The update_position method previously acquired a read lock to get the
Arc<AtomicPosition>, dropped it, then called update_with_execution
outside any lock. Two concurrent fills for the same position could both
load the same old_quantity via Acquire, compute independent new quantities,
and the last Release store would silently discard the other fill — causing
the position to show e.g. 10 shares when it should show 20.
Now holds the positions write lock across the entire get-or-create +
update_with_execution sequence, serializing concurrent fills per position.
The validate_position_update call remains outside the lock to minimize
hold time (it does its own async reads and risk checks).
Also removes excessive per-step RDTSC latency tracking from the critical
path (total latency tracking is preserved) and adds a multi-threaded
regression test that spawns 10 concurrent +1 fills and asserts the final
quantity equals 10.
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>
The old code computed `_execution_value = quantity * price` but
discarded it (underscore prefix). Only commission was subtracted,
so the account cash balance never reflected the cost of buying or
the proceeds from selling.
Now `update_from_execution` matches on the new `execution.side` field:
- Buy → cash -= execution_value + commission
- Sell → cash += execution_value − commission
Test assertions updated to expect the corrected balances.
Addresses audit item H2 (account never deducting trade value).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ExecutionResult.executed_quantity is always a positive magnitude, so the
old `is_buy = executed_quantity > ZERO` check was always true — every
fill was treated as a buy regardless of order side.
Add an explicit `pub side: OrderSide` field to ExecutionResult and use
`execution.side == OrderSide::Buy` in PositionManager. All construction
sites (source, tests, benchmarks) updated; sell-side tests now use
positive quantities with `side: OrderSide::Sell` instead of the former
negative-quantity hack.
Addresses audit item H4 (position direction always buy).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
clone_for_async() was creating independent AtomicU64 instances for
message_count, drop_count, last_heartbeat, and reconnect_attempts
instead of sharing the originals. This meant spawned tasks incremented
their own counters while get_stats() read the original's (always 0),
and the heartbeat monitor watched a counter never updated by the
connection task.
Also fixes last_heartbeat initializing to 0, which caused the first
heartbeat check to compute a huge elapsed time and immediately trigger
a false "connection appears dead" alert.
Changes:
- Change 4 struct fields from AtomicU64 to Arc<AtomicU64>
- Initialize last_heartbeat to HardwareTimestamp::now() instead of 0
- clone_for_async() now clones the Arcs (shared counters)
- Add test verifying counters are shared between original and clone
- Add test verifying heartbeat initialized to current time
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
4 symbols (ES, NQ, ZN, 6E) x 9 quarters (2024-Q1 through 2026-Q1),
723 days of 1-minute OHLCV bars from GLBX.MDP3 dataset.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The download binary was using the default SType::RawSymbol which
returned empty DBN files (95 bytes) for parent symbols like ES.FUT.
Adding SType::Parent resolves the symbol mapping. Also adjusted end
date to 2026-02-22 (latest available data).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Casting negative f64 to u64 saturates to 0, corrupting avg_price and all
downstream PnL calculations. Add price_to_fixed_checked() that rejects
non-finite, zero, and negative prices with PositionError::InvalidPrice.
The critical update_with_execution path now uses the checked variant.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The get_orders() status filter used `matches!(order.status, _status)` which
creates a new wildcard binding instead of comparing against the captured
`status` variable. This caused every order to match regardless of filter,
meaning get_orders(Some(Filled)) returned ALL orders — inflating exposure
calculations. Replaced with direct equality comparison `order.status == *status`.
Added test_get_orders_status_filter_only_returns_matching to prevent regression.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add #![allow(unused_crate_dependencies)] for consistency with other ml tests
- Extract EXPECTED_FEATURE_DIM constant (replaces magic number 51)
- Add validated_folds counter to prevent vacuous pass in walk-forward test
- Replace unwrap_or_else(panic) with match pattern
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three integration tests validating the full pipeline end-to-end:
1. Feature extraction: 90-day synthetic bars -> 51-dim features, no NaN/Inf
2. Walk-forward with normalization: 24-month bars -> windows -> NormStats from train only -> normalized mean ~0
3. No lookahead bias: train timestamps < val start, val timestamps < test start
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Loads trained DQN/PPO checkpoints, runs greedy inference on walk-forward
test windows, computes Sharpe ratio, max drawdown, win rate, profit factor,
and total return per fold. Outputs a JSON evaluation report with aggregate
metrics and sanity checks (beats-random, action diversity, fold consistency).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add ml/examples/train_baseline.rs that trains DQN and PPO models using
expanding walk-forward windows on real Databento OHLCV data.
Features:
- CLI args via clap (--model, --epochs, --batch-size, --data-dir, etc.)
- Recursive .dbn.zst file discovery and OHLCV bar loading
- 51-dim feature extraction via extract_ml_features()
- Walk-forward window generation with NormStats per fold
- DQN training loop with epsilon-greedy, experience replay, early stopping
- PPO training loop with GAE, trajectory collection, early stopping
- PnL-based reward (BUY/SELL/HOLD)
- Safetensors checkpoint saving per fold
- NormStats JSON export for evaluation reproducibility
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>
Adds `ml/examples/download_baseline.rs` that downloads 730 days of
Databento OHLCV-1m data in quarterly chunks for 4 CME futures symbols.
Features:
- Reads universe config from TOML (symbols, date range, dataset)
- Splits date range into calendar-quarter chunks (~90 days each)
- Resume support: skips existing non-empty files
- Uses `get_range_to_file` for streaming writes to .dbn.zst
- Dry-run mode with cost estimate ($0.12/symbol/day)
- Confirmation prompt (skippable with --yes)
- Per-file progress with timing and byte counts
- Failure-tolerant: logs errors and continues
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ubuntu 22.04's protobuf-compiler (v3.12) lacks proto3 optional field
support. Install protoc v25.1 from GitHub releases instead, which
matches the protoc version available in Debian bookworm-based images
used by the other 5 services.
Validated: docker build completes successfully, binary starts with
ml_training_service --help.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 6 service Dockerfiles were missing newly-added workspace crates
(web-gateway, ctrader-openapi, foxhunt-deploy, broker_gateway_service),
causing cargo workspace resolution failures during Docker builds.
Changes across all Dockerfiles:
- Add COPY directives for web-gateway, ctrader-openapi, foxhunt-deploy,
broker_gateway_service (new workspace members since Dockerfiles written)
- Add SQLX_OFFLINE=true env and .sqlx cache copy where missing
- Add perl and make system deps (needed for OpenSSL build from source)
- Remove COPY migrations (dir excluded by .dockerignore, not needed)
- Expand broker_gateway_service from 3-crate to full workspace copy
Validated: docker build --check passes all 6, cargo check -p passes all 6.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Essential workflow fixes:
- ci.yml: add SQLX_OFFLINE=true, replace fictitious cargo subcommands
(test-unit, ci-lint, audit-deps, etc.) with real cargo commands,
remove broken 9-way test matrix, remove instrument-coverage RUSTFLAGS
- test.yml: add SQLX_OFFLINE=true to build-check job, fix conflicting
clippy flags (-D and -W on clippy::all), update JWT secret to 32+ chars
- compilation-guard.yml: add SQLX_OFFLINE=true, remove references to
non-existent paths (services/trading-engine, crates/common/types),
remove dangerous auto-commit-to-main, remove MIRI on missing packages
Deleted duplicates (justification):
- comprehensive_testing.yml: duplicate of comprehensive-testing.yml
(same purpose, underscore vs hyphen naming)
- production-deploy.yml: duplicate of production-deployment.yml
(both named "Production Deployment Pipeline", this one has stale paths)
- coverage-fixed.yml: duplicate of coverage.yml
(uses deprecated actions-rs/toolchain@v1 and actions/cache@v3)
- performance.yml: duplicate of benchmark_regression.yml
(both "Performance Regression Detection", this one less mature)
- quality-baseline.json: not a workflow, stale fake data (999 warnings)
- quality-metrics.json: not a workflow, stale fake data
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Proves the critical trading pipeline path works end-to-end:
OHLCV -> features -> ensemble -> prediction. Uses all 10 real candle
inference adapters (DQN, PPO, TFT, Mamba2, Liquid-CfC, TGGN, TLOB,
KAN, xLSTM, Diffusion) with random weights, 60 synthetic OHLCV bars,
and asserts confidence/action/model-count invariants. No external
dependencies (no DB, no Docker).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Spawn a background tokio task that feeds synthetic OHLCV bars into the
ensemble coordinator's per-symbol feature extractors. The extractors
require ~51 bars of warmup before they can produce real 51-dim feature
vectors; without this feed they remain cold and return zeros.
The task uses a random-walk price generator with realistic base prices
for the four baseline futures symbols (ES, NQ, ZN, 6E) and configurable
interval (MARKET_FEED_INTERVAL_MS, default 1s) and symbol list
(MARKET_FEED_SYMBOLS). In production this will be replaced by a
Databento or exchange data feed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Register TGGN, TLOB, KAN, xLSTM, and Diffusion inference adapters
alongside the existing DQN, PPO, TFT, Mamba2, and Liquid-CfC.
Rebalance weights to 0.10 each (equal weighting across 10 models).
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>
Detailed TDD implementation plan for wiring all 10 ML models into
the ensemble coordinator. Covers 5 new inference adapters (KAN, TGGN,
xLSTM, TLOB, Diffusion), main.rs registration with equal weights,
market data wiring, E2E integration test, Docker/CI validation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire existing components into end-to-end pipeline: 5 missing inference
adapters, 10-model registration, market data feeding, integration test,
Docker build validation, and CI pipeline triage.
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>
10-task plan: unify AssetClass, add trading_symbol field, futures_baseline()
preset, TOML config loading, DatasetSpec::from_universe() wiring, data
reorganization into cache structure, stale data cleanup, manifest generation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Define futures baseline universe (ES, NQ, ZN, 6E) with micro
contract mapping for limited capital. Reorganize scattered test
data into cache structure. Download spec for 730 days of OHLCV-1m.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Use tokio::task::block_in_place for safe sync→async bridging
- Replace count(*) with count(1) in division context (QuestDB parser bug)
- Add coalesce() for stddev NULL handling (single-row edge case)
- Consolidate integration tests into single lifecycle test with DROP+CREATE
- All 4 QuestDB tests pass against real QuestDB 8.2.3
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace stub metrics provider with real QuestDBMetricsProvider that
queries QuestDB via PostgreSQL wire protocol (port 8812) for rolling
model Sharpe, win rates, confidence buckets, and ensemble metrics.
- Add QuestDB service to docker-compose.yml (8.2.3, ports 9009/8812/9003)
- Create questdb_metrics.rs with 5 SQL queries and graceful fallback
- Wire QuestDBMetricsProvider into main.rs (replaces StubMetricsProvider)
- Fix unused Instant import in feedback_loop.rs (#[cfg(test)] scope)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
FeedbackLoop runs as a background tokio task, periodically running
weight and gate optimization cycles with kill switch monitoring and
retraining trigger detection. Wired into main.rs with a stub
MetricsProvider until QuestDB is deployed.
7 new tests covering kill switch, freeze/unfreeze, retraining triggers.
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>
