4399a56d76
Five tests in crates/ml/src/trainers/dqn/smoke_tests had pass gates that validated existence rather than the invariant their doc-comment claimed to test. A trainer returning all-zero diagnostics (a plausible wiring regression) would have passed them. - reward_component_audit: was `is_finite() && >= 0.0` on 5 slots — passes trivially on all-zero stubs. Added `cf_flip > 0.1` and `trail_r > 0.01` floors (known-wired slots in smoke config; popart/micro/loss_aversion remain finite-only as they are legitimately near-zero in smoke). Run-observed values: cf=0.614, trail=0.295, la=0.006 — well above floors. - exploration_coverage: `.unwrap_or(0.0)` silently substituted 0 for a missing epoch, conflating "emission regressed" with "exploration collapsed". Now panics with a distinct message on missing entries, also asserts len >= 20, normalized range [0,1], and spread > 1e-6 to catch constant-output emitters. Run-observed spread: 0.275. - training_stability::50_epoch_convergence: entropy assertion was guarded behind `if entropy.is_finite()`, so NaN entropy (the more severe failure) silently passed. Fail hard on NaN first. - training_stability::trading_model_behavior: same `is_finite` guard pattern on action_entropy — now fails hard when the diagnostic is missing or NaN rather than skipping. - training_stability::gpu_collector_auto_initializes: only asserted training returned `Ok(_)`. A collector producing silent zeros would pass. Now also verifies epochs_trained, loss finiteness, and gradient flow. - walk_forward::no_overfitting_50_epochs: had two tautological "finite check" assertions (`x < x + 1` and the signum-adjusted ratio) that always passed regardless of divergence. Replaced with real `.is_finite()` checks plus a `div_ratio < 10.0` stability gate. Tests run under CUBLAS_WORKSPACE_CONFIG=:4096:8 + FOXHUNT_TEST_DATA=test_data/futures-baseline, release-test profile. reward_component_audit and exploration_coverage both PASS on the local RTX 3050. No threshold relaxation or quickfixes applied; any future wiring regression will now be caught by a meaningful assertion instead of a near-tautology. 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.