fe24987690
Gate 1 catastrophic failure (smoke vjmwc, commit 3d8f12deb):
- 62× hyperactivity (157,470 trades vs 2,511 baseline)
- 9,347% max-drawdown ($327M loss on $3.5M base)
- Sharpe -15.63 (2.4× worse than baseline)
Root cause: A2's formula
final_size *= (conv_ema / raw_max_conv)
amplified weak signals because EMA tracks the MEAN of raw_max_conv, NOT a
running max as the author's comment claimed. When raw_max_conv < conv_ema
(normal during quiet periods between strong signals), the multiplier was
> 1, blowing up small-signal positions:
raw=0.05, ema=0.3 → 6× amplification
raw=0.01, ema=0.3 → 30× amplification
Fix: clamp scale ∈ [0, 1] in BOTH decision_policy_default and
decision_policy_program (OP_WRITE_ORDER). Only damp when raw is stronger
than EMA (scale < 1); never amplify when raw is weaker (scale capped at 1).
Math:
scale = conv_ema / raw_max_conv // can be 0..∞
scale_clamped = min(scale, 1.0) // can only be 0..1
final_size *= scale_clamped
Test: conviction_ema_rescale_never_amplifies_weak_signal validates
target_lots stays ≤ 1 when alpha=0.51 (raw_conv=0.02) after EMA warmup
at alpha=0.7 (ema~=0.4). Without the clamp the broken multiplier is 20×.
Co-Authored-By: Claude Sonnet 4.6 <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.