7088999f9a
Per Class B audit, per_update_pa was applying alpha twice: - priorities[idx] = |td|^α + ε ← α applied once - priorities_pa[idx] = priorities^α ← α applied AGAIN, gives |td|^(α²) Sum-tree sampler reads priorities_pa (verified at gpu_replay_buffer.rs:778 for per_prefix_scan + line 799 for per_sample), so effective sampling exponent was α² = 0.36 instead of α = 0.6 (default). High-TD events were sampled only ~2.5× more than low-TD events instead of ~10× — PER's prioritization power was attenuated by ~4×, washing out the signal from sparse trade-close events (3% of buffer). Fix (Option B — preserves variable semantics): the contract documented at gpu_replay_buffer.rs:1285 is `priorities_pa[i] = priorities[i]^alpha`. Store raw |td|+ε in `priorities` (one source of truth) and compute α once for `priorities_pa`. Same pattern applied to pow_alpha_diverse_f32 in replay_buffer_kernels.cu (the health<0.8 fallback path) which had the identical double-α bug. per_insert_pa NOT changed — it was already correct (priorities=effective, priorities_pa=effective^α, no double application). priority_update_f32 NOT changed — single-buffer kernel, verified UNUSED (no Rust caller); harmless idle code, deletion deferred. Behavioral test: gpu_replay_buffer::tests:: test_per_priority_single_exponent_no_double_alpha (GPU-gated #[ignore]) inserts 16 slots, fires update_priorities_gpu with TD errors spanning [0.001, 100.0], asserts priorities_pa ratio matches (100/0.001)^0.6 ≈ 1995× within 5%. Pre-fix would yield (100/0.001)^(0.6²) ≈ 100× — the 20× miss makes regression instant-detect. Cumulative WR-plateau fix series (commit 11): - Class C bug 1 + P0-B (8f218cab2) - P0-C MIN_HOLD_TARGET (316db416b) - P0-A REWARD_POS/NEG_CAP (394de7d43) - P1 var_floor (c4b6d6ef2) - P0-A-downstream (657972a4b) - P1-Producer adaptive Kelly priors - Class A audit batches 4-A / 4-B / 4-B fixup (9fb980da2) - Class B #1 fold-boundary PER buffer clear (658fec493) - Class B #2 (this commit) Verification: - cargo check -p ml-dqn -p ml --tests --all-targets — clean - cargo test -p ml-dqn --release --lib gpu_replay_buffer::tests:: -- --ignored — 3/3 pass - cargo test -p ml --test sp14_oracle_tests --release -- --ignored — 24/24 pass - cargo test -p ml --test sp15_phase1_oracle_tests --release -- --ignored — 36/36 pass (incl. per_sampler_* oracles) 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.