All 32 MCP tool handlers now make real gRPC calls to the API Gateway
instead of returning stub responses. Covers: service monitoring,
training management, trading operations, ML inference, risk metrics,
broker status, data acquisition, cluster resources, agent control,
config management, and hyperopt tuning.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
end_to_end_tests.rs and error_handling_tests.rs imported non-existent
modules (fxt::client, fxt::prelude, etc.) from a prior architecture.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- StreamSystemStatus now queries real Prometheus data (was all zeros)
- StreamAlerts returns honest unimplemented (was sending empty events)
- Training epoch shows "N" not "N/0" (max_epochs not in proto)
- GPU power shows "300W" not "300W / 0W" when limit unavailable
- Services cockpit shows error reason when service is DOWN
- System status polling detects staleness after 3 consecutive failures
- Training cockpit header shows active K8s job count
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 30 MCP tool handlers now return structured JSON error with
isError=true instead of fake "stub: would ..." placeholder responses.
Tool registrations preserved so list_tools still works.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
config_service.proto (package foxhunt.config, 4 RPCs) was a subset of
config.proto (package config, 12 RPCs). Migrated api_gateway to implement
ConfigService from config.proto directly. Removed dead config_service()
method from fxt client.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
57 references updated across source, tests, benches, and config.
Proto package name foxhunt.tli kept for wire compatibility.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace three occurrences of String::new() used as "fetch all" filter
values in gRPC request construction with a named FILTER_ALL constant,
making the intent explicit.
The api_gateway monitoring handler was reviewed but its Prometheus URL
appears only once in tests, so no constant was extracted (single-use).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Use em-dash (—) consistently instead of mix of - and — for "no data"
placeholders across all cockpits (data, trading, risk, services, pods)
- Collapse pipeline status section to single "not connected" message
instead of showing 4 misleading rows of dashes
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Same class of bug as the DQN fix (4c88498b): network outputs were
being cast to F32 mid-pipeline, defeating tensor-core acceleration
on H100/L40S. Now the full training loop stays in training_dtype()
(BF16 on CUDA Ampere+, F32 on CPU), with F32 casts only at scalar
extraction boundaries (to_scalar, to_vec1).
Files fixed:
- ppo.rs: Actor/Critic forward, act_with_log_prob, compute_losses,
update_mlp, LSTM recurrent loop, predict method
- lstm_networks.rs: removed F32 output casts from both networks
- continuous_ppo.rs: one_tensor + scalar extractions
- hidden_state_manager.rs: zeros/ones use training_dtype()
- flow_policy/mod.rs: log_det accumulators + dummy log_std
- ensemble/adapters/ppo.rs + dqn.rs: F32 cast at extraction
2704 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove premature F32 output casts from network forward passes
(Sequential, NetworkLayers, DistributionalDueling) that were negating
tensor-core acceleration. Instead, cast F32 input tensors (rewards,
dones, gamma, weights, atoms) to training_dtype() at the boundary
and only escape to F32 at scalar extraction points (loss value,
TD errors, logging vectors).
465 DQN tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add ServiceAccount, Role, and RoleBinding so the api-gateway pod can
list/get/watch pods and pod metrics in the foxhunt namespace. Also sets
serviceAccountName on the Deployment to bind the new SA.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Allow api-gateway to reach the Kubernetes API server for pod listing
(Pods cockpit). Two CIDR blocks are needed because Cilium applies
NetworkPolicy after DNAT:
- 10.32.0.0/12:443 — kubernetes.default.svc ClusterIP
- 172.16.0.0/16:6443 — real node IP after Cilium DNAT
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add kube 3.0 + k8s-openapi 0.27 dependencies and create pods_handler.rs
that queries the Kubernetes API for pod status. Replace the UNIMPLEMENTED
stub in monitoring_handler.rs with a real streaming implementation that
polls pods every N seconds and maps them to PodInfo proto messages.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a new gRPC stream spawner that subscribes to SubscribeClusterPods
and groups pods by service name for the pods cockpit view. Follows the
same backoff/reconnect pattern used by all other stream spawners.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add query_cluster_resources() for real CPU/RAM from Prometheus
- Wire into build_response() replacing hardcoded zeros
- Fix NetworkPolicy: add ports 8080/9100/9400 for KSM/node-exporter/dcgm
- Add CiliumNetworkPolicy for hostNetwork pod scraping
- Add SubscribeClusterPods stub to unblock proto addition from Task 5
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add K8s pod data structures to TUI state layer for the upcoming pods
cockpit view. PodData represents individual pods, PodGroupData groups
them by service with ready/total counts. The StateUpdate::Pods variant
and apply_update handler wire the data path from gRPC streams to state.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
NoisyLinear layers have different parameter names (sigma_weight/sigma_bias)
than standard Linear layers. Hardcoding use_noisy_nets=false caused
checkpoint loading to silently fail when the training run used noisy nets,
resulting in empty folds in the eval report.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Pool ci-training-h100x2 (H100-2-80G, 160GB VRAM total).
MultiGpuConfig::detect() in ml crate auto-enables data parallelism.
Tags: kapsule,h100x2. Autoscale 0→1.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Clear separation: l40s tag → L40S pool, h100 tag → H100 pool.
Updated runner-rl-values.yaml and CI base templates to match.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Create ci-training-h100 runner (runner-h100-values.yaml)
- Route hyperopt-dqn and hyperopt-ppo to H100 via kapsule,h100 tags
- H100 is ~2x faster and cheaper per run vs L40S for hyperopt workloads
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
num_atoms, dueling_hidden_dim, v_min/v_max, gamma were using defaults
instead of hyperopt values — causing tensor shape mismatch on checkpoint
load (e.g. output layer 45×200=9000 vs 45×51=2295). Also fixed use_iqn
to read from use_qr_dqn key (matching trainer's field mapping).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
evaluate_baseline was using default hidden_dim_base=128 instead of the
hyperopt-optimized 4096, causing checkpoint loading to fail silently
(tensor shape mismatch). This produced empty folds: [] in eval reports.
Also adds consistent --tx-cost-bps/--tick-size/--spread-ticks to
train_baseline_rl and evaluate_baseline in the hyperopt-dqn job so all
three pipeline stages (hyperopt, train, eval) use the same cost model.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Hyperopt found hold_penalty_weight=1.818, curiosity_weight=0.402, etc.
but walk-forward training used tiny defaults (0.01, 0.1), causing the
agent to learn "holding is optimal" → 0 trades across all 50 epochs.
Now passes: hold_penalty_weight, max_position_absolute, huber_delta,
entropy_coefficient, curiosity_weight, weight_decay, kelly_fractional,
kelly_max_fraction from hyperopt JSON to DQNHyperparameters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Walk-forward DQN training hardcoded epsilon_start=1.0 with noisy_nets=true,
forcing pure random exploration for all 50 epochs (0 trades, 0 Sharpe).
Now reads epsilon, PER, dueling, distributional, noisy nets, and QR-DQN
params from the hyperopt JSON. When noisy_nets=true, defaults epsilon to
0.05 instead of 1.0.
Evaluator now falls back to highest dqn_fold{N}_epoch{E}.safetensors when
_best.safetensors doesn't exist (early stopping saves epoch checkpoints).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
17-task plan for replacing hardcoded mock data in fxt watch
with real gRPC streaming data from the API Gateway.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Streaming-first architecture: 11 new poll-to-stream gateway adapters,
channel-based DataFetcher in the TUI, auto-reconnect with backoff.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Flip 4 positions in the base64 payload instead of 1 to eliminate
any chance of environmental flakiness from OsRng contention.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Added select_action_inference(&self) — pure greedy forward pass with
no exploration mutations (epsilon, noisy nets, count bonus, step counter
are all skipped). Returns (FactoredAction, f32) where confidence is
softmax probability of the selected action, clamped [0.5, 0.95].
Also added select_action_with_confidence(&mut self) for training path
with the same confidence computation.
Production wrapper now uses read lock (was write lock), enabling
concurrent predictions. Confidence is real softmax probability
instead of hardcoded 0.85.
323 trading_service tests, 15 DQN core tests, 0 clippy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Auto-refresh: before non-auth commands, if the access token is expired
but a refresh token exists, silently regenerate tokens. Prints
"Token refreshed." to stderr so users know what happened.
- FXT_PASSWORD: login reads password from this env var when set,
skipping the interactive prompt. Safer than a --password flag
(env vars don't appear in ps output or shell history).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
