da21feb1b1
Production smoke completed end-to-end but produced n_trades=0 across 99,969 decisions — `decision_policy_default` and `decision_policy_program` both applied a sentinel-skip pattern: if `isv_kelly_d` had not been seeded (pnl_ema_win == 0), each horizon's signed-size stayed zero, AND each horizon's aggregation weight (= recent_sharpe) also stayed zero. The cross-horizon w_sum was therefore 0, final_size was 0, every market_target was noop. State only updates on trade close → no trade ever fires → infinite cold-start. Per pearl_blend_formulas_must_have_permanent_floor (`max(real, floor)`, not blend) and pearl_kelly_cap_signal_driven_floors, replace the sentinel- skip with a two-layer floor on each kernel: 1. Kelly fraction: `max(kelly_frac_floor, computed_kelly)` — when state is sentinel, falls back to the floor directly. Cap_lots falls back to `max_lots` when realised_return_var is sentinel. 2. Aggregation weight: `max(sharpe_weight_floor, recent_sharpe)` — lets cross-horizon sum produce a non-zero size before recent_sharpe is populated. Once a horizon shows positive sharpe it dominates. Plumbed through `step_decision_with_latency` / `step_decision` as two new f32 args (atomic contract change, every caller migrated). Defaults 0.20 / 0.10 chosen so a strong-conviction signal (sig_mag ≥ 0.5) fires 1 lot at cold-start under max_lots=5 while weaker signals stay flat (see `default_kelly_frac_floor` comment for the arithmetic). Exposed as CLI flags + sweep-grid base/cell overrides. Regression test `decision_floor_coldstart` proves: - default floors (0.20/0.10) fire a 1-lot buy with p_h=0.8 and zero state - zero floors reproduce the original noop bug Also moves `aggregate` step to the GPU pool because fxt-backtest is dynamically linked against libcuda.so.1 (the ci-compile-cpu hosts don't expose CUDA driver libs). Verified locally on RTX 3050 Ti — workspace cargo check passes, both regression tests pass, trunk save/load roundtrip still passes. 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.