f67ede94fb
Smoke smoke-test-ftdjz (commitd9a4d98a3, Mech 6 + Mech 7) regressed both F0 (44 -> 38.82, per-element cap clipped legitimate outliers) and F1 (grad-collapse at step 2040 vs 3720 with Mech 6 alone). Slots 36-42 STILL fired — Mech 7's per-element clip didn't prevent Adam EMA saturation, just slowed training to grad-collapse. Re-analysis: Mech 6's 100x multiplier on the upper-bound formula was mismatched with the slot 36 threshold ratio. Per-element gradient max <= adaptive_clip = 100 x slow_ema x isv ~= 200. Adam m_X steady-state reaches 200, exceeding slot 36 threshold of 100*isv = 100. Mech 6 was doing its job but the bound was wider than the diagnostic threshold. Two coordinated changes (per feedback_no_partial_refactor): 1. REVERT Mech 7 (per-element clip in dqn_adam_update_kernel). The per-element approach was misdiagnosis — clipping post-global-clip gradients tighter than legitimate per-element variance harms F0 training without addressing Adam saturation root cause. Kernel returns to its post-Mech-6 state (blob546feee48). 2. TIGHTEN Mech 6's upper-bound multiplier from 100x to 5x. Standard DL practice (5-10x steady-state grad norm). Per-element gradient max becomes <= 5 x slow_ema x isv ~= 10, well below slot 36 threshold of 100. Adam m_X EMA stays bounded <= 10 — slots 36-42 should not fire. Why 5x and not 10x: slot 36 threshold is 100 x isv. 5x slow_ema (~=10 absolute) leaves 10x headroom against the threshold, providing robust margin. 10x slow_ema would be 20 absolute, only 5x margin — risk of fluctuations triggering slot 36. Why not tighter (e.g., 2x): per-step gradient norms can legitimately spike to 5x slow_ema in normal training (e.g., gradient resumption after warmup); tighter bounds would over-clip. F0 risk: low — F0 typical adaptive_clip values are bounded by Mech 6's upper anchor only when the EMA-driven clip exceeds 5x slow_ema, which is rare in steady F0 training. Cap should be a no-op for F0. F1 risk: prevents the saturation pathway diagnosed by smoke smoke-test-ftdjz. Validates by running the next smoke at this commit. 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.