docs(phase-e): E.4 temporal encoder design + E.4.A implementation plan
Design doc (specs/): TFT-style architecture for Phase E execution
policy — sliding window → Mamba2 SSM → GRN trunk → MoE regime gate
→ C51 head → Thompson selector. Two core pillars added per user:
A) Full L1-L10 LOB depth input via hybrid MBP-10 peek
B) ISV-continual-learning: controllers fire at training AND
inference; Q-net weights frozen at inference but effective
policy adapts via ISV modulation
Plan doc (plans/): 14-task implementation plan for E.4.A foundation
(window buffer + L1-L10 depth + Mamba2 forward+backward + ISV-eval
controllers). Falsification gates: smoke R_mean improvement ≥ 50%,
backtest cost=0 Sharpe ≥ +8 (no regression vs C51-flat +10.41),
half-tick Sharpe ≥ -8 (closes 5pt+ of 10pt gap to Phase 1d.4
baseline -4.0).
TGGN (foxhunt Temporal Graph Gated Network) explicitly deferred to
Phase E.5+: existing CPU graph implementation + GPU adapter at
ml-supervised/src/tgnn/ — marginal benefit for single-instrument ES
futures vs the TFT-Mamba2 stack; revisit for multi-instrument
extension or production HFT inference layer.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
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docs/superpowers/plans/2026-05-15-phase-e-4-a-temporal-foundation.md
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# Phase E Temporal Encoder + Reasoning Architecture — Design
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**Status:** Design doc (not yet implementation plan). Awaiting user approval.
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**Date:** 2026-05-15
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**Author:** session 9c4e48ad-0f44-4989-8dab-09ec6870540c
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**Predecessors:** `pearl_c51_thompson_closed_phase_e3_gap.md`, `pearl_action_pruning_falsified.md`, `project_phase_e3_close.md`
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**Production reference stack:** `mamba2_temporal_kernel.cu`, `tlob_kernel.cu`, `attention_kernel.cu`, `grn_kernel.cu`, `vsn_feature_selection_kernel.cu`, `moe_kernels.cu`, `aux_heads_kernel.cu`, `aux_trunk_forward_kernel.cu`, `pearl_1_atom_kernel.cu`
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## 1. Goal
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Lift the Phase E execution policy from **stateless linear C51 Q** to a **stateful, regime-aware, auxiliary-supervised** Q-network. Close the remaining 10pt Sharpe gap to the Phase 1d.4 baseline at half-tick by adopting the production trainer's temporal-reasoning stack — same components that lifted Phase 1d.2's snapshot-stream AUC from 0.50 → 0.66 at K=6000.
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## 2. Why now
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The Phase E.3 close + C51 follow-up establish:
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- ✅ Alpha signal is real and gets transmitted (rvr = +1.045σ across linear and C51).
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- ✅ Calibration was the dominant bottleneck (C51 closed +26pt of Sharpe gap at cost=0 vs linear Q).
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- ✅ Action variance was NOT the bottleneck (pruning falsified, -22pt Sharpe).
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- ✅ Fill economics was NOT the bottleneck (real spread ≈ fixed for ES — 76% of bars at 1-tick floor).
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- ⚠️ The remaining 10pt half-tick gap is **trade-count economics × value-estimation precision** under current stateless architecture.
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The production trainer already implements the techniques needed:
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- **Temporal memory** (Mamba2, TLOB, attention) — `pearl_state_amplifies_short_horizon_into_long_horizon` proved this works on ES futures.
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- **Regime-aware decisions** (MoE) — `pearl_snapshot_alpha_is_regime_conditional` showed spread-Q4 acc=0.747 vs middle quintiles below chance; the alpha lives in specific regimes.
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- **Dense auxiliary supervision** (aux heads + separate aux trunk) — `pearl_separate_aux_trunk_when_shared_starves` is the canonical fix for sparse-PnL training.
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- **Adaptive atom support** (`pearl_per_branch_c51_atom_span`) — eliminates the hardcoded [-10, +10] choice.
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Smart-borrow philosophy: adopt the **techniques** (kernels, controller signals, ISV slots) without importing the **specialization** (4-branch action factorization, magnitude bins, direction-specific reward biases). Same approach that worked for the C51 borrow.
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## 2.5. Core architectural pillars (added by user 2026-05-15)
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### Pillar A: Full L1-L10 LOB depth input
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The DBN MBP-10 files contain real L1-L10 bid/ask data (verified
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`dbn_parser.rs:721-728` and `866-873` correctly copy all 10 levels).
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The current Phase E env synthesizes L2/L3 at ±tick offsets because
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the **fxcache** doesn't carry depth — only L1-derived features
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(spread_bps, l1_imbalance) plus the 81-dim Block-S feature vector.
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To use real L4-L10, options:
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1. **Hybrid loader**: at env construction, peek MBP-10 by timestamp
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for each fxcache bar — adds depth without breaking alpha_cache
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alignment.
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2. **Fxcache rebuild**: extend the fxcache schema to store full depth.
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3. **MBP-10 direct mode**: skip fxcache entirely; loses alpha_cache.
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Pick (1) for the staged rollout. Adds ~120 floats per snapshot
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(L1-L10 × bid+ask × {px, sz, ct} = 60 fields × 2 sides = up to 120).
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Phase E.4.A.2 task.
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### Pillar B: ISV-continual-learning (train AND inference)
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Foxhunt's controller pattern already produces and consumes ISV slots
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GPU-resident via `pearl_engagement_rate_self_correction` and friends.
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Phase E currently freezes ISV at training end. **The proposal: keep
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controllers firing during inference / eval / deployment.**
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**What stays frozen at inference:** Q-net weights (W, b for the C51
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head; Mamba2 SSM parameters; GRN/MoE/VSN weights).
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**What adapts at inference via ISV controllers:**
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- Slot 543 — stacker threshold (engagement-rate controller continues
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responding to observed trade rate)
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- Slot 545 — observed-rate Wiener-α EMA (always updating)
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- Slot 546 — Kelly attenuation (tightens after drawdowns)
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- Slot atom_headroom (Pearl-1) — adaptive C51 support widens/narrows
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as observed Q-scale shifts
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- Slot ~126 — MoE gate entropy EMA (drives MoE λ controller)
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- ISV[12] — health composition (if we adopt it) gates ALL downstream
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adaptations
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**Effective dynamic weight:** the policy's decision rule
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`action = ThompsonSelect(MoE_mix(experts), threshold_gate)` is a
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*function* of the ISV slots. As ISV evolves at inference,
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the effective policy adapts WITHOUT touching neural-network parameters.
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This is "dynamic weight via ISV modulation" — lightweight, safe,
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composable with frozen Q-net.
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**Optional aggressive variant — LoRA at inference:** add a low-rank
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adapter `W' = W + α·U·V^T` where `U ∈ R^{H×r}`, `V ∈ R^{r×H}`,
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`r << H`. Update U, V via online SGD on observed reward error during
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inference. Adds literal dynamic weights (not just ISV). Out of scope
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for Phase E.4 core — possible Phase E.5 extension if ISV-only
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adaptation isn't enough.
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**Why this composes:** Mamba2 hidden state accumulates within-episode
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temporal context (fast adaptation). ISV controllers accumulate
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across-episode regime context (slow adaptation). Together they form
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a two-timescale online adaptation system on top of a frozen
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representational core.
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**Failure modes to guard against:**
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- ISV runaway in low-data regimes — bound all controller updates by
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Wiener-α floor per `pearl_wiener_alpha_floor_for_nonstationary`.
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- Threshold spiraling to no-trade — engagement-rate controller has
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a target floor; verify it activates at inference.
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- Adversarial market manipulation against the live ISV — for HFT
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deployment, controllers should have rate limits on update magnitude.
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## 3. Architecture (target)
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```
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┌──────────────────────────────────────────────────────────────────────┐
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│ Per-step input: state[10] + rolling window buffer[K, 10] │
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│ (current state pushed into a circular buffer of last K snapshots) │
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└────────────────────────────────┬─────────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────────────────────────────────────────┐
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│ Variable Selection Network (VSN) │
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│ • Per-state softmax gate over feature groups → vsn_mask[10] │
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│ • Element-wise gated features → x_in[K, 10] │
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│ • mask EMA → ISV[~120..125] (interpretability) │
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└────────────────────────────────┬─────────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────────────────────────────────────────┐
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│ Mamba2 SSM Temporal Encoder │
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│ • State-space accumulation over the K-bar window │
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│ • Output: h_temporal ∈ R^H (compressed temporal context) │
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│ • SAME kernel as Phase 1d.2 — proven on ES futures │
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└────────────────────────────────┬─────────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────────────────────────────────────────┐
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│ Gated Residual Network (GRN) Trunk │
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│ • h_temporal → h_s2 ∈ R^H' (shared trunk encoding) │
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│ • Gating + residual; more expressive than linear, cheaper than │
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│ full transformer │
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└──────────────────────────────┬─┴─────────────────────────────────────┐
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│ │
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┌────────────────┘ │
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│ (main path) │
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▼ ▼
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┌──────────────────────────────────┐ ┌──────────────────────────────┐
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│ MoE Regime Gate │ │ Aux Trunk (SEPARATE) │
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│ • K_e=4 experts, gate by h_s2 │ │ • Linear→ELU→Linear→ELU→ │
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│ • Each expert is a C51 head │ │ Linear MLP │
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│ [9 actions × N_atoms atoms] │ │ • stop_grad at encoder │
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│ • Output: gate-mixed probs │ │ boundary │
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│ p(a, k | s) │ │ • Independent Adam │
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│ • gate_entropy_ema → ISV[126] │ │ • Aux heads: │
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│ drives MoE λ controller │ │ 1) next-bar return MSE │
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│ (existing in production) │ │ 2) 5-class regime CE │
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└────────────────────────┬─────────┘ └──────────────────────────────┘
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│
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▼
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┌──────────────────────────────────────────────────────────────────────┐
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│ C51 categorical output: probs[B, 9, N_atoms] │
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│ • Adaptive atom support [v_min, v_max] from Pearl-1 controller │
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│ • ISV-driven instead of hardcoded │
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└────────────────────────────────┬─────────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────────────────────────────────────────┐
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│ GPU Thompson selector (existing) + alpha_confidence threshold gate │
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│ • Inverse-CDF over each expert's mixed probs │
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│ • Mapped-pinned action output (current implementation, unchanged) │
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└──────────────────────────────────────────────────────────────────────┘
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```
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### Components (justification)
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| Component | Production kernel | Phase E role | Compat note |
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|---|---|---|---|
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| Sliding-window state buffer | new (CPU or GPU circular buffer) | Provides K bars of context per step | New buffer mgmt; reset on episode boundary |
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| VSN | `vsn_feature_selection_kernel.cu` | Per-state feature-importance gating | Use 10 features instead of 6 groups; mask EMA into ISV |
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| Mamba2 SSM | `mamba2_temporal_kernel.cu` | Temporal encoder | Same kernel; D=10 input, configure hidden width |
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| GRN | `grn_kernel.cu` | Gated trunk encoding | Direct reuse |
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| MoE gate | `moe_kernels.cu` + `moe_lambda_eff_kernel.cu` | Regime-aware Q-head dispatch | K_e=4 (matches spread-quintile regime structure) |
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| C51 head | `alpha_c51.cu` (existing) | Per-expert categorical output | Already works |
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| Aux trunk | `aux_trunk_forward/backward_kernel.cu` | Separate trunk for aux supervision | Stop-grad at encoder per `pearl_separate_aux_trunk_when_shared_starves` |
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| Aux heads | `aux_heads_kernel.cu` + `aux_heads_loss_ema_kernel.cu` | Next-bar MSE + regime CE | Adapt label-builders to Phase E targets |
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| Pearl-1 atom span | `pearl_1_atom_kernel.cu` | Adaptive [v_min, v_max] for C51 | Replaces hardcoded support; ISV-driven |
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| Thompson selector | `alpha_c51.cu` (existing) | Action selection | Reads gate-mixed probs; no kernel change |
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### TFT correspondence
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| TFT component | Provided by |
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|---|---|
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| Variable Selection Network | `vsn_feature_selection_kernel.cu` (direct) |
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| Gated Residual Network | `grn_kernel.cu` (direct) |
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| Static covariate encoder | absent — Phase E has no static covariates; OK |
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| LSTM seq-to-seq encoder | **substituted by Mamba2 SSM** (better; subquadratic) |
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| Interpretable multi-head attention | optional Phase E.4.E follow-up via `attention_kernel.cu` |
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| Quantile output | **substituted by C51 categorical** (already working) |
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The proposed architecture is structurally a TFT, with two strict upgrades vs the original Lim et al. 2021 design:
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1. Mamba2 instead of LSTM (linear-time state accumulation, no vanishing-gradient).
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2. C51 categorical output instead of quantile loss (proven calibration win).
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## 4. State representation changes
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Current Phase E state vector (10-dim, single timestep):
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```
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[alpha_logit, alpha_confidence, spread_bps, l1_imbalance, ofi_sum_5,
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mid_drift_5, position, step_normalized, log_tau, log_event_rate]
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```
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Proposed: same 10 features but **K-bar history** for all features. Window length K starts at 16 (~16 bars of context); validate empirically against K=32, K=64. Mamba2 handles long-horizon well (Phase 1d.2 used K=6000); the cost is GPU memory not architectural limit.
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Per-step input shape: `[B, K, 10]`. The buffer is maintained as a circular array; reset on episode start zeros it (or seeds with the first K observed states once available).
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## 5. Training changes
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- **Forward**: per step, push current state to buffer, run window → VSN → Mamba2 → GRN → (MoE → C51 head) + (aux trunk → aux heads).
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- **Backward**: Q-loss (C51 CE) flows through MoE→GRN→Mamba2→VSN. Aux-loss flows through aux trunk only (stop-grad at encoder boundary, per `pearl_separate_aux_trunk_when_shared_starves`).
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- **Loss balance**: per `pearl_loss_balance_controller`, signal-modulated target × Wiener-α. Phase E currently uses Q-loss only; aux-loss weight starts at 1.0 and is controller-driven.
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- **Optimizer**: keep plain SGD initially (C51 works with SGD). Switch to Adam in Phase E.4.D if signal plateaus.
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- **PER**: NOT in scope for E.4 (current on-policy training works); revisit only if learning is sample-starved.
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## 6. Phasing
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Each phase produces a runnable smoke + backtest. Each phase ships as a discrete experiment we can A/B against the prior best.
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**Phase E.4.A — Foundation (1 week)**
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- Phase E.4.A.1: Sliding-window state buffer (GPU-resident, circular)
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- Phase E.4.A.2: Hybrid MBP-10 depth loader — extend
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`load_snapshots_from_fxcache` to peek MBP-10 by timestamp and
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populate real L4-L10 bid/ask in `SnapshotRow.bid_l[3..10]` /
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`ask_l[3..10]`. Preserves alpha_cache alignment.
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- Phase E.4.A.3: Extend state vector to include depth features
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(L1-L10 cumulative size, spread-curve slope, etc.)
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- Phase E.4.A.4: Mamba2 forward + backward (already exists; wire into Phase E)
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- Phase E.4.A.5: GRN trunk (already exists; wire)
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- Phase E.4.A.6: C51 head reads h_s2 instead of state directly
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- Phase E.4.A.7: **ISV-continual-learning toggle** — controllers fire
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at eval time too (already in smoke at training; lift to backtest eval).
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- Smoke + backtest vs C51-flat-baseline (target: maintain +10.4 at cost=0, lift half-tick)
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- **Falsification: if Sharpe at cost=0 drops, the temporal architecture isn't lifting — pause and diagnose before adding more.**
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**Phase E.4.B — Regime gating (1 week)**
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- Add VSN feature selection
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- Add MoE 4-expert gating + λ controller (existing kernel)
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- Validate expert utilization (should specialize across spread quintiles)
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- A/B vs E.4.A
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**Phase E.4.C — Aux supervision (3-4 days)**
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- Add aux trunk + heads (next-bar return MSE, 5-class regime CE)
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- Validate aux losses converge (`pearl_separate_aux_trunk_when_shared_starves` gate: CE < 0.1 AND dir_acc > 0.95)
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- Validate Q-learning isn't destabilized
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- A/B vs E.4.B
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**Phase E.4.D — Adaptive atoms + polish (3-4 days)**
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- Pearl-1 atom span controller (replaces hardcoded [-10, +10])
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- Add health composition (ISV[12]) feeding controller / atom span
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- Trade-rate target sweep at high-cost regime (closes the trade-count economics)
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- Final 2D sweep, write close-out memo
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**Phase E.4.E — Optional follow-ups (defer)**
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- TFT interpretable multi-head attention
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- IQN quantile head composed with C51
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- Pearl-4 Adam adaptive hyperparams
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## 7. Falsification criteria
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For the architecture upgrade to be worth the engineering cost:
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**Smoke must show (vs C51-flat-baseline R_mean = -1.1):**
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- R_mean improvement ≥ 50% (i.e., R_mean ≥ -0.5)
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- rvr maintained (≥ +1.04σ)
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- Action entropy NOT collapsed to Wait-only (>0.5 × ln(9))
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**Backtest must show (vs C51-flat-baseline Sharpe_ann at half-tick = -13.8):**
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- Half-tick Sharpe_ann ≥ -8 (closes 5pt+ of the remaining 10pt gap to Phase 1d.4 baseline at -4.0)
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- Trade rate ≤ 70/ep at best τ (moving toward Phase 1d.4's 20-50/ep regime)
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- Cost=0 Sharpe_ann ≥ +8 (not regressed below the C51-flat result)
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If these don't hit by Phase E.4.B end: rollback to C51-flat and reconsider. The temporal architecture is a STRATEGIC bet — if it doesn't deliver, we have a known-good fallback (the C51-flat policy already shipped in this session).
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## 8. Risks
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- **State-buffer memory**: K=64 × 10 features × float = 2.5KB per env-instance. For batch backtest with 500 episodes the working set is fine. K=600 (full smoke horizon) is 24KB/instance — also fine.
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- **Mamba2 init**: Phase 1d.2 used random init + trainable end-to-end (`pearl_tlob_no_pretraining`). Should work for Phase E too.
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- **Aux head label leak**: `pearl_trend_scanning_purged_cv_doesnt_sterilize_forward_features` warns that forward-window labels leak into forward-horizon targets. Mitigation: aux supervision uses NEXT BAR (single-step) target, not multi-bar trend-scanning label.
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- **MoE expert collapse**: if all 4 experts learn the same C51 distribution, MoE adds parameters without benefit. Mitigation: `moe_lambda_eff_kernel.cu` λ controller penalizes gate entropy collapse; existing kernel does this.
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- **Loss balance instability**: with Q-loss + 2 aux losses, the encoder receives 3 gradient streams. `pearl_loss_balance_controller` is the production answer; adopt at Phase E.4.C.
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## 9. Open questions
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- **Window length K**: start at 16, sweep 16/32/64. Mamba2 doesn't impose a ceiling.
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- **MoE expert count K_e**: start at 4 (matches spread-quintile structure from Phase 1c). Validate by checking expert utilization per regime.
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- **Aux head regime classes**: 5 classes for spread quintiles, OR 5 classes for volatility quintiles? Pick spread (alpha-conditional dimension from Phase 1c).
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- **Should we keep the threshold gate?** The current `--train-threshold` is a CPU-side gate. Once the Q-net is temporal + regime-aware, the gate may be redundant. Test with `--train-threshold 0.0` in E.4.B.
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## 10. Out of scope / future research
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**Out of scope (Phase E.4 core):**
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- Different markets (validated as not the bottleneck for ES futures spread economics)
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- Action-space changes (pruning falsified; full 9 stays)
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- PER / off-policy replay (current on-policy training has no observable sample-starvation)
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- LoRA dynamic weights at inference (aggressive ISV-continual-learning variant — Phase E.5 if needed)
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**Future research (Phase E.5+ or production-tier):**
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- **TGGN graph reasoning over L1-L10 depth**
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(`ml-supervised/src/tgnn/`): foxhunt has an existing CPU-based
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Temporal Graph Gated Network for HFT (Performance targets <500ns
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graph build, <1μs GNN inference). The trainable adapter is
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GPU-native (`crates/ml/src/tgnn/trainable_adapter.rs` using cuBLAS
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GpuLinear + GpuAdamW) but the message-passing layer is CPU.
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Honest assessment for Phase E.4: marginal benefit over the
|
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TFT-Mamba2 stack — the 81-dim Block-S features already encode
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LOB structure and Mamba2 captures temporal context. TGGN's
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strongest value is in (a) multi-instrument cross-asset graphs,
|
||||
(b) full MBP-10 depth utilization, (c) production HFT
|
||||
sub-1μs latency. With Pillar A (L4-L10 input wired), the depth
|
||||
case becomes stronger — revisit as Phase E.5 candidate.
|
||||
- **TFT interpretable multi-head attention** —
|
||||
`attention_kernel.cu` + `attention_backward_kernel.cu`.
|
||||
Adds variable-importance via attention weights. Already deferred
|
||||
in section 6 as Phase E.4.E.
|
||||
- **IQN dual head composed with C51** — joint sampling
|
||||
(`pearl_thompson_for_distributional_action_selection`). Trade-off:
|
||||
+2-3× training overhead for marginal exploration gain.
|
||||
- **Pearl-4 Adam adaptive hyperparams** — replace plain SGD.
|
||||
- **xLSTM / KAN / Liquid alternative encoders** — adapters exist
|
||||
in `crates/ml/src/hyperopt/adapters/`. Test only if Mamba2
|
||||
plateaus.
|
||||
- **Multi-instrument extension** (ES + NQ + RTY graph for
|
||||
cross-asset signals) — opens up TGGN's strongest use case.
|
||||
|
||||
## 11. Approval gate
|
||||
|
||||
**This is a design proposal, not yet an implementation plan.** Before invoking `superpowers:writing-plans` to produce a step-by-step implementation:
|
||||
|
||||
- [x] User approved the TFT-Mamba2 architecture ("the idea is great
|
||||
we use that", 2026-05-15)
|
||||
- [x] TGGN clarified — foxhunt-specific Temporal Graph Gated
|
||||
Networks (`ml-supervised/src/tgnn/`). Existing CPU
|
||||
implementation + GPU adapter. Deferred to Phase E.5+ research
|
||||
per Section 10.
|
||||
- [x] L4-L10 depth source verified — DBN MBP-10 files contain real
|
||||
L1-L10. Pillar A (Section 2.5) integrates them via hybrid
|
||||
MBP-10 peek alongside fxcache.
|
||||
- [x] ISV-continual-learning concept adopted as Pillar B
|
||||
(Section 2.5) — core architectural feature, not optional.
|
||||
- [ ] User confirms phasing (4 phases × ~3 weeks total)
|
||||
- [ ] User confirms scope: stop after E.4.D? Or extend with E.4.E?
|
||||
- [ ] User approves the falsification criteria (smoke + backtest thresholds)
|
||||
|
||||
After approval, the writing-plans skill produces task-by-task TDD implementation steps for Phase E.4.A first.
|
||||
Reference in New Issue
Block a user