oficcejo
463 lines
18 KiB
Python
463 lines
18 KiB
Python
"""
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智策板块数据采集模块
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使用AKShare获取板块相关数据
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"""
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import akshare as ak
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import pandas as pd
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from datetime import datetime, timedelta
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import warnings
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import time
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warnings.filterwarnings('ignore')
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class SectorStrategyDataFetcher:
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"""板块策略数据获取类"""
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def __init__(self):
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print("[智策] 板块数据获取器初始化...")
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self.max_retries = 3 # 最大重试次数
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self.retry_delay = 2 # 重试延迟（秒）
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self.request_delay = 1 # 请求间隔（秒）
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def _safe_request(self, func, *args, **kwargs):
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"""安全的请求函数，包含重试机制"""
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for attempt in range(self.max_retries):
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try:
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result = func(*args, **kwargs)
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# 添加请求延迟，避免请求过快
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time.sleep(self.request_delay)
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return result
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except Exception as e:
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if attempt < self.max_retries - 1:
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print(f" 请求失败，{self.retry_delay}秒后重试... (尝试 {attempt + 1}/{self.max_retries})")
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time.sleep(self.retry_delay)
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else:
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print(f" 请求失败，已达最大重试次数: {e}")
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raise e
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def get_all_sector_data(self):
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"""
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获取所有板块的综合数据
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Returns:
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dict: 包含多个维度的板块数据
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"""
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print("[智策] 开始获取板块综合数据...")
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data = {
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"success": False,
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"timestamp": datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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"sectors": {},
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"sector_fund_flow": {},
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"market_overview": {},
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"north_flow": {},
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"news": []
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}
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try:
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# 1. 获取行业板块数据
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print(" [1/6] 获取行业板块行情...")
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sectors_data = self._get_sector_performance()
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if sectors_data:
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data["sectors"] = sectors_data
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print(f" ✓ 成功获取 {len(sectors_data)} 个行业板块数据")
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# 2. 获取概念板块数据
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print(" [2/6] 获取概念板块行情...")
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concept_data = self._get_concept_performance()
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if concept_data:
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data["concepts"] = concept_data
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print(f" ✓ 成功获取 {len(concept_data)} 个概念板块数据")
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# 3. 获取板块资金流向
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print(" [3/6] 获取行业资金流向...")
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fund_flow_data = self._get_sector_fund_flow()
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if fund_flow_data:
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data["sector_fund_flow"] = fund_flow_data
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print(f" ✓ 成功获取资金流向数据")
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# 4. 获取市场总体情况
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print(" [4/6] 获取市场总体情况...")
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market_data = self._get_market_overview()
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if market_data:
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data["market_overview"] = market_data
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print(f" ✓ 成功获取市场概况")
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# 5. 获取北向资金流向
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print(" [5/6] 获取北向资金流向...")
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north_flow = self._get_north_money_flow()
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if north_flow:
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data["north_flow"] = north_flow
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print(f" ✓ 成功获取北向资金数据")
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# 6. 获取财经新闻
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print(" [6/6] 获取财经新闻...")
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news_data = self._get_financial_news()
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if news_data:
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data["news"] = news_data
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print(f" ✓ 成功获取 {len(news_data)} 条新闻")
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data["success"] = True
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print("[智策] ✓ 板块数据获取完成！")
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except Exception as e:
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print(f"[智策] ✗ 数据获取出错: {e}")
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data["error"] = str(e)
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return data
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def _get_sector_performance(self):
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"""获取行业板块表现"""
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try:
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# 获取行业板块实时行情（使用重试机制）
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df = self._safe_request(ak.stock_board_industry_name_em)
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if df is None or df.empty:
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return {}
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# 转换为字典格式
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sectors = {}
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for idx, row in df.iterrows():
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sector_name = row.get('板块名称', '')
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if sector_name:
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sectors[sector_name] = {
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"name": sector_name,
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"change_pct": row.get('涨跌幅', 0),
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"turnover": row.get('换手率', 0),
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"total_market_cap": row.get('总市值', 0),
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"top_stock": row.get('领涨股票', ''),
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"top_stock_change": row.get('领涨股票涨跌幅', 0),
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"up_count": row.get('上涨家数', 0),
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"down_count": row.get('下跌家数', 0)
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}
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return sectors
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except Exception as e:
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print(f" 获取行业板块数据失败: {e}")
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return {}
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def _get_concept_performance(self):
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"""获取概念板块表现"""
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try:
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# 获取概念板块实时行情（使用重试机制）
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df = self._safe_request(ak.stock_board_concept_name_em)
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if df is None or df.empty:
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return {}
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# 转换为字典格式
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concepts = {}
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for idx, row in df.iterrows():
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concept_name = row.get('板块名称', '')
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if concept_name:
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concepts[concept_name] = {
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"name": concept_name,
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"change_pct": row.get('涨跌幅', 0),
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"turnover": row.get('换手率', 0),
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"total_market_cap": row.get('总市值', 0),
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"top_stock": row.get('领涨股票', ''),
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"top_stock_change": row.get('领涨股票涨跌幅', 0),
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"up_count": row.get('上涨家数', 0),
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"down_count": row.get('下跌家数', 0)
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}
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return concepts
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except Exception as e:
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print(f" 获取概念板块数据失败: {e}")
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return {}
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def _get_sector_fund_flow(self):
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"""获取行业资金流向"""
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try:
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# 获取行业资金流向（使用重试机制）
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df = self._safe_request(ak.stock_sector_fund_flow_rank, indicator="今日")
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if df is None or df.empty:
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return {}
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# 转换为字典格式
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fund_flow = {
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"today": [],
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"update_time": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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for idx, row in df.head(50).iterrows(): # 取前50个
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fund_flow["today"].append({
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"sector": row.get('名称', ''),
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"main_net_inflow": row.get('今日主力净流入-净额', 0),
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"main_net_inflow_pct": row.get('今日主力净流入-净占比', 0),
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"super_large_net_inflow": row.get('今日超大单净流入-净额', 0),
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"large_net_inflow": row.get('今日大单净流入-净额', 0),
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"medium_net_inflow": row.get('今日中单净流入-净额', 0),
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"small_net_inflow": row.get('今日小单净流入-净额', 0),
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"change_pct": row.get('今日涨跌幅', 0)
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})
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return fund_flow
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except Exception as e:
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print(f" 获取行业资金流向失败: {e}")
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return {}
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def _get_market_overview(self):
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"""获取市场总体情况"""
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try:
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# 获取A股市场统计
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overview = {}
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# 涨跌家数
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try:
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df_stat = self._safe_request(ak.stock_zh_a_spot_em)
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if df_stat is not None and not df_stat.empty:
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total_count = len(df_stat)
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up_count = len(df_stat[df_stat['涨跌幅'] > 0])
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down_count = len(df_stat[df_stat['涨跌幅'] < 0])
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flat_count = total_count - up_count - down_count
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overview["total_stocks"] = total_count
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overview["up_count"] = up_count
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overview["down_count"] = down_count
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overview["flat_count"] = flat_count
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overview["up_ratio"] = round(up_count / total_count * 100, 2) if total_count > 0 else 0
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# 涨停跌停
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limit_up = len(df_stat[df_stat['涨跌幅'] >= 9.5])
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limit_down = len(df_stat[df_stat['涨跌幅'] <= -9.5])
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overview["limit_up"] = limit_up
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overview["limit_down"] = limit_down
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except:
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pass
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# 大盘指数
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try:
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# 上证指数
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df_sh = ak.stock_zh_index_spot_em(symbol="上证指数")
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if df_sh is not None and not df_sh.empty:
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overview["sh_index"] = {
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"code": "000001",
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"name": "上证指数",
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"close": df_sh.iloc[0].get('最新价', 0),
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"change_pct": df_sh.iloc[0].get('涨跌幅', 0),
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"change": df_sh.iloc[0].get('涨跌额', 0)
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}
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# 深证成指
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df_sz = self._safe_request(ak.stock_zh_index_spot_em, symbol="深证成指")
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if df_sz is not None and not df_sz.empty:
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overview["sz_index"] = {
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"code": "399001",
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"name": "深证成指",
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"close": df_sz.iloc[0].get('最新价', 0),
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"change_pct": df_sz.iloc[0].get('涨跌幅', 0),
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"change": df_sz.iloc[0].get('涨跌额', 0)
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}
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# 创业板指
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df_cyb = self._safe_request(ak.stock_zh_index_spot_em, symbol="创业板指")
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if df_cyb is not None and not df_cyb.empty:
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overview["cyb_index"] = {
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"code": "399006",
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"name": "创业板指",
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"close": df_cyb.iloc[0].get('最新价', 0),
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"change_pct": df_cyb.iloc[0].get('涨跌幅', 0),
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"change": df_cyb.iloc[0].get('涨跌额', 0)
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}
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except:
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pass
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return overview
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except Exception as e:
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print(f" 获取市场概况失败: {e}")
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return {}
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def _get_north_money_flow(self):
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"""获取北向资金流向"""
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try:
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# 获取沪深港通资金流向（使用重试机制）
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df = self._safe_request(ak.stock_hsgt_fund_flow_summary_em)
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if df is None or df.empty:
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return {}
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# 获取最新数据
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latest = df.iloc[0]
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north_flow = {
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"date": str(latest.get('日期', '')),
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"north_net_inflow": latest.get('北向资金-成交净买额', 0),
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"hgt_net_inflow": latest.get('沪股通-成交净买额', 0),
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"sgt_net_inflow": latest.get('深股通-成交净买额', 0),
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"north_total_amount": latest.get('北向资金-成交金额', 0)
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}
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# 获取历史趋势（最近10天）
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history = []
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for idx, row in df.head(10).iterrows():
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history.append({
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"date": str(row.get('日期', '')),
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"net_inflow": row.get('北向资金-成交净买额', 0)
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})
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north_flow["history"] = history
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return north_flow
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except Exception as e:
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print(f" 获取北向资金失败: {e}")
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return {}
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def _get_financial_news(self):
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"""获取财经新闻"""
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try:
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# 获取东方财富财经新闻（使用重试机制）
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df = self._safe_request(ak.stock_news_em, symbol="全球")
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if df is None or df.empty:
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return []
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news_list = []
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for idx, row in df.head(150).iterrows(): # 取前150条
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news_list.append({
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"title": row.get('新闻标题', ''),
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"content": row.get('新闻内容', ''),
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"publish_time": str(row.get('发布时间', '')),
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"source": row.get('文章来源', ''),
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"url": row.get('新闻链接', '')
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})
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return news_list
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except Exception as e:
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print(f" 获取财经新闻失败: {e}")
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return []
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def format_data_for_ai(self, data):
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"""
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将数据格式化为适合AI分析的文本格式
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"""
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if not data.get("success"):
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return "数据获取失败"
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text_parts = []
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# 市场概况
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if data.get("market_overview"):
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market = data["market_overview"]
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text_parts.append(f"""
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【市场总体情况】
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时间: {data.get('timestamp', 'N/A')}
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大盘指数:
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""")
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if market.get("sh_index"):
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sh = market["sh_index"]
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text_parts.append(f" 上证指数: {sh['close']} ({sh['change_pct']:+.2f}%)")
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if market.get("sz_index"):
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sz = market["sz_index"]
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text_parts.append(f" 深证成指: {sz['close']} ({sz['change_pct']:+.2f}%)")
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if market.get("cyb_index"):
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cyb = market["cyb_index"]
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text_parts.append(f" 创业板指: {cyb['close']} ({cyb['change_pct']:+.2f}%)")
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if market.get("total_stocks"):
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text_parts.append(f"""
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市场统计:
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总股票数: {market['total_stocks']}
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上涨: {market['up_count']} ({market['up_ratio']:.1f}%)
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下跌: {market['down_count']}
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平盘: {market['flat_count']}
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涨停: {market['limit_up']}
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跌停: {market['limit_down']}
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""")
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# 北向资金
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if data.get("north_flow"):
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north = data["north_flow"]
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text_parts.append(f"""
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【北向资金流向】
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日期: {north.get('date', 'N/A')}
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北向资金净流入: {north.get('north_net_inflow', 0):.2f} 万元
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沪股通: {north.get('hgt_net_inflow', 0):.2f} 万元
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深股通: {north.get('sgt_net_inflow', 0):.2f} 万元
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""")
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# 行业板块表现（前20）
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if data.get("sectors"):
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sectors = data["sectors"]
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sorted_sectors = sorted(sectors.items(), key=lambda x: x[1]["change_pct"], reverse=True)
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text_parts.append(f"""
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【行业板块表现 TOP20】
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涨幅榜前10:
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""")
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for name, info in sorted_sectors[:10]:
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text_parts.append(f" {name}: {info['change_pct']:+.2f}% | 领涨: {info['top_stock']} ({info['top_stock_change']:+.2f}%)")
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text_parts.append(f"""
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跌幅榜前10:
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""")
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for name, info in sorted_sectors[-10:]:
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text_parts.append(f" {name}: {info['change_pct']:+.2f}% | 领跌: {info['top_stock']} ({info['top_stock_change']:+.2f}%)")
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# 概念板块表现（前20）
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if data.get("concepts"):
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concepts = data["concepts"]
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sorted_concepts = sorted(concepts.items(), key=lambda x: x[1]["change_pct"], reverse=True)
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text_parts.append(f"""
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【概念板块表现 TOP20】
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涨幅榜前10:
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""")
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for name, info in sorted_concepts[:10]:
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text_parts.append(f" {name}: {info['change_pct']:+.2f}% | 领涨: {info['top_stock']} ({info['top_stock_change']:+.2f}%)")
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# 板块资金流向（前15）
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if data.get("sector_fund_flow") and data["sector_fund_flow"].get("today"):
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flow = data["sector_fund_flow"]["today"]
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text_parts.append(f"""
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【行业资金流向 TOP15】
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主力资金净流入前15:
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""")
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sorted_flow = sorted(flow, key=lambda x: x["main_net_inflow"], reverse=True)
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for item in sorted_flow[:15]:
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text_parts.append(f" {item['sector']}: {item['main_net_inflow']:.2f}万 ({item['main_net_inflow_pct']:+.2f}%) | 涨跌: {item['change_pct']:+.2f}%")
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# 重要新闻（前20条）
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if data.get("news"):
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text_parts.append(f"""
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【重要财经新闻 TOP20】
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""")
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for idx, news in enumerate(data["news"][:20], 1):
|
||
text_parts.append(f"{idx}. [{news['publish_time']}] {news['title']}")
|
||
if news.get('content') and len(news['content']) > 100:
|
||
text_parts.append(f" {news['content'][:100]}...")
|
||
|
||
return "\n".join(text_parts)
|
||
|
||
|
||
# 测试函数
|
||
if __name__ == "__main__":
|
||
print("=" * 60)
|
||
print("测试智策板块数据采集模块")
|
||
print("=" * 60)
|
||
|
||
fetcher = SectorStrategyDataFetcher()
|
||
data = fetcher.get_all_sector_data()
|
||
|
||
if data.get("success"):
|
||
print("\n" + "=" * 60)
|
||
print("数据采集成功！")
|
||
print("=" * 60)
|
||
|
||
formatted_text = fetcher.format_data_for_ai(data)
|
||
print(formatted_text[:3000]) # 显示前3000字符
|
||
print(f"\n... (总长度: {len(formatted_text)} 字符)")
|
||
else:
|
||
print(f"\n数据采集失败: {data.get('error', '未知错误')}")
|
||
|