ed19985c95
The hardcoded WeightedByRealizedSharpe path from C7 is now joined by
a stack-based bytecode interpreter that consumes Strategy::flatten()
output, unlocking RegimeSwitch / Portfolio / non-default Ensemble /
single-Leaf compositions specified via policy-grid YAML.
cuda/decision_policy.cu — new kernel `decision_policy_program`:
Stack-based VM with parallel (value, attribution_mask) stacks.
Opcodes mirror src/policy/mod.rs::OpCode exactly:
NoOp / PushScalar / EvalRegime / BranchIfRegime
EmitPerHorizonSize (computes sized intent from alpha[h] +
IsvKellyState[h] using same Kelly + ISV-cap formula as the
hardcoded default)
AggMean / AggWeightedSharpe / AggMaxConfidence (pop n values,
push aggregated, OR attribution masks)
ApplyConflict (v1 no-op, reserved for Portfolio)
WriteOrder (terminal — converts top-of-stack to market_target +
open_horizon_masks attribution if currently flat)
AggWeightedSharpe recovers the source horizon from a single-bit
attribution mask to look up recent_sharpe; multi-bit masks (nested
aggregators that collapsed horizons) fall back to uniform weight.
decision_policy_default extended with a program_lens param: skips any
backtest whose plen > 0 (the program kernel handled it). The two
kernels run sequentially in step_decision_with_latency with mutual
exclusivity on each backtest slot.
LobSimCuda gains:
upload_program(b, &Program) — uploads a Strategy::flatten() output
to backtest b's slot in program_table_d, updates program_lens_d.
set_regime(b, regime_id) — writes regimes_d for OP_EVAL_REGIME /
OP_BRANCH_IF_REGIME consumption.
BacktestHarnessConfig.strategies (Vec<Strategy>) — empty means every
cell uses the hardcoded default; non-empty len must equal n_parallel
and each strategy is flattened + uploaded at construction.
bin/fxt-backtest --policy-grid <yaml> path now actually plumbs through
to the kernel (was parsed-but-ignored in C9). Empty grid keeps the
default behaviour.
New fixture decision_program_h4_only: uploads Strategy::Leaf(h4_only)
flattened to (EmitPerHorizonSize, WriteOrder) — 2 instructions, 16
bytes — and verifies the bytecode kernel produces equivalent end-state
to the hardcoded default (3 lot buy at vwap=5500). Proves the VM
dispatcher works end-to-end.
12/12 GPU fixtures green. 33 lib unit tests + 3 Ring 2 fuzz tests
still green.
Co-Authored-By: Claude Opus 4.7 <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.