138 lines
4.5 KiB
Python
138 lines
4.5 KiB
Python
# -*- coding: utf-8 -*-
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"""拉取橡胶主连各时间周期数据并保存到 D:\国内期货K线数据。
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使用 tqsdk 天勤量化，前复权 adj_type="F"。
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周期: M15, M30, H1, D1
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"""
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import json
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import sys
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import time
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from pathlib import Path
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import pandas as pd
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from datetime import timezone, timedelta
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# 读取 tqsdk 凭证
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settings_path = Path(r"D:\cl\AlphaMaster\web_settings.json")
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try:
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settings = json.loads(settings_path.read_text(encoding="utf-8"))
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user = str(settings.get("tqsdk_user", "")).strip()
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pwd = str(settings.get("tqsdk_password", "")).strip()
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except Exception:
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user, pwd = "", ""
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if not user or not pwd:
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user, pwd = "七斗居士", "ghhkphs8"
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print(f"tqsdk 账号: {user}")
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print("=" * 60)
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# 橡胶主连 tqsdk 代码
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TQSDK_SYMBOL = "KQ.m@SHFE.ru"
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# 周期映射: tqsdk duration_seconds
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TIMEFRAMES = {
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"M15": 900,
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"M30": 1800,
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"H1": 3600,
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"D1": 86400,
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}
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# 数据长度（tqsdk 最多支持 8000+ 根）
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DATA_LENGTH = 8000
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BJ_TZ = timezone(timedelta(hours=8))
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out_dir = Path(r"D:\国内期货K线数据")
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out_dir.mkdir(parents=True, exist_ok=True)
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from tqsdk import TqApi, TqAuth, TqSim
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for tf_name, duration in TIMEFRAMES.items():
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print(f"\n正在拉取 橡胶主连 {tf_name}...")
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api = None
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try:
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api = TqApi(TqSim(), auth=TqAuth(user, pwd), disable_print=True)
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# adj_type="F" 前复权
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df = api.get_kline_serial(
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TQSDK_SYMBOL, duration, data_length=DATA_LENGTH, adj_type="F"
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)
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print(f" 拉取到 {len(df)} 根 K 线")
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except Exception as exc:
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print(f" [ERROR] tqsdk 连接失败: {exc}")
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if api is not None:
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try:
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api.close()
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except Exception:
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pass
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continue
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finally:
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if api is not None:
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try:
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api.close()
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except Exception:
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pass
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if df is None or df.empty:
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print(f" [ERROR] 未拉取到数据")
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continue
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# 过滤无效行
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df = df[df["datetime"] != 0].copy()
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df = df[df["volume"] > 0].copy()
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print(f" 过滤后有效 K 线: {len(df)} 根")
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if len(df) == 0:
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print(f" [ERROR] 过滤后无有效数据")
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continue
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# 转换为项目兼容格式
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result = pd.DataFrame()
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result["time"] = (df["datetime"].astype("int64") // 1_000_000_000).astype("int64")
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result["open"] = df["open"].astype("float64")
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result["high"] = df["high"].astype("float64")
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result["low"] = df["low"].astype("float64")
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result["close"] = df["close"].astype("float64")
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result["tick_volume"] = df["volume"].astype("int64")
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# 升序排列 + 去重
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result = result.sort_values("time").reset_index(drop=True)
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result = result.drop_duplicates(subset=["time"], keep="last").reset_index(drop=True)
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# 保存
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out_file = out_dir / f"橡胶主连_{tf_name}.parquet"
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result.to_parquet(out_file, index=False)
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# 统计
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first_dt = pd.to_datetime(result["time"].iloc[0], unit="s", utc=True).tz_convert(BJ_TZ)
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last_dt = pd.to_datetime(result["time"].iloc[-1], unit="s", utc=True).tz_convert(BJ_TZ)
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span_days = (result["time"].iloc[-1] - result["time"].iloc[0]) / 86400
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print(f" 保存: {out_file}")
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print(f" 总行数: {len(result)}")
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print(f" 时间范围: {first_dt.strftime('%Y-%m-%d %H:%M')} ~ {last_dt.strftime('%Y-%m-%d %H:%M')}")
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print(f" 跨度: {span_days:.0f} 天 ({span_days/365.25:.1f} 年)")
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print(f" 收盘价: {result['close'].min():.1f} ~ {result['close'].max():.1f}")
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print(f" 前3行:")
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for _, r in result.head(3).iterrows():
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dt = pd.to_datetime(int(r['time']), unit='s', utc=True).tz_convert(BJ_TZ).strftime('%Y-%m-%d %H:%M')
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print(f" {dt} O={r['open']:.1f} H={r['high']:.1f} L={r['low']:.1f} C={r['close']:.1f} V={int(r['tick_volume'])}")
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print(f" 后3行:")
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for _, r in result.tail(3).iterrows():
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dt = pd.to_datetime(int(r['time']), unit='s', utc=True).tz_convert(BJ_TZ).strftime('%Y-%m-%d %H:%M')
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print(f" {dt} O={r['open']:.1f} H={r['high']:.1f} L={r['low']:.1f} C={r['close']:.1f} V={int(r['tick_volume'])}")
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# 简单质量检查
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o, h, l, c = result["open"], result["high"], result["low"], result["close"]
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bad_h = ((h < o) | (h < c)).sum()
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bad_l = ((l > o) | (l > c)).sum()
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neg = (c < 0).sum()
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zero_vol = (result["tick_volume"] == 0).sum()
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print(f" 质量检查: bad_h={bad_h} bad_l={bad_l} neg_price={neg} zero_vol={zero_vol}")
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time.sleep(1) # 避免频繁连接
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print("\n" + "=" * 60)
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print("全部拉取完成!")
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print("=" * 60)
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