07728f9efc
Phase A3 of the SP22 H6 vNext trade-outcome aux head (per
docs/plans/2026-05-14-sp22-h6-vNext-trade-outcome-aux.md). Two atomic pieces:
1. NEW kernel `aux_trade_outcome_forward_kernel.cu` — K=3 softmax aux head
forward (Linear → ELU → Linear → stable softmax). Mirrors
`aux_next_bar_forward` (K=2) but emits {Profit, Stop, Timeout} probs.
Saved tensors {hidden_out, logits_out, softmax_out} ready for A4 (loss
reduce) and A5 (backward). Dead code at this commit — no Rust launcher
yet. Registered in build.rs::kernels_with_common, cubin verified.
2. Save-for-backward buffers `pnl_vs_target_at_close_per_env` +
`pnl_vs_stop_at_close_per_env` ([alloc_episodes] f32 device-resident).
Producer: `experience_env_step::segment_complete` writes the trade's
realized P&L ratios vs profit_target / stop_loss at close (inline-
computed from `pre_trade_position × (raw_close − entry_price) /
(ps[PS_PLAN_PROFIT_TARGET] × prev_equity)`, symmetric-clamped to
[-2, +2] per pearl_symmetric_clamp_audit — same formula as the
sibling experience_state_gather's plan_isv[PLAN_ISV_PNL_VS_TARGET/_STOP]
slots). Consumer (eventual A4/A5 wireup): trade_outcome_label_kernel
classifies each close into {Profit, Stop, Timeout} via the ≥1.0
threshold-hit predicate.
Wireup discipline per feedback_registry_entries_need_dispatch_arms:
- New struct fields on GpuExperienceCollector
- stream.alloc_zeros at construct site
- Kernel-launch .arg() threading at experience_env_step launch
- StateResetRegistry entries (FoldReset sentinel 0.0)
- training_loop::reset_named_state dispatch arms
- All 10 registry pin tests pass including
every_fold_and_soft_reset_entry_has_dispatch_arm
Audit doc updated: docs/dqn-wire-up-audit.md Phase A3 section.
Next phases (per spec): A4 = aux_trade_outcome_loss_reduce (sparse CE,
mask=-1), A5 = aux_trade_outcome_backward, Phase B = 262-dim input
(h_s2_aux || plan_params), Phase C = 3-slot state assembly, Phase D =
12-weight W atom-shift, Phase E = dW + Adam, Phase F = validation smoke.
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.