- 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>
Add PositionFeatures struct that extracts 3 features for RL agent
position awareness (54→57 dim state vector):
- unrealized_pnl: normalized by EMA volatility
- bars_in_position: log-scaled to [0, 1]
- cost_basis: relative to price in bps, clamped [-500, 500]
8 unit tests, 0 clippy warnings. Not yet wired into trainers.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DQN: wire SAC-style entropy regularizer into TD loss, fix diversity
penalty normalization from ln(3) to ln(45), add epsilon floor (0.05)
for noisy nets, enable sigma scheduling (0.8→0.4 with 0.3 floor),
add UCB count-based exploration bonus, expand hyperopt search space
from 43D to 45D.
PPO: replace fabricated entropy metric (value_loss×0.5) with real
Shannon entropy from action distribution, fix EntropyRegularizer
normalization from ln(3) to ln(45), implement Monte Carlo entropy
estimate for flow policy (was returning zeros), fix hyperopt VRAM
bound from 3 to 45 actions.
Monitoring: add normalized action entropy and diversity Prometheus
gauges, add exploration diagnostics logging per epoch.
2503 ml tests pass, 277 common tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- ml_training_service: handlers delegate to promotion_manager
- api_gateway: proxy pass-through for both new RPCs
- Proto messages added to service-side ml_training.proto
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DashboardState with 4 tab substates (Training/Trading/Risk/System),
ring buffers for loss history and fills, scroll support.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- ratatui 0.29, crossterm 0.28 (event-stream), tokio-stream to deps
- ApproveModel + RejectModel RPCs added to MLTrainingService
- 4 new proto messages for model promotion workflow
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ratatui streaming dashboard (fxt watch) with 4 tabs, stub command
wiring plan, and new ApproveModel/RejectModel proto RPCs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
5 tests: training loop cycle, priority-weighted sampling, KS distribution
test, IS weight range validation, ring buffer overwrite correctness.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Collect loss tensors on GPU during accumulation loop and perform a
single mean+to_scalar readback at the accumulation boundary instead
of N per-step CPU readbacks. Falls back to CPU sum when GPU tensors
are unavailable. Also adds missing loss_tensor_gpu field to
RegimeConditionalDQN's GradientResult construction.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GradientResult and ComputeLossResult gain td_errors_gpu/indices_gpu
Option<Tensor> fields for GPU-resident TD errors. Trainer accumulates
GPU tensors and calls update_priorities_gpu() when available, falling
back to CPU path otherwise. DqnAgent delegates via memory().
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
New variant delegates to GpuReplayBuffer for all operations. Sample
returns BatchSample with gpu_batch populated (empty CPU vecs). Added
update_priorities_gpu() for tensor-based priority updates, as_gpu_buffer()
for direct access from trainer. Existing tests unchanged.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Proportional: cumsum + binary search over alpha-weighted priorities.
Rank-based: sort descending, rank probabilities 1/rank^alpha, cumsum sample.
Priority update: |td_error|^alpha + epsilon scatter into buffer.
All methods return GpuBatch with IS weights normalized by max.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Slice-scatter based insertion handles wrap-around by splitting into
tail + head copies. New experiences get max_priority. Tests verify
correctness at capacity boundary.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Pre-allocates all experience tensors on device at init (~47 MB for 100K
buffer). Ring buffer cursor and beta annealing state tracked CPU-side.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GPU-resident batch tensors allow compute_gradients() to skip CPU→GPU
transfer when sampling from the GPU replay buffer.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PerformanceMetrics::from_trades() already returns win_rate as percentage
(e.g. 57.78), but TRIAL_SUMMARY and backtest details log lines multiplied
by 100 again, producing values like 5778%. Remove the extra * 100.0.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
TDD plan covering GpuReplayBuffer, proportional + rank-based GPU
sampling, priority scatter updates, async loss readback, trainer
integration, OOM fallback, and distribution correctness tests.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Approved design for moving Prioritized Experience Replay entirely to
GPU — flat priority array with parallel prefix-sum sampling, GPU-resident
ring buffer for experiences, async loss readback. Eliminates the last
major CPU bottleneck in the DQN training loop.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
CI training jobs run as GitLab runner pods, not K8s Jobs, so the
kube_job_status_active query always returned 0. Switch to
foxhunt_training_active_workers which is emitted by all training
binaries regardless of deployment method.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- DQN/PPO trainers now record epoch, loss, val_loss, batch/s every epoch
- CI training jobs get Prometheus scrape annotations via runner overrides
- Allow Prometheus (foxhunt namespace) to scrape foxhunt-ci pods
- Skip no-model metrics in monitoring service session grouping
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add DNS record, nginx gRPC proxy block, network policy for Tailscale
ingress, and auto-detect fxhnt.ai in fxt monitor URL derivation.
Show GPU telemetry even when no training sessions are active.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
monitoring-service needs egress to Prometheus (port 80/9090) and ingress
from web-gateway (port 50057). web-gateway needs egress to monitoring-service.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The compile-services job already builds monitoring_service but the deploy
job was missing it from the PVC copy loop and rollout restart/wait loops.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The GPU experience collector gate checked only `dqn.dueling_q_network`
(plain dueling), but with both `use_dueling: true` AND `use_distributional: true`
(the defaults), DQN creates hybrid `dist_dueling_q_network` instead, leaving
the plain dueling fields as None. This meant the GPU collector never initialized
despite curiosity being enabled.
Fix: Add else-if fallback to check `dist_dueling_q_network`/`dist_dueling_target_network`
when plain dueling fields are None. Same fix applied to the weight sync site.
Also fix test_train_with_empty_data_completes_gracefully: reduce to 5 epochs with
early stopping disabled. The debug-mode async state machine is large enough that
empty-data epochs run ~180ms each (vs ~3ms in release), triggering both plateau
and patience-based early stopping. The test purpose is crash-freedom, not timing.
2497 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
