Add DeviceConfig and GpuCapabilities re-exports to ml::prelude so
downstream crates can import GPU management types via a single
`use ml::prelude::*` statement.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace hardcoded max_batch_size: 230 in CampaignConfig::dqn_default()
and ppo_default() with dynamic GPU detection via GpuCapabilities::detect()
and resolve_batch_size(). Update test to no longer assert <= 230.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace Device::cuda_if_available(0).unwrap_or(Device::Cpu) with
DeviceConfig::Auto.resolve().unwrap_or(Device::Cpu) in DQN, PPO, TFT,
and Mamba2 ensemble inference adapters so device selection goes through
the centralized GPU detection module.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace local DeviceConfig enum definition in liquid/candle_cfc.rs with
a re-export from the central crate::gpu::DeviceConfig, eliminating
duplication while preserving all existing API and tests.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- NoiseScheduler: precomputed cosine/linear alpha_bar schedules
- Denoiser: FC network with sinusoidal time embedding + SiLU + residual
- DDIMSampler: deterministic fast sampling (10 steps from 1000 timesteps)
- DiffusionTrainableAdapter: UnifiedTrainable for unified training pipeline
- Hyperopt adapter with ParameterSpace (9 params, batch ≤64 for 4GB GPU)
- ModelType::Diffusion registered in common + coordinator
- 41 tests passing (config=3, noise=7, denoiser=4, sampler=5, trainable=12, hyperopt=7)
- OOM-safe: FC denoiser instead of U-Net, small hidden dims, conservative defaults
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Phase 2-3 of ensemble expansion:
- KAN (Kolmogorov-Arnold Network): B-spline basis, layer, network, trainable adapter
- TLOB UnifiedTrainable adapter with 3D input support (batch, seq, features)
- Hyperopt adapters for both KAN and TLOB (ParameterSpace + metrics)
- ModelType::KAN variant registered in common, coordinator, lib.rs
- 44 new tests, all passing, zero warnings
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Closes the integration gap between Liquid CfC and DQN/PPO by adding:
- LiquidTrainer with gRPC progress callbacks, early stopping, and
checkpoint management (ml/src/trainers/liquid.rs)
- LiquidModel wrapper in enhanced_ml.rs for hot-loading from safetensors
- Ensemble weight rebalance: DQN 0.25, PPO 0.25, TFT 0.20, Mamba2 0.15,
Liquid-CfC 0.15
Verified: 81 liquid unit tests + 3 integration tests + 211 trading_service
tests pass, 0 compile errors across workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implements TGGNTrainableAdapter wrapping a candle-based projection
network (input→hidden→ReLU→output) with full UnifiedTrainable interface
including checkpoint save/load, validation, and gradient tracking.
11 tests covering forward, backward, optimizer step, checkpoint
roundtrip, validation, learning rate, and metrics collection.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add load_from_safetensors() to DQNAgent for weight loading via VarMap
- Update RealDQNModel::from_checkpoint to try safetensors first, fall back to JSON
- Replace std::mem::forget(prediction_shutdown_tx) with proper Vec-based storage
that sends shutdown signal and drops senders during graceful shutdown
- Wire order_manager.get_open_orders() into emergency_stop response so callers
see which orders were active when the kill switch engaged
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add CandleCfCTrainer to training.rs with Candle-based gradient training
- Update mod.rs with full CfC v2 re-exports (CandleCfCNetwork, CfCCell, etc.)
- Fix CUDA variance bug (undefined variable) and kernel compilation stub
- Add 3 integration tests: full training loop, checkpoint roundtrip, validation
- 3 new unit tests for CandleCfCTrainer (creation, single epoch, loss decrease)
73 liquid tests pass, 0 errors, 0 clippy warnings in liquid module.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove 520 lines of ndarray-based placeholder code that never performed real
gradient descent. Production training uses Candle-based trainers in ml::trainers/.
Deleted: SimpleNeuralNetwork, TrainingPipeline, NetworkConfig, ActivationType,
TrainingMetrics, NetworkInterface, MockNetwork, and 9 associated tests.
Kept: DeviceCapabilities, TrainingConfig, sub-module re-exports.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace load_and_split_data() mock that generated 2000 synthetic samples
with an error-returning stub directing users to ml_training_service.
The binary retains its real infrastructure (TFTTrainer, CLI, checkpoint
storage, progress callbacks) — only the fake data generation is removed.
-120 lines of mock data, +16 lines error stub with documentation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fix leftover conflict markers from rebase onto main. Align as_str()
with main's semantics (general-purpose model names), add separate
s3_prefix() for S3 storage paths, and fix to_db_string() to preserve
per-variant database values.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Full adapter bridging CandleCfCNetwork to the unified training pipeline
with VarMap-based checkpointing, AdamW optimizer, and gradient norm
tracking. Includes 10 unit tests covering creation, training steps,
validation, metrics, learning rate, checkpoint roundtrip, and error cases.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Bridge the ML crate's parking_lot::RwLock-based CircuitBreaker to the
shared CircuitBreakerTrait from common. Maps synchronous methods
(allow_request, record_success, etc.) to the async trait interface and
converts CircuitState variants to common::CircuitBreakerState.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Consolidate 4 separate ModelType enum definitions (ml 15 variants,
model_loader 7, campaign 2, job_spawner 4) into a single canonical
definition in common/src/model_types.rs with the union of all variants
and all methods (file_extension, as_str, to_db_string, weight, from_str,
Display).
- ml/src/lib.rs: replace 15-variant enum with re-export
- model_loader/src/lib.rs: replace 7-variant enum with re-export,
update PascalCase names (Dqn->DQN, Tft->TFT, etc)
- ml/hyperopt/campaign.rs: replace 2-variant enum with re-export
- services/ml_training_service/job_spawner.rs: replace 4-variant enum
with re-export, MAMBA2->MAMBA
- Remove orphan impl ToString in ml/observability/metrics.rs (Display
now provided by canonical type)
- Update backtesting_service and model_loader tests for new names
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The ml crate had a stub ErrorCategory with only 1 variant (System).
Replace it with a re-export of the canonical 24-variant ErrorCategory
from common::error. Also rename the unused ErrorCategory in
data/src/providers/common.rs to ProviderErrorCategory to avoid
name collision.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Deduplicate four identical OrderType enums (Market/LimitMaker/IoC) into a
single definition at ml/src/common/action.rs with all methods from all copies
(transaction_cost, cost_bps, cost_decimal, from_index, Display). Original
locations now re-export via `pub use crate::common::action::OrderType`.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add 6 integration tests that verify model weights survive a full
checkpoint cycle (serialize -> save to disk -> load -> predict):
- DQN raw Q-network weight roundtrip via safetensors
- DQN adapter roundtrip via DqnInferenceAdapter::from_checkpoint
- DQN CheckpointManager + Checkpointable trait flow
- PPO actor/critic checkpoint roundtrip with exact output comparison
- PPO inference after checkpoint load (probability validation)
- PPO adapter deterministic prediction verification
Fix a bug in DQN::load_from_safetensors where inserting new Var
objects into the VarMap HashMap left the Linear layers pointing at
stale data. The fix uses VarMap::load() which correctly updates
existing Vars in-place via Var::set(), preserving the shared
Arc<RwLock<Storage>> between VarMap entries and Linear layer tensors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds two gradient verification tests: test_cfc_gradient_flow confirms
backprop produces non-empty gradients for all parameters, and
test_cfc_optimizer_step_reduces_loss runs 10 AdamW steps and verifies
MSE loss decreases, proving the full CfC v2 compute graph is
differentiable end-to-end.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wraps CfCCell with output projection for end-to-end sequence processing.
forward_sequence unrolls the cell over [batch, seq_len, features] input
and projects the final hidden state to [batch, output_size]. Includes
forward() convenience method with default dt=0.01 for UnifiedTrainable
compatibility.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implements CfCCell with the core h(t+dt) = h*decay + f*(1-decay) update
rule where decay = exp(-dt/tau) and tau is a learned sigmoid-scaled time
constant. Uses manual_sigmoid for CUDA compatibility.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add candle_cfc module with CfCTrainConfig (Candle training path) and
DeviceConfig (CPU/CUDA/Auto device selection). This is the foundation
for the Liquid CfC v2 differentiable training implementation, parallel
to the existing FixedPoint inference path in cells.rs/network.rs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Removed 3 duplicate ModelType enums (model_loader, hyperopt campaign,
job_spawner). Canonical definition in ml/src/lib.rs with 15 variants.
model_loader and job_spawner now re-export from ml. Added as_str(),
s3_prefix(), Display, to_db_string(), and weight() to canonical enum.
Replaced conflicting ToString impl with Display. Fixed variant name
mismatches (Dqn->DQN, Mamba2->MAMBA, Liquid->LNN, TlobTransformer->TLOB).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Removed duplicate DQNConfig from agent.rs (13 fields, pre-Rainbow with
f64 gamma/epsilon) and adaptive-strategy stub (unit struct). Canonical
definition in dqn/dqn.rs now has 51 fields covering full Rainbow DQN
plus agent-level trading parameters (minimum_profit_factor, weight_decay).
Key changes:
- agent.rs imports DQNConfig from dqn.rs instead of defining its own
- Fixed f32/f64 type mismatches (epsilon_start/end/decay cast to f64
where QNetworkConfig expects f64)
- Renamed replay_buffer_size -> replay_buffer_capacity across all callers
- Updated 13 files across ml, adaptive-strategy, and trading_service
- All 2009 ml tests pass, 0 clippy warnings in modified files
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
dqn_all_fixes_integration_test.rs had pre-existing async/await error.
kelly_position_sizing_integration.rs had 4 failures from eval engine change.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove 45+ AGENT_*, WAVE_*, and completion report files that were
one-time swarm deliverables with no living documentation value.
Remove reports/2025-11-16_17_hyperopt_analysis/ (55 files, code
changes already landed). Content preserved in git history.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- trading_engine: replace 20 drop(Copy) with let _ = (drop on Copy is no-op)
- data: remove 4 unnecessary crate::error:: qualifications
- ml: remove stale #[allow] attribute on inference.rs
- web-gateway: allow dead_code on stub route body fields
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Delete 22 orphaned files (.backup, .broken_backup, .old, .rej, .disabled)
- Remove duplicate KillSwitch stub from risk_engine.rs, use AtomicKillSwitch
- Deduplicate UnixSocketKillSwitch via re-export from unix_socket module
- Rename StreamingConfig → EventStreamingConfig to resolve naming collision
- Guard MockTradingRepository behind #[cfg(test)] in trading_service
- Replace adaptive-strategy EnsembleConfig with re-export from ml crate
- Merge error_recovery.rs fields into canonical RetryConfig (circuit breaker,
jitter, HFT precision mode) and delete the 328-line dead module
- Replace local 3-variant RiskError with risk::error::RiskError import
- Fix all RetryConfig struct literals with ..Default::default()
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- ensemble/model.rs: replace silent .read().ok() with map_err+tracing::error for both models and signals RwLock in health_check
- batch_tuning_manager.rs: replace let _ = stop_tuning_job() with if let Err + tracing::warn
- orchestrator.rs: replace let _ = broadcaster.send() with if let Err + tracing::warn
- tuning_manager.rs: replace let _ = progress_tx.send() with if let Err + tracing::warn
- trial_executor.rs: replace let _ = result_tx.send() with if let Err + tracing::warn
- Use {:?} for SendError types whose inner value does not implement Display
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replace barrier_label.unwrap() with unwrap_or(0) at two debug log sites (lines 1464, 1700)
- Replace self.nstep_buffer.take().unwrap() with let-else pattern to safely skip on None
- Replace self.safety_loss_history.back().unwrap() with let-else and intermediate prev_loss_raw
- Add #[allow(clippy::unwrap_used, clippy::expect_used)] on lazy_static! block in inference.rs
with SAFETY comment explaining Prometheus string-literal registration is infallible
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace 10 .as_ref().unwrap() calls on Option<QuantizedTensor> weight fields
(q_weights, k_weights, v_weights, o_weights) and attention_cache with
.as_ref().ok_or_else(|| MLError::ModelError(...))? to satisfy the
clippy::unwrap_used deny lint. Affected methods: build_cache,
forward_with_mask, compute_projections_slow, and the test
test_attention_weights_sum_to_one.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
