The batch span processor needs a tokio runtime for gRPC transport and
periodic flush. Async services already have one via #[tokio::main], but
sync training binaries (hyperopt, train, evaluate) don't.
Previous approach (making binaries async with #[tokio::main]) caused
"Cannot start a runtime from within a runtime" panics because the ML
crate's internal code creates its own tokio runtimes for block_on().
New approach: build_otel_tracer() detects runtime context via
Handle::try_current(). If absent, it creates a dedicated 1-worker
multi-thread runtime stored in a process-lifetime OnceLock. The worker
thread actively polls the OTLP batch export task.
Reverts training binaries to sync fn main() so internal runtime creation
(hyperopt adapters, DQN/PPO trainers) continues working as before.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 6 training binaries (hyperopt_baseline_rl, hyperopt_baseline_supervised,
train_baseline_rl, train_baseline_supervised, evaluate_baseline,
evaluate_supervised) used sync fn main() but the OTLP batch exporter
requires a tokio runtime (tonic/hyper-util gRPC transport). This caused
an immediate panic on CI when OTEL_EXPORTER_OTLP_ENDPOINT was set.
Fix: #[tokio::main(flavor = "current_thread")] on all 6 binaries.
Also fix pre-existing clippy warnings (shadow, let_underscore_must_use,
doc_markdown, cognitive_complexity, integer_division, unsafe_code).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Emit set_eval_metrics (directional_accuracy, sharpe, profit_factor, return)
per fold for supervised model evaluation. Start metrics server on :9094.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace tracing_subscriber::fmt() with init_observability() which adds
JSON structured logging + optional OTLP export to Tempo. When
OTEL_EXPORTER_OTLP_ENDPOINT env var is unset, falls back to fmt-only.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
evaluate_supervised looked for checkpoints at {models_dir}/{model}_fold{N}_best
but training saves to {models_dir}/{model}/{model}_fold{N}_best (model subdirectory).
Also saves NormStats JSON per fold during training so evaluation uses
training-time normalization instead of computing from test data (data leakage).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
New evaluate_supervised binary runs walk-forward inference on supervised
model checkpoints (TFT, Mamba2, etc.), converts directional predictions
to trading signals, and computes Sharpe/MaxDD/WinRate/DirAccuracy.
CI changes:
- train-validate-dqn → train-validate-rl (trains+evals DQN+PPO)
- train-validate-tft now runs evaluate_supervised after training
- web/api fallback rules added to train-validate and deploy stages
- evaluate_supervised added to compile-services and Docker images
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
