829ddfa62c
Adds 4 new kernel symbols alongside the existing single-sample ones —
zero changes to current call sites, so the in-flight qf5mj baseline is
unaffected. The next commit wires these into PerceptionTrainer's K
loop and exposes --batch-size in the CLI.
cfc_step_batched processes [n_batch, n_in/n_hid] tensors
cfc_step_backward_batched same; shared mem holds sd_pre[B, n_hid]
+ sdecay[n_hid] (~16 KiB at B=32, well
under L40S 48 KiB block limit). Param
grads (grad_b/grad_w_in/grad_w_rec/
grad_tau) accumulated via += — thread i
is sole writer to its row across all
samples, so no atomicAdd and no per-
batch scratch buffer.
multi_horizon_heads_batched [n_batch, 5] sigmoid outputs from
[n_batch, 128] hidden inputs.
multi_horizon_heads_backward_batched
shared mem holds sd_z[B, 5]. grad_w
/ grad_b += across batch (thread tid
sole writer). grad_h carries the
optional per-sample grad_h_carry
(cfc recurrence chain).
Design notes:
- Threading: one block of n_hid threads. Each thread loops over
b ∈ 0..B internally. This avoids cross-block races on grad_*
buffers and keeps the existing "no atomicAdd" discipline. Cost:
less raw parallelism than grid-batching, but the bottleneck is
Mamba2 (already batch-parallel via its own kernel grid).
- Per-thread accumulators: grad_b / grad_tau land in registers,
flushed once at end. grad_w_in / grad_w_rec written += per-b
(thread sole writer to its row, safe).
- All B samples processed in stream order inside one kernel launch
— saves K * (B-1) launches per sequence vs serialising B
independent calls.
77 ml-alpha tests pass (kernels not yet exercised — wiring is the
next commit).
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.