e0dbae3c99
Per pearl_controller_anchors_isv_driven.md: Fix 35 (CQL formula direction
flip) is necessary but insufficient — `const float MAX_BUDGET = 1.0f;` at
line 113 of loss_balance_controller_kernel.cu is regime-encoded the same
way the original CQL target_ratio formula was. Under healthy training
the cap is fine; under val-Flat-collapse it lets the controller saturate
budgets to 1.0 while the model is regressing into Flat.
The pearl prescribes the structural fix: pick the canary that fires
under the failure mode → train_active_frac (Long+Short / total during
training rollout). Lift it onto ISV with Pearls A+D smoothing; route it
through a producer kernel that derives per-(head, branch) cap via linear
interpolation FLOOR + active_frac × (CEIL - FLOOR); read the cap from
ISV in the controller kernel (no more hardcoded 1.0).
Atomic commit per feedback_no_partial_refactor:
- new kernel: train_active_frac_compute_kernel.cu — single-thread
reduction over monitoring_summary[5..17), writes scratch[250]; chained
apply_pearls_ad → ISV[TRAIN_ACTIVE_FRAC_INDEX=321]
- new kernel: loss_balance_max_budget_compute_kernel.cu — 8 threads
(2 heads × 4 branches), reads ISV[TRAIN_ACTIVE_FRAC_INDEX], writes
scratch[251..259); chained apply_pearls_ad ×8 →
ISV[LB_MAX_BUDGET_{CQL,C51}_BASE..+4)
- consumer: loss_balance_controller_kernel.cu — replace
`const float MAX_BUDGET = 1.0f;` with per-(head, branch) ISV reads,
defensive Pearl A bootstrap clamp
- 9 new ISV slots @ [321..330); ISV_TOTAL_DIM 321 → 330;
SP5_PRODUCER_COUNT linear span 147 → 156
- 9 new scratch slots @ [250..259); SP5_SCRATCH_TOTAL 250 → 259
- delete CPU train_active_frac compute (training_loop.rs:3392-3396) per
feedback_no_cpu_compute_strict; delete host field
`last_train_active_frac: f32`; replace with accessor reading from ISV
- 3 new state-reset registry entries (FoldReset; Pearl A bootstrap on
first observation) + dispatch arms in reset_named_state
- audit doc: Fix 36 entry with full provenance, files touched, pearls
applied, verification
Pre-existing 18 cargo warnings unchanged. State-reset contract test
`every_fold_and_soft_reset_entry_has_dispatch_arm` passes for all 3 new
entries; SP5 ISV slot layout test
`sp8_max_budget_slots_contiguous_and_above_activation_block` passes;
`SQLX_OFFLINE=true cargo check -p ml` clean.
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.