4b5ef093de
Refactors the encoder backward to make its INPUT contract explicit so
the integrated RL trainer (and any future caller) can seed it without
going through the supervised GRN/aux backward path.
Refactor:
- New field PerceptionTrainer::grad_h_new_per_k_d: [K × B × HIDDEN_DIM]
buffer. The Loop-1 GRN head backward kernel writes pure per-K head
contributions into this buffer (called with grad_h_carry = nullptr,
the kernel's existing null-check path); the Loop-2 CfC step backward
reads slot k after folding in the recurrent grad_h_carry_d via
aux_vec_add_inplace. Replaces the pre-E.3a single-slot
grad_h_new_d ping-pong (field removed).
- dispatch_train_step's fused (GRN bwd → CfC bwd) reverse K-loop split
into two passes:
Loop 1 (head backwards) → grad_h_new_per_k_d (slot k, no carry)
Loop 2 (encoder backward) reads grad_h_new_per_k_d, folds in
grad_h_carry_d via the existing aux_vec_add_inplace kernel, then
runs cfc_step_bwd; CfC carry semantics byte-identical to before.
- New private helper dispatch_encoder_backward containing the entire
encoder backward kernel-launch chain: Loop-2 K-loop CfC bwd → 3D
transpose → attn-pool bwd + reducer → LN_b bwd + 2 reducers →
Mamba2 L2 bwd → LN_a bwd + 2 reducers → Mamba2 L1 bwd → VSN bwd +
2 reducers → reduce_axis0 for CfC param grads (4 launches) →
encoder Adam steps (CfC ×4 + Controller B + LN ×2 + VSN + attn_q +
Mamba2 L1/L2 grouped). Both supervised and RL paths call this same
helper — single source of truth per
feedback_single_source_of_truth_no_duplicates.
- New public method backward_encoder_with_grad_h_t seeds the per-K
buffer from a caller-provided grad_h_t at slot K-1 (the only slot
the RL heads consumed h_t from) and dispatches the shared encoder
backward helper. All other slots stay zero — those positions had
no downstream loss signal.
- Reducer closure at the old fused-loop site split per Option (a) in
the SDD: reduce_encoder lives in the helper (CfC ×4), reduce_heads
stays in dispatch_train_step (GRN ×10).
Integration:
- IntegratedTrainer::step_synthetic now calls
backward_encoder_with_grad_h_t with the accumulated
grad_h_t_combined_d (Q + π + V contributions with loss-balance λ
scaling). The encoder now learns from the full per-head gradient —
closes the last deferred item from Phase E.2-DEFER (item 1: encoder
backward integration).
Byte-identical supervised behavior preserved: all 63 ml-alpha lib
tests pass after the refactor (baseline 63, post-refactor 63). The
K-loop split + per-K buffer seeding contract is mathematically
equivalent to the prior fused-loop + single-slot ping-pong:
Old: GRN bwd writes grad_h[k] = head_contrib[k] + carry[k+1] (folded
in via kernel's grad_h_carry arg); cfc bwd reads grad_h.
New: Loop 1 writes grad_h_new_per_k[k] = head_contrib[k] (nullptr
carry); Loop 2 at slot k does grad_h_new_per_k[k] += carry[k+1]
via aux_vec_add_inplace, then cfc bwd reads the slot. Same final
value before cfc_step_bwd consumes it.
Capture-graph safety preserved: the new aux_vec_add launches per K
iteration are kernel-only (no host branches, no host mallocs, no
event tracking) per pearl_no_host_branches_in_captured_graph.
Companion to E.2 (commit 2665669b5) and E.2-DEFER (commit 7356e3c7b).
Phase E.3b follows with LobSim integration + toy bandit activation +
2 more controller kernels (rl_rollout_steps + rl_per_alpha).
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.