fd24b53833
smoke-test-4rbv9 on b3b4d0278 (z-score implementation) showed bit-
identical w_pop=2.000 at ep1 to pre-z-score B1b smoke, proving the
z-score formula was structurally a no-op:
z[c] = mag[c] / fmaxf(sqrtf(0), EPS_DIV) = mag[c] x 1e6
ratio[c] = (mag[c] x 1e6) / (1e6 x sum(mag)) = mag[c] / sum(mag) -> linear
Root cause: launch_reward_component_ema_inplace at line 3707 ran
AFTER launch_sp11_mag_ratio_compute at line 3465. So ISV[64..68]
and ISV[362..366] held sentinel-0 values at ep1's canary read
(ep0 had no segment_complete fires). z[c]=0 for c=1..5 -> popart
ratio collapsed to 1.0 -> controller saturated.
This was structurally the same bug that motivated adding
launch_sp11_popart_component_ema at line 3441 (B1b follow-up).
That fix-up addressed popart but left cf/trail/micro/opp_cost/
bonus stale.
Moved launch_reward_component_ema_inplace from line 3707 to before
launch_sp11_popart_component_ema. Other launches at the original
site (trade_attempt_rate_ema, plan_threshold_update, etc.) stay
where they were — different consumers, different timing constraints.
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.