68888c5d8c
Per user: dd049d9a4 hit wr=0.567 with high trade frequency selective scalping.
Subsequent "architectural fixes" (drawdown-from-peak, reversal block, surfer
amplification) reduced wr to 0.30 trend-follower. Reverting to original
behavior — costs ($0.82/side) alone should make wr=0.567 dollar-positive.
Reverted to baseline:
- rl_trade_context_update.cu: unrealized_R back as feature [1] (NOT drawdown-from-peak)
- rl_min_hold_check.cu: original close-only block (NO reversal block in min_hold)
- ENTRY_COST: 40 → 15 (baseline)
- SHORT_HOLD_MIN_STEPS: 200 → 100 (baseline)
- SHORT_HOLD_PENALTY: 0.3 → 0.5 (baseline)
- HOLD_BONUS: 4.0 → 2.0 (baseline)
- MIN_HOLD_STEPS: 300 → γ-derived 138 (baseline)
- CONF_GATE_MAX_HOLD_FRAC: 0.95 → 0.85 (baseline 15% explore)
- THOMPSON_FLOOR: 0.02 → 0.05 (baseline)
Kept (safe non-behavioral fixes):
- Realistic costs $0.82/side (the fix that should make 0.567 profitable)
- Step-based max_hold (safety net only)
- l_q double-divide fix
- Mega-graph + perf optimizations
- cuBLAS replacement in DQN/IQN
- Advantage normalization (gradient hygiene only)
- Thompson floor at baseline 0.05
The unit_peak_unrealized_r_d buffer remains allocated but unused — no kernel
consumes it after revert. Leaving for now; cleanup is non-critical.
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.