commit;
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parent
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src/app.py
69
src/app.py
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@ -1,3 +1,4 @@
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# -*- coding: utf-8 -*-
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import sys
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import os
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from datetime import datetime, timedelta
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@ -44,6 +45,7 @@ from src.scripts.stock_daily_data_collector import collect_stock_daily_data
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from valuation_analysis.financial_analysis import FinancialAnalyzer
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from src.valuation_analysis.stock_price_collector import StockPriceCollector
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from src.quantitative_analysis.batch_stock_price_collector import fetch_and_store_stock_data, get_stock_realtime_info_from_redis
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# 设置日志
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logging.basicConfig(
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@ -227,7 +229,7 @@ def run_rzrq_initial_collection1():
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}), 200
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@app.route('/scheduler/industry/crowding', methods=['GET'])
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@app.route('/scheduler/industry/crowding1/bak', methods=['GET'])
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def precalculate_industry_crowding1():
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"""预计算部分行业和概念板块的拥挤度指标 晚上10点开始"""
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try:
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@ -337,6 +339,27 @@ def scheduler_financial_analysis():
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"status": "success"
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}), 200
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@app.route('/scheduler/industry/crowding', methods=['GET'])
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def precalculate_industry_crowding_batch():
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"""批量预计算行业和概念板块的拥挤度指标"""
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try:
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from src.valuation_analysis.industry_analysis import IndustryAnalyzer
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analyzer = IndustryAnalyzer()
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# 固定行业和概念板块
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industries = ["煤炭开采", "焦炭加工", "油气开采", "石油化工", "油服工程", "日用化工", "化纤", "化学原料", "化学制品", "塑料", "橡胶", "农用化工", "非金属材料", "冶钢原料", "普钢", "特钢", "工业金属", "贵金属", "能源金属", "稀有金属", "金属新材料", "水泥", "玻璃玻纤", "装饰建材", "种植业", "养殖业", "林业", "渔业", "饲料", "农产品加工", "动物保健", "酿酒", "饮料乳品", "调味品", "休闲食品", "食品加工", "纺织制造", "服装家纺", "饰品", "造纸", "包装印刷", "家居用品", "文娱用品", "白色家电", "黑色家电", "小家电", "厨卫电器", "家电零部件", "一般零售", "商业物业经营", "专业连锁", "贸易", "电子商务", "乘用车", "商用车", "汽车零部件", "汽车服务", "摩托车及其他", "化学制药", "生物制品", "中药", "医药商业", "医疗器械", "医疗服务", "医疗美容", "电机制造", "电池", "电网设备", "光伏设备", "风电设备", "其他发电设备", "地面兵装", "航空装备", "航天装备", "航海装备", "军工电子", "轨交设备", "通用设备", "专用设备", "工程机械", "自动化设备", "半导体", "消费电子", "光学光电", "元器件", "其他电子", "通信设备", "通信工程", "电信服务", "IT设备", "软件服务", "云服务", "产业互联网", "游戏", "广告营销", "影视院线", "数字媒体", "出版业", "广播电视", "全国性银行", "地方性银行", "证券", "保险", "多元金融", "房屋建设", "基础建设", "专业工程", "工程咨询服务", "装修装饰", "房地产开发", "房产服务", "体育", "教育培训", "酒店餐饮", "旅游", "专业服务", "公路铁路", "航空机场", "航运港口", "物流", "电力", "燃气", "水务", "环保设备", "环境治理", "环境监测", "综合类"]
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concepts = ["通达信88", "海峡西岸", "海南自贸", "一带一路", "上海自贸", "雄安新区", "粤港澳", "ST板块", "次新股", "含H股", "含B股", "含GDR", "含可转债", "国防军工", "军民融合", "大飞机", "稀缺资源", "5G概念", "碳中和", "黄金概念", "物联网", "创投概念", "航运概念", "铁路基建", "高端装备", "核电核能", "光伏", "风电", "锂电池概念", "燃料电池", "HJT电池", "固态电池", "钠电池", "钒电池", "TOPCon电池", "钙钛矿电池", "BC电池", "氢能源", "稀土永磁", "盐湖提锂", "锂矿", "水利建设", "卫星导航", "可燃冰", "页岩气", "生物疫苗", "基因概念", "维生素", "仿制药", "创新药", "免疫治疗", "CXO概念", "节能环保", "食品安全", "白酒概念", "代糖概念", "猪肉", "鸡肉", "水产品", "碳纤维", "石墨烯", "3D打印", "苹果概念", "阿里概念", "腾讯概念", "小米概念", "百度概念", "华为鸿蒙", "华为海思", "华为汽车", "华为算力", "特斯拉概念", "消费电子概念", "汽车电子", "无线耳机", "生物质能", "地热能", "充电桩", "新能源车", "换电概念", "高压快充", "草甘膦", "安防服务", "垃圾分类", "核污染防治", "风沙治理", "乡村振兴", "土地流转", "体育概念", "博彩概念", "赛马概念", "分散染料", "聚氨酯", "云计算", "边缘计算", "网络游戏", "信息安全", "国产软件", "大数据", "数据中心", "芯片", "MCU芯片", "汽车芯片", "存储芯片", "互联金融", "婴童概念", "养老概念", "网红经济", "民营医院", "特高压", "智能电网", "智能穿戴", "智能交通", "智能家居", "智能医疗", "智慧城市", "智慧政务", "机器人概念", "机器视觉", "超导概念", "职业教育", "物业管理概念", "虚拟现实", "数字孪生", "钛金属", "钴金属", "镍金属", "氟概念", "磷概念", "无人机", "PPP概念", "新零售", "跨境电商", "量子科技", "无人驾驶", "ETC概念", "胎压监测", "OLED概念", "MiniLED", "MicroLED", "超清视频", "区块链", "数字货币", "人工智能", "租购同权", "工业互联", "知识产权", "工业大麻", "工业气体", "人造肉", "预制菜", "种业", "化肥概念", "操作系统", "光刻机", "第三代半导体", "远程办公", "口罩防护", "医废处理", "虫害防治", "超级电容", "C2M概念", "地摊经济", "冷链物流", "抖音概念", "降解塑料", "医美概念", "人脑工程", "烟草概念", "新型烟草", "有机硅概念", "新冠检测", "BIPV概念", "地下管网", "储能", "新材料", "工业母机", "一体压铸", "汽车热管理", "汽车拆解", "NMN概念", "国资云", "元宇宙概念", "NFT概念", "云游戏", "天然气", "绿色电力", "培育钻石", "信创", "幽门螺杆菌", "电子纸", "新冠药概念", "免税概念", "PVDF概念", "装配式建筑", "绿色建筑", "东数西算", "跨境支付CIPS", "中俄贸易", "电子身份证", "家庭医生", "辅助生殖", "肝炎概念", "新型城镇", "粮食概念", "超临界发电", "虚拟电厂", "动力电池回收", "PCB概念", "先进封装", "热泵概念", "EDA概念", "光热发电", "供销社", "Web3概念", "DRG-DIP", "AIGC概念", "复合铜箔", "数据确权", "数据要素", "POE胶膜", "血氧仪", "旅游概念", "中特估", "ChatGPT概念", "CPO概念", "数字水印", "毫米波雷达", "工业软件", "6G概念", "时空大数据", "可控核聚变", "知识付费", "算力租赁", "光通信", "混合现实", "英伟达概念", "减速器", "减肥药", "合成生物", "星闪概念", "液冷服务器", "新型工业化", "短剧游戏", "多模态AI", "PEEK材料", "小米汽车概念", "飞行汽车", "Sora概念", "人形机器人", "AI手机PC", "低空经济", "铜缆高速连接", "军工信息化", "玻璃基板", "商业航天", "车联网", "财税数字化", "折叠屏", "AI眼镜", "智谱AI", "IP经济", "宠物经济", "小红书概念", "AI智能体", "DeepSeek概念", "AI医疗概念", "海洋经济", "外骨骼机器人", "军贸概念"]
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# 批量计算行业和概念板块拥挤度
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analyzer.batch_calculate_industry_crowding(industries, concepts)
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logger.info("批量计算行业和概念板块的拥挤度指标完成")
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except Exception as e:
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logger.error(f"批量计算行业拥挤度指标失败: {str(e)}")
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return jsonify({
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"status": "success"
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}), 200
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@app.route('/')
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def index():
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"""渲染主页"""
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@ -2863,17 +2886,53 @@ def get_pep_stock_info_by_shortname():
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@app.route('/api/pep_stock_info_by_code', methods=['GET'])
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def get_pep_stock_info_by_code():
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"""根据股票简称查询pep_stock_info集合中的全部字段"""
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"""根据股票代码查询Redis中的实时行情并返回指定结构"""
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short_code = request.args.get('code')
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if not short_code:
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return jsonify({'success': False, 'message': '缺少必要参数: short_code'}), 400
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try:
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analyzer = FinancialAnalyzer()
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result = analyzer.get_pep_stock_info_by_code(short_code)
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return jsonify(result)
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# 兼容600001.SH/SH600001等格式
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from src.quantitative_analysis.batch_stock_price_collector import get_stock_realtime_info_from_redis
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result = get_stock_realtime_info_from_redis(short_code)
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if result:
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return jsonify(result)
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else:
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return jsonify({'success': False, 'message': f'未找到股票 {short_code} 的实时行情'}), 404
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except Exception as e:
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return jsonify({'success': False, 'message': f'服务器错误: {str(e)}'}), 500
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@app.route('/api/industry/crowding/filter', methods=['GET'])
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def filter_industry_crowding():
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"""根据拥挤度百分位区间筛选行业和概念板块"""
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try:
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min_val = float(request.args.get('min', 0))
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max_val = float(request.args.get('max', 100))
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from src.valuation_analysis.industry_analysis import IndustryAnalyzer
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analyzer = IndustryAnalyzer()
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result = analyzer.filter_crowding_by_percentile(min_val, max_val)
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return jsonify({
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'status': 'success',
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'min': min_val,
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'max': max_val,
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'result': result
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})
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except Exception as e:
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logger.error(f"筛选行业/概念拥挤度接口异常: {str(e)}")
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return jsonify({
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'status': 'error',
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'message': str(e)
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}), 500
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@app.route('/scheduler/batch_stock_price/collection', methods=['GET'])
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def run_batch_stock_price_collection():
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"""批量采集A股行情并保存到数据库"""
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try:
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fetch_and_store_stock_data()
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return jsonify({"status": "success", "message": "批量采集A股行情并保存到数据库成功"})
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except Exception as e:
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logger.error(f"批量采集A股行情失败: {str(e)}")
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return jsonify({"status": "error", "message": str(e)})
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if __name__ == '__main__':
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# 启动Web服务器
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@ -0,0 +1,724 @@
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代码,日期,开盘价(元),最高价(元),最低价(元),收盘价(元),涨跌幅
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0020.HK,2022-07-05,2.4,2.78,2.38,2.75,0.0827
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0020.HK,2022-07-06,2.77,2.79,2.53,2.6,-0.0545
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0020.HK,2022-07-07,2.56,2.68,2.56,2.6,0
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0020.HK,2022-07-08,2.64,2.69,2.54,2.58,-0.0077
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0020.HK,2022-07-11,2.6,2.6,2.52,2.54,-0.0155
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0020.HK,2022-07-12,2.5,2.52,2.4,2.43,-0.0433
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||||
0020.HK,2022-07-13,2.45,2.48,2.38,2.39,-0.0165
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0020.HK,2022-07-14,2.36,2.38,2.25,2.29,-0.0418
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0020.HK,2022-07-15,2.28,2.36,2.15,2.16,-0.0568
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0020.HK,2022-07-18,2.13,2.23,2.11,2.14,-0.0093
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0020.HK,2022-07-19,2.14,2.17,2.04,2.1,-0.0187
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0020.HK,2022-07-20,2.18,2.42,2.17,2.33,0.1095
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0020.HK,2022-07-21,2.33,2.46,2.29,2.39,0.0258
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0020.HK,2022-07-22,2.41,2.49,2.39,2.43,0.0167
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0020.HK,2022-07-25,2.41,2.43,2.34,2.4,-0.0123
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0020.HK,2022-07-26,2.31,2.34,2.19,2.27,-0.0542
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||||
0020.HK,2022-07-27,2.23,2.37,2.22,2.34,0.0308
|
||||
0020.HK,2022-07-28,2.38,2.5,2.37,2.47,0.0556
|
||||
0020.HK,2022-07-29,2.5,2.52,2.3,2.33,-0.0567
|
||||
0020.HK,2022-08-01,2.32,2.35,2.26,2.28,-0.0215
|
||||
0020.HK,2022-08-02,2.22,2.22,2.12,2.17,-0.0482
|
||||
0020.HK,2022-08-03,2.22,2.33,2.19,2.2,0.0138
|
||||
0020.HK,2022-08-04,2.24,2.25,2.18,2.2,0
|
||||
0020.HK,2022-08-05,2.23,2.24,2.18,2.23,0.0136
|
||||
0020.HK,2022-08-08,2.25,2.26,2.2,2.2,-0.0135
|
||||
0020.HK,2022-08-09,2.2,2.23,2.18,2.19,-0.0045
|
||||
0020.HK,2022-08-10,2.2,2.21,2.14,2.16,-0.0137
|
||||
0020.HK,2022-08-11,2.19,2.21,2.16,2.19,0.0139
|
||||
0020.HK,2022-08-12,2.19,2.29,2.19,2.23,0.0183
|
||||
0020.HK,2022-08-15,2.26,2.29,2.23,2.25,0.009
|
||||
0020.HK,2022-08-16,2.25,2.28,2.18,2.2,-0.0222
|
||||
0020.HK,2022-08-17,2.2,2.22,2.16,2.16,-0.0182
|
||||
0020.HK,2022-08-18,2.18,2.18,2.06,2.08,-0.037
|
||||
0020.HK,2022-08-19,2.09,2.13,2.06,2.1,0.0096
|
||||
0020.HK,2022-08-22,2.15,2.22,2.12,2.19,0.0429
|
||||
0020.HK,2022-08-23,2.18,2.23,2.16,2.2,0.0046
|
||||
0020.HK,2022-08-24,2.21,2.3,2.21,2.24,0.0182
|
||||
0020.HK,2022-08-25,2.25,2.35,2.24,2.35,0.0491
|
||||
0020.HK,2022-08-26,2.34,2.34,2.26,2.29,-0.0255
|
||||
0020.HK,2022-08-29,2.23,2.32,2.21,2.29,0
|
||||
0020.HK,2022-08-30,2.29,2.34,2.27,2.31,0.0087
|
||||
0020.HK,2022-08-31,2.28,2.49,2.26,2.47,0.0693
|
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0020.HK,2022-09-01,2.47,2.49,2.36,2.39,-0.0324
|
||||
0020.HK,2022-09-02,2.38,2.39,2.25,2.26,-0.0544
|
||||
0020.HK,2022-09-05,2.25,2.35,2.25,2.29,0.0133
|
||||
0020.HK,2022-09-06,2.29,2.35,2.29,2.32,0.0131
|
||||
0020.HK,2022-09-07,2.29,2.31,2.26,2.29,-0.0129
|
||||
0020.HK,2022-09-08,2.3,2.35,2.26,2.29,0
|
||||
0020.HK,2022-09-09,2.28,2.35,2.28,2.28,-0.0044
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0020.HK,2022-09-13,2.3,2.3,2.15,2.17,-0.0482
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||||
0020.HK,2022-09-14,2.11,2.14,2.09,2.13,-0.0184
|
||||
0020.HK,2022-09-15,2.12,2.15,2.07,2.08,-0.0235
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0020.HK,2022-09-16,2.05,2.06,1.99,2,-0.0385
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0020.HK,2022-09-19,1.98,1.98,1.85,1.91,-0.045
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||||
0020.HK,2022-09-20,1.94,1.98,1.92,1.94,0.0157
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||||
0020.HK,2022-09-21,1.93,1.99,1.89,1.9,-0.0206
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||||
0020.HK,2022-09-22,1.89,1.95,1.84,1.94,0.0211
|
||||
0020.HK,2022-09-23,1.94,1.99,1.91,1.94,0
|
||||
0020.HK,2022-09-26,1.91,1.96,1.84,1.87,-0.0361
|
||||
0020.HK,2022-09-27,1.86,1.87,1.81,1.84,-0.016
|
||||
0020.HK,2022-09-28,1.82,1.83,1.72,1.73,-0.0598
|
||||
0020.HK,2022-09-29,1.78,1.79,1.58,1.63,-0.0578
|
||||
0020.HK,2022-09-30,1.63,1.67,1.57,1.6,-0.0184
|
||||
0020.HK,2022-10-03,1.57,1.64,1.56,1.61,0.0063
|
||||
0020.HK,2022-10-05,1.67,1.77,1.67,1.72,0.0683
|
||||
0020.HK,2022-10-06,1.71,1.75,1.65,1.66,-0.0349
|
||||
0020.HK,2022-10-07,1.66,1.66,1.58,1.59,-0.0422
|
||||
0020.HK,2022-10-10,1.54,1.56,1.49,1.5,-0.0566
|
||||
0020.HK,2022-10-11,1.5,1.53,1.32,1.33,-0.1133
|
||||
0020.HK,2022-10-12,1.33,1.34,1.2,1.28,-0.0376
|
||||
0020.HK,2022-10-13,1.3,1.32,1.2,1.21,-0.0547
|
||||
0020.HK,2022-10-14,1.25,1.28,1.21,1.24,0.0248
|
||||
0020.HK,2022-10-17,1.21,1.29,1.2,1.27,0.0242
|
||||
0020.HK,2022-10-18,1.3,1.37,1.27,1.35,0.063
|
||||
0020.HK,2022-10-19,1.34,1.34,1.27,1.29,-0.0444
|
||||
0020.HK,2022-10-20,1.25,1.31,1.22,1.28,-0.0078
|
||||
0020.HK,2022-10-21,1.29,1.31,1.25,1.26,-0.0156
|
||||
0020.HK,2022-10-24,1.26,1.29,1.13,1.17,-0.0714
|
||||
0020.HK,2022-10-25,1.2,1.26,1.11,1.21,0.0342
|
||||
0020.HK,2022-10-26,1.21,1.3,1.2,1.29,0.0661
|
||||
0020.HK,2022-10-27,1.34,1.35,1.24,1.26,-0.0233
|
||||
0020.HK,2022-10-28,1.26,1.28,1.18,1.19,-0.0556
|
||||
0020.HK,2022-10-31,1.2,1.26,1.18,1.19,0
|
||||
0020.HK,2022-11-01,1.21,1.29,1.18,1.28,0.0756
|
||||
0020.HK,2022-11-02,1.28,1.33,1.25,1.28,0
|
||||
0020.HK,2022-11-03,1.25,1.3,1.23,1.24,-0.0313
|
||||
0020.HK,2022-11-04,1.26,1.38,1.25,1.33,0.0726
|
||||
0020.HK,2022-11-07,1.34,1.8,1.33,1.8,0.3534
|
||||
0020.HK,2022-11-08,1.84,2.04,1.72,1.75,-0.0278
|
||||
0020.HK,2022-11-09,1.78,1.89,1.65,1.69,-0.0343
|
||||
0020.HK,2022-11-10,1.64,1.65,1.53,1.57,-0.071
|
||||
0020.HK,2022-11-11,1.78,1.82,1.65,1.7,0.0828
|
||||
0020.HK,2022-11-14,1.76,1.87,1.71,1.75,0.0294
|
||||
0020.HK,2022-11-15,1.69,1.85,1.63,1.79,0.0229
|
||||
0020.HK,2022-11-16,1.78,2.1,1.76,2.02,0.1285
|
||||
0020.HK,2022-11-17,2,2.2,1.88,2.13,0.0545
|
||||
0020.HK,2022-11-18,2.18,2.24,2.01,2.01,-0.0563
|
||||
0020.HK,2022-11-21,1.98,1.98,1.89,1.94,-0.0348
|
||||
0020.HK,2022-11-22,1.94,2.02,1.87,1.87,-0.0361
|
||||
0020.HK,2022-11-23,1.9,1.96,1.81,1.84,-0.016
|
||||
0020.HK,2022-11-24,1.87,1.89,1.81,1.85,0.0054
|
||||
0020.HK,2022-11-25,1.86,1.86,1.74,1.77,-0.0432
|
||||
0020.HK,2022-11-28,1.69,1.75,1.65,1.73,-0.0226
|
||||
0020.HK,2022-11-29,1.78,1.85,1.75,1.83,0.0578
|
||||
0020.HK,2022-11-30,1.86,1.97,1.84,1.91,0.0437
|
||||
0020.HK,2022-12-01,2,2.02,1.93,1.94,0.0157
|
||||
0020.HK,2022-12-02,1.95,2.02,1.91,2.01,0.0361
|
||||
0020.HK,2022-12-05,2.08,2.17,2.05,2.13,0.0597
|
||||
0020.HK,2022-12-06,2.08,2.13,2.04,2.06,-0.0329
|
||||
0020.HK,2022-12-07,2.06,2.46,2.05,2.2,0.068
|
||||
0020.HK,2022-12-08,2.26,2.39,2.19,2.37,0.0773
|
||||
0020.HK,2022-12-09,2.38,2.47,2.28,2.4,0.0127
|
||||
0020.HK,2022-12-12,2.34,2.54,2.25,2.28,-0.05
|
||||
0020.HK,2022-12-13,2.31,2.37,2.26,2.3,0.0088
|
||||
0020.HK,2022-12-14,2.31,2.35,2.19,2.27,-0.013
|
||||
0020.HK,2022-12-15,2.27,2.27,2.11,2.14,-0.0573
|
||||
0020.HK,2022-12-16,2.12,2.16,2.05,2.12,-0.0093
|
||||
0020.HK,2022-12-19,2.13,2.2,2.1,2.13,0.0047
|
||||
0020.HK,2022-12-20,2.1,2.12,2.05,2.09,-0.0188
|
||||
0020.HK,2022-12-21,2.11,2.16,2.11,2.16,0.0335
|
||||
0020.HK,2022-12-22,2.23,2.24,2.17,2.21,0.0231
|
||||
0020.HK,2022-12-23,2.16,2.21,2.14,2.16,-0.0226
|
||||
0020.HK,2022-12-28,2.18,2.31,2.18,2.29,0.0602
|
||||
0020.HK,2022-12-29,2.23,2.29,2.17,2.18,-0.048
|
||||
0020.HK,2022-12-30,2.25,2.25,2.18,2.22,0.0183
|
||||
0020.HK,2023-01-03,2.19,2.22,2.08,2.19,-0.0135
|
||||
0020.HK,2023-01-04,2.21,2.3,2.2,2.3,0.0502
|
||||
0020.HK,2023-01-05,2.35,2.37,2.26,2.26,-0.0174
|
||||
0020.HK,2023-01-06,2.27,2.28,2.11,2.15,-0.0487
|
||||
0020.HK,2023-01-09,2.18,2.23,2.15,2.19,0.0186
|
||||
0020.HK,2023-01-10,2.19,2.21,2.14,2.21,0.0091
|
||||
0020.HK,2023-01-11,2.23,2.34,2.18,2.22,0.0045
|
||||
0020.HK,2023-01-12,2.25,2.25,2.14,2.16,-0.027
|
||||
0020.HK,2023-01-13,2.17,2.22,2.15,2.22,0.0278
|
||||
0020.HK,2023-01-16,2.23,2.25,2.17,2.18,-0.018
|
||||
0020.HK,2023-01-17,2.18,2.19,2.11,2.13,-0.0229
|
||||
0020.HK,2023-01-18,2.11,2.16,2.1,2.13,0
|
||||
0020.HK,2023-01-19,2.11,2.13,2.08,2.11,-0.0094
|
||||
0020.HK,2023-01-20,2.1,2.17,2.1,2.17,0.0284
|
||||
0020.HK,2023-01-26,2.2,2.27,2.19,2.26,0.0415
|
||||
0020.HK,2023-01-27,2.34,2.74,2.31,2.71,0.1991
|
||||
0020.HK,2023-01-30,2.72,2.85,2.59,2.75,0.0148
|
||||
0020.HK,2023-01-31,2.78,2.84,2.62,2.84,0.0327
|
||||
0020.HK,2023-02-01,2.89,3.1,2.86,2.94,0.0352
|
||||
0020.HK,2023-02-02,3,3.07,2.71,2.73,-0.0714
|
||||
0020.HK,2023-02-03,2.74,2.88,2.74,2.82,0.033
|
||||
0020.HK,2023-02-06,2.79,3.03,2.75,3,0.0638
|
||||
0020.HK,2023-02-07,2.99,3.06,2.86,2.88,-0.04
|
||||
0020.HK,2023-02-08,2.87,2.88,2.68,2.69,-0.066
|
||||
0020.HK,2023-02-09,2.65,2.8,2.61,2.78,0.0335
|
||||
0020.HK,2023-02-10,2.75,2.81,2.68,2.69,-0.0324
|
||||
0020.HK,2023-02-13,2.66,2.71,2.6,2.67,-0.0074
|
||||
0020.HK,2023-02-14,2.73,2.79,2.7,2.74,0.0262
|
||||
0020.HK,2023-02-15,2.78,2.86,2.69,2.73,-0.0036
|
||||
0020.HK,2023-02-16,2.76,2.82,2.63,2.65,-0.0293
|
||||
0020.HK,2023-02-17,2.66,2.7,2.48,2.5,-0.0566
|
||||
0020.HK,2023-02-20,2.5,2.56,2.46,2.48,-0.008
|
||||
0020.HK,2023-02-21,2.5,2.57,2.48,2.49,0.004
|
||||
0020.HK,2023-02-22,2.46,2.49,2.37,2.42,-0.0281
|
||||
0020.HK,2023-02-23,2.44,2.46,2.38,2.4,-0.0083
|
||||
0020.HK,2023-02-24,2.4,2.56,2.39,2.51,0.0458
|
||||
0020.HK,2023-02-27,2.48,2.58,2.46,2.54,0.012
|
||||
0020.HK,2023-02-28,2.58,2.61,2.46,2.52,-0.0079
|
||||
0020.HK,2023-03-01,2.54,2.73,2.54,2.71,0.0754
|
||||
0020.HK,2023-03-02,2.69,2.74,2.66,2.71,0
|
||||
0020.HK,2023-03-03,2.74,2.76,2.63,2.69,-0.0074
|
||||
0020.HK,2023-03-06,2.67,2.71,2.58,2.66,-0.0112
|
||||
0020.HK,2023-03-07,2.65,2.7,2.5,2.53,-0.0489
|
||||
0020.HK,2023-03-08,2.5,2.51,2.4,2.5,-0.0119
|
||||
0020.HK,2023-03-09,2.5,2.54,2.45,2.51,0.004
|
||||
0020.HK,2023-03-10,2.45,2.54,2.44,2.51,0
|
||||
0020.HK,2023-03-13,2.52,2.59,2.47,2.52,0.004
|
||||
0020.HK,2023-03-14,2.53,2.59,2.45,2.48,-0.0159
|
||||
0020.HK,2023-03-15,2.56,2.6,2.5,2.55,0.0282
|
||||
0020.HK,2023-03-16,2.52,2.58,2.44,2.47,-0.0314
|
||||
0020.HK,2023-03-17,2.54,2.73,2.52,2.73,0.1053
|
||||
0020.HK,2023-03-20,2.72,2.74,2.61,2.66,-0.0256
|
||||
0020.HK,2023-03-21,2.7,2.7,2.63,2.68,0.0075
|
||||
0020.HK,2023-03-22,2.7,2.74,2.67,2.68,0
|
||||
0020.HK,2023-03-23,2.7,2.89,2.68,2.88,0.0746
|
||||
0020.HK,2023-03-24,2.87,2.92,2.77,2.9,0.0069
|
||||
0020.HK,2023-03-27,2.95,2.98,2.8,2.81,-0.031
|
||||
0020.HK,2023-03-28,2.81,2.83,2.64,2.68,-0.0463
|
||||
0020.HK,2023-03-29,2.72,2.76,2.56,2.62,-0.0224
|
||||
0020.HK,2023-03-30,2.64,2.77,2.6,2.64,0.0076
|
||||
0020.HK,2023-03-31,2.68,2.68,2.58,2.66,0.0076
|
||||
0020.HK,2023-04-03,2.65,2.9,2.65,2.89,0.0865
|
||||
0020.HK,2023-04-04,3.12,3.4,3.06,3.26,0.128
|
||||
0020.HK,2023-04-06,3.13,3.5,3.11,3.33,0.0215
|
||||
0020.HK,2023-04-11,3.62,3.7,3.13,3.3,-0.009
|
||||
0020.HK,2023-04-12,3.34,3.52,3.1,3.16,-0.0424
|
||||
0020.HK,2023-04-13,3.11,3.17,2.9,2.92,-0.0759
|
||||
0020.HK,2023-04-14,2.97,2.99,2.8,2.93,0.0034
|
||||
0020.HK,2023-04-17,2.92,2.93,2.76,2.81,-0.041
|
||||
0020.HK,2023-04-18,2.79,2.82,2.7,2.77,-0.0142
|
||||
0020.HK,2023-04-19,2.76,2.83,2.71,2.73,-0.0144
|
||||
0020.HK,2023-04-20,2.73,2.8,2.71,2.73,0
|
||||
0020.HK,2023-04-21,2.72,2.74,2.38,2.42,-0.1136
|
||||
0020.HK,2023-04-24,2.42,2.54,2.4,2.48,0.0248
|
||||
0020.HK,2023-04-25,2.48,2.5,2.38,2.42,-0.0242
|
||||
0020.HK,2023-04-26,2.4,2.47,2.4,2.44,0.0083
|
||||
0020.HK,2023-04-27,2.45,2.5,2.4,2.48,0.0164
|
||||
0020.HK,2023-04-28,2.51,2.63,2.5,2.6,0.0484
|
||||
0020.HK,2023-05-02,2.61,2.67,2.54,2.57,-0.0115
|
||||
0020.HK,2023-05-03,2.52,2.59,2.49,2.57,0
|
||||
0020.HK,2023-05-04,2.58,2.64,2.56,2.6,0.0117
|
||||
0020.HK,2023-05-05,2.6,2.65,2.56,2.59,-0.0038
|
||||
0020.HK,2023-05-08,2.5,2.51,2.4,2.43,-0.0618
|
||||
0020.HK,2023-05-09,2.43,2.43,2.22,2.27,-0.0658
|
||||
0020.HK,2023-05-10,2.28,2.35,2.27,2.31,0.0176
|
||||
0020.HK,2023-05-11,2.31,2.32,2.26,2.28,-0.013
|
||||
0020.HK,2023-05-12,2.31,2.34,2.28,2.28,0
|
||||
0020.HK,2023-05-15,2.25,2.29,2.19,2.25,-0.0132
|
||||
0020.HK,2023-05-16,2.28,2.29,2.2,2.22,-0.0133
|
||||
0020.HK,2023-05-17,2.23,2.25,2.16,2.17,-0.0225
|
||||
0020.HK,2023-05-18,2.2,2.3,2.18,2.24,0.0323
|
||||
0020.HK,2023-05-19,2.24,2.25,2.19,2.21,-0.0134
|
||||
0020.HK,2023-05-22,2.21,2.26,2.21,2.23,0.009
|
||||
0020.HK,2023-05-23,2.25,2.26,2.19,2.19,-0.0179
|
||||
0020.HK,2023-05-24,2.18,2.18,2.07,2.08,-0.0502
|
||||
0020.HK,2023-05-25,2.07,2.13,2.04,2.1,0.0096
|
||||
0020.HK,2023-05-29,2.15,2.22,2.12,2.15,0.0238
|
||||
0020.HK,2023-05-30,2.15,2.2,2.1,2.19,0.0186
|
||||
0020.HK,2023-05-31,2.16,2.18,2.06,2.1,-0.0411
|
||||
0020.HK,2023-06-01,2.1,2.21,2.07,2.12,0.0095
|
||||
0020.HK,2023-06-02,2.2,2.23,2.16,2.2,0.0377
|
||||
0020.HK,2023-06-05,2.24,2.26,2.16,2.18,-0.0091
|
||||
0020.HK,2023-06-06,2.16,2.2,2.1,2.12,-0.0275
|
||||
0020.HK,2023-06-07,2.18,2.18,2.12,2.14,0.0094
|
||||
0020.HK,2023-06-08,2.12,2.14,2.08,2.11,-0.014
|
||||
0020.HK,2023-06-09,2.12,2.19,2.07,2.18,0.0332
|
||||
0020.HK,2023-06-12,2.18,2.2,2.15,2.17,-0.0046
|
||||
0020.HK,2023-06-13,2.16,2.34,2.15,2.33,0.0737
|
||||
0020.HK,2023-06-14,2.36,2.36,2.23,2.26,-0.03
|
||||
0020.HK,2023-06-15,2.29,2.32,2.24,2.28,0.0088
|
||||
0020.HK,2023-06-16,2.29,2.33,2.26,2.29,0.0044
|
||||
0020.HK,2023-06-19,2.27,2.36,2.22,2.28,-0.0044
|
||||
0020.HK,2023-06-20,2.28,2.3,2.21,2.25,-0.0132
|
||||
0020.HK,2023-06-21,2.22,2.24,2.1,2.11,-0.0622
|
||||
0020.HK,2023-06-23,2.14,2.14,2.08,2.1,-0.0047
|
||||
0020.HK,2023-06-26,2.1,2.14,2.05,2.13,0.0143
|
||||
0020.HK,2023-06-27,2.13,2.16,2.12,2.13,0
|
||||
0020.HK,2023-06-28,2.13,2.16,2.07,2.15,0.0094
|
||||
0020.HK,2023-06-29,2.15,2.16,2.09,2.11,-0.0186
|
||||
0020.HK,2023-06-30,2.11,2.12,2.07,2.07,-0.019
|
||||
0020.HK,2023-07-03,2.1,2.23,2.09,2.18,0.0531
|
||||
0020.HK,2023-07-04,2.16,2.16,1.97,1.99,-0.0872
|
||||
0020.HK,2023-07-05,1.99,2,1.87,1.9,-0.0452
|
||||
0020.HK,2023-07-06,1.9,1.94,1.86,1.89,-0.0053
|
||||
0020.HK,2023-07-07,1.89,1.92,1.86,1.87,-0.0106
|
||||
0020.HK,2023-07-10,1.91,1.93,1.87,1.87,0
|
||||
0020.HK,2023-07-11,1.89,1.89,1.78,1.79,-0.0428
|
||||
0020.HK,2023-07-12,1.8,1.85,1.78,1.79,0
|
||||
0020.HK,2023-07-13,1.83,1.94,1.81,1.93,0.0782
|
||||
0020.HK,2023-07-14,1.94,1.96,1.89,1.92,-0.0052
|
||||
0020.HK,2023-07-18,1.9,1.9,1.8,1.8,-0.0625
|
||||
0020.HK,2023-07-19,1.8,1.83,1.78,1.79,-0.0056
|
||||
0020.HK,2023-07-20,1.8,1.82,1.68,1.69,-0.0559
|
||||
0020.HK,2023-07-21,1.64,1.84,1.61,1.72,0.0178
|
||||
0020.HK,2023-07-24,1.72,1.75,1.67,1.68,-0.0233
|
||||
0020.HK,2023-07-25,1.73,1.79,1.71,1.77,0.0536
|
||||
0020.HK,2023-07-26,1.77,1.79,1.73,1.76,-0.0056
|
||||
0020.HK,2023-07-27,1.77,1.81,1.76,1.8,0.0227
|
||||
0020.HK,2023-07-28,1.76,1.84,1.75,1.82,0.0111
|
||||
0020.HK,2023-07-31,1.85,1.91,1.84,1.85,0.0165
|
||||
0020.HK,2023-08-01,1.87,1.88,1.8,1.82,-0.0162
|
||||
0020.HK,2023-08-02,1.8,1.83,1.75,1.76,-0.033
|
||||
0020.HK,2023-08-03,1.75,1.79,1.74,1.76,0
|
||||
0020.HK,2023-08-04,1.79,1.81,1.75,1.76,0
|
||||
0020.HK,2023-08-07,1.74,1.74,1.69,1.7,-0.0341
|
||||
0020.HK,2023-08-08,1.68,1.72,1.67,1.68,-0.0118
|
||||
0020.HK,2023-08-09,1.67,1.7,1.66,1.69,0.006
|
||||
0020.HK,2023-08-10,1.68,1.68,1.62,1.65,-0.0237
|
||||
0020.HK,2023-08-11,1.65,1.65,1.56,1.59,-0.0364
|
||||
0020.HK,2023-08-14,1.54,1.62,1.5,1.61,0.0126
|
||||
0020.HK,2023-08-15,1.61,1.62,1.55,1.57,-0.0248
|
||||
0020.HK,2023-08-16,1.55,1.57,1.51,1.52,-0.0318
|
||||
0020.HK,2023-08-17,1.5,1.56,1.5,1.55,0.0197
|
||||
0020.HK,2023-08-18,1.55,1.56,1.5,1.51,-0.0258
|
||||
0020.HK,2023-08-21,1.51,1.52,1.48,1.49,-0.0132
|
||||
0020.HK,2023-08-22,1.49,1.53,1.47,1.52,0.0201
|
||||
0020.HK,2023-08-23,1.52,1.53,1.48,1.5,-0.0132
|
||||
0020.HK,2023-08-24,1.51,1.58,1.5,1.57,0.0467
|
||||
0020.HK,2023-08-25,1.54,1.55,1.51,1.53,-0.0255
|
||||
0020.HK,2023-08-28,1.58,1.6,1.5,1.5,-0.0196
|
||||
0020.HK,2023-08-29,1.51,1.55,1.37,1.54,0.0267
|
||||
0020.HK,2023-08-30,1.57,1.58,1.48,1.51,-0.0195
|
||||
0020.HK,2023-08-31,1.56,1.62,1.53,1.56,0.0331
|
||||
0020.HK,2023-09-04,1.58,1.71,1.57,1.68,0.0769
|
||||
0020.HK,2023-09-05,1.68,1.68,1.55,1.6,-0.0476
|
||||
0020.HK,2023-09-06,1.59,1.61,1.54,1.59,-0.0063
|
||||
0020.HK,2023-09-07,1.58,1.58,1.5,1.51,-0.0503
|
||||
0020.HK,2023-09-11,1.48,1.55,1.45,1.55,0.0265
|
||||
0020.HK,2023-09-12,1.55,1.55,1.49,1.5,-0.0323
|
||||
0020.HK,2023-09-13,1.51,1.52,1.47,1.48,-0.0133
|
||||
0020.HK,2023-09-14,1.49,1.5,1.47,1.48,0
|
||||
0020.HK,2023-09-15,1.49,1.51,1.43,1.49,0.0068
|
||||
0020.HK,2023-09-18,1.47,1.49,1.45,1.46,-0.0201
|
||||
0020.HK,2023-09-19,1.45,1.47,1.43,1.44,-0.0137
|
||||
0020.HK,2023-09-20,1.43,1.43,1.4,1.41,-0.0208
|
||||
0020.HK,2023-09-21,1.4,1.42,1.37,1.38,-0.0213
|
||||
0020.HK,2023-09-22,1.37,1.45,1.36,1.45,0.0507
|
||||
0020.HK,2023-09-25,1.46,1.47,1.42,1.42,-0.0207
|
||||
0020.HK,2023-09-26,1.43,1.46,1.4,1.42,0
|
||||
0020.HK,2023-09-27,1.43,1.44,1.38,1.39,-0.0211
|
||||
0020.HK,2023-09-28,1.39,1.42,1.37,1.37,-0.0144
|
||||
0020.HK,2023-09-29,1.39,1.44,1.38,1.43,0.0438
|
||||
0020.HK,2023-10-03,1.41,1.42,1.35,1.37,-0.042
|
||||
0020.HK,2023-10-04,1.37,1.37,1.31,1.32,-0.0365
|
||||
0020.HK,2023-10-05,1.33,1.37,1.32,1.34,0.0152
|
||||
0020.HK,2023-10-06,1.36,1.43,1.35,1.43,0.0672
|
||||
0020.HK,2023-10-09,1.46,1.46,1.4,1.41,-0.014
|
||||
0020.HK,2023-10-10,1.41,1.45,1.41,1.42,0.0071
|
||||
0020.HK,2023-10-11,1.45,1.49,1.43,1.47,0.0352
|
||||
0020.HK,2023-10-12,1.5,1.5,1.46,1.48,0.0068
|
||||
0020.HK,2023-10-13,1.46,1.47,1.43,1.44,-0.027
|
||||
0020.HK,2023-10-16,1.44,1.45,1.4,1.43,-0.0069
|
||||
0020.HK,2023-10-17,1.45,1.45,1.4,1.43,0
|
||||
0020.HK,2023-10-18,1.42,1.43,1.39,1.41,-0.014
|
||||
0020.HK,2023-10-19,1.4,1.43,1.38,1.4,-0.0071
|
||||
0020.HK,2023-10-20,1.4,1.4,1.37,1.38,-0.0143
|
||||
0020.HK,2023-10-24,1.38,1.39,1.34,1.35,-0.0217
|
||||
0020.HK,2023-10-25,1.4,1.43,1.37,1.39,0.0296
|
||||
0020.HK,2023-10-26,1.38,1.41,1.36,1.38,-0.0072
|
||||
0020.HK,2023-10-27,1.39,1.42,1.37,1.4,0.0145
|
||||
0020.HK,2023-10-30,1.4,1.46,1.4,1.42,0.0143
|
||||
0020.HK,2023-10-31,1.42,1.43,1.38,1.4,-0.0141
|
||||
0020.HK,2023-11-01,1.4,1.41,1.38,1.4,0
|
||||
0020.HK,2023-11-02,1.41,1.43,1.38,1.39,-0.0071
|
||||
0020.HK,2023-11-03,1.41,1.46,1.39,1.42,0.0216
|
||||
0020.HK,2023-11-06,1.45,1.54,1.43,1.53,0.0775
|
||||
0020.HK,2023-11-07,1.51,1.56,1.5,1.54,0.0065
|
||||
0020.HK,2023-11-08,1.55,1.57,1.52,1.53,-0.0065
|
||||
0020.HK,2023-11-09,1.53,1.56,1.48,1.49,-0.0261
|
||||
0020.HK,2023-11-10,1.47,1.48,1.43,1.44,-0.0336
|
||||
0020.HK,2023-11-13,1.46,1.54,1.46,1.53,0.0625
|
||||
0020.HK,2023-11-14,1.53,1.55,1.5,1.54,0.0065
|
||||
0020.HK,2023-11-15,1.58,1.6,1.53,1.56,0.013
|
||||
0020.HK,2023-11-16,1.57,1.57,1.51,1.52,-0.0256
|
||||
0020.HK,2023-11-17,1.5,1.52,1.47,1.48,-0.0263
|
||||
0020.HK,2023-11-20,1.5,1.55,1.48,1.55,0.0473
|
||||
0020.HK,2023-11-21,1.57,1.57,1.48,1.5,-0.0323
|
||||
0020.HK,2023-11-22,1.49,1.5,1.47,1.47,-0.02
|
||||
0020.HK,2023-11-23,1.48,1.53,1.46,1.52,0.034
|
||||
0020.HK,2023-11-24,1.51,1.52,1.46,1.46,-0.0395
|
||||
0020.HK,2023-11-27,1.47,1.47,1.43,1.44,-0.0137
|
||||
0020.HK,2023-11-28,1.44,1.45,1.3,1.37,-0.0486
|
||||
0020.HK,2023-11-29,1.37,1.38,1.33,1.36,-0.0073
|
||||
0020.HK,2023-11-30,1.36,1.37,1.33,1.36,0
|
||||
0020.HK,2023-12-01,1.36,1.39,1.34,1.38,0.0147
|
||||
0020.HK,2023-12-04,1.39,1.4,1.34,1.36,-0.0145
|
||||
0020.HK,2023-12-05,1.35,1.36,1.31,1.32,-0.0294
|
||||
0020.HK,2023-12-06,1.32,1.34,1.25,1.29,-0.0227
|
||||
0020.HK,2023-12-07,1.28,1.3,1.25,1.28,-0.0078
|
||||
0020.HK,2023-12-08,1.3,1.33,1.27,1.29,0.0078
|
||||
0020.HK,2023-12-11,1.29,1.29,1.23,1.25,-0.031
|
||||
0020.HK,2023-12-12,1.26,1.27,1.23,1.24,-0.008
|
||||
0020.HK,2023-12-13,1.24,1.24,1.2,1.22,-0.0161
|
||||
0020.HK,2023-12-14,1.24,1.25,1.21,1.22,0
|
||||
0020.HK,2023-12-15,1.24,1.29,1.24,1.26,0.0328
|
||||
0020.HK,2023-12-18,1.08,1.15,1.03,1.12,-0.1111
|
||||
0020.HK,2023-12-19,1.12,1.17,1.11,1.12,0
|
||||
0020.HK,2023-12-20,1.13,1.18,1.13,1.15,0.0268
|
||||
0020.HK,2023-12-21,1.14,1.17,1.13,1.15,0
|
||||
0020.HK,2023-12-22,1.17,1.17,1.08,1.08,-0.0609
|
||||
0020.HK,2023-12-27,1.09,1.12,1.06,1.09,0.0093
|
||||
0020.HK,2023-12-28,1.1,1.17,1.09,1.15,0.055
|
||||
0020.HK,2023-12-29,1.15,1.17,1.14,1.16,0.0087
|
||||
0020.HK,2024-01-02,1.18,1.19,1.14,1.16,0
|
||||
0020.HK,2024-01-03,1.14,1.16,1.11,1.12,-0.0345
|
||||
0020.HK,2024-01-04,1.12,1.13,1.09,1.1,-0.0179
|
||||
0020.HK,2024-01-05,1.1,1.12,1.08,1.09,-0.0091
|
||||
0020.HK,2024-01-08,1.09,1.09,1.02,1.03,-0.055
|
||||
0020.HK,2024-01-09,1.04,1.07,1.03,1.05,0.0194
|
||||
0020.HK,2024-01-10,1.05,1.06,1.02,1.02,-0.0286
|
||||
0020.HK,2024-01-11,1.03,1.07,1.02,1.05,0.0294
|
||||
0020.HK,2024-01-12,1.05,1.07,1.04,1.05,0
|
||||
0020.HK,2024-01-15,1.05,1.06,1.03,1.06,0.0095
|
||||
0020.HK,2024-01-16,1.05,1.07,1.03,1.03,-0.0283
|
||||
0020.HK,2024-01-17,1.03,1.03,0.91,0.91,-0.1165
|
||||
0020.HK,2024-01-18,0.91,0.94,0.89,0.91,0
|
||||
0020.HK,2024-01-19,0.91,0.93,0.87,0.89,-0.022
|
||||
0020.HK,2024-01-22,0.89,0.9,0.82,0.83,-0.0674
|
||||
0020.HK,2024-01-23,0.84,0.93,0.83,0.91,0.0964
|
||||
0020.HK,2024-01-24,0.92,0.93,0.88,0.92,0.011
|
||||
0020.HK,2024-01-25,0.93,0.93,0.89,0.92,0
|
||||
0020.HK,2024-01-26,0.91,0.92,0.87,0.87,-0.0543
|
||||
0020.HK,2024-01-29,0.87,0.9,0.86,0.87,0
|
||||
0020.HK,2024-01-30,0.87,0.87,0.83,0.84,-0.0345
|
||||
0020.HK,2024-01-31,0.83,0.85,0.78,0.79,-0.0595
|
||||
0020.HK,2024-02-01,0.8,0.83,0.79,0.79,0
|
||||
0020.HK,2024-02-02,0.81,0.84,0.76,0.78,-0.0127
|
||||
0020.HK,2024-02-05,0.77,0.82,0.76,0.78,0
|
||||
0020.HK,2024-02-06,0.78,0.88,0.78,0.86,0.1026
|
||||
0020.HK,2024-02-07,0.88,0.88,0.82,0.83,-0.0349
|
||||
0020.HK,2024-02-08,0.83,0.88,0.83,0.86,0.0361
|
||||
0020.HK,2024-02-09,0.85,0.85,0.81,0.82,-0.0465
|
||||
0020.HK,2024-02-14,0.81,0.85,0.78,0.8,-0.0244
|
||||
0020.HK,2024-02-15,0.81,0.82,0.78,0.8,0
|
||||
0020.HK,2024-02-16,0.8,0.86,0.8,0.85,0.0625
|
||||
0020.HK,2024-02-19,0.88,0.91,0.85,0.86,0.0118
|
||||
0020.HK,2024-02-20,0.86,0.87,0.83,0.85,-0.0116
|
||||
0020.HK,2024-02-21,0.84,0.94,0.83,0.91,0.0706
|
||||
0020.HK,2024-02-22,0.92,0.93,0.9,0.93,0.022
|
||||
0020.HK,2024-02-23,0.93,0.96,0.91,0.93,0
|
||||
0020.HK,2024-02-26,0.93,0.96,0.91,0.92,-0.0108
|
||||
0020.HK,2024-02-27,0.92,0.97,0.88,0.96,0.0435
|
||||
0020.HK,2024-02-28,0.97,0.99,0.89,0.9,-0.0625
|
||||
0020.HK,2024-02-29,0.89,0.94,0.89,0.9,0
|
||||
0020.HK,2024-03-01,0.9,0.91,0.88,0.89,-0.0111
|
||||
0020.HK,2024-03-04,0.9,0.93,0.89,0.89,0
|
||||
0020.HK,2024-03-05,0.88,0.89,0.82,0.83,-0.0674
|
||||
0020.HK,2024-03-06,0.84,0.86,0.82,0.84,0.012
|
||||
0020.HK,2024-03-07,0.84,0.89,0.83,0.83,-0.0119
|
||||
0020.HK,2024-03-08,0.84,0.87,0.83,0.86,0.0361
|
||||
0020.HK,2024-03-11,0.87,0.91,0.86,0.91,0.0581
|
||||
0020.HK,2024-03-12,0.92,0.93,0.9,0.92,0.011
|
||||
0020.HK,2024-03-13,0.92,0.94,0.88,0.89,-0.0326
|
||||
0020.HK,2024-03-14,0.89,0.9,0.84,0.85,-0.0449
|
||||
0020.HK,2024-03-15,0.84,0.85,0.82,0.84,-0.0118
|
||||
0020.HK,2024-03-18,0.85,0.85,0.83,0.84,0
|
||||
0020.HK,2024-03-19,0.84,0.85,0.82,0.82,-0.0238
|
||||
0020.HK,2024-03-20,0.82,0.84,0.82,0.82,0
|
||||
0020.HK,2024-03-21,0.84,0.87,0.83,0.84,0.0244
|
||||
0020.HK,2024-03-22,0.84,0.85,0.8,0.8,-0.0476
|
||||
0020.HK,2024-03-25,0.81,0.81,0.78,0.78,-0.025
|
||||
0020.HK,2024-03-26,0.78,0.79,0.76,0.78,0
|
||||
0020.HK,2024-03-27,0.79,0.79,0.7,0.7,-0.1026
|
||||
0020.HK,2024-03-28,0.7,0.74,0.7,0.71,0.0143
|
||||
0020.HK,2024-04-02,0.71,0.74,0.71,0.73,0.0282
|
||||
0020.HK,2024-04-03,0.73,0.74,0.68,0.68,-0.0685
|
||||
0020.HK,2024-04-05,0.68,0.69,0.62,0.65,-0.0441
|
||||
0020.HK,2024-04-08,0.66,0.68,0.64,0.65,0
|
||||
0020.HK,2024-04-09,0.65,0.68,0.65,0.67,0.0308
|
||||
0020.HK,2024-04-10,0.68,0.7,0.67,0.68,0.0149
|
||||
0020.HK,2024-04-11,0.67,0.7,0.66,0.68,0
|
||||
0020.HK,2024-04-12,0.68,0.71,0.66,0.66,-0.0294
|
||||
0020.HK,2024-04-15,0.65,0.66,0.61,0.62,-0.0606
|
||||
0020.HK,2024-04-16,0.61,0.62,0.58,0.59,-0.0484
|
||||
0020.HK,2024-04-17,0.59,0.62,0.58,0.62,0.0508
|
||||
0020.HK,2024-04-18,0.62,0.63,0.6,0.61,-0.0161
|
||||
0020.HK,2024-04-19,0.61,0.61,0.58,0.58,-0.0492
|
||||
0020.HK,2024-04-22,0.58,0.61,0.58,0.6,0.0345
|
||||
0020.HK,2024-04-23,0.61,0.63,0.6,0.61,0.0167
|
||||
0020.HK,2024-04-24,0.63,0.83,0.62,0.8,0.3115
|
||||
0020.HK,2024-04-25,0.94,0.96,0.82,0.83,0.0375
|
||||
0020.HK,2024-04-26,0.84,1.23,0.84,1.19,0.4337
|
||||
0020.HK,2024-04-29,1.22,1.32,1.16,1.21,0.0168
|
||||
0020.HK,2024-04-30,1.25,1.28,1.11,1.22,0.0083
|
||||
0020.HK,2024-05-02,1.21,1.68,1.19,1.66,0.3607
|
||||
0020.HK,2024-05-03,1.76,1.76,1.54,1.6,-0.0361
|
||||
0020.HK,2024-05-06,1.58,1.73,1.51,1.68,0.05
|
||||
0020.HK,2024-05-07,1.7,1.77,1.63,1.65,-0.0179
|
||||
0020.HK,2024-05-08,1.68,1.74,1.38,1.41,-0.1455
|
||||
0020.HK,2024-05-09,1.42,1.49,1.4,1.45,0.0284
|
||||
0020.HK,2024-05-10,1.47,1.5,1.34,1.47,0.0138
|
||||
0020.HK,2024-05-13,1.48,1.55,1.42,1.46,-0.0068
|
||||
0020.HK,2024-05-14,1.5,1.54,1.42,1.45,-0.0068
|
||||
0020.HK,2024-05-16,1.47,1.48,1.37,1.38,-0.0483
|
||||
0020.HK,2024-05-17,1.39,1.44,1.36,1.4,0.0145
|
||||
0020.HK,2024-05-20,1.4,1.6,1.38,1.57,0.1214
|
||||
0020.HK,2024-05-21,1.56,1.59,1.5,1.5,-0.0446
|
||||
0020.HK,2024-05-22,1.52,1.53,1.46,1.48,-0.0133
|
||||
0020.HK,2024-05-23,1.5,1.55,1.47,1.48,0
|
||||
0020.HK,2024-05-24,1.49,1.52,1.37,1.4,-0.0541
|
||||
0020.HK,2024-05-27,1.4,1.42,1.29,1.37,-0.0214
|
||||
0020.HK,2024-05-28,1.39,1.4,1.3,1.31,-0.0438
|
||||
0020.HK,2024-05-29,1.31,1.46,1.27,1.37,0.0458
|
||||
0020.HK,2024-05-30,1.37,1.4,1.35,1.36,-0.0073
|
||||
0020.HK,2024-05-31,1.38,1.42,1.31,1.32,-0.0294
|
||||
0020.HK,2024-06-03,1.33,1.38,1.32,1.36,0.0303
|
||||
0020.HK,2024-06-04,1.36,1.42,1.35,1.39,0.0221
|
||||
0020.HK,2024-06-05,1.39,1.45,1.37,1.38,-0.0072
|
||||
0020.HK,2024-06-06,1.4,1.5,1.38,1.48,0.0725
|
||||
0020.HK,2024-06-07,1.49,1.51,1.44,1.45,-0.0203
|
||||
0020.HK,2024-06-11,1.44,1.45,1.38,1.4,-0.0345
|
||||
0020.HK,2024-06-12,1.39,1.43,1.37,1.4,0
|
||||
0020.HK,2024-06-13,1.42,1.43,1.37,1.38,-0.0143
|
||||
0020.HK,2024-06-14,1.37,1.39,1.33,1.35,-0.0217
|
||||
0020.HK,2024-06-17,1.34,1.36,1.32,1.33,-0.0148
|
||||
0020.HK,2024-06-18,1.34,1.38,1.32,1.34,0.0075
|
||||
0020.HK,2024-06-19,1.35,1.46,1.35,1.43,0.0672
|
||||
0020.HK,2024-06-20,1.44,1.45,1.32,1.32,-0.0769
|
||||
0020.HK,2024-06-21,1.26,1.37,1.23,1.36,0.0303
|
||||
0020.HK,2024-06-24,1.37,1.38,1.32,1.37,0.0074
|
||||
0020.HK,2024-06-25,1.37,1.39,1.36,1.37,0
|
||||
0020.HK,2024-06-26,1.37,1.44,1.33,1.4,0.0219
|
||||
0020.HK,2024-06-27,1.4,1.41,1.33,1.34,-0.0429
|
||||
0020.HK,2024-06-28,1.33,1.37,1.32,1.32,-0.0149
|
||||
0020.HK,2024-07-02,1.32,1.41,1.31,1.38,0.0455
|
||||
0020.HK,2024-07-03,1.41,1.62,1.41,1.62,0.1739
|
||||
0020.HK,2024-07-04,1.66,1.67,1.56,1.61,-0.0062
|
||||
0020.HK,2024-07-05,1.61,1.63,1.35,1.35,-0.1615
|
||||
0020.HK,2024-07-08,1.36,1.39,1.28,1.31,-0.0296
|
||||
0020.HK,2024-07-09,1.32,1.35,1.29,1.33,0.0153
|
||||
0020.HK,2024-07-10,1.34,1.37,1.33,1.33,0
|
||||
0020.HK,2024-07-11,1.34,1.36,1.34,1.35,0.015
|
||||
0020.HK,2024-07-12,1.36,1.39,1.35,1.38,0.0222
|
||||
0020.HK,2024-07-15,1.37,1.37,1.32,1.33,-0.0362
|
||||
0020.HK,2024-07-16,1.32,1.35,1.32,1.34,0.0075
|
||||
0020.HK,2024-07-17,1.34,1.36,1.33,1.34,0
|
||||
0020.HK,2024-07-18,1.34,1.35,1.29,1.31,-0.0224
|
||||
0020.HK,2024-07-19,1.29,1.31,1.28,1.3,-0.0076
|
||||
0020.HK,2024-07-22,1.3,1.31,1.19,1.26,-0.0308
|
||||
0020.HK,2024-07-23,1.26,1.26,1.19,1.2,-0.0476
|
||||
0020.HK,2024-07-24,1.21,1.23,1.15,1.16,-0.0333
|
||||
0020.HK,2024-07-25,1.15,1.18,1.13,1.16,0
|
||||
0020.HK,2024-07-26,1.17,1.22,1.15,1.17,0.0086
|
||||
0020.HK,2024-07-29,1.19,1.21,1.16,1.18,0.0085
|
||||
0020.HK,2024-07-30,1.18,1.18,1.13,1.14,-0.0339
|
||||
0020.HK,2024-07-31,1.14,1.22,1.13,1.21,0.0614
|
||||
0020.HK,2024-08-01,1.21,1.23,1.17,1.18,-0.0248
|
||||
0020.HK,2024-08-02,1.16,1.18,1.14,1.16,-0.0169
|
||||
0020.HK,2024-08-05,1.14,1.17,1.05,1.07,-0.0776
|
||||
0020.HK,2024-08-06,1.1,1.12,1.05,1.09,0.0187
|
||||
0020.HK,2024-08-07,1.1,1.12,1.08,1.09,0
|
||||
0020.HK,2024-08-08,1.08,1.1,1.05,1.08,-0.0092
|
||||
0020.HK,2024-08-09,1.1,1.12,1.09,1.11,0.0278
|
||||
0020.HK,2024-08-12,1.12,1.12,1.07,1.1,-0.009
|
||||
0020.HK,2024-08-13,1.11,1.11,1.08,1.1,0
|
||||
0020.HK,2024-08-14,1.11,1.12,1.07,1.07,-0.0273
|
||||
0020.HK,2024-08-15,1.07,1.15,1.06,1.11,0.0374
|
||||
0020.HK,2024-08-16,1.12,1.14,1.1,1.11,0
|
||||
0020.HK,2024-08-19,1.11,1.15,1.11,1.13,0.018
|
||||
0020.HK,2024-08-20,1.13,1.14,1.09,1.1,-0.0265
|
||||
0020.HK,2024-08-21,1.09,1.12,1.07,1.11,0.0091
|
||||
0020.HK,2024-08-22,1.11,1.13,1.08,1.09,-0.018
|
||||
0020.HK,2024-08-23,1.09,1.11,1.08,1.1,0.0092
|
||||
0020.HK,2024-08-26,1.11,1.17,1.1,1.17,0.0636
|
||||
0020.HK,2024-08-27,1.16,1.19,1.14,1.18,0.0085
|
||||
0020.HK,2024-08-28,1.16,1.17,1.1,1.12,-0.0508
|
||||
0020.HK,2024-08-29,1.1,1.18,1.08,1.16,0.0357
|
||||
0020.HK,2024-08-30,1.17,1.2,1.15,1.18,0.0172
|
||||
0020.HK,2024-09-02,1.17,1.18,1.13,1.14,-0.0339
|
||||
0020.HK,2024-09-03,1.14,1.16,1.13,1.14,0
|
||||
0020.HK,2024-09-04,1.13,1.13,1.09,1.11,-0.0263
|
||||
0020.HK,2024-09-05,1.11,1.14,1.11,1.13,0.018
|
||||
0020.HK,2024-09-09,1.12,1.13,1.08,1.09,-0.0354
|
||||
0020.HK,2024-09-10,1.1,1.11,1.07,1.09,0
|
||||
0020.HK,2024-09-11,1.08,1.09,1,1.03,-0.055
|
||||
0020.HK,2024-09-12,1.04,1.06,1.03,1.04,0.0097
|
||||
0020.HK,2024-09-13,1.04,1.08,1.04,1.04,0
|
||||
0020.HK,2024-09-16,1.05,1.07,1.03,1.07,0.0288
|
||||
0020.HK,2024-09-17,1.08,1.11,1.06,1.1,0.028
|
||||
0020.HK,2024-09-19,1.09,1.13,1.07,1.1,0
|
||||
0020.HK,2024-09-20,1.11,1.2,1.11,1.17,0.0636
|
||||
0020.HK,2024-09-23,1.17,1.21,1.16,1.19,0.0171
|
||||
0020.HK,2024-09-24,1.21,1.24,1.18,1.23,0.0336
|
||||
0020.HK,2024-09-25,1.26,1.33,1.24,1.25,0.0163
|
||||
0020.HK,2024-09-26,1.26,1.4,1.24,1.39,0.112
|
||||
0020.HK,2024-09-27,1.44,1.5,1.4,1.46,0.0504
|
||||
0020.HK,2024-09-30,1.51,1.75,1.51,1.72,0.1781
|
||||
0020.HK,2024-10-02,1.72,1.88,1.68,1.85,0.0756
|
||||
0020.HK,2024-10-03,1.87,1.9,1.63,1.77,-0.0432
|
||||
0020.HK,2024-10-04,1.74,2.15,1.73,2.12,0.1977
|
||||
0020.HK,2024-10-07,2.23,2.34,2.11,2.33,0.0991
|
||||
0020.HK,2024-10-08,2.33,2.35,1.82,1.83,-0.2146
|
||||
0020.HK,2024-10-09,1.89,1.99,1.65,1.74,-0.0492
|
||||
0020.HK,2024-10-10,1.82,1.83,1.68,1.73,-0.0057
|
||||
0020.HK,2024-10-14,1.7,1.71,1.55,1.62,-0.0636
|
||||
0020.HK,2024-10-15,1.62,1.68,1.5,1.53,-0.0556
|
||||
0020.HK,2024-10-16,1.49,1.55,1.47,1.5,-0.0196
|
||||
0020.HK,2024-10-17,1.52,1.61,1.5,1.52,0.0133
|
||||
0020.HK,2024-10-18,1.53,1.68,1.48,1.66,0.0921
|
||||
0020.HK,2024-10-21,1.68,1.69,1.59,1.59,-0.0422
|
||||
0020.HK,2024-10-22,1.6,1.68,1.58,1.64,0.0314
|
||||
0020.HK,2024-10-23,1.66,1.74,1.62,1.66,0.0122
|
||||
0020.HK,2024-10-24,1.63,1.66,1.58,1.59,-0.0422
|
||||
0020.HK,2024-10-25,1.59,1.65,1.58,1.6,0.0063
|
||||
0020.HK,2024-10-28,1.6,1.63,1.57,1.6,0
|
||||
0020.HK,2024-10-29,1.63,1.66,1.54,1.57,-0.0188
|
||||
0020.HK,2024-10-30,1.58,1.6,1.52,1.55,-0.0127
|
||||
0020.HK,2024-10-31,1.55,1.58,1.53,1.56,0.0065
|
||||
0020.HK,2024-11-01,1.56,1.57,1.48,1.52,-0.0256
|
||||
0020.HK,2024-11-04,1.52,1.55,1.51,1.53,0.0066
|
||||
0020.HK,2024-11-05,1.53,1.65,1.5,1.64,0.0719
|
||||
0020.HK,2024-11-06,1.64,1.69,1.59,1.62,-0.0122
|
||||
0020.HK,2024-11-07,1.62,1.74,1.59,1.74,0.0741
|
||||
0020.HK,2024-11-08,1.78,1.8,1.68,1.71,-0.0172
|
||||
0020.HK,2024-11-11,1.65,1.73,1.63,1.72,0.0058
|
||||
0020.HK,2024-11-12,1.74,1.75,1.6,1.61,-0.064
|
||||
0020.HK,2024-11-13,1.59,1.62,1.57,1.61,0
|
||||
0020.HK,2024-11-14,1.6,1.7,1.59,1.61,0
|
||||
0020.HK,2024-11-15,1.63,1.66,1.58,1.58,-0.0186
|
||||
0020.HK,2024-11-18,1.6,1.61,1.52,1.53,-0.0316
|
||||
0020.HK,2024-11-19,1.55,1.57,1.52,1.56,0.0196
|
||||
0020.HK,2024-11-20,1.56,1.6,1.54,1.58,0.0128
|
||||
0020.HK,2024-11-21,1.58,1.6,1.54,1.55,-0.019
|
||||
0020.HK,2024-11-22,1.55,1.59,1.42,1.43,-0.0774
|
||||
0020.HK,2024-11-25,1.44,1.46,1.39,1.44,0.007
|
||||
0020.HK,2024-11-26,1.44,1.48,1.42,1.42,-0.0139
|
||||
0020.HK,2024-11-27,1.43,1.5,1.4,1.48,0.0423
|
||||
0020.HK,2024-11-28,1.49,1.49,1.44,1.44,-0.027
|
||||
0020.HK,2024-11-29,1.45,1.52,1.45,1.49,0.0347
|
||||
0020.HK,2024-12-02,1.49,1.51,1.47,1.5,0.0067
|
||||
0020.HK,2024-12-03,1.5,1.51,1.46,1.5,0
|
||||
0020.HK,2024-12-04,1.51,1.53,1.47,1.49,-0.0067
|
||||
0020.HK,2024-12-05,1.49,1.52,1.48,1.49,0
|
||||
0020.HK,2024-12-06,1.49,1.74,1.47,1.71,0.1477
|
||||
0020.HK,2024-12-09,1.71,1.86,1.68,1.85,0.0819
|
||||
0020.HK,2024-12-10,1.91,1.92,1.6,1.6,-0.1351
|
||||
0020.HK,2024-12-11,1.58,1.64,1.54,1.58,-0.0125
|
||||
0020.HK,2024-12-12,1.59,1.62,1.56,1.56,-0.0127
|
||||
0020.HK,2024-12-13,1.55,1.56,1.49,1.55,-0.0064
|
||||
0020.HK,2024-12-16,1.54,1.54,1.48,1.49,-0.0387
|
||||
0020.HK,2024-12-17,1.48,1.5,1.46,1.48,-0.0067
|
||||
0020.HK,2024-12-18,1.49,1.53,1.49,1.51,0.0203
|
||||
0020.HK,2024-12-19,1.48,1.58,1.48,1.51,0
|
||||
0020.HK,2024-12-20,1.51,1.54,1.5,1.5,-0.0066
|
||||
0020.HK,2024-12-23,1.51,1.52,1.48,1.48,-0.0133
|
||||
0020.HK,2024-12-24,1.48,1.5,1.47,1.48,0
|
||||
0020.HK,2024-12-27,1.49,1.56,1.48,1.53,0.0338
|
||||
0020.HK,2024-12-30,1.54,1.55,1.51,1.51,-0.0131
|
||||
0020.HK,2024-12-31,1.51,1.52,1.49,1.49,-0.0132
|
||||
0020.HK,2025-01-02,1.49,1.49,1.41,1.41,-0.0537
|
||||
0020.HK,2025-01-03,1.42,1.43,1.33,1.33,-0.0567
|
||||
0020.HK,2025-01-06,1.34,1.38,1.32,1.33,0
|
||||
0020.HK,2025-01-07,1.31,1.34,1.26,1.33,0
|
||||
0020.HK,2025-01-08,1.33,1.34,1.28,1.31,-0.015
|
||||
0020.HK,2025-01-09,1.3,1.35,1.3,1.31,0
|
||||
0020.HK,2025-01-10,1.32,1.34,1.28,1.28,-0.0229
|
||||
0020.HK,2025-01-13,1.27,1.3,1.26,1.3,0.0156
|
||||
0020.HK,2025-01-14,1.3,1.37,1.3,1.34,0.0308
|
||||
0020.HK,2025-01-15,1.35,1.39,1.32,1.33,-0.0075
|
||||
0020.HK,2025-01-16,1.35,1.39,1.34,1.36,0.0226
|
||||
0020.HK,2025-01-17,1.36,1.4,1.36,1.37,0.0074
|
||||
0020.HK,2025-01-20,1.4,1.44,1.39,1.41,0.0292
|
||||
0020.HK,2025-01-21,1.42,1.45,1.4,1.44,0.0213
|
||||
0020.HK,2025-01-22,1.42,1.45,1.39,1.41,-0.0208
|
||||
0020.HK,2025-01-23,1.42,1.48,1.42,1.43,0.0142
|
||||
0020.HK,2025-01-24,1.44,1.54,1.43,1.52,0.0629
|
||||
0020.HK,2025-01-27,1.55,1.73,1.55,1.63,0.0724
|
||||
0020.HK,2025-01-28,1.64,1.66,1.59,1.61,-0.0123
|
||||
0020.HK,2025-02-03,1.61,1.7,1.55,1.69,0.0497
|
||||
0020.HK,2025-02-04,1.71,1.74,1.65,1.73,0.0237
|
||||
0020.HK,2025-02-05,1.71,1.71,1.59,1.65,-0.0462
|
||||
0020.HK,2025-02-06,1.64,1.72,1.63,1.72,0.0424
|
||||
0020.HK,2025-02-07,1.73,1.8,1.68,1.73,0.0058
|
||||
0020.HK,2025-02-10,1.76,1.85,1.75,1.77,0.0231
|
||||
0020.HK,2025-02-11,1.78,1.8,1.68,1.71,-0.0339
|
||||
0020.HK,2025-02-12,1.72,1.74,1.67,1.72,0.0058
|
||||
0020.HK,2025-02-13,1.74,1.82,1.67,1.69,-0.0174
|
||||
0020.HK,2025-02-14,1.72,1.83,1.71,1.82,0.0769
|
||||
0020.HK,2025-02-17,1.87,1.9,1.78,1.83,0.0055
|
||||
0020.HK,2025-02-18,1.84,1.92,1.77,1.82,-0.0055
|
||||
0020.HK,2025-02-19,1.84,1.84,1.77,1.83,0.0055
|
||||
0020.HK,2025-02-20,1.81,1.82,1.73,1.73,-0.0546
|
||||
0020.HK,2025-02-21,1.78,1.9,1.75,1.88,0.0867
|
||||
0020.HK,2025-02-24,1.94,1.98,1.89,1.92,0.0213
|
||||
0020.HK,2025-02-25,1.83,1.88,1.79,1.81,-0.0573
|
||||
0020.HK,2025-02-26,1.84,1.86,1.79,1.82,0.0055
|
||||
0020.HK,2025-02-27,1.83,1.87,1.74,1.78,-0.022
|
||||
0020.HK,2025-02-28,1.77,1.77,1.62,1.64,-0.0787
|
||||
0020.HK,2025-03-03,1.68,1.69,1.6,1.64,0
|
||||
0020.HK,2025-03-04,1.56,1.66,1.56,1.64,0
|
||||
0020.HK,2025-03-05,1.66,1.69,1.63,1.68,0.0244
|
||||
0020.HK,2025-03-06,1.72,1.81,1.72,1.78,0.0595
|
||||
0020.HK,2025-03-07,1.76,1.8,1.72,1.75,-0.0169
|
||||
0020.HK,2025-03-10,1.74,1.76,1.69,1.73,-0.0114
|
||||
0020.HK,2025-03-11,1.68,1.76,1.67,1.75,0.0116
|
||||
0020.HK,2025-03-12,1.76,1.78,1.69,1.71,-0.0229
|
||||
0020.HK,2025-03-13,1.72,1.74,1.66,1.69,-0.0117
|
||||
0020.HK,2025-03-14,1.71,1.73,1.67,1.71,0.0118
|
||||
0020.HK,2025-03-17,1.71,1.73,1.67,1.68,-0.0175
|
||||
0020.HK,2025-03-18,1.71,1.73,1.7,1.72,0.0238
|
||||
0020.HK,2025-03-19,1.72,1.8,1.68,1.74,0.0116
|
||||
0020.HK,2025-03-20,1.74,1.75,1.68,1.68,-0.0345
|
||||
0020.HK,2025-03-21,1.68,1.69,1.6,1.61,-0.0417
|
||||
0020.HK,2025-03-24,1.62,1.63,1.58,1.61,0
|
||||
0020.HK,2025-03-25,1.6,1.61,1.56,1.57,-0.0248
|
||||
0020.HK,2025-03-26,1.57,1.6,1.57,1.59,0.0127
|
||||
0020.HK,2025-03-27,1.55,1.55,1.45,1.49,-0.0629
|
||||
0020.HK,2025-03-28,1.51,1.53,1.47,1.5,0.0067
|
||||
0020.HK,2025-03-31,1.5,1.5,1.45,1.49,-0.0067
|
||||
0020.HK,2025-04-01,1.49,1.51,1.46,1.47,-0.0134
|
||||
0020.HK,2025-04-02,1.48,1.54,1.47,1.53,0.0408
|
||||
0020.HK,2025-04-03,1.5,1.6,1.49,1.55,0.0131
|
||||
0020.HK,2025-04-07,1.4,1.44,1.25,1.28,-0.1742
|
||||
0020.HK,2025-04-08,1.31,1.35,1.25,1.31,0.0234
|
||||
0020.HK,2025-04-09,1.27,1.38,1.24,1.36,0.0382
|
||||
0020.HK,2025-04-10,1.41,1.45,1.39,1.41,0.0368
|
||||
0020.HK,2025-04-11,1.41,1.44,1.39,1.42,0.0071
|
||||
0020.HK,2025-04-14,1.45,1.51,1.45,1.47,0.0352
|
||||
0020.HK,2025-04-15,1.48,1.49,1.43,1.45,-0.0136
|
||||
0020.HK,2025-04-16,1.44,1.44,1.39,1.4,-0.0345
|
||||
0020.HK,2025-04-17,1.4,1.42,1.39,1.4,0
|
||||
0020.HK,2025-04-22,1.41,1.43,1.39,1.43,0.0214
|
||||
0020.HK,2025-04-23,1.47,1.47,1.43,1.43,0
|
||||
0020.HK,2025-04-24,1.44,1.45,1.4,1.43,0
|
||||
0020.HK,2025-04-25,1.45,1.47,1.42,1.42,-0.007
|
||||
0020.HK,2025-04-28,1.43,1.45,1.4,1.45,0.0211
|
||||
0020.HK,2025-04-29,1.45,1.48,1.44,1.46,0.0069
|
||||
0020.HK,2025-04-30,1.49,1.53,1.47,1.5,0.0274
|
||||
0020.HK,2025-05-02,1.5,1.56,1.48,1.55,0.0333
|
||||
0020.HK,2025-05-06,1.55,1.56,1.51,1.53,-0.0129
|
||||
0020.HK,2025-05-07,1.56,1.58,1.52,1.52,-0.0065
|
||||
0020.HK,2025-05-08,1.52,1.55,1.51,1.52,0
|
||||
0020.HK,2025-05-09,1.52,1.52,1.48,1.49,-0.0197
|
||||
0020.HK,2025-05-12,1.52,1.58,1.5,1.56,0.047
|
||||
0020.HK,2025-05-13,1.57,1.58,1.5,1.51,-0.0321
|
||||
0020.HK,2025-05-14,1.53,1.53,1.48,1.49,-0.0132
|
||||
0020.HK,2025-05-15,1.49,1.51,1.45,1.46,-0.0201
|
||||
0020.HK,2025-05-16,1.44,1.46,1.44,1.45,-0.0068
|
||||
0020.HK,2025-05-19,1.44,1.45,1.42,1.43,-0.0138
|
||||
0020.HK,2025-05-20,1.44,1.45,1.41,1.42,-0.007
|
||||
0020.HK,2025-05-21,1.43,1.44,1.41,1.41,-0.007
|
||||
0020.HK,2025-05-22,1.41,1.42,1.4,1.4,-0.0071
|
||||
0020.HK,2025-05-23,1.4,1.42,1.39,1.4,0
|
||||
0020.HK,2025-05-26,1.4,1.42,1.37,1.4,0
|
||||
0020.HK,2025-05-27,1.4,1.41,1.38,1.39,-0.0071
|
||||
0020.HK,2025-05-28,1.39,1.4,1.38,1.38,-0.0072
|
||||
0020.HK,2025-05-29,1.38,1.43,1.37,1.42,0.029
|
||||
0020.HK,2025-05-30,1.41,1.42,1.39,1.4,-0.0141
|
||||
0020.HK,2025-06-02,1.39,1.39,1.33,1.38,-0.0143
|
||||
0020.HK,2025-06-03,1.38,1.41,1.36,1.37,-0.0072
|
||||
0020.HK,2025-06-04,1.37,1.39,1.36,1.36,-0.0073
|
||||
0020.HK,2025-06-05,1.37,1.42,1.37,1.4,0.0294
|
||||
0020.HK,2025-06-06,1.4,1.41,1.36,1.4,0
|
||||
0020.HK,2025-06-09,1.41,1.5,1.4,1.47,0.05
|
||||
0020.HK,2025-06-10,1.48,1.48,1.42,1.46,-0.0068
|
||||
0020.HK,2025-06-11,1.46,1.49,1.45,1.47,0.0068
|
||||
0020.HK,2025-06-12,1.46,1.48,1.43,1.47,0
|
||||
0020.HK,2025-06-13,1.46,1.47,1.4,1.4,-0.0476
|
||||
|
|
|
@ -0,0 +1,158 @@
|
|||
# 量化分析工程模块
|
||||
|
||||
## 功能介绍
|
||||
|
||||
这个模块主要用于A股量化分析，包括：
|
||||
|
||||
1. **财务数据采集** - 自动从东方财富网采集上市公司财务报表数据
|
||||
2. **数据存储** - 将数据结构化存储到MongoDB数据库
|
||||
3. **量化分析** - 基于财务数据进行量化分析和策略开发
|
||||
|
||||
## 目录结构
|
||||
|
||||
```
|
||||
quantitative_analysis/
|
||||
├── __init__.py # 模块初始化文件
|
||||
├── financial_data_collector.py # 财务数据采集器
|
||||
├── README.md # 使用说明
|
||||
└── examples/ # 使用示例
|
||||
```
|
||||
|
||||
## 财务数据采集器
|
||||
|
||||
### 功能特点
|
||||
|
||||
- **全面的财务数据**：采集资产负债表、利润表、现金流量表、杜邦分析等
|
||||
- **批量处理**：支持单只股票和批量股票数据采集
|
||||
- **数据完整性**：2019-2024年完整的季度和年度财务数据
|
||||
- **智能存储**：自动去重，使用股票代码+报告日期作为唯一标识
|
||||
- **错误处理**：完善的异常处理和日志记录
|
||||
|
||||
### 使用方法
|
||||
|
||||
#### 1. 单只股票采集
|
||||
|
||||
```python
|
||||
from src.quantitative_analysis.financial_data_collector import FinancialDataCollector
|
||||
|
||||
# 创建采集器实例
|
||||
collector = FinancialDataCollector()
|
||||
|
||||
# 采集单只股票财务数据
|
||||
success = collector.collect_financial_data('300750.SZ') # 宁德时代
|
||||
|
||||
if success:
|
||||
print("财务数据采集成功")
|
||||
else:
|
||||
print("财务数据采集失败")
|
||||
|
||||
# 关闭连接
|
||||
collector.close_connection()
|
||||
```
|
||||
|
||||
#### 2. 批量股票采集
|
||||
|
||||
```python
|
||||
from src.quantitative_analysis.financial_data_collector import FinancialDataCollector
|
||||
|
||||
# 创建采集器实例
|
||||
collector = FinancialDataCollector()
|
||||
|
||||
# 股票代码列表
|
||||
stock_list = [
|
||||
'300750.SZ', # 宁德时代
|
||||
'000858.SZ', # 五粮液
|
||||
'002415.SZ', # 海康威视
|
||||
'000001.SZ', # 平安银行
|
||||
'600519.SH' # 贵州茅台
|
||||
]
|
||||
|
||||
# 批量采集
|
||||
results = collector.batch_collect_financial_data(stock_list)
|
||||
print(f"采集结果: {results}")
|
||||
|
||||
# 关闭连接
|
||||
collector.close_connection()
|
||||
```
|
||||
|
||||
### 数据结构
|
||||
|
||||
采集的数据会以以下结构存储到MongoDB：
|
||||
|
||||
```json
|
||||
{
|
||||
"stock_code": "300750.SZ",
|
||||
"report_date": "2024-06-30",
|
||||
"collect_time": "2024-07-15T10:30:00",
|
||||
"dupont_analysis": {
|
||||
"roe": 15.2,
|
||||
"dupont_assetstoequity": 1.8,
|
||||
"assetsturn": 0.9,
|
||||
"dupont_np": 8.5,
|
||||
"profittogr": 12.3
|
||||
},
|
||||
"balance_sheet": {
|
||||
"property": {
|
||||
"monetary_cap": 1000000000,
|
||||
"tot_assets": 5000000000,
|
||||
...
|
||||
},
|
||||
"liabilities": {
|
||||
"tot_liab": 2000000000,
|
||||
...
|
||||
},
|
||||
"owner_equity": {
|
||||
"tot_equity": 3000000000,
|
||||
...
|
||||
}
|
||||
},
|
||||
"profit_statement": {
|
||||
"tot_oper_rev": 800000000,
|
||||
"net_profit_is": 100000000,
|
||||
...
|
||||
},
|
||||
"cash_flow_statement": {
|
||||
"net_cash_flows_oper_act": 150000000,
|
||||
...
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 配置说明
|
||||
|
||||
- 使用 `src/valuation_analysis/config.py` 中的 `MONGO_CONFIG2` 配置
|
||||
- MongoDB服务器：192.168.20.110:27017
|
||||
- 数据库：judge
|
||||
- 集合：wind_financial_analysis
|
||||
|
||||
### 注意事项
|
||||
|
||||
1. **请求频率控制**：代码中已添加请求间隔，避免被东方财富网限制
|
||||
2. **数据完整性**：会检查四类财务数据是否都获取成功
|
||||
3. **错误处理**：单个股票失败不会影响其他股票的采集
|
||||
4. **日志记录**：详细的日志记录，便于排查问题
|
||||
|
||||
### 运行测试
|
||||
|
||||
直接运行财务数据采集器进行测试：
|
||||
|
||||
```bash
|
||||
cd src/quantitative_analysis
|
||||
python financial_data_collector.py
|
||||
```
|
||||
|
||||
这将测试采集宁德时代(300750.SZ)的财务数据。
|
||||
|
||||
## 扩展开发
|
||||
|
||||
后续可以在此模块基础上开发：
|
||||
|
||||
1. **财务指标计算** - 基于原始财务数据计算各类财务指标
|
||||
2. **估值模型** - DCF、PE、PB等估值模型
|
||||
3. **量化策略** - 基于财务数据的选股策略
|
||||
4. **风险分析** - 财务风险评估模型
|
||||
5. **回测框架** - 策略回测和绩效分析
|
||||
|
||||
## 联系方式
|
||||
|
||||
如有问题或建议，请联系开发团队。
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
"""
|
||||
量化分析工程模块
|
||||
|
||||
主要功能：
|
||||
1. 财务数据采集
|
||||
2. 数据处理和分析
|
||||
3. 量化策略开发
|
||||
"""
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
|
@ -0,0 +1,183 @@
|
|||
import requests
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
import sys
|
||||
import os
|
||||
import redis
|
||||
import json
|
||||
|
||||
# 添加项目根目录到路径，便于导入scripts.config
|
||||
project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
sys.path.append(project_root)
|
||||
|
||||
# 读取雪球headers和Redis配置
|
||||
try:
|
||||
from src.scripts.config import XUEQIU_HEADERS
|
||||
from src.valuation_analysis.config import REDIS_CONFIG
|
||||
except ImportError:
|
||||
XUEQIU_HEADERS = {
|
||||
'User-Agent': 'Mozilla/5.0',
|
||||
'Cookie': '', # 需要填写雪球cookie
|
||||
}
|
||||
REDIS_CONFIG = {
|
||||
'host': 'localhost',
|
||||
'port': 6379,
|
||||
'db': 0,
|
||||
'password': None
|
||||
}
|
||||
|
||||
REDIS_KEY = 'xq_stock_changes_latest' # 存放行情的主键
|
||||
|
||||
|
||||
def get_redis_conn():
|
||||
"""获取Redis连接"""
|
||||
pool = redis.ConnectionPool(
|
||||
host=REDIS_CONFIG['host'],
|
||||
port=REDIS_CONFIG['port'],
|
||||
db=REDIS_CONFIG.get('db', 0),
|
||||
password=REDIS_CONFIG.get('password', None),
|
||||
decode_responses=True
|
||||
)
|
||||
return redis.Redis(connection_pool=pool)
|
||||
|
||||
|
||||
def fetch_and_store_stock_data(page_size=90):
|
||||
"""
|
||||
批量采集雪球A股（上证、深证、科创板）股票的最新行情数据，并保存到Redis。
|
||||
:param page_size: 每页采集数量
|
||||
"""
|
||||
base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
|
||||
types = ['sha', 'sza', 'kcb'] # 上证、深证、科创板
|
||||
headers = XUEQIU_HEADERS
|
||||
|
||||
all_data = []
|
||||
|
||||
for stock_type in types:
|
||||
params = {
|
||||
'page': 1,
|
||||
'size': page_size,
|
||||
'order': 'desc',
|
||||
'order_by': 'percent',
|
||||
'market': 'CN',
|
||||
'type': stock_type
|
||||
}
|
||||
|
||||
# 初次请求以获取总页数
|
||||
response = requests.get(base_url, headers=headers, params=params)
|
||||
if response.status_code != 200:
|
||||
print(f"请求 {stock_type} 数据失败，状态码：{response.status_code}")
|
||||
continue
|
||||
|
||||
data = response.json()
|
||||
total_count = data['data']['count']
|
||||
total_pages = (total_count // page_size) + 1
|
||||
|
||||
for page in range(1, total_pages + 1):
|
||||
params['page'] = page
|
||||
response = requests.get(base_url, headers=headers, params=params)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
all_data.extend(data['data']['list'])
|
||||
else:
|
||||
print(f"请求 {stock_type} 数据第 {page} 页失败，状态码：{response.status_code}")
|
||||
# 转换为 DataFrame
|
||||
df = pd.DataFrame(all_data)
|
||||
|
||||
if not df.empty:
|
||||
df['fetch_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
# 存入Redis，使用hash结构，key为symbol，value为json字符串
|
||||
r = get_redis_conn()
|
||||
pipe = r.pipeline()
|
||||
# 先清空旧数据
|
||||
r.delete(REDIS_KEY)
|
||||
for _, row in df.iterrows():
|
||||
symbol = row.get('symbol')
|
||||
if not symbol:
|
||||
continue
|
||||
# 只保留必要字段，也可直接存row.to_dict()
|
||||
value = row.to_dict()
|
||||
pipe.hset(REDIS_KEY, symbol, json.dumps(value, ensure_ascii=False))
|
||||
pipe.execute()
|
||||
print(f"成功将数据写入Redis哈希 {REDIS_KEY}，共{len(df)}条记录。")
|
||||
else:
|
||||
print("未获取到任何数据。")
|
||||
|
||||
|
||||
def format_stock_code(stock_code):
|
||||
"""
|
||||
统一股票代码格式，支持600001.SH、SH600001、000001.SZ、SZ000001等
|
||||
返回雪球格式（如SH600001、SZ000001）和Redis存储格式（如SZ000978）
|
||||
"""
|
||||
stock_code = stock_code.upper()
|
||||
if '.' in stock_code:
|
||||
code, market = stock_code.split('.')
|
||||
if market == 'SH':
|
||||
return f'SH{code}', f'{market}{code}'
|
||||
elif market == 'SZ':
|
||||
return f'SZ{code}', f'{market}{code}'
|
||||
elif market == 'BJ':
|
||||
return f'BJ{code}', f'{market}{code}'
|
||||
else:
|
||||
return stock_code, stock_code
|
||||
elif stock_code.startswith(('SH', 'SZ', 'BJ')):
|
||||
return stock_code, stock_code
|
||||
else:
|
||||
# 默认返回原始
|
||||
return stock_code, stock_code
|
||||
|
||||
|
||||
def get_stock_realtime_info_from_redis(stock_code):
|
||||
"""
|
||||
根据股票代码从Redis查询实时行情，并封装为指定结构。
|
||||
:param stock_code: 支持600001.SH、SH600001、000001.SZ、SZ000001等
|
||||
:return: dict or None
|
||||
"""
|
||||
_, redis_code = format_stock_code(stock_code)
|
||||
r = get_redis_conn()
|
||||
value = r.hget(REDIS_KEY, redis_code)
|
||||
if not value:
|
||||
return None
|
||||
try:
|
||||
data = json.loads(value)
|
||||
except Exception:
|
||||
return None
|
||||
# 封装为指定结构
|
||||
result = {
|
||||
"code": None,
|
||||
"crawlDate": None,
|
||||
"marketValue": None,
|
||||
"maxPrice": None,
|
||||
"minPrice": None,
|
||||
"nowPrice": None,
|
||||
"pbRate": None,
|
||||
"rangeRiseAndFall": None,
|
||||
"shortName": None,
|
||||
"todayStartPrice": None,
|
||||
"ttm": None,
|
||||
"turnoverRate": None,
|
||||
"yesterdayEndPrice": None
|
||||
}
|
||||
# 赋值映射
|
||||
result["code"] = data.get("symbol")
|
||||
result["crawlDate"] = data.get("fetch_time")
|
||||
result["marketValue"] = data.get("market_capital")
|
||||
result["maxPrice"] = data.get("high") if "high" in data else data.get("high52w")
|
||||
result["minPrice"] = data.get("low") if "low" in data else data.get("low52w")
|
||||
result["nowPrice"] = data.get("current")
|
||||
result["pbRate"] = data.get("pb")
|
||||
result["rangeRiseAndFall"] = data.get("percent")
|
||||
result["shortName"] = data.get("name")
|
||||
result["todayStartPrice"] = data.get("open")
|
||||
result["ttm"] = data.get("pe_ttm")
|
||||
result["turnoverRate"] = data.get("turnover_rate")
|
||||
result["yesterdayEndPrice"] = data.get("last_close") if "last_close" in data else data.get("pre_close")
|
||||
# 兼容部分字段缺失
|
||||
if result["maxPrice"] is None and "high" in data:
|
||||
result["maxPrice"] = data["high"]
|
||||
if result["minPrice"] is None and "low" in data:
|
||||
result["minPrice"] = data["low"]
|
||||
return result
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
fetch_and_store_stock_data()
|
||||
|
|
@ -0,0 +1,220 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
|
||||
# 新增：设置matplotlib支持中文和负号
|
||||
import matplotlib
|
||||
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
|
||||
matplotlib.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
# 新增：屏蔽UserWarning
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
# 回测参数
|
||||
INIT_CASH = 4000000
|
||||
FEE_RATE = 0.003 # 千分之3
|
||||
GRID_NUM = 15 # 网格数量
|
||||
PER_GRID_CASH = INIT_CASH / GRID_NUM # 每格买入金额
|
||||
|
||||
def generate_composite_grids(price_min, price_max):
|
||||
"""生成等差网格"""
|
||||
step = (price_max - price_min) / GRID_NUM
|
||||
return np.arange(price_min, price_max, step)
|
||||
|
||||
def should_trade(macd, signal):
|
||||
"""MACD趋势过滤
|
||||
金叉时做多，死叉时暂停
|
||||
"""
|
||||
return macd > signal
|
||||
|
||||
def composite_grid_backtest(prices):
|
||||
cash = INIT_CASH
|
||||
position = 0
|
||||
cost = 0
|
||||
history = [] # 记录每日资金、持仓
|
||||
trade_log = [] # 买卖点
|
||||
last_grid = None
|
||||
|
||||
# 计算MACD
|
||||
exp1 = prices['close'].ewm(span=12, adjust=False).mean()
|
||||
exp2 = prices['close'].ewm(span=26, adjust=False).mean()
|
||||
prices['MACD'] = exp1 - exp2
|
||||
prices['Signal'] = prices['MACD'].ewm(span=9, adjust=False).mean()
|
||||
|
||||
# 生成固定网格
|
||||
price_min = prices['close'].min()
|
||||
price_max = prices['close'].max()
|
||||
grids = generate_composite_grids(price_min, price_max)
|
||||
|
||||
def get_grid_index(price):
|
||||
"""获取价格所在的网格索引"""
|
||||
grid_idx = np.searchsorted(grids, price, side='right') - 1
|
||||
if grid_idx < 0:
|
||||
grid_idx = 0
|
||||
if grid_idx >= len(grids)-1:
|
||||
grid_idx = len(grids)-2
|
||||
return grid_idx
|
||||
|
||||
def execute_trade(price, trade_type, grid_idx, last_grid_idx, date, allow_trade):
|
||||
"""执行交易"""
|
||||
nonlocal cash, position, cost
|
||||
if not allow_trade:
|
||||
return False
|
||||
if trade_type == 'buy' and cash >= PER_GRID_CASH:
|
||||
buy_price = price * (1 + FEE_RATE)
|
||||
buy_amount = PER_GRID_CASH / buy_price
|
||||
cash -= buy_amount * buy_price
|
||||
position += buy_amount
|
||||
cost += buy_amount * buy_price
|
||||
trade_log.append({'date': date, 'price': price, 'type': 'buy'})
|
||||
return True
|
||||
elif trade_type == 'sell' and position > 0:
|
||||
sell_price = price * (1 - FEE_RATE)
|
||||
sell_amount = PER_GRID_CASH / price
|
||||
if sell_amount > position:
|
||||
sell_amount = position
|
||||
cash += sell_amount * sell_price
|
||||
position -= sell_amount
|
||||
cost -= sell_amount * price
|
||||
trade_log.append({'date': date, 'price': price, 'type': 'sell'})
|
||||
return True
|
||||
return False
|
||||
|
||||
for i, row in prices.iterrows():
|
||||
date = row['date']
|
||||
open_price = row['open']
|
||||
close_price = row['close']
|
||||
|
||||
# 判断是否允许交易（MACD趋势过滤）
|
||||
allow_trade = should_trade(row['MACD'], row['Signal'])
|
||||
|
||||
# 获取开盘价和收盘价所在的网格位置
|
||||
open_grid_idx = get_grid_index(open_price)
|
||||
close_grid_idx = get_grid_index(close_price)
|
||||
|
||||
# 开盘价交易处理
|
||||
if last_grid is not None:
|
||||
# 开盘价买入处理
|
||||
if open_grid_idx < last_grid:
|
||||
for grid_idx in range(open_grid_idx, last_grid):
|
||||
if not execute_trade(open_price, 'buy', grid_idx, last_grid, date, allow_trade):
|
||||
break
|
||||
# 开盘价卖出处理
|
||||
elif open_grid_idx > last_grid:
|
||||
for grid_idx in range(last_grid + 1, open_grid_idx + 1):
|
||||
if not execute_trade(open_price, 'sell', grid_idx, last_grid, date, allow_trade):
|
||||
break
|
||||
|
||||
# 收盘价交易处理
|
||||
if last_grid is not None:
|
||||
# 收盘价买入处理
|
||||
if close_grid_idx < open_grid_idx:
|
||||
for grid_idx in range(close_grid_idx, open_grid_idx):
|
||||
if not execute_trade(close_price, 'buy', grid_idx, open_grid_idx, date, allow_trade):
|
||||
break
|
||||
# 收盘价卖出处理
|
||||
elif close_grid_idx > open_grid_idx:
|
||||
for grid_idx in range(open_grid_idx + 1, close_grid_idx + 1):
|
||||
if not execute_trade(close_price, 'sell', grid_idx, open_grid_idx, date, allow_trade):
|
||||
break
|
||||
|
||||
# 更新last_grid为收盘价所在网格
|
||||
last_grid = close_grid_idx
|
||||
|
||||
# 记录每日净值
|
||||
total = cash + position * close_price
|
||||
history.append({
|
||||
'date': date,
|
||||
'net': total,
|
||||
'cash': cash,
|
||||
'position': position,
|
||||
'price': close_price,
|
||||
'macd': row['MACD'],
|
||||
'signal': row['Signal'],
|
||||
'open_grid': open_grid_idx,
|
||||
'close_grid': close_grid_idx
|
||||
})
|
||||
|
||||
# 统计
|
||||
df_hist = pd.DataFrame(history)
|
||||
max_drawdown = ((df_hist['net'].cummax() - df_hist['net']) / df_hist['net'].cummax()).max()
|
||||
total_return = (df_hist['net'].iloc[-1] - INIT_CASH) / INIT_CASH
|
||||
|
||||
# 胜率统计
|
||||
win, lose = 0, 0
|
||||
for i in range(1, len(trade_log)):
|
||||
if trade_log[i-1]['type']=='buy' and trade_log[i]['type']=='sell':
|
||||
if trade_log[i]['price'] > trade_log[i-1]['price']:
|
||||
win += 1
|
||||
else:
|
||||
lose += 1
|
||||
win_rate = win / (win+lose) if (win+lose)>0 else 0
|
||||
|
||||
return df_hist, trade_log, total_return, max_drawdown, len([t for t in trade_log if t['type']=='buy']), len([t for t in trade_log if t['type']=='sell']), win_rate
|
||||
|
||||
def plot_results(df_hist, trade_log, grids=None):
|
||||
"""绘制回测结果"""
|
||||
fig, (ax1, ax3) = plt.subplots(2, 1, figsize=(14,10), height_ratios=[3, 1])
|
||||
ax2 = ax1.twinx()
|
||||
|
||||
# 绘制价格和净值
|
||||
ax1.plot(df_hist['date'], df_hist['price'], label='收盘价', color='gray', alpha=0.7)
|
||||
relative_net = df_hist['net'] / INIT_CASH
|
||||
ax2.plot(df_hist['date'], relative_net, label='净值', color='blue')
|
||||
|
||||
# 绘制交易点
|
||||
for t in trade_log:
|
||||
if t['type']=='buy':
|
||||
ax1.scatter(t['date'], t['price'], marker='^', color='red', label='买入' if '买入' not in ax1.get_legend_handles_labels()[1] else "")
|
||||
else:
|
||||
ax1.scatter(t['date'], t['price'], marker='v', color='green', label='卖出' if '卖出' not in ax1.get_legend_handles_labels()[1] else "")
|
||||
|
||||
# 绘制网格线
|
||||
if grids is not None:
|
||||
for g in grids:
|
||||
ax1.axhline(g, color='orange', linestyle='--', alpha=0.2)
|
||||
|
||||
# 绘制MACD
|
||||
ax3.plot(df_hist['date'], df_hist['macd'], label='MACD', color='blue')
|
||||
ax3.plot(df_hist['date'], df_hist['signal'], label='Signal', color='red')
|
||||
ax3.fill_between(df_hist['date'], df_hist['macd'] - df_hist['signal'],
|
||||
0, where=(df_hist['macd'] >= df_hist['signal']),
|
||||
color='green', alpha=0.3, label='做多区域')
|
||||
ax3.grid(True, alpha=0.3)
|
||||
ax3.legend(loc='upper left')
|
||||
|
||||
plt.title('复合策略网格回测')
|
||||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
ax1.legend(lines1 + lines2, labels1 + labels2)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 读取数据
|
||||
DATA_PATH = os.path.join(os.path.dirname(__file__), '0020.HK.csv')
|
||||
# 处理文件头乱码和尾部注释
|
||||
raw = pd.read_csv(DATA_PATH, header=None, skiprows=1, names=['code','date','open','high','low','close','pct'])
|
||||
raw = raw[raw['close'].apply(lambda x: str(x).replace('.','',1).isdigit())] # 去除空行和注释
|
||||
raw['date'] = pd.to_datetime(raw['date'])
|
||||
raw['close'] = raw['close'].astype(float)
|
||||
raw['open'] = raw['open'].astype(float)
|
||||
raw = raw.sort_values('date').reset_index(drop=True)
|
||||
|
||||
# 运行回测
|
||||
df_hist, trade_log, total_return, max_drawdown, buy_cnt, sell_cnt, win_rate = composite_grid_backtest(raw)
|
||||
|
||||
# 打印统计结果
|
||||
print('\n==== 复合策略网格回测结果 ===')
|
||||
print(f'总收益率: {total_return*100:.2f}%')
|
||||
print(f'最大回撤: {max_drawdown*100:.2f}%')
|
||||
print(f'买入次数: {buy_cnt}，卖出次数: {sell_cnt}')
|
||||
print(f'胜率: {win_rate*100:.2f}%')
|
||||
|
||||
# 绘制结果
|
||||
price_min = raw['close'].min()
|
||||
price_max = raw['close'].max()
|
||||
grids = generate_composite_grids(price_min, price_max)
|
||||
plot_results(df_hist, trade_log, grids)
|
||||
|
|
@ -0,0 +1,232 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
|
||||
# 新增：设置matplotlib支持中文和负号
|
||||
import matplotlib
|
||||
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
|
||||
matplotlib.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
# 新增：屏蔽UserWarning
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
# 回测参数
|
||||
INIT_CASH = 4000000
|
||||
FEE_RATE = 0.003 # 千分之3
|
||||
GRID_NUM = 15 # 网格数量
|
||||
PER_GRID_CASH = INIT_CASH / GRID_NUM # 每格买入金额
|
||||
|
||||
def generate_dynamic_grids(price, ma120):
|
||||
"""生成动态网格
|
||||
根据价格与MA120的偏离程度动态调整网格密度
|
||||
核心区（±8%）：密集网格（1.5%-2%）
|
||||
缓冲区（±8%-20%）：中等间距（3%-4%）
|
||||
极端区（超出±20%）：疏松间距（5%-6%）
|
||||
"""
|
||||
deviation = (price - ma120) / ma120 # 计算价格相对MA120的偏离率
|
||||
|
||||
if abs(deviation) <= 0.08: # 核心区（±8%）
|
||||
# 在当前价格上下8%范围内生成密集网格
|
||||
grid_min = price * 0.92
|
||||
grid_max = price * 1.08
|
||||
return np.linspace(grid_min, grid_max, GRID_NUM) # 约1.5-2%间距
|
||||
|
||||
elif abs(deviation) <= 0.20: # 缓冲区（±8%-20%）
|
||||
# 扩大网格范围，增加间距
|
||||
if deviation > 0: # 价格在MA120上方
|
||||
grid_min = ma120 * 1.08 # 从MA120+8%开始
|
||||
grid_max = price * 1.15 # 到当前价格+15%
|
||||
else: # 价格在MA120下方
|
||||
grid_min = price * 0.85 # 从当前价格-15%开始
|
||||
grid_max = ma120 * 0.92 # 到MA120-8%
|
||||
return np.linspace(grid_min, grid_max, GRID_NUM) # 约3-4%间距
|
||||
|
||||
else: # 极端区（超出±20%）
|
||||
# 在极端区域使用更大的网格间距
|
||||
if deviation > 0: # 价格远高于MA120
|
||||
grid_min = ma120 * 1.20 # 从MA120+20%开始
|
||||
grid_max = price * 1.10 # 到当前价格+10%
|
||||
else: # 价格远低于MA120
|
||||
grid_min = price * 0.90 # 从当前价格-10%开始
|
||||
grid_max = ma120 * 0.80 # 到MA120-20%
|
||||
return np.linspace(grid_min, grid_max, GRID_NUM) # 约5-6%间距
|
||||
|
||||
def dynamic_grid_backtest(prices):
|
||||
cash = INIT_CASH
|
||||
position = 0
|
||||
cost = 0
|
||||
history = [] # 记录每日资金、持仓
|
||||
trade_log = [] # 买卖点
|
||||
last_grid = None
|
||||
|
||||
# 计算120日移动平均作为动态参考价
|
||||
prices['MA120'] = prices['close'].rolling(window=120).mean()
|
||||
|
||||
def get_grid_index(price, current_grids):
|
||||
"""获取价格所在的网格索引"""
|
||||
if len(current_grids) == 0:
|
||||
return -1
|
||||
grid_idx = np.searchsorted(current_grids, price, side='right') - 1
|
||||
if grid_idx < 0:
|
||||
grid_idx = 0
|
||||
if grid_idx >= len(current_grids)-1:
|
||||
grid_idx = len(current_grids)-2
|
||||
return grid_idx
|
||||
|
||||
def execute_trade(price, trade_type, grid_idx, last_grid_idx, date):
|
||||
"""执行交易"""
|
||||
nonlocal cash, position, cost
|
||||
if trade_type == 'buy' and cash >= PER_GRID_CASH:
|
||||
buy_price = price * (1 + FEE_RATE)
|
||||
buy_amount = PER_GRID_CASH / buy_price
|
||||
cash -= buy_amount * buy_price
|
||||
position += buy_amount
|
||||
cost += buy_amount * buy_price
|
||||
trade_log.append({'date': date, 'price': price, 'type': 'buy'})
|
||||
return True
|
||||
elif trade_type == 'sell' and position > 0:
|
||||
sell_price = price * (1 - FEE_RATE)
|
||||
sell_amount = PER_GRID_CASH / price
|
||||
if sell_amount > position:
|
||||
sell_amount = position
|
||||
cash += sell_amount * sell_price
|
||||
position -= sell_amount
|
||||
cost -= sell_amount * price
|
||||
trade_log.append({'date': date, 'price': price, 'type': 'sell'})
|
||||
return True
|
||||
return False
|
||||
|
||||
for i, row in prices.iterrows():
|
||||
if pd.isna(row['MA120']): # 跳过MA120未形成的时期
|
||||
continue
|
||||
|
||||
date = row['date']
|
||||
open_price = row['open']
|
||||
close_price = row['close']
|
||||
ma120 = row['MA120']
|
||||
|
||||
# 生成当前的动态网格
|
||||
open_grids = generate_dynamic_grids(open_price, ma120)
|
||||
close_grids = generate_dynamic_grids(close_price, ma120)
|
||||
|
||||
# 获取开盘价和收盘价所在的网格位置
|
||||
open_grid_idx = get_grid_index(open_price, open_grids)
|
||||
close_grid_idx = get_grid_index(close_price, close_grids)
|
||||
|
||||
# 开盘价交易处理
|
||||
if last_grid is not None and open_grid_idx >= 0:
|
||||
# 开盘价买入处理
|
||||
if open_grid_idx < last_grid:
|
||||
for grid_idx in range(open_grid_idx, last_grid):
|
||||
if not execute_trade(open_price, 'buy', grid_idx, last_grid, date):
|
||||
break
|
||||
# 开盘价卖出处理
|
||||
elif open_grid_idx > last_grid:
|
||||
for grid_idx in range(last_grid + 1, open_grid_idx + 1):
|
||||
if not execute_trade(open_price, 'sell', grid_idx, last_grid, date):
|
||||
break
|
||||
|
||||
# 收盘价交易处理
|
||||
if last_grid is not None and close_grid_idx >= 0:
|
||||
# 收盘价买入处理
|
||||
if close_grid_idx < open_grid_idx:
|
||||
for grid_idx in range(close_grid_idx, open_grid_idx):
|
||||
if not execute_trade(close_price, 'buy', grid_idx, open_grid_idx, date):
|
||||
break
|
||||
# 收盘价卖出处理
|
||||
elif close_grid_idx > open_grid_idx:
|
||||
for grid_idx in range(open_grid_idx + 1, close_grid_idx + 1):
|
||||
if not execute_trade(close_price, 'sell', grid_idx, open_grid_idx, date):
|
||||
break
|
||||
|
||||
# 更新last_grid为收盘价所在网格
|
||||
last_grid = close_grid_idx
|
||||
|
||||
# 记录每日净值
|
||||
total = cash + position * close_price
|
||||
history.append({
|
||||
'date': date,
|
||||
'net': total,
|
||||
'cash': cash,
|
||||
'position': position,
|
||||
'price': close_price,
|
||||
'ma120': ma120,
|
||||
'open_grid': open_grid_idx,
|
||||
'close_grid': close_grid_idx
|
||||
})
|
||||
|
||||
# 统计
|
||||
df_hist = pd.DataFrame(history)
|
||||
max_drawdown = ((df_hist['net'].cummax() - df_hist['net']) / df_hist['net'].cummax()).max()
|
||||
total_return = (df_hist['net'].iloc[-1] - INIT_CASH) / INIT_CASH
|
||||
|
||||
# 胜率统计
|
||||
win, lose = 0, 0
|
||||
for i in range(1, len(trade_log)):
|
||||
if trade_log[i-1]['type']=='buy' and trade_log[i]['type']=='sell':
|
||||
if trade_log[i]['price'] > trade_log[i-1]['price']:
|
||||
win += 1
|
||||
else:
|
||||
lose += 1
|
||||
win_rate = win / (win+lose) if (win+lose)>0 else 0
|
||||
|
||||
return df_hist, trade_log, total_return, max_drawdown, len([t for t in trade_log if t['type']=='buy']), len([t for t in trade_log if t['type']=='sell']), win_rate
|
||||
|
||||
def plot_results(df_hist, trade_log, grids=None):
|
||||
"""绘制回测结果"""
|
||||
fig, ax1 = plt.subplots(figsize=(14,6))
|
||||
ax2 = ax1.twinx()
|
||||
|
||||
# 绘制价格、MA120和净值
|
||||
ax1.plot(df_hist['date'], df_hist['price'], label='收盘价', color='gray', alpha=0.7)
|
||||
ax1.plot(df_hist['date'], df_hist['ma120'], label='MA120', color='purple', alpha=0.5)
|
||||
relative_net = df_hist['net'] / INIT_CASH
|
||||
ax2.plot(df_hist['date'], relative_net, label='净值', color='blue')
|
||||
|
||||
# 绘制交易点
|
||||
for t in trade_log:
|
||||
if t['type']=='buy':
|
||||
ax1.scatter(t['date'], t['price'], marker='^', color='red', label='买入' if '买入' not in ax1.get_legend_handles_labels()[1] else "")
|
||||
else:
|
||||
ax1.scatter(t['date'], t['price'], marker='v', color='green', label='卖出' if '卖出' not in ax1.get_legend_handles_labels()[1] else "")
|
||||
|
||||
# 绘制最后一个时间点的网格线
|
||||
if grids is not None and len(grids) > 0:
|
||||
for g in grids:
|
||||
ax1.axhline(g, color='orange', linestyle='--', alpha=0.2)
|
||||
|
||||
plt.title('动态网格回测')
|
||||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
ax1.legend(lines1 + lines2, labels1 + labels2)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 读取数据
|
||||
DATA_PATH = os.path.join(os.path.dirname(__file__), '0020.HK.csv')
|
||||
# 处理文件头乱码和尾部注释
|
||||
raw = pd.read_csv(DATA_PATH, header=None, skiprows=1, names=['code','date','open','high','low','close','pct'])
|
||||
raw = raw[raw['close'].apply(lambda x: str(x).replace('.','',1).isdigit())] # 去除空行和注释
|
||||
raw['date'] = pd.to_datetime(raw['date'])
|
||||
raw['close'] = raw['close'].astype(float)
|
||||
raw['open'] = raw['open'].astype(float)
|
||||
raw = raw.sort_values('date').reset_index(drop=True)
|
||||
|
||||
# 运行回测
|
||||
df_hist, trade_log, total_return, max_drawdown, buy_cnt, sell_cnt, win_rate = dynamic_grid_backtest(raw)
|
||||
|
||||
# 打印统计结果
|
||||
print('\n==== 动态网格回测结果 ===')
|
||||
print(f'总收益率: {total_return*100:.2f}%')
|
||||
print(f'最大回撤: {max_drawdown*100:.2f}%')
|
||||
print(f'买入次数: {buy_cnt}，卖出次数: {sell_cnt}')
|
||||
print(f'胜率: {win_rate*100:.2f}%')
|
||||
|
||||
# 绘制结果
|
||||
last_price = df_hist['price'].iloc[-1]
|
||||
last_ma120 = df_hist['ma120'].iloc[-1]
|
||||
last_grids = generate_dynamic_grids(last_price, last_ma120)
|
||||
plot_results(df_hist, trade_log, last_grids)
|
||||
|
|
@ -0,0 +1,603 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
东方财富财务数据采集器 V2.0
|
||||
适配2025年新版接口
|
||||
|
||||
从东方财富网自动采集A股上市公司的财务报表数据，包括：
|
||||
1. 资产负债表
|
||||
2. 利润表
|
||||
3. 现金流量表
|
||||
|
||||
数据存储到MongoDB数据库中，保留所有原始字段
|
||||
"""
|
||||
|
||||
import requests
|
||||
import pymongo
|
||||
import logging
|
||||
import datetime
|
||||
import time
|
||||
import json
|
||||
import pandas as pd
|
||||
from typing import List, Dict, Optional
|
||||
import sys
|
||||
import os
|
||||
from sqlalchemy import create_engine, text
|
||||
|
||||
# 添加项目根目录到路径
|
||||
project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
sys.path.append(project_root)
|
||||
|
||||
# 导入配置
|
||||
try:
|
||||
from valuation_analysis.config import MONGO_CONFIG2, DB_URL
|
||||
except ImportError:
|
||||
# 如果上面的导入失败，尝试直接导入
|
||||
import importlib.util
|
||||
config_path = os.path.join(project_root, 'valuation_analysis', 'config.py')
|
||||
spec = importlib.util.spec_from_file_location("config", config_path)
|
||||
config_module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(config_module)
|
||||
MONGO_CONFIG2 = config_module.MONGO_CONFIG2
|
||||
DB_URL = config_module.DB_URL
|
||||
|
||||
# 配置日志
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
handlers=[
|
||||
logging.FileHandler('financial_data_collector_v2.log', encoding='utf-8'),
|
||||
logging.StreamHandler()
|
||||
]
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 为StreamHandler也设置编码（如果可能）
|
||||
try:
|
||||
if hasattr(sys.stdout, 'reconfigure'):
|
||||
sys.stdout.reconfigure(encoding='utf-8')
|
||||
if hasattr(sys.stderr, 'reconfigure'):
|
||||
sys.stderr.reconfigure(encoding='utf-8')
|
||||
except:
|
||||
pass # 如果设置失败就忽略
|
||||
|
||||
|
||||
class FinancialDataCollectorV2:
|
||||
"""财务数据采集器 V2.0 - 适配新版东方财富接口"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化"""
|
||||
self.mongo_client = None
|
||||
self.db = None
|
||||
# 使用新的集合名称存储新版本数据
|
||||
self.collection_name = 'eastmoney_financial_data_v2'
|
||||
self.collection = None
|
||||
|
||||
# 初始化MySQL连接
|
||||
self.mysql_engine = None
|
||||
|
||||
self.connect_mongodb()
|
||||
self.connect_mysql()
|
||||
|
||||
def connect_mongodb(self):
|
||||
"""连接MongoDB数据库"""
|
||||
try:
|
||||
# 使用参数形式连接MongoDB，而不是连接字符串
|
||||
self.mongo_client = pymongo.MongoClient(
|
||||
host=MONGO_CONFIG2['host'],
|
||||
port=MONGO_CONFIG2['port'],
|
||||
username=MONGO_CONFIG2['username'],
|
||||
password=MONGO_CONFIG2['password']
|
||||
)
|
||||
self.db = self.mongo_client[MONGO_CONFIG2['db']]
|
||||
self.collection = self.db[self.collection_name]
|
||||
|
||||
# 测试连接
|
||||
self.mongo_client.admin.command('ping')
|
||||
logger.info(f"MongoDB连接成功，使用集合: {self.collection_name}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"MongoDB连接失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def connect_mysql(self):
|
||||
"""连接MySQL数据库"""
|
||||
try:
|
||||
self.mysql_engine = create_engine(
|
||||
DB_URL,
|
||||
pool_size=5,
|
||||
max_overflow=10,
|
||||
pool_recycle=3600
|
||||
)
|
||||
|
||||
# 测试连接
|
||||
with self.mysql_engine.connect() as conn:
|
||||
conn.execute(text("SELECT 1"))
|
||||
|
||||
logger.info("MySQL数据库连接成功")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"MySQL数据库连接失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def get_all_stock_codes(self) -> List[str]:
|
||||
"""
|
||||
从数据库中获取所有股票代码
|
||||
|
||||
Returns:
|
||||
List[str]: 股票代码列表
|
||||
"""
|
||||
try:
|
||||
query = "SELECT DISTINCT gp_code_two FROM gp_code_all WHERE gp_code_two IS NOT NULL AND gp_code_two != ''"
|
||||
|
||||
with self.mysql_engine.connect() as conn:
|
||||
df = pd.read_sql(text(query), conn)
|
||||
|
||||
stock_codes = df['gp_code_two'].tolist()
|
||||
logger.info(f"从数据库获取到 {len(stock_codes)} 只股票")
|
||||
|
||||
return stock_codes
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取股票代码列表失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def build_date_filter(self, stock_code: str, periods: int = 21) -> str:
|
||||
"""
|
||||
构建日期过滤条件 - 适配新版接口
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码，如'300750.SZ'
|
||||
periods: 获取多少个报告期，默认21个季度
|
||||
|
||||
Returns:
|
||||
str: 日期过滤字符串
|
||||
"""
|
||||
# 计算需要的日期范围，从当前日期往前推
|
||||
current_date = datetime.datetime.now()
|
||||
dates = []
|
||||
|
||||
# 生成最近21个季度的日期
|
||||
for i in range(periods):
|
||||
# 从当前季度往前推
|
||||
year = current_date.year
|
||||
month = current_date.month
|
||||
|
||||
# 计算季度
|
||||
if month <= 3:
|
||||
quarter_month = 3
|
||||
year_offset = i // 4
|
||||
quarter_offset = i % 4
|
||||
if quarter_offset > 0:
|
||||
year_offset += 1
|
||||
quarter_month = [12, 9, 6, 3][quarter_offset - 1]
|
||||
target_year = year - year_offset
|
||||
elif month <= 6:
|
||||
quarter_month = 6
|
||||
year_offset = i // 4
|
||||
quarter_offset = i % 4
|
||||
if quarter_offset >= 1:
|
||||
if quarter_offset == 1:
|
||||
quarter_month = 3
|
||||
elif quarter_offset == 2:
|
||||
quarter_month = 12
|
||||
target_year = year - 1
|
||||
elif quarter_offset == 3:
|
||||
quarter_month = 9
|
||||
target_year = year - 1
|
||||
else:
|
||||
target_year = year
|
||||
if quarter_offset >= 2:
|
||||
year_offset += 1
|
||||
target_year = year - year_offset
|
||||
elif month <= 9:
|
||||
quarter_month = 9
|
||||
year_offset = i // 4
|
||||
quarter_offset = i % 4
|
||||
if quarter_offset >= 1:
|
||||
if quarter_offset == 1:
|
||||
quarter_month = 6
|
||||
elif quarter_offset == 2:
|
||||
quarter_month = 3
|
||||
elif quarter_offset == 3:
|
||||
quarter_month = 12
|
||||
target_year = year - 1
|
||||
else:
|
||||
target_year = year
|
||||
if quarter_offset >= 3:
|
||||
year_offset += 1
|
||||
target_year = year - year_offset
|
||||
else:
|
||||
quarter_month = 12
|
||||
year_offset = i // 4
|
||||
quarter_offset = i % 4
|
||||
if quarter_offset >= 1:
|
||||
if quarter_offset == 1:
|
||||
quarter_month = 9
|
||||
elif quarter_offset == 2:
|
||||
quarter_month = 6
|
||||
elif quarter_offset == 3:
|
||||
quarter_month = 3
|
||||
else:
|
||||
target_year = year
|
||||
target_year = year - year_offset
|
||||
|
||||
# 简化逻辑：直接从2025年开始往前推21个季度
|
||||
base_year = 2025
|
||||
base_quarters = [
|
||||
(2025, 3), (2024, 12), (2024, 9), (2024, 6), (2024, 3),
|
||||
(2023, 12), (2023, 9), (2023, 6), (2023, 3),
|
||||
(2022, 12), (2022, 9), (2022, 6), (2022, 3),
|
||||
(2021, 12), (2021, 9), (2021, 6), (2021, 3),
|
||||
(2020, 12), (2020, 9), (2020, 6), (2020, 3)
|
||||
]
|
||||
|
||||
if i < len(base_quarters):
|
||||
year, month = base_quarters[i]
|
||||
if month == 3:
|
||||
date_str = f'{year}-03-31'
|
||||
elif month == 6:
|
||||
date_str = f'{year}-06-30'
|
||||
elif month == 9:
|
||||
date_str = f'{year}-09-30'
|
||||
else:
|
||||
date_str = f'{year}-12-31'
|
||||
dates.append(date_str)
|
||||
|
||||
# 构建过滤字符串
|
||||
date_filter = f'(SECUCODE%3D%22{stock_code}%22)(REPORT_DATE%20in%20('
|
||||
date_filter += '%2C'.join([f'%27{date}%27' for date in dates])
|
||||
date_filter += '))'
|
||||
|
||||
return date_filter
|
||||
|
||||
def fetch_profit_statement(self, stock_code: str, periods: int = 21) -> List[Dict]:
|
||||
"""获取利润表数据"""
|
||||
date_filter = self.build_date_filter(stock_code, periods)
|
||||
url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_GINCOME&sty=APP_F10_GINCOME&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC'
|
||||
|
||||
headers = {"Content-Type": "application/json"}
|
||||
|
||||
try:
|
||||
response = requests.get(url, headers=headers, timeout=30)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
if 'result' in data and 'data' in data['result']:
|
||||
logger.info(f"成功获取利润表数据，共 {len(data['result']['data'])} 个报告期")
|
||||
return data['result']['data']
|
||||
else:
|
||||
logger.warning("利润表数据格式异常")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取利润表失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def fetch_balance_sheet(self, stock_code: str, periods: int = 21) -> List[Dict]:
|
||||
"""获取资产负债表数据"""
|
||||
date_filter = self.build_date_filter(stock_code, periods)
|
||||
url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_GBALANCE&sty=F10_FINANCE_GBALANCE&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=012481899342117453'
|
||||
|
||||
headers = {"Content-Type": "application/json"}
|
||||
|
||||
try:
|
||||
response = requests.get(url, headers=headers, timeout=30)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
if 'result' in data and 'data' in data['result']:
|
||||
logger.info(f"成功获取资产负债表数据，共 {len(data['result']['data'])} 个报告期")
|
||||
return data['result']['data']
|
||||
else:
|
||||
logger.warning("资产负债表数据格式异常")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取资产负债表失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def fetch_cash_flow_statement(self, stock_code: str, periods: int = 21) -> List[Dict]:
|
||||
"""获取现金流量表数据"""
|
||||
date_filter = self.build_date_filter(stock_code, periods)
|
||||
url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_GCASHFLOW&sty=APP_F10_GCASHFLOW&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=04664977872701077'
|
||||
|
||||
headers = {"Content-Type": "application/json"}
|
||||
|
||||
try:
|
||||
response = requests.get(url, headers=headers, timeout=30)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
if 'result' in data and 'data' in data['result']:
|
||||
logger.info(f"成功获取现金流量表数据，共 {len(data['result']['data'])} 个报告期")
|
||||
return data['result']['data']
|
||||
else:
|
||||
logger.warning("现金流量表数据格式异常")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取现金流量表失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def process_financial_data(self, stock_code: str, profit_data: List[Dict],
|
||||
balance_data: List[Dict], cash_data: List[Dict]) -> List[Dict]:
|
||||
"""
|
||||
处理财务数据，按报告期合并三张表的数据
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
profit_data: 利润表数据
|
||||
balance_data: 资产负债表数据
|
||||
cash_data: 现金流量表数据
|
||||
|
||||
Returns:
|
||||
List[Dict]: 处理后的财务数据列表
|
||||
"""
|
||||
financial_data_list = []
|
||||
|
||||
# 创建按报告日期索引的字典
|
||||
profit_dict = {item['REPORT_DATE']: item for item in profit_data}
|
||||
balance_dict = {item['REPORT_DATE']: item for item in balance_data}
|
||||
cash_dict = {item['REPORT_DATE']: item for item in cash_data}
|
||||
|
||||
# 获取所有报告日期
|
||||
all_dates = set()
|
||||
all_dates.update(profit_dict.keys())
|
||||
all_dates.update(balance_dict.keys())
|
||||
all_dates.update(cash_dict.keys())
|
||||
|
||||
# 按日期排序
|
||||
sorted_dates = sorted(all_dates, reverse=True)
|
||||
|
||||
for report_date in sorted_dates:
|
||||
try:
|
||||
# 构建完整的财务数据记录
|
||||
financial_record = {
|
||||
'stock_code': stock_code,
|
||||
'report_date': report_date[:10] if report_date else None, # 只取日期部分
|
||||
'collect_time': datetime.datetime.now(),
|
||||
'data_source': 'eastmoney_v2',
|
||||
'profit_statement': profit_dict.get(report_date, {}),
|
||||
'balance_sheet': balance_dict.get(report_date, {}),
|
||||
'cash_flow_statement': cash_dict.get(report_date, {})
|
||||
}
|
||||
|
||||
# 只有当至少有一张表有数据时才添加记录
|
||||
if (financial_record['profit_statement'] or
|
||||
financial_record['balance_sheet'] or
|
||||
financial_record['cash_flow_statement']):
|
||||
financial_data_list.append(financial_record)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理报告期 {report_date} 数据时出错: {str(e)}")
|
||||
continue
|
||||
|
||||
logger.info(f"成功处理 {len(financial_data_list)} 个报告期的数据")
|
||||
return financial_data_list
|
||||
|
||||
def save_to_mongodb(self, financial_data_list: List[Dict]) -> bool:
|
||||
"""
|
||||
保存数据到MongoDB，如果数据已存在则跳过，只新增不存在的数据
|
||||
|
||||
Args:
|
||||
financial_data_list: 财务数据列表
|
||||
|
||||
Returns:
|
||||
bool: 是否保存成功
|
||||
"""
|
||||
try:
|
||||
if not financial_data_list:
|
||||
logger.warning("没有数据需要保存")
|
||||
return False
|
||||
|
||||
# 统计插入和跳过的数量
|
||||
inserted_count = 0
|
||||
skipped_count = 0
|
||||
|
||||
for data in financial_data_list:
|
||||
# 使用股票代码和报告日期作为唯一标识
|
||||
filter_condition = {
|
||||
'stock_code': data['stock_code'],
|
||||
'report_date': data['report_date']
|
||||
}
|
||||
|
||||
# 检查数据是否已存在
|
||||
existing_record = self.collection.find_one(filter_condition)
|
||||
|
||||
if existing_record:
|
||||
# 数据已存在，跳过
|
||||
skipped_count += 1
|
||||
logger.debug(f"跳过已存在的数据: {data['stock_code']} - {data['report_date']}")
|
||||
else:
|
||||
# 数据不存在，插入新数据
|
||||
self.collection.insert_one(data)
|
||||
inserted_count += 1
|
||||
logger.debug(f"插入新数据: {data['stock_code']} - {data['report_date']}")
|
||||
|
||||
logger.info(f"数据保存完成 - 新增: {inserted_count} 条，跳过: {skipped_count} 条")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"保存数据到MongoDB失败: {str(e)}")
|
||||
return False
|
||||
|
||||
def collect_financial_data(self, stock_code: str, periods: int = 21) -> bool:
|
||||
"""
|
||||
采集单只股票的财务数据
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码，如'300750.SZ'
|
||||
periods: 获取多少个报告期，默认21个季度
|
||||
|
||||
Returns:
|
||||
bool: 是否采集成功
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始采集股票 {stock_code} 的财务数据（{periods}个报告期）")
|
||||
|
||||
# 获取三张财务报表数据
|
||||
profit_data = self.fetch_profit_statement(stock_code, periods)
|
||||
time.sleep(1) # 避免请求过于频繁
|
||||
|
||||
balance_data = self.fetch_balance_sheet(stock_code, periods)
|
||||
time.sleep(1)
|
||||
|
||||
cash_data = self.fetch_cash_flow_statement(stock_code, periods)
|
||||
time.sleep(1)
|
||||
|
||||
# 检查至少有一张表有数据
|
||||
if not any([profit_data, balance_data, cash_data]):
|
||||
logger.error(f"股票 {stock_code} 没有获取到任何财务数据")
|
||||
return False
|
||||
|
||||
# 处理财务数据
|
||||
financial_data_list = self.process_financial_data(
|
||||
stock_code, profit_data, balance_data, cash_data
|
||||
)
|
||||
|
||||
if not financial_data_list:
|
||||
logger.error(f"股票 {stock_code} 的财务数据处理失败")
|
||||
return False
|
||||
|
||||
# 保存到MongoDB
|
||||
success = self.save_to_mongodb(financial_data_list)
|
||||
|
||||
if success:
|
||||
logger.info(f"股票 {stock_code} 的财务数据采集完成")
|
||||
|
||||
return success
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"采集股票 {stock_code} 的财务数据失败: {str(e)}")
|
||||
return False
|
||||
|
||||
def batch_collect_financial_data(self, stock_codes: List[str], periods: int = 21) -> Dict:
|
||||
"""
|
||||
批量采集多只股票的财务数据
|
||||
|
||||
Args:
|
||||
stock_codes: 股票代码列表
|
||||
periods: 获取多少个报告期，默认21个季度
|
||||
|
||||
Returns:
|
||||
Dict: 采集结果统计
|
||||
"""
|
||||
results = {'success': 0, 'failed': 0, 'failed_stocks': []}
|
||||
total_stocks = len(stock_codes)
|
||||
|
||||
logger.info(f"开始批量采集 {total_stocks} 只股票的财务数据")
|
||||
|
||||
for index, stock_code in enumerate(stock_codes, 1):
|
||||
try:
|
||||
# 显示进度
|
||||
progress = (index / total_stocks) * 100
|
||||
logger.info(f"进度 [{index}/{total_stocks}] ({progress:.1f}%) - 正在处理: {stock_code}")
|
||||
|
||||
success = self.collect_financial_data(stock_code, periods)
|
||||
if success:
|
||||
results['success'] += 1
|
||||
logger.info(f"SUCCESS [{index}/{total_stocks}] {stock_code} 采集成功")
|
||||
else:
|
||||
results['failed'] += 1
|
||||
results['failed_stocks'].append(stock_code)
|
||||
logger.warning(f"FAILED [{index}/{total_stocks}] {stock_code} 采集失败")
|
||||
|
||||
# 每只股票之间暂停一下，避免请求过于频繁
|
||||
time.sleep(2)
|
||||
|
||||
# 每100只股票输出一次统计信息
|
||||
if index % 100 == 0:
|
||||
current_success_rate = (results['success'] / index) * 100
|
||||
logger.info(f"阶段性统计: 已处理 {index}/{total_stocks} 只股票，成功率: {current_success_rate:.2f}%")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理股票 {stock_code} 时出错: {str(e)}")
|
||||
results['failed'] += 1
|
||||
results['failed_stocks'].append(stock_code)
|
||||
|
||||
# 继续处理下一只股票，不中断整个流程
|
||||
continue
|
||||
|
||||
success_rate = (results['success'] / total_stocks) * 100
|
||||
logger.info(f"批量采集完成: 成功{results['success']}只，失败{results['failed']}只，成功率: {success_rate:.2f}%")
|
||||
|
||||
if results['failed_stocks']:
|
||||
logger.info(f"失败的股票数量: {len(results['failed_stocks'])}")
|
||||
# 记录前10个失败的股票到日志
|
||||
failed_sample = results['failed_stocks'][:10]
|
||||
logger.info(f"失败股票示例: {', '.join(failed_sample)}")
|
||||
|
||||
return results
|
||||
|
||||
def close_connection(self):
|
||||
"""关闭数据库连接"""
|
||||
if self.mongo_client:
|
||||
self.mongo_client.close()
|
||||
logger.info("MongoDB连接已关闭")
|
||||
|
||||
if self.mysql_engine:
|
||||
self.mysql_engine.dispose()
|
||||
logger.info("MySQL连接已关闭")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数 - 批量采集所有股票的财务数据"""
|
||||
collector = FinancialDataCollectorV2()
|
||||
|
||||
try:
|
||||
# 从数据库获取所有股票代码
|
||||
logger.info("正在从数据库获取股票列表...")
|
||||
stock_codes = collector.get_all_stock_codes()
|
||||
|
||||
if not stock_codes:
|
||||
logger.error("未获取到任何股票代码，程序退出")
|
||||
return
|
||||
|
||||
logger.info(f"从数据库获取到 {len(stock_codes)} 只股票")
|
||||
|
||||
# 可以选择采集所有股票或者部分股票进行测试
|
||||
# 如果要测试，可以取前几只股票
|
||||
# 测试模式：只采集前10只股票
|
||||
TEST_MODE = False # 设置为False将采集所有股票
|
||||
|
||||
if TEST_MODE:
|
||||
test_count = min(10, len(stock_codes)) # 最多取10只股票测试
|
||||
stock_codes = stock_codes[:test_count]
|
||||
logger.info(f"TEST MODE: 仅采集前 {test_count} 只股票")
|
||||
else:
|
||||
logger.info(f"PRODUCTION MODE: 将采集全部 {len(stock_codes)} 只股票")
|
||||
|
||||
logger.info(f"开始批量采集 {len(stock_codes)} 只股票的财务数据")
|
||||
|
||||
# 批量采集
|
||||
results = collector.batch_collect_financial_data(stock_codes, periods=21)
|
||||
|
||||
# 输出最终结果
|
||||
print(f"\n{'='*50}")
|
||||
print(f"批量采集完成统计")
|
||||
print(f"{'='*50}")
|
||||
print(f"SUCCESS 成功采集: {results['success']} 只股票")
|
||||
print(f"FAILED 采集失败: {results['failed']} 只股票")
|
||||
print(f"SUCCESS RATE 成功率: {(results['success'] / len(stock_codes) * 100):.2f}%")
|
||||
|
||||
if results['failed_stocks']:
|
||||
print(f"\n失败的股票列表:")
|
||||
for i, stock in enumerate(results['failed_stocks'][:10], 1): # 只显示前10个
|
||||
print(f" {i}. {stock}")
|
||||
if len(results['failed_stocks']) > 10:
|
||||
print(f" ... 还有 {len(results['failed_stocks']) - 10} 只股票")
|
||||
|
||||
logger.info("所有任务已完成")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
logger.info("用户中断程序执行")
|
||||
print("\n警告: 程序被用户中断")
|
||||
except Exception as e:
|
||||
logger.error(f"采集过程中出现错误: {str(e)}")
|
||||
print(f"\n错误: 程序执行出错: {str(e)}")
|
||||
finally:
|
||||
collector.close_connection()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,200 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
|
||||
# 新增：设置matplotlib支持中文和负号
|
||||
import matplotlib
|
||||
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
|
||||
matplotlib.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
# 新增：屏蔽UserWarning
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
# 读取数据
|
||||
DATA_PATH = os.path.join(os.path.dirname(__file__), '0020.HK.csv')
|
||||
# 处理文件头乱码和尾部注释
|
||||
raw = pd.read_csv(DATA_PATH, header=None, skiprows=1, names=['code','date','open','high','low','close','pct'])
|
||||
raw = raw[raw['close'].apply(lambda x: str(x).replace('.','',1).isdigit())] # 去除空行和注释
|
||||
raw['date'] = pd.to_datetime(raw['date'])
|
||||
raw['close'] = raw['close'].astype(float)
|
||||
raw = raw.sort_values('date').reset_index(drop=True)
|
||||
|
||||
# 回测参数
|
||||
INIT_CASH = 4000000
|
||||
FEE_RATE = 0.003 # 千分之3
|
||||
GRID_TYPES = ['等差网格', '等比网格', 'ATR网格']
|
||||
GRID_NUM = 15 # 网格数量
|
||||
PER_GRID_CASH = INIT_CASH / GRID_NUM # 每格买入金额
|
||||
|
||||
# 自动确定网格上下限
|
||||
price_min = raw['close'].min()
|
||||
price_max = raw['close'].max()
|
||||
price_mean = raw['close'].mean()
|
||||
price_std = raw['close'].std()
|
||||
|
||||
# 网格参数生成
|
||||
# 1. 等差网格
|
||||
step_linear = (price_max - price_min) / GRID_NUM
|
||||
linear_grids = np.arange(price_min, price_max, step_linear)
|
||||
# 2. 等比网格
|
||||
step_ratio = (price_max / price_min) ** (1/GRID_NUM)
|
||||
ratio_grids = [price_min * (step_ratio ** i) for i in range(GRID_NUM+1)]
|
||||
# 3. ATR网格
|
||||
atr_window = 20
|
||||
raw['tr'] = np.maximum(raw['high']-raw['low'], np.abs(raw['high']-raw['close'].shift(1)), np.abs(raw['low']-raw['close'].shift(1)))
|
||||
raw['atr'] = raw['tr'].rolling(atr_window).mean()
|
||||
avg_atr = raw['atr'].mean()
|
||||
atr_step = avg_atr * 1.0 # 可调参数
|
||||
atr_grids = np.arange(price_min, price_max, atr_step)
|
||||
|
||||
# 网格集合
|
||||
grid_dict = {
|
||||
'等差网格': linear_grids,
|
||||
'等比网格': ratio_grids,
|
||||
'ATR网格': atr_grids
|
||||
}
|
||||
|
||||
# 回测主函数
|
||||
def grid_backtest(prices, grids, grid_type):
|
||||
cash = INIT_CASH
|
||||
position = 0
|
||||
cost = 0
|
||||
history = [] # 记录每日资金、持仓
|
||||
trade_log = [] # 买卖点
|
||||
last_grid = None
|
||||
|
||||
def get_grid_index(price):
|
||||
"""获取价格所在的网格索引"""
|
||||
grid_idx = np.searchsorted(grids, price, side='right') - 1
|
||||
if grid_idx < 0:
|
||||
grid_idx = 0
|
||||
if grid_idx >= len(grids)-1:
|
||||
grid_idx = len(grids)-2
|
||||
return grid_idx
|
||||
|
||||
def execute_trade(price, trade_type, grid_idx, last_grid_idx):
|
||||
"""执行交易"""
|
||||
nonlocal cash, position, cost
|
||||
if trade_type == 'buy' and cash >= PER_GRID_CASH:
|
||||
buy_price = price * (1 + FEE_RATE)
|
||||
buy_amount = PER_GRID_CASH / buy_price
|
||||
cash -= buy_amount * buy_price
|
||||
position += buy_amount
|
||||
cost += buy_amount * buy_price
|
||||
trade_log.append({'date': date, 'price': price, 'type': 'buy'})
|
||||
return True
|
||||
elif trade_type == 'sell' and position > 0:
|
||||
sell_price = price * (1 - FEE_RATE)
|
||||
sell_amount = PER_GRID_CASH / price
|
||||
if sell_amount > position:
|
||||
sell_amount = position
|
||||
cash += sell_amount * sell_price
|
||||
position -= sell_amount
|
||||
cost -= sell_amount * price
|
||||
trade_log.append({'date': date, 'price': price, 'type': 'sell'})
|
||||
return True
|
||||
return False
|
||||
|
||||
for i, row in prices.iterrows():
|
||||
date = row['date']
|
||||
open_price = row['open']
|
||||
close_price = row['close']
|
||||
|
||||
# 获取开盘价和收盘价所在的网格位置
|
||||
open_grid_idx = get_grid_index(open_price)
|
||||
close_grid_idx = get_grid_index(close_price)
|
||||
|
||||
# 开盘价交易处理
|
||||
if last_grid is not None:
|
||||
# 开盘价买入处理
|
||||
if open_grid_idx < last_grid:
|
||||
# 处理多个网格的买入
|
||||
for grid_idx in range(open_grid_idx, last_grid):
|
||||
if not execute_trade(open_price, 'buy', grid_idx, last_grid):
|
||||
break
|
||||
# 开盘价卖出处理
|
||||
elif open_grid_idx > last_grid:
|
||||
# 处理多个网格的卖出
|
||||
for grid_idx in range(last_grid + 1, open_grid_idx + 1):
|
||||
if not execute_trade(open_price, 'sell', grid_idx, last_grid):
|
||||
break
|
||||
|
||||
# 收盘价交易处理
|
||||
if last_grid is not None:
|
||||
# 收盘价买入处理
|
||||
if close_grid_idx < open_grid_idx:
|
||||
# 处理多个网格的买入
|
||||
for grid_idx in range(close_grid_idx, open_grid_idx):
|
||||
if not execute_trade(close_price, 'buy', grid_idx, open_grid_idx):
|
||||
break
|
||||
# 收盘价卖出处理
|
||||
elif close_grid_idx > open_grid_idx:
|
||||
# 处理多个网格的卖出
|
||||
for grid_idx in range(open_grid_idx + 1, close_grid_idx + 1):
|
||||
if not execute_trade(close_price, 'sell', grid_idx, open_grid_idx):
|
||||
break
|
||||
|
||||
# 更新last_grid为收盘价所在网格
|
||||
last_grid = close_grid_idx
|
||||
|
||||
# 记录每日净值
|
||||
total = cash + position * close_price
|
||||
history.append({
|
||||
'date': date,
|
||||
'net': total,
|
||||
'cash': cash,
|
||||
'position': position,
|
||||
'price': close_price,
|
||||
'open_grid': open_grid_idx,
|
||||
'close_grid': close_grid_idx
|
||||
})
|
||||
|
||||
# 统计
|
||||
df_hist = pd.DataFrame(history)
|
||||
max_drawdown = ((df_hist['net'].cummax() - df_hist['net']) / df_hist['net'].cummax()).max()
|
||||
total_return = (df_hist['net'].iloc[-1] - INIT_CASH) / INIT_CASH
|
||||
|
||||
# 胜率统计
|
||||
win, lose = 0, 0
|
||||
for i in range(1, len(trade_log)):
|
||||
if trade_log[i-1]['type']=='buy' and trade_log[i]['type']=='sell':
|
||||
if trade_log[i]['price'] > trade_log[i-1]['price']:
|
||||
win += 1
|
||||
else:
|
||||
lose += 1
|
||||
win_rate = win / (win+lose) if (win+lose)>0 else 0
|
||||
|
||||
return df_hist, trade_log, total_return, max_drawdown, len([t for t in trade_log if t['type']=='buy']), len([t for t in trade_log if t['type']=='sell']), win_rate
|
||||
|
||||
# 主流程
|
||||
if __name__ == '__main__':
|
||||
for grid_type in GRID_TYPES:
|
||||
grids = grid_dict[grid_type]
|
||||
df_hist, trade_log, total_return, max_drawdown, buy_cnt, sell_cnt, win_rate = grid_backtest(raw, grids, grid_type)
|
||||
print(f'\n==== {grid_type} 回测结果 ===')
|
||||
print(f'总收益率: {total_return*100:.2f}%')
|
||||
print(f'最大回撤: {max_drawdown*100:.2f}%')
|
||||
print(f'买入次数: {buy_cnt}，卖出次数: {sell_cnt}')
|
||||
print(f'胜率: {win_rate*100:.2f}%')
|
||||
# 绘图
|
||||
fig, ax1 = plt.subplots(figsize=(14,6))
|
||||
ax2 = ax1.twinx()
|
||||
ax1.plot(df_hist['date'], df_hist['price'], label='收盘价', color='gray', alpha=0.7)
|
||||
# 将净值转换为相对净值（从1开始）
|
||||
relative_net = df_hist['net'] / INIT_CASH
|
||||
ax2.plot(df_hist['date'], relative_net, label='净值', color='blue')
|
||||
for t in trade_log:
|
||||
if t['type']=='buy':
|
||||
ax1.scatter(t['date'], t['price'], marker='^', color='red', label='买入' if '买入' not in ax1.get_legend_handles_labels()[1] else "")
|
||||
else:
|
||||
ax1.scatter(t['date'], t['price'], marker='v', color='green', label='卖出' if '卖出' not in ax1.get_legend_handles_labels()[1] else "")
|
||||
for g in grids:
|
||||
ax1.axhline(g, color='orange', linestyle='--', alpha=0.2)
|
||||
plt.title(f'{grid_type} 网格回测')
|
||||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
ax1.legend(lines1 + lines2, labels1 + labels2)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
|
@ -0,0 +1,214 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
|
||||
# 新增：设置matplotlib支持中文和负号
|
||||
import matplotlib
|
||||
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
|
||||
matplotlib.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
# 新增：屏蔽UserWarning
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
# 回测参数
|
||||
INIT_CASH = 4000000
|
||||
FEE_RATE = 0.003 # 千分之3
|
||||
GRID_NUM = 15 # 网格数量
|
||||
PER_GRID_CASH = INIT_CASH / GRID_NUM # 每格买入金额
|
||||
|
||||
def generate_trend_following_grids(price, ma20):
|
||||
"""生成趋势跟随网格
|
||||
上升趋势：
|
||||
- 买入：每跌1%加仓
|
||||
- 卖出：每涨3%减仓
|
||||
下降趋势：
|
||||
- 买入：每跌3%加仓
|
||||
- 卖出：每涨1%减仓
|
||||
"""
|
||||
if price > ma20: # 上升趋势
|
||||
buy_steps = np.arange(price * 0.99, price * 0.85, -price * 0.01) # 每跌1%加仓
|
||||
sell_steps = np.arange(price * 1.03, price * 1.30, price * 0.03) # 每涨3%减仓
|
||||
else: # 下降趋势
|
||||
buy_steps = np.arange(price * 0.97, price * 0.70, -price * 0.03) # 每跌3%加仓
|
||||
sell_steps = np.arange(price * 1.01, price * 1.15, price * 0.01) # 每涨1%减仓
|
||||
return np.sort(np.concatenate([buy_steps, sell_steps]))
|
||||
|
||||
def trend_following_grid_backtest(prices):
|
||||
cash = INIT_CASH
|
||||
position = 0
|
||||
cost = 0
|
||||
history = [] # 记录每日资金、持仓
|
||||
trade_log = [] # 买卖点
|
||||
last_grid = None
|
||||
|
||||
# 计算MA20
|
||||
prices['MA20'] = prices['close'].rolling(window=20).mean()
|
||||
|
||||
def get_grid_index(price, current_grids):
|
||||
"""获取价格所在的网格索引"""
|
||||
if len(current_grids) == 0:
|
||||
return -1
|
||||
grid_idx = np.searchsorted(current_grids, price, side='right') - 1
|
||||
if grid_idx < 0:
|
||||
grid_idx = 0
|
||||
if grid_idx >= len(current_grids)-1:
|
||||
grid_idx = len(current_grids)-2
|
||||
return grid_idx
|
||||
|
||||
def execute_trade(price, trade_type, grid_idx, last_grid_idx, date):
|
||||
"""执行交易"""
|
||||
nonlocal cash, position, cost
|
||||
if trade_type == 'buy' and cash >= PER_GRID_CASH:
|
||||
buy_price = price * (1 + FEE_RATE)
|
||||
buy_amount = PER_GRID_CASH / buy_price
|
||||
cash -= buy_amount * buy_price
|
||||
position += buy_amount
|
||||
cost += buy_amount * buy_price
|
||||
trade_log.append({'date': date, 'price': price, 'type': 'buy'})
|
||||
return True
|
||||
elif trade_type == 'sell' and position > 0:
|
||||
sell_price = price * (1 - FEE_RATE)
|
||||
sell_amount = PER_GRID_CASH / price
|
||||
if sell_amount > position:
|
||||
sell_amount = position
|
||||
cash += sell_amount * sell_price
|
||||
position -= sell_amount
|
||||
cost -= sell_amount * price
|
||||
trade_log.append({'date': date, 'price': price, 'type': 'sell'})
|
||||
return True
|
||||
return False
|
||||
|
||||
for i, row in prices.iterrows():
|
||||
if pd.isna(row['MA20']): # 跳过MA20未形成的时期
|
||||
continue
|
||||
|
||||
date = row['date']
|
||||
open_price = row['open']
|
||||
close_price = row['close']
|
||||
ma20 = row['MA20']
|
||||
|
||||
# 生成当前的趋势跟随网格
|
||||
open_grids = generate_trend_following_grids(open_price, ma20)
|
||||
close_grids = generate_trend_following_grids(close_price, ma20)
|
||||
|
||||
# 获取开盘价和收盘价所在的网格位置
|
||||
open_grid_idx = get_grid_index(open_price, open_grids)
|
||||
close_grid_idx = get_grid_index(close_price, close_grids)
|
||||
|
||||
# 开盘价交易处理
|
||||
if last_grid is not None and open_grid_idx >= 0:
|
||||
# 开盘价买入处理
|
||||
if open_grid_idx < last_grid:
|
||||
for grid_idx in range(open_grid_idx, last_grid):
|
||||
if not execute_trade(open_price, 'buy', grid_idx, last_grid, date):
|
||||
break
|
||||
# 开盘价卖出处理
|
||||
elif open_grid_idx > last_grid:
|
||||
for grid_idx in range(last_grid + 1, open_grid_idx + 1):
|
||||
if not execute_trade(open_price, 'sell', grid_idx, last_grid, date):
|
||||
break
|
||||
|
||||
# 收盘价交易处理
|
||||
if last_grid is not None and close_grid_idx >= 0:
|
||||
# 收盘价买入处理
|
||||
if close_grid_idx < open_grid_idx:
|
||||
for grid_idx in range(close_grid_idx, open_grid_idx):
|
||||
if not execute_trade(close_price, 'buy', grid_idx, open_grid_idx, date):
|
||||
break
|
||||
# 收盘价卖出处理
|
||||
elif close_grid_idx > open_grid_idx:
|
||||
for grid_idx in range(open_grid_idx + 1, close_grid_idx + 1):
|
||||
if not execute_trade(close_price, 'sell', grid_idx, open_grid_idx, date):
|
||||
break
|
||||
|
||||
# 更新last_grid为收盘价所在网格
|
||||
last_grid = close_grid_idx
|
||||
|
||||
# 记录每日净值
|
||||
total = cash + position * close_price
|
||||
history.append({
|
||||
'date': date,
|
||||
'net': total,
|
||||
'cash': cash,
|
||||
'position': position,
|
||||
'price': close_price,
|
||||
'ma20': ma20,
|
||||
'open_grid': open_grid_idx,
|
||||
'close_grid': close_grid_idx
|
||||
})
|
||||
|
||||
# 统计
|
||||
df_hist = pd.DataFrame(history)
|
||||
max_drawdown = ((df_hist['net'].cummax() - df_hist['net']) / df_hist['net'].cummax()).max()
|
||||
total_return = (df_hist['net'].iloc[-1] - INIT_CASH) / INIT_CASH
|
||||
|
||||
# 胜率统计
|
||||
win, lose = 0, 0
|
||||
for i in range(1, len(trade_log)):
|
||||
if trade_log[i-1]['type']=='buy' and trade_log[i]['type']=='sell':
|
||||
if trade_log[i]['price'] > trade_log[i-1]['price']:
|
||||
win += 1
|
||||
else:
|
||||
lose += 1
|
||||
win_rate = win / (win+lose) if (win+lose)>0 else 0
|
||||
|
||||
return df_hist, trade_log, total_return, max_drawdown, len([t for t in trade_log if t['type']=='buy']), len([t for t in trade_log if t['type']=='sell']), win_rate
|
||||
|
||||
def plot_results(df_hist, trade_log, grids=None):
|
||||
"""绘制回测结果"""
|
||||
fig, ax1 = plt.subplots(figsize=(14,6))
|
||||
ax2 = ax1.twinx()
|
||||
|
||||
# 绘制价格、MA20和净值
|
||||
ax1.plot(df_hist['date'], df_hist['price'], label='收盘价', color='gray', alpha=0.7)
|
||||
ax1.plot(df_hist['date'], df_hist['ma20'], label='MA20', color='purple', alpha=0.5)
|
||||
relative_net = df_hist['net'] / INIT_CASH
|
||||
ax2.plot(df_hist['date'], relative_net, label='净值', color='blue')
|
||||
|
||||
# 绘制交易点
|
||||
for t in trade_log:
|
||||
if t['type']=='buy':
|
||||
ax1.scatter(t['date'], t['price'], marker='^', color='red', label='买入' if '买入' not in ax1.get_legend_handles_labels()[1] else "")
|
||||
else:
|
||||
ax1.scatter(t['date'], t['price'], marker='v', color='green', label='卖出' if '卖出' not in ax1.get_legend_handles_labels()[1] else "")
|
||||
|
||||
# 绘制最后一个时间点的网格线
|
||||
if grids is not None and len(grids) > 0:
|
||||
for g in grids:
|
||||
ax1.axhline(g, color='orange', linestyle='--', alpha=0.2)
|
||||
|
||||
plt.title('趋势跟随网格回测')
|
||||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
ax1.legend(lines1 + lines2, labels1 + labels2)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 读取数据
|
||||
DATA_PATH = os.path.join(os.path.dirname(__file__), '0020.HK.csv')
|
||||
# 处理文件头乱码和尾部注释
|
||||
raw = pd.read_csv(DATA_PATH, header=None, skiprows=1, names=['code','date','open','high','low','close','pct'])
|
||||
raw = raw[raw['close'].apply(lambda x: str(x).replace('.','',1).isdigit())] # 去除空行和注释
|
||||
raw['date'] = pd.to_datetime(raw['date'])
|
||||
raw['close'] = raw['close'].astype(float)
|
||||
raw['open'] = raw['open'].astype(float)
|
||||
raw = raw.sort_values('date').reset_index(drop=True)
|
||||
|
||||
# 运行回测
|
||||
df_hist, trade_log, total_return, max_drawdown, buy_cnt, sell_cnt, win_rate = trend_following_grid_backtest(raw)
|
||||
|
||||
# 打印统计结果
|
||||
print('\n==== 趋势跟随网格回测结果 ===')
|
||||
print(f'总收益率: {total_return*100:.2f}%')
|
||||
print(f'最大回撤: {max_drawdown*100:.2f}%')
|
||||
print(f'买入次数: {buy_cnt}，卖出次数: {sell_cnt}')
|
||||
print(f'胜率: {win_rate*100:.2f}%')
|
||||
|
||||
# 绘制结果
|
||||
last_price = df_hist['price'].iloc[-1]
|
||||
last_ma20 = df_hist['ma20'].iloc[-1]
|
||||
last_grids = generate_trend_following_grids(last_price, last_ma20)
|
||||
plot_results(df_hist, trade_log, last_grids)
|
||||
|
|
@ -11,7 +11,7 @@ XUEQIU_HEADERS = {
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'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
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'Client-Version': 'v2.44.75',
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|
||||
'Cookie': 'cookiesu=811743062689927; device_id=33fa3c7fca4a65f8f4354e10ed6b7470; HMACCOUNT=8B64A2E3C307C8C0; s=c611ttmqlj; xq_is_login=1; u=8493411634; bid=4065a77ca57a69c83405d6e591ab5449_m8r2nhs8; snbim_minify=true; xq_a_token=ef79e6da376751a4bf6c1538103e9894d44473e1; xqat=ef79e6da376751a4bf6c1538103e9894d44473e1; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOjg0OTM0MTE2MzQsImlzcyI6InVjIiwiZXhwIjoxNzUxNTE1MDgxLCJjdG0iOjE3NDg5MjMwODE2NDQsImNpZCI6ImQ5ZDBuNEFadXAifQ.gQrIt4VI73JLUFGVSTKpXidhFIMwlusBKyrzYwClwCBszXCooQY3WnFqlbXqSX3SwnMapuveOFUM5sGIOoZ8oDF8cZYs3HDz5vezR-2nes9gfZr2nZcUfZzNRJ299wlX3Zis5NbnzNlfnisUhv9GUfEZjQ_Rs37B4qRbQZVC2kdN1Z0xB8j1MplSTOsYj4IliQntuaTo-8SBh-4zz5244dnF85xREBVxtFzzCtHUhn9B-mzxE81_42nwrDscvow-4_jtlJXlqbehiAFxld-dCWDXwmCju9lRWu_WzdoQe19n-c6jhCZZ1pU1JGsYyhIAsd1gV064jQ6FxfN38so1Eg; xq_r_token=30a80318ebcabffbe194e7deecb108b665e8c894; Hm_lvt_1db88642e346389874251b5a1eded6e3=1749028611; is_overseas=0; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1750034926; ssxmod_itna=eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40Q07hqDyliiYwirG4tshiQBKD/KlYeDZDGFdDqx0Ei6FiBFKCezjCGbKBACQ5xC3o0aOyndbV3Ab3t8NXiK3y4xB3DExGkR0iYeK4DxrPD5xDTDWeDGDD3WxGaDmeDeho+D0bmHUOvrU7oD7eDXxGCDQFor4GWDiPD7Po45iim=KxD0xD1ESkEDDPDaroxDG5NlQ9weDi3rfgsLFbLDdwIqQimD753DlcqwXLXmktxGfoyzd=bdI8fDCKDjxdIx93QW33vimn4xqiDb37Yn4qe2qW+QKGxAiUmrKiirFBDFAQQDT7GN0xq3DPQGQBwOb7Y3FrjAPdmrRiYIvZRHm9HV4hY3Ybz777DbRGxi4T/48Bxt3GhUhtBhNfqtRDWq2qADPfYYsihWYPeD; ssxmod_itna2=eqGxBDnGKYuxcD4kDRgxYq7ueYKS8DBP01Dp2xQyP08D60DB40Q07hqDyliiYwirG4tshiQYeDA4pg2w+RYD7POqKepgqDlcb9wAqieP5+6V+KK5w6Q4dMdqCLomgLyxQn93CdLjc3pXEYq0/0h+jCdiOWudEmrIjmRf1+lLX0OGj02GXiiblBod5++dyGbWSnufTL+nxBWIQimWCI3ueZSne50WYT6afRSyCo79FGa6WEk2j30a5d9LFRZFb==8bO73cfarqe=kkkK09RmTUISi6qQwqZfChNd3Ktj6E3tj9GjXLWwV59vpUqOnFXIp9/rujWHt7v3KhIHMUrH70=mn1em1A7ujba3Y4jwqKyWRDR4q7/rCDFoyF7AiK4rNz018Ix0rYfYx+OYm2=nxNlxPGTKYStcOPuEDD',
|
||||
'Referer': 'https://weibo.com/u/7735765253',
|
||||
'Sec-Ch-Ua': '"Chromium";v="122", "Not(A:Brand";v="24", "Google Chrome";v="122"',
|
||||
'Sec-Ch-Ua-Mobile': '?0',
|
||||
|
|
|
|||
|
|
@ -13,7 +13,14 @@ DB_CONFIG = {
|
|||
'password': 'Chlry#$.8',
|
||||
'database': 'db_gp_cj'
|
||||
}
|
||||
|
||||
# redis配置
|
||||
REDIS_CONFIG = {
|
||||
'host': '192.168.18.208',
|
||||
'port': 6379,
|
||||
'password': 'wlkj2018',
|
||||
'db': 13,
|
||||
'socket_timeout': 5
|
||||
}
|
||||
# 创建数据库连接URL
|
||||
DB_URL = f"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['database']}"
|
||||
|
||||
|
|
|
|||
|
|
@ -877,11 +877,12 @@ class FinancialAnalyzer:
|
|||
|
||||
# 缓存结果，有效期1天（86400秒）
|
||||
try:
|
||||
redis_client.set(
|
||||
kkk = redis_client.set(
|
||||
cache_key,
|
||||
json.dumps(result, default=str), # 使用default=str处理日期等特殊类型
|
||||
ex=86400 # 1天的秒数
|
||||
)
|
||||
print(kkk)
|
||||
logger.info(f"已缓存股票 {stock_code} 的财务分析数据，有效期为1天")
|
||||
except Exception as cache_error:
|
||||
logger.warning(f"缓存财务分析数据失败: {cache_error}")
|
||||
|
|
|
|||
|
|
@ -824,4 +824,178 @@ class IndustryAnalyzer:
|
|||
|
||||
except Exception as e:
|
||||
logger.error(f"获取概念板块综合分析失败: {e}")
|
||||
return {"success": False, "message": f"获取概念板块综合分析失败: {e}"}
|
||||
return {"success": False, "message": f"获取概念板块综合分析失败: {e}"}
|
||||
|
||||
def batch_calculate_industry_crowding(self, industries: List[str], concepts: List[str] = None) -> None:
|
||||
"""
|
||||
批量计算多个行业和概念板块的拥挤度指标
|
||||
|
||||
Args:
|
||||
industries: 行业列表
|
||||
concepts: 概念板块列表，默认为None
|
||||
"""
|
||||
try:
|
||||
# 1. 获取3年数据的时间范围
|
||||
end_date = datetime.datetime.now().strftime('%Y-%m-%d')
|
||||
start_date = (datetime.datetime.now() - datetime.timedelta(days=3*365)).strftime('%Y-%m-%d')
|
||||
|
||||
# 2. 一次性获取全市场所有股票的交易数据
|
||||
query = text("""
|
||||
SELECT
|
||||
symbol,
|
||||
`timestamp` AS trade_date,
|
||||
amount
|
||||
FROM
|
||||
gp_day_data
|
||||
WHERE
|
||||
`timestamp` BETWEEN :start_date AND :end_date
|
||||
""")
|
||||
|
||||
with self.engine.connect() as conn:
|
||||
df_all = pd.read_sql(query, conn, params={"start_date": start_date, "end_date": end_date})
|
||||
|
||||
# 3. 计算每日市场总成交额
|
||||
df_total = df_all.groupby('trade_date')['amount'].sum().reset_index()
|
||||
df_total.columns = ['trade_date', 'total_market_amount']
|
||||
|
||||
# 4. 获取所有行业和概念板块的股票映射
|
||||
industry_stocks = {}
|
||||
for industry in industries:
|
||||
stocks = self.get_industry_stocks(industry)
|
||||
if stocks:
|
||||
industry_stocks[industry] = stocks
|
||||
|
||||
if concepts:
|
||||
concept_stocks = {}
|
||||
for concept in concepts:
|
||||
stocks = self.get_concept_stocks(concept)
|
||||
if stocks:
|
||||
concept_stocks[concept] = stocks
|
||||
|
||||
# 5. 批量计算行业拥挤度
|
||||
for industry, stocks in industry_stocks.items():
|
||||
try:
|
||||
# 计算行业成交额
|
||||
df_industry = df_all[df_all['symbol'].isin(stocks)].groupby('trade_date')['amount'].sum().reset_index()
|
||||
df_industry.columns = ['trade_date', 'sector_amount']
|
||||
|
||||
# 合并数据
|
||||
df = pd.merge(df_total, df_industry, on='trade_date', how='inner')
|
||||
|
||||
# 计算指标
|
||||
df['industry_amount_ratio'] = (df['sector_amount'] / df['total_market_amount']) * 100
|
||||
df['percentile'] = df['industry_amount_ratio'].rank(pct=True) * 100
|
||||
df['crowding_level'] = pd.cut(
|
||||
df['percentile'],
|
||||
bins=[0, 20, 40, 60, 80, 100],
|
||||
labels=['不拥挤', '较不拥挤', '中性', '较为拥挤', '极度拥挤']
|
||||
)
|
||||
|
||||
# 缓存结果
|
||||
cache_key = f"industry_crowding:{industry}"
|
||||
redis_client.set(
|
||||
cache_key,
|
||||
json.dumps(df.to_dict(orient='records'), default=str),
|
||||
ex=86400
|
||||
)
|
||||
|
||||
logger.info(f"成功计算行业 {industry} 的拥挤度指标，共 {len(df)} 条记录")
|
||||
except Exception as e:
|
||||
logger.error(f"计算行业 {industry} 的拥挤度指标时出错: {str(e)}")
|
||||
continue
|
||||
|
||||
# 6. 批量计算概念板块拥挤度
|
||||
if concepts:
|
||||
for concept, stocks in concept_stocks.items():
|
||||
try:
|
||||
# 计算概念板块成交额
|
||||
df_concept = df_all[df_all['symbol'].isin(stocks)].groupby('trade_date')['amount'].sum().reset_index()
|
||||
df_concept.columns = ['trade_date', 'sector_amount']
|
||||
|
||||
# 合并数据
|
||||
df = pd.merge(df_total, df_concept, on='trade_date', how='inner')
|
||||
|
||||
# 计算指标
|
||||
df['industry_amount_ratio'] = (df['sector_amount'] / df['total_market_amount']) * 100
|
||||
df['percentile'] = df['industry_amount_ratio'].rank(pct=True) * 100
|
||||
df['crowding_level'] = pd.cut(
|
||||
df['percentile'],
|
||||
bins=[0, 20, 40, 60, 80, 100],
|
||||
labels=['不拥挤', '较不拥挤', '中性', '较为拥挤', '极度拥挤']
|
||||
)
|
||||
|
||||
# 缓存结果
|
||||
cache_key = f"concept_crowding:{concept}"
|
||||
redis_client.set(
|
||||
cache_key,
|
||||
json.dumps(df.to_dict(orient='records'), default=str),
|
||||
ex=86400
|
||||
)
|
||||
|
||||
logger.info(f"成功计算概念板块 {concept} 的拥挤度指标，共 {len(df)} 条记录")
|
||||
except Exception as e:
|
||||
logger.error(f"计算概念板块 {concept} 的拥挤度指标时出错: {str(e)}")
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"批量计算行业拥挤度指标失败: {str(e)}")
|
||||
|
||||
def filter_crowding_by_percentile(self, min_percentile: float, max_percentile: float) -> dict:
|
||||
"""
|
||||
查询所有缓存中的行业和概念板块拥挤度，筛选最后一个交易日拥挤度百分位在[min, max]区间的行业/概念。
|
||||
返回格式：{'industry': [...], 'concept': [...]}，每个元素包含名称、最后日期、拥挤度百分位、拥挤度等级等。
|
||||
"""
|
||||
result = {'industry': [], 'concept': []}
|
||||
try:
|
||||
# 获取所有行业和概念板块的缓存key
|
||||
industry_keys = redis_client.keys('industry_crowding:*')
|
||||
concept_keys = redis_client.keys('concept_crowding:*')
|
||||
|
||||
# 行业
|
||||
for key in industry_keys:
|
||||
try:
|
||||
name = key.split(':', 1)[1]
|
||||
cached_data = redis_client.get(key)
|
||||
if not cached_data:
|
||||
continue
|
||||
df = pd.DataFrame(json.loads(cached_data))
|
||||
if df.empty:
|
||||
continue
|
||||
last_row = df.iloc[-1]
|
||||
percentile = float(last_row['percentile'])
|
||||
if min_percentile <= percentile <= max_percentile:
|
||||
result['industry'].append({
|
||||
'name': name,
|
||||
'last_date': last_row['trade_date'],
|
||||
'percentile': percentile,
|
||||
'crowding_level': last_row.get('crowding_level', None)
|
||||
})
|
||||
except Exception as e:
|
||||
logger.warning(f"处理行业缓存 {key} 时出错: {e}")
|
||||
continue
|
||||
|
||||
# 概念板块
|
||||
for key in concept_keys:
|
||||
try:
|
||||
name = key.split(':', 1)[1]
|
||||
cached_data = redis_client.get(key)
|
||||
if not cached_data:
|
||||
continue
|
||||
df = pd.DataFrame(json.loads(cached_data))
|
||||
if df.empty:
|
||||
continue
|
||||
last_row = df.iloc[-1]
|
||||
percentile = float(last_row['percentile'])
|
||||
if min_percentile <= percentile <= max_percentile:
|
||||
result['concept'].append({
|
||||
'name': name,
|
||||
'last_date': last_row['trade_date'],
|
||||
'percentile': percentile,
|
||||
'crowding_level': last_row.get('crowding_level', None)
|
||||
})
|
||||
except Exception as e:
|
||||
logger.warning(f"处理概念缓存 {key} 时出错: {e}")
|
||||
continue
|
||||
except Exception as e:
|
||||
logger.error(f"筛选拥挤度缓存时出错: {e}")
|
||||
return result
|
||||
Loading…
Reference in New Issue