779c03b9d7
Spec: docs/superpowers/specs/2026-06-02-trainer-rollout-buffer-gae.md Plan: docs/superpowers/plans/2026-06-02-trainer-rollout-buffer-gae-implementation.md §Phase 1B-B Builds on commit5cd2f8703(1B-A foundation). Wires the RolloutBuffer into the per-step training loop via a new RolloutCollection helper, gated behind FOXHUNT_USE_ROLLOUT=1 env flag. Mode 0 (default unset) is bit-equal to HEAD; Mode 1 (flag=1) snapshots per-step data into the rollout buffer and invokes GAE every T_rollout steps. This phase is a STRUCTURAL validation, not an empirical pnl test. Phase 1B-C will replace the per-step training in Mode 1 with multi- epoch PPO over the rollout buffer. New components: * crates/ml-alpha/cuda/rollout_pack.cu (42 LOC): Single-thread-per-batch deterministic f32 -> u8 packer for dones. Used because RolloutBuffer.dones_d is u8 (per spec for memory) but the trainer's dones_d is f32 (per existing pipeline). * crates/ml-alpha/src/trainer/rollout_collection.rs (375 LOC): RolloutCollection helper with three methods: - new(ctx): loads rollout_pack cubin - snapshot_step(trainer, buf, t, b_size, t_max): DtoD scatter via cuMemcpy2DAsync_v2 for rewards/v_t/actions/log_pi/h_t at offset b*t_max+t in the [B × T] buffer; rollout_pack kernel for dones - copy_bootstrap_v(trainer, buf, b_size): DtoD V(s_T) from trainer.v_pred_tp1_d into buf.v_t_bootstrap_d at rollout end All transfers use mapped-pinned / DtoD only — no raw memcpy_dtoh on regular slices (`feedback_no_htod_htoh_only_mapped_pinned`). * crates/ml-alpha/examples/alpha_rl_train.rs (+137 LOC at lines 59-66, 410-466, 591-666): - FOXHUNT_USE_ROLLOUT env-flag parse at startup - Conditional RolloutBuffer + RolloutCollection allocation - Per-step snapshot_step() after step_with_lobsim_gpu - Every T_rollout steps: copy_bootstrap_v + compute_gae + stderr log `[rollout] step=N GAE complete (T=…, γ=…, λ=0.95): adv_mean=… adv_abs_mean=… returns_mean=…` Adaptations from the dispatch (documented inline): 1. T_rollout fallback to 32 when slot 758 is unbootstrapped. Slot 758 (RL_PPO_ROLLOUT_HORIZON_INDEX) was allocated in 1B-A but no kernel bootstraps it — `read_isv_host(758)` returns 0.0. Binary uses `.clamp(32, 1024)`, flooring to T=32 for the smoke. Phase 1B-C will add the trainer bootstrap entry (or a controller kernel) to make T_rollout=256 the production default. 2. Mode 1 still runs per-step training as a side effect; the rollout buffer is populated as a parallel snapshot. The "stop training in Mode 1" separation would require a ~1500 LOC refactor of step_with_lobsim_gpu (single-step/training-coupled per pearl_foxhunt_trainer_is_genuinely_single_step). The dispatch explicitly authorized this "skeleton with correct semantics" adaptation — the load-bearing 1B-B gate is "collection + GAE pipeline works structurally", which is verified. Phase 1B-C will replace the per-step training with multi-epoch PPO over the rollout buffer. Validation (all gates PASS): * cargo build --release --example alpha_rl_train -p ml-alpha: exit 0 (~60s) * cargo test rollout_buffer_invariants --release: 5/5 PASS (no regression) * Mode 0 (flag unset, default): ./scripts/determinism-check.sh --quick exit 0 - DETERMINISTIC: all checksums.* leaves match across all 200 rows - pipeline output bit-equal to HEAD5cd2f8703baseline * Mode 1 (FOXHUNT_USE_ROLLOUT=1): smoke completes in 2m 53s - 500 train + 100 eval steps, exit 0, completed_clean=true - 16 GAE windows logged (T=32 each) - adv_abs_mean range [4.4e-3, 1.92e-1] — always > 1e-3 sanity gate - eval_summary written: total_pnl_usd=-$101,487.70, n_trades=301, wr=0.346 * Cross-source consistency BOTH modes within $50 (Mode 0: $0.00 diff; Mode 1: $0.01 diff). Phase 0 contract preserved. * Pre-commit hook PASS (0 memcpy_dtoh raw violations, 0 atomicAdd). Next: Phase 1B-C will (a) bootstrap slot 758 to production T_rollout=256, (b) replace Mode 1's per-step training with multi-epoch PPO over the rollout buffer using the existing PPO surrogate + V regression kernels operating on minibatches of [B × T] flattened to [B*T]. Co-Authored-By: Claude Opus 4.7 <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.