Add #![deny(clippy::unwrap_used, clippy::expect_used)] to 11 crates that
were missing it, and standardize 3 existing crates to deny both lints.
Test code is exempted via #![cfg_attr(test, allow(...))].
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace _param suppression pattern with actual usage across 21 files:
- adaptive-strategy: wire EpistemicConfig/AleatoricConfig into
UncertaintyQuantifier, KellyConfig into DrawdownTracker,
TLOBConfig into TLOBTransformer
- trading_engine/compliance: store config in 26 compliance structs
(audit_trails, best_execution, sox, iso27001, transaction_reporting,
compliance_reporting, automated_reporting) with public accessors
- fxt: store Channel in LoginClient, ConnectionConfig in ConnectionManager
- ml: remove unused path param from ReplayBuffer::new(), wire
Mamba2Config.target_latency_us into HardwareOptimizer
- services: store TrainingConfig in GpuConfigManager, symbol in
TechnicalIndicatorCalculator
- database: change let _result to let _ (intentional discard)
- trading_engine/brokers: store BrokerConnectorConfig in BrokerConnector
Result: 0 warnings across all 37+ workspace crates.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replace 44 incorrect drop(write!()) patterns with let _ = write!()
(drop() on fmt::Result triggers clippy warning; let _ = is idiomatic)
- Fix syntax errors from botched drop→let_ replacement (extra closing paren)
- Remove unused imports in ml/src/dqn/agent.rs (std::fs::File, std::io::Read)
- Remove unused #[allow(clippy::expect_used)] in ml/src/inference.rs
- Fix backtesting_service binary re-declaring library modules (mod x instead
of use backtesting_service::x), which caused false dead_code warnings
Result: 0 warnings across all 37+ workspace crates.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Coordinator (ml/src/integration/coordinator.rs):
- Delete 9 fake heuristic methods (500+ lines) that pretended to be
real DQN/TFT/TGGN/LNN/Mamba predictions using sin()/tanh() math
- Make generate_model_specific_prediction() return Err instead of
fake predictions — prevents trading on fabricated signals
- Make ensemble fault-tolerant: skip failed models instead of
failing entire ensemble (execute_parallel/execute_sequential)
- Remove double-fallback in execute_single_model error path
Enhanced ML (trading_service):
- get_model_performance(): compute accuracy from real
inference_count/error_count instead of returning all zeros
- get_feature_importance(): return Status::unavailable instead of
hardcoded fake values — honest about missing SHAP implementation
Autonomous scaling (trading_agent_service):
- diversification_score: compute real HHI from instrument volume
distribution instead of hardcoded 0.8
- ml_confidence: keep liquidity heuristic but remove warn!() spam
Position limiter (risk):
- Remove redundant portfolio_id field from CachedPosition — the
DashMap key already provides account-based isolation
- Remove misleading "stub" comment — was not a stub
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add debug_assert_eq! guards in 4 train_baseline functions to catch
bar/feature length misalignment at debug time (#4)
- Remove "last sample targets itself" block in hyperopt PPO adapter
that created ~0 return sample biasing toward HOLD (#5)
- Align hyperopt state_dim 54→51 and num_actions 45→3 to match
train_baseline architecture, making tuned hyperparams transferable (#6)
- Use greedy_action() in evaluate_baseline PPO eval for deterministic results
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three critical PPO fixes:
1. Add PPO::act_with_log_prob() returning (action, log_prob, value).
The existing act() discarded the policy log-probability, making
PPO importance sampling use wrong ratios during training.
2. Cap trajectory length with --max-steps-per-epoch in train_baseline
PPO path. DQN already had this limit; PPO iterated all features
(~500K per fold), causing OOM on 4GB GPU.
3. Replace random actions and fake log_prob/value in hyperopt PPO
adapter with real agent.act_with_log_prob() calls. Trajectories
now reflect actual policy behavior for meaningful hyperopt.
Also adds warmup offset alignment to PPO trajectory collection
(matching the DQN fix) and fixes .unwrap() in test.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Use chrono-tz America/New_York for correct EST/EDT trading session
boundaries (was hardcoded UTC-5, off by 1h during daylight saving)
- Normalize effective weights to sum to 1.0 before signal aggregation
(raw weights could sum to anything, biasing the ensemble)
- Skip adapter predictions that return NaN/Inf direction or confidence
instead of letting them poison the weighted average
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PPO hyperopt: Split DATA 80/20 for train/val (not trajectory count).
Previously indexed training_data[..num_trajectories] which was only
16 samples from 56K — causing num_batches=0 and NaN from 0/0 division.
Now properly splits data array and uses min(64, num_train) for batch
episodes with ceil division for batch count.
DQN: Fix hardcoded "45 actions" in diagnostic log (now uses actual
q_vec.len()). Replace indexed[..top_n] slice with .take(top_n)
iterator to prevent potential slice panic.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Databento data pipeline stored files as .dbn.zst in symbol subdirectories
(e.g., ES.FUT/ES.FUT_2024-Q1.dbn.zst), but the hyperopt adapters and DQN
data loader only matched .dbn extension and used flat directory listing.
Three fixes:
- Match both .dbn and .dbn.zst file extensions in collect_dbn_files_recursive
- Use recursive directory traversal instead of flat read_dir
- Branch on extension to use Decoder::from_zstd_file() for .dbn.zst files
(from_file() doesn't handle zstd decompression)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implements expanding-window walk-forward cross-validation for time-series
ML models, preventing lookahead bias by always evaluating on unseen future
data. Includes z-score NormStats computed from training splits only.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Sequence-buffered adapter with 3-layer MLP projection
(flat_dim -> hidden -> hidden/2 -> 1). Ring buffer collects
seq_len feature vectors, returns neutral prediction until full,
then flattens and projects through the MLP with sigmoid output
mapping to direction [-1,1] and confidence [0,1].
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove panic!() calls from test_futures_baseline_micro_mapping,
use map()+Some() pattern consistent with rest of test suite
- Make DatasetSpec::from_universe() accept a name parameter instead
of hardcoding "futures-baseline"
- Add doc comment to UniverseConfigMeta explaining dead_code fields
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove duplicate AssetClass enum from data_pipeline module and replace
with a re-export from asset_selection, which is now the canonical
location. This ensures data_pipeline::AssetClass and
asset_selection::AssetClass are the same type, enabling direct
comparison and preventing subtle type mismatch bugs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Gate optimizer adjusts conviction gate thresholds based on win-rate per
confidence bucket with cooldown and kill switch safety rails. Model
registry provides lifecycle management (Candidate → Staging → Production
→ Archived) with InMemoryModelRegistry for testing. P&L attribution
decomposes realized trade P&L into per-model contributions using signal
alignment.
24 new tests across 3 modules.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Introduces the asset_selection module with AssetClass enum (Equity/ETF/Future),
UniverseAsset struct, and AssetUniverse with filtering, lookup, and a us_starter()
preset of 7 highly liquid US assets. Includes scorer/selector placeholder stubs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adjusts model weights based on rolling Sharpe ratios with:
- EMA smoothing (alpha=0.1)
- Bounds: [0.05, 0.40] per model
- Max step: 0.03 per cycle
- 24h cooldown, 7-day grace period for new models
- Kill switch freeze/unfreeze
8 tests.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds optional ConvictionGateEvaluator field to EnsembleCoordinator.
When configured, predict() evaluates all 7 gates before returning.
If any gate rejects, returns HOLD with rejection metadata.
Includes quorum ratio, trading session, and regime volatility helpers.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
7-gate conviction system, autonomous feedback loop with kill switch,
and Rust-native model registry — backed by PostgreSQL + QuestDB.
Resolve merge conflicts in hyperopt/adapters/mod.rs and enhanced_ml.rs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Scale model_memory_mb by activation_multiplier in resolve_batch_size()
so TFT (2.5x) gets proportionally smaller batches than DQN (1.0x)
- Add cached_capabilities() with OnceLock to avoid spawning nvidia-smi
on every call to CampaignConfig::dqn_default/ppo_default/PpoTrainer::new
- Add PartialEq to GpuCapabilities and simplify serde roundtrip test
- Update stale "RTX 3050 Ti" and "Exceeds GPU limit (230)" comments
to reflect dynamic detection
- Document Auto variant silent CPU fallback behavior (no callers affected)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add DeviceConfig and GpuCapabilities re-exports to ml::prelude so
downstream crates can import GPU management types via a single
`use ml::prelude::*` statement.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace hardcoded max_batch_size: 230 in CampaignConfig::dqn_default()
and ppo_default() with dynamic GPU detection via GpuCapabilities::detect()
and resolve_batch_size(). Update test to no longer assert <= 230.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
