freedakgmail
358 lines
13 KiB
Python
358 lines
13 KiB
Python
"""
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股票数据服务 - 获取、缓存、分析
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"""
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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import traceback
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import json
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import os
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from config import Config
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# ========== 股票名称缓存 ==========
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_stock_name_cache = {}
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def _load_stock_name_cache():
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"""从本地文件加载股票名称缓存"""
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global _stock_name_cache
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try:
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if os.path.exists(Config.STOCK_NAME_CACHE_FILE):
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with open(Config.STOCK_NAME_CACHE_FILE, 'r', encoding='utf-8') as f:
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_stock_name_cache = json.load(f)
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print(f"加载股票名称缓存：{len(_stock_name_cache)}条")
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except Exception as e:
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print(f"加载股票名称缓存失败: {e}")
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def _save_stock_name_cache():
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"""保存股票名称缓存到本地"""
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try:
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with open(Config.STOCK_NAME_CACHE_FILE, 'w', encoding='utf-8') as f:
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json.dump(_stock_name_cache, f, ensure_ascii=False, indent=2)
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except Exception as e:
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print(f"保存股票名称缓存失败: {e}")
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def get_stock_name(stock_code):
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"""获取股票名称 — 使用腾讯财经API"""
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global _stock_name_cache
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if stock_code in _stock_name_cache:
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return _stock_name_cache[stock_code]
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# 腾讯财经API获取股票名称
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try:
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import requests as _req
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tcode = ('sh' if stock_code.startswith('6') else 'sz') + stock_code
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_r = _req.get(f'http://qt.gtimg.cn/q={tcode}', timeout=5,
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headers={'Referer': 'https://finance.qq.com'})
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if _r.status_code == 200 and '\"' in _r.text:
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_fields = _r.text.split('\"')[1].split('~')
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if len(_fields) > 2 and _fields[1]:
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_stock_name_cache[stock_code] = _fields[1]
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_save_stock_name_cache()
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return _fields[1]
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except Exception as e:
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print(f"获取股票名称失败(腾讯): {e}")
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return None
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# ========== 股票数据缓存 ==========
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def _get_cache_file_path(stock_code):
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"""获取缓存文件路径"""
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return os.path.join(Config.STOCK_DATA_CACHE_DIR, f'{stock_code}.json')
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def load_cached_data(stock_code):
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"""加载缓存的股票数据"""
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cache_file = _get_cache_file_path(stock_code)
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if os.path.exists(cache_file):
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try:
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with open(cache_file, 'r', encoding='utf-8') as f:
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data = json.load(f)
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df = pd.DataFrame(data['records'])
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if not df.empty and '日期' in df.columns:
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df['日期'] = pd.to_datetime(df['日期'])
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return df, data.get('stock_name'), data.get('last_update')
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except Exception as e:
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print(f"加载缓存数据失败: {e}")
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return None, None, None
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def save_cached_data(stock_code, df, stock_name):
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"""保存股票数据到缓存"""
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cache_file = _get_cache_file_path(stock_code)
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try:
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df_copy = df.copy()
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df_copy['日期'] = df_copy['日期'].dt.strftime('%Y-%m-%d')
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records = df_copy.to_dict('records')
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data = {
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'stock_code': stock_code,
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'stock_name': stock_name,
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'last_update': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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'records': records
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}
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with open(cache_file, 'w', encoding='utf-8') as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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print(f"已保存 {stock_code} 数据，共 {len(records)} 条")
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except Exception as e:
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print(f"保存缓存数据失败: {e}")
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# ========== 获取股票资金流向数据 ==========
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def get_stock_fund_flow(stock_code, start_date, end_date, force_refresh=False):
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"""
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获取股票资金流向数据（支持缓存，增量获取）
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force_refresh: 强制刷新缓存
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返回: (DataFrame, stock_name, error_msg)
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"""
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try:
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# 判断市场
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if stock_code.startswith('6'):
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market = 'sh'
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elif stock_code.startswith('0') or stock_code.startswith('3'):
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market = 'sz'
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else:
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return None, None, "无法识别股票代码所属市场"
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stock_name = get_stock_name(stock_code)
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start = pd.to_datetime(start_date)
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end = pd.to_datetime(end_date)
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# 加载缓存
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cached_df, cached_name, last_update = load_cached_data(stock_code)
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need_fetch = force_refresh
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new_data_df = None
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if cached_df is not None and not cached_df.empty:
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# 如果缓存是今天的，直接使用
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if last_update:
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try:
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update_date = pd.to_datetime(last_update.split()[0])
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today = pd.to_datetime(datetime.now().strftime('%Y-%m-%d'))
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if update_date >= today and not force_refresh:
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# 今天已更新，直接使用缓存
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df = cached_df[(cached_df['日期'] >= start) & (cached_df['日期'] <= end)]
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df = df.sort_values('日期').reset_index(drop=True)
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return df, cached_name or stock_name, None
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except:
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pass
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cached_max_date = cached_df['日期'].max()
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today = pd.to_datetime(datetime.now().strftime('%Y-%m-%d'))
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# 如果缓存数据不超过2天，直接使用（优化分析速度）
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if cached_max_date >= today - timedelta(days=2) and not force_refresh:
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if cached_df['日期'].min() <= start:
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need_fetch = False
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new_data_df = cached_df
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print(f"使用缓存数据: {stock_code}, 最新日期: {cached_max_date.strftime('%Y-%m-%d')}")
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if stock_name is None and cached_name:
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stock_name = cached_name
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if need_fetch:
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# 东方财富资金流向API已不可用(腾讯云网络限制)，使用缓存数据
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print(f"资金流向API不可用，使用缓存: {stock_code}")
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if cached_df is not None:
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new_data_df = cached_df
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else:
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return None, None, "资金流向API不可用(东方财富已封锁)，且无缓存数据"
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if new_data_df is None or new_data_df.empty:
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# 最后尝试使用缓存数据（即使不在日期范围内）
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if cached_df is not None and not cached_df.empty:
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print(f"使用全部缓存数据: {stock_code}")
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df = cached_df.sort_values('日期').reset_index(drop=True)
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return df, cached_name or stock_name, None
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return None, None, "无法获取数据"
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# 筛选日期范围
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df = new_data_df[(new_data_df['日期'] >= start) & (new_data_df['日期'] <= end)]
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# 如果筛选后为空，使用全部数据
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if df.empty and not new_data_df.empty:
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print(f"日期范围无数据，使用全部缓存: {stock_code}")
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df = new_data_df
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df = df.sort_values('日期').reset_index(drop=True)
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return df, stock_name, None
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except Exception as e:
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traceback.print_exc()
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return None, None, f"获取数据失败: {str(e)}"
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# ========== 分析股票数据 ==========
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def analyze_fund_flow_impact(df):
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"""分析资金流向对股价的影响（含成交量分析）"""
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if df is None or df.empty:
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return None
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df = df.sort_values('日期').reset_index(drop=True)
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threshold = 2.0
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if '超大单净流入-净占比' not in df.columns:
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return None
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df['超大单净流入-净占比'] = df['超大单净流入-净占比'].fillna(0)
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df['主力净流入-净占比'] = df['主力净流入-净占比'].fillna(0)
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df['超大单流向'] = df['超大单净流入-净占比'].apply(
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lambda x: '大额流入' if x >= threshold else ('大额流出' if x <= -threshold else '普通')
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)
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df['主力流向'] = df['主力净流入-净占比'].apply(
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lambda x: '大额流入' if x >= threshold else ('大额流出' if x <= -threshold else '普通')
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)
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# 计算价格位置（改为60日）
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latest = df.iloc[-1]
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lookback = 60 # 从20日改为60日
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try:
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actual_lookback = min(len(df), lookback)
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if actual_lookback >= 5: # 至少需要5天数据
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recent = df.tail(actual_lookback)
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high = float(recent['收盘价'].max() or 0)
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low = float(recent['收盘价'].min() or 0)
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current_price = float(latest.get('收盘价') or 0)
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price_position = (current_price - low) / (high - low) * 100 if high != low else 50
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else:
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price_position = 50
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except:
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price_position = 50
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# 成交量分析（基于主力净流入-净额作为成交额指标）
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volume_ratio = 1.0 # 默认值
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volume_trend = '普通'
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try:
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if '主力净流入-净额' in df.columns and len(df) >= 10:
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# 使用主力净流入绝对值作为活跃度指标
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df['活跃度'] = df['主力净流入-净额'].abs()
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recent_5 = df.tail(5)['活跃度'].mean()
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recent_20 = df.tail(min(20, len(df)))['活跃度'].mean()
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if recent_20 > 0:
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volume_ratio = recent_5 / recent_20
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if volume_ratio >= 1.5:
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volume_trend = '放量'
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elif volume_ratio <= 0.5:
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volume_trend = '缩量'
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else:
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volume_trend = '正常'
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except:
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pass
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# 计算均线MA5和MA20
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ma5 = 0
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ma20 = 0
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try:
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if '收盘价' in df.columns and len(df) >= 5:
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ma5 = df.tail(5)['收盘价'].mean()
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if '收盘价' in df.columns and len(df) >= 20:
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ma20 = df.tail(20)['收盘价'].mean()
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except:
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pass
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# 处理日期格式（可能是datetime或字符串）
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def format_date(d):
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if hasattr(d, 'strftime'):
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return d.strftime('%Y-%m-%d')
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return str(d)[:10] if d else ''
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def safe_float(val, default=0):
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try:
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return float(val) if val is not None else default
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except:
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return default
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return {
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'最新数据': {
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'日期': format_date(latest['日期']),
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'收盘价': safe_float(latest.get('收盘价')),
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'涨跌幅': safe_float(latest.get('涨跌幅')),
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'价格位置': safe_float(price_position),
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'超大单净流入占比': safe_float(latest.get('超大单净流入-净占比')),
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'主力净流入占比': safe_float(latest.get('主力净流入-净占比')),
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'超大单流向': latest.get('超大单流向', '普通'),
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'主力流向': latest.get('主力流向', '普通'),
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'成交量比': safe_float(volume_ratio, 1.0),
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'量能趋势': volume_trend,
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'MA5': safe_float(ma5),
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'MA20': safe_float(ma20)
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},
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'数据概览': {
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'总交易日数': len(df),
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'计算周期': min(len(df), lookback),
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'日期范围': {
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'开始': format_date(df['日期'].min()),
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'结束': format_date(df['日期'].max())
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}
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}
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}
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# ========== 实时价格 ==========
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def get_realtime_price(stock_code):
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"""获取实时价格（使用mairuiapi，更稳定）"""
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try:
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from services.mairui_api import get_realtime_price as mairui_get_price
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result = mairui_get_price(stock_code)
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if result['success']:
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return result
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except Exception as e:
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print(f"mairuiapi获取实时价格失败({stock_code}): {e}")
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# 备用方案2：使用腾讯财经API（腾讯云可用）
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try:
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import requests as _req
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tcode = ('sh' if stock_code.startswith('6') else 'sz') + stock_code
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_r = _req.get(f'http://qt.gtimg.cn/q={tcode}', timeout=5,
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headers={'Referer': 'https://finance.qq.com'})
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if _r.status_code == 200 and '\"' in _r.text:
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_fields = _r.text.split('\"')[1].split('~')
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if len(_fields) > 35 and _fields[3]:
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return {
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'success': True,
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'data': {
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'code': stock_code,
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'name': _fields[1],
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'price': float(_fields[3]),
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'change': float(_fields[32]) if _fields[32] else 0,
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}
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}
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except Exception as e:
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print(f"腾讯财经备用方案失败({stock_code}): {e}")
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return {'success': False, 'error': '获取失败'}
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def get_realtime_prices_batch(stock_codes):
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"""批量获取实时价格"""
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try:
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from services.mairui_api import get_realtime_prices_batch as mairui_batch
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return mairui_batch(stock_codes)
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except Exception as e:
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print(f"mairuiapi批量获取失败: {e}")
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return {}
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# ========== 热门股票 ==========
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def get_hot_stocks(limit=100):
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"""获取热门股票 — 东方财富API已不可用，返回空"""
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# stock_hot_rank_em 为东方财富API，已在腾讯云被封锁
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return []
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# 初始化时加载缓存
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_load_stock_name_cache()
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