0f34843253
Integrated spec for the continuous-reasoning trader rework. Replaces
the rule-based discrete policy with a 4-layer signal-driven architecture:
Layer A: continuous control loop (every event, not every stride)
Layer B: signal-driven position management (multi-horizon fusion,
continuous sizing, conviction-degradation exit, open_trade_state
expanded 24→128 bytes)
Layer C: adaptive risk envelope (ISV-derived max_lots, threshold,
target_annual_vol)
Layer D: online weight adaptation (LoRA + EWC++, offline-batch
per-session, shadow-eval gated)
Phased gates: A unlocks B; B is where alpha lives; C amplifies B;
D amplifies whatever's working. Each gate has explicit pass criteria.
Conviction definition: ISV-weighted multi-horizon agreement.
weight_h = max(pnl_ema_win - pnl_ema_loss, 0) / (var + cost^2).
Net edge x SNR per horizon, scale-normalised, cold-start uniform fallback.
Pearl conformance: 13 existing pearls referenced and respected
(ISV-driven anchors, first-observation bootstrap, Wiener-alpha floor,
blend-with-floor, z-score normalisation, one-unbounded multiplicand,
trade-level vol bootstrap, deadline cadence, single-source-of-truth,
atomic refactor, adaptive-not-tuned).
Hardcoded boundary preserved for hardware/exchange realities (latency,
cost, annualisation, instrument bounds). Everything else adaptive.
Status: design — awaiting user review before implementation plan.
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.