9d4fda36ab
Sweep YAML now supports the batched flow per spec §3.3 + Task 6:
- SweepBase.sim_variants: Vec<SimVariant> — list of (cost, latency,
threshold, ...) variants. When non-empty, each cell runs ONE harness
at n_parallel=variants.len() with BatchedSimConfig::from_grid instead
of the legacy one-harness-per-cell fan-out.
- SweepBase.data_template: Option<String> — when set with `{window}`
placeholder, each cell's `window` field interpolates the per-cell
data path. Replaces single scalar `data` for the windowed flow.
- SweepCell.window: Option<String> — window identifier (e.g., "2025-Q2").
- SimVariant: threshold + cost_per_lot_per_side required (the spec's
primary axes); other fields optional overrides on top of SweepBase
scalars.
New runner pieces:
- BatchedSimConfig::from_grid(&[ResolvedSimVariant]) in
crates/ml-backtesting/src/sim/batched_config.rs.
- ResolvedSimVariant — per-variant fully-resolved sim params.
- resolve_sim_variants(&SweepBase) in main.rs — layers per-variant
overrides over base scalars.
- run_batched_cell() in main.rs — builds the harness with
sim_config_override + variant_names plumbed through. Writes per-
backtest artifacts to sim_<variant_name>/ subdirs (spec §3.3).
Harness side:
- BacktestHarnessConfig gains variant_names + sim_config_override
Option fields. When sim_config_override is Some, harness uses that
directly instead of building from_uniform off scalar cfg. When
variant_names is Some, write_artifacts uses sim_<name>/ instead of
cell_NNNN/ subdirs. Both None preserve legacy single-cell behaviour
(smoke, fixtures unchanged).
YAML configs:
- config/ml/sweep_threshold_tuning.yaml: 1 cell (W0) × 8 sim_variants
(p60-p95 in 5pt steps) with cost=0.125 (1-tick anchor). Threshold
pre-registration pass.
- config/ml/sweep_deployability.yaml: 4 cells (W1-W4) × 140 variants
each (7 costs × 4 latencies × 5 thresholds). Generated by
scripts/generate_sweep_variants.py — placeholder threshold values
(p60-p95) until threshold-tuning publishes calibrated absolutes to
config/ml/v2_prod_thresholds.json.
Deferred to P7 (operational glue):
- argo-lob-sweep.sh adaptation for the batched flow (cells = windows,
not sim-variants; one Argo task per window invokes `fxt-backtest sweep`
end-to-end inside the pod rather than `fxt-backtest run`).
Regression: all 7 existing CUDA tests pass through the new harness
construction path (sim_config_override = None → from_uniform fallback).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.