Commit graph Warrior_EA/Signals/SignalHYBRID.mqh
Author SHA1 Message Date
AnimateDread
70cdec2717 fix(ai): drop the conv pooling stage - it reduced across filters, not time
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>
2026-07-29 19:28:44 -04:00
AnimateDread
d9a4f91717 refactor: compose topologies from named stages; drop dead code
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>
2026-07-29 00:38:05 -04:00
AnimateDread
e043e565eb feat: implement hybrid AI signal with CNN-LSTM architecture and add pooling parameters 2026-07-27 22:08:55 -04:00