00da163078
Three correlated fixes addressing the architectural inconsistency
surfaced by the 3-fold ISV CV: we built a horizon-aware gradient
controller (ISV) but suppressed its target horizon (h6000) to 0.36%
of the loss via auto-horizon-weights, then used a ratio formula
that never approached its own clamp ceiling. ISV's lambda was
operating on rounding error.
(1) Uniform BCE weights as auto-default
trainer/perception.rs: `auto_horizon_weights` now returns
[1.0; 5] regardless of seq_len. Prior schedule `min(1, K/h)`
gave h6000 weight 0.0053 at K=32 — combined with lambda ~1.04,
h6000's effective loss contribution was ~0.37%, indistinguishable
from zero. With uniform weights, each horizon contributes 20% and
ISV's lambda actually has something to scale.
(2) Z-score lambda derivation
cuda/horizon_lambda.cu: replace `ratio = ema_h / mean(ema)` with
`z_h = (ema_h - mean) / std(ema); lambda = clamp(1.0 + 0.5*z, 1.0, 2.0)`.
Per `pearl_zscore_normalization_for_magnitude_asymmetric_signals.md`
z-score makes lambda spread scale-invariant of the absolute EMA
level. The ratio formula gave lambdas ≤ 1.04 in our data because
per-horizon BCE clusters tightly (range ~0.04) while mean is
~0.65. Z-score fills the [1.0, 2.0] envelope: 1σ → 1.5, 2σ →
ceiling. Boost-only asymmetric clamp preserved.
Test verification on the existing smoke (after 5 steps):
ema = [0.526, 0.522, 0.608, 0.641, 0.553]
lambda = [1.00, 1.00, 1.40, 1.76, 1.00]
Previously with ratio formula, max lambda on the same data
would have been ~1.05. h1000 (1.5σ above mean BCE here) now
gets a 76% trunk-gradient boost vs uniform.
(3) Default --seq-len 32 → 64
examples/alpha_train.rs: K=32 gives the model 0.5% of the
h6000 prediction window as in-window context. K=64 doubles
that, giving Mamba2's SSM state more material to build
long-horizon predictions. Within the kernel's MAMBA2_KERNEL_SEQ_MAX
cap of 96. Per-epoch wall scales ~K (more K-loop launches in
the captured graph, ~2× wall at K=64 vs K=32 for the K-loop
portion of dispatch).
Cache-bust v5 in build.rs to force nvcc recompile against the new
horizon_lambda.cu formula on the cluster's /cargo-target PVC. Old
cubins compute a numerically different lambda; running them against
the new Rust loop would silently apply the wrong gradient scaler.
Validation: 7 perception_overfit tests + 26 lib + 23 integration
ml-alpha tests pass. Synthetic overfit still converges to 0.0006.
horizon_ema_and_lambda_track_after_training observes the new
lambda spread (1.0-1.76) and asserts the asymmetric clamp envelope.
Co-Authored-By: Claude Opus 4.7 <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.