#!/usr/bin/env python3 """ 资产异常波动日频监控脚本 使用方法: python scripts/monitor.py python scripts/monitor.py --notify """ from WindPy import w import pandas as pd import numpy as np from datetime import datetime, timedelta import os import sys # 监控资产配置 ASSET_CONFIG = { "sw3_industry": { "name": "申万三级行业", "type": "sector", "sectorid": "a39901011i000000", # 259个 }, "ashare_index": { "name": "A股主要指数", "type": "direct", "codes": [ "000300.SH", "000905.SH", "000016.SH", "000852.SH", "000001.SH", "399001.SZ", "399006.SZ", "000688.SH", "883985.WI" ], }, "bond_index": { "name": "中债指数", "type": "direct", "codes": [ "CBA00101.CS", "CBA00301.CS", "CBA00401.CS", "CBA00501.CS", "CBA00601.CS" ], }, "etf": { "name": "主流ETF", "type": "direct", "codes": [ "510300.SH", "510500.SH", "510050.SH", "159915.SZ", "588000.SH", "512480.SH", "515030.SH", "512760.SH" ], }, "commodity": { "name": "商品期货", "type": "direct", "codes": [ "AU00.SHF", "AG00.SHF", "CU00.SHF", "AL00.SHF", "ZN00.SHF", "RB00.SHF", "SC00.INE", "TA00.CZC" ], }, "global_index": { "name": "全球指数", "type": "direct", "codes": [ "SPX.GI", "IXIC.GI", "DJI.GI", "VIX.GI", "HSI.HI", "N225.GI", "KS11.GI", "GDAXI.GI", "FTSE.GI" ], }, } def analyze_asset(code, name, category, threshold_z=2.0, min_days=30): """分析单个资产的波动""" try: one_year_ago = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d') today_str = datetime.now().strftime('%Y%m%d') hist = w.wsd(code, "pct_chg", one_year_ago, today_str, "", usedf=True) if hist[0] == 0 and len(hist[1]) > min_days: returns = hist[1]['PCT_CHG'].dropna() if len(returns) > min_days: mean_ret = returns.mean() std_ret = returns.std() today_ret = returns.iloc[-1] if len(returns) > 0 else None if today_ret is not None and std_ret > 0: z_score = (today_ret - mean_ret) / std_ret if abs(z_score) > threshold_z: return { 'category': category, 'code': code, 'name': name, 'today_return': float(today_ret), 'z_score': float(z_score), 'direction': '大涨' if z_score > 0 else '大跌' } except Exception: pass return None def monitor_sector(config, threshold_z=2.0): """监控板块类资产""" print(f"\n[监控] {config['name']}") today_str = datetime.now().strftime('%Y%m%d') result = w.wset("sectorconstituent", f"date={today_str};sectorid={config['sectorid']}") if result.ErrorCode != 0 or len(result.Data) < 2: print(f" ⚠️ 未获取到数据") return [] codes = result.Data[1] names = result.Data[2] print(f" 共 {len(codes)} 个资产") anomalies = [] for code, name in zip(codes, names): result = analyze_asset(code, name, config['name'], threshold_z) if result: anomalies.append(result) print(f" ⚠️ {name}: {result['today_return']:+.2f}% (Z={result['z_score']:+.2f})") print(f" 发现 {len(anomalies)} 个异常") return anomalies def monitor_direct(config, threshold_z=2.0): """监控直接代码类资产""" print(f"\n[监控] {config['name']} ({len(config['codes'])}个)") # 获取名称 try: result = w.wss(','.join(config['codes']), "sec_name", "", usedf=True) name_map = dict(zip(result[1].index, result[1]['SEC_NAME'])) if result[0] == 0 else {} except: name_map = {code: code for code in config['codes']} anomalies = [] for code in config['codes']: name = name_map.get(code, code) result = analyze_asset(code, name, config['name'], threshold_z) if result: anomalies.append(result) print(f" ⚠️ {name}: {result['today_return']:+.2f}% (Z={result['z_score']:+.2f})") print(f" 发现 {len(anomalies)} 个异常") return anomalies def run_monitoring(threshold_z=2.0): """运行完整监控""" today = datetime.now() print("="*70) print(f"📊 资产异常波动监控") print(f"时间: {today.strftime('%Y-%m-%d %H:%M')}") print(f"Z值阈值: {threshold_z}") print("="*70) all_anomalies = [] for key, config in ASSET_CONFIG.items(): try: if config['type'] == 'sector': anomalies = monitor_sector(config, threshold_z) else: anomalies = monitor_direct(config, threshold_z) all_anomalies.extend(anomalies) except Exception as e: print(f" ❌ {config['name']} 监控失败: {e}") print(f"\n{'='*70}") print(f"✅ 监控完成，共发现 {len(all_anomalies)} 个异常") print(f"{'='*70}\n") return all_anomalies def generate_excel_report(anomalies, output_dir="output"): """生成 Excel 报告""" if not anomalies: return None os.makedirs(output_dir, exist_ok=True) df = pd.DataFrame(anomalies) df = df.sort_values('z_score', key=abs, ascending=False) today_str = datetime.now().strftime('%Y%m%d') excel_path = os.path.join(output_dir, f"asset_anomaly_report_{today_str}.xlsx") df.to_excel(excel_path, index=False, sheet_name='异常波动资产') print(f"✅ Excel 报告: {excel_path}") return excel_path def generate_text_report(anomalies): """生成文本报告""" if not anomalies: return "📊 资产异常监控\n\n✅ 今日无异常资产。" lines = [ "📊 资产异常波动报告", f"报告时间: {datetime.now().strftime('%Y-%m-%d')}", "", f"共发现 {len(anomalies)} 个异常资产:", "", ] for i, item in enumerate(anomalies, 1): emoji = "🚀" if item['z_score'] > 0 else "📉" lines.append( f"{i}. {emoji} {item['name']} ({item['category']})\n" f" 涨跌幅: {item['today_return']:+.2f}% | Z值: {item['z_score']:+.2f}" ) return '\n'.join(lines) def main(): import argparse parser = argparse.ArgumentParser(description='资产异常波动监控') parser.add_argument('--threshold', type=float, default=2.0, help='Z-Score阈值') parser.add_argument('--output', type=str, default='output', help='输出目录') parser.add_argument('--notify', action='store_true', help='打印报告') args = parser.parse_args() # 连接 Wind print("正在连接 Wind...") w.start() print("✅ Wind 连接成功\n") try: # 运行监控 anomalies = run_monitoring(threshold_z=args.threshold) # 生成报告 if anomalies: excel_path = generate_excel_report(anomalies, args.output) if args.notify: print("\n" + generate_text_report(anomalies)) else: print("✅ 今日无异常资产") finally: w.stop() print("\nWind 连接已断开") if __name__ == "__main__": main()