These files were part of the Phase 2 cuda_pipeline but were untracked
and not included in previous commits. Required for compilation with
--features ml/cuda.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Phase 3 of the CUDA pipeline: both trainers now take a GPU-first path
for experience collection (128×500 = 64K experiences per kernel launch,
zero CPU-GPU roundtrips per timestep) with automatic CPU fallback.
- DQN: upload features alongside targets, GPU collection branch before
CPU loop, gpu_batch_to_experiences() conversion into replay buffer
- PPO: set_raw_market_data() for CudaSlice upload, GPU collection
branch bypasses collect_rollouts + prepare_training_batch entirely,
gpu_batch_to_trajectory_batch() conversion with in-kernel GAE
- Configurable GPU batch sizes (gpu_n_episodes, gpu_timesteps_per_episode)
and trading params (initial_capital, avg_spread) via hyperparameters
- SAFETY training diagnostics downgraded from warn! to debug!
- 4 new batch index-math validation tests in cuda_pipeline
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 10 shared device functions (gpu_random, leaky_relu, matvec_leaky_relu,
action_to_exposure, action_to_tx_cost, barrier_init/check/reset,
diversity_entropy, curiosity_inference) and shared constants from
dqn_experience_kernel.cu into a new common_device_functions.cuh header.
The DQN kernel now expects the common header to be prepended via NVRTC
source concatenation at compile time. DQN-specific functions
(q_forward_dueling, argmax_q) remain in the kernel file. This enables
the upcoming PPO kernel to reuse the same shared functions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds GpuExperienceCollector field to DQNTrainer, initializes it when
dueling networks and curiosity module are present on CUDA device, and
syncs GPU weight copies after each training epoch.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three fixes from spec review:
1. diversity_entropy returns -0.1 penalty (threshold < 1.0) instead of raw entropy
2. barrier_done included in episode termination (barrier hit ends episode)
3. step_in_episode counter resets properly on mid-loop episode reset
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace raw f64 price/quantity in SmallBatchOptimizer with typed
financial values. Conversion to f64 happens at the SIMD boundary
(_mm256_loadu_pd) only. Delete 3 orphaned dead-code files:
financial_safe.rs, simd_optimizations.rs, tests/financial_tests.rs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Aliased re-exports avoid collision with existing types::Price/Quantity/Money
during migration. Will rename to canonical names in Phase 6 cleanup.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Ratio arithmetic (Add, Sub, Mul, Div) now clamps overflow to
f64::MAX/f64::MIN instead of producing Inf from finite inputs
- Cross-type Price*Quantity→Money and Money/Quantity→Price use
saturating i128→i64 conversion via clamp instead of silent truncation
- Added clamp_finite() and saturating_i128_to_i64() helper functions
- Added 9 edge-case tests: 89/89 passing, clippy clean
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add Serialize/Deserialize to all four financial types
- Ratio::ln returns 0.0 for non-positive inputs (prevents NaN leak)
- Ratio::exp clamps to f64::MAX on overflow (prevents Inf leak)
- Ratio::powi clamps to f64::MAX/MIN on overflow
- Add 4 invariant-preservation tests (80 total)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace all `.to_string().parse::<f32/f64>()` patterns with
`num_traits::ToPrimitive` methods (`.to_f32()`, `.to_f64()`).
Each string roundtrip heap-allocated per conversion — fatal in
DQN hot loop (300K+ bars × epochs). Decimal stays as canonical
financial type; conversions happen at GPU/float boundaries only.
Also fixes blocking_read() in async context (risk_integration.rs).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
P2-C reversal/cash warns fire thousands of times per hyperopt trial
during backtest simulation. Also removes stale Kelly patch file (already
applied).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
These fire every training step when portfolio isn't populated yet —
extremely noisy during hyperopt with parallel trials.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The previous "auto-detect" logic forced CPU which was wrong — GPU is
faster for forward/backward even with parallel trials (DQN/PPO use
<350MB of 24GB VRAM across 7 threads).
Changes:
- Remove --device flag, always require CUDA GPU
- Add DQNTrainer::new_with_device() to share CUDA context across trials
- Propagate hyperopt device to internal DQN trainer (was ignoring it)
- PPO/DQN adapters error on missing GPU instead of silent CPU fallback
- Downgrade batch-size clamping from warn to debug
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DQN/PPO networks are tiny (3 layers × 128 neurons). Running parallel
hyperopt on GPU wastes cores because CUDA context serializes across
threads — 5 trials on L4 only used 2000m of 6000m requested CPU.
Changes:
- Add --device flag to hyperopt_baseline_rl (auto/cpu/cuda)
- Auto mode forces CPU for parallel runs (no CUDA contention)
- CPU mode uses all available cores (no 2-core reserve)
- Add with_device() builder to DQN/PPO hyperopt trainers
- Downgrade "portfolio value <= 0" and GPU utilization warnings
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Portfolio value <= 0 during early exploration is expected behavior,
not a warning. GPU memory utilization advisory is informational.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Enable concurrent trial evaluation for DQN/PPO hyperparameter
optimization via clone-per-particle pattern — each PSO particle
clones the trainer and trains independently, replacing the previous
Arc<Mutex> serialization bottleneck. On L4 (8 vCPU) this yields
~4-5x throughput improvement.
Changes:
- DQNTrainer/PPOTrainer: Clone with Arc<AtomicUsize> trial counter
- DQNTrainer: replace unsafe mutable aliasing with Arc<Mutex> for
best_trial tracking
- ArgminOptimizer: add optimize_parallel() with ParallelObjectiveFunction
and scoped-thread LHS evaluation
- CLI: --parallel 0 (auto-detect CPUs-2), --initial-capital 35000,
--tx-cost-bps 0.1 (IBKR ES all-in)
- CI: both hyperopt jobs use --parallel 0 + IBKR ES cost defaults
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
train_from_parquet and load_training_data_from_parquet had zero
callers — TFT hyperopt uses train_from_bars via DBN data.
Also removes unused NormalizationParams struct from this module.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Feature vectors are [f64; 51] after WAVE 10 (Proxy OFI removal):
[0..5] static, [5..15] known, [15..51] unknown (36 features)
Old code used fv[15..54].min(fv.len()) which silently produced 36
features per step but expected 39 in Array2 shape → ShapeError.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
small_batch_ring::test_performance_characteristics asserts <500ns
latency which is unreliable on shared L4 nodes running parallel jobs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three critical stability fixes for PPO training:
1. Gradient clipping: Added clip_grads() that scales gradients by
max_norm/norm when L2 norm exceeds max_grad_norm (0.5). Previously
the code only logged a warning — gradient norms of 27-171x above
the threshold were applied raw to weights, causing divergence.
Fixed in all 8 locations across PpoTrainer (6) and ContinuousPPO (2).
2. Return normalization: Value loss now normalizes returns to N(0,1)
before computing MSE. Raw cumulative returns (±1000s) caused enormous
value gradients that destabilized the critic network.
3. Hyperopt search space: Tightened value LR upper bound from 1e-3 to
1e-4 (1e-3 is documented unstable), policy LR from 1e-3 to 3e-4.
Also fixed LSTM path where optimizer.step() ran BEFORE gradient norm
check — now clip → step (not step → warn).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The adaptive-strategy crate (~28K lines, 22 source files) was an orphaned
framework with zero external consumers. Its only valuable piece — ensemble
uncertainty quantification — has been ported to ml/src/ensemble/confidence.rs.
Ported: ConfidenceAggregator, UncertaintyQuantifier, ReliabilityScorer,
IntervalCombiner, DisagreementTracker + all config/output types. Removed
gratuitous async from pure-math methods. 6 tests (4 ported + 2 edge cases).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Extend hyperopt infrastructure to support all 10 ML models (DQN, PPO +
8 supervised). Previously only TFT and Mamba2 had hyperopt trainers.
- Add HyperparameterOptimizable impl for Liquid, TGGN, TLOB, KAN, xLSTM, Diffusion
- Create shared_data.rs with common data prep utilities (build_flat_pairs,
build_sequence_pairs, write_trial_result_json)
- Extend hyperopt_baseline_supervised binary to dispatch all 8 models
(individual, "both" for tft+mamba2, "all" for all 8)
- Add CI jobs: 7 train-validate + 10 hyperopt jobs for all models
- Fix DiffusionMetrics NaN default, XLSTMMetrics serde, safe indexing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
evaluate_supervised looked for checkpoints at {models_dir}/{model}_fold{N}_best
but training saves to {models_dir}/{model}/{model}_fold{N}_best (model subdirectory).
Also saves NormStats JSON per fold during training so evaluation uses
training-time normalization instead of computing from test data (data leakage).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The 10μs threshold is too tight for shared K8s pods with noisy-neighbor
jitter (failed at 16μs on L4 CI pool). Bump to 100μs — still validates
sub-millisecond HFT compliance without flaking in CI.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
New evaluate_supervised binary runs walk-forward inference on supervised
model checkpoints (TFT, Mamba2, etc.), converts directional predictions
to trading signals, and computes Sharpe/MaxDD/WinRate/DirAccuracy.
CI changes:
- train-validate-dqn → train-validate-rl (trains+evals DQN+PPO)
- train-validate-tft now runs evaluate_supervised after training
- web/api fallback rules added to train-validate and deploy stages
- evaluate_supervised added to compile-services and Docker images
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The validate() method called loss.to_scalar::<f64>() on an F32 tensor,
causing "unexpected dtype, expected: F64, got: F32" at runtime.
Add .to_dtype(F64) before scalar extraction, matching the pattern
used in all other TFT gradient/loss code paths.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
These tests assert sub-millisecond latency thresholds that only hold
with opt-level=3. Now that dev/test builds use opt-level=0 (correct),
mark them #[ignore] so they run only via `cargo test --release`.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add dimension validation in DQN, PPO, Mamba2, TGGN, TLOB, Liquid,
KAN, xLSTM, Diffusion constructors (fail-fast on zero-dim inputs
that would cause CUDA_ERROR_INVALID_VALUE at runtime)
- Add num_unknown_features > 0 guard to TFT (temporal input required)
- Fix 12 dead-code/unused warnings in test compilation
- Remove opt-level=3 and codegen-units=1 from target rustflags
(was forcing O3 + single-thread codegen on dev/test builds)
- Remove hardcoded jobs=16 cap (cargo now auto-detects CPU count)
- Switch linker to clang+lld (2-5x faster linking)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The training binary creates flat [batch, 51] feature vectors (one per
bar), but TFTConfig::default() had sequence_length=50, making the
adapter expect [batch, 51*50=2550]. Set sequence_length=1 and
prediction_horizon=1 to match the actual point-wise sample format.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
CUDA cannot handle linear layers with zero input dimensions.
When num_static_features=0 or num_known_features=0, the
VariableSelectionNetwork constructor creates linear(0, 0, ...)
which triggers CUDA_ERROR_INVALID_VALUE on GPU.
- Make static/future VSN and GRN encoder fields Option<T>
- Skip layer creation when feature count is 0
- Forward pass skips absent feature paths gracefully
- Trainable adapter creates empty placeholder tensors
- Add cilium CNI toleration to training job template and runner
All 103 TFT tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
TFT create_model used TFTConfig::default() values for num_known_features(10)
and num_unknown_features(210) totaling 220, but input_dim was 51 from the
feature extractor. Set both explicitly: known=0, unknown=feature_dim.
S3 uploader now uses path-style requests (required for Scaleway S3) and
explicitly passes AWS credentials from env vars instead of relying on the
instance metadata credential provider (unavailable on Kapsule).
Also fix runner tags lost during session (kapsule, rust, docker restored).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
