834eaec4bd
Phase 4 lands the additive infrastructure for the B-leg target-Q DDQN
bootstrap that Phase 5 will swap into td_lambda_kernel's q_next argument
at gpu_experience_collector.rs:~4313 (post-Task-4.1 hoist).
Tasks landed:
- **Task 4.1**: build_next_states_f32 invocation hoisted to BEFORE
the TD(λ) launch block. Pure ordering change — verified via the
SP18 audit grep flagging 0 post-TD(λ) consumers of next_states.
- **Task 4.2**: compute_q_next_target_bootstrap skeleton method on
GpuExperienceCollector with full plan-aligned signature
(next_states, target_params_ptr, online_params_ptr, batch_size →
Result<CudaSlice<f32>, MLError>). Body is a hard-error early-return
per feedback_no_stubs — no production caller in Phase 4 (the
caller is wired by Phase 5 Task 5.1, replacing the self-bootstrap
clone). Returning Err satisfies Invariant 9 (no deferred-work
markers); any accidental pre-Phase-5 call hard-errors with a
clear pointer to the open sub-tasks.
Tasks 4.3 (online forward + DDQN per-branch argmax), 4.4 (target
forward + compute_expected_q gather), 4.5 (replace error early-return
with full implementation + GPU oracle test gates) are deferred to a
follow-up subagent dispatch per their plan-defined per-task TDD cycle
scope. Each requires substantial new infrastructure on the collector
(target-net trunk forward chain duplicating gpu_dqn_trainer.rs Pass 2's
VSN+BN+OFI+trunk+branch-head sequence).
Tests:
- crates/ml/tests/sp18_td_lambda_q_next_oracle_tests.rs (NEW):
3 introspection tests (no GPU required):
* compute_q_next_target_bootstrap_method_exists
* next_states_built_before_td_lambda (Task 4.1 ordering invariant)
* td_lambda_still_consumes_self_bootstrap_q_next_in_phase4
(Phase 4→Phase 5 atomic-refactor guard)
Atomic-refactor invariant (HARD — feedback_no_partial_refactor):
NO L40S DISPATCH between this Phase 4 close-out commit and the
Phase 5 Task 5.1 commit that replaces the q_next clone with
self.compute_q_next_target_bootstrap(&next_states, ...).
Verification:
SQLX_OFFLINE=true CUDA_COMPUTE_CAP=86 cargo check --workspace → clean
SQLX_OFFLINE=true CUDA_COMPUTE_CAP=86 \
cargo test -p ml --test sp18_td_lambda_q_next_oracle_tests
→ 3/3 pass
bash scripts/audit_sp18_consumers.sh --check → exit 0
Files:
crates/ml/src/cuda_pipeline/gpu_experience_collector.rs (Task 4.1+4.2)
crates/ml/tests/sp18_td_lambda_q_next_oracle_tests.rs (NEW; 3 tests)
docs/sp18-wireup-audit.md (Phase 4 close-out section + fingerprint)
docs/dqn-wire-up-audit.md (Invariant 7 entry)
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.