""" 市场情绪数据获取和计算模块 使用akshare获取市场情绪相关指标，包括ARBR、恐慌指数、市场资金情绪等 """ import pandas as pd import numpy as np import akshare as ak from datetime import datetime, timedelta import warnings import sys import io from data_source_manager import data_source_manager warnings.filterwarnings('ignore') # 设置标准输出编码为UTF-8（仅在命令行环境，避免streamlit冲突） def _setup_stdout_encoding(): """仅在命令行环境设置标准输出编码""" if sys.platform == 'win32' and not hasattr(sys.stdout, '_original_stream'): try: # 检测是否在streamlit环境中 import streamlit # 在streamlit中不修改stdout return except ImportError: # 不在streamlit环境，可以安全修改 try: sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='ignore') except: pass _setup_stdout_encoding() class MarketSentimentDataFetcher: """市场情绪数据获取和计算类""" def __init__(self): self.arbr_period = 26 # ARBR计算周期 def get_market_sentiment_data(self, symbol, stock_data=None): """ 获取完整的市场情绪分析数据 Args: symbol: 股票代码 stock_data: 股票历史数据（如果已有） Returns: dict: 包含各类市场情绪指标的字典 """ sentiment_data = { "symbol": symbol, "arbr_data": None, # ARBR指标数据 "market_index": None, # 大盘指数数据 "sector_index": None, # 板块指数数据 "turnover_rate": None, # 换手率数据 "limit_up_down": None, # 涨跌停数据 "margin_trading": None, # 融资融券数据 "fear_greed_index": None, # 市场恐慌贪婪指数 "data_success": False } try: # 判断是否为中国股票 is_chinese = self._is_chinese_stock(symbol) if is_chinese: # 1. 计算ARBR指标 print("📊 正在计算ARBR情绪指标...") arbr_data = self._calculate_arbr(symbol, stock_data) if arbr_data: sentiment_data["arbr_data"] = arbr_data # 2. 获取换手率数据 print("📊 正在获取换手率数据...") turnover_data = self._get_turnover_rate(symbol) if turnover_data: sentiment_data["turnover_rate"] = turnover_data # 3. 获取大盘情绪 print("📊 正在获取大盘情绪数据...") market_data = self._get_market_index_sentiment() if market_data: sentiment_data["market_index"] = market_data # 4. 获取涨跌停数据 print("📊 正在获取涨跌停数据...") limit_data = self._get_limit_up_down_stats() if limit_data: sentiment_data["limit_up_down"] = limit_data # 5. 获取融资融券数据 print("📊 正在获取融资融券数据...") margin_data = self._get_margin_trading_data(symbol) if margin_data: sentiment_data["margin_trading"] = margin_data # 6. 获取市场恐慌指数 print("📊 正在计算市场恐慌指数...") fear_greed = self._get_fear_greed_index() if fear_greed: sentiment_data["fear_greed_index"] = fear_greed sentiment_data["data_success"] = True print("✅ 市场情绪数据获取完成") else: # 美股的情绪指标（简化版） print("ℹ️ 美股暂不支持完整的市场情绪数据") sentiment_data["error"] = "美股暂不支持完整的市场情绪数据" except Exception as e: print(f"❌ 获取市场情绪数据失败: {e}") sentiment_data["error"] = str(e) return sentiment_data def _is_chinese_stock(self, symbol): """判断是否为中国股票""" return symbol.isdigit() and len(symbol) == 6 def _calculate_arbr(self, symbol, stock_data=None): """ 计算ARBR指标 AR = (N日内(H-O)之和 / N日内(O-L)之和) × 100 BR = (N日内(H-CY)之和 / N日内(CY-L)之和) × 100 """ try: # 如果没有提供stock_data，则重新获取（支持akshare和tushare自动切换） if stock_data is None or stock_data.empty: end_date = datetime.now().strftime('%Y%m%d') start_date = (datetime.now() - timedelta(days=150)).strftime('%Y%m%d') # 使用数据源管理器获取数据 df = data_source_manager.get_stock_hist_data( symbol=symbol, start_date=start_date, end_date=end_date, adjust='qfq' ) if df is None or df.empty: return None # 数据源管理器返回的数据列名已经是小写，无需重命名 else: # 使用已有数据 df = stock_data.copy() # 确保列名正确 if 'Open' in df.columns: df = df.rename(columns={ 'Open': 'open', 'Close': 'close', 'High': 'high', 'Low': 'low', 'Volume': 'volume' }) df = df.reset_index() if 'Date' in df.columns: df = df.rename(columns={'Date': 'date'}) # 确保日期列为datetime类型 if 'date' in df.columns: df['date'] = pd.to_datetime(df['date']) # 计算各项差值 df['HO'] = df['high'] - df['open'] # 最高价-开盘价 df['OL'] = df['open'] - df['low'] # 开盘价-最低价 df['HCY'] = df['high'] - df['close'].shift(1) # 最高价-前收 df['CYL'] = df['close'].shift(1) - df['low'] # 前收-最低价 # 计算AR指标 df['AR'] = (df['HO'].rolling(window=self.arbr_period).sum() / df['OL'].rolling(window=self.arbr_period).sum()) * 100 # 计算BR指标 df['BR'] = (df['HCY'].rolling(window=self.arbr_period).sum() / df['CYL'].rolling(window=self.arbr_period).sum()) * 100 # 处理无穷大和空值 df['AR'] = df['AR'].replace([np.inf, -np.inf], np.nan) df['BR'] = df['BR'].replace([np.inf, -np.inf], np.nan) # 移除空值 df = df.dropna(subset=['AR', 'BR']) if df.empty: return None # 获取最新值和统计信息 latest = df.iloc[-1] ar_value = latest['AR'] br_value = latest['BR'] # 解读ARBR interpretation = self._interpret_arbr(ar_value, br_value) # 生成交易信号 signals = self._generate_arbr_signals(ar_value, br_value) # 计算历史统计 stats = { "ar_mean": df['AR'].mean(), "ar_std": df['AR'].std(), "ar_min": df['AR'].min(), "ar_max": df['AR'].max(), "br_mean": df['BR'].mean(), "br_std": df['BR'].std(), "br_min": df['BR'].min(), "br_max": df['BR'].max(), } # 计算信号统计 df['ar_signal'] = 0 df['br_signal'] = 0 df.loc[df['AR'] > 150, 'ar_signal'] = -1 df.loc[df['AR'] < 70, 'ar_signal'] = 1 df.loc[df['BR'] > 300, 'br_signal'] = -1 df.loc[df['BR'] < 50, 'br_signal'] = 1 df['combined_signal'] = df['ar_signal'] + df['br_signal'] buy_signals = len(df[df['combined_signal'] > 0]) sell_signals = len(df[df['combined_signal'] < 0]) neutral_signals = len(df) - buy_signals - sell_signals signal_stats = { "buy_signals": buy_signals, "sell_signals": sell_signals, "neutral_signals": neutral_signals, "total_signals": len(df), "buy_ratio": f"{buy_signals/len(df)*100:.1f}%" if len(df) > 0 else "0%", "sell_ratio": f"{sell_signals/len(df)*100:.1f}%" if len(df) > 0 else "0%" } return { "latest_ar": float(ar_value), "latest_br": float(br_value), "interpretation": interpretation, "signals": signals, "statistics": stats, "signal_statistics": signal_stats, "calculation_date": latest.get('date', datetime.now()).strftime('%Y-%m-%d') if pd.notna(latest.get('date')) else datetime.now().strftime('%Y-%m-%d'), "period": self.arbr_period } except Exception as e: print(f"计算ARBR指标失败: {e}") return None def _interpret_arbr(self, ar_value, br_value): """解读ARBR数值的含义""" interpretation = [] # AR指标解读 if ar_value > 180: interpretation.append("AR极度超买（>180），市场过热，风险极高，建议谨慎") elif ar_value > 150: interpretation.append("AR超买（>150），市场情绪过热，注意回调风险") elif ar_value < 40: interpretation.append("AR极度超卖（<40），市场过冷，可能存在机会") elif ar_value < 70: interpretation.append("AR超卖（<70），市场情绪低迷，可关注反弹机会") else: interpretation.append(f"AR处于正常区间（{ar_value:.2f}），市场情绪相对平稳") # BR指标解读 if br_value > 400: interpretation.append("BR极度超买（>400），投机情绪过热，警惕泡沫") elif br_value > 300: interpretation.append("BR超买（>300），投机情绪旺盛，注意风险") elif br_value < 30: interpretation.append("BR极度超卖（<30），投机情绪冰点，可能触底") elif br_value < 50: interpretation.append("BR超卖（<50），投机情绪低迷，关注企稳信号") else: interpretation.append(f"BR处于正常区间（{br_value:.2f}），投机情绪适中") # ARBR关系解读 if ar_value > 100 and br_value > 100: interpretation.append("多头力量强劲（AR>100且BR>100），但需警惕过热风险") elif ar_value < 100 and br_value < 100: interpretation.append("空头力量占优（AR<100且BR<100），市场情绪偏空") if ar_value > br_value: interpretation.append("人气指标强于意愿指标（AR>BR），市场基础较好，投资者信心相对稳定") else: interpretation.append("意愿指标强于人气指标（BR>AR），投机性较强，需注意资金稳定性") return interpretation def _generate_arbr_signals(self, ar_value, br_value): """生成ARBR交易信号""" signals = [] signal_strength = 0 # AR信号 if ar_value > 150: signals.append("AR卖出信号") signal_strength -= 1 elif ar_value < 70: signals.append("AR买入信号") signal_strength += 1 # BR信号 if br_value > 300: signals.append("BR卖出信号") signal_strength -= 1 elif br_value < 50: signals.append("BR买入信号") signal_strength += 1 # 综合信号 if signal_strength >= 2: overall = "强烈买入信号" elif signal_strength == 1: overall = "买入信号" elif signal_strength == -1: overall = "卖出信号" elif signal_strength <= -2: overall = "强烈卖出信号" else: overall = "中性信号" return { "individual_signals": signals if signals else ["中性"], "overall_signal": overall, "signal_strength": signal_strength } def _get_turnover_rate(self, symbol): """获取换手率数据（支持akshare和tushare自动切换）""" try: # 优先使用akshare获取最近的换手率数据 print(f" [Akshare] 正在获取换手率数据...") # 获取A股实时行情数据（不需要参数） df = ak.stock_zh_a_spot_em() if df is not None and not df.empty: stock_data = df[df['代码'] == symbol] if not stock_data.empty: row = stock_data.iloc[0] turnover_rate = row.get('换手率', 'N/A') # 解读换手率 interpretation = "" if turnover_rate != 'N/A': try: turnover = float(turnover_rate) if turnover > 20: interpretation = "换手率极高（>20%），资金活跃度极高，可能存在炒作" elif turnover > 10: interpretation = "换手率较高（>10%），交易活跃" elif turnover > 5: interpretation = "换手率正常（5%-10%），交易适中" elif turnover > 2: interpretation = "换手率偏低（2%-5%），交易相对清淡" else: interpretation = "换手率很低（<2%），交易清淡" except: pass print(f" [Akshare] ✅ 成功获取换手率: {turnover_rate}%") return { "current_turnover_rate": turnover_rate, "interpretation": interpretation } except Exception as e: print(f" [Akshare] ❌ 获取换手率失败: {e}") # akshare失败，尝试tushare if data_source_manager.tushare_available: try: print(f" [Tushare] 正在获取换手率数据（备用数据源）...") ts_code = data_source_manager._convert_to_ts_code(symbol) # 获取最近一个交易日的数据 df = data_source_manager.tushare_api.daily_basic( ts_code=ts_code, trade_date=datetime.now().strftime('%Y%m%d') ) if df is not None and not df.empty: row = df.iloc[0] turnover_rate = row.get('turnover_rate', 'N/A') # 解读换手率 interpretation = "" if turnover_rate != 'N/A': try: turnover = float(turnover_rate) if turnover > 20: interpretation = "换手率极高（>20%），资金活跃度极高，可能存在炒作" elif turnover > 10: interpretation = "换手率较高（>10%），交易活跃" elif turnover > 5: interpretation = "换手率正常（5%-10%），交易适中" elif turnover > 2: interpretation = "换手率偏低（2%-5%），交易相对清淡" else: interpretation = "换手率很低（<2%），交易清淡" except: pass print(f" [Tushare] ✅ 成功获取换手率: {turnover_rate}%") return { "current_turnover_rate": turnover_rate, "interpretation": interpretation } except Exception as te: print(f" [Tushare] ❌ 获取失败: {te}") return None def _get_market_index_sentiment(self): """获取大盘指数情绪（支持akshare和tushare自动切换）""" try: # 优先使用akshare获取上证指数实时数据 print(f" [Akshare] 正在获取大盘指数数据...") # 使用正确的symbol参数 df = ak.stock_zh_index_spot_em(symbol="上证系列指数") if df is not None and not df.empty: # 查找上证指数（代码为000001） sh_index = df[df['代码'] == '000001'] if not sh_index.empty: row = sh_index.iloc[0] change_pct = row.get('涨跌幅', 0) # 获取涨跌家数 try: market_summary = ak.stock_zh_a_spot_em() if market_summary is not None and not market_summary.empty: up_count = len(market_summary[market_summary['涨跌幅'] > 0]) down_count = len(market_summary[market_summary['涨跌幅'] < 0]) total_count = len(market_summary) flat_count = total_count - up_count - down_count # 计算市场情绪指数 sentiment_score = (up_count - down_count) / total_count * 100 # 解读市场情绪 if sentiment_score > 30: sentiment = "市场情绪极度乐观" elif sentiment_score > 10: sentiment = "市场情绪偏多" elif sentiment_score > -10: sentiment = "市场情绪中性" elif sentiment_score > -30: sentiment = "市场情绪偏空" else: sentiment = "市场情绪极度悲观" print(f" [Akshare] ✅ 成功获取大盘数据") return { "index_name": "上证指数", "change_percent": change_pct, "up_count": up_count, "down_count": down_count, "flat_count": flat_count, "total_count": total_count, "sentiment_score": f"{sentiment_score:.2f}", "sentiment_interpretation": sentiment } except Exception as e: print(f" [Akshare] 获取涨跌家数失败: {e}") print(f" [Akshare] ✅ 成功获取指数涨跌幅") return { "index_name": "上证指数", "change_percent": change_pct } except Exception as e: print(f" [Akshare] ❌ 获取大盘指数失败: {e}") # akshare失败，尝试tushare if data_source_manager.tushare_available: try: print(f" [Tushare] 正在获取大盘指数数据（备用数据源）...") # 获取上证指数数据 df = data_source_manager.tushare_api.index_daily( ts_code='000001.SH', start_date=datetime.now().strftime('%Y%m%d'), end_date=datetime.now().strftime('%Y%m%d') ) if df is not None and not df.empty: row = df.iloc[0] change_pct = row.get('pct_chg', 0) print(f" [Tushare] ✅ 成功获取大盘指数涨跌幅: {change_pct}%") return { "index_name": "上证指数", "change_percent": change_pct } except Exception as te: print(f" [Tushare] ❌ 获取失败: {te}") return None def _get_limit_up_down_stats(self): """获取涨跌停统计数据""" try: # 获取今日涨停和跌停统计 today = datetime.now().strftime('%Y%m%d') # 获取涨停股票 try: limit_up_df = ak.stock_zt_pool_em(date=today) limit_up_count = len(limit_up_df) if limit_up_df is not None and not limit_up_df.empty else 0 except: limit_up_count = 0 # 获取跌停股票 try: limit_down_df = ak.stock_zt_pool_dtgc_em(date=today) limit_down_count = len(limit_down_df) if limit_down_df is not None and not limit_down_df.empty else 0 except: limit_down_count = 0 # 计算涨跌停比例 if limit_up_count + limit_down_count > 0: limit_ratio = limit_up_count / (limit_up_count + limit_down_count) * 100 else: limit_ratio = 50 # 解读涨跌停情况 if limit_ratio > 70: interpretation = "涨停股远多于跌停股，市场情绪火热" elif limit_ratio > 60: interpretation = "涨停股多于跌停股，市场情绪较好" elif limit_ratio > 40: interpretation = "涨跌停数量相当，市场情绪分化" elif limit_ratio > 30: interpretation = "跌停股多于涨停股，市场情绪较弱" else: interpretation = "跌停股远多于涨停股，市场情绪低迷" return { "limit_up_count": limit_up_count, "limit_down_count": limit_down_count, "limit_ratio": f"{limit_ratio:.1f}%", "interpretation": interpretation, "date": today } except Exception as e: print(f"获取涨跌停数据失败: {e}") return None def _get_margin_trading_data(self, symbol): """获取融资融券数据""" try: # 获取个股融资融券数据（尝试多个API） try: # 方法1：获取沪深融资融券明细 df = ak.stock_margin_underlying_info_szse(date=datetime.now().strftime('%Y%m%d')) if df is not None and not df.empty: stock_data = df[df['证券代码'] == symbol] if not stock_data.empty: latest = stock_data.iloc[0] margin_balance = latest.get('融资余额', 0) short_balance = latest.get('融券余额', 0) # 解读融资融券 interpretation = [] if margin_balance > short_balance * 10: interpretation.append("融资余额远大于融券余额，投资者看多情绪强") elif margin_balance > short_balance * 3: interpretation.append("融资余额大于融券余额，投资者偏看多") else: interpretation.append("融资融券相对平衡") return { "margin_balance": margin_balance, "short_balance": short_balance, "interpretation": interpretation, "date": datetime.now().strftime('%Y-%m-%d') } except: pass # 方法2：获取融资融券汇总数据 try: df = ak.stock_margin_szsh() if df is not None and not df.empty: # 获取最新数据 latest = df.iloc[-1] return { "margin_balance": latest.get('融资余额', 'N/A'), "short_balance": latest.get('融券余额', 'N/A'), "interpretation": ["市场整体融资融券数据"], "date": latest.get('交易日期', 'N/A') } except: pass except Exception as e: print(f"获取融资融券数据失败: {e}") return None def _get_fear_greed_index(self): """计算市场恐慌贪婪指数（基于多个指标综合计算）""" try: # 基于多个市场指标计算恐慌贪婪指数 # 1. 涨跌家数比例 # 2. 涨跌停比例 # 3. 成交量变化 score = 50 # 基准分数 factors = [] # 获取涨跌家数 try: market_summary = ak.stock_zh_a_spot_em() if market_summary is not None and not market_summary.empty: up_count = len(market_summary[market_summary['涨跌幅'] > 0]) down_count = len(market_summary[market_summary['涨跌幅'] < 0]) total = len(market_summary) up_ratio = up_count / total # 根据涨跌家数比例调整分数（权重30%） score += (up_ratio - 0.5) * 60 factors.append(f"涨跌家数比例: {up_ratio:.1%}") except: pass # 确保分数在0-100之间 score = max(0, min(100, score)) # 解读恐慌贪婪指数 if score >= 75: level = "极度贪婪" interpretation = "市场情绪极度乐观，投资者贪婪，需警惕回调风险" elif score >= 60: level = "贪婪" interpretation = "市场情绪乐观，投资者偏向贪婪" elif score >= 40: level = "中性" interpretation = "市场情绪中性，投资者相对理性" elif score >= 25: level = "恐慌" interpretation = "市场情绪悲观，投资者偏向恐慌" else: level = "极度恐慌" interpretation = "市场情绪极度悲观，投资者恐慌，可能存在超卖机会" return { "score": f"{score:.1f}", "level": level, "interpretation": interpretation, "factors": factors } except Exception as e: print(f"计算恐慌贪婪指数失败: {e}") return None def format_sentiment_data_for_ai(self, sentiment_data): """ 将市场情绪数据格式化为适合AI阅读的文本 """ if not sentiment_data or not sentiment_data.get("data_success"): return "未能获取市场情绪数据" text_parts = [] # ARBR指标 if sentiment_data.get("arbr_data"): arbr = sentiment_data["arbr_data"] text_parts.append(f""" 【ARBR市场情绪指标】 - 计算周期：{arbr.get('period', 26)}日 - AR值：{arbr.get('latest_ar', 'N/A'):.2f}（人气指标） - BR值：{arbr.get('latest_br', 'N/A'):.2f}（意愿指标） - 信号：{arbr.get('signals', {}).get('overall_signal', 'N/A')} - 解读： {chr(10).join([' * ' + item for item in arbr.get('interpretation', [])])} ARBR统计数据： - AR历史均值：{arbr.get('statistics', {}).get('ar_mean', 0):.2f} - BR历史均值：{arbr.get('statistics', {}).get('br_mean', 0):.2f} - 历史买入信号比例：{arbr.get('signal_statistics', {}).get('buy_ratio', 'N/A')} - 历史卖出信号比例：{arbr.get('signal_statistics', {}).get('sell_ratio', 'N/A')} """) # 换手率 if sentiment_data.get("turnover_rate"): turnover = sentiment_data["turnover_rate"] text_parts.append(f""" 【换手率数据】 - 当前换手率：{turnover.get('current_turnover_rate', 'N/A')}% - 解读：{turnover.get('interpretation', 'N/A')} """) # 大盘情绪 if sentiment_data.get("market_index"): market = sentiment_data["market_index"] text_parts.append(f""" 【大盘市场情绪】 - 指数：{market.get('index_name', 'N/A')} - 涨跌幅：{market.get('change_percent', 'N/A')}% """) if market.get('sentiment_score'): text_parts.append(f"""- 市场情绪得分：{market.get('sentiment_score', 'N/A')} - 涨家数：{market.get('up_count', 'N/A')}只 - 跌家数：{market.get('down_count', 'N/A')}只 - 平家数：{market.get('flat_count', 'N/A')}只 - 市场情绪：{market.get('sentiment_interpretation', 'N/A')} """) # 涨跌停统计 if sentiment_data.get("limit_up_down"): limit = sentiment_data["limit_up_down"] text_parts.append(f""" 【涨跌停统计】 - 涨停股数量：{limit.get('limit_up_count', 0)}只 - 跌停股数量：{limit.get('limit_down_count', 0)}只 - 涨停占比：{limit.get('limit_ratio', 'N/A')} - 解读：{limit.get('interpretation', 'N/A')} """) # 融资融券 if sentiment_data.get("margin_trading"): margin = sentiment_data["margin_trading"] text_parts.append(f""" 【融资融券数据】 - 融资余额：{margin.get('margin_balance', 'N/A')}元 - 融券余额：{margin.get('short_balance', 'N/A')}元 - 融资买入额：{margin.get('margin_buy', 'N/A')}元 - 解读：{'; '.join(margin.get('interpretation', []))} """) # 恐慌贪婪指数 if sentiment_data.get("fear_greed_index"): fear_greed = sentiment_data["fear_greed_index"] text_parts.append(f""" 【市场恐慌贪婪指数】 - 指数得分：{fear_greed.get('score', 'N/A')}/100 - 情绪等级：{fear_greed.get('level', 'N/A')} - 解读：{fear_greed.get('interpretation', 'N/A')} """) return "\n".join(text_parts) # 测试函数 if __name__ == "__main__": print("测试市场情绪数据获取...") fetcher = MarketSentimentDataFetcher() # 测试平安银行 symbol = "000001" print(f"\n正在获取 {symbol} 的市场情绪数据...") sentiment_data = fetcher.get_market_sentiment_data(symbol) if sentiment_data.get("data_success"): print("\n" + "="*60) print("市场情绪数据获取成功！") print("="*60) formatted_text = fetcher.format_sentiment_data_for_ai(sentiment_data) print(formatted_text) else: print(f"\n获取失败: {sentiment_data.get('error', '未知错误')}")