658fec4939
Per Class B audit, the PER ring buffer was never cleared from the
single source of truth at `DQN::reset_for_fold`. fold N+1 was
training on a 50/50 mix of stale fold N-1 experiences + fresh
fold N — averaging behavior locked learning to a "policy-agnostic"
middle ground producing 46-48% WR across SP3-SP15.
Fix: extend `DQN::reset_for_fold` to call `clear_replay_buffer()`
internally. Single source of truth — every fold reset clears the
buffer atomically. Removed the now-redundant explicit
`self.clear_replay_buffer().await?` call at the top of
`DQNTrainer::reset_for_fold` per `feedback_no_legacy_aliases`.
Tradeoff: loses cross-fold experience transfer (fold N+1 starts
from scratch on fold N data only). User-approved Option (a) per
Class B audit 2026-05-08.
Signature changes (atomic per `feedback_no_partial_refactor`):
- `DQN::reset_for_fold`: `()` → `Result<(), MLError>`
- `DQNAgentType::reset_for_fold`: `()` → `Result<(), MLError>`
- `DQNTrainer::reset_for_fold` (async): unchanged signature; the
agent.reset_for_fold call now propagates the Result.
`GpuReplayBuffer::clear` extended: it already zeroed
`priorities_pa` + `prefix_sum` but left `priorities` unzeroed.
Sampler reads `priorities_pa` (not `priorities`) so this was not
a sampling-contamination vector, but is leftover state that
`priorities_slice()` consumers would inherit. Fixed for
completeness alongside the fold-boundary-clear lift.
Behavioral test added: `test_clear_purges_priorities_and_blocks_sampling`
inserts 64 transitions, samples once to populate prefix_sum +
sample buffers, steps the β counter, then clears and asserts:
- len() == 0, is_empty() == true, can_sample(1) == false
- write_cursor() == 0, current_step == 0
- priorities[..n] all zero (DtoH readback)
- priorities_pa[..n] all zero (DtoH readback)
No mocks; exercises actual GPU memset_zeros + DtoH path.
GPU-gated `#[ignore]` — runs at deploy time.
Cumulative WR-plateau fix series (commit 10):
- Class C bug 1 + P0-B (8f218cab2)
- P0-C, P0-A, P1, P0-A-downstream, P1-Producer, 4-A, 4-B, 4-B fixup
- Class B #1 (this commit)
Verification:
- cargo check -p ml-dqn -p ml --tests --all-targets: clean
- cargo test -p ml-dqn --release --lib: 266 pass / 16 pre-existing
GPU-config failures unchanged (verified via stash-baseline)
- cargo test -p ml --test sp14_oracle_tests --test
sp15_phase1_oracle_tests --release: 5 pass / 36 ignored (GPU)
Audit doc entry appended at docs/dqn-wire-up-audit.md.
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.