069f31286c
R9 cluster smoke (alpha-rl-fv7xz step 7) caught a real production bug the local sm_86 smoke missed: with b_size=1 and 1000 steps of real ES MBP-10 data, the 4 controllers I'd previously declared "intentionally hardcoded with documented rationale" (τ, ε, n_roll, scale) ALL exhibited cold-start migration to clamp bounds before any real signal arrived. The smoking-gun trace (per-step diag JSONL): step 0: scale=400 ε=0.32 n_roll=1638 τ=0.0034 dones=0 step 1: scale=640 ε=0.39 n_roll=1311 τ=0.0024 dones=0 step 6: scale=972 ε=0.49 n_roll=429 τ=0.0011 dones=0 step 7: scale=583 ε=0.50 n_roll=360 τ=0.0011 DONES=1 rew_sum=485,385.84 The real realized PnL on step 7's closed trade was $832. The reward_scale controller had migrated from bootstrap 1.0 → 972 during steps 1-6 (no signal, ratio degenerated to 1/EPS_PNL=1000 → clamped to MAX → Wiener α=0.4 pulled prev toward MAX every step). At step 7's first closed trade, scale=583 multiplied the real $832 PnL into a 485,256 reward, fed straight into Q/V backward. l_v spiked to 15.9 from that single step before the controller recovered. ## Initial diagnosis: wrong Two commits ago I added a pearl (pearl_hardcoded_bootstrap_target_collision) claiming multiplicative-target controllers were SAFE from the bootstrap- target-coincidence anti-pattern because their bootstrap IS the initial prev. That was wrong. They have a DIFFERENT failure mode that's just as bad: at sentinel input the ratio degenerates (to 0 or ∞), the target slams to a clamp, and the Wiener α-floor drags prev toward the clamp every step until signal arrives. ## Fix: cold-start gate (4 kernels, ~2 lines each) ```cuda const float input_ema = isv[input_slot]; if (input_ema == 0.0f) return; // hold bootstrap; adapt only on real signal ``` Applied to: * rl_target_tau_controller (multiplicative, q_div input) * rl_ppo_clip_controller (multiplicative, kl_pi input) * rl_rollout_steps_controller (multiplicative, adv_var_ratio input) * rl_reward_scale_controller (reciprocal, mean_abs_pnl input) The bootstrap value stays canonical (PPO ε=0.2, target-net τ=0.005, PPO rollout=2048, reward scale=1.0 raw passthrough) until the first non-zero EMA observation. Only then does the per-step Wiener blend begin moving prev toward the formula's target. This matches `pearl_first_observation_bootstrap`'s "sentinel = 0 means no data — adapt against signal, not noise" mandate, just applied at the controller layer rather than the EMA producer layer. ## Why local sm_86 smoke missed this The R9 G3 local test seeded every EMA input with a non-zero value BEFORE firing the controllers. That's a real-signal scenario by construction — the cold-start gate is a no-op there. The test still proves "controllers respond to real signal" but cannot detect "controllers misbehave at sentinel input" because it never feeds sentinel input. Adding a `g3b_controllers_hold_bootstrap_at_sentinel_input` test would be sensible for follow-up. For now the cluster smoke is the canonical witness — re-run will confirm scale/ε/n_roll/τ all hold at bootstrap until the first closed trade. ## Pearl updated The existing `pearl_hardcoded_bootstrap_target_collision.md` is amended to document BOTH failure modes (additive vs multiplicative/ reciprocal) and BOTH fixes (derive-from-input for additive, cold- start gate for multiplicative). The canonical incident (alpha-rl-fv7xz step 7) is captured with the actual scale=583 × $832 = 485k trace. ## Verified gates (post-fix, local sm_86) G1 isv_bootstrap ✅ unchanged (bootstrap values intact) G3 controllers_emit ✅ all 7 still move when fed real EMAs (test pre-seeds non-zero inputs) G4 target_soft_update ✅ unchanged G6 r7d_per_wiring ✅ unchanged R3 ema/advantage (3 tests) ✅ unchanged R4 action kernels (3 tests) ✅ unchanged end integrated_trainer_smoke ✅ all 5 head losses finite, unchanged ## Next: re-submit cluster smoke The fix is local. Push + ./scripts/argo-alpha-rl.sh --n-steps 1000 will re-validate on real ES MBP-10 with the cold-start gates active. Expected diag at step 7: scale=1.0 (bootstrap, unchanged) for the first trade close — no 485k reward spike. The diff between this and the prior smoke is the load-bearing signal that the fix worked. 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.