ecab584c32
Three architectural changes in one unified design to push the model from
HFT noise extraction (62% trade rate, sharpe-gaming) toward MFT alpha-hunting:
1. Asymmetric bounded cap (-10/+5) — restores loss aversion erased by
SP11 symmetric cap. 2:1 ratio matches Kahneman/Tversky prospect theory.
Anchor: pearl_audit_unboundedness_for_implicit_asymmetry.
2. Min-hold soft penalty with temperature curriculum — patience requirement
at exit. Soft factor = deficit/(deficit+T), T anneals 50→5 over 50 epochs.
Forces commitment without paralysis in early training.
3. Zero per-bar shaping (gate micro/opp_cost on events) — eliminates
continuous-reward gradient that pulls toward continuous exposure.
Anchor: pearl_event_driven_reward_density_alignment.
Combined: reward fires only on trade events with prospect-theory loss
aversion + commitment requirement. Pure per-trade event-driven Q-learning
properly aligned with per-trade P&L objective.
~50 LOC across 3-4 files. No new ISV slots in Phase 1 (constants only).
Cost ~€1.30 (€0.30 smoke + €1.00 30-epoch validation).
Empirical motivation: train-multi-seed-pmbwn 50-epoch on commit 6a259942e
showed sharpe-gaming pattern (PnL -30% over 8 epochs while sharpe held).
SP11 cap fix unmasked the per-bar shaping bias plus erased loss-aversion
that the unbounded loss path was implicitly providing.
Continues on sp11-reward-as-controlled-subsystem branch — SP12 is
architectural continuation of SP11, not separate work.
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.