feat: phase 1 — data downloader, backtrader runner, SMA strategy

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

data/downloader.py

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+"""Unified data downloader: akshare primary, baostock fallback."""
+import logging
+from datetime import date, datetime
+from typing import Optional
+import pandas as pd
+import akshare as ak
+import baostock as bs
+
+logger = logging.getLogger(__name__)
+
+BAOSTOCK_FREQ_MAP = {"d": "d", "w": "w", "m": "m"}  # baostock only supports daily
+
+
+def _download_akshare(symbol: str, start: str, end: str, adjust: str = "qfq") -> Optional[pd.DataFrame]:
+    """Download daily bars from akshare. Returns DataFrame with OHLCV columns."""
+    try:
+        # symbol format: 'sh600000' in akshare stock_zh_a_hist expects raw code like '600000'
+        # strip exchange prefix for akshare
+        raw = symbol.replace("sh", "").replace("sz", "")
+        df = ak.stock_zh_a_hist(
+            symbol=raw,
+            period="daily",
+            start_date=start,
+            end_date=end,
+            adjust=adjust,
+        )
+        if df is None or df.empty:
+            return None
+        # Standardize columns
+        col_map = {
+            "日期": "date",
+            "开盘": "open",
+            "最高": "high",
+            "最低": "low",
+            "收盘": "close",
+            "成交量": "volume",
+            "成交额": "amount",
+        }
+        df = df.rename(columns=col_map)
+        df["symbol"] = symbol
+        return df[["symbol", "date", "open", "high", "low", "close", "volume", "amount"]]
+    except Exception as e:
+        logger.warning(f"akshare download failed for {symbol}: {e}")
+        return None
+
+
+def _download_baostock(symbol: str, start: str, end: str, frequency: str = "d") -> Optional[pd.DataFrame]:
+    """Download daily bars from baostock as fallback."""
+    try:
+        bs.login()
+        # baostock format: sh.600000
+        code = f"{symbol[:2]}.{symbol[2:]}"
+        rs = bs.query_history_k_data_plus(
+            code=code,
+            fields="date,open,high,low,close,volume,amount",
+            start_date=start,
+            end_date=end,
+            frequency=frequency,
+            adjustflag="2",  # qfq
+        )
+        if rs.error_code != "0":
+            logger.warning(f"baostock error for {symbol}: {rs.error_msg}")
+            return None
+        data_list = []
+        while rs.next():
+            data_list.append(rs.get_row_data())
+        bs.logout()
+        if not data_list:
+            return None
+        df = pd.DataFrame(data_list, columns=["date", "open", "high", "low", "close", "volume", "amount"])
+        df[["open", "high", "low", "close", "volume", "amount"]] = df[
+            ["open", "high", "low", "close", "volume", "amount"]
+        ].astype(float)
+        df["symbol"] = symbol
+        return df[["symbol", "date", "open", "high", "low", "close", "volume", "amount"]]
+    except Exception as e:
+        logger.warning(f"baostock download failed for {symbol}: {e}")
+        try:
+            bs.logout()
+        except Exception:
+            pass
+        return None
+
+
+def download_daily(
+    symbol: str,
+    start: str,
+    end: str,
+    adjust: str = "qfq",
+    source: str = "auto",
+) -> pd.DataFrame:
+    """
+    Download daily OHLCV data. Tries akshare first, falls back to baostock.
+
+    Args:
+        symbol: Stock symbol like 'sh600000' or 'sz000001'
+        start: Start date 'YYYY-MM-DD'
+        end: End date 'YYYY-MM-DD'
+        adjust: 'qfq' (forward-adjusted), 'hfq' (backward), '' (none)
+        source: 'auto' (akshare then baostock fallback), 'akshare' only,
+            or 'baostock' only
+
+    Returns:
+        DataFrame with columns: symbol, date, open, high, low, close, volume, amount
+    """
+    df = None
+    if source in ("akshare", "auto"):
+        df = _download_akshare(symbol, start, end, adjust)
+    if df is None and source in ("baostock", "auto"):
+        df = _download_baostock(symbol, start, end)
+
+    if df is None or df.empty:
+        raise RuntimeError(f"Failed to download data for {symbol} from {start} to {end}")
+
+    df["date"] = pd.to_datetime(df["date"])
+    df = df.sort_values("date").reset_index(drop=True)
+    return df
+
+
+def download_batch(
+    symbols: list[str],
+    start: str,
+    end: str,
+    adjust: str = "qfq",
+) -> dict[str, pd.DataFrame]:
+    """Download daily data for multiple symbols. Returns {symbol: DataFrame}."""
+    results = {}
+    for sym in symbols:
+        try:
+            results[sym] = download_daily(sym, start, end, adjust)
+            logger.info(f"Downloaded {sym}: {len(results[sym])} bars")
+        except Exception as e:
+            logger.error(f"Failed {sym}: {e}")
+    return results