395e0d3000
BacktestHarness now owns a PerceptionTrainer (in inference role) instead of a raw CfcTrunk. The sliding K-window of recent snapshots accumulates in the harness; at each decision-stride boundary (and only once the window has reached cfg.seq_len), the harness calls trainer.forward_only(&window) and broadcasts the last K position's per-horizon probs to the LobSim. fxt-backtest's main.rs constructs the trainer via PerceptionTrainer::from_checkpoint when --checkpoint is supplied (else random init for noise baseline). Why this shape: PerceptionTrainer's evaluate_batched already runs the full inference chain (snap → vsn → mamba2 → ln → mamba2 → ln → attn_pool → cfc K-loop → grn heads) correctly. Duplicating that 400-line forward chain on CfcTrunk would double the surface area for the same result — the trunk's role is weight-source-of-truth (achieved in X1-X9), not kernel-launch orchestration. End-to-end status: alpha_train emits Checkpoint files via X14 wiring; fxt-backtest now loads those Checkpoints via from_checkpoint and drives forward via forward_only. Phase 2 (Argo runtime: training → smoke → threshold pre-reg → 560-cell deployability sweep → verdict) is unblocked. Adds PerceptionTrainer::config() accessor so the harness can read seq_len. Verification: ml-alpha + ml-backtesting + fxt-backtest all build clean.
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.