1aa92f57f0
The parent eval-boundary fix (72684ed3e) preserved env.max/clamp EMAs but
left the σ explosion intact. Diagnosis from alpha-rl-jnct8 (10k+2500 eval,
fold-1, -$106M eval pnl, σ → 4451 by eval step 5) revealed two pre-existing
mechanisms compounding the eval shock:
ISSUE A — reward_scale snaps to 0.10 at boundary
================================================
rl_fused_controllers' reward_scale block had a bootstrap-fraction-floor
gate: `(cumulative_dones < min_trades) → boot_floor = 0.10`. The intent
was cold-start protection (prevent scale crash before any closed-trade
ground truth). But cumulative_dones (slot 660) is reset by
reset_session_state for Kelly's PREDICTIVE-warmup purpose. At fold
boundary the gate fires again, clamping the preserved scale (0.0046 in
jnct8) UP to 0.10 — a 22× upward jump. Eval rewards are then 22× larger,
overwhelming env.max preservation; σ explodes regardless.
FIX A — decouple via monotonic warmed_flag (slot 716, never reset).
Set ONCE when cumulative_dones first crosses min_trades; persists across
all subsequent fold boundaries. boot_floor reads the flag instead of
re-evaluating trade_count. Math: at cold-start flag=0 → boot_floor=0.10
(original protection preserved). Post-warmup flag=1 → boot_floor=scale_min
(~1e-4) → preserved scale survives boundaries.
ISSUE B — pos_max_ema growth unbounded (train-phase fat-tail spike)
====================================================================
Pre-existing fat-tail behavior: at alpha-rl-jnct8 step 3895 a single
account had pre_clamp scaled reward 724. The clamp's Wiener-α EMA
(α=0.4 floor) admitted 40% of the observation, jumping pos_max_ema
112 → 834 in 5 steps. clamp_win = MARGIN × pos_max_ema followed
magnitude up rather than bounding it; env.max captured the unclamped
reward (121 → 1306). Recovery via slow-decay over 600 steps, but
during the spike PPO gradients were mis-scaled.
FIX B — asymmetric per-step growth cap on pos_max_ema (1.5× max).
Same Schulman-bounded-step pattern as reward_scale's 2% per-step
movement clamp. Decreases unbounded (allows fast recovery from spike).
Bootstrap path unchanged (ema_prev=0 → ema_new=pos_max, no cap).
Math: 5-step max growth = 1.5^5 ≈ 7.6× vs prior uncapped 7.4× in
practice — similar steady-state, bounded transient.
Local validation (b=16, 800+200 fold-1):
- σ preserved across boundary (51.9 → 51.4 at eval[1])
- σ stays bounded in 23-57 range across full eval phase (no 60× explosion)
- scale=0.10 stable across boundary
- warmed_flag stays 0 at b=16 (cumulative_dones never crosses min_trades
at this scale — flip behavior tested at cluster b=1024)
Spec: docs/superpowers/specs/2026-05-31-eval-boundary-addendum-reward-scale-and-train-spike.md
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.