ee24f0a303
Two `pearl_tests_must_prove_not_lock_observations` violations
surfaced during the R9 pre-cluster validation sweep on the dev RTX
3050 Ti (sm_86). Neither was a bug in the trainer or kernels — both
were test fixtures that asserted observed-value coincidences rather
than invariants. Per the canonical pearl, observed-value tests
become bug-locks (the SP16 T3 sp16_phase3_alpha_low_in_steady_state
incident was an assertion `α<0.40` matching the bug itself).
## g3_per_step_controllers_move_isv_outputs_when_fed_real_emas
The fixture fed `RL_TD_KURTOSIS_EMA = 10.0` to the rl_per_alpha
controller, expecting ISV[405] to move off its bootstrap 0.6 after
the Wiener blend. But the kernel's target formula at td_kurtosis=10
maps to **exactly** the bootstrap:
target = 0.4 + 0.2 × (10 − 3) / 7 = 0.6
The Wiener blend `(1−α)·prev + α·target` then produces 0.6 from any
α, so the controller can't move off bootstrap. The assertion was
asserting a coincidence — fixed by picking `td_kurtosis = 20.0`
which lands at `target = 0.886`, distinct from the 0.6 bootstrap.
With the fix all 7 controllers move (γ→0.9, τ→0.023, ε→0.14,
coef→0.0154, n_roll→2867, per_α→0.714, scale→0.608).
The kernel itself is correct — the test was wrong.
## integrated_trainer_step_with_lobsim_runs_without_panic
Asserted `λ_sum ≈ 1.0` for the loss-balance λs. But
`LossLambdas::default()` returns each λ=1.0 (sum = 5.0) with the
`/5.0` divide applied at the trainer's loss-combine site so each
head's contribution is `lambda/5.0`. The "sum=1" assertion was
based on a normalization that the trainer never used. Loosened to
the actual invariant we care about ("every head has a finite
positive λ so the encoder receives real-valued gradient") which
survives any future controller-driven λ re-weighting.
## R9 local-smoke results (all gates green on sm_86)
```
G1 isv_bootstrap ✅ γ=0.99 τ=0.005 ε=0.2
coef=0.01 n_roll=2048
per_α=0.6 scale=1.0
R3 r3_ema_advantage (3 tests) ✅ bootstrap + per-step EMA +
advantage/return formula
R4 r4_action_kernels (3 tests) ✅ Thompson + argmax + log_pi
G3 controllers_emit ✅ all 7 ISV outputs moved
G4 target_soft_update ✅ Polyak τ=0.005 applied
G6 r7d_per_wiring ✅ buffer 0→5→8 + sample size
end integrated_trainer_smoke ✅ all 5 head losses finite
```
Confirms R7c-data + R7d run end-to-end on real CUDA. Next R9 step
(cluster smoke via scripts/argo-alpha-rl.sh + multi-fold G8) requires
git push + cluster credits — paused per the chosen R9 path "stop
before cluster submission."
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.