63fc16f173
alpha-rl-mjsmr (4a4953b01 step 99-249, 2026-06-02): v5.1 with wr_ema=0.248
(below break_even 0.30) produced scaffold_weight = 0.500, not the expected
1.0. Math: `max(0, wr_ema - break_even) = 0` → sigmoid(0) = 0.5 → competence
= 0.5 → w_competence = 0.5. So a novice agent below break-even got
HALF-scaffold instead of full scaffold (entropy dropped to 1.27 at step 249
because of the early directional signal, then ramped back up to 1.96 once
w_decay took over — unstable).
Fix: drop the `max(0, ...)` clamp. Use signed `wr_distance = wr_ema -
break_even` so:
wr=0.20 → sigmoid(30·-0.10) ≈ 0.05 → w_competence ≈ 0.95 (full scaffold ✓)
wr=0.30 → sigmoid(0) = 0.50 → w_competence = 0.50 (half at BE)
wr=0.40 → sigmoid(30·+0.10) ≈ 0.95 → w_competence ≈ 0.05 (pure pnl ✓)
This is the proper symmetric fade around break_even; the asymmetry was a
v5 original-design error. Combined with v5.1 `w_decay = max(0, 2·frac-1)`,
the scaffold should now:
- hold near 1.0 while wr < break_even (novice)
- smoothly fade through 0.5 as wr crosses break_even
- drop toward 0 as wr settles above break_even AND PH decay subsides
Local invariant test:
reward_alignment_surfer_scaffold_invariants OK: 251 rows validated
train[0] bootstrap = 0.966 (unchanged)
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.