70d5fc29cf
Decision-stride S lets a length-K sequence span ((K-1)*S + 1) raw snapshots instead of K consecutive ones — expands the effective time-window covered by each sequence at the same K-positions compute cost. With K=64 and S=4, the window covers 256 ticks (~5s on ES MBP-10 at 20ms-tick) instead of 64 ticks (~1.3s). Loader (crates/ml-alpha/src/data/loader.rs): - `MultiHorizonLoaderConfig.decision_stride: usize` (default 1, must pre-existing call sites add the new field). - `next_sequence` reads snapshot at `anchor + k * stride`; labels at the same indices (labels stay in absolute-snapshot horizons regardless of stride, e.g. h=6000 always means "predict 6000 raw snapshots forward"). - `prev` snapshot for microstructure features (prev_mid, prev_ts_ns) now points to the prior K-position (`anchor + (k-1)*stride`), NOT the consecutive-snapshot prior, so `Δt = ts_ns - prev_ts_ns` carries the actual elapsed time between K-positions (consumed by Mamba2's dt_s and the planned Phase 2C TGN Fourier features). - New `#[ignore]` real-data test: `loader_stride_4_yields_correct_spacing` asserts Δt monotonicity at stride=4. Mamba2 dt_s (crates/ml-alpha/src/trainer/perception.rs): - `PerceptionTrainerConfig.decision_stride: usize` plumbs the stride through. dispatch_train_step + evaluate_batched now use `dt_s = decision_stride as f32` so Mamba2's selective scan `exp(-dt * sigmoid(a))` reflects the real elapsed time. With stride=1 the behaviour is identical to before. CLI (crates/ml-alpha/examples/alpha_train.rs): - `--decision-stride <S>` flag (default 1) wired into both train and val loaders + PerceptionTrainerConfig. Argo workflow: - `decision-stride` parameter on the template (default "1") + `--decision-stride` script flag + propagation into the train pod's alpha_train invocation. Synthetic smoke (tests/perception_overfit.rs): - `stacked_trainer_loss_shrinks_with_stride_4` proves the trainer-level dt_s=4.0 keeps the Mamba2+LN+CfC+GRN chain numerically stable. Converges 0.32 → 0.0000 (matches stride=1 smoke trajectory — dt_s scaling didn't break the SSM dynamics). 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.