The ensemble chart UI had a shared-namespace defect that answered the user
question "what do the arrows represent?" with "a bug": all four members drew
arrows under the same WarSig_<bartime> object names, so the chart showed
whichever member rendered LAST, one member Neutral deleted another member Buy
at the same bar, each member init sweep wiped the arrows the previous member
had just restored, and SaveChartSignals - which rebuilds the sidecar by
SCANNING the chart - persisted every other member arrows into its own history
(the exact cross-model laundering its own header warns about, now happening
BETWEEN ensemble members).
Arrows are now namespaced per member (WarSig_PAI_, WarSig_CONV_, WarSig_LSTM_,
WarSig_HYB_): draw, delete, restore, prune, member init sweep, destructor
purge and the sidecar scan are all member-scoped, and the tooltip names the
model. Global purges keep matching the bare WarSig_ prefix, which covers all
member namespaces plus old-format leftovers from earlier builds.
Labels: the ensemble panel header no longer says "HYBRID ensemble" (HYBRID is
one member; the header is the ensemble) and the CONVLSTM member displays as
ConvLSTM instead of Hybrid. Its SHORT id stays HYB deliberately - it names the
model folder and changing it would orphan every model trained under that path.
Deinit: the alt-data mapping dialog namespace (WarriorAltMap_) joins
WarriorChartPrefixes, so both the OnInit purge and the deinit final sweep now
cover it - it was in neither list, so a dialog starved of its own Destroy()
left its controls on the chart permanently.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The plateau/regression line printed balancedOosEra as the current value while
comparing against m_bestBalancedOos, which has held the SELECTION score since
a142749. Two different metrics in one sentence, so HYBRID logged "regressed
from best 14.4% to 34.0%" a hundred times - a regression to a higher number,
which is not a thing. The comparison itself was right (selectionScore, coverage
weighted, genuinely below best); only the print was wrong. 1039ad9 relabelled
these strings but missed that this site passes the wrong variable.
The startup config line had the same shape of gap: it printed the dense taper
and called itself self-verifying while the DERIVED conv and recurrent stages -
the ones that dominate CONV/LSTM/HYBRID - were invisible. It now shows the
width into and out of each front-end stage, and flags the case where the dense
stack is wider than the vector reaching it (a linear fan-out cannot recover
what the bottleneck discarded; it only adds parameters). Flagged, not silently
reshaped - that would re-key trained topologies mid-comparison.
UsesConvStage()/UsesLstmStage() replace HasConvBeforeLstm() as the primitive,
so each subclass declares its composition once and both the capacity budget and
the config line derive from it rather than restating it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
ResetWeights already deletes the whole model set - .nnw, .cfg, _ckpt.tmp,
.stats, _shadow.nnw - and clears both the .arrows sidecar and the drawn
chart objects. What undid it was PersistWeightsOnShutdown: detaching the EA
after a reset but before an era completed re-created a .nnw from the
freshly-built, never-run net, so the next attach loaded an era-0 stub
instead of starting clean. For LSTM/HYBRID that stub is worse than nothing -
a layer that has never run a forward pass has m_iInputs<=0, so Save omits
every LSTM buffer (see 413ff7e). Skip the save when no era completed and no
model was loaded; that is exactly the post-reset and first-attach state.
Also sweep _shadowclone.tmp, which the reset did not cover.
Separately, ComputeLstmHiddenSize budgeted every topology against the
flattened input (historyBars x neuronsCount). True for LSTM, wrong for
HYBRID, where AddConvStage runs first and the LSTM is fed the conv feature
map - historyBars x convFilterCount, 160 rather than 420 at H1 defaults.
The quadratic is dominated by the inputs term, so overstating the fan-in
2.6x cost a full ladder step (16 units where the budget affords 32). New
virtual HasConvBeforeLstm() feeds LstmFanIn(), so composition decides this
rather than an AIType check. desc.window is advisory only - CNet never
passes it to the layer - but is now truthful for the same reason.
Derived values stay out of the weights-filename fingerprint and are adopted
from the .cfg, so existing models keep their saved width; only fresh ones
pick up the corrected budget.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
FeedForwardConv emits POSITION-MAJOR output, matrix_o[out + window_out * i],
so one bar's window_out filter responses are contiguous and consecutive bars
sit window_out apart. Both pooling implementations (FeedForwardProof and
CPU_FeedForwardProof) slide FLAT over that buffer - pos = i * step, reducing
`window` CONSECUTIVE elements. On a position-major layout those neighbours
are different FILTERS of the same bar, never one filter across time.
At the shipped 3/2 the pool computed max(bar0_f0, bar0_f1, bar0_f2), then
max(bar0_f2, bar0_f3, bar0_f4), with every 8th window straddling a bar
boundary. So it collapsed unrelated feature detectors into whichever fired
hardest, passed gradient to that winner only, and halved the feature map
while doing it - all below every learnable layer, where nothing above can
recover it. The removed inputs' own labels ("3 Bars") show time-axis pooling
was the intent throughout.
Measured cost: CONV sat pinned at ~40% balanced accuracy for 510 eras with
Sell recall 0%, while plain MLPs on the same data reached 57-61%. HYBRID,
which also carried this stage, came second-worst of the batch-norm group.
Not fixable in the topology: pooling one filter across time needs a stride
of window_out BETWEEN samples within a window, which a consecutive-window
kernel cannot express at any window/step. That needs a stride-aware kernel
in Network.cl + WarriorCPU.cpp + WarriorDML.cpp and a DLL rebuild, and is
only worth doing if a conv front-end earns its place without downsampling
first - with 20 sliding positions there is little to gain by halving them.
ConvPoolWindow/ConvPoolStep and their enums are removed with it, along with
the |CP: fingerprint term added earlier today.
Both builds compile 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
DRY - topology construction
---------------------------
CSignalCONV and CSignalHYBRID each built the Conv+Pool front-end from scratch;
CSignalLSTM and CSignalHYBRID each built the LSTM stage from scratch. The
duplicates had already drifted: HYBRID guarded the LSTM step with
MathMax(1, historyBars/2), CSignalLSTM divided unguarded, so a historyBars of 1
gave two different steps for what is documented as the same layer.
Extracted AddConvPoolStage() and AddLstmStage() onto CExpertSignalAIBase. The
three overrides are now compositions:
CONV = AddConvPoolStage
LSTM = AddLstmStage
HYBRID = AddConvPoolStage && AddLstmStage
HYBRID's "matches the standalone CONV front-end exactly, then adds LSTM" is
enforced by construction instead of by comment. Took the guarded step for both.
Also fixed a descriptor leak the duplicates shared: on a failed topology.Add()
the CLayerDescription was neither owned by the array nor deleted.
Dead code
---------
- CNet::SaveCheckpoint / CNet::LoadCheckpoint (123 lines). Superseded by the
in-memory CaptureWeights/RestoreWeights pair; Network.mqh:1312 already said so
("This replaces the file-based SaveCheckpoint/LoadCheckpoint"). Zero call
sites - every remaining mention was a comment. The five comments that
referenced them have been reworded rather than left dangling.
- CExpertSignalCustom::CheckForDuplicateTrade / FindLastTradeIndex /
UpdateTradeStatusAndExit: declared, never defined anywhere, never called.
They only made it look as though duplicate-trade detection existed.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
