jgrusewski 9ec43fdb9d feat(rl): expose forward_encoder() for RL head consumption (Phase B)
Adds PerceptionTrainer::forward_encoder() returning a borrowed slice
to h_t — the CfC h_new at the FINAL window position (K-1), where the
trade decision is made. The integrated RL trainer (ml_alpha::rl,
Phase E) consumes this to dispatch its 5 loss heads (BCE direction,
C51 Q, PPO pi, PPO V, aux prof+size) on the same encoder
representation that a supervised step() would have used.

Implementation strategy: reuse the existing forward_only() captured
graph (snap_features -> VSN -> Mamba2 x2 -> CfC K-loop -> BCE GRN
heads) rather than splitting the encoder forward out of the captured
graph. The BCE heads still run but their output (probs_per_k_d) is
discarded; the overhead is one fused GRN kernel per k (small vs.
Mamba2+CfC) and avoids the risk of breaking
pearl_cudarc_disable_event_tracking_for_graph_capture or
pearl_no_host_branches_in_captured_graph. Phase E end-to-end
profiling can revisit if needed.

A new dedicated h_t_d: CudaSlice<f32> field of size [B * HIDDEN_DIM]
receives a stream-ordered DtoD copy from h_new_per_k_d at slot K-1
after each forward_encoder() call. This gives RL callers a stable
borrowed reference even if a subsequent forward_encoder() overwrites
the per-K scratch.

Phase B (this commit) lands forward only. The backward path
(per-head loss -> lambda-weighted combine -> encoder backward via
existing step_backward_* machinery) is wired in Phase E once the
heads (Phases C+D) exist.

Existing step()/step_batched()/forward_only() semantics are
unchanged — the new field is initialised once at construction and
written only by forward_encoder(). All 56 ml-alpha lib tests still
pass; the new tests/encoder_gradient.rs locks the API contract
(borrow length B*HIDDEN_DIM, captured-graph determinism across two
calls, finite values, at least one non-zero element).
2026-05-22 22:28:19 +02:00

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.

Description
No description provided
Readme 849 MiB
Languages
Rust 88.2%
Cuda 7.7%
Python 1.3%
Shell 1.1%
PLpgSQL 0.8%
Other 0.8%