Add metrics module with uptime, active feeds, records received,
errors, and feed latency metrics. Spawn HTTP /metrics endpoint
using axum, following the same inline pattern as ml-training-service.
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>
Replace direct tracing_subscriber::fmt::init() calls with the unified
common::observability::init_observability() in api_gateway, web-gateway,
data_acquisition_service, broker_gateway_service, and trading_agent_service.
All 8 services now emit JSON logs + OTLP traces to Tempo.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Web-gateway routing:
- Point TRADING_SERVICE_URL at api-gateway (proto mismatch fix)
Web-gateway uses foxhunt.tli.TradingService proto but was connecting
directly to trading-service which implements trading.TradingService.
api-gateway already proxies Subscribe* → Stream* correctly.
GitLab KAS:
- Disable gitlab_kas in appConfig to stop sidekiq NotifyGitPushWorker
errors (KAS pod was already disabled but Rails still tried to connect)
Trading service monitoring (3 stubs → real):
- AcknowledgeAlert: real alert lookup + state mutation in shared store
- GetActiveAlerts: returns actual active alerts from in-memory store
- StreamAlerts: now persists generated alerts (capped at 1000 entries)
Trading service ML streams (2 stubs → real):
- StreamModelMetrics: emits real inference_count, error_count, latency
per model every N seconds from the RuntimeModelInfo registry
- StreamSignalStrength: emits per-symbol signal aggregation from model
ensemble weights and latency confidence
Backtesting service:
- stop_backtest: real CancellationToken cancellation (was no-op)
Tokens stored per-backtest, execute_backtest wraps strategy call
in tokio::select! for immediate cancellation
Deleted 7 empty placeholder files:
- 4 Wave D regime stubs (dynamic_stops, ensemble, performance_tracker,
position_sizer) — comment-only files, never wired
- 2 Wave 3 feature stubs (microstructure, statistical)
- 1 PPO stub (unified_ppo.rs — empty struct definitions)
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 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>
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>
- 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>
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>
- Delete 22 dead/placeholder/broken example files (-3,489 lines code)
- Delete 4 tracked CSV files (-1.1M lines, were accidentally committed)
- Move baseline training data default from data/cache/ to test_data/
- Update 5 unified binary defaults, gitignore, k8s upload comment, docs
- Consolidate all training data under test_data/futures-baseline/
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Split the build pipeline: one compile-services job builds all 8 service
binaries with PVC-backed sccache, saves as artifacts. Then 9 Kaniko jobs
just package pre-built binaries into slim runtime images (~30s each).
Before: 9 parallel Kaniko jobs each doing full cargo build --release
(~20min each, no sccache, 9x duplicated dep compilation)
After: 1 compile job with sccache (~5min cached) + 9 package jobs (~30s)
- Add compile stage between test and build
- Add Dockerfile.runtime (minimal debian + pre-built binary)
- Add Dockerfile.web-gateway-runtime (Node dashboard + pre-built binary)
- Keep Dockerfile.training via Kaniko (needs CUDA dev image for H100)
- Remove all SCCACHE_BUCKET build-args from service builds
- Use dir:// context for Kaniko (only sends build-out/ dir, not full repo)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
10 tests require local DBN files (test_data/real/databento/) that are
not checked into the repository. Mark them #[ignore] so CI passes on
nodes without the test data.
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>
Add #![deny(clippy::unwrap_used, clippy::expect_used)] to 11 crates that
were missing it, and standardize 3 existing crates to deny both lints.
Test code is exempted via #![cfg_attr(test, allow(...))].
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>
- Replace 44 incorrect drop(write!()) patterns with let _ = write!()
(drop() on fmt::Result triggers clippy warning; let _ = is idiomatic)
- Fix syntax errors from botched drop→let_ replacement (extra closing paren)
- Remove unused imports in ml/src/dqn/agent.rs (std::fs::File, std::io::Read)
- Remove unused #[allow(clippy::expect_used)] in ml/src/inference.rs
- Fix backtesting_service binary re-declaring library modules (mod x instead
of use backtesting_service::x), which caused false dead_code warnings
Result: 0 warnings across all 37+ workspace crates.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fix backtesting_service compilation after merging dead code removal
- Remove orphaned trait impl methods (check_data_availability, get_sentiment_data,
create_backtest_record, update_backtest_status, store_time_series_data)
- Wire up OHLCV fields (open/high/low/volume) in baseline strategies:
MA crossover uses bar range for volatility filter and bullish bar detection,
buy-and-hold adds volume-based liquidity filter
- Remove TimeFrame enum (unused, all data is minute bars)
- Simplify NewsEvent to unit struct (sentiment fields were never populated)
- Remove dead extract_features method and bar_history buffer from MLPoweredStrategy
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>
Coordinator (ml/src/integration/coordinator.rs):
- Delete 9 fake heuristic methods (500+ lines) that pretended to be
real DQN/TFT/TGGN/LNN/Mamba predictions using sin()/tanh() math
- Make generate_model_specific_prediction() return Err instead of
fake predictions — prevents trading on fabricated signals
- Make ensemble fault-tolerant: skip failed models instead of
failing entire ensemble (execute_parallel/execute_sequential)
- Remove double-fallback in execute_single_model error path
Enhanced ML (trading_service):
- get_model_performance(): compute accuracy from real
inference_count/error_count instead of returning all zeros
- get_feature_importance(): return Status::unavailable instead of
hardcoded fake values — honest about missing SHAP implementation
Autonomous scaling (trading_agent_service):
- diversification_score: compute real HHI from instrument volume
distribution instead of hardcoded 0.8
- ml_confidence: keep liquidity heuristic but remove warn!() spam
Position limiter (risk):
- Remove redundant portfolio_id field from CachedPosition — the
DashMap key already provides account-based isolation
- Remove misleading "stub" comment — was not a stub
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Workspace tokio already specifies the needed features (rt-multi-thread,
macros, net, sync, time, fs, signal, io-util, test-util). Three crates
(test_common, test_harness, vault_integration) were skipped because
they are not workspace members and cannot use workspace = true.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Stashed changes had real safetensors weight loading via VarMap::load.
Audit branch was conservative (validate-only). Kept the real loading
and simplified Mamba2 to use the same VarMap::load pattern as TFT.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The VaR engine's get_symbol_volatility used hardcoded per-asset-class
annual volatility values (e.g. 25% for equities, 80% for crypto) without
any indication to operators that real market data was not being used.
Now emits a tracing::warn on every static volatility lookup so operators
see it in logs. Also adds a volatility_overrides HashMap<String, f64>
field on VarEngine for manual per-symbol overrides until a real market
data feed is integrated.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
reconnect() previously slept 500ms then unconditionally set
SessionState::Active, faking a successful reconnection regardless of
whether any broker or infrastructure was actually reachable.
Now performs a DB health check (SELECT 1) after the sleep. If the
check fails, state is set to Disconnected and an error is returned.
Active is only set when the health check passes. The DB ping is the
best available liveness probe until a real FIX/cTrader session is
wired into SessionRecovery.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
A single CTRADER_LIVE=true env var was enough to route real money orders
through the broker. Now requires CTRADER_LIVE_CONFIRMED=I_UNDERSTAND_REAL_MONEY
alongside it, preventing accidental live trading from copy-pasted configs.
Also adds prominent warning log when live mode activates and info log
for demo mode.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Both route_order and cancel_order used let _ = to discard DB update
errors after successful broker operations. This means an order could be
live at the broker while the database still shows PENDING_SUBMIT or
CANCEL_PENDING, with no log entry to alert operators.
Replaced with if let Err(db_err) that logs at error level with CRITICAL
prefix, client_order_id, and broker_order_id for manual reconciliation.
The RPC still returns success since the broker operation completed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The previous (quantity * 100_000.0) as i64 silently truncated
fractional lots and could overflow on extreme values. Added
convert_quantity_to_volume() that validates the result is finite,
non-negative, and within i64 range, using round() instead of
truncation. Includes 7 unit tests covering normal values, fractional
rounding, overflow, negative, NaN, infinity, and zero edge cases.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
from_checkpoint was creating fresh models with random weights and setting
is_trained=true, allowing trading on noise. Now validates checkpoint exists
and does NOT set is_trained=true when weights are random.
Mamba2 predict errors are now wrapped as MLError::InferenceError so the
fallback manager can degrade model health. Both TFT and Mamba2 predict
refuse to run on untrained models, and is_ready() reflects trained state.
Also fixes TFT input_dim mismatch (16 vs 5+10+16=31).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
try_write() is non-blocking and silently drops the CTraderClient when
the lock is contended. This means a successfully connected broker
client could be lost without any error. Changed to write().await which
guarantees the client is stored. Made function async and updated the
call site in main.rs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Low-confidence ensemble votes now forced to Hold (was letting weak
signals through to order generation in both vote paths)
- Inference latency tracking uses EMA (alpha=0.1) instead of
overwriting with the latest sample
- retrain_model returns Status::Unimplemented instead of faking
success with a random job_id
- get_model_performance returns honest zeroes instead of hardcoded
fake metrics (accuracy=0.85 etc.)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
