641aa0dfde
Per train-multi-seed-hjzss validation: SP16 T1+T2 chain was structurally
landed but BEHAVIORALLY INERT in 5-epoch smoke (bit-identical to pfh9n
baseline through epoch 3). Root cause: hardcoded `alpha = 0.05f` in both
producer kernels violates feedback_isv_for_adaptive_bounds AND prevents
convergence in short runs (~60 epochs needed from cold start).
Fix per pearl_wiener_optimal_adaptive_alpha:
α = diff_var / (diff_var + sample_var + ε)
Where sample_var = running variance of target signal (Welford accumulator)
and diff_var = running variance of consecutive one-step differences.
Cold-start: target jumps 1.0 → 6.4 → 7.0 → high diff_var → α ≈ 0.6+
→ near-bootstrap responsiveness in epochs 1-3
Steady-state: signal stabilizes → diff_var drops → α decays naturally
→ smoothing emerges without hardcoded constant
Adds 12 new ISV slots (6 per producer):
- HCS_TARGET_MEAN/M2, HCS_DIFF_MEAN/M2, HCS_PREV_TARGET, HCS_SAMPLE_COUNT
- MHT_TARGET_MEAN/M2, MHT_DIFF_MEAN/M2, MHT_PREV_TARGET, MHT_SAMPLE_COUNT
ISV_TOTAL_DIM 462 → 474.
Both kernels migrated atomically. Pearl-A bootstrap preserved (sentinel
on prev_blended triggers REPLACE; cold-start α=1.0 when N<3 samples).
Defensive bounds [WELFORD_ALPHA_MIN=0.01, WELFORD_ALPHA_MAX=0.95] on the
Wiener-derived α to guard against denormal/underflow corner cases.
HEALTH_DIAG[N] emit extended with `alpha=...` and `sample_count=...` for
direct trajectory observation in validation smoke.
Behavioral tests verify:
- α high during signal jumps (>0.3 at epoch 3 post-cold-start)
- α low in steady state (mean tail α<0.4 under converging signal)
- Pearl-A bootstrap fires on first observation (Welford state advances
regardless of REPLACE branch)
- α stays within [WELFORD_ALPHA_MIN, WELFORD_ALPHA_MAX] over 50 epochs
(post-cold-start; cold-start α=1.0 by design)
- No 0.05f hardcoded literal remains in blend math (regression-locked
via host-only string scan)
5 GPU + host tests pass: sp16_phase3_alpha_high_during_signal_jump,
alpha_low_in_steady_state, pearl_a_bootstrap_first_obs,
alpha_naturally_bounded, no_hardcoded_alpha. sp14 + sp15 oracle suites
unchanged (34 GPU tests + 4 host tests).
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.