c51_loss and mse_loss kernels use NUM_ATOMS for loop bounds AND shared
memory. Precompiling with NUM_ATOMS=51 then running with num_atoms=101
causes wrong loop iterations. These 2 kernels MUST use runtime #define
injection to match the hyperopt config.
35 of 37 kernels remain precompiled. Only c51_loss and mse_loss use
runtime NVRTC (they're the only kernels where a #define controls logic).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Same class of bug as IQN_HIDDEN: compile-time array float local_proj[51]
overflows when hyperopt picks num_atoms=101. Crashed at ~35 min when
PSO explored larger atom counts.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause (found via Zen debug + Gemini 2.5 Pro):
IQN_DIST_MAX = (IQN_HIDDEN + 31) / 32 = 8 (from IQN_HIDDEN=256).
Register arrays h_dist[8], embed_dist[8], comb_dist[8] overflow when
hyperopt picks hidden_dim > 256. With hidden_dim=512: loops iterate
16 times on 8-element arrays → stack corruption → SIGSEGV.
Fix: IQN_HIDDEN=2048 → IQN_DIST_MAX=64. Runtime loops still use
actual hidden_dim param. Only register array sizing affected — no
perf impact (unused elements are never accessed).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
IQN_EMBED_DIM (cosine embedding dimension, default 64) converted from
compile-time #define to runtime kernel parameter `embed_dim`. This was
the last remaining hardcoded dimension in the precompiled IQN cubin
that could diverge from GpuIqnConfig.embed_dim at runtime, causing
SIGSEGV from out-of-bounds weight offset computation.
All 9 IQN kernels (forward_loss, backward, grad_norm, adam, forward,
trunk_forward, ema, decode_actions, sample_taus) now receive embed_dim
as the final parameter. The iqn_compute_offsets() device helper uses
the runtime embed_dim for W_EMBED→B_EMBED offset calculation.
The other 7 kernel files in the task spec were verified safe:
- c51/mse_loss_kernel.cu: NVRTC-compiled with config values injected
- attention/attention_backward: already parameterized (state_dim, num_heads)
- dt_kernels.cu: NVRTC-compiled with config values injected
- curiosity/ppo: architecturally constant values matching Rust constants
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Build-time #define was 256 but runtime config can be 128 (hidden_dim_base).
Caused CUDA_ERROR_ILLEGAL_ADDRESS in IQN trunk gradient. Now passed as
kernel params matching the STATE_DIM parameterization pattern.
Also: warn→error for training failures in optimizer, eprintln for debugging.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Build-time #define was 256 but runtime config can be 128 (hidden_dim_base).
Caused CUDA_ERROR_ILLEGAL_ADDRESS in IQN trunk gradient. Now passed as
kernel params — matches the pattern used for STATE_DIM parameterization.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
increment_trial() was never called — budget enforcement was broken because
TrialBudgetObserver had its own internal Arc<AtomicUsize> disconnected from
the trial_counter used by evaluate_point() and cost(). With num_trials=2,
PSO ran 20 particle evaluations instead of stopping at 2.
Now TrialBudgetObserver::new() takes the caller-owned Arc<AtomicUsize> so
all evaluation paths (LHS via evaluate_point + PSO via cost()) share one
counter. ObjectiveFunction::cost() and ParallelObjectiveFunction::cost()
increment trial_counter directly (fetch_add SeqCst) and check budget inline;
observe_iter() reads the same counter to terminate PSO between iterations.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Backtest portfolio now tracks hold_time at index [5]. min_hold_bars
param enforces hold constraint during evaluation, matching training.
Fixes train/eval mismatch that caused NaN Sharpe → 1e6 penalty.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The kernel was updated with portfolio_states, min_hold_bars, max_position
params (Task 3 of trade lifecycle fix) but the backtest evaluator's launch
site wasn't updated. It passed 10 args to a 13-param kernel, causing
SIGSEGV from reading garbage for the missing params.
Fix: pass NULL/0/0.0 for the 3 new params (hold enforcement disabled
during evaluation for now — will be enabled in eval cleanup plan).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Extend build.rs to compile all 25 .cu kernel files to cubins via nvcc.
Common header prepended to 24 kernels; experience_kernels.cu is standalone.
Total cubin size ~1.1 MB embedded in binary.
- cargo:rerun-if-changed tracks all .cu and .cuh files
- CUDA_COMPUTE_CAP env var selects target arch (sm_86, sm_90)
- Graceful skip when nvcc unavailable (non-CUDA builds)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The reversal P&L block (Kelly stats, entry_price reset) ran BEFORE the
hold enforcement guard. If hold_time < min_hold_bars cancelled a reversal,
the P&L was already booked for a trade that didn't execute, corrupting
portfolio_states. This caused SIGSEGV in the hyperopt path where the
corrupted state triggered invalid memory access in subsequent kernels.
Fix: moved the reversal P&L block from line 751 to after line 862
(after hold enforcement resolves final reversing_trade value).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Trade count dropped from 77% turnover (620K/800K) to 16.5% (527/3200)
with min_hold_bars=3. The hold enforcement is working correctly in the
standard training path. SIGSEGV in hyperopt path is a separate issue
(likely buffer sizing in the hyperopt adapter's experience collector).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
With v_range ±240 (vs old ±2), C51 distributional loss is naturally
~100-400 instead of ~3-6. Updated smoke test assertion accordingly.
Trade lifecycle fix validated: 467 trades (was 620K) with min_hold_bars=3.
PF=1.87, WinRate=49.7%, MaxDD=41.7% — model learns multi-bar swing trading.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add build.rs that compiles .cu kernels to cubins at cargo build time via nvcc.
Replace OnceLock<Ptx> + compile_ptx_for_device() in gpu_action_selector.rs with
include_bytes! + Ptx::from_binary() loading from the precompiled cubin.
- build.rs gracefully skips when nvcc is not available (non-CUDA builds)
- CUDA_COMPUTE_CAP env var controls target GPU arch (86=RTX 3050, 90=H100)
- common_device_functions.cuh prepended to kernel sources at build time
- Zero runtime compiler invocations for epsilon_greedy kernel
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
7 tasks: build.rs POC → parameterize #define → update launches → build all
→ replace runtime compilation → delete NVRTC infra → integration test.
Key insight: convert #define STATE_DIM/NUM_ATOMS to kernel params so each
kernel has ONE version regardless of model config. No multi-variant compilation.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Integer parameter (rounded from continuous). Lets PSO discover
optimal hold duration for ES futures regime characteristics.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
During min_hold period, forces exposure to current position index.
Covers both greedy (Q-value) and random (epsilon) paths.
Portfolio state already GPU-resident — zero-copy parameter pass.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add min_hold_bars to DQNHyperparameters (usize, default 5), ExperienceSection
in training_profile, and both TOML configs (smoketest=3, production=5).
Wired through apply_to() so TOML overrides land correctly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Fixed struct name: ExperienceCollectorConfig (not GpuExperienceCollectorConfig)
- Fixed max_pos scoping: moved declaration before action_select launch
- Fixed exiting_trade ordering: preliminary variable before trailing stop
- Documented hardcoded 5-action limitation in branching_action_select
- Zero Q-gap during hold periods (conviction meaningless when forced)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Layer 1 now targets experience_action_select (GPU-fused training path)
AND branching_action_select (backtest/fallback), not just the fallback
- Action masking covers both greedy AND random exploration paths
- Exposure index uses parameterized b0_size, not hardcoded 5 or 9
- Fixed end-of-episode variable name (total_bars, not timesteps_per_episode)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Added BARS_PER_DAY, BARS_PER_YEAR, ANNUALIZATION_FACTOR to common::thresholds::time
- Added bars_per_day field to DQNHyperparameters (default 390.0, configurable)
- compute_epoch_financials() now takes bars_per_day parameter from hyperparams
- coordinator_extended.rs + ab_testing.rs use centralized ANNUALIZATION_FACTOR
- When switching to tick data, change one constant in common/thresholds.rs
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Compounding per-bar returns over 10K+ bars still produces extreme numbers
(+1.8M% return, 2687% MaxDD). Replaced with:
- Return: mean_per_bar × bars_per_year (annualized, no compounding)
- MaxDD: windowed over last 10K bars AND capped at 1.0 (100%)
These are monitoring metrics — the hyperopt objective uses the backtest
evaluator which already has correct windowed metrics.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The feature cache auto-invalidates via content hash — manual rm -f
wasted ~2 minutes of MBP-10 + OFI recomputation per trial start.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Training epoch metrics compound step_returns multiplicatively. With 2M+
bars per epoch on H100, this produces Return=+2.1e24% and MaxDD=99.5% —
meaningless numbers that corrupt risk monitoring and early stopping.
Fixed: total_return and max_drawdown now use only the last 10K bars
(~25 trading days), matching the backtest evaluator's window cap.
Sharpe/Sortino are unaffected (use arithmetic mean/std, not compounding).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
dqn-smoke: Walk-forward validation DQN uses raw price returns (~0.001),
not production reward_scale=10. Set v_min/v_max to ±10 for the small
16-dim 3-action test network (was inheriting ±240 from production default).
dqn-early-stop: Gradient collapse threshold = lr × multiplier must exceed
the actual gradient norm to trigger collapse. With v_range ±240, gradient
norms reach 100-10000 (was ~0.5-2.0 with old ±2.0 range). Updated
multiplier from 1e9 to 1e12 to guarantee threshold (10000) > grad norm.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Noisy nets are now always on (mandatory feature since config unification).
Epsilon start always uses the noisy-nets-aware default (0.05).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- dqn_action_collapse_fix_test: search space grew from 39D to 45D
(added c51_warmup, her_ratio, curiosity, cvar, dt_pretrain). Updated
assertions to use >= 39 and dynamic index lookup for cql_alpha.
- training_stability: Q-value assertion widened from 2.5 to 300.0
to match v_range ±240 (computed from reward_scale=10, gamma=0.95).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds mbp10_data_dir and trades_data_dir to CampaignConfig, passed to
DQNTrainer via with_ofi_data_dirs(). Enables OFI features (VPIN,
Kyle's Lambda, etc.) in hyperopt campaigns. dqn_full() now defaults
to 50 epochs/trial for baseline runs.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: backtest windows of 300K bars produced ±billions% returns via
multiplicative compounding, and sqrt(252) annualization was wrong for
1-minute bars.
Fixes:
- Window size capped to 10K bars (~25 trading days), evenly distributed
across the full validation set (was clustered in first 6%)
- Annualization: configurable bars_per_day field in GpuBacktestConfig
(default 390.0 for 1-min), produces sqrt(98280) ≈ 313.5
- tanh normalization recalibrated: Sharpe/5, Sortino/8 (was /2, /3)
- CVaR threshold scaled to per-bar: 0.003 with slope 1400 (was 0.05/200)
- VaR/CVaR strided sampling covers full window (was first 4096 only)
- financials.rs + ab_testing.rs: sqrt(252) → sqrt(98280) for consistency
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Hybrid approach: each of 256 threads exports boundary metadata (first/last
action, prefix/suffix return, interior trade count). Thread 0 stitches
boundaries in O(256) to produce exact trade count and win rate.
Fixes: trades spanning chunk boundaries were fragmented (returns lost,
counts incorrect). Now every trade's cumulative return is tracked exactly
regardless of which thread chunks it spans.
7 boundary values stored in s_sorted[stride..8*stride] — zero extra
shared memory (reuses sort scratch between equity reduction and bitonic sort).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
s_max_dd, s_trades, s_wins parallel reductions were made redundant by
the exact sequential drawdown scan and upcoming boundary stitching.
Reduces shared memory from 25KB to 22KB and removes 3 dead ops/bar
from the per-thread loop. Trade count/win rate temporarily zeroed —
restored by boundary stitching in next commit.
The stress tester creates a SECOND DQNTrainer for validation, which
OOMs on 4GB GPUs. Changed from fatal error (?) to warning + skip.
Hyperopt now successfully trains on RTX 3050 — 2 trials × 10 epochs
completed in 19 minutes. Backtest metrics still show extreme returns
(multiplicative compounding over 895K bars) — needs separate fix.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The DQNTrainer adapter in hyperopt silently returns penalty (1000000)
because build_hyperparams() fails with the cleaned config struct.
Needs: update DQNParams→DQNHyperparameters mapping for removed use_
booleans and new reward_scale field.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add 7 new fields to DQNHyperparameters (fill_ioc_fill_prob,
fill_limit_fill_min, fill_limit_fill_max, fill_spread_cost_frac,
fill_spread_capture_frac, q_clip_min, q_clip_max) and wire them
through training_profile.rs into ExperienceCollectorConfig construction
in training_loop.rs. Previously these 7 values were hardcoded at the
construction site; now they flow from TOML [experience.fill_simulation]
and [risk] sections. Default values match the prior hardcoded constants
so existing behavior is unchanged.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove use_double_dqn, use_dueling, use_per, use_branching,
use_distributional, use_noisy_nets, use_huber_loss, and use_cql
from DQNConfig, DQNHyperparameters, and DqnParams structs.
These features are always enabled (Rainbow DQN standard). The boolean
flags were dead code — every constructor set them to true, and the
only code paths that set them to false were in tests that disabled
features for simplicity. With the fields removed, the features are
unconditionally active, eliminating ~490 lines of dead configuration.
Key changes:
- Struct field declarations removed from 3 core config structs
- Conditional branches (if use_X { ... } else { ... }) simplified:
dueling/branching/PER network creation is now unconditional
- Checkpoint metadata hardcodes "true" for backward compatibility
- Hyperopt search space index 11 (use_branching) fixed at 1.0
- TOML/YAML config files cleaned of removed fields
- Tests that toggled these flags updated or rewritten
45 files changed, -487 net lines. Zero new test failures.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
