a92ff28a98
Per spec §6.2 per-side semantics:
cost_t = commission_per_rt × rt_ind[t]
+ half_spread[t] × |pos[t]| × side_ind[t]
+ ofi_lambda × |pos[t]| × |ofi[t]| × side_ind[t]
Commission charged at close; half-spread × |pos| at entry AND exit
(sums to one full spread per RT); OFI impact same per-side. Initial
λ=2.0e-4 in ISV[OFI_IMPACT_LAMBDA_INDEX=407] as Invariant-1 anchor
(constructor-write + FoldReset rewrite per feedback_isv_for_adaptive_bounds);
per-fold ISV refit may overwrite in later phases. Mean cost-per-bar
emitted to ISV[COST_PER_BAR_AVG_INDEX=408] (stateful kernel output;
FoldReset sentinel 0 + Pearl A first-observation bootstrap).
Same kernel reads LobBar fields from synthetic markets (Phase 2A) and
real fxcache LOB (prod) — dev/prod parity per Q3.
Phase 1.2 lands kernel + launcher + anchor seed + 2 registry entries
+ 2 dispatch arms only; consumer migration deferred to a follow-up
commit per feedback_no_partial_refactor (mirrors Phase 1.1 atomic
pattern). Oracle test cost_net_sharpe_round_trip_charges_full_spread
validates single round-trip cost = 2.00 / 10 bars = 0.20/bar with
mean_pnl_net = 0.80 on 1.69s RTX 3050 Ti (sm_86). Zero regressions
introduced (946 passed / 13 pre-existing failures, same as Task 1.1).
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.