dd9c70df98
Lands the producer→ring→consumer schema plumbing for per-bar aux_conf
(the K=2 peak-softmax-above-uniform confidence value the Phase 5
Aux→Q gate consumes at the Bellman target). Atomic across all struct
boundaries per `feedback_no_partial_refactor`.
Data flow (TWO struct boundaries, not one — plan errata Gap 11):
ExperienceCollector → GpuExperienceBatch → insert_batch()
(.aux_conf field) (8th arg)
→ ring → sample() → GpuBatchPtrs → Trainer
(.aux_conf_ptr) (Phase 5 consumer)
Phase 5 lands the consumer (Aux→Q gate kernel at the Bellman target);
this commit is the producer-side schema plumbing only.
Spec §4.4 Phase 5 forward-reference contract:
At each replay batch step:
aux_conf = ptrs.aux_conf_ptr[k] # SAMPLED bar
threshold = ISV[AUX_CONF_THRESHOLD_INDEX] # [0.01, 0.20]
temp = ISV[AUX_GATE_TEMP_INDEX]
gate = sigmoid((aux_conf - threshold) / temp) # ∈ [0, 1]
Q_target = gate × Q_full + (1 - gate) × mean_a Q(s, a)
Producer (experience_env_step):
- 1 new kernel arg `aux_conf_per_sample: float* [N×L×cf_mult]`.
- Per-bar write at kernel entry: `aux_conf_per_sample[out_off] =
sp20_compute_per_bar_aux_conf_k2(aux_logits + i*K)`.
- CF slot write: `aux_conf_per_sample[cf_off] =
aux_conf_per_sample[out_off]` (CF state shares the same env state
so shares the same aux signal).
- NULL-tolerant: defaults to 0.0f (K=2 uniform-prior sentinel).
GpuExperienceCollector:
- +`aux_conf_per_sample: CudaSlice<f32>` field.
- alloc with `total_output * cf_mult` for both on-policy + CF.
- Threads as kernel arg of experience_env_step.
- Clones into `GpuExperienceBatch.aux_conf` at end of
collect_experiences_gpu (size `total = base_total × 2`).
GpuExperienceBatch: +`aux_conf: CudaSlice<f32>` field.
GpuReplayBuffer (crates/ml-dqn):
- +`GpuBatchPtrs.aux_conf_ptr: u64`.
- +ring storage `aux_conf: CudaSlice<f32>` [capacity].
- +sample buffer `sample_aux_conf: CudaSlice<f32>` [max_batch_size].
- +`trainer_aux_conf_ptr: u64` for direct path.
- +`set_trainer_aux_conf_ptr` setter + `trainer_aux_conf_ptr`
accessor (mirrors `set_isv_signals_ptr` pattern).
- `insert_batch` adds 8th arg `aux_conf_in: &CudaSlice<f32>` —
scatters via `scatter_insert_f32` (reuses the rewards/dones
scatter kernel).
- `sample_proportional` adds gather: direct-to-trainer if
`trainer_aux_conf_ptr != 0`, else fallback into
`sample_aux_conf`. Both paths populate `GpuBatchPtrs.aux_conf_ptr`.
Atomic caller updates (insert_batch arg from 7 to 8):
- training_loop.rs:2522 (production)
- gpu_residency.rs:77 (smoke)
- performance.rs:144 (smoke)
- training_stability.rs:154+201 (smoke ×2)
- gpu_per_integration_test.rs:128 (integration)
- sp15_phase1_oracle_tests.rs:2170+2189+2356 (oracle ×3)
- 3 inline tests in gpu_replay_buffer.rs
Tests:
- test_aux_conf_schema_round_trip (GPU): inserts 8 transitions with
distinct aux_conf [0.0, 0.05, ..., 0.35]; samples 1; asserts the
gathered slot value matches one of the inserted values
(round-trip integrity invariant).
- test_aux_conf_trainer_ptr_wiring_contract (CPU): asserts
set_trainer_aux_conf_ptr / trainer_aux_conf_ptr setter+accessor
behavior matches the SP15 Phase 3.5.5.b mirror pattern.
Verification:
SQLX_OFFLINE=true cargo check -p ml --tests --features cuda # green
SQLX_OFFLINE=true cargo check -p ml-dqn --tests --features cuda # green
SQLX_OFFLINE=true cargo test -p ml --lib sp20 # 21/21 pass
SQLX_OFFLINE=true cargo test -p ml-dqn test_aux_conf_trainer_ptr_wiring_contract # CPU pass
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.