0efdee4b0c
Audited the v1 plan against the full project memory catalog. Found
several rule violations and gaps:
A3 (was: 'host memcpy_htod canonical defaults to ISV[400..406]') →
violated feedback_no_htod_htoh_only_mapped_pinned ('tests not exempt')
and short-circuited pearl_first_observation_bootstrap. Replaced with
launch-once-at-init: each controller fires once with sentinel-zero
input, kernel's first-observation-bootstrap path writes the canonical
value. No host write to ISV. Canonical pattern across the codebase.
G1-G7 gates (was: 'matches host reference' for argmax_expected_q) →
violated feedback_no_cpu_test_fallbacks. Replaced every CPU oracle
with GPU oracle: analytical synthetic inputs, property assertions,
cross-kernel validation only.
Added explicit catalog of memory rules the rebuild MUST honor (30+
rules grouped by domain). Every R-phase + every architectural decision
now cites which rules it applies.
Added A9 (PER actually wired into step) per feedback_always_per — the
flawed branch had ReplayBuffer struct but never sampled from it.
Added new ISV slots for 7 EMA inputs (RL_*_EMA_INDEX) → RL_SLOTS_END
extends to 424. The controllers' inputs live on device, not host.
Added cluster smoke discipline section: per pearl_single_window_oos
the G8 backtest gate requires >=3 walk-forward folds. Per
feedback_kill_runs_on_anomaly_quickly the dispatcher kills on NaN /
ISV saturation / kernel hang. Per pearl_q_spread misaligned, the
dispatcher MUST NOT kill on Q_SPREAD. Per feedback_argo_template_must_apply
the dispatcher refuses to submit if template not applied since edit.
Per feedback_push_before_deploy the dispatcher hard-errors if local
HEAD != origin HEAD.
Added pre-cluster validation checklist (R9 prerequisite): full
local-CUDA gate matrix that must be green before push.
Reconciled feedback_mbp10_mandatory: --mbp10-data-dir is mandatory;
--trades-data-dir is flagged as a gap (ml-alpha's MultiHorizonLoader
does not currently consume a separate trades stream — OFI is derived
from MBP-10 snapshot deltas). Either wire trades in a future plan or
ship explicitly without; the rebuild does the latter and flags it.
Plan grew from 381 to ~580 lines because the memory audit exposed
several decisions that needed explicit treatment instead of implicit
'follow project pattern' references.
Co-Authored-By: Claude Opus 4.7 <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.