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