3b71d21834
Aux's original label was (p_{t+1} > p_t) — pure HFT-scale microstructure
noise that's unlearnable at our HFT-MFT trading frequency. Migrated to
(p_{t+H} > p_t) where H is read from ISV[AUX_PRED_HORIZON_BARS_INDEX=450].
Adaptive producer drives H from observed avg winning hold time:
- Pearl-A first-observation bootstrap: replace sentinel H=60 directly
on first valid observation
- Steady-state Wiener-α EMA blend, slow (α=0.01) for stable horizon
(no target-variance EMA available, fallback per
pearl_wiener_optimal_adaptive_alpha)
- "No winning trades yet" guard keeps sentinel until first valid observation
Lookahead truncation: labels at t where t+H >= total_bars are masked
(sentinel -1, loss-reduce skips). The existing aux_next_bar_loss_reduce
in aux_heads_kernel.cu already supports the -1 mask convention via the
B_valid count — no new valid_mask parameter needed.
Step 5b finding: Case B — existing per-sample buffers
(hold_at_exit_per_sample, trade_profitable_per_sample) populated by
unified_env_step_core, but no aggregate ISV slot. Added new aggregator
slot AVG_WIN_HOLD_TIME_BARS_INDEX=451 + new producer kernel
avg_win_hold_time_update_kernel.cu (block-tree-reduce, no atomicAdd).
ISV_TOTAL_DIM bumped 450 → 452.
ATOMIC migration per feedback_no_partial_refactor: both label kernels
(aux_sign_label_kernel.cu trajectory + aux_sign_label_per_step_kernel.cu
per-rollout-step) migrated together to the new
(targets, bar_indices, isv, isv_h_idx, out_labels, total, total_bars)
signature. The lookahead host-passed scalar argument is removed; H is
read from ISV inside the kernel (broadcast value, single read per
thread, on-device clamp [1, 240]).
Producer chain (per-epoch boundary): new
GpuDqnTrainer::launch_aux_horizon_chain orchestrates
avg_win_hold_time_update → aux_horizon_update sequentially alongside
launch_kelly_cap_update at the existing epoch-boundary slot in
training_loop.rs.
Trunk math (C.2/C.3/C.4) unchanged — separate aux trunk is label-
agnostic. Validation in C.10 will use H=60 cold-start; the adaptive
producer drives H from real winning-trade observations.
Tests (8 oracle, 5 new + 3 preserved):
- aux_trunk_forward_matches_numpy_reference (C.3) ✓
- aux_trunk_backward_gradient_check (C.4) ✓
- aux_trunk_backward_does_not_write_dx (C.4) ✓
- aux_sign_label_h_bar_horizon (NEW) ✓
- aux_sign_label_lookahead_mask (NEW) ✓
- aux_horizon_pearl_a_bootstrap (NEW) ✓
- aux_horizon_converges_to_steady_target (NEW) ✓
- aux_horizon_holds_sentinel_with_no_winning_trades (NEW) ✓
8/8 pass on RTX 3050 Ti.
Phase C.4b of SP14 Layer C separate-aux-trunk refactor.
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.