c295fa9c92
R9 cluster smoke alpha-rl-qzstj step 7 caught the second half of
the cold-start fix: even with the input==0 gate holding controllers
at bootstrap until the first observation, the Wiener α-floor=0.4
blend then produced `0.6 × bootstrap + 0.4 × target` on the FIRST
warm step — 60% bootstrap contamination distorting the controller's
first emit.
For `rl_reward_scale` this was the load-bearing failure: with
prev=1.0 (bootstrap) and target=1/832=0.0012 on the first closed
trade, blend gave scale=0.600 → real $832 × 0.6 = $499 fed to V
regression → l_v = 249,782. Replace-directly: scale = 0.0012
immediately → V target = 1.0 → l_v ≈ 1. Three orders of magnitude
reduction in cold-start contamination.
## Fix
For each of the 4 cold-start-gated controllers (τ, ε, n_roll, scale),
detect "first warm observation" via `prev == BOOTSTRAP_VALUE` and
write target directly instead of Wiener blending. Subsequent steps
(where prev has drifted via earlier blends) take the Wiener path
unchanged.
```cuda
// (cold-start gate, then target computation already done)
if (prev == HARDCODED_BOOTSTRAP_VALUE) {
isv[OUTPUT_INDEX] = target;
return;
}
// ... Wiener blend
```
The `prev == HARDCODED_BOOTSTRAP` check uses float equality but is
safe: the sentinel-bootstrap path WROTE that exact value, and the
cold-start gate prevents any arithmetic from touching it until input
becomes non-zero. The first non-zero input triggers this branch
exactly once.
This is `pearl_first_observation_bootstrap` ("sentinel = 0; first
observation replaces directly") applied at the controller's bootstrap
→ warm transition. The pearl was originally framed for EMA producers;
the R9 audit shows it applies equally to adaptive controllers whose
hardcoded bootstrap doubles as a "no data yet" sentinel.
## Test impact
`g3_per_step_controllers_move_isv_outputs_when_fed_real_emas` now
shows stronger first-observation moves (replace-directly hits target
cleanly):
Before R9 fixes: τ 0.005 → 0.023 ε 0.2 → 0.14 scale 1 → 0.608
After cold-gate: τ 0.005 → 0.023 ε 0.2 → 0.14 scale 1 → 0.608
After this fix: τ 0.005 → 0.05 ε 0.2 → 0.05 scale 1 → 0.02
All gates still green:
G1 isv_bootstrap ✅
G3 controllers_emit ✅ (stronger first-emit movements)
G4 target_soft_update ✅
G6 r7d_per_wiring ✅
R3, R4, smoke ✅
## What's NOT in this commit
The 6 missing EMA input wirings (kl_pi, q_divergence,
entropy_observed, advantage_var_ratio, td_kurtosis, trade_duration)
remain. Six of seven controllers will still hold at bootstrap during
the cluster smoke because their input EMAs receive no signal. That
fix is the next commit — it requires new reduce kernels (var-over-
abs-mean, kurtosis, KL-approx, L2-diff-norm) and a per-batch
trade-duration counter.
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.