91e2c5dc8a
Second of three FRD backward stages. Given dL/dlogits from F.3a's
softmax_ce_grad and the cached hidden activation from F.2's forward,
computes the layer-2 weight gradients via the standard chain rule
and emits the upstream gradient for layer-1 backward (F.3c).
Kernel `cuda/rl_frd_layer2_bwd.cu`:
* grid_dim = (B, 1, 1), block_dim = (FRD_HIDDEN_DIM=64, 1, 1)
* Phase 0: stage 63-slot grad_logits into shared (thread 63 idle)
* Phase 1: each thread i (i < 64) computes one row of per-batch
dW2 scratch: grad_w2_per_batch[b, i, 0..63] = h_bi × grad_logits[0..63]
(63 writes per thread, no atomics)
* Phase 2: each thread i computes dL/dhidden[b, i] = Σ_j W2[i, j] × grad_logits[j]
* Phase 3: thread i (i < 63) writes grad_b2_per_batch[b, i] = grad_logits[b, i]
* Per-batch scratch shape [B, FRD_HIDDEN_DIM, FRD_OUT_DIM] reduces
across batch via existing reduce_axis0 infra (caller's job, same
pattern as v_head_bwd / aux_heads_bwd)
Rust wiring `FrdHead::layer2_bwd`:
* Takes hidden (forward cache), grad_logits (from softmax_ce_grad),
self.w2_d
* Writes grad_w2_per_batch, grad_b2_per_batch, grad_hidden — all
sized to caller-allocated buffers
* Sole &self method (Adam step is the caller's responsibility)
Tests (2 new, 8/8 file total):
* frd_layer2_bwd_finite_diff_w2 — perturb W2[10, 5] by ±ε=1e-3,
compare (L(+) - L(-))/(2ε) to per-batch grad scratch. rel_err
= 6.27e-5 (better than F.3a's softmax-CE finite-diff because
the gradient magnitude here is larger so rounding error is
relatively smaller). Helper `ce_total_loss` re-uses
`softmax_ce_grad` to compute total CE for the perturbed forward
pass — pure GPU-oracle, no CPU softmax/CE reference impl.
* frd_layer2_bwd_db2_equals_grad_logits — analytical invariant:
db2_per_batch[b, j] must equal grad_logits[b, j] exactly (the
bias gradient is the identity passthrough at this layer). Cheap
structural check that catches dimension-shuffle bugs in the
kernel before they corrupt the reduce_axis0 step.
The kernel restores W2 to its original values after the perturbation
to keep test isolation clean — `&mut head` access pattern (proper
Rust borrowing, no UB const→mut casts).
F.3c (layer-1 backward: dW1, db1, dh_t with ReLU mask via the
cached hidden activation) is next.
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.