jgrusewski a4127935a8 feat(ml-backtesting): BacktestHarness orchestrator + Ring 1b parity (C8)
BacktestHarness::new(cfg, &dev, trunk) constructs a MultiHorizonLoader
(inference_only=true), captures the trunk's perception Graph A using
loader.peek_first() as the template, then allocates a LobSimCuda.
run() walks the chronological snapshot stream, calls apply_snapshot on
every event, and at decision-stride boundaries:
  trunk.update_input_buffers(raw)
  → trunk.perception_forward_captured() → (probs[N_HORIZONS], proj)
  → sim.broadcast_alpha(&probs)
  → sim.step_decision(ts, target_vol, ann_factor, max_lots)

Returns RunStats { events_processed, decisions_taken }. The harness
deliberately accepts an externally-built CfcTrunk (random-init in v1)
because a checkpoint format isn't pinned in ml-alpha yet; when one
lands, the binary CLI (C9) can switch from new_random to load_checkpoint.

trainer_parity.rs Ring 1b — two ignored tests:

  peek_first_byte_equal_across_modes
    Verifies the Mbp10RawInput produced by the loader path used by the
    backtest harness (inference_only=true) is BYTE-EQUAL to what the
    trainer's loader (inference_only=false) produces from the same
    source. Guards against any future refactor accidentally diverging
    the two paths (e.g. someone special-casing inference path to skip
    regime feature computation). All 50 f32 fields + scalars compared
    via .to_bits() equality.

  inference_iteration_matches_chronological_snapshots
    Verifies next_inference_input() yields monotone-ts snapshots with
    correct cur==prev semantics on the first read and prev_ts==prior_ts
    afterwards.

Both tests skip gracefully when FOXHUNT_TEST_DATA fixtures lack
populated sidecars (the placeholder-empty bins committed in the
test_data/ tree).

GPU-side inference parity (probs[N_HORIZONS] bit-equal across loader
modes) is deferred until ml-alpha pins a checkpoint format and we have
a small checkpoint fixture to gate it on FOXHUNT_TEST_CKPT.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-18 08:52:49 +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
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