total_return_pct used simple pnl/initial_capital (arithmetic).
Omega/Sharpe/Sortino used pnl/current_equity (compounding per-trade).
These diverge after drawdown: Omega>1 but negative total_return was
mathematically impossible yet appeared in every run.
Fix: total_return_pct = ∏(1+r_i)-1 using the same per-trade returns
that feed Omega. Now Omega>1 ⟺ positive total_return. Always.
Single-pass equity curve + returns computation. No separate loops.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Returns were computed as pnl/initial_capital for ALL trades. After a
49% drawdown (equity=51K), a $500 loss was recorded as 500/100K=0.5%
instead of 500/51K=0.98%. This deflated losses after drawdown,
inflating Omega (5.5x) while total return was -9.5% — contradictory.
Fix: track running equity and compute each trade's return relative to
equity AT TIME OF TRADE. Now Omega, Sharpe, Sortino all reflect the
actual impact of each trade on the current portfolio.
This was the 'sneaky bug' causing Omega>5 with negative returns.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Sharpe/Sortino were annualized with √252 (daily trading assumption).
For intraday strategies with hundreds of trades per eval window, this
inflated magnitudes ~10x, causing Sharpe=-1.4 while Omega=10.9 on
the same return series — mathematically contradictory.
Fix: scale by √N where N = actual number of returns in the series.
This gives the Sharpe of the evaluation window, not a synthetic annual.
- evaluation/metrics.rs: Sharpe, Sortino, Calmar all fixed
- trainer/metrics.rs: val_loss Sharpe (compute_validation_loss)
- ppo.rs: epoch Sharpe proxy
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The stability penalty (15% of PSO objective) was dead weight:
- Gradient norm threshold of 20,000 NEVER fires because gradient
clipping is at 10.0 and avg raw grad norms are typically 5-50.
- Q-value std threshold of 100 rarely fires with DSR and v_range=[10,50].
Fix: log-scale ramp for gradient (threshold=50, cap=3.0) gives smooth
PSO gradient across the 50→5000 range where clip engagement indicates
instability. Linear ramp for Q-std (threshold=15, cap=3.0).
Also: delete unused smooth_transition() + calculate_exponential_sharpe_incentive()
(-160 lines dead code), fix stale docstring on HFT activity fn args.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GPU experience kernel was zero-padding OFI features (state[45..53]),
creating a train/eval mismatch where GPU-collected experiences had no
OFI context but CPU validation used real OFI data.
- Add OFI_DIM compile-time constant to common_device_functions.cuh
(default 0, set to 8 when state_dim >= market_dim + portfolio_dim + 8)
- Add ofi_features parameter to both kernel signatures (full + warp)
- Replace zero-padding loop with direct OFI load from GPU memory
- Add upload_ofi_features() method to GpuExperienceCollector
- Wire OFI upload in DQN trainer at collector init time
- Cap MaxDD at 100% in evaluation metrics (margin call boundary)
- Add running_equity + margin_called tracking to EvaluationEngine
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add backticks to type names in doc comments (doc_markdown)
- Mark eligible functions as const fn (missing_const_for_fn)
No behavior changes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
