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>
PPO now reads architecture config (state_dim, num_actions, hidden_dims,
clip_epsilon, entropy_coeff, max_grad_norm) from the _metadata.json
saved alongside the actor checkpoint. Falls back to hardcoded defaults
for legacy checkpoints without metadata.
Also fixes predict() to use self.feature_count instead of hardcoded 54,
matching the DQN pattern.
323 tests, 0 clippy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
egobox is not a dependency (replaced by argmin PSO long ago).
The egobox_tuner.rs module was pure dead code — optimize_mamba2()
just returned a deprecation error. Deleted the module, its 26 tests,
and the integration test. Cleaned up stale egobox references in
ParameterSpace trait docs.
174 hyperopt tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
FoxhuntClient now holds an AuthInterceptor<FileTokenStorage> and
all 11 typed service accessors use with_interceptor() instead of
bare Channel::new(). This ensures every outgoing RPC automatically
includes the JWT Bearer token when one is cached on disk.
The bare channel() accessor is preserved for LoginClient which
needs unauthenticated access for the initial login RPC.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DQN: disable all exploration for production (warmup=0, epsilon=0,
noisy_nets=false, count_bonus=false), read feature_count from
checkpoint metadata instead of hardcoding 54, add loaded guard
and NaN check on predict output.
PPO: fix confidence formula — use act_with_log_prob() instead of
act() which returns value_estimate not log_prob, add loaded guard
and NaN check, remove WorkingPPO alias.
Liquid: add loaded flag for is_ready()/predict() guards.
Remove EgoboxOptimizer/EgoboxOptimizerBuilder backward-compat
aliases — replaced with canonical ArgminOptimizer everywhere.
323 trading_service tests, 200 hyperopt tests, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Extract 5 model wrappers from enhanced_ml.rs into dedicated files:
dqn_model.rs, ppo_model.rs, tft_model.rs, mamba2_model.rs, liquid_model.rs
- Rename enhanced_ml.rs → ml.rs, EnhancedMLServiceImpl → MLService
- Drop legacy "Real" prefix: DQNModel, PPOModel, TFTModel, Mamba2Model, LiquidModel
- Delete dead ml.rs stub (was never wired in mod.rs)
- Move model tests into their respective modules with proper test helpers
- DQNModel uses clean DQN API with FactoredAction (45 actions), checkpoint-driven config
- 312 tests passing, 0 clippy warnings
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add BROKER_GATEWAY_SERVICE_URL, DATA_ACQUISITION_SERVICE_URL, and
ML_SERVICE_URL env vars so the gateway can connect to all backend services.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace 3 stub RPCs with real implementations:
- get_system_status: fan-out gRPC health checks to all backends, reports SystemStatus
- get_health_check: fan-out checks with response time measurement, returns HealthCheck entries
- get_metrics: queries Prometheus for requested metric names
MonitoringServiceHandler now accepts backend URLs via with_backends() builder.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Read new env vars: BROKER_GATEWAY_SERVICE_URL, DATA_ACQUISITION_SERVICE_URL,
ML_SERVICE_URL, TRADING_AGENT_SERVICE_URL
- Create lazy channels: trading channel shared by trading/risk/config proxies,
separate channels for ML, broker, data_acq, trading_agent
- Register 7 new services on router with auth interceptor
- Update reflection service with all 6 new proto descriptors
- Add broker/data_acq/trading_agent to health check backend list
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fxt 2.0 overhaul: consolidated proto/, absorbed monitoring into API Gateway,
new 15-command CLI with gRPC, MCP server mode, TUI cockpit framework.
159 files changed, net -11,835 lines.
Wire LR scheduler to actually update the Adam optimizer (was logged but
never applied). Add update_learning_rate to DQN, RegimeConditionalDQN,
and DQNAgentType so decay_factor propagates through all agent variants.
Fix train/eval parity: CLI defaults 51→54 features, 3→45 actions;
DQN eval always uses 3-layer hidden_dims; PPO eval uses 5-layer value
network matching trainer. enhanced_ml.rs hardcoded config updated from
state_dim=16/num_actions=3 to 54/45.
Fix Candle F32/BF16 traps: replace `Tensor * 0.5` (f64 literal) with
broadcast_mul(Tensor::full(0.5_f32)) in quantile_regression.rs (2x),
dqn.rs Huber loss, and IQN gamma multiplication. Prevents panics on
Ampere+ BF16 GPUs.
Add urgency_weight() multiplier to training cost model in reward.rs
and portfolio_tracker.rs — urgency dimension (Patient/Normal/Aggressive)
now affects learned value function, matching evaluate_baseline.rs.
Fix equity tracking: additive (equity += ret) → multiplicative
(equity *= 1.0 + ret) in compute_metrics. Fix total return calc.
Fix PER beta annealing: epochs*70 → epochs*1000 (~1015 actual steps
per epoch for 130k bars / batch 128).
Fix silent target network freeze: mutex lock failure now propagates
error instead of silently skipping weight update.
Make save_checkpoint atomic (write .tmp then rename). Make NormStats
write atomic with error logging instead of silent discard.
Convert EnsembleConfig::new assert! → Result<Self, MLError> with
14 call site updates. Fix hyperopt result serialization to warn
instead of silently dropping to Value::Null.
Fix clippy MSRV mismatch: clippy.toml 1.75 → 1.85 matching Cargo.toml.
2732 tests pass, 0 clippy warnings across workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- DQNAgent::load_from_safetensors: validate architecture hash before
loading weights (was bypassing validation entirely - P0 production gap)
- DQNEnsemble::save_to_directory: embed architecture metadata via
safetensors::serialize_to_file instead of bare VarMap::save (save/load
round-trip was guaranteed to fail)
- architecture_hash: hash hidden_dims.len() before values to prevent
theoretical collision between different-length configs
- train_baseline_rl: NormStats write now atomic (write .tmp then rename)
with cleanup guard on rename failure; serialization error is now loud
(error! + return None) instead of silently discarded
- train_baseline_rl: checkpoint rename failure cleans up orphaned .tmp
- hyperopt_baseline_rl: fix clippy single_match_else (match on equality
check -> if/else)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace `let _ = sync_tensor.to_vec0::<f32>()` with `drop()` to
satisfy clippy's let_underscore_must_use lint on the Result return.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
