d646c1eb3c
Critical review surfaced 9 issues; all fixed inline:
1. §1 — Reframed "140× amortization" as "amortization on forward; sim
runs parallel". True win is on the ~2ms forward shared across 140
cells, not on sim work which scales linearly with n_backtests.
2. §2 — Made the 1h target vs firm bound explicit (≤1h target, ≤2h
firm). Acknowledges Graph capture realistic speedup is 1.2-1.5×,
not 2×.
3. §5 — Dropped atomicAdd. Plain `+=` under the single-writer-per-block
convention (existing pattern across sim kernels). No race.
4. §4 — Documented max-over-horizons threshold rationale (vs per-
horizon or aggregate-conviction). Flagged per-horizon as a follow-up
tweak if dilution pathway matters in the verdict.
5. §7 P5 + §10 risk — Captured-vs-uncaptured tolerance is 1e-5 relative,
NOT strict bit-identity. CUDA Graph capture can reorder reductions
harmlessly by 1 ULP; strict bit-identity would be a false-positive.
6. §8 — Made explicit that parallel_sim_equivalence + independence
tests are BOTH required. Equivalence alone is necessary but not
sufficient (a shadow-backtest[0] bug still passes equivalence).
7. §3.3 — Specified output schema: `cell_W{n}/sim_<variant_name>/`
with summary.json carrying a resolved `sim_config` block (verdict
emitter reads that, not the directory name).
8. §7 P4 — Enumerated apply_fill_to_pos call sites + added grep-verify
step before commit, so no fill path silently loses cost.
9. §9 — Tightened rate-validation gates with hard targets (P2 ≤90s,
P5 ≤60s) instead of generous minute envelopes. Added §9.1 stride=8
fallback as explicit Plan-B if Graph capture under-delivers.
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.