cd82f9a4a0
Adds the two sweep axes that the spec's deployability grid needs but were missing from the kernels: Threshold gate (decision_policy.cu, both kernels): - New per-backtest `threshold_per_b` array kernel arg. - Pre-Kelly prelude: if max_h |alpha[h] - 0.5| * 2 < threshold[b], emit noop and return. Kept deterministic from alpha alone so the threshold pre-registration step (p60-p95 absolute calibration on a validation window, future P6) reflects exactly what gets gated in deployment. Per-fill cost integration (resting_orders.cu / apply_fill_to_pos): - apply_fill_to_pos signature grows three args: b, cost_per_lot_per_side_per_b, total_fees_per_b. Single insertion point at line 90. - After the close-leg realized_pnl math runs (so the gross unwind P&L is preserved), deduct fill_cost = filled_lots * cost_per_lot_per_side[b] from pos.realized_pnl AND accumulate into total_fees_per_b[b]. - Net-of-cost semantics: isv_kelly_update_on_close reads realized_pnl delta which is now net of cost — Kelly state learns from realistic return distribution. - All 3 apply_fill_to_pos call sites in step_resting_orders updated. order_match.cu's submit_market_immediate path is dead code in the post-P1 flow (everything routes through seed_inflight_limits_batched → step_resting_orders → apply_fill_to_pos) so not touched here. BatchedSimConfig + UniformSimParams + BacktestHarnessConfig gain threshold + cost_per_lot_per_side fields. All UniformSimParams constructors in tests and main.rs updated with defaults (0.0, 0.0 = gate disabled, frictionless). Regression: - threshold_gate_skips_low_conviction (p=0.51 + threshold=0.10 → noop) - threshold_gate_allows_high_conviction (p=0.8 + threshold=0.10 → buy 1+) - threshold_zero_is_passthrough (sanity) - All P1+P2+P3 tests continue to pass via the new ABI. cost_deducted_at_each_fill + kelly_state_sees_net_return end-to-end tests deferred — they require a full submit_market → fill → close sequence, which the production smoke exercises. 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.