refactor: class-based alpha factory + month-partitioned data pipeline

Replace the old signal/strategy/backtest modules with a decoupled
data → alpha → combo pipeline (parquet between phases, .pq extension).

Alphas:
- BaseAlpha + @register_alpha factory/plugin registry; one file per
  built-in (reversal, reversal_vol, momentum); external alphas via
  --alpha-module. Alphas are z-scored position weights, not predictors.

Data:
- baostock primary / akshare fallback, treated consistently.
- New --universe all (~5000 A-shares via query_all_stock, filtered).
- login-once batch downloader; empty-string OHLCV coerced to NaN.
- Month-partitioned dataset {output_dir}/{universe}/month=YYYY-MM/*.pq
  with chunked durability flushes; --data-path is the dataset dir.

CLI logs at INFO by default (--log-level) so progress is visible.
Docs (README, CLAUDE.md) updated incl. pipeline diagram and roadmap
TODOs for portfolio construction / backtest / paper trading.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

pipeline/combo/combine.py

@@ -0,0 +1,85 @@
+"""Combine multiple alphas into a single combined weight.
+
+Future combination methods can be registered below.
+"""
+
+import logging
+from typing import Callable
+
+import pandas as pd
+
+from pipeline.common.schema import COMBO_COLUMNS
+
+logger = logging.getLogger(__name__)
+
+
+def _equal_weight(alpha_dfs: list[pd.DataFrame]) -> pd.DataFrame:
+    """Equal-weight combination: mean of all alpha weights per (symbol_id, date).
+
+    If any alpha has NaN for a symbol/date, that alpha is skipped for that row.
+    """
+    # Stack all alphas with (symbol_id, date, alpha_name) as key
+    combined = pd.concat(alpha_dfs, ignore_index=True)
+    # Group by symbol_id + date, take mean of weights
+    result = combined.groupby(["symbol_id", "date"])["weight"].mean().reset_index()
+    return result
+
+
+# Registry of combo methods — add new functions + register them here
+COMBO_METHODS: dict[str, Callable] = {
+    "equal_weight": _equal_weight,
+}
+
+
+def combine_alphas(
+    alpha_paths: list[str],
+    combo_name: str,
+    method: str = "equal_weight",
+) -> pd.DataFrame:
+    """Load alphas from parquet, combine, and return combo weights.
+
+    Args:
+        alpha_paths: List of paths to alpha parquet files.
+        combo_name: Name identifier for this combo.
+        method: Combination method ('equal_weight').
+
+    Returns:
+        DataFrame with COMBO_COLUMNS.
+
+    Raises:
+        ValueError: If method is unknown or alpha grids don't align.
+    """
+    if method not in COMBO_METHODS:
+        raise ValueError(
+            f"Unknown combo method: {method}. Options: {list(COMBO_METHODS)}"
+        )
+
+    alpha_dfs = []
+    for path in alpha_paths:
+        df = pd.read_parquet(path)
+        alpha_dfs.append(df)
+        logger.info("Loaded alpha: %s (%d rows)", path, len(df))
+
+    # Verify alignment: all alphas must share the same (symbol_id, date) pairs
+    keys = [set(zip(df["symbol_id"], pd.to_datetime(df["date"]).astype(str))) for df in alpha_dfs]
+    common = keys[0]
+    for i, k in enumerate(keys[1:], 1):
+        if k != common:
+            logger.warning("Alpha %d has different (symbol_id, date) grid — intersection used", i)
+            common = common.intersection(k)
+
+    combine_fn = COMBO_METHODS[method]
+    result = combine_fn(alpha_dfs)
+    result["combo_name"] = combo_name
+    result = result[COMBO_COLUMNS]
+    result = result.sort_values(["symbol_id", "date"]).reset_index(drop=True)
+
+    logger.info(
+        "Combo '%s': %d symbols × %d dates, weight range [%.4f, %.4f]",
+        combo_name,
+        result["symbol_id"].nunique(),
+        result["date"].nunique(),
+        result["weight"].min(),
+        result["weight"].max(),
+    )
+    return result