7c850a6c02
Per spec §4.0 + plan C1.3. Without this gate, every fractional change in target_lots seeds an order via target-delta semantics; combined with the continuous controller invocation (every event after A1) this generates hyperactivity even with the multi-horizon §4.4 conviction formula (C1.2). v2 Gate-1 failures confirmed: scalar EMA + amplification clamp + per-event controller = 155k trades on a 2M-event smoke. The band absorbs fractional target adjustments so most events produce no order. When the signal genuinely shifts and target moves enough to cross the band, the kernel seeds. Continuous evaluation, sparse action. Greenfields atomic refactor — single config knob threaded through every layer in one commit: SweepBase.delta_floor: f32 (YAML, default 1.0 = 1 lot) SimVariant.delta_floor: Option<f32> (per-variant override) ResolvedSimVariant.delta_floor: f32 UniformSimParams.delta_floor: f32 BatchedSimConfig.delta_floor: Vec<f32> LobSimCuda.delta_floor_d: CudaSlice<f32> seed_inflight_limits_batched kernel param + skip-if-below-floor logic New accessor: LobSimCuda::read_inflight_count(b) — counts active != 0 limit slots for backtest b. Not cfg(test); follows existing read_limit_slot pattern. Test: no_trade_band_blocks_micro_delta verifies that a second decision with the same strong-bullish alpha (same target, effective = in-flight lots) produces delta=0 and does not re-seed. max_lots=1 keeps the arithmetic unambiguous. Existing tests: all UniformSimParams struct literals updated with delta_floor=0.0 (band disabled) so existing behaviour is preserved. harness.rs from_uniform path uses delta_floor=1.0 (production default). Hot-path discipline: the delta_floor_d upload happens once per run in the existing config-upload block (alongside threshold, cost, max_hold_ns); the kernel reads the device slot per-event without any host roundtrip. No memcpy_htod / dtoh / dtov / synchronize introduced on the per-event path. Per pearl_controller_anchors_isv_driven, this floor is currently a config constant; CRT.2 (Phase 2) makes it ISV-derived from rolling spread cost / signal volatility. 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.