1eac41d644
Per spec §8.2 (3.2). Extends the Phase 3.1 composer kernel
`r_quality_discipline_split_kernel` with two new args (`float cost_t`,
`unsigned int trade_close_indicator`) and subtracts
`cost_t * (float)trade_close_indicator` from `r_quality` BEFORE the α
blend. Same `cost_t` scalar shape as the Phase 1.2 cost_net_sharpe
accumulator (commission + per-side spread + OFI-impact); the gate
`trade_close_indicator=1` on round-trip-close bars (rt_ind=1) and 0
otherwise so non-close bars receive a structural no-op identical to
the pre-Phase-3.2 behaviour. Model SEES the bill in the gradient
signal during training, not just in eval-time metrics.
Approach: kernel-signature-extension (NOT wrapper-kernel). The only
existing call site in the tree is the Phase 3.1 oracle test
(`training_loop.rs` has dispatch-arm reset wiring but does NOT yet
invoke the launcher per the Phase 3.1 commit's deferred-consumer
note), so the cascade is bounded to that single test — extending the
existing kernel is cleaner than a parallel wrapper that would have to
be retired the moment the Phase 3.1 deferred consumer migration
lands.
Anchor test 2.4 cost_sensitivity (Phase 2B contract) — green via this
commit + Phase 3.1 split structure (already landed in 2d226e6e7).
Per established Phase precedent: kernel/launcher signature change +
existing-test migration land atomically per
feedback_no_partial_refactor; production reward-composition wire-up
that feeds real `cost_t` from cost_net_sharpe is the same deferred
follow-up Phase 3.1 declared (no new debt added — both share one
follow-up commit).
cargo check -p ml --features cuda: clean (18 pre-existing warnings).
cargo test -p ml --test sp15_phase1_oracle_tests --features cuda
-- --ignored r_quality_subtracts_explicit_cost: 1 passed.
cargo test -p ml --test sp15_phase1_oracle_tests --features cuda
-- --ignored r_split_uses_sentinel_alpha_at_cold_start: 1 passed
(Phase 3.1 sentinel test post-migration).
cargo test -p ml --features cuda: 946 passed / 13 failed
(same 13 failures as parent 2d226e6e7; zero introduced).
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.