Replace the stub OCSP implementation with a fully functional RFC 6960
compliant revocation checker that:
- Builds OCSP requests using SHA-1 hashes of issuer name/key via the
ocsp crate's CertId and OneReq types with proper DER encoding
- Sends requests via HTTP POST with correct Content-Type headers
- Parses DER-encoded OCSP responses to extract response status
(successful/malformed/internal-error/try-later/unauthorized)
- Determines certificate status (Good/Revoked/Unknown) by locating
the serial number and scanning for ASN.1 context-specific tags
- Caches results in the existing LRU cache with TTL
- Increments Prometheus metrics for all failure paths
Replace the 2-feature placeholder (close, volume) in create_training_samples()
with the full 51-dimension extract_ml_features() pipeline. The UnifiedDataLoader
now converts MarketDataContainers to OHLCVBars and runs the production feature
extractor when use_unified_extractor is enabled, falling back to the basic
2-feature path if extraction fails or the extractor is disabled. Also populates
feature metadata with the 51 named features matching extraction.rs ordering.
Fix three compilation errors in the observability module that had been
commented out due to type errors with tracing_subscriber:
- Fix lifetime issue in set_correlation_id by cloning Arc before async
- Fix Option<CorrelationId> vs CorrelationId type mismatch in get_correlation_id
- Update deprecated opentelemetry_sdk::trace::Config API to builder methods
- Remove unused imports across all observability submodules
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add #![deny(clippy::unwrap_used, clippy::expect_used)] to api_gateway and
backtesting_service crate roots. The ml and common crates already had these
deny attributes.
Production code fixes:
- api_gateway: replace expect with unwrap_or for cache stats and LRU eviction
- api_gateway: add #[allow] on NonZeroU32 constants and Prometheus metrics
- backtesting_service: replace expect/unwrap with ok_or_else/? for OHLCV
bar bounds checks and chrono timestamp resampling
- backtesting_service: add #[allow] on Prometheus Lazy metric statics
Test code: cfg_attr(test, allow) at crate root for both crates.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace three placeholder stubs that returned Err("pending") with
working implementations that create real trainers and run actual
training loops:
- train_ppo: Creates PpoTrainer with conservative defaults,
loads market data as state vectors, runs PPO training with
progress callback, saves checkpoint metadata
- train_mamba2: Creates Mamba2Trainer with validated hyperparams,
generates training/validation tensor pairs, runs MAMBA-2
sequence training, collects training statistics
- train_tft: Creates TFTTrainer with FileSystemStorage, attempts
Parquet data loading first with synthetic data fallback,
runs TFT training with OOM retry support
Also fixes 10 pre-existing compilation errors:
- Import PPOHyperparameters/PPOTrainer -> PpoHyperparameters/PpoTrainer
- Import TFTHyperparameters -> TFTTrainerConfig (correct type name)
- ModelType::LIQUID -> ModelType::LNN (correct enum variant)
- DQNHyperparameters struct literal -> conservative() with overrides
- checkpoint_manager.config() -> direct PathBuf construction
- DQN checkpoint callback 2-arg -> 3-arg (epoch, data, is_best)
- opts partial move -> clone version_tag before unwrap_or_else
- Add catch-all arm for exhaustive ModelType matching
- Add FileSystemStorage import for TFT trainer construction
- Prefix unused variables with underscore
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace the TODO placeholder with proper CRL validation that checks:
- thisUpdate is not in the future (CRL not yet valid)
- nextUpdate is not in the past (CRL expired)
- CRL issuer matches the certificate issuer (prevents CRL substitution)
All checks use x509-parser's ASN1Time for accurate time comparison
and log warnings/errors via tracing for operational visibility.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Production trading must never rely on simulated predictions. The ensemble
coordinator now requires real model adapters and returns errors when none
are registered or all fail inference, instead of silently falling back to
mock/simulated predictions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace empty placeholder safetensors file with actual model weight
serialization using the VarMap, matching the DQN trainable adapter pattern.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace the TODO stub in validate_certificate_chain with full
cryptographic chain-of-trust verification using x509-parser's ring-backed
verify feature:
1. Issuer CN matching -- client cert issuer must equal CA subject CN
2. CA validity period -- reject expired or not-yet-valid CA certificates
3. CA Basic Constraints -- verify the signer actually has cA=true
4. Cryptographic signature -- verify client cert TBS data signature
against the CA's SubjectPublicKeyInfo (RSA/ECDSA/Ed25519 via ring)
Also enables the "verify" feature on x509-parser and extends the method
signature to accept ca_cert_pem for proper chain validation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace 6 unwrap() calls with safe error handling in DQN IQN code:
- Production: 3 unwrap() on iqn_network/iqn_target_network replaced with
ok_or_else returning MLError::ModelError for clear diagnostics
- Tests: 2 result.unwrap() replaced with ?, 2 DQN::new().unwrap() replaced
with ? after changing test signatures to return anyhow::Result<()>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Bite-sized steps for each task with exact file paths, code snippets,
verification commands, and parallel execution strategy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Safety, live trading, security, and observability gaps identified
from comprehensive codebase audit. Organized for parallel swarm execution.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add #[allow(unsafe_code)] on each unsafe impl Send/Sync for the four
inference adapters (DQN, PPO, TFT, Mamba2). The SAFETY comments explain
why these are sound — Mutex provides exclusive access.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement real quantile-based VaR at 95% and 99% confidence, proper
Expected Shortfall (CVaR) as tail mean, sqrt-of-time 10-day scaling,
and safe .get() access instead of array indexing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire S3-compatible upload/exists/delete using object_store crate.
Fix generate_object_key to return Result instead of panicking.
Add 4 unit tests with InMemory backend.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace empty MLEngine struct with actual EnsembleCoordinator from ml
crate. Registers DQN, PPO, TFT, Mamba2 models with configurable
weights loaded from config repository.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- fix(trading_engine): replace Prometheus panic! with graceful registration
- fix(trading_service): implement partial fill matching in order book
- feat(trading_service): replace feature extraction stub with real 51-dim pipeline
- feat(trading_service): wire RiskEngine with real VaR calculator
- fix(api_gateway): implement real ML prediction proxy
- feat(data_acquisition): implement DBN data downloader
- feat(data): wire DBN uploader with MinIO integration
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace stub Poll::Pending with actual stream polling that reads from
underlying data source and converts DBN messages to MarketEvents.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Validates full pipeline: DBN converter roundtrip, replay engine streaming,
model loading + prediction, 51-dim feature extraction, and synthetic
backtest with DQN model producing real predictions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PerformanceTracker now records open entries, computes realized PnL on
position close, tracks equity curve snapshots, and populates trades
and performance_timeline in finalize() instead of returning empty vecs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace stub update_position() and record_trade() with real accounting:
weighted-average entry price, realized PnL on close, partial fills,
position flips (long→short), and trade history recording.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
BacktestMLModel bridges ModelInferenceAdapter → MLModel trait for the
global ModelRegistry. load_models_for_backtest() creates DQN/PPO
adapters from checkpoint files and registers them for inference.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace local 3-5 feature FeatureExtractor with ProductionFeatureExtractorAdapter
from ml::features. Falls back to legacy extractor during warmup period.
Production extractor produces exactly 51 features matching DQN/PPO training.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DbnReplayEngine loads events and streams them through mpsc channels,
compatible with StrategyTester. Supports from_events() for testing
and from_bytes() stub for DBN file parsing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Converts Trade, Ohlcv, and Quote variants from data::ProcessedMessage
to trading_engine::MarketEvent. Handles HardwareTimestamp→DateTime<Utc>,
Decimal→Quantity, and String→Symbol type conversions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The catch-all match arm in convert_to_common_event was creating synthetic
TradeEvent objects with symbol "UNKNOWN", price zero, and trade_id "placeholder"
for any MarketDataEvent variant not explicitly handled (Bar, OrderBook, News, etc.).
These fake trades corrupt downstream analytics pipelines. Now returns None and
logs at debug level instead, allowing callers to filter unsupported types cleanly.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
8-task TDD plan to bridge 8 disconnected layers in the backtesting
pipeline: DBN converter, replay engine, 51-dim feature wiring,
model loader, registry startup, position tracking, PnL tracking,
and end-to-end integration test.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
13-task plan covering data pipeline fixes (Databento stream, data acquisition,
DBN uploader) and trading service core wiring (RiskEngine, MLEngine, VaR,
feature extraction, order matching, API gateway, Prometheus panics).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
8-task plan to bridge disconnected backtesting pipeline layers:
DBN parser → feature extraction → ML inference → position tracking → PnL
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Covers data pipeline, trading service core, broker connectivity
(FIX 4.4 + TWS), execution algorithms, security/compliance, and
observability — based on comprehensive non-ML audit.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two-phase design:
A) Real 6E.FUT data → 4-model ensemble → PaperBroker → P&L metrics
B) Best hyperopt params → train → deploy to ensemble → ValidationHarness
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PaperBroker: simulated fills with configurable slippage and commission.
PnLTracker: rolling Sharpe ratio (252-day window), max drawdown, cumulative return.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
CampaignResults with serde serialization. run_campaign() orchestrates
ArgminOptimizer for DQN hyperopt, saves best_params.json and
campaign_summary.json to timestamped results directory.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Constructs DQN, PPO, Mamba2, and TFT adapters with small configs,
pre-warms the sequence-based models, then feeds 5 feature vectors
through InferenceEnsemble and verifies bounded predictions from
all 4 models.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Lightweight synchronous coordinator that wraps N ModelInferenceAdapter
instances. Aggregates predictions via confidence-weighted voting with
graceful degradation for unready or failing models.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
