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>
- 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>
containerd on Kapsule nodes uses host DNS which can't resolve
.svc.cluster.local names. Switch all image references from
gitlab-registry.foxhunt.svc.cluster.local:5000 to localhost:30500
(NodePort on the GitLab registry). Also make training runtime image
configurable via TRAINING_RUNTIME_IMAGE env var.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- JWT issuer now foxhunt-api across all 16 files (services, tests, config, docker-compose)
- Remove serde alias api_gateway_url from FxtConfig (no backwards compat)
- Remove api_gateway CLI alias from e2e orchestrator
- All services must deploy simultaneously for JWT validation to match
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Rename 7 service binaries from snake_case to kebab-case to match
K8s deployment names. Update Cargo.toml package/bin names, K8s
manifest S3 paths and commands, and cross-crate dependency keys.
- api_gateway → api-gateway
- trading_service → trading-service
- broker_gateway_service → broker-gateway
- ml_training_service → ml-training-service
- backtesting_service → backtesting-service
- trading_agent_service → trading-agent-service
- data_acquisition_service → data-acquisition-service
broker-gateway gets an explicit [lib] name = "broker_gateway_service"
since its new package name maps to broker_gateway (not the original
broker_gateway_service used in source code). All other services map
correctly with Rust's automatic hyphen-to-underscore conversion.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Delete per-service proto/ directories. All 7 services now compile
from the single canonical proto/ at workspace root.
- trading_service: 5 protos + ml_training client -> ../../proto/
- ml_training_service: ml_training server + fxt_trading client -> ../../proto/
- broker_gateway_service: broker_gateway -> ../../proto/
- trading_agent_service: trading_agent + ml client -> ../../proto/
- data_acquisition_service: data_acquisition -> ../../proto/
- api_gateway: 8 proto compilations -> ../../proto/
- monitoring_service: keeps local proto (3 training RPCs only, deleted in Task 5)
Added proto/fxt_trading.proto (fat-client proto, package foxhunt.tli)
separate from proto/trading.proto (backend, package trading) since the
API gateway needs both for protocol translation.
Added unimplemented stubs for merged monitoring.proto RPCs:
- trading_service: 3 training RPCs (served by monitoring_service)
- api_gateway monitoring_proxy: 13 system health RPCs (Task 4)
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>
- 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>
Remove unnecessary async from init_observability -- body was fully sync.
Change otlp_endpoint from &str to Option<&str> -- when None, OTLP layer
is skipped (fmt-only mode). Update all 8 service callers.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Create 3 missing service READMEs (api_gateway, data_acquisition_service,
trading_agent_service). Create bin/fxt/README.md. Create
testing/service-integration/README.md. Update existing service and testing
READMEs to standard template. Delete 4 subdirectory READMEs from
testing/integration/ and testing/e2e/.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove stale test reports, quick-start guides, benchmark analyses,
profiling reports, and tool artifacts from across the workspace.
Keeps only root README.md per crate/service.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Switch all MinIO communication from HTTP to HTTPS across the entire stack:
- MinIO deployment: mount TLS secret, tcpSocket probes, HTTPS init-buckets
- 11 service YAMLs: HTTPS rclone endpoint + CA cert volume mount
- Training job template + train.sh: HTTPS for fetch-binaries and uploader
- CI pipeline (.gitlab-ci.yml): all 7 rclone exports use HTTPS + CA cert
- GitLab runner values: minio-ca-cert ConfigMap volume for CI pods
- Rust: CA cert loading via MINIO_CA_CERT_PATH in training_uploader,
storage backend, data_acquisition uploader, and k8s_dispatcher
- Cert generation script (infra/scripts/generate-minio-tls.sh)
Fixes training uploader S3 upload failures caused by with_allow_http(false)
connecting to an HTTP endpoint.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add tracing::instrument(skip_all) to gRPC handlers across all services
for distributed trace spans via OTLP. Pairs with Prometheus scrape
annotations from previous commit.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add --hyperopt, --trials, and --parallel flags to `fxt train start` so
users can dispatch PSO hyperparameter optimization jobs alongside
regular training. The mode=hyperopt tag propagates through the service
layer which selects the correct binary (hyperopt_baseline_rl for DQN/PPO,
hyperopt_baseline_supervised for all others) and builds hyperopt-specific
CLI arguments via a new extra_args field on TrainingJobParams.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Align test assertions with the production job spec changes from b5fca4d1:
- Binary paths use /binaries/ prefix
- Args use --symbol=X format
- Config uses runtime_image and binaries_bucket fields
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replace separate training/uploader images with single runtime_image
- Add fetch-binaries initContainer (rclone from S3 binaries bucket)
- Switch to emptyDir for output and binaries (no output PVC needed)
- Add Cilium CNI toleration for fresh scale-from-zero nodes
- Extract symbol from file_path last component in data_source
- Increase active_deadline_seconds to 6 hours for hyperopt jobs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace stub approve_promotion and reject_promotion gRPC handlers with
real implementations that call PromotionManager.approve() and .reject().
- approve_promotion: removes from pending, promotes to active model map
- reject_promotion: removes from pending with operator-supplied reason
- Input validation: empty model_id returns INVALID_ARGUMENT
- Error handling: nonexistent model_id returns success=false with message
- Add 4 tests covering approve, reject, and not-found error paths
- Add #[cfg(test)] active_models_for_test() accessor on PromotionManager
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace the stub list_pending_promotions gRPC handler with a real
implementation that queries PromotionManager::list_pending() and maps
each PendingModel to the proto PendingPromotion message. Adds a unit
test verifying the field mapping from domain type to proto type.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace the stub report_job_completion gRPC handler with a real
implementation that:
- Validates job_id as UUID upfront
- On failure: updates DB status to Failed (best-effort), returns "failed"
- On success: updates DB status to Completed, looks up model_type and
symbol from child_jobs table, registers with PromotionManager, and
returns the actual promotion status string
Also adds JobSpawner::get_job_by_id() to fetch individual child jobs
from PostgreSQL, and two new tests covering promotion status mapping
and the PromotionManager integration path.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Background tokio task polls JobSpawner.get_next_pending_job() every 5s,
marks found jobs as Running, builds TrainingJobParams, and dispatches
to K8s via K8sDispatcher. On dispatch failure the job is marked Failed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add JobSpawner as the highest-priority dispatch path in start_training.
When job_spawner is available, training requests are persisted to
PostgreSQL via spawn_batch() and a batch ID is returned immediately.
The queue consumer (Task 3) will later poll for pending jobs and
dispatch them to K8s.
Priority order: JobSpawner (DB) → K8sDispatcher (direct) → orchestrator (in-process).
Also adds test_start_training_model_binary_mapping unit test.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire JobSpawner into the gRPC service struct so that training jobs can
be persisted to PostgreSQL before being dispatched to K8s. This is the
first step toward a durable job queue that survives pod restarts.
- Add `job_spawner: Option<Arc<JobSpawner>>` to MLTrainingServiceImpl
- Extend `new()` constructor to accept the spawner parameter
- Add `DatabaseManager::pg_pool()` accessor for cheap PgPool cloning
- Construct JobSpawner in main.rs and pass `Some(job_spawner)` to service
- Add compile-time test verifying the field exists
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Extend ml_training_service to dispatch GPU training jobs as K8s batch/v1
Jobs, collect results via a Rust sidecar uploader, and support model
promotion with operator approval via fxt CLI.
- K8s dispatcher creates Jobs on gpu-training pool with native sidecar
- training_uploader crate: watches DONE/FAILED marker, uploads to S3,
reports completion via ReportJobCompletion gRPC
- PromotionManager compares metrics, queues better models for approval
- 4 new proto RPCs: ReportJobCompletion, ListPendingPromotions,
ApprovePromotion, RejectPromotion
- fxt commands: train start, model list/approve/reject
- Training binaries write DONE/FAILED markers + metrics.json
- Dockerfile, K8s job template, and CI pipeline updated
- StartTraining gracefully falls back to in-process when outside K8s
- 27 new tests (16 service + 11 promotion), 141 total service tests pass
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 17 library crates into crates/, CLI binary into bin/fxt,
consolidate 10 test crates into testing/, split config crate
from deployment config files.
Root directory reduced from 38+ to ~17 directories.
All Cargo.toml paths and build.rs proto refs updated.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace _param suppression pattern with actual usage across 21 files:
- adaptive-strategy: wire EpistemicConfig/AleatoricConfig into
UncertaintyQuantifier, KellyConfig into DrawdownTracker,
TLOBConfig into TLOBTransformer
- trading_engine/compliance: store config in 26 compliance structs
(audit_trails, best_execution, sox, iso27001, transaction_reporting,
compliance_reporting, automated_reporting) with public accessors
- fxt: store Channel in LoginClient, ConnectionConfig in ConnectionManager
- ml: remove unused path param from ReplayBuffer::new(), wire
Mamba2Config.target_latency_us into HardwareOptimizer
- services: store TrainingConfig in GpuConfigManager, symbol in
TechnicalIndicatorCalculator
- database: change let _result to let _ (intentional discard)
- trading_engine/brokers: store BrokerConnectorConfig in BrokerConnector
Result: 0 warnings across all 37+ workspace crates.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Strip all 413 #[allow(dead_code)] annotations from 139 files and remove
the actual dead code they were suppressing: unused struct fields (and their
constructor sites), unused methods/functions, and entire dead structs.
Key removals:
- trading_engine compliance: ~50 dead structs/fields across audit, reporting, SOX modules
- trading_service: dead execution engine fields, broker routing, paper trading methods
- ml_training_service: dead TLS validation (~340 lines), GPU state, monitoring fields
- backtesting_service: dead model cache, TLS validation, TradeSignal fields
- risk: dead VaR engine fields, safety coordinator fields, position tracker fields
- adaptive-strategy: dead ensemble methods, regime detection, sizing functions
147 files changed, -4264 net lines. Workspace compiles with 0 errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ubuntu 22.04's protobuf-compiler (v3.12) lacks proto3 optional field
support. Install protoc v25.1 from GitHub releases instead, which
matches the protoc version available in Debian bookworm-based images
used by the other 5 services.
Validated: docker build completes successfully, binary starts with
ml_training_service --help.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 6 service Dockerfiles were missing newly-added workspace crates
(web-gateway, ctrader-openapi, foxhunt-deploy, broker_gateway_service),
causing cargo workspace resolution failures during Docker builds.
Changes across all Dockerfiles:
- Add COPY directives for web-gateway, ctrader-openapi, foxhunt-deploy,
broker_gateway_service (new workspace members since Dockerfiles written)
- Add SQLX_OFFLINE=true env and .sqlx cache copy where missing
- Add perl and make system deps (needed for OpenSSL build from source)
- Remove COPY migrations (dir excluded by .dockerignore, not needed)
- Expand broker_gateway_service from 3-crate to full workspace copy
Validated: docker build --check passes all 6, cargo check -p passes all 6.
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>
Move identical TLS type definitions from 4 service crates into
common/src/tls.rs, eliminating ~435 lines of duplicated code.
Each service retains its own TlsConfig struct and validation logic
(async vs sync, delegated vs monolithic) while sharing the type
definitions. Services re-export the types for backward compatibility.
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>
- broker_gateway: use 100ms circuit breaker timeout in tests (was 60s)
- ml_training: relax GPU count assertions to >= 1 (env-dependent)
- icmarkets: mark 4 live-credential tests as #[ignore]
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>
