c7fdc617dc
Three follow-ups to the cold-start floor fix: 1. Kernel: MIN_TRADES_FOR_VAR_CAP gate. After the first trade closed, `isv_kelly_update_on_close` set `realised_return_var = ret²` — a single-sample variance proxy that systematically collapses `cap_units = target_vol / sqrt(var × ann_factor)` near zero for any biased return. cap_lots → 0 → no further trades despite strong alpha. Gate the variance-derived cap behind `n_trades_seen >= 10`; below the threshold cap falls back to host-supplied `max_lots`, same as the pre-first-trade path. Same gate applied to both `decision_policy_default` and `decision_policy_program`. Regression: `post_first_loss_state_does_not_lock_out_further_trades` reproduces the exact pre-fix state from the smoke (n_trades_seen=1, var=103.6) and asserts the kernel still fires a long with p=0.8. 2. Aggregate: add `nvidia.com/gpu: 1` resource request. Scaleway's L40S device plugin mounts libcuda.so.1 into the container only on GPU-requesting pods; the aggregate logic is CPU-only but the binary's dynamic loader needs the driver libs. Cheapest correct fix until a separate CPU-only aggregator binary exists. 3. Smoke YAML: `max_events: 0` (exhaust loader). 100k events is minutes of ES.FUT, far shorter than the h6000 holding horizon the model was trained on. Full quarter exercises sustained trading + variance estimate ramp-up. All three regression tests pass locally on RTX 3050 Ti. 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.