ROOT CAUSE 1: Stop-loss 0.3%→1%, take-profit 0.5%→2% (2:1 R:R).
Old 0.3% = 8.3 ticks on ES. Normal 1-min noise is 4-6 ticks.
99.88% of trades were stopped out by NOISE, not by bad entries.
ROOT CAUSE 2: Dense shaping 0.1x→0.01x. Over 50 bars, old dense
signal = 5.0 vs completion ±2.0 — dense dominated. Now dense = 0.5
vs completion ±2.0 — trade completion is the primary signal.
ROOT CAUSE 3: Action aliasing in 5-bar hold override. When model
chose Flat but was forced to Hold, replay stored (state, Flat, Hold's
reward) — corrupting Q-values for Flat. Now overwrites out_actions
with the ACTUAL held exposure action.
ROOT CAUSE 4: Hyperopt HFT activity weight 25%→5%. Old objective
penalized selective trading. MIN_VIABLE_TRADES 100→20.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
GPU experience collection is ALWAYS active in CUDA builds. The boolean
toggle was dead code — no CPU fallback exists. Removed from config,
hyperopt adapter, constructor, smoke tests, and training loop guard.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
IQN train_iqn_step_gpu() was doing a synchronous DtoH readback of the
loss scalar EVERY training step. This serializes the GPU pipeline.
Fix: return 0.0 placeholder from train step. Actual loss available via
read_loss() method — call only at epoch boundaries for logging.
Remaining readbacks (all once-per-epoch, acceptable):
- epoch_state: 32 bytes (DSR monitoring)
- q_stats: 20 bytes (Q-value diagnostics)
- gradient accum: dead code path (fused CUDA always active)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The compute_cvar_scales() was doing GPU→CPU→sort→CPU→GPU for quantile
CVaR computation. With batch_size=307 × 32 quantiles × 15 actions,
this was ~150KB of DtoH + sort + HtoD per epoch — likely the source
of the 10s/epoch overhead vs 3s baseline.
New inline CUDA kernel (iqn_cvar_kernel):
- One thread per sample (256 threads/block)
- Insertion sort of alpha_count smallest quantiles in registers
- Fast path for alpha_count=1 (just find minimum)
- Zero CPU readback, zero CPU allocation
Compiled once via OnceLock, cached for all subsequent calls.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Full Kelly implementation in CUDA kernel, no CPU path:
Enhanced Kelly = 0.7 × continuous (μ/σ²) + 0.3 × discrete ((bp-q)/b)
× confidence scaling (1 - 1/√n_trades)
× half-Kelly (0.5)
Clamped to [0.05, 0.25], normalized to position scale.
Tracks 6 statistics in portfolio state (PORTFOLIO_STRIDE 18→20):
ps[14] win_count, ps[15] loss_count, ps[16] sum_wins,
ps[17] sum_losses, ps[18] sum_returns, ps[19] sum_sq_returns
Removed CPU kelly_scale parameter — was a shortcut that violated
the zero-CPU-in-hot-path principle. All Kelly computation now
happens per-step in the env_step kernel from accumulated trade
statistics. Activates after ≥20 trade completions.
Also noted: IQN CVaR compute_cvar_scales() still does a CPU readback
for sorting quantiles. Should be a GPU kernel in next iteration.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fixed TP/SL produced bimodal reward (all trades hit exactly -0.3% or
+0.5%), making the reward distribution too deterministic. Model learned
"all actions produce the same bounded return" → Q-values converged.
Fix: stops scale with Q-gap conviction (0.5x to 2.0x of base levels):
- High conviction (Q-gap=2.0): SL=-0.6%, TP=+1.0% (let it run)
- Low conviction (Q-gap=0.5): SL=-0.15%, TP=+0.25% (quick exit)
- Time stop also scales: 50-200 bars based on conviction
This makes the reward distribution CONTINUOUS, not bimodal. High-conviction
entries with wider stops produce different returns than low-conviction entries
with tight stops → the model can learn that conviction matters.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
GpuBacktestEvaluator, GpuDqnTrainer, and hyperopt adapter had hardcoded
branch_0_size=5. Training produced 9-action indices (0-8) but backtest
only allocated 5-action buffers → SIGSEGV (exit 139) during walk-forward
evaluation after Trial 0 training.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three critical fixes from H100 Trial 0 analysis:
1. REMOVE IDLE PENALTY — was making Flat the worst Q-value (Q=-19.4),
forcing the model to trade on 95% of bars. Now Flat = reward 0.0.
DSR already penalizes inaction through the Sharpe denominator.
2. TRADE COMPLETION SCALING — ×1000 clamped to ±1 made ALL trades look
the same (can't distinguish 1-tick from 10-tick return). Now ×200
with clamp ±2, plus sqrt(hold_time) bonus for holding longer.
3. DYNAMIC TRADE MANAGEMENT — model learns ENTRY quality, exits are
managed by the system:
- Stop loss: -0.3% × vol_mult (CUSUM-adaptive, up to -0.75%)
- Take profit: +0.5% × vol_mult × trend_mult (up to +2.5% in trends)
- Time stop: 100 bars × vol_mult (up to 250 in volatile markets)
- Min hold: 5 bars before model can exit voluntarily
- Auto-exit forces flat and computes trade_completion_reward
Regime-adaptive via ADX (trend strength) and CUSUM (volatility)
features already in the GPU memory.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
CRITICAL: The DQN constructor had hardcoded state_dim=45/53 (old PORTFOLIO_DIM=3).
With PORTFOLIO_DIM=8, raw state_dim is 50 (no OFI) or 58 (with OFI).
The network was sized for 56 dimensions but OFI features (8 dims) were uploaded
and ignored because state_dim didn't include them.
Also fixed:
- constructor.rs: num_actions 5→9
- All GPU trainer/head defaults: state_dim 48→56
- metrics.rs: state_dim calculation
- mod.rs test: state_dim assertions
- Argo template reapplied for stale cache clearing
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
CRITICAL: gpu_monitoring.rs had [usize; 5] for action_counts but kernel
writes 9 exposure levels → memory corruption. Fixed array, buffer (12→16),
readback size, and the CUDA monitoring_reduce kernel (s_counts[5]→[9],
summary offsets updated). Also fixed remaining 45→81 references in
metrics.rs, training_loop.rs, monitoring.rs, config.rs comments.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Complete dual distributional pipeline:
1. After training: fused_ctx.compute_cvar_device_ptr() runs IQN forward
2. CVaR at α=5% computed per sample → [0.25, 1.0] position scaling
3. Device pointer set on collector via set_cvar_scales()
4. Next epoch: env_step kernel applies target_position *= cvar_scales[i]
C51 picks direction (argmax expected Q), IQN sizes risk (CVaR tail).
High tail risk → smaller position. Safe distribution → full size.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- experience_env_step kernel: new cvar_scales parameter (NULL = no scaling)
- target_position *= cvar_scales[i] when buffer is non-NULL
- GpuExperienceCollector: cvar_scales_ptr field + set_cvar_scales() setter
- Default: NULL (0) = no scaling until IQN CVaR is wired from training loop
The GpuIqnHead.compute_cvar_scales() produces the buffer, the collector
passes it to the kernel. Full wiring through training_loop.rs is the
remaining step — needs the collector to receive the device pointer from
the fused training context after each IQN training step.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New method on GpuIqnHead: samples τ values, runs forward-only kernel,
computes CVaR at α=5% per sample, returns [0.25, 1.0] scaling factors.
CVaR ≥ 0 (safe) → full position. CVaR < 0 (risky) → reduced position.
Next: wire into env_step kernel for position scaling during experience
collection, and into fused_training.rs to call after IQN training step.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
use_qr_dqn was a hacky toggle that gated the IQN dual-head behind
a num_atoms threshold. IQN is now always enabled when iqn_lambda > 0
(default 0.25). C51 remains the main loss; IQN is the auxiliary head
for CVaR risk quantification.
Removed use_qr_dqn from: DQNParams, DQNHyperparameters, fused_training,
hyperopt adapter, all tests, all examples.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Updated all fixed-size arrays across monitoring.rs, financials.rs,
metrics.rs, training_loop.rs from [_;5]→[_;9] and [_;45]→[_;81].
DIRECTION_LUT expanded to 9 levels (-1.0 to +1.0 in 0.25 steps).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
RTH/ETH cost differential, market impact (done), book depth fill quality,
macro events, weekend gap risk, margin utilization feature.
All configurable via TOML. We trade against real markets — simulation must match.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1-lot fills at bid/ask. 4-lot moves the market.
impact_scale = 1 + (|delta|/max_position)^2, ranges [1.0, 2.0].
Teaches the model that max-size positions are 2x more expensive to enter.
Makes the 9-exposure granularity meaningful — 25% positions are cheaper
to enter/exit than 100% positions.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
PDT $25K rule protection: if equity drops below 75% of peak_equity,
the episode terminates (done=1) with a -1.0 penalty reward and forced
flat. The model learns that approaching the floor = game over.
Uses peak_equity (not initial_capital) so it adapts as account grows.
With $35K initial, floor triggers at ~$26.25K — above the $25K minimum.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The model thinks in TRADES, not in bars. Per-bar 1-minute noise is like
flipping a coin — the real signal is the full trade return (entry→exit).
Three reward levels:
1. TRADE EXIT (sparse, strong): full trade return entry→exit, the PRIMARY
learning signal. 10x stronger than dense shaping.
2. IN TRADE (dense, weak): 0.1x DSR + PnL shaping per bar. Just enough
gradient for direction, doesn't overwhelm the completion signal.
3. FLAT (near-zero): doing nothing is almost free.
New portfolio state fields (PORTFOLIO_STRIDE 12→14):
- ps[12] = entry_price (price at trade entry)
- ps[13] = trade_start_pnl (cumulative PnL snapshot at trade entry)
Trade lifecycle detection: entering_trade / exiting_trade / in_trade
from position transitions (was_flat→positioned / positioned→is_flat).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
C51 for action selection (expected Q), IQN for risk quantification (CVaR).
Disagreement between C51 and IQN expected Q = uncertainty signal.
Architecture already 90% built — IQN head exists but output unused.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
With num_atoms=101 (just above the old threshold of 100), QR-DQN activated
alongside C51, doubling distributional computation. H100 epochs went from
~2s to ~35s. Raised threshold to 200 so num_atoms=101 uses C51 only.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- DSR warm-up: skip first 50 steps when EMA has insufficient history
- Phase Fast num_atoms: 51 → 101 (H100 can afford finer resolution,
1.19 per atom vs 2.35 — critical for distinguishing Q-values)
- Argo template: clear stale feature cache before hyperopt (ensures
fresh computation with VPIN/trades enrichment)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Bug hunt findings:
- epsilon_greedy_kernel.cu: all 3 kernels (select, routed, branching) lacked
the Q-gap conviction filter. Training learned with q_gap=0.1 but inference
paths bypassed it entirely. Now all action selection paths are consistent.
- c51_loss_kernel.cu: Bellman projection boundary fix — b_pos clamped away
from exact NUM_ATOMS-1 to prevent phantom upper==lower atom collapse.
- experience_kernels.cu: NaN/Inf guards on DSR and normalized PnL outputs.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
ROOT CAUSE: 5 interlocking bugs made learning impossible:
1. DSR denominator floor 1e-12 produced values in millions → drowned all signal
2. Global [-1,+1] clamp destroyed Bellman equation signal (can't distinguish
catastrophic loss from mild loss)
3. v_range=20 exactly equals V_max for gamma=0.95 → Bellman target pins at
ceiling → Q-values saturate → Q-gap collapses to 0.0000
4. num_atoms=11 over 40-unit range = 4.0 per atom (C51 paper min is 51)
5. 6/7 reward components were penalties → mean_reward=-0.311 regardless of action
FIXES:
- DSR denominator floor: 1e-12 → 0.01 (prevents million-scale spikes)
- Each component individually clamped BEFORE weighting (DSR to [-1,+1],
z-score to [-3,+3], drawdown to [0,1], time decay to [0,0.3])
- Removed global [-1,+1] clamp (no longer needed with bounded components)
- profit_take_bonus: 0.1 → 0.01 (was 100x too large, caused reward hacking)
- Removed confidence scaling (positive feedback loop destabilized learning)
- Removed regime scaling (non-stationary reward confused the model)
- Dynamic v_range from gamma: v_range = 2.5/(1-gamma)*1.2 (always covers Q range)
- num_atoms minimum: 11 → 51 (C51 paper standard)
- gamma default: 0.99 → 0.95
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Q-gap was 0.0 (disabled) meaning the model traded on every bar regardless
of conviction. With 0.1, the model must have Q(best) - Q(flat) > 0.1
before entering a position. Local test showed 21% fewer trades.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The DBN feature cache was keyed ONLY on OHLCV .dbn files. If a cache was
created without trades data (VPIN/Kyle's Lambda), subsequent runs with trades
would silently serve stale features from cache, dropping VPIN enrichment.
Now cache key hashes OHLCV + MBP-10 + trades dirs together. Adding or removing
data sources invalidates the cache correctly.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
max_batch_size() was expanding batch_size upper bound to 4096 (VRAM capacity),
overriding the TOML's configured [64, 512]. This caused all local hyperopt
trials to sample batch_size > 1024 and OOM on RTX 3050 (4GB).
Fix: TOML upper bound is the ceiling; VRAM sizing only REDUCES, never expands.
Added defensive clamp in from_continuous as a hard guard.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add q_gap_threshold to action selection kernel: when greedy Q(best) - Q(flat)
< threshold, default to flat. Teaches model to trade only with conviction.
39D search space (was 38D). Default 0.0 (disabled), hyperopt range [0.0, 0.5].
Remove use_branching parameter from experience_action_select — GPU pipeline
always uses branching DQN. Flat mode was dead code.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three new reward intelligence features, all zero-state GPU-native:
1. Spread-aware transaction costs: tx_cost scales by CUSUM volatility.
Trading in choppy markets costs more — teaches the model to reduce
frequency in volatile regimes. Real spread DOES widen with volatility.
2. Kelly-inspired confidence scaling: when realized_pnl > 0 (model has
been right), amplify PnL weight 1.5x. When losing, amplify drawdown
penalty 1.5x. Self-reinforcing: good decisions → stronger signal →
better Q-values. Bad decisions → defensive mode → more exploration.
3. Profit-taking bonus: +0.1 reward when model reduces a position toward
flat while cumulative episode PnL is positive. Explicitly rewards the
ACT of taking profit, not just being in a winner. Teaches the model
to lock in gains rather than riding them back to breakeven.
Total kernel additions: ~20 lines, ~10 FLOPs. Zero extra state beyond
what PORTFOLIO_STRIDE=12 already provides.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Time decay now only applies when position is underwater (raw_pnl < 0).
Profitable positions pay zero rent, encouraging the model to hold
winners. Losers accumulate time_decay_rate per step, compounding with
drawdown penalty to force quick exits.
"Let profits run, cut losses short" — the #1 rule in systematic trading.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Without clamping, DSR + drawdown penalty + idle penalty can produce
rewards of ±50 which exceed C51's atom support [-v_range, +v_range].
Values outside the support get clamped by C51, destroying the
distributional signal and causing train_loss to explode (94M-289M).
Clamping to [-1, +1] keeps all reward values within C51's representable
range, ensuring every atom contributes meaningful probability mass.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
6% drawdown tolerance too generous for HFT. Tightened search range
to 0.5%-3%, default 1%. Forces aggressive loss cutting.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Reward normalization and DSR computation have been moved to the GPU
experience-collection kernel (experience_kernels.cu). This removes the
now-dead CPU-side code:
- Remove RewardNormalizer struct (150 lines) — GPU pnl_ema/pnl_var replaces it
- Remove hold_reward, hold_penalty_weight, enable_normalization from RewardConfig
- Remove use_dsr toggle from RewardConfig and RewardConfigBuilder — DSR is always on
- Remove calculate_hold_reward() — inlined as Decimal::ZERO (GPU w_idle replaces it)
- Make RewardFunction.dsr non-optional (always enabled)
- Make training_loop.rs DSR sync + epoch reset unconditional
- Clean up constructor.rs RewardConfig construction (6 fewer fields)
- Mark DQNHyperparameters.use_dsr and hold_penalty_weight as deprecated
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Expand the DQN hyperopt parameter space from 31D to 38D by adding 7 GPU
composite reward weights (w_dsr, w_pnl, w_dd, w_idle, dd_threshold,
loss_aversion, time_decay_rate) at indices 31-37.
- Remove hold_penalty_weight from DQNParams (replaced by w_idle)
- Add #[serde(default)] for backward compat with old JSON results
- Phase Fast fixes reward weights to defaults (not searched)
- Wire reward weights from DQNParams → DQNHyperparameters in train_with_params
- Fix all tests: 134 hyperopt + 7 ensemble + 5 JSON export tests pass
- Fix stale 40D ensemble tests → 38D layout (were already broken pre-change)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add 7 composite reward fields to DQNHyperparameters: w_dsr, w_pnl,
w_dd, w_idle, dd_threshold, loss_aversion, time_decay_rate.
Add RewardSection to training_profile.rs with Option<f64> fields and
apply_to() mapping. Add [reward] section to all 3 DQN TOML profiles
(production, smoketest, hyperopt) with identical defaults.
Remove hold_reward from ExperienceSection (replaced by w_idle).
Add 7 reward search bounds to SearchSpaceSection and bound() match.
Add 7 reward phase_fast defaults to PhaseFastSection.
hold_penalty kept as deprecated field for hyperopt adapter compat
(Task 4 will clean it up).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
