Wire 8 OFI features (OFI L1/L5, depth imbalance, VPIN, Kyle's lambda,
bid/ask slopes, trade imbalance) through the DQN training pipeline:
- Add mbp10_data_dir config field to DQNHyperparameters
- Dynamic state_dim: 43 (no OFI) or 51 (with OFI) based on config
- Compute OFI per bar during data loading, store on trainer
- Pass OFI features through regime_features slot in TradingState
- Configurable MBP-10 path with recursive .dbn/.dbn.zst discovery
- Add zstd auto-detection to DbnParser::parse_mbp10_file()
- Add --mbp10-data-dir CLI flag to train_baseline_rl
- Fix hardcoded [f64; 51] → FeatureVector51 ([f64; 40]) across
examples, walk_forward, GPU memory profile, and test fixtures
- Fix stale state_dim=51 in dqn_config_2025() and DQN tests
2747 tests pass, 0 failures.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ephemeral Argo workflow pods terminate after training completes, causing
Prometheus to lose all scraped metrics. Add push_to_gateway() to POST
final metrics to the existing pushgateway service so they persist on the
Grafana training dashboard after pod completion.
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>
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>
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>
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>
- Wire position-delta cost model: costs only apply on position changes,
proportional to delta (not flat per-bar)
- Differentiate order types: Market=15bps, LimitMaker=5bps, IoC=10bps
- Apply urgency weight: Patient=0.5x, Normal=1.0x, Aggressive=1.5x
- Append 3 portfolio features (position, unrealized PnL, drawdown) to match
training's 54-dim state vector (was 51, causing shape mismatch on load)
- Fix default num_actions 3->45 to match training's factored action space
- Add --bars-per-year CLI flag for per-symbol annualization
(ES/NQ=347760, 6E=345000, ZN=105840)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Atomic checkpoint writes: write to .tmp then fs::rename() (POSIX atomic)
- Remove redundant thread::sleep(50ms) after CUDA tensor readback sync
- NormStats: bail on missing file instead of computing from test data (lookahead bias)
- q_value_std: warn when missing from training metrics instead of silent 0.0 fallback
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
Both DQN and PPO eval paths used old 3-action index matching (0=Buy,
1=Sell, 2=Hold). Now uses FactoredAction.target_exposure() for
exposure-weighted returns and order-type-specific transaction costs.
PPO path had .to_int() which doesn't exist on FactoredAction.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Emit set_eval_metrics (win_rate, sharpe, profit_factor, return) per fold
for DQN/PPO evaluation. Start metrics server on :9094. Feeds training
cockpit Grafana dashboard eval panels.
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>
Wire BF16/FP16 mixed precision end-to-end for DQN and PPO with auto-detection
from GPU name (Ampere+ → BF16, Volta/Turing → FP16). Add hidden_dim_base to
hyperopt and wire through training/eval binaries. Reduce GPU sync points: make
DQN NaN checks periodic (every 100 steps), replace PPO GAE GPU round-trip with
pure CPU implementation. Cache training data across hyperopt trials for all 10
models via Arc. Batch DQN experience storage (128x fewer lock acquisitions).
Correct VRAM constants and batch bounds for all 9 supervised model adapters.
28 files changed, +1207/-208 lines. 2418 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>
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>
