu_only_build cfg (never referenced)
- evaluate_baseline.rs: CPU gpu_handled fallback
- testing/integration/gpu: CPU test stubs
Zero #[cfg(not(feature = "cuda"))] remains in the codebase.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove ~850 lines of unreachable CPU fallback code from 3 hot-path files:
- hyperopt/dqn.rs: delete 250-line CPU backtest path (GPU eval mandatory)
- evaluate_baseline.rs: delete CPU PPO eval + non-CUDA fallback (147 lines)
- trainers/ppo.rs: delete 6 dead CPU rollout methods (~370 lines),
split train() into dispatcher + #[cfg(feature = "cuda")] train_gpu()
All .to_vec1() GPU hot-path guard violations eliminated.
GPU failures are now hard errors, not silent CPU fallbacks.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Cast input to weight dtype in DQN residual, rmsnorm, noisy_layers
- Set use_gpu=true in QNetworkConfig defaults and all config sites
- Resolve BF16 boundary mismatches in attention, curiosity, branching,
distributional_dueling across ml-dqn
- GPU-resident regime ops with BF16 boundary casts, eliminate .expect() in CUDA paths
- Eliminate all Device::Cpu fallbacks — GPU-only across 10 ML crates
- PPO: cast logits to F32 before softmax, cast batch tensors to training dtype
- Gradient collapse detection for RegimeConditionalDQN
- Wire halt_grad_collapse from CUDA guard kernel to halt training
- Dead neuron detection uses active network VarMap + squeeze factored readback
- Increment gradient_logging_step in GPU PER path
- Gradient collapse warmup guards use original buffer_size
- Cap training steps per epoch + tracing migration
- Replace Tensor::all() with sum_all() for pinned Candle compatibility
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace closure-based evaluate() with evaluate_dqn_graphed() for non-OFI
walk-forward backtest path. Extracts DuelingWeightSet from VarMap (branching
or standard dueling) and runs hand-written warp-cooperative CUDA forward
kernel with CUDA Graph capture — zero Candle dispatch overhead per step.
Key changes:
- GpuBacktestEvaluator::stream() getter for weight extraction on eval stream
- DQNAgentType::is_using_branching() / network_dims() for CUDA kernel config
- Hyperopt evaluate_gpu() non-OFI path: extract_dueling_weights_branching()
→ evaluate_dqn_graphed() (CUDA Graph accelerated)
- OFI path: retains Candle closure for state permutation (gather kernel
layout mismatch — future CUDA permutation kernel)
- 66+ GPU hot-path violations hardened to hard errors across DQN/PPO/supervised
- Stripped all gpu-ok suppression comments
- Proper #[cfg(feature = "cuda")] gating for CUDA-only code paths
77 files, 0 errors, 0 warnings across workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The hyperopt binary was crashing with "trials (5) must be greater than
n_initial (5)" when --trials 0 was passed. Root cause: the bump logic
set trials = n_initial instead of max(5, n_initial + 1).
Fix: both hyperopt_baseline_rl and hyperopt_baseline_supervised now clamp
trials to max(5, n_initial + 1). Also add `when` condition to the
compile-and-train DAG so hyperopt step is skipped when trials=0, and
train-best gracefully handles missing hyperopt results.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When trials=0 (or any value <= n_initial), both RL and supervised
hyperopt binaries now auto-bump to n_initial+1 instead of bailing.
Previously the RL binary bumped to 5 which equalled n_initial=5,
triggering "trials must be greater than n_initial" error. The
supervised binary lacked the bump entirely and just crashed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The GpuBacktestConfig hardcoded max_position=1.0 with a 2× leverage cap,
reducing effective position to 0.2026 contracts on ES at $35K capital.
Training uses max_position_absolute from PSO params (1.0-4.0 contracts)
with no leverage cap — a 5.4× mismatch that makes transaction costs
overwhelm any alpha in walk-forward evaluation (0% win rate, -688% return).
Fix: Pass max_position_absolute through evaluate_gpu() and disable
leverage cap (max_leverage=0) to match training conditions. Same fix
applied to evaluate_baseline.rs via --max-position CLI arg.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All three GPU evaluation functions (DQN, PPO, supervised) computed
market_feature_dim = args.feature_dim - 3, which could be >42 when
OFI is enabled. Since extract_ml_features produces [f64; 42], the
feature vectors have exactly 42 market features. Using a larger dim
caused the gather kernel to place live portfolio at the wrong index.
Hardcode market_feature_dim = 42 to match the actual feature extraction output.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
CUBLAS_WORKSPACE_CONFIG and NVIDIA_TF32_OVERRIDE are set once at
startup before any threads or CUDA work begins. The unsafe block is
correct but triggers -W unsafe-code. Adding #[allow(unsafe_code)] at
the call site silences the warning while keeping the safety comment.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- gradient_utils: Add TensorId-based fallback when Var identity mismatch
causes 0/N vars to match GradStore (Candle Adam clones Arcs). Fallback
computes norm AND clips via insert_id. Throttled warning (1st + every
1000th). 7 unit tests including mismatch-actually-clips.
- monitoring: Track full 45-action factored space (5 exposure × 3 order
× 3 urgency). Fix validate_rewards false alarm on GPU path where single
aggregated mean_reward per epoch gives N=1 → std=0.
- trainer: GPU experience collection routes exposure actions through
route_action() for factored tracking instead of exposure-only counts.
Applied in both per-step and epoch-summary paths.
- train_baseline_rl: Auto-detect VRAM <8GB → disable GPU replay buffer
to prevent OOM on RTX 3050 Ti class GPUs.
- smoke_test_real_data: E2E DQN training test with 6 assertions (epoch
completion, loss decrease, finite losses, Q-value divergence, 45-action
space, finite gradient norms).
Validated: 1642 tests pass (ml=915, ml-core=311, ml-dqn=416), 0 clippy
warnings, baseline RL trains 10 epochs on CUDA with Sharpe +5.45.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Capture the full DQN backtest step loop (gather → forward → DtoD →
env_step × max_len) as a replayable CUDA Graph:
- evaluate_dqn_graphed(): captures on first call via
CudaStream::begin_capture/end_capture, replays cached graph on
subsequent calls. Falls back to evaluate_dqn() on any failure.
- invalidate_dqn_graph(): discards cached graph when weights change.
- SendSyncGraph: newtype wrapper for CudaGraph (single-threaded use).
- Full unrolled capture: each step's step_i32 argument is baked in at
capture time, avoiding GPU-resident step counter complexity.
Eliminates ~5-10μs per kernel launch × 4 kernels × max_len steps
of CUDA driver overhead per evaluation.
evaluate_baseline.rs: added --cuda-graphs CLI flag to opt in.
14 backtest evaluator tests pass, 0 clippy errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Multi-stream pipeline (evaluate() closure path):
- Secondary env_stream with CudaEvent sync allows env_step to overlap
with gather/forward of the next iteration on H100's 132 SMs
Pure-CUDA DQN forward (evaluate_dqn):
- backtest_forward_kernel.cu: warp-cooperative dueling Q forward via NVRTC
- Eliminates candle per-op dispatch overhead, enables future CUDA Graph capture
- evaluate_baseline.rs: auto-detects dueling network and uses pure-CUDA path
Pure-CUDA PPO forward (evaluate_ppo):
- backtest_forward_ppo_kernel.cu: actor MLP → softmax → 45→5 exposure collapse → argmax
- One thread per window, bypasses candle entirely
CUDA supervised signal→action (evaluate_supervised):
- backtest_forward_supervised_kernel.cu: threshold bucketing kernel
- Candle still used for model forward (TFT/Mamba/etc), but argmax is GPU-native
Shared helpers: launch_gather, launch_env_step_on, launch_metrics_and_download
deduplicate code across all four evaluation paths.
914 tests pass, 0 clippy errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Restore 45-action factored space via Branching DQN (Tavakoli 2018),
outputting 11 Q-values (5+3+3) instead of 45. This was reduced to 5
exposure-only actions during debugging and was never intended as permanent.
- Enable use_branching: true by default in DQNConfig and DQNHyperparameters
- Add branching paths to select_action_with_confidence and select_action_inference
- Update agent.rs select_action_factored for branching-aware selection
- Expand CountBonus to per-branch tracking with bonuses_branched()
- Add order_type + urgency distribution tracking in monitoring
- Add DQN_ORDER_ACTIONS=3, DQN_URGENCY_ACTIONS=3, DQN_TOTAL_ACTIONS=45 to CUDA header
- Fix 7 pre-existing clippy doc_markdown errors in regime_conditional.rs
- Fix pre-existing cognitive_complexity in replay_buffer_type.rs (extract helpers)
- Fix flaky GPU test OOM under parallel execution (CPU fallback + test VRAM safety)
- Delete unused flash_attention submodules (block_sparse, causal_masking, etc.)
- Add GPU hot-path guard scripts and ensemble/hyperopt adapter improvements
Tests: ml-dqn 416/0, ml 905/0, clippy 0 errors on both crates
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- evaluate_supervised_fold_gpu(): loads any of 8 supervised models via
UnifiedTrainable, runs GPU backtest with signal threshold mapping
- TFT uses tft_quantile_to_signal() to extract median quantile before
threshold comparison; scalar models use signal_to_action_scores() directly
- create_supervised_model() factory + find_supervised_checkpoint() helper
- Main loop dispatches GPU-first for all supervised models when --gpu-eval
- RefCell wrapper bridges &mut self forward() into Fn(&Tensor) closure
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- evaluate_ppo_fold_gpu(): loads PPO checkpoint, runs GPU backtest with
ppo_to_exposure_scores (45→5 collapse), uses GpuBacktestEvaluator
- PPO main loop: tries GPU path first when --gpu-eval (default), falls
back to CPU if CUDA unavailable or error
- PPOMetrics: backtest_sharpe/backtest_trades fields for GPU walk-forward
- PPOTrainer::run_gpu_backtest(): runs backtest on validation 20% split
- extract_objective(): prefers backtest_fitness when GPU results available,
falls back to -avg_episode_reward on non-CUDA builds
- 2 new tests: backtest fitness priority + few-trades penalty
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Flip --gpu-eval default to true (--no-gpu-eval to opt out)
- Move actions_history scatter-write into env kernel (zero CPU accumulation)
- Remove done-flag periodic download (env kernel handles per-thread)
- Only GPU→CPU transfer is final metrics readback (n_windows × 40 bytes)
- Add spread cost discrepancy warning when GPU path is active
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add --gpu-eval flag that routes DQN fold evaluation through
GpuBacktestEvaluator instead of the per-bar CPU path. The GPU path
uploads the full test fold as a single window, runs the env kernel
loop on-device, and downloads only the final WindowMetrics (10 floats).
Key design choices:
- GPU path uses greedy argmax (not hierarchical softmax); results differ
slightly from CPU path — this is documented and intentional
- Falls back to CPU path on any GPU error (no silent failures)
- Also adds --initial-capital flag needed by GpuBacktestConfig
- Fix pre-existing clippy::wildcard_enum_match_arm in
GpuBacktestEvaluator::new (Device::_ → explicit variants)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- DQN hyperopt adapter: remove static VRAM gate for GPU experience
collector and GPU PER — dynamic scaling handles constraints at
runtime with graceful CPU fallback on init failure
- Remove is_parquet_file branching from hyperopt eval path (all data
loads via DBN pipeline now)
- Update training example CLI args for consistency
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Expand FeatureVector from 40 to 42 dimensions by including ADX(14) at
index 40 and CUSUM direction at index 41 from the existing CPU feature
extraction pipeline. This eliminates proxy-based regime classification
and enables GPU-native regime detection via tensor narrow/comparison ops.
Key changes:
- extraction.rs: wire RegimeADXFeatures + RegimeCUSUMFeatures into
extract_current_features_v2(), output 42 features per bar
- regime_conditional.rs: classify_regime_masks_gpu() creates per-regime
mask tensors entirely on GPU (ADX > 0.25 = trending, |CUSUM| > 0.7 =
volatile, else ranging). Zero CPU roundtrip in training hot path.
- trainer.rs/config.rs: state_dim 43→45 (no OFI), 51→53 (with OFI),
aligned dims unchanged (48/56). GPU batch insertion for all 3 heads.
- CUDA header: MARKET_DIM 40→42
- walk_forward.rs: FEATURE_DIM 40→42
- 42 files updated, all [f64;40]→[f64;42] propagated across workspace
Test results: ml=874/0, ml-dqn=354/0, ml-features=282/0, ml-core=274/0
Real data GPU smoke tests: 7/7 passed (OHLCV + OFI + trade enrichment)
Hyperopt baseline RL: 2 trials completed on local RTX 3050 Ti
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Move UnifiedTrainable trait, TrainingMetrics, CheckpointMetadata, and
checkpoint helpers from ml to ml-core (zero new dependencies — ml-core
already had candle-core + serde_json)
- Wrap Mamba2SSM in Mamba2TrainableAdapter to satisfy orphan rule (trait
in ml-core, type in ml-supervised — all other 9 models already used
wrapper pattern)
- Make Mamba2SSM::validate() pub for cross-crate adapter access
- Delete 5 permanently disabled deployment modules (cfg(any()) — never
compiled): registry, hot_swap, validation, monitoring, endpoints
(-5,842 lines)
- Delete 2 undeclared dead files in training/: dqn_trainer.rs,
transformer_trainer.rs (-138 lines)
- Fix pre-existing compute_loss test shape mismatch in mamba adapter
UnifiedTrainable in ml-core unblocks future trainers/ extraction (17.8K
lines) since model-specific trainers can now depend on ml-core for the
trait without pulling in the full ml monolith.
14 files changed, +128 -6,497 (net -6,369 lines)
Tests: 274 ml-core + 948 ml = 1,222 passed, 0 failed
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add QuestDB ILP sink for training metrics, update Prometheus scrape
configs, and fix network policies for monitoring stack connectivity.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add --mbp10-data-dir and --trades-data-dir CLI args to
hyperopt_baseline_rl binary so hyperopt trials can use real
order book and trade data for VPIN/Kyle's Lambda features.
- DQNTrainer: add mbp10_data_dir/trades_data_dir fields + with_ofi_data_dirs() builder
- DQNHyperparameters: pipe through from trainer instead of hardcoded None
- download-trades-job: fix nodeSelector to ci-compile-cpu (platform pool full)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Expand training-data-pvc from 10Gi → 200Gi (OHLCV + MBP-10 + headroom)
- Add data-sync-job: rclone sync from MinIO → PVC (delta-only, fast repeats)
- Add sync-training-data init container to training job template
(auto-syncs data from MinIO before each training run)
- Add training-job + data-sync-job NetworkPolicies (MinIO, Tempo, Pushgateway)
- Add app.kubernetes.io/part-of: foxhunt to training pod template
- download_baseline: add --parallel N flag for concurrent Databento downloads
- download_baseline: delete local files after MinIO upload (saves scratch space)
- Bump download job scratch volume to 100Gi
Architecture: Databento → MinIO (source of truth) → PVC (local cache).
First sync pulls everything; subsequent runs only sync deltas (seconds).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire 8 OFI features (OFI L1/L5, depth imbalance, VPIN, Kyle's lambda,
bid/ask slopes, trade imbalance) through the DQN training pipeline:
- Add mbp10_data_dir config field to DQNHyperparameters
- Dynamic state_dim: 43 (no OFI) or 51 (with OFI) based on config
- Compute OFI per bar during data loading, store on trainer
- Pass OFI features through regime_features slot in TradingState
- Configurable MBP-10 path with recursive .dbn/.dbn.zst discovery
- Add zstd auto-detection to DbnParser::parse_mbp10_file()
- Add --mbp10-data-dir CLI flag to train_baseline_rl
- Fix hardcoded [f64; 51] → FeatureVector51 ([f64; 40]) across
examples, walk_forward, GPU memory profile, and test fixtures
- Fix stale state_dim=51 in dqn_config_2025() and DQN tests
2747 tests pass, 0 failures.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- download_baseline now reads schema from universe TOML config
(was hardcoded to ohlcv-1m, now supports mbp-10 and other schemas)
- Add --rclone-dest flag for direct upload to MinIO after each download
- Add config/universe-es-mbp10.toml: ES.FUT MBP-10, same date range
- Add infra/k8s/training/download-mbp10-job.yaml: K8s Job to download
ES.FUT MBP-10 data in-cluster and upload to MinIO
Usage:
kubectl apply -f infra/k8s/training/download-mbp10-job.yaml
Prereqs: databento-credentials secret, download_baseline binary in MinIO
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Critical bug: all 3 DQN action selection methods (select_action,
select_action_with_confidence, select_action_inference) used
FactoredAction::from_index() which maps indices 0-4 to exposure_idx=0
(Short100) via division by 9. This is the root cause of action
diversity collapse during both training and production inference.
Fix: ExposureLevel::from_index() + OrderRouter::route_default() in all
DQN paths. Also fixes hyperopt objective thresholds (<10 → <3 for
5-action degenerate detection), stale defaults/comments, integration
test configs.
Files: dqn.rs (3 methods), trainer.rs (validation + select_action),
hyperopt/adapters/dqn.rs (thresholds), dqn_model.rs (comments),
train_baseline_rl.rs (default), reward.rs (comment),
dqn_integration.rs + ensemble_integration.rs (num_actions).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Multi-window backtest: splits validation data into 3 non-overlapping
windows and aggregates with mean(Sharpe) - 0.5*std(Sharpe), penalizing
inconsistency and reducing overfit to a single data segment.
Top-K ensemble: hyperopt now emits top_k_params (top 5 trials) in JSON
output. train_baseline_rl gains --ensemble-top-k flag to train multiple
models per fold from different hyperopt configs, saving checkpoints as
dqn_ensemble_{k}_fold_{n}.safetensors.
Workflow template: adds ensemble-top-k parameter (default 5) and passes
--ensemble-top-k to the train-best step.
2720 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ephemeral Argo workflow pods terminate after training completes, causing
Prometheus to lose all scraped metrics. Add push_to_gateway() to POST
final metrics to the existing pushgateway service so they persist on the
Grafana training dashboard after pod completion.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replace Silverman's bandwidth (h = 1.06σn^(-1/5)) with Scott's rule
(h = 0.7σn^(-1/(d+4))) for tighter kernels in high-D parameter spaces
- Add best-trial injection: always evaluate EI at best known point plus
5 small perturbations (±5%), preventing optimizer from forgetting peaks
- Scale n_candidates dynamically: max(256, 8*n_dims) instead of fixed 100
- Reduce gamma from 0.25 to 0.15 when trials < 50 for tighter exploitation
- Wire model_name through PSO/TPE paths for per-trial Prometheus metrics
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fixes from deep investigation audit (LOW/MEDIUM priority):
1. CVaR penalty: hard cliff (0 or 10) → smooth ramp with gradient signal
for PSO. Formula: min(10, max(0, -cvar-0.05)*200).
2. Clip outliers leakage: data_loading.rs now computes clip bounds from
training portion only (first 80%), then applies to full series.
Log returns and windowed normalize are causal (no leakage).
3. Noisy sigma scheduler: hyperopt now matches conservative() defaults
(enabled, initial=0.8, final=0.4) so hyperopt-found params
generalize to train_best without scheduler mismatch.
4. evaluate_supervised.rs: NormStats fallback from test data (leakage)
replaced with bail! matching evaluate_baseline.rs behavior.
5. Doc comments: stale 27D references updated to 31D (4 locations).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add device_pool to DQN/PPO hyperopt trainers for round-robin GPU assignment
per trial. Binary detects all CUDA devices, scales VRAM budget by GPU count.
Single-GPU: no behavior change (pool of 1).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
NoisyLinear layers have different parameter names (sigma_weight/sigma_bias)
than standard Linear layers. Hardcoding use_noisy_nets=false caused
checkpoint loading to silently fail when the training run used noisy nets,
resulting in empty folds in the eval report.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
