201 lines
6.6 KiB
Python
201 lines
6.6 KiB
Python
"""东方财富分钟K线数据客户端
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通过 push2his.eastmoney.com 获取 A 股分钟级 K 线数据，
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用于盘中量能分布分析和短线进场时机判断。
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免费接口，无需认证，注意限频。
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"""
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import logging
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import httpx
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import pandas as pd
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from datetime import datetime
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from app.data.cache import cache
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from app.config import settings
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logger = logging.getLogger(__name__)
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# 东方财富分钟K线接口
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EASTMONEY_KLINE_URL = "http://push2his.eastmoney.com/api/qt/stock/kline/get"
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HEADERS = {
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"Referer": "http://finance.eastmoney.com",
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"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36",
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}
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def _ts_code_to_eastmoney(ts_code: str) -> str:
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"""600519.SH -> 1.600519 (上海=1, 深圳=0)"""
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code, market = ts_code.split(".")
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prefix = "1" if market == "SH" else "0"
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return f"{prefix}.{code}"
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async def get_min_kline(
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ts_code: str,
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period: str = "5",
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count: int = 48,
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) -> pd.DataFrame:
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"""获取分钟级 K 线数据
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Args:
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ts_code: Tushare 格式代码，如 600519.SH
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period: 分钟周期 ("1", "5", "15", "30", "60")
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count: 请求条数（5分钟×48=4小时≈一个交易日）
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Returns:
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DataFrame with columns: time, open, close, high, low, volume, amount
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空 DataFrame if failed
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"""
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cache_key = f"min_kline:{ts_code}:{period}"
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cached = cache.get(cache_key)
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if cached is not None:
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return cached
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secid = _ts_code_to_eastmoney(ts_code)
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params = {
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"secid": secid,
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"fields1": "f1,f2,f3,f4,f5,f6",
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"fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
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"klt": period, # 分钟周期
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"fqt": "1", # 前复权
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"beg": "0",
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"end": "20500101",
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"lmt": str(count), # 返回条数限制
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}
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try:
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async with httpx.AsyncClient() as client:
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resp = await client.get(
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EASTMONEY_KLINE_URL,
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params=params,
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headers=HEADERS,
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timeout=10,
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)
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data = resp.json()
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klines = data.get("data", {}).get("klines", [])
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if not klines:
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logger.debug(f"东方财富分钟K线 {ts_code} 无数据")
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return pd.DataFrame()
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# 解析：格式 "2026-04-15 09:30,5.71,5.73,5.74,5.70,12345,70000.00"
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rows = []
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for line in klines:
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parts = line.split(",")
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if len(parts) < 7:
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continue
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rows.append({
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"time": parts[0],
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"open": float(parts[1]),
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"close": float(parts[2]),
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"high": float(parts[3]),
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"low": float(parts[4]),
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"volume": float(parts[5]),
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"amount": float(parts[6]),
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})
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df = pd.DataFrame(rows)
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if df.empty:
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return df
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# 缓存（分钟数据盘中3分钟过期，盘后30分钟过期）
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ttl = 180 if _is_trading_hours() else 1800
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cache.set(cache_key, df, ttl)
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return df
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except Exception as e:
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logger.error(f"东方财富分钟K线获取失败 {ts_code}: {e}")
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return pd.DataFrame()
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def analyze_intraday_volume_distribution(min_df: pd.DataFrame) -> dict:
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"""分析盘中量能分布（基于5分钟K线）
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Returns:
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{
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"morning_volume_ratio": float, # 上午成交额占比
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"afternoon_volume_ratio": float, # 下午成交额占比
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"opening_strength": float, # 开盘30分钟成交额/全天占比
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"closing_strength": float, # 尾盘30分钟成交额/全天占比
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"volume_trend": str, # "morning_dominant" / "afternoon_dominant" / "balanced"
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"key_periods": list[str], # 关键放量时段描述
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}
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"""
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if min_df.empty or len(min_df) < 4:
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return {
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"morning_volume_ratio": 0.5,
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"afternoon_volume_ratio": 0.5,
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"opening_strength": 0.25,
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"closing_strength": 0.25,
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"volume_trend": "balanced",
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"key_periods": [],
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}
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total_amount = min_df["amount"].sum()
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if total_amount == 0:
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return {
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"morning_volume_ratio": 0.5,
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"afternoon_volume_ratio": 0.5,
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"opening_strength": 0.25,
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"closing_strength": 0.25,
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"volume_trend": "balanced",
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"key_periods": [],
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}
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# 按时间段分割
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morning = min_df[min_df["time"].str.contains("09:|10:|11:0", na=False)]
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afternoon = min_df[min_df["time"].str.contains("13:|14:0|14:1|14:2|14:3|14:4|14:5", na=False)]
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morning_ratio = morning["amount"].sum() / total_amount if not morning.empty else 0.5
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afternoon_ratio = afternoon["amount"].sum() / total_amount if not afternoon.empty else 0.5
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# 开盘30分钟（9:30-10:00）
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opening_30 = min_df[min_df["time"].str.contains("09:3|09:4|09:5|10:0", na=False)]
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opening_strength = opening_30["amount"].sum() / total_amount if not opening_30.empty else 0.25
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# 尾盘30分钟（14:30-15:00）
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closing_30 = min_df[min_df["time"].str.contains("14:3|14:4|14:5|15:0", na=False)]
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closing_strength = closing_30["amount"].sum() / total_amount if not closing_30.empty else 0.25
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# 量能趋势判断
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if morning_ratio > 0.6:
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volume_trend = "morning_dominant"
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elif afternoon_ratio > 0.6:
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volume_trend = "afternoon_dominant"
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else:
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volume_trend = "balanced"
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# 关键放量时段
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key_periods = []
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avg_amount = min_df["amount"].mean()
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hot_bars = min_df[min_df["amount"] > avg_amount * 2]
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for _, bar in hot_bars.iterrows():
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time_str = bar["time"].split(" ")[1] if " " in bar["time"] else bar["time"]
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pct = bar["amount"] / total_amount * 100
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key_periods.append(f"{time_str}放量(占比{pct:.0f}%)")
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return {
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"morning_volume_ratio": round(morning_ratio, 2),
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"afternoon_volume_ratio": round(afternoon_ratio, 2),
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"opening_strength": round(opening_strength, 2),
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"closing_strength": round(closing_strength, 2),
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"volume_trend": volume_trend,
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"key_periods": key_periods[:3],
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}
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def _is_trading_hours() -> bool:
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"""简单判断是否在交易时间"""
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now = datetime.now()
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if now.weekday() >= 5: # 周六周日
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return False
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hour = now.hour
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minute = now.minute
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# 9:30 - 11:30 或 13:00 - 15:00
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if (hour == 9 and minute >= 30) or (hour == 10) or (hour == 11 and minute <= 30):
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return True
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if (hour == 13) or (hour == 14) or (hour == 15 and minute == 0):
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return True
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return False |