0611d32b06
V7 audit of the 7 adaptive controllers named in spec §5.3 against the
baseline controller_activity smoke run (3 folds × 20 epochs = 60 epochs,
intervention-based fire detection per commit ed4b30b49):
* C1 anti_lr: DIAGNOSTIC (0/60; wiring surprise — fire detector reads
base scheduler, not post-anti_lr LR, flagged for Phase 2 fix)
* C2 adaptive tau: DIAGNOSTIC (2/60; fires at fold boundaries only)
* C3 adaptive gamma: DIAGNOSTIC (1/60; pinned to floor by health-coupled
correction; re-measure at L40S when health is real)
* C4 adaptive grad_clip: CANDIDATE FOR DELETE pending ablation (12/60,
20% — above diagnostic, below load-bearing; needs Track 3 Step 2)
* C5 cql_alpha: DIAGNOSTIC (2/60; regime_stability gate uninformative
at smoke scale)
* C6 cost_anneal: DIAGNOSTIC by design (deterministic sigmoid; fire_cost
hard-coded false at training_loop.rs:2475)
* C7 base LR scheduler: DIAGNOSTIC by construction (pure open-loop, not
in the closed-loop controller battery)
Cross-controller finding: no controller fires in > 50% of epochs, so
the policy is not currently held on the rails by adaptive intervention
at smoke scale. However, C2 / C3 / C5 are structurally inert here —
their trigger signals (health, regime_stability) are degenerate at
smoke scale, so their 0-ish fire rates are lower bounds, not
representative. Promote to final after L40S validation.
Wiring surprise (anti_lr): fire_lr detection reads lr_scheduler.get_lr()
*before* the anti_lr multiplier applies. Under smoke Constant LR this
reports 0 correctly-for-the-wrong-reason; under L40S Cosine/Linear it
will false-positive on pure decay drift. Recommend detecting via the
anti_mult != 1.0 branch directly (training_loop.rs:2936–2942) for an
unambiguous intervention semantic matching C4/C6.
Follow-ups flagged for Phase 2:
- Fix C1 fire-detection wiring before L40S re-run
- Run C4 grad-clip ablation (25 min, gates the final C4 verdict)
- Optional: reset fire_counts per fold to remove C2/C5 boundary artefact
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.