feat(data): expanding walk-forward splits

Aggiunge expanding_walk_forward e dataclass Split per generare fold
walk-forward con train che cresce e test sulla finestra successiva.
Rispetta min_train_days e ritorna lista vuota su fold troppo piccoli.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tests/unit/test_splits.py

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+import pandas as pd
+import pytest
+
+from multi_swarm.data.splits import expanding_walk_forward
+
+
+@pytest.fixture
+def daily_index():
+    return pd.date_range("2024-01-01", "2024-12-31", freq="D", tz="UTC")
+
+
+def test_expanding_split_count(daily_index: pd.DatetimeIndex):
+    splits = expanding_walk_forward(
+        daily_index, train_ratio=0.7, n_folds=4, min_train_days=30
+    )
+    assert len(splits) == 4
+
+
+def test_expanding_split_train_grows(daily_index: pd.DatetimeIndex):
+    splits = expanding_walk_forward(
+        daily_index, train_ratio=0.7, n_folds=4, min_train_days=30
+    )
+    train_lengths = [len(s.train_idx) for s in splits]
+    assert train_lengths == sorted(train_lengths)
+    assert train_lengths[0] < train_lengths[-1]
+
+
+def test_no_overlap_train_test(daily_index: pd.DatetimeIndex):
+    splits = expanding_walk_forward(
+        daily_index, train_ratio=0.7, n_folds=4, min_train_days=30
+    )
+    for s in splits:
+        assert s.train_idx[-1] < s.test_idx[0]
+
+
+def test_min_train_days_respected():
+    idx = pd.date_range("2024-01-01", "2024-02-15", freq="D", tz="UTC")
+    splits = expanding_walk_forward(idx, train_ratio=0.7, n_folds=2, min_train_days=20)
+    for s in splits:
+        assert len(s.train_idx) >= 20