b6dfd89903
Replaces the single-linear heads (5 × 128) with per-horizon TFT GRN:
trunk h[B, 128]
→ W1 (HIDDEN→HEAD_MID) + GELU = eta_2 [B, 5, HEAD_MID]
→ W2 (HEAD_MID→HEAD_MID) = eta_1 [B, 5, HEAD_MID]
→ (W_gate, W_main) split = (gate_lin, main) [B, 5]
→ W_skip (HIDDEN→1) = skip [B, 5]
→ skip + sigma(gate_lin) * main = logit [B, 5]
→ sigma(logit) = p [B, 5]
10 trainable param tensors per horizon (5 weight + 5 bias), 10 AdamW
state objects (biases get wd=0), 6 per-K-position intermediate buffers
sized [K, B, 5, HEAD_MID] (z1, a1, z2) and [K, B, 5] (gate_logit, main,
logit). All saved during fwd, consumed by GRN bwd for the chain rule.
K-loop forward replaces `multi_horizon_heads_batched` with the new
`multi_horizon_heads_grn_fwd_batched` kernel. K-loop backward replaces
`multi_horizon_heads_backward_batched` with `multi_horizon_heads_grn_bwd_batched`.
ISV `lambda_d` is consumed by the GRN bwd kernel to scale ONLY the
trunk gradient (per pearl_adam_normalizes_loss_weights.md — the
effective lever is the trunk gradient, not loss weight).
Eval path also uses the GRN fwd kernel so eval sees the same
distribution downstream layers trained on (same pattern as Phase 1.6
LN wiring).
Xavier-uniform init: scale = sqrt(1 / fan_in) per tensor:
W1, W_skip: scale = sqrt(1/HIDDEN) ≈ 0.088
W2, W_gate, W_main: scale = sqrt(1/HEAD_MID) ≈ 0.125
Parameter count per horizon: W1 (8192) + b1 (64) + W2 (4096) + b2 (64)
+ W_gate (64) + b_gate (1) + W_main (64) + b_main (1) + W_skip (128)
+ b_skip (1) = 12,675 params × 5 horizons = 63,375 head params total
(vs single-linear's 5*(128+1)=645). Trunk grad dimensionality is
unchanged.
Synthetic overfit smoke: loss 0.32 → 0.0000 by step 50 (faster than
single-linear's 0.37 → 0.0006 in 250 steps); all 7 perception_overfit
tests PASS. Confirms the full Mamba2 + LN + CfC + GRN backward chain
is wired correctly and all 17 AdamW optimisers move weights.
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.