ce1a81552b
Workflow train-cr9hl on sp20-aux-h-fixed @ 7fc979934, terminated at
epoch=1 end after 36m58s wall-clock per
`feedback_kill_runs_on_anomaly_quickly`.
Pre-smoke gates (all clean): CAPTURE_PHASE_AUX_DONE + POST_AUX_DONE,
13 child graphs captured, no CUDA errors, no panics, no OOM. The new
`aux_softmax_to_per_env_kernel` launched inside the captured forward
graph without breaking recording (pure per-thread gather, no host
branches — passes `pearl_no_host_branches_in_captured_graph`).
Epoch 1 trade stats: 489959 trades, 245998 wins, 243961 losses,
PF=0.947 — WR = 50.21%, squarely in the runbook's pre-declared
falsification band (50.1–50.2%).
Aux head was producing non-trivial directional content (HEALTH_DIAG[0]
pred_tanh = 0.6626, batch mean of softmax[1]-softmax[0]) — the bridge
was conducting signal; the policy just couldn't gradient-couple to it.
LOW EXPOSURE DIVERSITY warnings at epoch 1 (S_Small=3.8%, H_Half=0.4%,
H_Full=0.8%, L_Small=4.2%, F_Half=1.7%, F_Full=3.5%) corroborate the
V/A unidentifiability hypothesis (`project_dueling_va_unidentifiable`).
Combined with `hold_pct_ema=0.2004` against `target_hold_pct=0.1151`
and `hold_reward_ema=-0.2044`, the cost-dominance pattern from
`pearl_event_driven_reward_density_alignment` is the more upstream
candidate.
Phase 1 H6 wiring STAYS merged per `feedback_no_functionality_removal`:
slot 121 is allocated, the buffer is initialized via pure-GPU fill,
the copy kernel runs inside captured graphs. None is harmful; if a
future fix produces policy gradient-coupling to directional features,
the bridge is already in place.
A2 (eval-side aux integration) is deferred indefinitely — A3 NULL
fallback is sufficient for eval and there is no production case for
A2 until training-side evidence shows the bridge is doing useful work.
Next direction (framing only, not implementation):
- H3 (reward density mismatch per
`pearl_event_driven_reward_density_alignment`) is the upstream
candidate; V/A unidentifiability fix is downstream.
- Sequencing: heal gradient signal first, then test if V/A still
pathologizes with healthy rewards.
Audit doc append: `## 2026-05-12 — SP22 H6 implementation` section gets
a new "Smoke result (2026-05-12) — H6 FALSIFIED" subsection capturing
the verdict, ruling-out table, and next-investigation framing.
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.