aszerW
4cee249823
缓存策略改进： - Key改为(code, today_date)：每天缓存一次全量数据 - 下载全量数据：从DEFAULT_START_DATE(2015-01-01)到今天 - 返回时切片：从缓存数据中按start-end范围切片返回 新增功能： - DEFAULT_START_DATE配置项（可通过环境变量覆盖） - _fetch_full_data_cached：缓存全量数据 - _slice_data_from_cache：从缓存切片指定日期范围 优势： - 同一天内不同日期范围请求不会重复下载 - 第二天请求自动更新缓存（today_date变化） - 减少对外部数据源的请求次数 修改文件： - datasource/flask_server.py
697 lines
21 KiB
Python
697 lines
21 KiB
Python
"""
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Flask 数据服务 API
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==================
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提供 RESTful API 接口，支持获取各类资产的 K 线数据
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特性:
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- 分层架构：各资产类型独立实现
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- LRU + TTL 双缓存机制
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- SSH隧道支持（港美股）
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- ETF净值获取（计算溢价率）
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运行:
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python datasource/flask_server.py
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API 文档:
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GET / - 服务信息
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GET /health - 健康检查
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GET /api/v1/asset-type - 检测资产类型
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GET /api/v1/ohlcv - 获取K线数据
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POST /api/v1/ohlcv/batch - 批量获取K线数据
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GET /api/v1/etf/nav - 获取ETF净值
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POST /api/v1/cache/clear - 清理缓存
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GET /api/v1/cache/stats - 缓存统计
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"""
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import os
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import sys
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import json
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from pathlib import Path
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from datetime import datetime, timedelta
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from typing import Optional, Dict, Any, List, Tuple
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from functools import lru_cache
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# 添加项目根目录到路径
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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from dotenv import load_dotenv
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load_dotenv()
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from flask import Flask, request, jsonify
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from flask_cors import CORS
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import pandas as pd
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from datasource.universal_fetcher import UniversalDataFetcher
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from datasource.asset_type_detector import AssetTypeDetector, AssetType
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# ============================================================
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# Flask 应用配置
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# ============================================================
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app = Flask(__name__)
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CORS(app) # 启用跨域支持
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# 全局数据获取器实例
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fetcher: Optional[UniversalDataFetcher] = None
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ssh_config: Optional[Dict] = None
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# 缓存配置
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CACHE_MAXSIZE = int(os.getenv('CACHE_MAXSIZE', '128'))
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CACHE_TTL_SECONDS = int(os.getenv('CACHE_TTL_SECONDS', '7200')) # 默认2小时
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# 默认数据起点（下载全量数据时使用）
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DEFAULT_START_DATE = os.getenv('DEFAULT_START_DATE', '2015-01-01')
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class TimedCacheEntry:
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"""带时间戳的缓存条目"""
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def __init__(self, data: Any):
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self.data = data
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self.timestamp = datetime.now()
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def is_expired(self) -> bool:
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return (datetime.now() - self.timestamp).total_seconds() > CACHE_TTL_SECONDS
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# TTL缓存存储
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_ttl_cache: Dict[Tuple, TimedCacheEntry] = {}
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# ============================================================
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# 初始化
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# ============================================================
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def get_ssh_config() -> Optional[Dict]:
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"""从环境变量获取 SSH 配置"""
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enabled = os.getenv('SSH_ENABLED', 'false').lower() == 'true'
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if not enabled:
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return None
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return {
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"enabled": True,
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"host": os.getenv('SSH_HOST', ''),
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"port": int(os.getenv('SSH_PORT', '22')),
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"username": os.getenv('SSH_USERNAME', ''),
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"key_path": os.getenv('SSH_KEY_PATH', 'hk_ecs.pem'),
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"local_port": int(os.getenv('SSH_LOCAL_PORT', '1080')),
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}
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def get_fetcher() -> UniversalDataFetcher:
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"""获取或创建数据获取器实例"""
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global fetcher, ssh_config
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if fetcher is None:
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ssh_config = get_ssh_config()
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fetcher = UniversalDataFetcher(ssh_config=ssh_config)
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return fetcher
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# ============================================================
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# 缓存机制
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# ============================================================
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@lru_cache(maxsize=CACHE_MAXSIZE)
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def _fetch_full_data_cached(code: str, today: str) -> Optional[str]:
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"""
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缓存全量数据（从 DEFAULT_START_DATE 到 today）
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缓存Key: (code, today_date)
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- today: 实际的今天日期，用于每日更新缓存
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- 每天下载一次全量数据，避免重复请求
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Returns:
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JSON 序列化的全量数据
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"""
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f = get_fetcher()
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try:
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with f:
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# 下载全量数据：从默认起点到今天
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df = f.fetch(code, DEFAULT_START_DATE, today)
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if df is None or len(df) == 0:
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return None
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# 保存为 DataFrame 格式（方便后续切片）
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result = {
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'df_json': dataframe_to_json(df),
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'code': code,
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'asset_type': AssetTypeDetector.detect(code).value,
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'data_start': df.index.min().strftime('%Y-%m-%d') if len(df) > 0 else None,
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'data_end': df.index.max().strftime('%Y-%m-%d') if len(df) > 0 else None,
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}
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return json.dumps(result)
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except Exception as e:
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return json.dumps({"error": str(e)})
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def _slice_data_from_cache(cached_data: Dict, start: str, end: str) -> Dict:
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"""
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从缓存的全量数据中切片指定日期范围
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Args:
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cached_data: 缓存的全量数据
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start: 用户请求的开始日期
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end: 用户请求的结束日期
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Returns:
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切片后的数据（JSON格式）
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"""
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if 'df_json' not in cached_data or 'data' not in cached_data['df_json']:
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return cached_data
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# 从缓存数据中重建 DataFrame
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records = cached_data['df_json']['data']
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if not records:
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return cached_data
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# 转换为 DataFrame
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df = pd.DataFrame(records)
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if 'date' in df.columns:
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df['date'] = pd.to_datetime(df['date'])
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df = df.set_index('date')
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# 切片日期范围
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start_dt = pd.to_datetime(start)
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end_dt = pd.to_datetime(end)
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# 确保索引已排序
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df = df.sort_index()
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# 切片（使用 loc 进行日期范围选择）
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sliced_df = df.loc[start_dt:end_dt]
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if len(sliced_df) == 0:
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return {
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'data': [],
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'count': 0,
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'code': cached_data['code'],
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'asset_type': cached_data['asset_type'],
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'requested_range': {'start': start, 'end': end},
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'available_range': {'start': cached_data['data_start'], 'end': cached_data['data_end']},
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}
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# 转换为 JSON 格式
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result = dataframe_to_json(sliced_df)
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result['code'] = cached_data['code']
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result['asset_type'] = cached_data['asset_type']
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result['requested_range'] = {'start': start, 'end': end}
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result['available_range'] = {'start': cached_data['data_start'], 'end': cached_data['data_end']}
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return result
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def fetch_data_with_ttl(
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code: str,
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start: str,
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end: str,
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nocache: bool = False
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) -> Tuple[Optional[Dict], bool]:
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"""
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获取数据，支持 TTL 缓存
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缓存策略：
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- Key: (code, today_date) 缓存全量数据
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- 每天下载一次全量数据（从 DEFAULT_START_DATE 到今天）
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- 用户请求时从缓存切片 start-end 范围返回
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Args:
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code: 标的代码
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start: 用户请求的开始日期
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end: 用户请求的结束日期
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nocache: 是否跳过缓存
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Returns:
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(data, is_cached): 切片后的数据和是否命中缓存
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"""
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# 获取今天的实际日期（用于缓存Key）
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today = datetime.now().strftime('%Y-%m-%d')
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full_cache_key = (code, today)
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# 跳过缓存：清理缓存后重新下载
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if nocache:
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_fetch_full_data_cached.cache_clear()
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global _ttl_cache
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_ttl_cache.clear()
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result_json = _fetch_full_data_cached(code, today)
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if result_json is None:
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return None, False
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full_data = json.loads(result_json)
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return (_slice_data_from_cache(full_data, start, end), False)
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# 检查 TTL 缓存（全量数据缓存）
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if full_cache_key in _ttl_cache:
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entry = _ttl_cache[full_cache_key]
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if not entry.is_expired():
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# 从缓存切片
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sliced_data = _slice_data_from_cache(entry.data, start, end)
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return sliced_data, True
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# 过期，删除
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del _ttl_cache[full_cache_key]
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# 从 LRU 缓存获取全量数据
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result_json = _fetch_full_data_cached(code, today)
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if result_json is None:
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return None, False
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full_data = json.loads(result_json)
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# 检查是否有错误
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if "error" in full_data:
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return full_data, False
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# 存入 TTL 缓存（存全量数据）
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_ttl_cache[full_cache_key] = TimedCacheEntry(full_data)
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# 从全量数据切片返回用户请求的范围
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sliced_data = _slice_data_from_cache(full_data, start, end)
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return sliced_data, False
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def clear_cache():
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"""清理所有缓存"""
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global _ttl_cache
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_fetch_full_data_cached.cache_clear()
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_ttl_cache.clear()
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def get_cache_info() -> Dict:
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"""获取缓存统计信息"""
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info = _fetch_full_data_cached.cache_info()
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return {
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"lru_cache": {
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"hits": info.hits,
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"misses": info.misses,
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"maxsize": info.maxsize,
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"currsize": info.currsize,
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},
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"ttl_cache_size": len(_ttl_cache),
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"ttl_seconds": CACHE_TTL_SECONDS,
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"default_start_date": DEFAULT_START_DATE,
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"cache_strategy": "full_data_by_code_and_today",
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}
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# ============================================================
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# DataFrame 转换
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# ============================================================
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def dataframe_to_json(df: pd.DataFrame) -> Dict:
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"""将 DataFrame 转换为 JSON 可序列化的字典"""
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if df is None or len(df) == 0:
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return {"data": [], "count": 0}
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# 重置索引
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df_reset = df.reset_index()
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# 处理日期列
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date_columns = ['date', 'Date', 'index', 'trade_date', 'datetime']
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for col in date_columns:
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if col in df_reset.columns:
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try:
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df_reset[col] = pd.to_datetime(df_reset[col]).dt.strftime('%Y-%m-%d')
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if col != 'date':
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df_reset = df_reset.rename(columns={col: 'date'})
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break
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except Exception:
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pass
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# 转换为字典列表
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records = df_reset.to_dict(orient='records')
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return {
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"data": records,
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"count": len(records),
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"columns": list(df_reset.columns),
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"date_range": {
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"start": df.index.min().strftime('%Y-%m-%d') if len(df) > 0 else None,
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"end": df.index.max().strftime('%Y-%m-%d') if len(df) > 0 else None,
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}
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}
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def validate_date(date_str: str) -> bool:
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"""验证日期格式"""
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try:
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datetime.strptime(date_str, '%Y-%m-%d')
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return True
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except ValueError:
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return False
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def get_default_dates() -> Tuple[str, str]:
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"""获取默认日期范围（最近3个月）"""
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end = datetime.now()
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start = end - timedelta(days=90)
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return start.strftime('%Y-%m-%d'), end.strftime('%Y-%m-%d')
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# ============================================================
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# API 路由
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# ============================================================
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@app.route('/')
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def index():
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"""首页 - API 信息"""
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return jsonify({
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"name": "Universal Data Fetcher API",
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"version": "2.0.0",
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"description": "统一数据获取服务（分层架构）",
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"architecture": "Unified entry + Asset-specific methods",
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"features": [
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"分层架构（各资产类型独立实现）",
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"LRU + TTL 双缓存机制",
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"SSH隧道支持（港美股）",
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"ETF净值获取（计算溢价率）",
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],
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"endpoints": {
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"info": "/",
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"health": "/health",
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"asset_type": "/api/v1/asset-type?code={code}",
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"ohlcv": "/api/v1/ohlcv?code={code}&start={YYYY-MM-DD}&end={YYYY-MM-DD}",
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"ohlcv_nocache": "/api/v1/ohlcv?code={code}&nocache=true",
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"batch": "POST /api/v1/ohlcv/batch",
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"etf_nav": "/api/v1/etf/nav?code={code}",
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"cache_clear": "POST /api/v1/cache/clear",
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"cache_stats": "/api/v1/cache/stats",
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},
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"supported_assets": {
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"china_index": ["000300.SH", "399006.SZ", "H30269.CSI"],
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"china_etf": ["159915.SZ", "513100.SH", "518880.SH"],
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"hk_index": ["HSI", "HSTECH.HK"],
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"us_index": ["NDX", "SPX", "N225", "GDAXI"],
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"futures": ["AU.SHF", "CU.SHF", "CL.NYM"],
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"crypto": ["BTC", "ETH"],
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},
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"cache_config": get_cache_info(),
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"ssh_status": "enabled" if ssh_config and ssh_config.get('enabled') else "disabled",
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})
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@app.route('/health')
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def health():
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"""健康检查"""
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return jsonify({
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"status": "healthy",
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"timestamp": datetime.now().isoformat(),
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"ssh_configured": ssh_config is not None and ssh_config.get('enabled', False),
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})
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@app.route('/api/v1/asset-type')
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def detect_asset_type():
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"""检测资产类型"""
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code = request.args.get('code', '').strip()
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if not code:
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return jsonify({
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"error": "Missing required parameter: code",
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"example": "/api/v1/asset-type?code=000300.SH"
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}), 400
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asset_type = AssetTypeDetector.detect(code)
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description = AssetTypeDetector.get_description(asset_type)
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return jsonify({
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"code": code,
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"asset_type": asset_type.value,
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"description": description,
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})
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@app.route('/api/v1/ohlcv')
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def get_ohlcv():
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"""
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获取单只标的的 OHLCV 数据
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Query Parameters:
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code: 标的代码 (required)
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start: 开始日期 YYYY-MM-DD (optional, 默认90天前)
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end: 结束日期 YYYY-MM-DD (optional, 默认今天)
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nocache: 是否跳过缓存 (optional, 默认false)
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"""
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code = request.args.get('code', '').strip()
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start = request.args.get('start', '').strip()
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end = request.args.get('end', '').strip()
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nocache = request.args.get('nocache', 'false').lower() == 'true'
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# 参数验证
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if not code:
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return jsonify({
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"error": "Missing required parameter: code",
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"example": "/api/v1/ohlcv?code=000300.SH&start=2024-01-01&end=2024-03-31"
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}), 400
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# 设置默认日期
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if not start or not end:
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start, end = get_default_dates()
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# 日期格式验证
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if not validate_date(start) or not validate_date(end):
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return jsonify({
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"error": "Invalid date format. Use YYYY-MM-DD",
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"start": start,
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"end": end,
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}), 400
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# 使用缓存获取数据
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result, is_cached = fetch_data_with_ttl(code, start, end, nocache)
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if result is None:
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return jsonify({
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"code": code,
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"asset_type": AssetTypeDetector.detect(code).value,
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"error": "No data available",
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"start": start,
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"end": end,
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}), 404
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||
if "error" in result:
|
||
return jsonify({
|
||
"code": code,
|
||
"asset_type": AssetTypeDetector.detect(code).value,
|
||
"error": result["error"],
|
||
}), 500
|
||
|
||
result['cached'] = is_cached
|
||
return jsonify(result)
|
||
|
||
|
||
@app.route('/api/v1/ohlcv/batch', methods=['POST'])
|
||
def batch_ohlcv():
|
||
"""批量获取多只标的的 OHLCV 数据"""
|
||
data = request.get_json()
|
||
|
||
if not data:
|
||
return jsonify({
|
||
"error": "Missing request body",
|
||
"example": {
|
||
"codes": ["000300.SH", "NDX"],
|
||
"start": "2024-01-01",
|
||
"end": "2024-03-31",
|
||
}
|
||
}), 400
|
||
|
||
codes = data.get('codes', [])
|
||
start = data.get('start', '').strip()
|
||
end = data.get('end', '').strip()
|
||
|
||
if not codes or not isinstance(codes, list):
|
||
return jsonify({
|
||
"error": "Missing or invalid parameter: codes (must be a list)"
|
||
}), 400
|
||
|
||
if not start or not end:
|
||
start, end = get_default_dates()
|
||
|
||
if not validate_date(start) or not validate_date(end):
|
||
return jsonify({
|
||
"error": "Invalid date format. Use YYYY-MM-DD",
|
||
}), 400
|
||
|
||
# 获取数据
|
||
f = get_fetcher()
|
||
results = {}
|
||
success_count = 0
|
||
failed_count = 0
|
||
|
||
try:
|
||
with f:
|
||
for code in codes:
|
||
result, _ = fetch_data_with_ttl(code, start, end)
|
||
|
||
if result is not None and "error" not in result:
|
||
results[code] = result
|
||
success_count += 1
|
||
else:
|
||
results[code] = {
|
||
"code": code,
|
||
"asset_type": AssetTypeDetector.detect(code).value,
|
||
"error": result.get("error", "No data") if result else "No data",
|
||
"data": [],
|
||
"count": 0,
|
||
}
|
||
failed_count += 1
|
||
except Exception as e:
|
||
return jsonify({"error": f"Batch fetch failed: {str(e)}"}), 500
|
||
|
||
return jsonify({
|
||
"results": results,
|
||
"success_count": success_count,
|
||
"failed_count": failed_count,
|
||
"total": len(codes),
|
||
"start": start,
|
||
"end": end,
|
||
})
|
||
|
||
|
||
@app.route('/api/v1/etf/nav')
|
||
def get_etf_nav():
|
||
"""获取ETF净值数据（用于计算溢价率）"""
|
||
code = request.args.get('code', '').strip()
|
||
start = request.args.get('start', '').strip()
|
||
end = request.args.get('end', '').strip()
|
||
|
||
if not code:
|
||
return jsonify({
|
||
"error": "Missing required parameter: code",
|
||
"example": "/api/v1/etf/nav?code=513100.SH"
|
||
}), 400
|
||
|
||
if not start or not end:
|
||
start, end = get_default_dates()
|
||
|
||
# 检查是否为ETF
|
||
asset_type = AssetTypeDetector.detect(code)
|
||
if asset_type != AssetType.CHINA_ETF:
|
||
return jsonify({
|
||
"error": f"Not an ETF: {code} (type: {asset_type.value})",
|
||
"hint": "Only A股ETF (codes starting with 51/52/15/16) supported",
|
||
}), 400
|
||
|
||
# 获取净值和溢价率
|
||
f = get_fetcher()
|
||
try:
|
||
with f:
|
||
price_df, nav_df, premium_series = f.fetch_etf_with_nav(code, start, end)
|
||
|
||
result = {
|
||
"code": code,
|
||
"price": dataframe_to_json(price_df) if price_df else {"data": [], "count": 0},
|
||
"nav": dataframe_to_json(nav_df) if nav_df else {"data": [], "count": 0},
|
||
}
|
||
|
||
# 添加历史溢价率序列
|
||
if premium_series is not None and len(premium_series) > 0:
|
||
# 转换为日期-溢价率列表
|
||
premium_data = [
|
||
{"date": date.strftime('%Y-%m-%d'), "premium": round(premium, 6)}
|
||
for date, premium in premium_series.items()
|
||
]
|
||
result['premium_series'] = premium_data
|
||
|
||
# 最新溢价率
|
||
latest_premium = premium_series.iloc[-1]
|
||
latest_date = premium_series.index[-1].strftime('%Y-%m-%d')
|
||
result['latest_premium'] = round(latest_premium, 6)
|
||
result['premium_date'] = latest_date
|
||
|
||
# 溢价率统计
|
||
result['premium_stats'] = {
|
||
"mean": round(premium_series.mean(), 6),
|
||
"std": round(premium_series.std(), 6),
|
||
"min": round(premium_series.min(), 6),
|
||
"max": round(premium_series.max(), 6),
|
||
"median": round(premium_series.median(), 6),
|
||
}
|
||
|
||
|
||
return jsonify(result)
|
||
|
||
except Exception as e:
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
|
||
@app.route('/api/v1/cache/clear', methods=['POST'])
|
||
def clear_cache_endpoint():
|
||
"""清理缓存"""
|
||
info_before = get_cache_info()
|
||
clear_cache()
|
||
return jsonify({
|
||
"message": "Cache cleared successfully",
|
||
"before": info_before,
|
||
"after": get_cache_info()
|
||
})
|
||
|
||
|
||
@app.route('/api/v1/cache/stats')
|
||
def cache_stats():
|
||
"""获取缓存统计"""
|
||
return jsonify(get_cache_info())
|
||
|
||
|
||
# ============================================================
|
||
# 错误处理
|
||
# ============================================================
|
||
|
||
@app.errorhandler(404)
|
||
def not_found(error):
|
||
return jsonify({
|
||
"error": "Endpoint not found",
|
||
"available_endpoints": [
|
||
"/", "/health",
|
||
"/api/v1/asset-type",
|
||
"/api/v1/ohlcv",
|
||
"/api/v1/ohlcv/batch",
|
||
"/api/v1/etf/nav",
|
||
"/api/v1/cache/clear",
|
||
"/api/v1/cache/stats",
|
||
]
|
||
}), 404
|
||
|
||
|
||
@app.errorhandler(500)
|
||
def internal_error(error):
|
||
return jsonify({
|
||
"error": "Internal server error",
|
||
"message": str(error)
|
||
}), 500
|
||
|
||
|
||
# ============================================================
|
||
# 启动服务
|
||
# ============================================================
|
||
|
||
if __name__ == '__main__':
|
||
import argparse
|
||
|
||
parser = argparse.ArgumentParser(description='Universal Data Fetcher API Server')
|
||
parser.add_argument('--host', default='0.0.0.0', help='Host to bind')
|
||
parser.add_argument('--port', type=int, default=5000, help='Port to bind')
|
||
parser.add_argument('--debug', action='store_true', help='Enable debug mode')
|
||
|
||
args = parser.parse_args()
|
||
|
||
# 预加载 SSH 配置
|
||
ssh_config = get_ssh_config()
|
||
if ssh_config and ssh_config.get('enabled'):
|
||
print(f"✓ SSH 隧道已配置: {ssh_config['host']}:{ssh_config['port']}")
|
||
else:
|
||
print("✗ SSH 隧道未启用（仅支持A股数据）")
|
||
|
||
print(f"\n🚀 Universal Data Fetcher API Server v2.0")
|
||
print(f" Host: {args.host}")
|
||
print(f" Port: {args.port}")
|
||
print(f" Cache: LRU({CACHE_MAXSIZE}) + TTL({CACHE_TTL_SECONDS}s)")
|
||
print(f"\n📖 API: http://{args.host}:{args.port}/")
|
||
print(f" 健康检查: http://{args.host}:{args.port}/health")
|
||
|
||
app.run(host=args.host, port=args.port, debug=args.debug) |