754aea6995
Phase 2 verdict gave mechanistic evidence that the encoder's weights
for state[121] are essentially unused (action distribution
bit-identical across Phase 1 and Phase 2 encodings; drift < 0.5% on
every action bin). The aux signal reaches state[121] (`pred_tanh =
0.66`) but the policy ignores it because slot 121's encoder weights
need many training steps to grow from zero.
Phase 3 routes aux around the cold-encoder bottleneck via two
non-overlapping mechanisms (combined per
`pearl_no_deferrals_for_complementary_fixes`):
α (post-encoder bypass-head)
────────────────────────────
- New learnable param `W_aux_to_Q_dir: [4]` f32 (one weight per
direction action). Adam states `m`, `v` ([4] each). 12 floats total.
- Forward (rollout + training graphs, post-Q_dir):
Q_dir[b, a] += W_aux_to_Q_dir[a] * state_121[b]
- Backward (training only):
dW[a] = Σ_b state_121[b] * dQ_dir[b, a] (per-action block reduce)
dstate_121[b] += Σ_a W[a] * dQ_dir[b, a] (per-sample sum)
- New cubins:
aux_to_q_dir_bias_kernel.cu (forward)
aux_to_q_dir_bias_backward_kernel.cu (backward)
- Zero-init; Phase-3 step-0 output identical to Phase-2 baseline.
Adam-driven growth tracks aux-Q correlation.
- Reads state_121 from the encoder INPUT (batch_states[121]), bypassing
the encoder entirely.
β (event-driven aux-aligned trade-close bonus)
──────────────────────────────────────────────
At trade-close (existing segment_complete branch in
experience_env_step):
position_sign = sign(prev_position)
aux_at_close = state_121[env]
alignment = max(0, aux_at_close * position_sign) ∈ [0, +1]
profit_term = max(0, realized_pnl) ∈ [0, +∞)
r_aux_align = scale_beta * alignment * profit_term
Added to existing r_trail (no new reward component for first test —
YAGNI). scale_beta = 0.5 fixed. Non-negative-only (no anti-alignment
penalty; let r_trail's loss carry the negative signal). Event-driven
per `pearl_event_driven_reward_density_alignment`. Bounded by
`pearl_one_unbounded_signal_per_reward` (realized_pnl is the single
unbounded multiplicand, which IS the natural per-trade scale).
Why combined
────────────
α: direct forward-pass channel (aux shifts Q in real-time).
β: indirect training-signal channel (aux-aligned profitable trades
get bigger gradient → encoder learns to use state[121] faster, AND
the policy learns to take aux-aligned trades).
Decision points captured (with rationale)
─────────────────────────────────────────
| Question | Decision | Rationale |
| α scope | 4-weight vector | aux is directionally signed |
| β formula | non-negative only | avoid confusing policy when aux wrong |
| β scale | fixed 0.5 | minimum scope for verdict experiment |
| state[121] grad routing | both paths open | simplest; α dominates initially |
| β placement | added to r_trail | YAGNI; 7th component deferred |
| W sharing | trainer holds, collector reads | mirrors GEMM pattern |
Verification (all required clean before commit)
───────────────────────────────────────────────
- cargo check -p ml --features cuda (0 errors, 21 pre-existing
warnings expected — parity with Phase 2 baseline)
- gpu_backtest_validation (6/6 pass; post-Phase-2-test-fix baseline)
- compute-sanitizer --tool=memcheck (0 errors)
- Captured-graph integrity check (CAPTURE_PHASE_FORWARD_DONE +
parent compose visible in smoke logs)
- α weight movement check (W_aux_to_Q_dir non-zero after epoch 1)
Verdict criteria
────────────────
- WR > 50.5% within 3 epochs → confirmed → justify A2.
- W_aux_to_Q_dir non-zero magnitudes → α gradient flowed.
- Action distribution diverges from Phase 1/2 baselines → policy is
exercising different actions.
- a_var [m, o, u] > 1e-3 → sub-branches gradient-coupled.
- WR pinned + W non-zero + action distribution moved → bypass routing
is working but aux signal isn't actionable on current fixtures →
pivot to deeper hypothesis (per-branch IQN tau audit, reward
density rewrite, longer horizon).
- WR pinned + W near-zero → gradient flow broken; kernel debug.
Estimated effort: ~10–16 hr engineering + ~37 min smoke wall-clock.
Refs
────
- docs/plans/2026-05-12-sp22-h6-aux-policy-state-bridge.md (Phase 1)
- docs/plans/2026-05-12-sp22-h6-phase2-recenter.md (Phase 2)
- pearl_no_deferrals_for_complementary_fixes (combined plan)
- pearl_event_driven_reward_density_alignment (β rationale)
- pearl_one_unbounded_signal_per_reward (β bound)
- pearl_first_observation_bootstrap (Phase 2 sentinel = 0)
- pearl_no_host_branches_in_captured_graph (kernel discipline)
- pearl_separate_aux_trunk_when_shared_starves (aux trunk untouched)
- pearl_no_atomicadd (backward reduce discipline)
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.