b8966fb1a6
Migrates target_annual_vol_units, annualisation_factor, max_lots, latency_ns, kelly_frac_floor, sharpe_weight_floor from scalar-broadcast kernel args to per-backtest device arrays via BatchedSimConfig. Atomic contract change per feedback_no_partial_refactor — kernel + sim + harness + all 3 existing test files migrate in this commit. - crates/ml-backtesting/src/sim.rs → sim/mod.rs (directory module) - crates/ml-backtesting/src/sim/batched_config.rs (NEW): BatchedSimConfig + UniformSimParams + validate(). from_uniform rebuilds the legacy uniform-broadcast behaviour at n=1 (smoke/fixtures). from_grid lands in P6 for the 140-variant sweep packing. - LobSimCuda gains 6 per-backtest device buffers (target_annual_vol_units_d, annualisation_factor_d, max_lots_d, latency_ns_d, kelly_frac_floor_d, sharpe_weight_floor_d). step_decision_with_latency uploads from BatchedSimConfig each call; both decision kernel launches now pass per-backtest array pointers. - decision_policy_default + decision_policy_program: scalar args become const float* / const int* per_b arrays; first lines of each kernel index by `b` into the arrays. Behaviour preserved at n=1 uniform. - dispatch_latent_market_orders: reads latency per-backtest from &BatchedSimConfig (host loop stays for P1; P2 replaces with kernel). - step_decision_with_latency now ALWAYS dispatches through the latency path; when cfg.latency_ns[b]=0 the in-flight slot's arrival_ts equals current_ts and gets promoted immediately on next snapshot. Eliminates the if/else branch and consolidates the launch path. - harness.rs: BacktestHarness gains a sim_config field, built via BatchedSimConfig::from_uniform at new() from the harness cfg's scalar fields. The run loop passes &self.sim_config to step_decision_with_latency. Regression coverage: - parallel_sim_correctness::parallel_sim_equivalence_with_uniform_config — n=8 with uniform config produces 8 bit-identical market_targets (proves per-backtest indexing reduces correctly). - Existing decision_floor_coldstart tests (3) all pass through the new ABI — proves cold-start floor + variance-cap gate behaviour preserved. - parallel_sim_independence_per_backtest deferred to P2 (needs read_first_inflight_arrival_ts helper that depends on LimitSlot layout). 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.