feat(quickadapter): add TimeSeriesSplit as alternative data splitting method (#48)

feat(quickadapter): add TimeSeriesSplit as alternative data splitting method (#48)

* feat(model): add TimeSeriesSplit support via train() override

* docs(readme): document TimeSeriesSplit configuration options

* fix: address PR review comments

- Use test_size parameter in TimeSeriesSplit
- Remove unused dk parameter from _make_timeseries_split_datasets()
- Assign dk.data_dictionary = dd before logging
- Fix typo: train_test_test -> train_test_split in README

* docs: integrate data_split_parameters into tunables table

Remove standalone section and add parameters to existing table
with freqai. prefix for consistency.

* refactor: use FreqAI APIs for weight calculation and data dictionary

- Use dk.set_weights_higher_recent() instead of duplicating weight formula
- Use dk.build_data_dictionary() for consistent data structure
- Respects feature_parameters.weight_factor configuration
- Fix bug: was using data_kitchen_thread_count instead of weight_factor

* refactor: extract _apply_pipelines() to reduce code duplication

- Move pipeline definition and application logic to helper method
- Reduces train() override complexity while keeping same behavior
- Helper can be reused by future custom split implementations

* style: harmonize namespace and remove inline comments

- Rename DATA_SPLIT_METHODS to _DATA_SPLIT_METHODS (private tuple pattern)
- Reference DATA_SPLIT_METHOD_DEFAULT from _DATA_SPLIT_METHODS[0]
- Remove 22 inline comments to match self-documenting codebase style

* fix: align TimeSeriesSplit weight calculation with FreqAI semantics

Calculate weights on combined train+test set before splitting to maintain
temporal weight continuity, matching FreqAI's make_train_test_datasets behavior.

* feat: add gap=0 warning and improve TimeSeriesSplit validation

- Warn when gap=0 about look-ahead bias risk (reference label_period_candles)
- Add _compute_timeseries_min_samples() for accurate minimum sample calculation
- Account for gap and test_size in minimum sample validation
- Improve error message with all relevant parameters

* style: harmonize error messages with codebase conventions

- Use 'Invalid {param} value {value!r}: {constraint}' pattern
- Align with existing validation error format (lines 718, 1145)

* style: add cached set accessor for data split methods

- Add _data_split_methods_set() with @staticmethod @lru_cache
- Use QuickAdapterRegressorV3 prefix for class attribute access
- Use cached set for O(1) membership check in validation

* fix: address PR review comments for TimeSeriesSplit

- Use dd consistently in training logs instead of dk.data_dictionary
- Use self.data_split_parameters consistently in _apply_pipelines
- Add explicit type coercion for n_splits, gap, max_train_size
- Add validation for gap >= 0 and max_train_size >= 1
- Improve test_size validation: float in (0,1) as fraction, int >= 1 as count
- Fix _compute_timeseries_min_samples formula: (n_splits+1)*test_size + n_splits*gap
- Optimize tscv.split() iteration to avoid unnecessary list materialization

* fix: correct min_samples formula to match sklearn validation

sklearn validates: n_samples - gap - (test_size * n_splits) > 0
Correct formula: test_size * n_splits + gap + 1

* feat: auto-calculate TimeSeriesSplit gap from label_period_candles

When gap=0 is configured, automatically set gap to label_period_candles
to prevent look-ahead bias from overlapping label windows. This ensures
temporal separation between train and test sets without requiring manual
configuration.

* fix: remove redundant time import shadowing module

* fix: correct min_samples formula for dynamic test_size and document test_size param

* refactor: remove redundant TimeSeriesSplit min_samples validation

* docs: clarify test_size default per split method

* refactor: move DependencyException import to file header

* style: use class name for class constant access

* Apply suggestion from @Copilot

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* docs: use Python None instead of null in README

* docs: fix train_test_split description (sequential, not random)

* fix: use explicit None check for max_train_size validation

* docs: clarify timeseries_split as chronological split, not cross-validation

* refactor(quickadapter): shorten log prefixes and tailor empty test set error by split method

* refactor(quickadapter): use index pattern for timeseries_split method constant

Replace string literals with index access pattern following existing
codebase convention for _DATA_SPLIT_METHODS.

Also renames variables for semantic clarity:
- test_size_param -> test_size
- feat_dict -> feature_parameters

* refactor(quickadapter): use _TEST_SIZE constant instead of hardcoded 0.1

* chore(quickadapter): bump version to 3.11.2

* fix(quickadapter): restore test_size parameter in TimeSeriesSplit

The test_size variable from data_split_parameters was being
immediately overwritten by a type annotation line, making it
always None regardless of user configuration.

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Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>