73fd49f4b5
Smoke (with mag_concat off-by-one fixed and fxcache regenerated):
- training MAG_DIST: Q=0.287 H=0.301 F=0.412 (healthy diversity)
- INTENT at eval: Q=0.045 H=0.045 F=0.911 (network learned Full)
- realised at eval: Q=0.592 H=0.408 F=0.000 (Full silenced)
Even though q_f >> q_h >> q_q (network strongly prefers Full), eval
realised Full = 0.000. Position-sizing pipeline composes four
multiplicative shrinks at experience_kernels.cu:1615-1672:
effective_max_pos = max_position
× cvar_scale (line 1621)
× q_gap_conviction (line 1628, clamped [0.25,1])
× kelly_f (line 1649, only if >20 trades)
× var_scale (line 1671, = 1/(1+sqrt(var_q)))
Each term well-bounded individually but composing them silences the
policy at validation when var_q persists high (var_q ≈ 30 → var_scale
≈ 0.15; even with conviction = 1.0, kelly_f = 1.0, the compound 0.15
falls in the Quarter bucket [0, 0.375)). Network INTENT reaches the
target_position kernel correctly; var_scale strips it back out.
Same family as val-Flat-collapse (warm-branch Kelly = 0 from balanced
priors) — fix is the same pearl
(`pearl_blend_formulas_must_have_permanent_floor.md`):
var_scale = max(var_scale, q_gap_conviction)
The q_gap-derived conviction is already an adaptive ISV-coupled signal
(line 1628), already clamped to [0.25, 1.0]. Using it as a permanent
floor on var_scale lets the policy's magnitude intent reach the
realised position when conviction is high, regardless of variance.
No tuned constants — feedback_adaptive_not_tuned + feedback_isv_for_adaptive_bounds
both honoured by reusing the existing adaptive bound rather than
introducing a new threshold.
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.