fxt 2.0 overhaul: consolidated proto/, absorbed monitoring into API Gateway,
new 15-command CLI with gRPC, MCP server mode, TUI cockpit framework.
159 files changed, net -11,835 lines.
Replace `let _ = sync_tensor.to_vec0::<f32>()` with `drop()` to
satisfy clippy's let_underscore_must_use lint on the Result return.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add quantile_huber_loss_per_sample() that preserves the batch dimension
(mean over quantiles only) so PER importance-sampling weights can be
applied per-sample before final batch reduction. Mirrors the existing
quantile_huber_loss() but returns [batch] instead of scalar.
Two tests verify shape correctness, non-negativity, consistency with
the scalar variant (mean of per-sample == scalar loss), and IS weight
application behavior.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Embed DQNConfig architecture hash (SHA-256 of state_dim, num_actions,
hidden_dims, dueling/distributional/noisy/IQN flags) in safetensors
file header on save. Validate hash on load to catch shape mismatches
before touching the VarMap - prevents silent corruption from loading
checkpoints trained with different network architectures.
All 5 save paths (trainable_adapter, trainer serialize_model,
DQNAgentType::save_checkpoint, RegimeConditionalDQN per-head) now
embed metadata. All 3 load paths (DQN::load_from_safetensors,
trainable_adapter::load_checkpoint, ensemble adapter) validate.
No backward compatibility: checkpoints without metadata are rejected
with a clear error message to re-train.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Wire position-delta cost model: costs only apply on position changes,
proportional to delta (not flat per-bar)
- Differentiate order types: Market=15bps, LimitMaker=5bps, IoC=10bps
- Apply urgency weight: Patient=0.5x, Normal=1.0x, Aggressive=1.5x
- Append 3 portfolio features (position, unrealized PnL, drawdown) to match
training's 54-dim state vector (was 51, causing shape mismatch on load)
- Fix default num_actions 3->45 to match training's factored action space
- Add --bars-per-year CLI flag for per-symbol annualization
(ES/NQ=347760, 6E=345000, ZN=105840)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Atomic checkpoint writes: write to .tmp then fs::rename() (POSIX atomic)
- Remove redundant thread::sleep(50ms) after CUDA tensor readback sync
- NormStats: bail on missing file instead of computing from test data (lookahead bias)
- q_value_std: warn when missing from training metrics instead of silent 0.0 fallback
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The DQNConfig construction in trainer.rs hardcoded weight_decay to 1e-4,
ignoring the hyperopt-tuned value in DQNHyperparameters. This meant PSO
optimization of weight_decay (search space index 17, range [1e-5, 1e-3])
had no effect on actual training runs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DQNConfig.weight_decay (default 1e-4, hyperopt range [1e-5, 1e-3]) was
silently ignored — ParamsAdam always received weight_decay: None. Now
conditionally passes Decay::DecoupledWeightDecay when weight_decay > 0,
enabling L2 regularization that hyperopt has been tuning for.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Integrate three standalone PPO modules into the hyperopt training loop:
- PPORewardShaper: hold penalty + rolling Sharpe + diversity bonus per step
- CompositeReward: risk-adjusted reward (downside dev + differential return)
- TrajectoryReplayBuffer: ExO-PPO M=4 rollouts with IS-weighted replay
Also fixes GAE hardcoded gamma=0.99/lambda=0.95 — now uses hyperopt params.
2726 tests pass, 0 clippy errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Integrate all PPO improvement modules into the core training paths:
- Symlog value predictions in compute_value_loss (MLP + LSTM)
- Adaptive entropy auto-tuning replaces fixed entropy_coeff
- Percentile P5/P95 advantage scaling for heavy-tailed returns
- DAPO clip_epsilon_high wired in all 7 PPOConfig construction sites
- Shape mismatch fix in adaptive_entropy (unsqueeze scalar)
2726 tests pass, 0 clippy errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Change --optimizer default from "pso" to "tpe" since TPE has better
sample efficiency in 25D spaces. Fix clippy let_underscore_must_use
on CUDA sync tensor readback.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fix 20 exotic parameters to validated defaults, keeping only the 25
that genuinely affect trading performance. This makes both PSO and
TPE dramatically more effective at exploring the parameter space.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add optimize_with_tpe() function that uses Tree-Parzen Estimator for
Bayesian hyperparameter optimization. Add --optimizer=tpe CLI flag
to hyperopt_baseline_rl binary (default: pso for backward compat).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace DType::F32 with training_dtype(&device) for action one-hot
encoding and state embedding conversion. On BF16 devices, model
weights are BF16 but inputs were F32, causing dtype mismatch errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Force CUDA synchronization between trials by creating a tiny tensor and
reading it back (forces cudaDeviceSynchronize). This ensures GPU memory
from dropped models is actually freed before the next trial starts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tighten max_position_absolute search range from [4,8] to [1,4] to
prevent catastrophic leverage during hyperopt. Add drawdown circuit
breaker to PortfolioTracker that force-closes positions at >20%
drawdown and refuses new trades while flat.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add a standalone TPE optimizer for Bayesian hyperparameter optimization that
replaces PSO by modeling good/bad trial distributions with per-dimension
kernel density estimates. Includes Latin Hypercube Sampling for initial
exploration, Silverman bandwidth selection, log-sum-exp numerical stability,
and JSON-based trial persistence. 15 tests covering splits, bounds, KDE
density, convergence on 1D/2D objectives, and history serialization.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
sample_noise(), sigma init, disable_noise(), and epsilon buffers all
used hardcoded DType::F32. When weights are BF16 on Ampere+ CUDA,
this caused dtype mismatch in mul/add operations during forward pass.
Fix: use training_dtype(device) for all noise-related tensors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
After converting all VarBuilder sites from F32 to training_dtype (BF16 on
Ampere+ GPUs), input tensors from callers remain F32, causing dtype
mismatches in matmul/add/mul operations. This commit adds systematic
boundary casts across all 10 model architectures:
- Input boundary: ensure_training_dtype() at each model forward() entry
- Output boundary: to_dtype(F32) at each model forward() exit
- Internal intermediates: hidden state init, gradient extraction,
positional encodings, causal masks, SSM state matrices, B-spline
basis values all cast to match computation dtype
- Ensemble adapters: ensure_training_dtype after Tensor::from_vec
36 files, +293/-72 lines. 2640 tests pass, 0 failures, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace DType::F32 with training_dtype(&device) in VarBuilder::from_varmap
calls across TFT, Mamba2, Liquid/CfC, KAN, xLSTM, Diffusion, TGGN, TLOB.
61 sites changed across 25 files (production constructors + test helpers).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace DType::F32 with training_dtype(&device) in all VarBuilder::from_varmap
calls across 16 DQN files (~55 call sites). This enables automatic BF16 weight
initialization on Ampere+ GPUs while keeping F32 on CPU and older hardware.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- gpu_replay_buffer: allocate states/next_states with training_dtype(),
cast incoming batches at ingestion boundary
- gpu_weights: cast BF16 model weights to F32 before extraction for
CUDA f32 kernels in both extract_one() and sync_one()
- mod.rs: cast DqnGpuData, GpuBufferPool, PpoGpuData uploads to
training_dtype(); cast portfolio tensors to match features dtype;
cast bar_target_values readback to F32
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Cast state tensors to BF16 (via training_dtype()) at all DQN network
input sites: compute_loss_internal states/next_states, select_actions_batch,
curiosity module state/next_state, validation batch, and Q-value logging
batch. Non-network tensors (actions, rewards, dones, weights) are left
as F32 since they participate in loss math, not forward passes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Previously scalar_tensor() rejected BF16 and F16 with an error,
blocking BF16 training for Mamba2. Now BF16/F16 scalars are created
as F32 then cast to the target dtype, matching Candle's internal
precision requirements while supporting half-precision training.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add detect_from_gpu_name_auto() which uses cached nvidia-smi GPU
capabilities to auto-detect mixed precision config, and training_dtype()
which returns BF16 for Ampere+ CUDA GPUs and F32 for everything else.
This is the single entry point for "what dtype should weights use?"
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- trade_ml.rs: Replace 3 mock data fallbacks (submit, predictions,
performance) with proper error propagation. Commands now fail
honestly when the API Gateway is unreachable instead of silently
returning fake data. Mark 3 integration tests as #[ignore].
- monitoring_service: Add tonic-health with set_serving for
MonitoringServiceServer. Enables grpc_health_probe readiness checks.
- ml_training_service: Add tonic-health with set_serving for
MlTrainingServiceServer. Wired into both TLS and non-TLS paths.
- data_acquisition_service: Add tonic-health with set_serving for
DataAcquisitionServiceServer.
- ml/cuda_streams: Fix pre-existing unused variable clippy warning.
All 8 services now have standard gRPC health checking enabled.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace per-bar GPU forward passes with chunked batch inference (1024 bars
per chunk). This reduces ~90K individual CUDA kernel launches to ~88 batched
forward passes — ~1000× fewer GPU round-trips.
Changes:
- Add DQN::batch_greedy_actions(&self) for immutable batched forward+argmax
- Add RegimeConditionalDQN::batch_greedy_actions with per-regime-head batching
- Add DQNTrainer::convert_to_state_vec (CPU-only, skips GPU tensor allocation)
- Add PortfolioTracker::set_position_direct for backtest state sync
- Rewrite hyperopt backtest loop: chunked batching with portfolio state
updates between chunks (1024-bar granularity ≈ 17h of 1-min data)
Portfolio features (last 3 of 54 dims) are refreshed between chunks via
set_portfolio_for_backtest(), keeping position/PnL/exposure accurate at
chunk boundaries while batching inference within each chunk.
2640 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Split model overhead into two constants: MODEL_OVERHEAD_MB (pure
model weights for batch-size capping) and TRIAL_VRAM_MB (total
per-trial VRAM for concurrent hyperopt planning). DQN trials
empirically consume ~7 GB each on L40S (model + GPU replay buffer +
experience collector + CUDA allocations + fragmentation), not the
200 MB previously estimated. This caused plan_hyperopt to compute
128 concurrent trials instead of the actual 5, inflating PSO
particles from 20→128 and total trials from 20→384 via .max()
instead of .min(), guaranteeing a 4h timeout kill.
Fix auto-scaling to: (1) match particles to GPU concurrency for
maximum hardware utilization on any node, (2) cap particles at
max_trials to never inflate the trial budget, (3) never auto-inflate
the total trial count.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The GPU experience collection path bypassed monitor.track_action(),
leaving action_counts all zeros and reporting 0/45 diversity on every
epoch. Feed batch.actions into monitor.action_counts after GPU
collection so the metric reflects actual policy behavior.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Monte Carlo entropy estimate with random (untrained) weights can dip
well below -1.0 under parallel test load. Widened the lower bound from
-1.0 to -5.0; the key invariant is finiteness, not positivity.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Rename SlippageModel trait → MarketImpactModel to avoid name collision
with PPO's SlippageModel enum
- Wrap ThompsonSamplingState in std::sync::Mutex for interior mutability
through Arc, enabling record_outcome during inference
- Add TrafficSplitter::record_outcome() method for callers
- Cap RegimeDetectionEngine::feature_data to window_size*3 entries
- Add trailing newline to ab_testing.rs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Extends TrafficSplitter with Thompson Sampling (Beta-distributed traffic
allocation) and PromotionCriteria for automated champion/challenger
decisions. 18 tests, 0 clippy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Per-trade online learning with Elastic Weight Consolidation to prevent
catastrophic forgetting. Rolling 10K experience buffer, mini-updates
every 100 trades. Safety rails: grad clip 1.0, LR×0.1, auto-rollback
at 20% Sharpe degradation, kill switch at Sharpe < -1.0.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
SlippageModel trait with two implementations:
- LinearImpactModel: sqrt market impact (spread/2 + eta*sqrt(V/ADV) + order premium)
- FixedCostModel: backward-compat wrapper for existing 15/5/10 bps constants
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
