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"""
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K 线数据服务 v2 - 缓存优先策略
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"""
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import logging
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from datetime import datetime
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from typing import List, Optional
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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from app.db.init_db import TimescaleSessionLocal
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from app.services.cache_service import cache_service
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from app.services.amazing_data_service import amazing_data_service
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logger = logging.getLogger(__name__)
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class KlineService:
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"""K 线数据服务"""
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@staticmethod
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def get_kline_data(
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symbol: str,
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period: str,
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start: datetime,
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end: datetime,
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page: int = 1,
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page_size: int = 1000
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) -> List[dict]:
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"""
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获取 K 线数据（支持分页）
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Args:
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symbol: 品种代码
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period: 周期 (1m, 5m, 1h, 1d 等)
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start: 开始时间
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end: 结束时间
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page: 页码 (默认 1)
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page_size: 每页数量 (默认 1000)
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Raises:
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ValueError: 当 start > end 时抛出异常
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"""
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# 边界条件验证：开始时间不能大于结束时间
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if start > end:
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raise ValueError("开始时间不能大于结束时间 (startTime > endTime)")
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with TimescaleSessionLocal() as db:
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offset = (page - 1) * page_size
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query = text("""
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SELECT time, open, high, low, close, volume, amount, open_interest
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FROM kline_data
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WHERE symbol = :symbol
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AND period = :period
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AND time >= :start
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AND time <= :end
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ORDER BY time ASC
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LIMIT :limit OFFSET :offset
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""")
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result = db.execute(
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query,
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{
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"symbol": symbol,
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"period": period,
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"start": start,
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"end": end,
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"limit": page_size,
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"offset": offset
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}
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)
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rows = result.fetchall()
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return [
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{
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"time": row[0].isoformat(),
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"open": float(row[1]),
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"high": float(row[2]),
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"low": float(row[3]),
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"close": float(row[4]),
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"volume": int(row[5]),
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"amount": float(row[6]) if row[6] else None,
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"open_interest": int(row[7]) if row[7] else None
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}
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for row in rows
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]
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@staticmethod
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def get_latest_kline(symbol: str, period: str) -> Optional[dict]:
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"""获取最新一条 K 线数据"""
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with TimescaleSessionLocal() as db:
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query = text("""
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SELECT time, open, high, low, close, volume, amount, open_interest
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FROM kline_data
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WHERE symbol = :symbol AND period = :period
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ORDER BY time DESC
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LIMIT 1
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""")
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result = db.execute(query, {"symbol": symbol, "period": period})
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row = result.fetchone()
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if row:
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return {
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"time": row[0].isoformat(),
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"open": float(row[1]),
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"high": float(row[2]),
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"low": float(row[3]),
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"close": float(row[4]),
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"volume": int(row[5]),
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"amount": float(row[6]) if row[6] else None,
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|
"open_interest": int(row[7]) if row[7] else None
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|
}
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|
return None
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|
|
@staticmethod
|
|
|
|
|
|
def insert_kline_data(
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|
symbol: str,
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|
period: str,
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|
|
kline_data: List[dict]
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) -> int:
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|
"""
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|
批量插入 K 线数据
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|
Returns:
|
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|
|
插入的记录数
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|
"""
|
|
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|
|
with TimescaleSessionLocal() as db:
|
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|
|
query = text("""
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|
|
INSERT INTO kline_data
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|
(time, symbol, period, open, high, low, close, volume, amount, open_interest)
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|
VALUES
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|
(:time, :symbol, :period, :open, :high, :low, :close, :volume, :amount, :open_interest)
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""")
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|
count = 0
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|
|
for kline in kline_data:
|
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|
|
db.execute(
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|
|
|
|
query,
|
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|
|
{
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|
|
"time": kline["time"],
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|
|
"symbol": symbol,
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|
|
"period": period,
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|
|
"open": kline["open"],
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|
"high": kline["high"],
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|
"low": kline["low"],
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|
"close": kline["close"],
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|
"volume": kline["volume"],
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|
|
"amount": kline.get("amount", 0),
|
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|
|
|
|
"open_interest": kline.get("open_interest", 0)
|
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|
|
}
|
|
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|
)
|
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|
|
count += 1
|
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|
|
db.commit()
|
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|
|
return count
|
|
|
|
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
|
|
def get_symbols() -> List[str]:
|
|
|
|
|
|
"""获取所有品种代码"""
|
|
|
|
|
|
with TimescaleSessionLocal() as db:
|
|
|
|
|
|
query = text("""
|
|
|
|
|
|
SELECT DISTINCT symbol FROM kline_data ORDER BY symbol
|
|
|
|
|
|
""")
|
|
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|
|
|
result = db.execute(query)
|
|
|
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|
|
return [row[0] for row in result.fetchall()]
|
|
|
|
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
|
|
def get_periods() -> List[str]:
|
|
|
|
|
|
"""获取所有周期"""
|
|
|
|
|
|
with TimescaleSessionLocal() as db:
|
|
|
|
|
|
query = text("""
|
|
|
|
|
|
SELECT DISTINCT period FROM kline_data ORDER BY period
|
|
|
|
|
|
""")
|
|
|
|
|
|
result = db.execute(query)
|
|
|
|
|
|
return [row[0] for row in result.fetchall()]
|
|
|
|
|
|
|
|
|
|
|
|
# ==================== V2 缓存优先策略 ====================
|
|
|
|
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
|
|
async def get_kline_data_v2(
|
|
|
|
|
|
symbol: str,
|
|
|
|
|
|
period: str,
|
|
|
|
|
|
start_date: datetime,
|
|
|
|
|
|
end_date: datetime,
|
|
|
|
|
|
page: int = 1,
|
|
|
|
|
|
page_size: int = 1000,
|
|
|
|
|
|
use_cache: bool = True
|
|
|
|
|
|
) -> List[dict]:
|
|
|
|
|
|
"""
|
|
|
|
|
|
获取 K 线数据 v2 - 缓存优先策略
|
|
|
|
|
|
|
|
|
|
|
|
核心逻辑：
|
|
|
|
|
|
1. 先查询 Redis 缓存
|
|
|
|
|
|
2. 缓存命中直接返回
|
|
|
|
|
|
3. 缓存未命中则调用 amazingData 获取数据
|
|
|
|
|
|
4. 写入缓存并返回
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
symbol: 品种代码
|
|
|
|
|
|
period: 周期 (1m, 5m, 1h, 1d 等)
|
|
|
|
|
|
start_date: 开始时间
|
|
|
|
|
|
end_date: 结束时间
|
|
|
|
|
|
page: 页码
|
|
|
|
|
|
page_size: 每页数量
|
|
|
|
|
|
use_cache: 是否使用缓存
|
|
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
|
K 线数据列表
|
|
|
|
|
|
"""
|
|
|
|
|
|
# 边界条件验证
|
|
|
|
|
|
if start_date > end_date:
|
|
|
|
|
|
raise ValueError("开始时间不能大于结束时间 (startTime > endTime)")
|
|
|
|
|
|
|
|
|
|
|
|
# 1. 尝试从缓存获取
|
|
|
|
|
|
if use_cache:
|
|
|
|
|
|
cached_data = await cache_service.get_kline(
|
|
|
|
|
|
symbol=symbol,
|
|
|
|
|
|
period=period,
|
|
|
|
|
|
start_date=start_date,
|
|
|
|
|
|
end_date=end_date
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
if cached_data:
|
|
|
|
|
|
logger.info(f"Cache hit for {symbol} {period} {start_date} to {end_date}")
|
|
|
|
|
|
# 分页处理
|
|
|
|
|
|
start_idx = (page - 1) * page_size
|
|
|
|
|
|
end_idx = start_idx + page_size
|
|
|
|
|
|
return cached_data[start_idx:end_idx]
|
|
|
|
|
|
|
|
|
|
|
|
logger.info(f"Cache miss for {symbol} {period}, fetching from amazingData")
|
|
|
|
|
|
|
|
|
|
|
|
# 2. 缓存未命中，调用 amazingData 获取数据
|
|
|
|
|
|
try:
|
|
|
|
|
|
# 确保连接
|
|
|
|
|
|
if not amazing_data_service.ensure_connected():
|
|
|
|
|
|
logger.warning("amazingData not connected, trying database fallback")
|
|
|
|
|
|
# 回退到数据库查询
|
|
|
|
|
|
return KlineService.get_kline_data(symbol, period, start_date, end_date, page, page_size)
|
|
|
|
|
|
|
|
|
|
|
|
# 从 amazingData 获取数据
|
|
|
|
|
|
kline_data = amazing_data_service.get_kline_data(
|
|
|
|
|
|
symbol=symbol,
|
|
|
|
|
|
period=period,
|
|
|
|
|
|
start_date=start_date.strftime("%Y-%m-%d"),
|
|
|
|
|
|
end_date=end_date.strftime("%Y-%m-%d")
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
if not kline_data:
|
|
|
|
|
|
logger.warning(f"No data from amazingData for {symbol} {period}")
|
|
|
|
|
|
# 回退到数据库查询
|
|
|
|
|
|
return KlineService.get_kline_data(symbol, period, start_date, end_date, page, page_size)
|
|
|
|
|
|
|
|
|
|
|
|
# 3. 写入缓存
|
|
|
|
|
|
if use_cache:
|
|
|
|
|
|
await cache_service.set_kline(
|
|
|
|
|
|
symbol=symbol,
|
|
|
|
|
|
period=period,
|
|
|
|
|
|
start_date=start_date,
|
|
|
|
|
|
end_date=end_date,
|
|
|
|
|
|
data=kline_data
|
|
|
|
|
|
)
|
|
|
|
|
|
logger.info(f"Cached {len(kline_data)} records for {symbol} {period}")
|
|
|
|
|
|
|
|
|
|
|
|
# 4. 分页返回
|
|
|
|
|
|
start_idx = (page - 1) * page_size
|
|
|
|
|
|
end_idx = start_idx + page_size
|
|
|
|
|
|
return kline_data[start_idx:end_idx]
|
|
|
|
|
|
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
|
logger.error(f"Failed to fetch from amazingData: {e}")
|
|
|
|
|
|
# 回退到数据库查询
|
|
|
|
|
|
return KlineService.get_kline_data(symbol, period, start_date, end_date, page, page_size)
|
|
|
|
|
|
|
|
|
|
|
|
@staticmethod
|
|
|
|
|
|
async def get_latest_kline_v2(
|
|
|
|
|
|
symbol: str,
|
|
|
|
|
|
period: str,
|
|
|
|
|
|
use_cache: bool = True
|
|
|
|
|
|
) -> Optional[dict]:
|
|
|
|
|
|
"""
|
|
|
|
|
|
获取最新一条 K 线数据（缓存优先）
|
|
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
|
symbol: 品种代码
|
|
|
|
|
|
period: 周期
|
|
|
|
|
|
use_cache: 是否使用缓存
|
|
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
|
最新一条 K 线数据，如果没有则返回 None
|
|
|
|
|
|
"""
|
|
|
|
|
|
from datetime import timedelta
|
|
|
|
|
|
|
|
|
|
|
|
# 查询最近 7 天的数据
|
|
|
|
|
|
end_date = datetime.utcnow()
|
|
|
|
|
|
start_date = end_date - timedelta(days=7)
|
|
|
|
|
|
|
|
|
|
|
|
# 获取数据
|
|
|
|
|
|
data = await KlineService.get_kline_data_v2(
|
|
|
|
|
|
symbol=symbol,
|
|
|
|
|
|
period=period,
|
|
|
|
|
|
start_date=start_date,
|
|
|
|
|
|
end_date=end_date,
|
|
|
|
|
|
page=1,
|
|
|
|
|
|
page_size=1,
|
|
|
|
|
|
use_cache=use_cache
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
return data[0] if data else None
|
