16cf9f260c
The addendum's rate-cap (B-1) only protected the Wiener-α path; the first-
observation bootstrap branch let cold-start fat-tail events seed pos_max_ema
unbounded. At alpha-rl-4xmxm step 5, a single $947 scaled reward bootstrapped
pos_max_ema=879 directly, cascading through clamp_win → unclamped subsequent
rewards → env.max=11375 by step 37 (1500× the eventual steady-state σ).
B-2 fix per docs/superpowers/specs/2026-05-31-pos-max-ema-cold-start-redesign.md:
1. Bootstrap RL_POS/NEG_SCALED_REWARD_MAX_EMA_INDEX to MIN_WIN=1.0 (was 0)
in `with_controllers_bootstrapped`. Conservative neutral value → clamp_win
starts at MARGIN × 1.0 = 1.5 → rewards heavily clipped until adaptation.
2. Remove the `if (ema_prev == 0.0f) ema_new = pos_max;` branch from
`rl_reward_clamp_controller.cu`. Single uniform update rule (Wiener-α +
rate-cap) applies from cold-start onward. Mirror change for neg_max_ema.
3. Replace `#define POS_MAX_EMA_MAX_GROWTH_PER_STEP 1.5f` with ISV-driven
reads (per feedback_isv_for_adaptive_bounds). Three new slots:
717 RL_POS_MAX_EMA_COLD_START_INDEX = 1.0
718 RL_POS_MAX_EMA_GROWTH_CAP_BASE_INDEX = 1.225 (√1.5 for
twice-per-step inv)
719 RL_POS_MAX_EMA_GROWTH_CAP_CV_GAIN_INDEX = 0.0 (adaptive layer
disabled by default)
4. Adaptive growth_cap from Welford CV of reward magnitude (slot 615-617)
when cv_gain > 0: stable regime → tight cap, volatile regime → loose.
Disabled by default; opt-in via ISV tuning.
Local smoke validation (800+200 fold-1 b=16):
- step 1: pos_ema=1.0 (initialized, NOT bootstrapped from observation)
- step 5: pos_ema=1.0 (no observation yet, sparse-skip working)
- step 25: pos_ema=63.8 (vs 1314 without B-2 — 21× reduction)
- step 37: pos_ema=32.5 (vs 7074 without B-2 — 218× reduction)
- env.max @ step 37: 126 (vs 11375 without B-2 — 90× reduction)
Generalizes the pattern: NORMALIZATION EMAs that gate signal magnitudes
should bootstrap to CONSERVATIVE neutral values, NEVER to first observation.
Cross-references pearl_first_observation_bootstrap which needs revision.
Phase 4 audit (other controllers with same anti-pattern) deferred to
follow-up — see spec §4 Phase 4 for the grep target list.
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.