26deaa5004
Closes the multi-step T2.2 work. Phase 1.5 captures entry_q (predicted
Q at trade open) on the per-trade tape; Phase 2 wires
enrichment::run_enrichments to the real GpuBacktestEvaluator
per-trade tape and deletes the fake-trade synthesizer
(extract_eval_trades_from_metrics) per feedback_no_stubs.
Plan amendment from on-paper design:
- Audit caught plan's "portfolio_state[ps+6] is unused" claim was
wrong — slot 6 is in active use as cum_return. Replaced with a
dedicated entry_q_state_buf [n_windows] separate from
portfolio_state. Doesn't touch shmem layout, gather kernel, or
model state-dim.
- E5 design question resolved as option (2): quartile-spread Sharpe
with ISV-driven significance anchor read from
ISV[VAL_SHARPE_VAR_EMA_INDEX=351] (per
pearl_controller_anchors_isv_driven). Hardcoded 1.5σ/0.5σ
thresholds eliminated; the noise floor is the val_sharpe variance
EMA already produced per epoch by the early-stopping pipeline.
- Single-step evaluate() launcher passes NULL q_values_per_window
(forward_fn closure exposes only action indices, not Q-values);
same NULL-tolerant pattern as exploration_scale_ptr et al. The
production val pipeline (chunked path) DOES wire real Q-values.
Files (atomic per feedback_no_partial_refactor):
- backtest_env_kernel.cu: 4 new args at end of both kernels
(entry_q_state, q_values_per_window, num_actions,
per_trade_predicted_q_out); pre_entry_q snapshot + close emit +
open/reverse capture.
- gpu_backtest_evaluator.rs: entry_q_state_buf + per_trade_predicted_q_buf
allocated; both reset in reset_evaluation_state; both launchers
migrated; EvalTrade.predicted_q field added; read_per_trade_tape
populates it.
- trainer/enrichment.rs: EvalTrade is now pub(crate) use re-export
from gpu_backtest_evaluator (drops ensemble_var); E5 refactored
to quartile-spread Sharpe with ISV-driven anchor;
extract_eval_trades_from_metrics deleted; HindsightExperience
retained (Phase 6 wiring).
- trainer/training_loop.rs: enrichment block replaced with
evaluator.read_per_trade_tape() + ISV slot 351 read; val_bars /
real_trade_count / real_total_pnl / real_win_rate plumbing
dropped.
- trainer/{mod,constructor,metrics}.rs: last_val_metrics field
removed (last consumer gone — feedback_no_hiding).
- docs/dqn-wire-up-audit.md: 2026-05-11 audit entry.
Verification (passing):
- SQLX_OFFLINE=true cargo check -p ml --tests --features cuda
- sp20_aggregate_inputs_test (12/12)
- sp20_phase1_4_wireup_test (2/2)
- sp20_emas_compute_test (4/4)
- sp20_controllers_compute_test (7/7)
After-this scope (Phase 3-7 + Phase 8 in T2.2 multi-phase):
- E1 q_correction → ISV slot consumer
- E4 per-branch LR scaling via per-group Adam
- E6 winner indices → PER priority bumps
- E7 hindsight → replay buffer injection
- E8 curriculum weights → segment sampling
- Signal-drive remaining controller GAINS (0.9/1.1 in E5; 2.0/0.5/-0.5 in E2)
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.