980f3b07f3
First Task 2.0 pass (commitd60e5375a) covered IQN/CQL/C51/Ens. Result: IQN+Ens are architecturally zero (don't touch branches); C51+CQL send 30-400× more gradient to magnitude than direction. But Task 0.4's grad_ratio_mag_dir=0.0000 at epoch end — so magnitude gradient gets cancelled somewhere in the aux-graph gap between CQL/C51 and epoch end. This commit instruments the 5 unmeasured writers/scalings: - apply_distillation_gradient - launch_recursive_confidence_backward - compute_predictive_coding_loss - apply_c51_budget_scale (multiplicative; signed delta reveals shrinkage) - apply_cql_saxpy (multiplicative) Schema extended 4 → 9 components. Pinned result slot grew 8 → 18 floats. 9 device scratch buffers (~90 MB total on RTX 3050 Ti — 2.2% of 4 GB, acceptable for diagnostic). HEALTH_DIAG now emits: grad_split_bwd [iqn=… cql=… c51=… ens=…] grad_split_aux [distill=… rec=… pred=… cql_sx=… c51_bs=…] No atomicAdd (block-internal tree reduction). memcpy_dtod_async is not captureable → used graph_safe_copy_f32 (same as first pass). Last 5 epochs (HEALTH_DIAG[15..19]), RTX 3050 Ti, magnitude_distribution smoke test: grad_split_bwd [iqn=0.0000 cql=18.4618 c51=102.2001 ens=0.0000] grad_split_aux [distill=1.2281 rec=0.0000 pred=0.0000 cql_sx=18.4618 c51_bs=102.2001] grad_split_bwd [iqn=0.0000 cql=55.0089 c51=125.1374 ens=0.0000] grad_split_aux [distill=1.2381 rec=0.0000 pred=0.0000 cql_sx=55.0089 c51_bs=125.1374] grad_split_bwd [iqn=0.0000 cql=30.1931 c51=262.0410 ens=0.0000] grad_split_aux [distill=0.7916 rec=0.0000 pred=0.0000 cql_sx=30.1931 c51_bs=262.0410] grad_split_bwd [iqn=0.0000 cql=70.2364 c51=126.6637 ens=0.0000] grad_split_aux [distill=0.7397 rec=0.0000 pred=0.0000 cql_sx=70.2364 c51_bs=126.6637] grad_split_bwd [iqn=0.0000 cql=103.3925 c51=104.4165 ens=0.0000] grad_split_aux [distill=0.8085 rec=0.0000 pred=0.0000 cql_sx=103.3925 c51_bs=104.4165] Keystone findings for Task 2.1: * rec (recursive confidence) + pred (predictive coding) report 0.0000 every epoch — architecturally expected: MSE/predictive gradients flow through bw_d_h_s2 / trunk tensors 0..8 only, they never touch branch tensors 8..16. Same architectural-zero class as IQN+Ens. * distill is the only aux writer that actually touches branches, but at ratios ~0.5–1.2 (nearly balanced mag/dir), so it cannot be the source of Task 0.4's grad_ratio_mag_dir=0 cancellation. * cql_sx ≡ cql and c51_bs ≡ c51 exactly (ratio, not norm). This is expected — multiplicative scalings (saxpy budget / c51 budget scale) uniformly scale BOTH direction and magnitude slices, so the mag/dir RATIO is invariant. These ops change amplitude, not balance. Conclusion: NONE of the 9 instrumented stages zero the magnitude signal. CQL+C51 reach grad_buf with huge mag/dir ratios (30-400×); distill adds a balanced micro-contribution; rec/pred/iqn/ens/scalings all architectur- ally neutral for the mag/dir ratio. Yet Task 0.4's post-step grad_ratio_mag_dir=0 (meaning dir < 1e-9). Re-reading Task 0.4's impl: `per_branch_grad_norms` returns [dir, mag, …] and the ratio is `mag / dir if dir > 1e-9 else 0.0`. So 0.0000 means dir_norm is zero, NOT mag cancelled. Task 0.4's measurement runs at EPOCH boundary after Adam consumes grad_buf — which means the "cancel" is either (a) Adam consuming/zeroing grad_buf pre-readback, (b) the next step's `submit_forward_ops_main` memset firing before Task 0.4's DtoH reads, or (c) something Adam-related that drops dir to zero. Five unmeasured writers verified present (all 5 exist via grep + read — none renamed or deleted since the first pass's report). Writers that DON'T touch branches (rec/pred) are now explicit 0.0000 evidence, not speculation. Feeds Task 2.1's decision tree: H4 is NOT caused by cancellation in the aux-graph gap. Next hypothesis: Adam grad_buf lifecycle / Task 0.4 timing relative to memset. Build clean; magnitude_distribution smoke green (21.90 s local, RTX 3050 Ti). Test result: 1 passed; 0 failed. Push status: deferred — first Task 2.0 pass reported network unreachable (d60e5375alocal-only). Will attempt in same commit cycle; if it fails again, that's consistent with the prior pass's finding. Per plan Task 2.0 extension (from Task 2.0 first-pass escalation report). 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.