fef5939556
The threshold-tuning smoke at 81decf40f produced n_trades=0 despite
74.6% of decisions having max_conv ≥ 0.30 — the linear-weighted-mean
aggregator in decision_policy_default is structurally dilution-bound
at cold-start (per spec §1).
Q1 stopgap: when sim_variants[i].use_cold_start_stopgap = true, the
harness uploads a max-confidence Strategy bytecode program for that
backtest, routing decisions through decision_policy_program with
OP_AGG_MAX_CONFIDENCE. Existing kernel; zero CUDA changes.
Field additions (atomically across BatchedSimConfig + UniformSimParams
+ ResolvedSimVariant + SweepBase.SimVariant) — every UniformSimParams
literal migrated to include use_cold_start_stopgap: false (default).
The sweep YAML's sim_variants entry sets it to true only for the
validation run; production deployability uses Q2's kernel fix instead.
Sweep YAML (config/ml/sweep_smoke.yaml) flipped to use_cold_start_stopgap=true
at threshold=0.0, cost=0.125 — same anchor as the threshold-tuning
smoke that produced n_trades=0, for direct comparison.
This is a VALIDATION step. Cluster smoke at this commit MUST produce
n_trades > 100 + finite metrics. Q2's kernel CBSW immediately follows
and deletes this entire stopgap atomically (field, harness branch,
YAML setting, every literal).
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.