b12483d91b
Final commit toward task #94 val plan_isv parity. Wires the plan machinery inside the chunked val backtest loop so state positions [86..92) carry real plan signals matching training instead of zero- fill. This is the structural distribution-shift fix behind the observed val trade count of 21 / 214,654 bars (0.0098%). Phase 6 inside `submit_dqn_step_loop_cublas`, per chunk, after env step advances portfolio: 1. Forward on the LAST-STEP N rows of chunked_states — a targeted `compute_q_values_to(last_step_states, N, scratch_q)` rewrites `save_h_s2` to exactly [N, SH2] for the final step. Needed because the batched `compute_q_values_to` for N*chunk_len rows leaves save_h_s2 at an arbitrary last-sub-batch slice. 2. `compute_plan_params(plan_params_buf, N)` runs the trade-plan MLP on that clean save_h_s2, producing [N, 6] plan_params. 3. `backtest_plan_state_isv` — Flat↔Positioned activation / deactivation on plan_state[N, 7] and writes plan_isv_buf[N, 6] consumed by the next chunk's first `launch_gather` for state positions [86..92). ch_q_values is reused as the Q-scratch for step (1) — its original chunk Q-values were already consumed by Phase 4 action-select. Epoch-boundary diagnostic `launch_plan_diag_and_log` after the step loop: launches `backtest_plan_diag_reduce` (1-block, 256 threads), DtoH-copies the 8-float summary, syncs, and emits a single `tracing::info!(target: "val_plan_diag", ...)` line with the six labelled plan_isv means plus active_frac / n_active. Scalars passed with exact i32 types (current_step, max_len, feat_dim, n_windows) per feedback_cudarc_f64_f32_abi. Kernels pulled via `plan_state_isv_kernel.as_ref().ok_or_else(...)` — they are loaded in `ensure_action_select_ready` on first eval. Not captured inside a CUDA Graph: the backtest step loop uses direct kernel launches (evaluator comment: graphs SIGSEGV on 500K+ launches). 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.