7883c5ca1be2883a1ad8119a59a13fe0d052d795
Post-mortem of the H6 Phase 1 smoke (WR = 50.21% verdict in `docs/dqn-wire-up-audit.md`) traced an actual pearl violation in the H6 implementation itself: state slot 121 uses the [0, 1] range (`aux_softmax[env, 1] = p_up`) with sentinel 0.5, while every OTHER state slot uses 0 as the "no signal" baseline (zero-padding, feature_mask, ofi-missing, mtf-missing). Per `pearl_first_observation_bootstrap`: "sentinel = 0; first observation replaces directly." The H6 design violated this for slot 121 alone. The encoder must learn TWO things about slot 121: (1) the directional mapping AND (2) the appropriate bias offset for the non-zero baseline — every other dim is single-step (mapping only). Phase 2 is the simplest possible amplification fix: rewrite the bridge to use the same convention as every other state slot. If the encoder can't gradient-couple even after this fix, H6 is truly falsified and we pivot to amplitude scaling or deeper hypothesis. Change scope (atomic per `feedback_no_partial_refactor`): - aux_softmax_to_per_env_kernel.cu: write `2*p_up - 1` instead of `p_up` - gpu_experience_collector.rs: cold-start + FoldReset sentinel 0.5 → 0.0 - experience_kernels.cu: NULL-fallback in 3 state-gather kernels 0.5f → 0.0f - state_layout.rs / state_layout.cuh: comment updates to reflect [-1, +1] range and 0 sentinel Estimated effort: ~45 min walltime (edits + verification gates) + ~25–40 min smoke wall-clock. Verdict criteria (cycle 1 WR + a_var [d/m/o/u] in HEALTH_DIAG[0]): - WR > 50.5% → recentering binding, H6 + Phase 2 sufficient → justify A2 - a_var for mag/ord/urg > 1e-3 → sub-branches learning under recentered signal - WR pinned at 50.1–50.2% → Phase 2 falsified, pivot to amplitude scaling or deeper hypothesis 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.
Description
Languages
Rust
88.2%
Cuda
7.7%
Python
1.3%
Shell
1.1%
PLpgSQL
0.8%
Other
0.8%