ed4b30b493
Redefine the "fire" semantic for adaptive controllers per the V7 audit (policy-quality-design spec §5.3): a controller fires iff it made an ADAPTIVE INTERVENTION this epoch, not merely because its observable output value changed. Previously fire detection was absolute-delta on the output: - grad_clip fired in 98.3% of epochs because the adaptive clip threshold is an EMA recomputed every training step. The EMA drifts by > 1e-3 every epoch regardless of whether the clip actually clamped a gradient. - cost_anneal fired in 98.3% of epochs because it is a deterministic sigmoid of current_epoch (1/(1+exp(-(epoch-10)/3))) with no adaptive or reactive component. Every epoch moves it by > 1e-4 by design. Neither was "load-bearing" in the V7 sense — one was a per-step EMA tracker, the other a pure curriculum schedule. The prior test output "controller 'grad_clip' fires in 98.3%" was a false positive from measuring the wrong signal. New semantics: - anti_lr / tau / gamma / cql_alpha: unchanged — absolute delta vs prior epoch on the effective output value (real adaptive controllers). - grad_clip: intervention-based latch `grad_clip_kicked_this_epoch`, set in run_training_steps_slices iff raw_grad_norm > active clip at any training step this epoch. Reset in reset_epoch_state. - cost_anneal: pure deterministic schedule → never load-bearing → always fires=false. The value is still tracked in prev_controller_values and the HEALTH_DIAG line still emits it for observability, but it cannot trip the 50% load-bearing gate. After fix, controller_activity smoke reports: anti_lr=0.000 tau=0.033 gamma=0.017 clip=0.233 cql=0.033 cost=0.000 All 6 rates ≤ 0.5. Test passes. Touched: - crates/ml/src/trainers/dqn/trainer/mod.rs (add grad_clip_kicked_this_epoch) - crates/ml/src/trainers/dqn/trainer/constructor.rs (init new field) - crates/ml/src/trainers/dqn/trainer/training_loop.rs (latch kick per step, redefine fire_clip + fire_cost in HEALTH_DIAG block) 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.