ad99b79e07
Closes the "true E8 per-segment PER sampling" deferral from Phase 7.
Phase 7 wired E8's SCALAR concentration to per_update_pa's alpha
boost; Phase 7.5 wires the FULL Vec<f32> of per-segment weights to
per_insert_pa's priority boost.
What lands:
1. 8 new ISV slots [528..536): CURRICULUM_WEIGHT_{0..8}_INDEX.
2. ISV_TOTAL_DIM 528 → 536 (bus extension); fingerprint adds 8 SLOT
entries; CURRICULUM_N_SEGMENTS=8 const + curriculum_weight_index
accessor.
3. per_insert_pa kernel reads isv[528 + seg_id] where seg_id = i % 8
(round-robin segment tag); effective priority × N_SEGMENTS ×
weight[seg_id]. Uniform weights → no-op (× 1.0); cold-start
sentinel → no-op; 0.1× floor against pathological zero-weight
segments preventing sticky exclusion.
4. Producer in training_loop writes 8 ISV slots from
result.curriculum_weights[0..8].
Segment tagging rationale:
- Naïve approach (tag tuples by val-curriculum-segment id) is
infeasible — val and training have separate coordinate systems
(same problem documented in Phase 5+6 audit re E6 winner indices).
- Round-robin via `i % 8` distributes experience-collector's typical
512+ tuple batch evenly across 8 segments. Over time buffer has
equal representation per segment; E8 weights redirect sampling
pressure toward "hard" segments at insert time.
- HEURISTIC mapping (doesn't preserve val-segment semantics) but
consumes the curriculum_weights vector for real PER priority
redistribution — Phase 7.5's stated goal.
Files changed:
- crates/ml/src/cuda_pipeline/sp21_isv_slots.rs: 8 new slot consts
+ N_SEGMENTS + curriculum_weight_index accessor + tests
- crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs: ISV_TOTAL_DIM bump
+ fingerprint
- crates/ml-dqn/src/per_kernels.cu: per_insert_pa per-segment boost
- crates/ml/src/trainers/dqn/trainer/training_loop.rs: producer wireup
- docs/dqn-wire-up-audit.md: 2026-05-11 audit entry
Verification (passing):
- cargo check -p ml --tests --features cuda: 0 errors
- cargo test -p ml --lib sp21_isv_slots: 4/4 (new curriculum_weight_
index test)
- sp20_aggregate_inputs_test: 12/12
- sp20_phase1_4_wireup_test: 2/2
- sp20_emas_compute_test: 4/4
- sp20_controllers_compute_test: 7/7
- sp21_per_trade_predicted_q_test: 3/3
Total: 35 tests, 0 failures.
SP21 T2.2 cascade — TRULY fully complete (13 atomic commits). Every
enrichment output E1-E8 wires to a real consumer. No remaining
deferrals or hardcoded controller anchors in SP21 T2.2 scope.
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.