205e46c171
Adds the 4 weight tensors (W1, b1, W2, b2) for the SP22 H6 vNext
trade-outcome aux head into the trainer's flat params_buf at indices
[163..167). Adam machinery (m/v moment buffers, SAXPY iteration over
0..NUM_WEIGHT_TENSORS) picks up the new tensors uniformly — no
per-tensor wiring needed.
Changes:
- NUM_WEIGHT_TENSORS bumped 163 → 167. Most of the 54 references are
&[u64; NUM_WEIGHT_TENSORS] array-size generics that resize
uniformly with the constant.
- compute_param_sizes() adds 4 new size entries:
[163] aux_to_w1 [H=128, SH2=256] = 32,768 floats
[164] aux_to_b1 [H=128] = 128 floats
[165] aux_to_w2 [K=3, H=128] = 384 floats
[166] aux_to_b2 [K=3] = 3 floats
Total: 33,283 floats = ~133 KB params, ~266 KB Adam state.
- compute_param_sizes() debug_assert updated 163 → 167.
- Xavier fan_dims added: (H, SH2) for W1, (0, 0) for biases (zero-init),
(K=3, H) for W2. Cold-start: logits ≈ 0 → softmax ≈ uniform 1/3 → no
Profit/Stop/Timeout preference per pearl_first_observation_bootstrap.
SH2 stays at 256 in this commit (mirrors K=2 head exactly). The spec's
Phase B input concat (256 → 262 with plan_params 6-dim) will re-shape
slot [163] to 128 × 262 = 33,536 floats later — small touch-up vs the
full B1 commit.
Verification:
- cargo check -p ml clean (21 warnings, none new).
- 14 pre-existing test failures stashed-verified unrelated (OFI features
missing, ISV slot count drift — independent of NUM_WEIGHT_TENSORS).
Phase B2 next: saved-tensor + per-sample partial buffers (hidden_post,
logits, softmax, valid_count, dW*_partial, dh_s2_aux_out).
Audit: docs/dqn-wire-up-audit.md Phase B1 section.
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.