Adds accessor methods for all 11 service clients (trading, ml, ml_training,
broker_gateway, trading_agent, monitoring, config, data_acquisition, risk,
health, config_service). All clients share a single gRPC channel.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Port Prometheus scraping and training metrics RPCs directly into
api_gateway, eliminating the monitoring-service backend proxy.
MonitoringServiceProxy replaced with MonitoringServiceHandler that
queries Prometheus directly for training metrics, GPU stats, and
active job counts.
Changes:
- monitoring_proxy.rs -> monitoring_handler.rs with embedded PrometheusClient
- Remove MonitoringBackendConfig and setup_monitoring_proxy from server.rs
- main.rs reads PROMETHEUS_URL + MONITORING_STREAM_INTERVAL instead of
MONITORING_SERVICE_URL
- Remove monitoring-service from backend health check list
- Monitoring is always available (no graceful degradation needed)
- Fix fxt build.rs compile_protos type mismatch
- All 9 ported unit tests + 0 clippy warnings
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fxt now compiles from the same service protos as all backend services.
Deleted 8 fat-client protos (3,286 lines) from bin/fxt/proto/.
Updated include_proto! macros to use new package names (trading, ml,
config instead of foxhunt.tli, foxhunt.ml, foxhunt.config).
Added risk, data_acquisition, and config_service proto modules.
Fixed duplicate include_proto in agent.rs to use crate::proto.
Existing command implementations will be rewritten to use new proto
types in Task 6.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add print_financial_metrics() to the monitor command, displaying
per-model Sharpe ratio (color-coded green/yellow/red), win rate,
max drawdown, profit factor, total return, trade count, and
action distribution (BUY/SELL/HOLD percentages). Only shown when
sessions report non-zero financial data.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Sharpe/Win% columns to training list table, financial summary lines
(Sharpe, Sortino, Win Rate, Max DD, PF, Return, Avg, Trades) to the
detail overview, action distribution (BUY/SELL/HOLD %) to current metrics,
and four new sparklines (Sharpe, Win Rate, Max DD, Total Return) to the
Metrics sub-tab.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add 11 financial fields (sharpe, sortino, win_rate, max_drawdown,
profit_factor, total_return, avg_return, total_trades, action
distribution) to TrainingSession (fields 36-46), a new
GetEpochHistory RPC with request/response messages, and wire the
Prometheus metric mapping in the monitoring service with a
stub RPC handler for task 7.
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>
The fxt client was using a hardcoded dev fallback secret for JWT signing,
causing InvalidSignature errors against the API gateway. Now reads
jwt_secret from ~/.foxhunt/config.toml with fallback chain:
env var > config file > dev secret.
Also updates default api_gateway_url to https://api.fxhnt.ai.
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>
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>
Adds a new CLI subcommand that connects to the monitoring service via
gRPC to display live training metrics. Supports single-snapshot mode
(--once), model filtering (--model), and configurable streaming
interval. Renders per-model epoch/loss table, GPU telemetry, hyperopt
progress, and health counters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- ibapi handshake: retry once after 1s on first failure (IB Gateway
needs time to release stale client_id slots after disconnection)
- test_load_nonexistent_config → test_load_config_succeeds: remove
default-value assertions that fail when ~/.foxhunt/config.toml exists
(defaults already tested by test_serde_defaults and test_default_config)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Match the actual K8s broker-gateway service port discovered during
live validation of fxt broker check.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The IB Gateway JVM uses non-blocking I/O on port 4002, which causes
EAGAIN errors with ibapi's blocking Client::connect(). The gnzsnz
image provides socat on port 4004 as a stable proxy that bridges
blocking clients to the non-blocking gateway socket.
Port 4004 is the correct client-facing port for paper mode (4003 for
live). The K8s readiness probe stays on 4002 since TCP probes work
fine with non-blocking sockets.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Broker variant to Commands enum with BrokerArgs (flatten),
route to execute_broker_command in match block (no JWT required),
exit(1) on check failure. Clone config.api_gateway_url to avoid
partial move. Add two CLI parsing tests for broker check.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Copy broker_gateway.proto from broker_gateway_service into fxt/proto/
and wire it into build.rs compile_protos + lib.rs proto module so the
CLI can call BrokerGatewayService RPCs (health check, account state,
session status).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds IbkrConfig (host/port/client_id with defaults for paper trading)
and BrokerConfig (gateway_url + ibkr nested) to the TLI config file.
Both structs derive serde defaults so existing config files remain
backward-compatible.
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>
Add nginx server block for api.fxhnt.ai with grpc_pass to the
api-gateway ClusterIP service. Add Scaleway DNS A record via Terraform
module. Fix fxt gRPC client to enable TLS when connecting to https://
endpoints (tonic requires explicit ClientTlsConfig).
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>
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>
test_machine_id_derivation reads /etc/machine-id which doesn't exist
in container environments.
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>
