9d0c124cee
Root cause from train-6fcml 5-epoch trajectory (commit 5608b866b after
producer cadence migration): HEALTH_DIAG[0] (post-experience-collection)
showed q_dis_s=0.0595 q_dis_l=0.1329 var_q=0.00091 — meaningful rollout
signal. HEALTH_DIAG[1+] (post-training, per-step launches) all showed
q_dis_s=0.0000 q_dis_l=0.0000 var_q=0.00000 — signal decayed to zero
inside ONE epoch.
The kernel's ISV write block ran unconditionally even when total_cnt
(non-masked-row count after Hold/Flat masking) was 0. Empty-batch
launches blended `batch_mean = 0/1 = 0` into the EMA, decaying the
rollout signal to 0 over ~178 training steps × 0.7^n. Per-step training
launches read replay batches whose Q-direction picks are dominated by
Hold/Flat (the natural distribution); so total_cnt = 0 was the common
case, not a corner case.
Fix (atomic, single kernel):
- Wrap the ISV write block in `if (total_cnt > 0.0f) { ... }`. When the
training batch has no non-masked rows, the kernel is a no-op for that
step — EMAs stay at the prior step's values. Stream-ordered launches
still run; only the ISV write is skipped.
- Remove redundant `&& (total_cnt > 0.0f)` clause from the `is_first`
bootstrap check (now guaranteed by the outer gate).
Per pearl_first_observation_bootstrap semantics: "no observation"
preserves prior; only "first observation" replaces sentinel. Decay-on-
empty was inconsistent with both rules.
Other EGF-chain kernels audited:
- alpha_grad_compute_kernel.cu — operates on persistent ISV state,
no batch concept; var_aux/var_alpha Welford updates use `diff` of
persistent EMAs, not batch means. No empty-batch path. SAFE.
- aux_dir_acc_reduce_kernel.cu — emits out_6[0..3] with sentinel
fallback (0.5) when denom==0; downstream apply_fixed_alpha_ema then
blends 0.5 toward EMA. The sentinel is the random-baseline (target
threshold lies above it), so empty-batch pulls EMA toward harmless
baseline rather than zero. Different semantics from q_disagreement
(which has 0 — far below baseline 0.5). SAFE.
- gradient_hack_detect_kernel.cu — single-thread state machine on
persistent ISV, no batch. SAFE.
Verification:
- 6 existing sp14_oracle_tests pass.
- New q_disagreement_empty_batch_preserves_ema test asserts bit-exact
preservation of pre-seeded EMAs (0.0595, 0.1329, 0.0009 — the
train-6fcml HEALTH_DIAG[0] values) across an all-Hold batch. Catches
the regression the existing all-hold test missed (its bound
`[0.0, 0.5]` accepted both decay-to-blend and preserve-prior; the new
test is strict bit-equality).
- L40S smoke validation pending — train-6fcml symptoms (alpha_smoothed
stuck at 0.0002, gate1 closed forever) expected to resolve.
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.