spec(sp19+20): WR-first reward + multi-horizon label utilization
Combines SP19 (multi-horizon labels, already landed) + SP20 (WR-first reward) into one spec per pearl_no_deferrals_for_complementary_fixes. Goal: WR ≥ 55%, textbook PF ≥ 2.0, walk-forward stable, per-regime stable. Six components, atomic ship per feedback_no_partial_refactor: 1. Reward kernel (event-driven, 4-quadrant, asymmetric clamp ramped from wr_ema, multi-horizon directional ground-truth check) 2. Hold opportunity-cost (per-bar, dual emission for real reward + Hold baseline buffer) 3. n-step credit distributor (uniform over trade duration, fixes SP18 B-leg self-bootstrap bug at gpu_experience_collector.rs:4154) 4. Aux→Q confidence gate (sigmoid threshold, mean_a Q baseline avoids Hold-everywhere punishment) 5. 3 fused producer kernels (sp20_emas, sp20_controllers, sp20_stats) driving 9 ISV slots in [510..520) 6. 7 behavioral tests on RTX 3050 Ti, gating L40S deployment ~1,550 LoC total. Spec includes data flow, error handling philosophy, 4-tier test gate, success criteria with explicit failure modes. Cross-references: - pearl_event_driven_reward_density_alignment (design principle) - pearl_audit_unboundedness_for_implicit_asymmetry (asymmetric clamp) - pearl_separate_aux_trunk_when_shared_starves (aux gate leverages SP14-C) - project_metric_pipeline_inflation_audit (WR honest, Sharpe honest, goal grounded) - project_goal_wr_55_pf_2 (the goal this spec implements)
This commit is contained in:
jgrusewski
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docs/superpowers/specs/2026-05-09-sp19-20-wr-first-design.md
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# SP19+20: WR-First Reward + Multi-Horizon Label Utilization
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**Spec date:** 2026-05-09
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**Branch:** `sp19-20-wr-first` forks from `feat/sp18-combined` HEAD `d39005c6f`
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**Combines:** SP19 (multi-horizon labels, already landed) + SP20 (WR-first reward design)
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**Per memory rule:** combined into one spec/plan per `pearl_no_deferrals_for_complementary_fixes` — non-overlapping refactor scopes (SP19 = producer-side label blend already on disk; SP20 = reward kernel + Q-target plumbing)
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## 1. Goal
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Hard goal: **WR ≥ 55%** closed-trade directional accuracy AND **textbook PF ≥ 2.0**, walk-forward stable across folds, per-regime stable across T/R/V buckets.
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Soft goal: profit comes from honest directional accuracy, not from payoff-asymmetry compensation. SP-chain pattern (WR=46% + omega 1.32:1 → positive Sharpe) is "fragile trading" and unacceptable as the terminal state.
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## 2. Background and motivation
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### 2.1 What we know honestly (post 2026-05-09 metric audit)
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- **WR = 46-50% across 16+ SP runs** is honest closed-trade directional accuracy. Verified by audit at `experience_kernels.cu:2857`, `backtest_metrics_kernel.cu:256, 424`, `trade_physics.cuh:515` — all use realized PnL > 0 predicate.
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- **Sharpe ≈ 78** (annualized at √(2880 × 252) = 852 for our volume bars) is honest under its per-bar definition.
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- **Per-trade EV ≈ +0.07** in avg_loss units. Positive but fragile to slippage/fees. PF ≈ 1.32 (textbook) implies wins compensate losses by 1.55×.
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- The **Q(Hold) attractor is structural across all SP-chain runs** (vtj9r → 5rbml → wgdc7). Hold% climbs to 50-62% by epoch 5; SP19 multi-horizon reward delayed but did not break it.
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- **Per-regime WR is uniformly ~46% across T/R/V** (wgdc7 instrumentation). No regime-conditional edge surfaces under current reward.
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### 2.2 What this spec attacks
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The SP-chain reward shapes (popart, cf, trail_r, micro, opp_cost, bonus, multi-horizon blend) all grade *cumulative per-bar PnL*, not closed-trade directional correctness. The model learns to maximize cumulative reward via payoff asymmetry (1.32:1 with WR=46%) rather than directional skill (would require WR>50%).
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This spec re-grades the model on **closed-trade directional correctness** with **multi-horizon ground-truth check** and **adaptive asymmetric loss aversion** that ramps in only after the model has learned the directional baseline. The mechanism is fundamentally event-driven (per `pearl_event_driven_reward_density_alignment`), not per-bar-shaped.
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### 2.3 What we are deliberately NOT doing
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- More architecture changes — capacity is not the constraint (per audit).
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- Per-bar reward shaping — `pearl_event_driven_reward_density_alignment` warns this creates exposure-positive bias for event-driven objectives.
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- Optimizing Sharpe — Sharpe is honest but compensation-based; targeting it directly perpetuates the fragile-trading failure mode.
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- Multi-instrument joint training, cross-asset features, instrument selection — flagged as future work per `project_label_horizon_is_bottleneck`, but addressed only after SP20 establishes whether the model can lift WR with current data.
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## 3. Architecture
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```
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┌──────── Encoder ────────┐
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│ │
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▼ ▼
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[Shared Trunk (GRN)] [Aux Trunk (SP14-C, separate)]
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│ │
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┌─────┼──────┐ │
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▼ ▼ ▼ ▼
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Q_dir Q_mag Q_ord/urg aux_direction (multi-bar, supervised by SP19 labels)
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│ │
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▼ ▼
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Thompson selector aux_conf, aux_dir_acc_ema (ISV)
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│ │
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▼ │
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Action a_t │
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│ │
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▼ │
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Trade physics ──── step_return │
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│ │
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┌─────┴──── is_close ? │
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│ │
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▼ (yes) │
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[Reward Kernel — Component 1] │
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R_event = direction_check(close_PnL, │
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SP19_label, │
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asym_ratio) │
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│ │
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▼ │
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alpha = R_event − hold_baseline_n_bars │
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│ │
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▼ │
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R_used = alpha − alpha_ema │
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│ │
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▼ │
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[n-step distributor — Component 3] │
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spread R_used over trade_duration bars │
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│ │
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└──────────► Replay buffer ◄───────────┘
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│ (aux_conf at trade open
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│ stored alongside reward)
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▼
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[Bellman target — Component 4]
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Q_target = aux_gate × full_target +
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(1 − aux_gate) × baseline_target
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where aux_gate = sigmoid((aux_conf − threshold) / temp)
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│
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▼
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Q-loss + Q-gradients
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│
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▼
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──── Per-bar (in Hold state) — Component 2 ────
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Hold_opp_cost = −aux_conf × cost_scale
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fires every bar Hold is held; aux_conf high → cost high
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──── Adaptive Curriculum (ISV) — Component 5 ────
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asym_ratio ← ramp(wr_ema)
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aux_threshold ← adapt(aux_dir_acc_ema)
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n_step ← adapt(trade_duration_ema)
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cost_scale ← adapt(hold_pct_ema)
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alpha_ema_α ← Wiener-α floor 0.4
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```
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Six components, each independently testable, all shipping atomically per `feedback_no_partial_refactor`.
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## 4. Components
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### 4.1 Component 1 — Reward kernel (event-driven)
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**Fires:** at every trade close (`is_close == 1`) detected by existing trade_physics.
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**Formula:**
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```
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At trade close (is_close=1):
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win_cap = +1.0
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loss_cap = -1.0 - clamp((wr_ema - 0.50) / 0.05, 0, 1)
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# = -1 below WR=50%, ramps to -2 by WR=55%
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label_at_open = sign(SP19_blended_label[trade_open_bar])
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dir_match = (sign(close_PnL) == label_at_open)
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R_event = match dir_match, sign(close_PnL):
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( true, +1) ⇒ +win_cap # right reason, right outcome
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(false, +1) ⇒ +0.5 # wrong reason, right outcome
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( true, -1) ⇒ -0.5 # right reason, wrong outcome
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(false, -1) ⇒ loss_cap # wrong reason, wrong outcome
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( *, 0) ⇒ 0.0 # zero PnL = no information
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hold_baseline = Σ over [trade_open .. trade_close] of hold_baseline_buffer[i]
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# see Component 2 for the buffer; negative number
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alpha = R_event - hold_baseline # subtraction: hold_baseline is negative, so alpha ≥ R_event
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alpha_ema_t+1 ← (1-α) × alpha_ema_t + α × alpha # Wiener-α floor 0.4
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R_used = alpha - alpha_ema_t
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→ distributed n-step credit by Component 3
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```
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**Implementation:**
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- New device function `sp20_compute_event_reward()` in `experience_kernels.cu` (~80 LoC)
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- Replaces SP18 D-leg `compute_sp18_hold_opportunity_cost` (returned 0 throughout 5rbml — empirically null)
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- Replaces SP12 v3 reward block (`compute_sp12_reward_with_cost` and per-bar additions)
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**ISV slots (3 new):**
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- `ASYM_RATIO_INDEX` — produced by Kernel 2 from `wr_ema`
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- `ALPHA_EMA_INDEX` — produced by Kernel 1 (Wiener-α EMA)
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- `WR_EMA_INDEX` — produced by Kernel 1 (per-trade EMA)
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### 4.2 Component 2 — Hold opportunity-cost (per-bar)
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**Fires:** every bar.
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Two parallel emissions:
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```
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Per bar i:
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aux_conf_i = max(softmax(aux_logits_i)) - 1/3 # range [0, 2/3]
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cost_scale = ISV[HOLD_COST_SCALE] # adaptive [0.01, 0.5]
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per_bar_opp_cost_i = -aux_conf_i × cost_scale
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# Path 1 — real reward, fires only on Hold action:
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R_per_bar_i = (action_i == Hold) ? per_bar_opp_cost_i : 0
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# Path 2 — counterfactual buffer, ALWAYS recorded:
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hold_baseline_buffer[i] = per_bar_opp_cost_i
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# consumed by Component 1 at trade close
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```
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**Adaptive cost_scale (Kernel 2 controller):**
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```
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target_hold_pct = clamp(0.8 - aux_conf_p50_ema × 1.5, 0.1, 0.8)
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hold_pct_ema = Wiener-α EMA of (action == Hold) indicator
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if hold_pct_ema > target_hold_pct + 0.05:
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cost_scale ← clamp(cost_scale × 1.05, 0.01, 0.5)
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elif hold_pct_ema < target_hold_pct - 0.05:
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cost_scale ← clamp(cost_scale × 0.95, 0.01, 0.5)
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```
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**Self-stabilizing properties:**
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- Aux untrained (warmup): `aux_conf_p50_ema ≈ 0.05` → `target_hold_pct ≈ 0.725` → controller pushes `cost_scale` toward floor 0.01 → per-bar cost effectively zero. **No explicit warmup gate needed.**
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- Flat action: treated as not-Hold (no cost). Flat is intentional close-all, conceptually different from Hold.
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**Implementation:**
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- 1 new producer kernel `hold_cost_scale_compute_kernel.cu` (~50 LoC, follows SP4 Pearls A+D)
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- Modification at `experience_kernels.cu` per-bar reward path: add `R_per_bar_i` term, write to `hold_baseline_buffer`
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**ISV slots (3 new):** `HOLD_COST_SCALE_INDEX`, `TARGET_HOLD_PCT_INDEX`, `HOLD_PCT_EMA_INDEX`.
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### 4.3 Component 3 — n-step credit distributor
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**Fires:** at each trade close, after Component 1 produces `R_used`.
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**Formula (uniform distribution):**
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```
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At trade close (is_close=1):
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R_used = α - α_ema # from Component 1
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n_step = ISV[N_STEP_INDEX] # = round(trade_duration_ema), bounded [1, 30]
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trade_duration_actual = bar_close - bar_open
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n_distribute = min(n_step, trade_duration_actual)
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for k in [0 .. n_distribute):
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bar_idx = bar_close - k
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per_bar_credit[bar_idx] += R_used / n_distribute
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```
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**Why uniform, not λ-decay:**
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The trade's reward is event-attributed: every bar in the trade contributed equally to the close-PnL outcome (open bar created position, close bar realized it, intermediate bars held). λ-decay would systematically under-credit the open bar — the bar that committed the directional decision.
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**Trade boundary discipline:**
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The eligibility trace state must reset at every trade close, not carry across. This fixes the SP18 B-leg `gpu_experience_collector.rs:4154` self-bootstrap bug as part of SP20 (per `pearl_no_deferrals_for_complementary_fixes` — non-overlapping scope, deferred no longer).
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**Implementation:**
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- 1 new kernel `n_step_credit_kernel.cu` (~80 LoC)
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- Trade-boundary tracking reuses existing `trade_close_event` buffer
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- SP18 B-leg fix at `gpu_experience_collector.rs:4154`: trace state resets at trade close (~30 LoC change)
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**ISV slots (1 new):** `N_STEP_INDEX`. Plus `TRADE_DURATION_EMA_INDEX` (for the producer).
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### 4.4 Component 4 — Aux→Q confidence gate
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**Fires:** at each replay batch step, in the Bellman target computation.
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**Formula:**
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```
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At each replay batch step:
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aux_conf = max(softmax(aux_logits_at_state)) - 1/3
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threshold = ISV[AUX_CONF_THRESHOLD] # adaptive
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temp = ISV[AUX_GATE_TEMP] # adaptive
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gate = sigmoid((aux_conf - threshold) / temp) # ∈ [0, 1]
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Q_target_full = r + γ × max_a' Q(s', a')
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Q_target_baseline = mean_a Q(s, a) # neutralizes action preference, doesn't punish
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Q_target = gate × Q_target_full + (1 - gate) × Q_target_baseline
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```
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**Why `mean_a Q(s, a)` baseline:**
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- `0` baseline punishes all actions for being in noise → drives Hold-everywhere.
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- `Q(s, a)` (no update) wastes uncertain-state data.
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- `mean_a Q` actively neutralizes action preference at uncertain states without freezing learning. Hold emerges from learned indifference, not from punishment.
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**Adaptive threshold and temperature:**
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```
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threshold = clamp(aux_dir_acc_ema - 0.50, 0.01, 0.20)
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temp = aux_conf_std_ema # gate sharpness adapts to actual aux confidence spread
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```
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**Self-stabilizing curriculum:**
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- Cold start (aux ≈ random): threshold ≈ 0.01 → gate ≈ 1 always → standard Q-learning
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- Aux improves: threshold ramps with `aux_dir_acc_ema − 0.50` → gate becomes selective
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- Aux strong: gate sharply discriminates by confidence
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- Aux anti-correlated (< 0.50): clamp floor → gate ≈ 1 → fall back to standard Q
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**Implementation:**
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- `gpu_dqn_trainer.rs` Bellman target site: ~40 LoC modification
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- 2 new ISV slots: `AUX_CONF_THRESHOLD_INDEX`, `AUX_GATE_TEMP_INDEX`
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- Producer kernel `aux_gate_params_compute_kernel.cu`: ~60 LoC (fused into Kernel 2)
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- Aux logits already exposed at training step (SP14-C wired); no new readback needed
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### 4.5 Component 5 — Adaptive Curriculum Producers
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All ISV-driven parameters come from 3 fused producer kernels (per `pearl_fused_per_group_statistics_oracle`):
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**Kernel 1: `sp20_emas_compute`** (~80 LoC, per-step + per-trade triggers)
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Consumes: trade events, aux logits, action history.
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Produces (Wiener-α EMAs with floor 0.4):
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- `wr_ema` — per-trade
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- `trade_duration_ema` — per-trade
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- `aux_conf_p50_ema` — per-step (from Kernel 3)
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- `aux_conf_std_ema` — per-step (from Kernel 3)
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- `aux_dir_acc_ema` — per-step (extends existing SP14-C signal)
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- `hold_pct_ema` — per-step
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- `alpha_ema` — per-trade
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**Kernel 2: `sp20_controllers_compute`** (~70 LoC, depends on Kernel 1)
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Consumes: above EMAs.
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Produces controller outputs:
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- `ASYM_RATIO_INDEX` = ramp from wr_ema (Section 4.1)
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- `N_STEP_INDEX` = round(trade_duration_ema), clamp [1, 30]
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- `AUX_CONF_THRESHOLD` = clamp(aux_dir_acc_ema − 0.50, 0.01, 0.20)
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- `AUX_GATE_TEMP` = aux_conf_std_ema (with floor)
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- `TARGET_HOLD_PCT` = clamp(0.8 − aux_conf_p50_ema × 1.5, 0.1, 0.8)
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- `HOLD_COST_SCALE` = two-sided controller (hold_pct_ema vs target)
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**Kernel 3: `sp20_stats_compute`** (~60 LoC, per-step)
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Consumes: aux_logits raw.
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Produces: `aux_conf_p50`, `aux_conf_std` (used by Kernel 1).
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Uses `sp4_histogram_p99.cuh` pattern for p50 (median, k=128).
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**Total new ISV slots: 9 in range [510..520).**
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### 4.6 Component 6 — Behavioral Test Suite
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7 synthetic-market tests on RTX 3050 Ti, gating L40S deployment.
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| Test | Synthetic market | Pass criterion | Proves |
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|---|---|---|---|
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| `sp20_pure_trend` | Strong monotonic trend, low noise | WR > 70%, PF > 2.0 | Model CAN learn directional skill when signal exists |
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| `sp20_pure_noise` | i.i.d. zero-mean returns | Hold% > 80%, trades < 50/run | Model doesn't gamble — Hold attractor correctly induced when no edge exists |
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| `sp20_confidence_correlation` | Mixed (50% trend + 50% noise bars) | corr(aux_conf, trade_WR) > 0.4 | Aux gate is functional |
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| `sp20_asymmetry_no_game` | Trending market, hard symmetric clamp | PF > 1.0 (small but positive) | WR optimization isn't an artifact of asymmetric clamp |
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| `sp20_n_step_distribution` | 3-bar trade, known close reward = +0.6, n_step = 3 | Each of 3 bars receives credit ≈ 0.2 | n-step distributor is correct |
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| `sp20_per_regime_wr` | T/R/V segments (40%/40%/20%) | WR_T > 65%, WR_R > 50% with Hold%_R > 60%, WR_V > 50% with low trade count | Model behaviorally adapts to regime |
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| `sp20_regime_transition` | Abrupt trend → noise at bar 50 | Trade frequency drops 50% within 10 bars of transition | Model recognizes regime change |
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All reuse SP15 Phase 2A LobBar harness. ~250 LoC test code. Each runs <30s on RTX 3050 Ti.
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**Hard rule:** No L40S dispatch until all 7 behavioral tests pass on dev. Per cross-cutting SP15 discipline.
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## 5. Data flow (one full step)
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```
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[Forward pass]
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encoder → trunk → Q-heads, aux head
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aux_logits → softmax → aux_conf (read at every step into sp20_stats_compute)
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Thompson selector → action a_t
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[Step]
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trade_physics(a_t) → step_return, is_close, position state
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per-bar reward path:
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if action == Hold: R_per_bar = -aux_conf × cost_scale (Component 2)
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else: R_per_bar = 0
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hold_baseline_buffer[t] = -aux_conf × cost_scale (always, for Component 1)
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[Trade close detected]
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R_event = 4-quadrant reward (Component 1)
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hold_baseline = sum(hold_baseline_buffer over trade duration)
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alpha = R_event - hold_baseline
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R_used = alpha - alpha_ema
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n_step_credit_kernel: distribute R_used over trade_duration bars (Component 3)
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Update wr_ema, trade_duration_ema, alpha_ema (Kernel 1)
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[Replay sample]
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Read state, action, distributed_per_bar_credit, aux_conf_at_state
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Q_target = gate(aux_conf, threshold) × Q_full + (1 - gate) × Q_baseline (Component 4)
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Loss, backprop, Adam update
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[Per epoch]
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Recompute controllers (Kernel 2)
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||||
Update aux_conf_p50/std (Kernel 3)
|
||||
```
|
||||
|
||||
## 6. Error handling philosophy
|
||||
|
||||
Per CLAUDE.md: trust internal contracts. No defensive validation between trusted modules. Validate only at system boundaries (data input, action emission). Use `feedback_no_stubs`: every kernel wired for real or deleted. Edge cases surfaced during design that turn out to be subsumed by natural controller dynamics are **not** explicitly handled (per `feedback_no_quickfixes`).
|
||||
|
||||
Verified-natural cases (no explicit handling needed):
|
||||
- Aux warmup → controller produces low cost_scale automatically
|
||||
- Aux anti-correlated (< 0.50 dir_acc) → threshold clamp produces gate ≈ 1
|
||||
- First observation → universal Pearl A bootstrap pattern
|
||||
- Aux NaN → indicates training crash, separate concern
|
||||
|
||||
## 7. Testing strategy
|
||||
|
||||
4-tier gate, in order. Each tier blocks the next:
|
||||
|
||||
1. **Per-kernel unit tests** (RTX 3050 Ti, ~50 tests)
|
||||
- Each kernel: oracle test, sentinel-bootstrap test, ISV signal independence test, reset behavior test
|
||||
2. **Component integration tests** (RTX 3050 Ti, ~20 tests)
|
||||
- Components 1+2 coupling, 1+3 n-step distribution, 4+aux signal flow
|
||||
3. **Behavioral synthetic tests** (RTX 3050 Ti, 7 tests from §4.6)
|
||||
4. **L40S integration smoke** (5-epoch × 2-fold) — ~75 min, reads HEALTH_DIAG behavioral metrics
|
||||
5. **Full validation** (L40S 50-epoch × 3-seed × 3-fold)
|
||||
|
||||
Anomaly at any tier: stop, root-cause, re-run from current tier. Per `feedback_kill_runs_on_anomaly_quickly`.
|
||||
|
||||
## 8. Implementation footprint
|
||||
|
||||
Approximate sizing per component (atomic ship per `feedback_no_partial_refactor`):
|
||||
|
||||
| Component | Files modified | New files | Approx LoC |
|
||||
|---|---|---|---|
|
||||
| 1: Reward kernel | `experience_kernels.cu`, `gpu_experience_collector.rs` | — | 100 |
|
||||
| 2: Hold opp-cost | `experience_kernels.cu` | `hold_cost_scale_compute_kernel.cu` | 70 |
|
||||
| 3: n-step distributor | `gpu_experience_collector.rs` | `n_step_credit_kernel.cu` | 110 |
|
||||
| 4: Aux→Q gate | `gpu_dqn_trainer.rs` | — | 40 |
|
||||
| 5: Adaptive curriculum | `sp14_isv_slots.rs`, `mod.rs`, `build.rs` | `sp20_emas_compute_kernel.cu`, `sp20_controllers_compute_kernel.cu`, `sp20_stats_compute_kernel.cu` | 280 |
|
||||
| 6: Behavioral tests | — | `sp19_20_behavioral_tests.rs` | 250 |
|
||||
| Per-component unit tests | — | various | 600 |
|
||||
| Audit doc + memory pearls | `audit/sp20_wireup.md` (new) | — | 100 |
|
||||
| **Total** | | | **~1,550** |
|
||||
|
||||
Comparable in scope to SP18 (~2,500 LoC). Smaller than SP15 (~5,000 LoC).
|
||||
|
||||
## 9. Cross-references
|
||||
|
||||
- `pearl_event_driven_reward_density_alignment` — design principle for §4.1, §4.3
|
||||
- `pearl_audit_unboundedness_for_implicit_asymmetry` — design principle for asymmetric clamp in §4.1
|
||||
- `pearl_one_unbounded_signal_per_reward` — informs that asym_ratio is the only unbounded multiplicand
|
||||
- `pearl_separate_aux_trunk_when_shared_starves` — §4.4 leverages the separate aux trunk (SP14-C)
|
||||
- `pearl_no_deferrals_for_complementary_fixes` — combining SP19+SP20 + SP18 B-leg into one spec
|
||||
- `pearl_controller_anchors_isv_driven` — every adaptive parameter in §4.5
|
||||
- `pearl_first_observation_bootstrap` — sentinel = 0, replace on first observation for all EMAs
|
||||
- `pearl_wiener_alpha_floor_for_nonstationary` — α floor 0.4 for control loops
|
||||
- `feedback_no_partial_refactor` — atomic ship across all 6 components
|
||||
- `feedback_isv_for_adaptive_bounds` — every adaptive bound in ISV
|
||||
- `feedback_no_atomicadd` — sp4_histogram_p99 pattern for p50
|
||||
- `feedback_no_stubs` — every kernel wired or deleted
|
||||
- `feedback_kill_runs_on_anomaly_quickly` — tier gates
|
||||
- `project_metric_pipeline_inflation_audit` — context establishing Sharpe is honest, WR is real
|
||||
- `project_goal_wr_55_pf_2` — the goal this spec implements
|
||||
- `project_label_horizon_is_bottleneck` — the now-partially-invalidated claim that triggered SP19
|
||||
|
||||
## 10. Success criteria
|
||||
|
||||
A successful SP20 final L40S run (50-epoch × 3-seed × 3-fold) produces:
|
||||
|
||||
- **WR ≥ 55%** (mean across folds), with no fold below 52%
|
||||
- **Textbook PF ≥ 2.0** (mean across folds), with no fold below 1.5
|
||||
- **Per-regime WR_T ≥ 60%, WR_R ≥ 52%, WR_V ≥ 52%** (no regime degenerate)
|
||||
- **Sharpe still positive** (don't care about magnitude — WR/PF are the goals)
|
||||
- **Hold% in [20%, 50%] across training** (not Hold-everywhere, not over-trading)
|
||||
- **Behavioral tests stable** (all 7 pass at every checkpoint, not just at start)
|
||||
|
||||
A failure mode: WR plateaus at 46% but Hold% drops to 30% (model trades more without learning direction). This indicates aux gating is firing too freely; fix by tightening `AUX_CONF_THRESHOLD` floor.
|
||||
|
||||
A success-with-caveat: WR=55% but PF=1.6. Means model has directional skill but exit timing isn't aligned with directional duration. Acceptable as a milestone; follow-up work focuses on exit heuristics.
|
||||
|
||||
A failure that demands stop-the-presses: WR stays at 46% across all 3 seeds × 3 folds. This invalidates the hypothesis that training-side leverage is exhausted only because of reward shape — implies bottleneck is genuinely upstream (data composition, instrument selection). Trigger pivot to multi-instrument joint training per `project_label_horizon_is_bottleneck` action list.
|
||||
Reference in New Issue
Block a user