146 lines
5.5 KiB
Python
146 lines
5.5 KiB
Python
import sys
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import os
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from pathlib import Path
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# Add real vnpy source code to sys.path
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_VNPY_SRC = os.path.join(os.path.dirname(__file__), "..", "vnpy_v4.4.0")
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_VNPY_SRC = os.path.abspath(_VNPY_SRC)
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if _VNPY_SRC not in sys.path:
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sys.path.insert(0, _VNPY_SRC)
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import pandas as pd
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from datetime import datetime, date
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from vnpy.trader.object import BarData
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from vnpy.trader.constant import Exchange, Interval
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from vnpy.trader.setting import SETTINGS
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def read_parquet_daily(symbol: str, start: str, end: str, cfg, dir_key: str = "daily_dir") -> list[BarData]:
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daily_dir = Path(cfg.data_paths[dir_key])
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start_dt = datetime.strptime(start, "%Y-%m-%d")
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end_dt = datetime.strptime(end, "%Y-%m-%d")
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bars: list[BarData] = []
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prefix = "sh" if guess_exchange(symbol) == Exchange.SSE else "sz"
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for year in range(start_dt.year, end_dt.year + 1):
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f = daily_dir / str(year) / f"{prefix}{symbol}_daily.parquet"
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if not f.exists():
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continue
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df = pd.read_parquet(f)
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for _, row in df.iterrows():
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d = pd.to_datetime(row["date"])
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if start_dt <= d <= end_dt:
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bars.append(_row_to_bar(symbol, row, Interval.DAILY))
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return bars
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def _row_to_bar(symbol: str, row, interval: Interval) -> BarData:
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return BarData(
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symbol=symbol,
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exchange=guess_exchange(symbol), # Task 4 已改为 guess_exchange
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datetime=pd.to_datetime(row["date"]).to_pydatetime(),
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interval=interval,
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open_price=float(row["open"]),
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high_price=float(row["high"]),
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low_price=float(row["low"]),
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close_price=float(row["close"]),
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volume=float(row["volume"]),
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gateway_name="DATA",
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)
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def guess_exchange(symbol: str) -> Exchange:
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"""按代码前缀判断交易所：6/68/5x→SSE，0/3/15x→SZSE"""
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if symbol.startswith(("60", "68", "51", "56", "58")):
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return Exchange.SSE
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if symbol.startswith(("00", "30", "15")):
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return Exchange.SZSE
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return Exchange.SSE
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def read_db_daily(symbol: str, start: str, end: str, cfg) -> list[BarData]:
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from vnpy.trader.database import get_database # lazy：避免模块 import 依赖数据库驱动
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# Configure vnpy database SETTINGS before calling get_database()
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SETTINGS["database.name"] = "sqlite"
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SETTINGS["database.database"] = cfg.data_paths["vnpy_db"]
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db = get_database()
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start_dt = datetime.strptime(start, "%Y-%m-%d")
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end_dt = datetime.strptime(end, "%Y-%m-%d")
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return db.load_bar_data(
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symbol=symbol,
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exchange=guess_exchange(symbol),
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interval=Interval.DAILY,
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start=start_dt,
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end=end_dt,
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)
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def read_parquet_15min(symbol: str, start: str, end: str, cfg, dir_key: str = "minute_15_dir") -> list[BarData]:
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"""读 15min parquet（NAS /volume1/stock/minute_kline/15min/sh{symbol}_15min.parquet）。"""
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minute_dir = Path(cfg.data_paths[dir_key])
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start_dt = datetime.strptime(start, "%Y-%m-%d")
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end_dt = datetime.strptime(end, "%Y-%m-%d")
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prefix = "sh" if guess_exchange(symbol) == Exchange.SSE else "sz"
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f = minute_dir / f"{prefix}{symbol}_15min.parquet"
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if not f.exists():
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return []
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df = pd.read_parquet(f)
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# baostock 15min 有完整时分 datetime 列；旧数据用 date 列
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time_col = ("datetime" if "datetime" in df.columns
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else ("date" if "date" in df.columns else df.columns[0]))
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bars: list[BarData] = []
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for _, row in df.iterrows():
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d = pd.to_datetime(row[time_col])
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if start_dt <= d <= end_dt:
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bars.append(BarData(
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symbol=symbol,
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exchange=guess_exchange(symbol),
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datetime=d.to_pydatetime(),
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interval=Interval.MINUTE,
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open_price=float(row["open"]),
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high_price=float(row["high"]),
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low_price=float(row["low"]),
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close_price=float(row["close"]),
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volume=float(row["volume"]),
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gateway_name="DATA",
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))
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return bars
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def read_index_daily(code: str, start: date, end: date, cfg) -> pd.DataFrame:
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"""
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读指数日线数据（sh000300/sz000905），复用 read_parquet_daily 的年分片 parquet 路径
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Args:
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code: 指数代码，如 "sh000300"（沪深300）、"sz000905"（中证500）
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start: 起始日期
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end: 结束日期
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cfg: 数据配置对象
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Returns:
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pd.DataFrame: 包含 date/open/high/low/close/volume 列的日线数据
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"""
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daily_dir = Path(cfg.data_paths["daily_dir"])
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start_dt = start if isinstance(start, datetime) else datetime.combine(start, datetime.min.time())
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end_dt = end if isinstance(end, datetime) else datetime.combine(end, datetime.max.time())
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dfs: list[pd.DataFrame] = []
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# 按年分片读取（与 read_parquet_daily 相同路径逻辑）
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for year in range(start_dt.year, end_dt.year + 1):
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f = daily_dir / str(year) / f"{code}_daily.parquet"
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if not f.exists():
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continue
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df = pd.read_parquet(f)
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# 过滤日期范围
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df["date"] = pd.to_datetime(df["date"])
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mask = (df["date"] >= start_dt) & (df["date"] <= end_dt)
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filtered_df = df[mask].copy()
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if not filtered_df.empty:
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dfs.append(filtered_df)
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if dfs:
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result = pd.concat(dfs, ignore_index=True)
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result = result.sort_values("date")
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return result.reset_index(drop=True)
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else:
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return pd.DataFrame(columns=["date", "open", "high", "low", "close", "volume"])
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