feat(multipair): G10 multi-pair pipeline + USE_MULTIPAIR knob (Option C)

- prepare_hourly.py: parameterize PAIR env var; OUT_DEFAULT per-pair; load_m1_from_zips(pair=)
- prepare_multipair.py: inner-join 5-pair hourly parquets on datetime → wide parquet
  cols: datetime, {pair}_ret, {pair}_rv × n_pairs; eurusd_rv = target
- fetch_multipair.py: download GBPUSD/USDJPY/USDCHF/AUDUSD M1 2008-2023 from histdata
- train.py: USE_MULTIPAIR knob (JEPA_USE_MULTIPAIR=1); build() reads multipair parquet
  with n_channels = n_pairs × 2; target = eurusd_rv
- Taskfile: data:fetch:multipair, data:prepare:pair, data:prepare:multipair, data:test updated
- 7 new tests in test_multipair.py; 34/35 pass (1 SKIP until multipair parquet built)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

scripts/prepare_multipair.py

@@ -0,0 +1,72 @@
+"""Merge per-pair hourly parquets into a single wide multipair parquet.
+
+Each pair contributes two features: {pair}_ret and {pair}_rv (realized vol).
+The merge is an INNER JOIN on datetime — only hours present in ALL pairs are kept.
+The target for train.py remains eurusd_rv.
+
+Output: data/processed/eurusd_multipair.parquet
+
+    python scripts/prepare_multipair.py
+    PROCESSED=data/processed python scripts/prepare_multipair.py
+"""
+import os
+import pandas as pd
+
+PAIRS = ["eurusd", "gbpusd", "usdjpy", "usdchf", "audusd"]
+
+PROCESSED_DEFAULT = "data/processed"
+OUT_DEFAULT       = "data/processed/eurusd_multipair.parquet"
+
+
+def merge_pair_parquets(pair_dfs: dict) -> pd.DataFrame:
+    """Inner-join hourly DataFrames from multiple pairs on datetime.
+
+    Args:
+        pair_dfs: dict mapping pair name (e.g. "eurusd") to hourly DataFrame
+                  with columns [datetime, close, ret, realized_vol, ...].
+    Returns:
+        Wide DataFrame with columns:
+          datetime, {pair}_ret, {pair}_rv  for each pair.
+    """
+    merged = None
+    for pair, df in pair_dfs.items():
+        sub = df[["datetime", "ret", "realized_vol"]].copy()
+        sub = sub.rename(columns={"ret": f"{pair}_ret", "realized_vol": f"{pair}_rv"})
+        sub = sub.set_index("datetime")
+        if merged is None:
+            merged = sub
+        else:
+            merged = merged.join(sub, how="inner")
+
+    return merged.reset_index()
+
+
+def build_multipair_parquet(
+    processed_dir: str = PROCESSED_DEFAULT,
+    out_path: str = OUT_DEFAULT,
+    pairs: list = None,
+) -> None:
+    if pairs is None:
+        pairs = PAIRS
+    pair_dfs = {}
+    for pair in pairs:
+        path = os.path.join(processed_dir, f"{pair}_hourly.parquet")
+        if not os.path.exists(path):
+            raise FileNotFoundError(
+                f"{pair}_hourly.parquet not found at {path} — run prepare_hourly.py for this pair first"
+            )
+        df = pd.read_parquet(path)
+        pair_dfs[pair] = df
+
+    merged = merge_pair_parquets(pair_dfs)
+    merged.to_parquet(out_path, index=False)
+    n_pairs = len(pairs)
+    n_ch    = n_pairs * 2
+    print(f"Multipair parquet: {len(merged):,} rows × {n_ch} feature channels ({n_pairs} pairs)")
+    print(f"Date range: {merged['datetime'].min()} → {merged['datetime'].max()}")
+    print(f"Written: {out_path}")
+
+
+if __name__ == "__main__":
+    processed_dir = os.environ.get("PROCESSED", PROCESSED_DEFAULT)
+    build_multipair_parquet(processed_dir=processed_dir)