feat(weights): add uniform pivot weighting strategy (#75)

feat(weights): add uniform pivot weighting strategy (#75)

* feat(weights): add uniform pivot weighting strategy

Adds "uniform" to WEIGHT_STRATEGIES (between "none" and the metric
names) which assigns weight=1.0 to every detected pivot. Off-pivot rows
remain governed by the existing fill_method (zero / epsilon / gaussian),
so uniform + gaussian collapses cleanly to a pure proximity kernel
around each pivot. Naming follows sklearn convention
(KNeighbors(weights="uniform"), DummyClassifier(strategy="uniform")).

* chore(quickadapter): bump strategy and regressor version 3.11.11 -> 3.11.12

* refactor(weights): hoist indices_array and valid_mask in compute_label_weights

Compute indices_array and valid_mask once at the top of the function
instead of after the strategy dispatch. The uniform branch can now use
indices_array.size instead of len(indices), and the duplicate
np.asarray / valid_mask construction lower in the function is removed.
Saves one np.asarray and one mask computation per call.

* refactor(weights): consolidate _scatter_weights signature

Drop the redundant indices: list[int] parameter now that the only
caller (compute_label_weights) hoists indices_array and valid_mask.
The function takes them positionally and uses indices_array.size for
size checks, removing three len(indices) calls and the optional-kwarg
fallback paths.

* refactor(weights): pipeline API consolidation pass

- compute_label_weights: drop Optional placeholder, accept
  Sequence[int] | NDArray[np.integer] for indices
- standardize Optional[X] -> X | None across module (PEP 604)
- _impute_weights: positional call instead of keyword on single arg
- _pivot_equivalent_count: remove unreachable threshold <= 0 branch
  (survivors.size > 0 implies survivors.max() > 0 because the input
  has been sanitized to non-negative values upstream)
- _scatter_weights: drop dead 'if not np.any(valid_mask)' early
  return; vectorized assignment is a no-op when the mask is all-False
- sanitize_and_renormalize: clarify empty-input semantics in docstring

* fix(weights): zero leading and trailing non-finite runs in _impute_weights

The boundary mask only covered the strict tip positions (index 0 and -1),
so multi-element non-finite runs at the boundary were median-imputed
instead of zeroed. With input [NaN, NaN, 1.0, 2.0, NaN, NaN] the function
returned [0.0, 1.5, 1.0, 2.0, 1.5, 0.0] instead of [0, 0, 1.0, 2.0, 0, 0],
silently extending pivot weight to the unconfirmed boundary candles.

Use np.argmax on the finite mask to detect the leading and trailing
non-finite runs and zero the entire run, matching the docstring contract.

* fix(weights): floor stacked metrics in geometric and harmonic aggregation

Power means with p<=0 collapse to 0 on a single zero in the stack:
pmean([1, 0, 3], p=-1) = 0.0 and pmean([1, 0, 3], p=0) = 0.0. Combined
with compose_sample_weights' (arr <= 0) drop_mask predicate, a single
metric returning 0 on a pivot silently drops that row entirely.

Floor stacked_metrics at np.finfo(float).tiny only inside the
geometric_mean and harmonic_mean branches so all-positive pivots
survive aggregation. arithmetic_mean, quadratic_mean, weighted_median
and softmax branches are untouched.

* fix(weights): log when out-of-range pivot indices are dropped

compute_label_weights silently filters out pivot indices outside
[0, n_values) via valid_mask. This made upstream contract violations
invisible: a stale or off-by-one index list would simply produce zero
training weight on those rows with no diagnostic.

Emit logger.warning with the count and dropped fraction whenever
n_dropped > 0 so the upstream caller can spot the issue.

* refactor(weights): collapse 4x label-config validators into a registry

Replace the four near-identical _validate_*_params + get_label_*_config
pairs with a single _LABEL_KIND_REGISTRY mapping each kind name to
(specs, defaults). _label_kind_validator builds the validator on the
fly and get_label_kind_config dispatches to _get_label_config with the
appropriate spec/default pair. The four public get_label_*_config
helpers remain as thin wrappers so existing callers in QuickAdapterV3
and QuickAdapterRegressorV3 are unaffected.

_LabelTransformerConfig.from_dict (LabelTransformer.py) is intentionally
out of scope: it would require propagating a logger through
BaseTransform's freqtrade-side interface, which is upstream-controlled.

* docs(weights): drop verbose empty-input note from sanitize_and_renormalize

The added sentence paraphrased the existing collapse line and restated
obvious facts about zero-length vectors without contractual information.
Revert to the concise pre-W1 docstring.

* docs(weights): drop get_label_kind_config docstring for consistency

The 4 sibling get_label_*_config wrappers have no docstrings; their
parameter names and the registry name self-document the contract. Drop
the redundant docstring on get_label_kind_config to match the family
style.

* fix(weights): drop tiny floor in geometric/harmonic aggregation

The floor at np.finfo(float).tiny added in b1f86a0 preserved pivots
whose metrics included an exact zero, but a zero metric is itself a
'signal absent' marker that downstream compose_sample_weights drops
via the (arr <= 0) mask. Floor was masking the intended drop.

Restore the upstream pmean behavior so a zero in any geometric or
harmonic input produces an exact 0.0 combined weight, allowing
drop_mask to drop the pivot as designed.

* docs(readme): document uniform label weighting strategy

* style(readme): re-align tunables table columns