f707c86df2
User explicit request: restore surfer philosophy. Previous wr=0.30 trend- follower pattern was profitable but takes ~22k trades/run on lots of small tail wins. Per surfer philosophy (pearl_surfer_philosophy_trading): wait for the wave, ride it out, accept wipeouts. Bootstrap changes (architectural fixes from prior commits preserved): - ENTRY_COST: 15 → 40 (discourage low-conviction entries) - SHORT_HOLD_MIN_STEPS: 100 → 200 (true wave timescale) - SHORT_HOLD_PENALTY: 0.5 → 0.3 (harder penalty for quick exits) - HOLD_BONUS: 2.0 → 4.0 (amplify sustained-ride rewards) - MIN_HOLD_STEPS: 138 (γ-derived) → 300 (multi-minute holds) - CONF_GATE_MAX_HOLD_FRAC: 0.85 → 0.95 (95% Hold = patient) - THOMPSON_FLOOR: 0.05 → 0.02 (less random, more Q-driven) Local smoke 1000 steps (b=128): - wr peaked 0.61 at step 200 (vs 0.30 before) - dones per step dropped to 1-7 (vs 17-40 before) - PnL stays positive: $285k cumulative - avg_hold growing toward 200+ Expected at scale: fewer trades, higher wr, dollar-positive after realistic $0.82/side costs because each trade is bigger. 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.