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Plan 3 Task 6c. Portfolio-state tail-append: - PS_PRE_ENTRY_CONVICTION_EMA = 41 (EMA mean of conviction_core during Flat) - PS_PRE_ENTRY_CONVICTION_VAR_EMA = 42 (EMA of squared deviations) - PS_STRIDE 41 -> 43 - All 6 hardcoded-stride sites migrated in lockstep Producer (experience_kernels.cu Flat branch): - Per-bar EMA update alpha=0.05 (matches Task 1 reward-ema convention) - Welford-style: delta = c - mean; var_ema = (1-alpha)*(var + alpha*delta^2) Consumer (experience_kernels.cu entering_trade block): - ratio = stddev/mean; stability = clamp(0, 1, 1 - ratio/0.2) - Fires only when ratio < 0.2 (stable pre-entry conviction) - bonus = shaping x vol_proxy x stability x conviction_core - All multiplicands in [0,1] except vol_proxy (<=0.01); max bonus ~ 0.01 - Mirrors B.2 novelty-bonus structure — one bounded shape replaced (novelty -> stability) - rc[5] += bonus; both EMA slots reset at entry, reversal, fold/episode boundary Per pearl_one_unbounded_signal_per_reward.md: exactly ONE unbounded multiplicand (vol_proxy); all others bounded. No `q_scale x |reward|` style blowout possible. No new ISV slot. 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.