Removed the legacy non-branching Sequential q_network and target_network
from DQNAgent. These were never used (branching+dueling always active)
but allocated VRAM and ran noise resets every step. -190 lines.
Config tuning for dense micro-reward system:
- n_steps: 5→1 (TD(0), micro-rewards cancel over n>1)
- tau: 0.007→0.01 (faster target tracking for TD(0))
- c51_alpha_max: 0.5→1.0 (full C51, PopArt handles normalization)
- curiosity_weight: 0.1→0.0 (dense micro-reward replaces curiosity)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
target_dim expansion: adds raw_open (OHLCV) and mid_price_open
(MBP-10 midpoint at bar formation) to fxcache targets. FXCACHE_VERSION
2→3 for auto-rebuild. Legacy v2 files handled with close-price fallback.
Spec v5 adds 3 pearls:
- Bar duration encoding in Mamba2 (continuous-time SSM awareness)
- Order book center of mass from all 10 MBP-10 levels (aggression signal)
- Retrospective hold quality bonus (teaches exit timing)
Plus: Hold action (4th direction), DSR Sharpe EMA fix, counterfactual
magnitude/order sign fix, MFT mid-price mark-to-market.
OFI embed MLP now 18→10 (was 16→8). Mamba2 width SH2+10 (was SH2+8).
Attention width D+10 (was D+8).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
target_dim 4→6: adds raw_open (OHLCV) and mid_price_open (MBP-10
best bid/ask midpoint at bar formation) to the targets buffer.
Micro-reward now uses real intra-bar move (close - open) / atr
instead of close-to-close approximation. Also adds spread cost
awareness via |open - mid| penalty. Requires fxcache recomputation.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Major changes from v1:
- Fix OFI data pipeline (ofi_gpu never wired, indices 42→66)
- Expand PORTFOLIO_STRIDE 30→38 for prev-OFI storage
- Add Mamba2 d_h_history backward (2 new cuBLAS GEMMs)
- Expose attention d_input_scratch for gradient flow
- Pre-compute OFI deltas in experience collection (state[74..82])
- Lower rank normalization threshold to 1e-5 for micro-rewards
- Use close-to-close return instead of unavailable open_price
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Dense per-bar reward using order flow momentum × price confirmation.
OFI embedding MLP (16→8 via cuBLAS) feeds into Mamba2 history AND
attention input. Replaces sparse exit-only reward with continuous
temporal signal for the SSM and attention heads to learn from.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Single cuGraphExecLaunch per step. PER sampling, gather, training,
priority update ALL as child graph nodes in one parent. Direct-to-trainer
gather eliminates DtoD copies. GPU-side counters eliminate host writes.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: CUfunction sharing between graphed children and ungraphed
outside-graph ops corrupts kernel state on Hopper → 3100ms adam replay.
Fix: capture EVERYTHING unconditionally in child graphs. Selectivity,
denoise, causal intervention, vaccine, Q-stats all become graphed children.
Zero ungraphed launches = zero CUfunction conflicts.
7 children, ~190 kernel nodes, one capture, one replay per step.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Phase 1 results: 4.0s/step GPU compute (graph replay = ungraphed, cuBLAS
replaced manual matmul but feature count increased). CPU pipeline fully
async (0.1ms/step). Total ~718s/epoch — 100% GPU bound.
Updated Phase 2 targets: aux parallelism (2b) is highest priority —
80/177 kernels with 4 independent trainers. nsys profiling should run
first to validate SM vs memory-bound assumptions.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Inventory penalty makes flat optimal — model learns to do nothing.
Instead: penalize inaction proportional to model's own predicted edge
(Q-value spread). Creates virtuous cycle: better temporal attention →
higher self-imposed penalty for missed trades → more trading on signal.
No hindsight bias (uses predicted edge, not actual price change).
Micro-reward (already exists) rewards correct positioning.
Churn penalty (new) prevents rapid flips.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
forward_child: 4 branch streams fork after h_s2, join before loss
aux_child: IQL/IQN/attention on parallel streams
Uses CU_STREAM_CAPTURE_MODE_RELAXED with fork-join events as graph edges
Existing branch_streams[4] + events in batched_forward.rs ready to activate
Target: <40s epochs (from <80s Phase 1)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Portfolio slots ps[15]-ps[19] are Kelly accumulators (active).
Plan params moved to ps[23]-ps[29]. PORTFOLIO_STRIDE grows 23→30.
Fixed asymmetry: scales profit_target only (not stop_loss).
Clarified epsilon mid-plan: overridden by direction lock (intended).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Components were fighting a broken signal, not broken themselves.
With correct signal: adapt DSR to trade-level, rank only non-zero
rewards, keep commitment as soft signal, let E1 enrichment handle
Q-drift instead of hardcoded kernel penalty.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: training environment differs from validation backtest in 3 ways:
1. 100-bar episodes force exits (val runs continuous) → position-gated done
2. Rank normalization destroys sparse trade-level reward → remove it
3. Different Sharpe computation (per-trade vs per-bar, different annualization)
Single execution path:
Phase 1: Episode alignment (100→5000 bars, position-gated done, soft reset)
Phase 2: Reward alignment (remove rank norm, raw trade P&L to replay)
Phase 3: Metrics alignment (un-annualized per-trade Sharpe, both paths)
Expected: training Sharpe within 2× of validation with same weights.
Removes 5 unnecessary shaping components that fought the broken signal.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Every epoch's validation backtest feeds back into the next epoch:
E1: Q-value reality check (bias correction, replaces drift penalty)
E2: Adaptive epsilon (performance-driven, replaces schedule)
E3: Dynamic gamma (from trade duration, replaces manual annealing)
E4: Trade autopsy (per-branch LR scaling from error rates)
E5: Ensemble agreement tuning (auto-tune epistemic gate)
E6: Winner distillation (boost top 10% trades in replay)
E7: Hindsight optimal labels (correct actions for losers)
E8: Curriculum weights (oversample failure regimes)
E9: State confidence (tradability scores from eval)
Zero hardcoded schedules. The model adapts from its own performance.
~215 lines Rust, no new CUDA kernels. ~15s overhead per epoch.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two fundamental fixes for training Sharpe breakthrough:
1. Trade-level rewards: replace per-bar noise (SNR=0.01) with trade
P&L attribution (SNR=0.1-0.5). C51 atoms model trade outcome
distributions, not random walk noise.
2. Exploration risk budget: protection stack (CVaR, epistemic gate,
commitment, DSR) scaled by iqn_readiness². Loose during exploration,
tight when converged. Model can discover edges before being punished.
Also: homeostatic regularization spec (unified adaptive penalties).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Primary goal: val/OOS Sharpe gap < 15%. If val_Sharpe drops to 25
post-generalization, OOS should be > 21. If val drops to 15, OOS > 13.
OOS Sharpe > 10 sustained as secondary target.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Walk-forward: strictly chronological, no future leakage.
Weight decay mask: indices 0-7 (trunk + value head), not just trunk.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
