├── .gitattributes ├── result.jpg ├── sh.600000_predict.jpg ├── sh.600004_predict.jpg ├── sh.600009_predict.jpg ├── sh.600010_predict.jpg ├── sh.600011_predict.jpg ├── requirements.txt ├── .gitignore ├── stock_concept.py ├── README.md ├── data_downloader.py ├── data ├── hs300_stocks.csv └── stocks │ └── sh.000300.csv ├── strategy.py ├── backtest.py ├── prediction.py ├── LICENSE └── stock_indicator.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /result.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AkatsukiYamisora/stock-prediction-with-DL/HEAD/result.jpg -------------------------------------------------------------------------------- /sh.600000_predict.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AkatsukiYamisora/stock-prediction-with-DL/HEAD/sh.600000_predict.jpg -------------------------------------------------------------------------------- /sh.600004_predict.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AkatsukiYamisora/stock-prediction-with-DL/HEAD/sh.600004_predict.jpg -------------------------------------------------------------------------------- /sh.600009_predict.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AkatsukiYamisora/stock-prediction-with-DL/HEAD/sh.600009_predict.jpg -------------------------------------------------------------------------------- /sh.600010_predict.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AkatsukiYamisora/stock-prediction-with-DL/HEAD/sh.600010_predict.jpg -------------------------------------------------------------------------------- /sh.600011_predict.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AkatsukiYamisora/stock-prediction-with-DL/HEAD/sh.600011_predict.jpg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | torch==1.7.0+cu101 2 | pandas>=1.0.5 3 | baostock>=0.8.8 4 | matplotlib>=3.2.2 5 | tqdm>=4.48.2 6 | numpy>=1.19.3 7 | tushare>=1.2.62 8 | requests 9 | bs4 10 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | /.idea/ 3 | /__pycache__/ 4 | *.csv 5 | *.pkl 6 | *.pt 7 | tushare_apikey.txt 8 | !./data/*.csv 9 | !./data/stocks/sh.000016.csv 10 | !./data/stocks/sh.000300.csv 11 | !./data/stocks/sh.000905.csv 12 | -------------------------------------------------------------------------------- /stock_concept.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | @version: 3.8.3 4 | @author: Yamisora 5 | @file: stock_concept.py 6 | """ 7 | import requests 8 | from bs4 import BeautifulSoup 9 | import pandas as pd 10 | 11 | 12 | base_data_path = './data/' 13 | url = 'http://stockpage.10jqka.com.cn/' 14 | header = { 15 | 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) ' 16 | 'Chrome/96.0.4664.55 Safari/537.36 Edg/96.0.1054.34 ' 17 | } 18 | hs300 = pd.read_csv(base_data_path + 'hs300_stocks.csv') 19 | codes = [a[3:]+'/' for a in hs300['code']] 20 | hs300['concept'] = '' 21 | 22 | for i in range(len(hs300)): 23 | print('正在爬取'+codes[i]+'概念数据') 24 | response = requests.get(url+codes[i], headers=header) 25 | soup = BeautifulSoup(response.text, 'lxml') 26 | company_details = soup.find('dl', class_='company_details') 27 | details = company_details.find_all('dd') 28 | concept = details[1]['title'] 29 | hs300.loc[i, 'concept'] = concept 30 | 31 | hs300.to_csv(base_data_path+'hs300_stocks.csv') 32 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # stock-prediction-with-DL/深度学习与股票分析预测 2 | 3 | ![GitHub](https://img.shields.io/github/license/AkatsukiYamisora/stock-prediction-with-DL) 4 | ![Python Version](https://img.shields.io/badge/python-3.8+-blue) 5 | ![Pytorch Version](https://img.shields.io/badge/torch-1.7.0+-blue) 6 | ![GitHub watchers](https://img.shields.io/github/watchers/AkatsukiYamisora/stock-prediction-with-DL?style=social) 7 | ![GitHub Repo stars](https://img.shields.io/github/stars/AkatsukiYamisora/stock-prediction-with-DL?style=social) 8 | 9 | 目前新增了100多个技术指标，优化了一部分代码，正在做图神经网络部分内容，预计做一个小论文，等论文见刊再更新仓库^-^ 10 | 11 | ## data_downloader.py 12 | 13 | 通过baostock下载上证50，沪深300，中证500的日线数据 14 | 15 | ## backtest.py 16 | 17 | 回测模块，根据调仓周期，通过模型选股，按开盘价买入卖出（暂定），计算收益，与指数比较并绘图 18 | 19 | 当前回测结果： 20 | 21 | ![result](result.jpg) 22 | 23 | ## strategy.py 24 | 25 | 按价值因子（选择账面市值比BM）进行选股（效果差，由于BM在大市值公司的效果不佳） 26 | 27 | 用CNN预测模型进行选股 28 | 29 | 用动量因子(MF)进行选股 30 | 31 | 用换手率因子(TR)进行选股(思路参考自Liu et al.(2019) Size and value in China, 修改了数据选取) 32 | 33 | 用LSTM与reluRNN预测模型进行选股 34 | 35 | 用GRU与tanhRNN预测模型进行选股(效果不佳已放弃) 36 | 37 | 用ResNet18,34,50预测模型进行选股 38 | 39 | 用DenseNet预测模型进行选股 40 | 41 | 用综合几种网络预测结果的集成学习策略进行选股 42 | 43 | 其余策略待更新 44 | 45 | ## prediction.py 46 | 47 | 使用CNN对股票数据进行预测 48 | 49 | 使用LSTM与reluRNN对股票数据进行预测(GRU与tanhRNN由于效果不佳放弃) 50 | 51 | 使用ResNet18,34,50对股票数据进行预测(101与152由于效果不佳放弃) 52 | 53 | 使用DenseNet对股票数据进行预测 54 | 55 | 当前预测结果 56 | 57 | sh.600000,浦发银行 58 | 59 | ![1](sh.600000_predict.jpg) 60 | 61 | sh.600004,白云机场 62 | 63 | ![2](sh.600004_predict.jpg) 64 | 65 | sh.600009,上海机场 66 | 67 | ![3](sh.600009_predict.jpg) 68 | 69 | sh.600010,包钢股份 70 | 71 | ![4](sh.600010_predict.jpg) 72 | 73 | sh.600011,华能国际 74 | 75 | ![5](sh.600011_predict.jpg) 76 | 77 | ## tushare_apikey.txt 78 | 79 | 本地保存tushare pro的apikey 80 | 81 | ## /data/hs300_stocks.csv 82 | 83 | 存放指数股票列表数据 84 | 85 | ## /data/stocks/sh.000300.csv 86 | 87 | 存放指数日线数据 88 | 89 | ## /data/stocks/*.csv 90 | 91 | 存放回测个股日线数据 92 | 93 | ## /data/train_data/*.csv 94 | 95 | 存放训练集个股日线数据 96 | 97 | ## /data/*.pkl 98 | 99 | 存放训练集数据 100 | 101 | ## /data/*.pt 102 | 103 | 存放训练完成的模型数据 104 | 105 | ## result.jpg 106 | 107 | 回测结果展示图片 108 | 109 | ## *_predict.jpg 110 | 111 | 单只股票预测结果展示图片 112 | -------------------------------------------------------------------------------- /data_downloader.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | @version: 3.8.3 4 | @time: 21/4/25 23:49 5 | @author: Yamisora 6 | @file: data_downloader.py 7 | """ 8 | import os 9 | import baostock as bs 10 | import tushare as ts 11 | import pandas as pd 12 | import numpy as np 13 | from tqdm import tqdm 14 | import time 15 | 16 | # 设置数据下载时间段 17 | data_start_date = '2019-01-01' 18 | data_end_date = '2021-06-02' 19 | train_data_start_date = '2015-01-01' 20 | train_data_end_date = '2018-12-31' 21 | # 存储路径 22 | base_data_path = './data/' 23 | data_path = './data/stocks/' 24 | train_data_path = './data/train_data/' 25 | 26 | # 使用tushare数据api 27 | apikey = '' 28 | if os.path.exists('tushare_apikey.txt'): 29 | with open('tushare_apikey.txt', 'r') as f: 30 | apikey = f.read() 31 | pro = ts.pro_api(apikey) 32 | 33 | # 设置是否下载数据 34 | download_stocks = True 35 | download_indexes = True 36 | # 下载个股参数列表 37 | fields = "date,code,open,high,low,close,preclose,volume," \ 38 | "amount,turn,peTTM,psTTM,pcfNcfTTM,pbMRQ" 39 | ts_basic_fields = "trade_date,total_share,float_share,free_share,total_mv,circ_mv" 40 | 41 | # baostock数据下载 42 | lg = bs.login() 43 | # 显示登陆返回信息 44 | if lg.error_code != '0': 45 | print('login respond error_code:' + lg.error_code) 46 | print('login respond error_msg:' + lg.error_msg) 47 | 48 | 49 | def status(raw): 50 | if raw.error_code != '0': 51 | print('query_history_k_data_plus respond error_code:' + rs.error_code) 52 | print('query_history_k_data_plus respond error_msg:' + rs.error_msg) 53 | 54 | 55 | def data_load(raw): 56 | """读取baostock数据""" 57 | data = [] 58 | while (raw.error_code == '0') & raw.next(): 59 | # 获取一条记录，将记录合并在一起 60 | data.append(raw.get_row_data()) 61 | df = pd.DataFrame(data, columns=raw.fields) 62 | return df 63 | 64 | 65 | def ts_c(bs_stock_code: str) -> str: 66 | """转换baostock股票代码为tushare股票代码""" 67 | return bs_stock_code[3:]+'.'+bs_stock_code[0:2].upper() 68 | 69 | 70 | def ts_d(date): 71 | """转换baostock日期为tushare日期""" 72 | return date.replace('-', '') 73 | 74 | 75 | def bs_d(ts_date_series): 76 | """转换tushare日期序列为baostock日期序列""" 77 | date_series = [] 78 | for ts_date in ts_date_series: 79 | date = str(ts_date) 80 | date_series.append(date[:4] + '-' + date[4:6] + '-' + date[6:]) 81 | return date_series 82 | 83 | 84 | if download_stocks: 85 | rs = bs.query_hs300_stocks() 86 | # 获取股票名称与代码 87 | result = data_load(rs) 88 | result.to_csv(base_data_path+'hs300_stocks.csv') 89 | for code in tqdm(result['code']): 90 | for start_date, end_date, path in ((data_start_date, data_end_date, data_path), 91 | (train_data_start_date, train_data_end_date, train_data_path)): 92 | rs = bs.query_history_k_data_plus(code, fields, 93 | start_date=start_date, end_date=end_date, 94 | frequency="d", adjustflag="3") 95 | status(rs) 96 | 97 | # baostock日线数据 98 | df1 = data_load(rs) 99 | df1.set_index('date', inplace=True) 100 | 101 | # tushare指标数据 102 | df2 = pro.daily_basic(ts_code=ts_c(code), start_date=ts_d(start_date), 103 | end_date=ts_d(end_date), fields=ts_basic_fields) 104 | df2['trade_date'] = bs_d(df2['trade_date']) 105 | df2.set_index('trade_date', inplace=True) 106 | 107 | # 按index横向拼接 108 | df1 = df1.join(df2) 109 | # 去除无效空值,补0 110 | df1.replace(to_replace=r'^\s*$', value=np.nan, regex=True, inplace=True) 111 | df1.fillna(0, inplace=True) 112 | 113 | df1.to_csv(path+code+'.csv') 114 | del df1, df2 115 | time.sleep(0.2) # 防止tushare调用到分钟上限 116 | 117 | 118 | if download_indexes: 119 | rs = bs.query_history_k_data_plus('sh.000300', 120 | "date,code,open,high,low,close,preclose,volume,amount,pctChg", 121 | start_date=data_start_date, end_date=data_end_date, frequency="d") 122 | status(rs) 123 | data_load(rs).to_csv(data_path+'sh.000300.csv') 124 | 125 | # 登出系统 126 | bs.logout() 127 | -------------------------------------------------------------------------------- /data/hs300_stocks.csv: -------------------------------------------------------------------------------- 1 | ,updateDate,code,code_name 2 | 0,2021-05-31,sh.600000,浦发银行 3 | 1,2021-05-31,sh.600004,白云机场 4 | 2,2021-05-31,sh.600009,上海机场 5 | 3,2021-05-31,sh.600010,包钢股份 6 | 4,2021-05-31,sh.600011,华能国际 7 | 5,2021-05-31,sh.600015,华夏银行 8 | 6,2021-05-31,sh.600016,民生银行 9 | 7,2021-05-31,sh.600018,上港集团 10 | 8,2021-05-31,sh.600019,宝钢股份 11 | 9,2021-05-31,sh.600025,华能水电 12 | 10,2021-05-31,sh.600027,华电国际 13 | 11,2021-05-31,sh.600028,中国石化 14 | 12,2021-05-31,sh.600029,南方航空 15 | 13,2021-05-31,sh.600030,中信证券 16 | 14,2021-05-31,sh.600031,三一重工 17 | 15,2021-05-31,sh.600036,招商银行 18 | 16,2021-05-31,sh.600048,保利地产 19 | 17,2021-05-31,sh.600050,中国联通 20 | 18,2021-05-31,sh.600061,国投资本 21 | 19,2021-05-31,sh.600066,宇通客车 22 | 20,2021-05-31,sh.600068,葛洲坝 23 | 21,2021-05-31,sh.600085,同仁堂 24 | 22,2021-05-31,sh.600104,上汽集团 25 | 23,2021-05-31,sh.600109,国金证券 26 | 24,2021-05-31,sh.600111,北方稀土 27 | 25,2021-05-31,sh.600115,东方航空 28 | 26,2021-05-31,sh.600118,中国卫星 29 | 27,2021-05-31,sh.600150,中国船舶 30 | 28,2021-05-31,sh.600161,天坛生物 31 | 29,2021-05-31,sh.600176,中国巨石 32 | 30,2021-05-31,sh.600177,雅戈尔 33 | 31,2021-05-31,sh.600183,生益科技 34 | 32,2021-05-31,sh.600196,复星医药 35 | 33,2021-05-31,sh.600208,新湖中宝 36 | 34,2021-05-31,sh.600233,圆通速递 37 | 35,2021-05-31,sh.600271,航天信息 38 | 36,2021-05-31,sh.600276,恒瑞医药 39 | 37,2021-05-31,sh.600297,广汇汽车 40 | 38,2021-05-31,sh.600299,安迪苏 41 | 39,2021-05-31,sh.600309,万华化学 42 | 40,2021-05-31,sh.600332,白云山 43 | 41,2021-05-31,sh.600340,华夏幸福 44 | 42,2021-05-31,sh.600346,恒力石化 45 | 43,2021-05-31,sh.600352,浙江龙盛 46 | 44,2021-05-31,sh.600362,江西铜业 47 | 45,2021-05-31,sh.600369,西南证券 48 | 46,2021-05-31,sh.600383,金地集团 49 | 47,2021-05-31,sh.600390,五矿资本 50 | 48,2021-05-31,sh.600406,国电南瑞 51 | 49,2021-05-31,sh.600436,片仔癀 52 | 50,2021-05-31,sh.600438,通威股份 53 | 51,2021-05-31,sh.600482,中国动力 54 | 52,2021-05-31,sh.600487,亨通光电 55 | 53,2021-05-31,sh.600489,中金黄金 56 | 54,2021-05-31,sh.600498,烽火通信 57 | 55,2021-05-31,sh.600519,贵州茅台 58 | 56,2021-05-31,sh.600522,中天科技 59 | 57,2021-05-31,sh.600547,山东黄金 60 | 58,2021-05-31,sh.600570,恒生电子 61 | 59,2021-05-31,sh.600584,长电科技 62 | 60,2021-05-31,sh.600585,海螺水泥 63 | 61,2021-05-31,sh.600588,用友网络 64 | 62,2021-05-31,sh.600600,青岛啤酒 65 | 63,2021-05-31,sh.600606,绿地控股 66 | 64,2021-05-31,sh.600637,东方明珠 67 | 65,2021-05-31,sh.600655,豫园股份 68 | 66,2021-05-31,sh.600660,福耀玻璃 69 | 67,2021-05-31,sh.600690,海尔智家 70 | 68,2021-05-31,sh.600703,三安光电 71 | 69,2021-05-31,sh.600705,中航资本 72 | 70,2021-05-31,sh.600741,华域汽车 73 | 71,2021-05-31,sh.600745,闻泰科技 74 | 72,2021-05-31,sh.600760,中航沈飞 75 | 73,2021-05-31,sh.600763,通策医疗 76 | 74,2021-05-31,sh.600795,国电电力 77 | 75,2021-05-31,sh.600809,山西汾酒 78 | 76,2021-05-31,sh.600837,海通证券 79 | 77,2021-05-31,sh.600845,宝信软件 80 | 78,2021-05-31,sh.600848,上海临港 81 | 79,2021-05-31,sh.600872,中炬高新 82 | 80,2021-05-31,sh.600886,国投电力 83 | 81,2021-05-31,sh.600887,伊利股份 84 | 82,2021-05-31,sh.600893,航发动力 85 | 83,2021-05-31,sh.600900,长江电力 86 | 84,2021-05-31,sh.600918,中泰证券 87 | 85,2021-05-31,sh.600919,江苏银行 88 | 86,2021-05-31,sh.600926,杭州银行 89 | 87,2021-05-31,sh.600958,东方证券 90 | 88,2021-05-31,sh.600989,宝丰能源 91 | 89,2021-05-31,sh.600998,九州通 92 | 90,2021-05-31,sh.600999,招商证券 93 | 91,2021-05-31,sh.601006,大秦铁路 94 | 92,2021-05-31,sh.601009,南京银行 95 | 93,2021-05-31,sh.601012,隆基股份 96 | 94,2021-05-31,sh.601021,春秋航空 97 | 95,2021-05-31,sh.601066,中信建投 98 | 96,2021-05-31,sh.601077,渝农商行 99 | 97,2021-05-31,sh.601088,中国神华 100 | 98,2021-05-31,sh.601100,恒立液压 101 | 99,2021-05-31,sh.601108,财通证券 102 | 100,2021-05-31,sh.601111,中国国航 103 | 101,2021-05-31,sh.601117,中国化学 104 | 102,2021-05-31,sh.601138,工业富联 105 | 103,2021-05-31,sh.601155,新城控股 106 | 104,2021-05-31,sh.601162,天风证券 107 | 105,2021-05-31,sh.601166,兴业银行 108 | 106,2021-05-31,sh.601169,北京银行 109 | 107,2021-05-31,sh.601186,中国铁建 110 | 108,2021-05-31,sh.601198,东兴证券 111 | 109,2021-05-31,sh.601211,国泰君安 112 | 110,2021-05-31,sh.601216,君正集团 113 | 111,2021-05-31,sh.601225,陕西煤业 114 | 112,2021-05-31,sh.601229,上海银行 115 | 113,2021-05-31,sh.601231,环旭电子 116 | 114,2021-05-31,sh.601236,红塔证券 117 | 115,2021-05-31,sh.601238,广汽集团 118 | 116,2021-05-31,sh.601288,农业银行 119 | 117,2021-05-31,sh.601318,中国平安 120 | 118,2021-05-31,sh.601319,中国人保 121 | 119,2021-05-31,sh.601328,交通银行 122 | 120,2021-05-31,sh.601336,新华保险 123 | 121,2021-05-31,sh.601360,三六零 124 | 122,2021-05-31,sh.601377,兴业证券 125 | 123,2021-05-31,sh.601390,中国中铁 126 | 124,2021-05-31,sh.601398,工商银行 127 | 125,2021-05-31,sh.601555,东吴证券 128 | 126,2021-05-31,sh.601577,长沙银行 129 | 127,2021-05-31,sh.601600,中国铝业 130 | 128,2021-05-31,sh.601601,中国太保 131 | 129,2021-05-31,sh.601607,上海医药 132 | 130,2021-05-31,sh.601618,中国中冶 133 | 131,2021-05-31,sh.601628,中国人寿 134 | 132,2021-05-31,sh.601633,长城汽车 135 | 133,2021-05-31,sh.601658,邮储银行 136 | 134,2021-05-31,sh.601668,中国建筑 137 | 135,2021-05-31,sh.601669,中国电建 138 | 136,2021-05-31,sh.601688,华泰证券 139 | 137,2021-05-31,sh.601696,中银证券 140 | 138,2021-05-31,sh.601698,中国卫通 141 | 139,2021-05-31,sh.601727,上海电气 142 | 140,2021-05-31,sh.601766,中国中车 143 | 141,2021-05-31,sh.601788,光大证券 144 | 142,2021-05-31,sh.601800,中国交建 145 | 143,2021-05-31,sh.601808,中海油服 146 | 144,2021-05-31,sh.601816,京沪高铁 147 | 145,2021-05-31,sh.601818,光大银行 148 | 146,2021-05-31,sh.601838,成都银行 149 | 147,2021-05-31,sh.601857,中国石油 150 | 148,2021-05-31,sh.601872,招商轮船 151 | 149,2021-05-31,sh.601877,正泰电器 152 | 150,2021-05-31,sh.601878,浙商证券 153 | 151,2021-05-31,sh.601881,中国银河 154 | 152,2021-05-31,sh.601888,中国中免 155 | 153,2021-05-31,sh.601899,紫金矿业 156 | 154,2021-05-31,sh.601901,方正证券 157 | 155,2021-05-31,sh.601916,浙商银行 158 | 156,2021-05-31,sh.601919,中远海控 159 | 157,2021-05-31,sh.601933,永辉超市 160 | 158,2021-05-31,sh.601939,建设银行 161 | 159,2021-05-31,sh.601985,中国核电 162 | 160,2021-05-31,sh.601988,中国银行 163 | 161,2021-05-31,sh.601989,中国重工 164 | 162,2021-05-31,sh.601990,南京证券 165 | 163,2021-05-31,sh.601998,中信银行 166 | 164,2021-05-31,sh.603019,中科曙光 167 | 165,2021-05-31,sh.603087,甘李药业 168 | 166,2021-05-31,sh.603156,养元饮品 169 | 167,2021-05-31,sh.603160,汇顶科技 170 | 168,2021-05-31,sh.603195,公牛集团 171 | 169,2021-05-31,sh.603259,药明康德 172 | 170,2021-05-31,sh.603288,海天味业 173 | 171,2021-05-31,sh.603369,今世缘 174 | 172,2021-05-31,sh.603392,万泰生物 175 | 173,2021-05-31,sh.603501,韦尔股份 176 | 174,2021-05-31,sh.603658,安图生物 177 | 175,2021-05-31,sh.603799,华友钴业 178 | 176,2021-05-31,sh.603833,欧派家居 179 | 177,2021-05-31,sh.603899,晨光文具 180 | 178,2021-05-31,sh.603986,兆易创新 181 | 179,2021-05-31,sh.603993,洛阳钼业 182 | 180,2021-05-31,sh.688008,澜起科技 183 | 181,2021-05-31,sh.688009,中国通号 184 | 182,2021-05-31,sh.688012,中微公司 185 | 183,2021-05-31,sh.688036,传音控股 186 | 184,2021-05-31,sz.000001,平安银行 187 | 185,2021-05-31,sz.000002,万科A 188 | 186,2021-05-31,sz.000063,中兴通讯 189 | 187,2021-05-31,sz.000066,中国长城 190 | 188,2021-05-31,sz.000069,华侨城A 191 | 189,2021-05-31,sz.000100,TCL科技 192 | 190,2021-05-31,sz.000157,中联重科 193 | 191,2021-05-31,sz.000166,申万宏源 194 | 192,2021-05-31,sz.000333,美的集团 195 | 193,2021-05-31,sz.000338,潍柴动力 196 | 194,2021-05-31,sz.000425,徐工机械 197 | 195,2021-05-31,sz.000538,云南白药 198 | 196,2021-05-31,sz.000568,泸州老窖 199 | 197,2021-05-31,sz.000596,古井贡酒 200 | 198,2021-05-31,sz.000625,长安汽车 201 | 199,2021-05-31,sz.000627,天茂集团 202 | 200,2021-05-31,sz.000651,格力电器 203 | 201,2021-05-31,sz.000656,金科股份 204 | 202,2021-05-31,sz.000661,长春高新 205 | 203,2021-05-31,sz.000671,阳光城 206 | 204,2021-05-31,sz.000703,恒逸石化 207 | 205,2021-05-31,sz.000708,中信特钢 208 | 206,2021-05-31,sz.000723,美锦能源 209 | 207,2021-05-31,sz.000725,京东方A 210 | 208,2021-05-31,sz.000728,国元证券 211 | 209,2021-05-31,sz.000768,中航西飞 212 | 210,2021-05-31,sz.000776,广发证券 213 | 211,2021-05-31,sz.000783,长江证券 214 | 212,2021-05-31,sz.000786,北新建材 215 | 213,2021-05-31,sz.000858,五粮液 216 | 214,2021-05-31,sz.000860,顺鑫农业 217 | 215,2021-05-31,sz.000876,新希望 218 | 216,2021-05-31,sz.000895,双汇发展 219 | 217,2021-05-31,sz.000938,紫光股份 220 | 218,2021-05-31,sz.000961,中南建设 221 | 219,2021-05-31,sz.000963,华东医药 222 | 220,2021-05-31,sz.000977,浪潮信息 223 | 221,2021-05-31,sz.001979,招商蛇口 224 | 222,2021-05-31,sz.002001,新和成 225 | 223,2021-05-31,sz.002007,华兰生物 226 | 224,2021-05-31,sz.002008,大族激光 227 | 225,2021-05-31,sz.002024,苏宁易购 228 | 226,2021-05-31,sz.002027,分众传媒 229 | 227,2021-05-31,sz.002032,苏泊尔 230 | 228,2021-05-31,sz.002044,美年健康 231 | 229,2021-05-31,sz.002049,紫光国微 232 | 230,2021-05-31,sz.002050,三花智控 233 | 231,2021-05-31,sz.002120,韵达股份 234 | 232,2021-05-31,sz.002129,中环股份 235 | 233,2021-05-31,sz.002142,宁波银行 236 | 234,2021-05-31,sz.002146,荣盛发展 237 | 235,2021-05-31,sz.002153,石基信息 238 | 236,2021-05-31,sz.002157,正邦科技 239 | 237,2021-05-31,sz.002179,中航光电 240 | 238,2021-05-31,sz.002202,金风科技 241 | 239,2021-05-31,sz.002230,科大讯飞 242 | 240,2021-05-31,sz.002236,大华股份 243 | 241,2021-05-31,sz.002241,歌尔股份 244 | 242,2021-05-31,sz.002252,上海莱士 245 | 243,2021-05-31,sz.002271,东方雨虹 246 | 244,2021-05-31,sz.002304,洋河股份 247 | 245,2021-05-31,sz.002311,海大集团 248 | 246,2021-05-31,sz.002352,顺丰控股 249 | 247,2021-05-31,sz.002371,北方华创 250 | 248,2021-05-31,sz.002384,东山精密 251 | 249,2021-05-31,sz.002410,广联达 252 | 250,2021-05-31,sz.002414,高德红外 253 | 251,2021-05-31,sz.002415,海康威视 254 | 252,2021-05-31,sz.002422,科伦药业 255 | 253,2021-05-31,sz.002456,欧菲光 256 | 254,2021-05-31,sz.002460,赣锋锂业 257 | 255,2021-05-31,sz.002463,沪电股份 258 | 256,2021-05-31,sz.002475,立讯精密 259 | 257,2021-05-31,sz.002493,荣盛石化 260 | 258,2021-05-31,sz.002508,老板电器 261 | 259,2021-05-31,sz.002555,三七互娱 262 | 260,2021-05-31,sz.002558,巨人网络 263 | 261,2021-05-31,sz.002594,比亚迪 264 | 262,2021-05-31,sz.002600,领益智造 265 | 263,2021-05-31,sz.002601,龙蟒佰利 266 | 264,2021-05-31,sz.002602,世纪华通 267 | 265,2021-05-31,sz.002607,中公教育 268 | 266,2021-05-31,sz.002624,完美世界 269 | 267,2021-05-31,sz.002673,西部证券 270 | 268,2021-05-31,sz.002714,牧原股份 271 | 269,2021-05-31,sz.002736,国信证券 272 | 270,2021-05-31,sz.002739,万达电影 273 | 271,2021-05-31,sz.002773,康弘药业 274 | 272,2021-05-31,sz.002812,恩捷股份 275 | 273,2021-05-31,sz.002821,凯莱英 276 | 274,2021-05-31,sz.002841,视源股份 277 | 275,2021-05-31,sz.002916,深南电路 278 | 276,2021-05-31,sz.002938,鹏鼎控股 279 | 277,2021-05-31,sz.002939,长城证券 280 | 278,2021-05-31,sz.002945,华林证券 281 | 279,2021-05-31,sz.002958,青农商行 282 | 280,2021-05-31,sz.003816,中国广核 283 | 281,2021-05-31,sz.300003,乐普医疗 284 | 282,2021-05-31,sz.300014,亿纬锂能 285 | 283,2021-05-31,sz.300015,爱尔眼科 286 | 284,2021-05-31,sz.300033,同花顺 287 | 285,2021-05-31,sz.300059,东方财富 288 | 286,2021-05-31,sz.300122,智飞生物 289 | 287,2021-05-31,sz.300124,汇川技术 290 | 288,2021-05-31,sz.300136,信维通信 291 | 289,2021-05-31,sz.300142,沃森生物 292 | 290,2021-05-31,sz.300144,宋城演艺 293 | 291,2021-05-31,sz.300347,泰格医药 294 | 292,2021-05-31,sz.300408,三环集团 295 | 293,2021-05-31,sz.300413,芒果超媒 296 | 294,2021-05-31,sz.300433,蓝思科技 297 | 295,2021-05-31,sz.300498,温氏股份 298 | 296,2021-05-31,sz.300529,健帆生物 299 | 297,2021-05-31,sz.300601,康泰生物 300 | 298,2021-05-31,sz.300628,亿联网络 301 | 299,2021-05-31,sz.300676,华大基因 302 | -------------------------------------------------------------------------------- /strategy.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | @version: 3.8.3 4 | @time: 21/4/29 9:30 5 | @author: Yamisora 6 | @file: strategy.py 7 | """ 8 | from prediction import * 9 | 10 | 11 | class Strategy: 12 | def __init__(self, data_days=10): 13 | """ 14 | 选股策略 15 | @data_days: 回测选择数据日期 16 | """ 17 | # 策略所需数据天数 18 | self.data_days = data_days 19 | # index选择指数组合 20 | self.index_name = 'hs300' 21 | self.index_code = 'sh.000300' 22 | # 存储路径 23 | self.base_data_path = './data/' 24 | self.data_path = './data/stocks/' 25 | self.train_data_path = './data/train_data/' 26 | # 指数组合内股票名称,代码数据 27 | self.stocks = pd.read_csv('{}{}_stocks.csv'.format(self.base_data_path, self.index_name)) 28 | self.stocks_codes = self.stocks['code'] 29 | # 指数日线数据 30 | self.index = pd.read_csv('{}{}.csv'.format(self.data_path, self.index_code)) 31 | # 交易日str序列 32 | self.trading_dates = self.index['date'] 33 | # 训练CNN模型 34 | self.dataset = StockDataset(data_days=data_days) 35 | self.prediction = Prediction(data_days=data_days, batch_size=50) 36 | self.prediction.train_cnn(self.dataset, retrain=False, epochs=2) 37 | self.prediction.train_lstm(self.dataset, retrain=False, epochs=2) 38 | # self.prediction.train_gru(self.dataset, retrain=False, epochs=2) 39 | # self.prediction.train_rnn_tanh(self.dataset, retrain=False, epochs=2) 40 | self.prediction.train_rnn_relu(self.dataset, retrain=False, epochs=2) 41 | self.prediction.train_resnet18(self.dataset, retrain=False, epochs=2) 42 | self.prediction.train_resnet34(self.dataset, retrain=False, epochs=2) 43 | self.prediction.train_resnet50(self.dataset, retrain=False, epochs=2) 44 | self.prediction.train_resnet101(self.dataset, retrain=False, epochs=2) 45 | self.prediction.train_resnet152(self.dataset, retrain=False, epochs=2) 46 | self.prediction.train_densenet(self.dataset, retrain=False, epochs=2) 47 | 48 | def choose_by_bm(self, today: tuple, number: int): 49 | """ 50 | 选择最近data_days中平均账面市值比(BM)最高的number只股票 51 | """ 52 | # 第一次买入策略应大于策略所需数据天数 53 | if today[1] < self.data_days: 54 | return pd.Series(None) 55 | # 建立用于计算平均BM的DF 56 | stocks_data = pd.DataFrame(self.stocks_codes) 57 | stocks_data['aver_BM'] = 0 58 | stocks_data = stocks_data.set_index('code') 59 | # 到交易日前一日为止共data_days日期序号 60 | days = range(today[1] - self.data_days, today[1]) 61 | for stock_code in self.stocks_codes: 62 | sum_BM = 0 63 | valid_days = self.data_days 64 | stock_data = pd.read_csv('{}{}.csv'.format(self.data_path, stock_code), index_col='date') 65 | for day in days: 66 | if self.trading_dates[day] in stock_data.index: 67 | # 加入每日市净率倒数 68 | pb = stock_data.loc[self.trading_dates[day], 'pbMRQ'] 69 | if pb != 0: 70 | sum_BM += 1.0 / pb 71 | else: 72 | sum_BM += 0 73 | else: 74 | valid_days -= 1 75 | if valid_days != 0: 76 | aver_BM = sum_BM / valid_days 77 | else: 78 | aver_BM = 0 79 | if aver_BM > 0: 80 | stocks_data.loc[stock_code, 'aver_BM'] = aver_BM 81 | # print(stocks_data) 82 | stocks_data.sort_values(by='aver_BM', ascending=False, inplace=True) 83 | # print(stocks_data.index) 84 | if len(stocks_data.index) > number: 85 | # 取0到number-1共number只股票 86 | return stocks_data.index[0:number] 87 | else: 88 | # 取全部股票 89 | return stocks_data.index[:] 90 | 91 | def choose_by_mf(self, today: tuple, number: int): 92 | """ 93 | 选择最近data_days中动量因子(Momentum Factor)最高的number只股票 94 | """ 95 | # 第一次买入策略应大于策略所需数据天数 96 | if today[1] < self.data_days: 97 | return pd.Series(None) 98 | # 建立用于计算平均MF的DF 99 | stocks_data = pd.DataFrame(self.stocks_codes) 100 | stocks_data['aver_MF'] = 0 101 | stocks_data = stocks_data.set_index('code') 102 | # 到交易日前一日为止共data_days日期序号 103 | days = range(today[1] - self.data_days, today[1]) 104 | for stock_code in self.stocks_codes: 105 | sum_MF = 0 106 | valid_days = self.data_days 107 | stock_data = pd.read_csv('{}{}.csv'.format(self.data_path, stock_code), index_col='date') 108 | for day in days: 109 | if self.trading_dates[day] in stock_data.index: 110 | # 加入收益率 111 | pc = stock_data.loc[self.trading_dates[day], 'preclose'] 112 | close = stock_data.loc[self.trading_dates[day], 'close'] 113 | sum_MF += close / pc 114 | else: 115 | valid_days -= 1 116 | if valid_days != 0: 117 | aver_MF = sum_MF / valid_days 118 | else: 119 | aver_MF = 0 120 | if aver_MF > 0: 121 | stocks_data.loc[stock_code, 'aver_MF'] = aver_MF 122 | # print(stocks_data) 123 | stocks_data.sort_values(by='aver_MF', ascending=False, inplace=True) 124 | # print(stocks_data.index) 125 | if len(stocks_data.index) > number: 126 | # 取0到number-1共number只股票 127 | return stocks_data.index[0:number] 128 | else: 129 | # 取全部股票 130 | return stocks_data.index[:] 131 | 132 | def choose_by_tr(self, today: tuple, number: int): 133 | """ 134 | 选择最近data_days中换手率因子(Unusual Turnover Rate, 异常换手率)最高的number只股票 135 | """ 136 | # 第一次买入策略应大于策略所需数据天数 137 | if today[1] < self.data_days: 138 | return pd.Series(None) 139 | # 建立用于计算平均TR的DF 140 | stocks_data = pd.DataFrame(self.stocks_codes) 141 | stocks_data['aver_TR'] = 0 142 | stocks_data = stocks_data.set_index('code') 143 | # 到交易日前一日为止共data_days日期序号 144 | days = range(today[1] - self.data_days, today[1]) 145 | for stock_code in self.stocks_codes: 146 | sum_TR = 0 147 | valid_days = self.data_days 148 | stock_data = pd.read_csv('{}{}.csv'.format(self.data_path, stock_code), index_col='date') 149 | for day in days: 150 | if self.trading_dates[day] in stock_data.index: 151 | # 加入换手率 152 | tr = stock_data.loc[self.trading_dates[day], 'turn'] 153 | sum_TR += tr 154 | else: 155 | valid_days -= 1 156 | if valid_days > 2: 157 | aver_TR = sum_TR / valid_days 158 | tr1 = stock_data.loc[self.trading_dates[days[-1]], 'turn'] 159 | tr2 = stock_data.loc[self.trading_dates[days[-1] - 1], 'turn'] 160 | ratio = (tr1 + tr2) / (aver_TR * 2) 161 | else: 162 | ratio = 0 163 | if ratio > 0: 164 | stocks_data.loc[stock_code, 'ratio'] = ratio 165 | 166 | # print(stocks_data) 167 | stocks_data.sort_values(by='ratio', ascending=False, inplace=True) 168 | # print(stocks_data.index) 169 | if len(stocks_data.index) > number: 170 | # 取0到number-1共number只股票 171 | return stocks_data.index[0:number] 172 | else: 173 | # 取全部股票 174 | return stocks_data.index[:] 175 | 176 | def __nn_choose(self, model_type: str, today: tuple, number: int): 177 | """ 178 | 选择指定NN预测未来data_days天涨幅最高的number只股票 179 | """ 180 | # 第一次买入策略应大于策略所需数据天数 181 | if today[1] < self.data_days: 182 | return pd.Series(None) 183 | # 建立用于计算预测涨跌幅的DF 184 | stocks_data = pd.DataFrame(self.stocks_codes) 185 | stocks_data['change'] = 0 186 | stocks_data = stocks_data.set_index('code') 187 | avail_num = 0 188 | # 预测每只股票未来data_days天涨跌幅 189 | for stock_code in self.stocks_codes: 190 | change = getattr(self.prediction, 'predict_' + model_type)(stock_code, today) 191 | if type(change) != int: 192 | # tensor直接取值 193 | change = change[0, 0].item() 194 | if change > 0: 195 | # 去除小于0的预测值 196 | stocks_data.loc[stock_code, 'change'] = change 197 | avail_num += 1 198 | # 排序 199 | stocks_data.sort_values(by='change', ascending=False, inplace=True) 200 | if avail_num > number: 201 | # 取0到number-1共number只股票 202 | return stocks_data.index[0:number] 203 | else: 204 | # 取全部有效股票 205 | return stocks_data.index[:avail_num] 206 | 207 | def choose_by_cnn(self, today: tuple, number: int): 208 | return self.__nn_choose('cnn', today, number) 209 | 210 | def choose_by_lstm(self, today: tuple, number: int): 211 | return self.__nn_choose('lstm', today, number) 212 | 213 | def choose_by_gru(self, today: tuple, number: int): 214 | return self.__nn_choose('gru', today, number) 215 | 216 | def choose_by_rnn_tanh(self, today: tuple, number: int): 217 | return self.__nn_choose('rnn_tanh', today, number) 218 | 219 | def choose_by_rnn_relu(self, today: tuple, number: int): 220 | return self.__nn_choose('rnn_relu', today, number) 221 | 222 | def choose_by_resnet18(self, today: tuple, number: int): 223 | return self.__nn_choose('resnet18', today, number) 224 | 225 | def choose_by_resnet34(self, today: tuple, number: int): 226 | return self.__nn_choose('resnet34', today, number) 227 | 228 | def choose_by_resnet50(self, today: tuple, number: int): 229 | return self.__nn_choose('resnet50', today, number) 230 | 231 | def choose_by_resnet101(self, today: tuple, number: int): 232 | return self.__nn_choose('resnet101', today, number) 233 | 234 | def choose_by_resnet152(self, today: tuple, number: int): 235 | return self.__nn_choose('resnet152', today, number) 236 | 237 | def choose_by_densenet(self, today: tuple, number: int): 238 | return self.__nn_choose('densenet', today, number) 239 | 240 | def choose_by_ensemble(self, today: tuple): 241 | chosen_num = pd.DataFrame() 242 | chosen_num['code'] = self.stocks_codes 243 | chosen_num['num'] = 0 244 | chosen_num.set_index('code', inplace=True) 245 | number = 300 246 | for chosen in (self.__nn_choose('cnn', today, number), 247 | self.__nn_choose('lstm', today, number), 248 | self.__nn_choose('rnn_relu', today, number), 249 | self.__nn_choose('resnet18', today, number), 250 | self.__nn_choose('resnet34', today, number), 251 | self.__nn_choose('resnet50', today, number), 252 | self.__nn_choose('densenet', today, number)): 253 | for stock_code in self.stocks_codes: 254 | if stock_code in chosen: 255 | chosen_num.loc[stock_code, 'num'] += 1 256 | return chosen_num[chosen_num['num'] >= 3].index 257 | -------------------------------------------------------------------------------- /backtest.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | @version: 3.8.3 4 | @time: 21/4/26 10:52 5 | @author: Yamisora 6 | @file: backtest.py 7 | """ 8 | # import matplotlib.pyplot as plt 9 | # import pandas as pd 10 | import time 11 | 12 | from strategy import * 13 | 14 | 15 | class Backtest: 16 | def __init__(self, start_cash=300000, fee=0.0003): 17 | """ 18 | 回测 19 | @start_cash: 初始资金 20 | @fee: 手续费比例 21 | """ 22 | # index选择指数组合 23 | self.index_name = 'hs300' 24 | self.index_code = 'sh.000300' 25 | # 存储路径 26 | self.base_data_path = './data/' 27 | self.data_path = './data/stocks/' 28 | # 默认万三手续费 29 | self.fee = fee 30 | # 指数组合内股票名称,代码数据 31 | self.stocks = pd.read_csv('{}{}_stocks.csv'.format(self.base_data_path, self.index_name)) 32 | self.stocks_codes = self.stocks['code'] 33 | # 指数日线数据 34 | self.index = pd.read_csv('{}{}.csv'.format(self.data_path, self.index_code)) 35 | # 交易日str序列 36 | self.trading_dates = self.index['date'] 37 | # 今日名称str与序列序号int 38 | self.today = (self.trading_dates[0], 0) 39 | # 初始资金 40 | self.start_cash = start_cash 41 | # 每日可用资金 42 | self.cash = pd.DataFrame(self.trading_dates) 43 | self.cash['cash'] = start_cash 44 | self.cash = self.cash.set_index('date') 45 | # 每日持股 46 | self.position = pd.DataFrame() 47 | self.position['date'] = self.trading_dates 48 | for stock_code in self.stocks_codes: 49 | self.position[stock_code] = 0 50 | self.position = self.position.set_index('date') 51 | 52 | def stocks_data(self, stock_codes, trading_date: str) -> pd.DataFrame(): 53 | """ 54 | 读取指定交易日内，一系列股票日线数据 55 | """ 56 | # 空输入情况 57 | if stock_codes.empty: 58 | return pd.DataFrame(None) 59 | stocks = pd.DataFrame() 60 | for stock_code in stock_codes: 61 | # 读取每只股票数据 62 | data = pd.read_csv('{}{}.csv'.format(self.data_path, stock_code), index_col='date') 63 | if trading_date in data.index: 64 | # 读入指定日期数据 65 | stocks = stocks.append(data.loc[trading_date], ignore_index=True) 66 | stocks = stocks.set_index('code') 67 | return stocks 68 | 69 | def buy(self, stocks_codes) -> bool: 70 | """ 71 | 全仓买入trading date的所有stock codes股票 72 | """ 73 | trading_date = self.today[0] 74 | # 读入当前交易日内所有待购买股票日线数据 75 | stocks = self.stocks_data(stocks_codes, trading_date) 76 | # 空输入情况 77 | if stocks.empty: 78 | return False 79 | # 预计购买股票数 80 | n = len(stocks_codes) 81 | # 每只股票可用购买资金 82 | single = self.cash.loc[trading_date, 'cash'] // n 83 | for stock_code in stocks_codes: 84 | # 每只股票开盘价作为买入价 85 | open_price = stocks.loc[stock_code, 'open'] 86 | # 计算除去手续费后可购买份额(整百) 87 | quantity = ((single / (1+self.fee)) / open_price) // 100 88 | # 买入并修改当日现金余额 89 | self.position.loc[trading_date, stock_code] += quantity * 100 90 | self.cash.loc[trading_date, 'cash'] -= open_price * quantity * 100 91 | del stocks 92 | return True 93 | 94 | def sell(self, stocks_codes): 95 | """ 96 | 空仓trading date的除去stock codes外股票 97 | """ 98 | trading_date = self.today[0] 99 | # 读入当前交易日内所有股票日线数据 100 | stocks = self.stocks_data(self.stocks_codes, trading_date) 101 | # 初始化卖出金额 102 | cash = 0 103 | # 卖出股票数量 104 | sell_num = 0 105 | # 卖出所有不继续持仓的股票并删除欲购买列表中已持仓的股票 106 | for stock_code in self.stocks_codes: 107 | position = self.position.loc[trading_date, stock_code] 108 | if position != 0: 109 | if stock_code not in stocks_codes: 110 | # 卖出 111 | cash += position * stocks.loc[stock_code, 'open'] * (1-self.fee) 112 | self.position.loc[trading_date, stock_code] = 0 113 | sell_num += 1 114 | else: 115 | # 去除已选股票中已持仓股票 116 | stocks_codes = stocks_codes.drop(stock_code) 117 | # 当日现金余额增加卖出金额 118 | self.cash.loc[trading_date, 'cash'] += cash 119 | del stocks 120 | return stocks_codes, sell_num 121 | 122 | def next_day(self) -> str: 123 | """ 124 | 返回下一个交易日str 125 | 延续前一天cash数量 126 | """ 127 | # 获取当日现金余额、持仓、日期序号 128 | cash = self.cash.loc[self.today[0], 'cash'] 129 | position = self.position.loc[self.today[0]] 130 | today = self.today[1] 131 | if today < len(self.trading_dates) - 1: 132 | # 更新下一日现金余额、持仓、日期元组 133 | self.today = (self.trading_dates[today+1], today+1) 134 | self.cash.loc[self.today[0], 'cash'] = cash 135 | self.position.loc[self.today[0]] = position 136 | return self.today[0] 137 | 138 | def calculate(self): 139 | """ 140 | 计算收益折线 141 | """ 142 | # 总收益百分比 143 | basic_position = self.start_cash 144 | position = [] 145 | for i, trading_date in enumerate(self.trading_dates): 146 | # 每隔5交易日计算持仓 147 | if i % 5 != 0: 148 | continue 149 | print('当前计算交易日: '+trading_date, end='\r') 150 | # 读入当前交易日内所有股票日线信息 151 | stock_data = self.stocks_data(self.stocks_codes, trading_date) 152 | # 初始化当前拥有总金额为现金金额 153 | daily_position = self.cash.loc[trading_date, 'cash'] 154 | for stock_code in self.stocks_codes: 155 | # 确认每只股票持股数 156 | quantity = self.position.loc[trading_date, stock_code] 157 | if quantity != 0: 158 | daily_position += quantity * stock_data.loc[stock_code, 'close'] 159 | position.append(daily_position) 160 | position = pd.Series(position) 161 | position /= basic_position 162 | return position 163 | 164 | 165 | if __name__ == '__main__': 166 | start_time = time.time() 167 | bt1 = Backtest(start_cash=10000000, fee=0.0003) 168 | bt2 = Backtest(start_cash=10000000, fee=0.0003) 169 | bt3 = Backtest(start_cash=10000000, fee=0.0003) 170 | bt4 = Backtest(start_cash=10000000, fee=0.0003) 171 | bt5 = Backtest(start_cash=10000000, fee=0.0003) 172 | # bt6 = Backtest(start_cash=10000000, fee=0.0003) 173 | # bt7 = Backtest(start_cash=10000000, fee=0.0003) 174 | bt8 = Backtest(start_cash=10000000, fee=0.0003) 175 | bt9 = Backtest(start_cash=10000000, fee=0.0003) 176 | bt10 = Backtest(start_cash=10000000, fee=0.0003) 177 | bt11 = Backtest(start_cash=10000000, fee=0.0003) 178 | # bt12 = Backtest(start_cash=10000000, fee=0.0003) 179 | # bt13 = Backtest(start_cash=10000000, fee=0.0003) 180 | bt14 = Backtest(start_cash=10000000, fee=0.0003) 181 | bt15 = Backtest(start_cash=10000000, fee=0.0003) 182 | strategy = Strategy(data_days=10) 183 | for date_key, date in bt1.trading_dates.items(): 184 | # 每10交易日调仓一次 185 | if date_key % 10 == 0: 186 | print('当前交易日: '+date) 187 | print('因子模型选股中...') 188 | chosen1 = strategy.choose_by_bm(bt1.today, 90) 189 | chosen3 = strategy.choose_by_mf(bt3.today, 90) 190 | chosen4 = strategy.choose_by_tr(bt4.today, 90) 191 | print('神经网络模型选股中...') 192 | chosen2 = strategy.choose_by_cnn(bt2.today, 90) 193 | chosen5 = strategy.choose_by_lstm(bt5.today, 90) 194 | # chosen6 = strategy.choose_by_gru(bt6.today, 90) 195 | # chosen7 = strategy.choose_by_rnn_tanh(bt7.today, 90) 196 | chosen8 = strategy.choose_by_rnn_relu(bt8.today, 90) 197 | chosen9 = strategy.choose_by_resnet18(bt9.today, 90) 198 | chosen10 = strategy.choose_by_resnet34(bt10.today, 90) 199 | chosen11 = strategy.choose_by_resnet50(bt11.today, 90) 200 | # chosen12 = strategy.choose_by_resnet101(bt12.today, 90) 201 | # chosen13 = strategy.choose_by_resnet152(bt13.today, 90) 202 | chosen14 = strategy.choose_by_densenet(bt14.today, 90) 203 | print('集成学习模型选股中...') 204 | chosen15 = strategy.choose_by_ensemble(bt15.today) 205 | 206 | to_buy1, sell1 = bt1.sell(chosen1) 207 | print('价值因子(BM)选股模型卖出', sell1, '只股票') 208 | to_buy2, sell2 = bt2.sell(chosen2) 209 | print('CNN选股模型卖出', sell2, '只股票') 210 | to_buy3, sell3 = bt3.sell(chosen3) 211 | print('动量因子(MF)选股模型卖出', sell3, '只股票') 212 | to_buy4, sell4 = bt4.sell(chosen4) 213 | print('换手率因子(TR)选股模型卖出', sell4, '只股票') 214 | to_buy5, sell5 = bt5.sell(chosen5) 215 | print('LSTM选股模型卖出', sell5, '只股票') 216 | # to_buy6, sell6 = bt6.sell(chosen6) 217 | # print('GRU选股模型卖出', sell6, '只股票') 218 | # to_buy7, sell7 = bt7.sell(chosen7) 219 | # print('RNN_tanh选股模型卖出', sell7, '只股票') 220 | to_buy8, sell8 = bt8.sell(chosen8) 221 | print('RNN_relu选股模型卖出', sell8, '只股票') 222 | to_buy9, sell9 = bt9.sell(chosen9) 223 | print('ResNet18选股模型卖出', sell9, '只股票') 224 | to_buy10, sell10 = bt10.sell(chosen10) 225 | print('ResNet34选股模型卖出', sell10, '只股票') 226 | to_buy11, sell11 = bt11.sell(chosen11) 227 | print('ResNet50选股模型卖出', sell11, '只股票') 228 | # to_buy12, sell12 = bt12.sell(chosen12) 229 | # print('ResNet101选股模型卖出', sell12, '只股票') 230 | # to_buy13, sell13 = bt13.sell(chosen13) 231 | # print('ResNet152选股模型卖出', sell13, '只股票') 232 | to_buy14, sell14 = bt14.sell(chosen14) 233 | print('DenseNet选股模型卖出', sell14, '只股票') 234 | to_buy15, sell15 = bt15.sell(chosen15) 235 | print('集成学习选股模型卖出', sell15, '只股票') 236 | bt1.buy(to_buy1) 237 | print('价值因子(BM)选股模型买入', len(to_buy1), '只股票') 238 | bt2.buy(to_buy2) 239 | print('CNN选股模型买入', len(to_buy2), '只股票') 240 | bt3.buy(to_buy3) 241 | print('动量因子(MF)选股模型买入', len(to_buy3), '只股票') 242 | bt4.buy(to_buy4) 243 | print('换手率因子(TR)选股模型买入', len(to_buy4), '只股票') 244 | bt5.buy(to_buy5) 245 | print('LSTM选股模型买入', len(to_buy5), '只股票') 246 | # bt6.buy(to_buy6) 247 | # print('GRU选股模型买入', len(to_buy6), '只股票') 248 | # bt7.buy(to_buy7) 249 | # print('RNN_tanh选股模型买入', len(to_buy7), '只股票') 250 | bt8.buy(to_buy8) 251 | print('RNN_relu选股模型买入', len(to_buy8), '只股票') 252 | bt9.buy(to_buy9) 253 | print('ResNet18选股模型买入', len(to_buy9), '只股票') 254 | bt10.buy(to_buy10) 255 | print('ResNet34选股模型买入', len(to_buy10), '只股票') 256 | bt11.buy(to_buy11) 257 | print('ResNet50选股模型买入', len(to_buy11), '只股票') 258 | # bt12.buy(to_buy12) 259 | # print('ResNet101选股模型买入', len(to_buy12), '只股票') 260 | # bt13.buy(to_buy13) 261 | # print('ResNet152选股模型买入', len(to_buy13), '只股票') 262 | bt14.buy(to_buy14) 263 | print('DenseNet选股模型买入', len(to_buy14), '只股票') 264 | bt15.buy(to_buy15) 265 | print('集成学习选股模型买入', len(to_buy15), '只股票') 266 | bt1.next_day() 267 | bt2.next_day() 268 | bt3.next_day() 269 | bt4.next_day() 270 | bt5.next_day() 271 | # bt6.next_day() 272 | # bt7.next_day() 273 | bt8.next_day() 274 | bt9.next_day() 275 | bt10.next_day() 276 | bt11.next_day() 277 | # bt12.next_day() 278 | # bt13.next_day() 279 | bt14.next_day() 280 | bt15.next_day() 281 | mid_time = time.time() 282 | span = mid_time - start_time 283 | print('回测模拟交易用时 {} 分 {} 秒'.format(int(span // 60), span % 60)) 284 | print('\n计算价值因子(BM)选股模型收益中') 285 | bm_position = bt1.calculate() 286 | print('\n计算CNN选股模型收益中') 287 | cnn_position = bt2.calculate() 288 | print('\n计算动量因子(MF)选股模型收益中') 289 | mf_position = bt3.calculate() 290 | print('\n计算换手率因子(TR)选股模型收益中') 291 | tr_position = bt4.calculate() 292 | print('\n计算LSTM选股模型收益中') 293 | lstm_position = bt5.calculate() 294 | # print('\n计算GRU选股模型收益中') 295 | # gru_position = bt6.calculate() 296 | # print('\n计算RNN_tanh选股模型收益中') 297 | # rnn_tanh_position = bt7.calculate() 298 | print('\n计算RNN_relu选股模型收益中') 299 | rnn_relu_position = bt8.calculate() 300 | print('\n计算ResNet18选股模型收益中') 301 | resnet18_position = bt9.calculate() 302 | print('\n计算ResNet34选股模型收益中') 303 | resnet34_position = bt10.calculate() 304 | print('\n计算ResNet50选股模型收益中') 305 | resnet50_position = bt11.calculate() 306 | # print('\n计算ResNet101选股模型收益中') 307 | # resnet101_position = bt12.calculate() 308 | # print('\n计算ResNet152选股模型收益中') 309 | # resnet152_position = bt13.calculate() 310 | print('\n计算DenseNet选股模型收益中') 311 | densenet_position = bt14.calculate() 312 | print('\n计算集成学习选股模型收益中') 313 | ensemble_position = bt15.calculate() 314 | # 指数收益百分比 315 | basic_index_price = bt1.index['close'][0] 316 | index_price = bt1.index['close'][::5] / basic_index_price 317 | x = range(0, len(bt1.trading_dates), 5) 318 | plt.rcParams['font.sans-serif'] = ['SimHei'] 319 | plt.rcParams['axes.unicode_minus'] = False 320 | plt.figure(figsize=[20, 15], dpi=160) 321 | plt.subplot(211) 322 | plt.plot(x, index_price, 'k:', label='沪深300指数收益率') 323 | plt.plot(x, bm_position, 'r:', label='价值因子(BM)选股模型持仓收益率') 324 | plt.plot(x, cnn_position, label='CNN选股模型持仓收益率') 325 | plt.plot(x, mf_position, 'g:', label='动量因子(MF)选股模型持仓收益率') 326 | plt.plot(x, tr_position, 'b:', label='换手率因子(TR)选股模型持仓收益率') 327 | plt.plot(x, lstm_position, label='LSTM选股模型持仓收益率') 328 | # plt.plot(x, gru_position, label='GRU选股模型持仓收益率') 329 | # plt.plot(x, rnn_tanh_position, label='RNN_tanh选股模型持仓收益率') 330 | plt.plot(x, rnn_relu_position, label='RNN_relu选股模型持仓收益率') 331 | plt.plot(x, resnet18_position, label='ResNet18选股模型持仓收益率') 332 | plt.plot(x, resnet34_position, label='ResNet34选股模型持仓收益率') 333 | plt.plot(x, resnet50_position, label='ResNet50选股模型持仓收益率') 334 | # plt.plot(x, resnet101_position, label='ResNet101选股模型持仓收益率') 335 | # plt.plot(x, resnet152_position, label='ResNet152选股模型持仓收益率') 336 | plt.plot(x, densenet_position, label='DenseNet选股模型持仓收益率') 337 | plt.plot(x, ensemble_position, label='集成学习选股模型持仓收益率') 338 | plt.ylabel('收益率/%') 339 | x_ticks = list(x[::len(x)//9]) 340 | x_ticks.append(x[-1]) 341 | x_labels = [bt1.trading_dates[i] for i in x_ticks] 342 | plt.xticks(x_ticks, x_labels) 343 | plt.legend() 344 | plt.subplot(212) 345 | plt.plot(x, [0]*len(x), 'k:', label='基准市场收益率(沪深300)', ) 346 | plt.plot(x, bm_position.values-index_price.values, 'r:', label='价值因子(BM)选股模型持仓超额收益率') 347 | plt.plot(x, cnn_position.values-index_price.values, label='CNN选股模型持仓超额收益率') 348 | plt.plot(x, mf_position.values-index_price.values, 'g:', label='动量因子(MF)选股模型持仓超额收益率') 349 | plt.plot(x, tr_position.values-index_price.values, 'b:', label='换手率因子(TR)选股模型持仓超额收益率') 350 | plt.plot(x, lstm_position.values-index_price.values, label='LSTM选股模型持仓超额收益率') 351 | # plt.plot(x, gru_position.values-index_price.values, label='GRU选股模型持仓超额收益率') 352 | # plt.plot(x, rnn_tanh_position.values-index_price.values, label='RNN_tanh选股模型持仓超额收益率') 353 | plt.plot(x, rnn_relu_position.values-index_price.values, label='RNN_relu选股模型持仓超额收益率') 354 | plt.plot(x, resnet18_position.values-index_price.values, label='ResNet18选股模型持仓超额收益率') 355 | plt.plot(x, resnet34_position.values-index_price.values, label='ResNet34选股模型持仓超额收益率') 356 | plt.plot(x, resnet50_position.values-index_price.values, label='ResNet50选股模型持仓超额收益率') 357 | # plt.plot(x, resnet101_position.values-index_price.values, label='ResNet101选股模型持仓超额收益率') 358 | # plt.plot(x, resnet152_position.values-index_price.values, label='ResNet152选股模型持仓超额收益率') 359 | plt.plot(x, densenet_position.values-index_price.values, label='DenseNet选股模型持仓超额收益率') 360 | plt.plot(x, ensemble_position.values-index_price.values, label='集成学习选股模型持仓超额收益率') 361 | plt.ylabel('超额收益率/%') 362 | plt.xticks(x_ticks, x_labels) 363 | plt.legend() 364 | plt.savefig('result.jpg') 365 | end_time = time.time() 366 | span = end_time - mid_time 367 | print('计算持仓收益用时 {} 分 {} 秒'.format(int(span // 60), span % 60)) 368 | span = end_time - start_time 369 | print('总计用时 {} 分 {:.2f} 秒'.format(int(span // 60), span % 60)) 370 | -------------------------------------------------------------------------------- /prediction.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | @version: 3.8.3 4 | @time: 21/5/11 8:33 5 | @author: Yamisora 6 | @file: prediction.py 7 | """ 8 | import torch 9 | import torch.nn as nn 10 | from torch.utils.data.dataset import Dataset 11 | from torch.utils.data.dataloader import DataLoader 12 | import torch.nn.functional as F 13 | import pandas as pd 14 | import numpy as np 15 | from tqdm import tqdm 16 | import os 17 | import matplotlib.pyplot as plt 18 | from collections import OrderedDict 19 | 20 | # 运行设备 21 | device = 'cuda' if torch.cuda.is_available() else 'cpu' 22 | 23 | 24 | def return_rate_transform(return_rate): 25 | """按收益率分布转换为数值分类模型""" 26 | if return_rate < -0.093: 27 | return -1.0 28 | elif return_rate < -0.053: 29 | return -0.8 30 | elif return_rate < -0.030: 31 | return -0.6 32 | elif return_rate < -0.014: 33 | return -0.4 34 | elif return_rate < 0.000: 35 | return -0.2 36 | elif return_rate < 0.016: 37 | return 0.2 38 | elif return_rate < 0.034: 39 | return 0.4 40 | elif return_rate < 0.058: 41 | return 0.6 42 | elif return_rate < 0.100: 43 | return 0.8 44 | elif return_rate >= 0.100: 45 | return 1.0 46 | 47 | 48 | class StockDataset(Dataset): 49 | """沪深300股票训练数据集""" 50 | def __init__(self, data_days=10, remake_data=False): 51 | super(StockDataset, self).__init__() 52 | # 存储路径 53 | self.base_data_path = './data/' 54 | self.data_path = './data/stocks/' 55 | self.train_data_path = './data/train_data/' 56 | # 策略所需数据天数 57 | self.data_days = data_days 58 | # 指数组合 59 | self.index_name = 'hs300' 60 | self.index_code = 'sh.000300' 61 | # 指数组合内股票名称,代码数据 62 | self.stocks = pd.read_csv('{}{}_stocks.csv'.format(self.base_data_path, self.index_name)) 63 | self.stocks_codes = self.stocks['code'] 64 | # 输入列 65 | self.input_columns = ('open', 'high', 'low', 'close', 'preclose', 66 | 'turn', 'peTTM', 'psTTM', 'pcfNcfTTM', 'pbMRQ',) 67 | # 数据集 68 | if not os.path.exists('{}{}.pkl'.format(self.base_data_path, self.index_name)): 69 | remake_data = True 70 | if remake_data: 71 | data = [] 72 | for stock_code in tqdm(self.stocks_codes): 73 | # 读取数据 74 | stock_data = pd.read_csv('{}{}.csv'.format(self.train_data_path, stock_code)) 75 | # 选择指定列 76 | stock_data = pd.DataFrame(stock_data, columns=self.input_columns) 77 | 78 | batches = len(stock_data.index) - 2 * self.data_days 79 | if batches <= 0: 80 | continue 81 | # 数据集存入个股训练数据 82 | for i in range(batches): 83 | # 清除无效数据(0) 84 | if 0 in stock_data[i:i + self.data_days].values: 85 | continue 86 | # 当前日期为data_days + i 87 | # data_days后收盘价 88 | next_price = stock_data.loc[2 * data_days + i, 'close'] 89 | # 当前日期收盘价 90 | this_price = stock_data.loc[data_days + i, 'close'] 91 | # high_change = stock_data.loc[data_days + i, 'high'] / stock_data.loc[data_days + i - 1, 'high']-1 92 | # low_change = stock_data.loc[data_days + i, 'low'] / stock_data.loc[data_days + i - 1, 'low'] - 1 93 | close_change = this_price / stock_data.loc[data_days + i - 1, 'close'] - 1 94 | predict_change = (next_price / this_price - 1) 95 | # 当前日期前一天到前data_days天 共data_days天数据 96 | data.append({'data': stock_data[i:i + self.data_days].values, 97 | 'label': [predict_change, close_change]}) 98 | # 'label': [predict_change, low_change, high_change, close_change]}) 99 | self.data = pd.DataFrame(data) 100 | self.data.to_pickle('{}{}.pkl'.format(self.base_data_path, self.index_name)) 101 | else: 102 | self.data = pd.read_pickle('{}{}.pkl'.format(self.base_data_path, self.index_name)) 103 | 104 | def __len__(self): 105 | """返回整个数据集的大小""" 106 | return len(self.data) 107 | 108 | def __getitem__(self, idx): 109 | """根据索引index返回dataset[index]""" 110 | data = torch.tensor(self.data.loc[idx, 'data'], dtype=torch.float32, device=device) 111 | label = torch.tensor(self.data.loc[idx, 'label'], dtype=torch.float32, device=device) 112 | return data, label 113 | 114 | 115 | class CNNModel(nn.Module): 116 | """类LeNet结构CNN模型""" 117 | def __init__(self, input_size, data_days=10): 118 | super(CNNModel, self).__init__() 119 | self.conv1 = nn.Conv2d(1, 6, 3) # 输入通道数为1，输出通道数为6 120 | self.conv2 = nn.Conv2d(6, 16, 3) # 输入通道数为6，输出通道数为16 121 | self.fc1 = nn.Linear((data_days - 4) * (input_size - 4) * 16, 120) 122 | self.fc2 = nn.Linear(120, 84) 123 | self.fc3 = nn.Linear(84, 2) 124 | 125 | def forward(self, x): 126 | x = x.view(x.size()[0], 1, x.size()[1], x.size()[2]) 127 | # 输入x (50, 1, 10, 10) -> conv1 (50, 6, 8, 8) -> relu 128 | x = self.conv1(x) 129 | x = F.relu(x) 130 | # 输入x (50, 6, 8, 8) -> conv2 (50, 16, 6, 6) -> relu 131 | x = self.conv2(x) 132 | x = F.relu(x) 133 | # view函数将张量x变形成一维向量形式，总特征数不变，为全连接层做准备 134 | x = x.view(x.size()[0], -1) 135 | x = F.relu(self.fc1(x)) 136 | x = F.relu(self.fc2(x)) 137 | x = self.fc3(x) 138 | return x 139 | 140 | 141 | class RNNModel(nn.Module): 142 | """LSTM,GRU,使用tanh与relu激活的RNN四种结构模型""" 143 | def __init__(self, rnn_type, input_size, hidden_size, n_layers): 144 | super(RNNModel, self).__init__() 145 | if rnn_type in ['LSTM', 'GRU']: 146 | self.rnn = getattr(nn, rnn_type)(input_size, hidden_size, n_layers, batch_first=True) 147 | else: 148 | try: 149 | non_linearity = {'RNN_TANH': 'tanh', 'RNN_RELU': 'relu'}[rnn_type] 150 | except KeyError: 151 | raise ValueError("""非可选RNN类型,可选参数:['LSTM', 'GRU', 'RNN_TANH', 'RNN_RELU']""") 152 | self.rnn = nn.RNN(input_size, hidden_size, n_layers, nonlinearity=non_linearity, batch_first=True) 153 | self.fc1 = nn.Linear(hidden_size, 120) 154 | self.fc2 = nn.Linear(120, 84) 155 | self.fc3 = nn.Linear(84, 2) 156 | # self.norm = nn.BatchNorm1d(10) 157 | # self.fc = nn.Linear(hidden_size, 3) 158 | 159 | self.rnn_type = rnn_type 160 | self.hidden_size = hidden_size 161 | self.n_layers = n_layers 162 | 163 | def forward(self, x): 164 | # x = self.norm(x) 165 | x, _ = self.rnn(x) 166 | x = F.relu(self.fc1(x[:, -1, :])) 167 | x = F.relu(self.fc2(x)) 168 | x = self.fc3(x) 169 | # print(x) 170 | # x = F.relu(x[:, -1, :]) 171 | # print(x) 172 | # x = self.fc(x) 173 | return x 174 | 175 | 176 | class BasicBlock(nn.Module): 177 | """用于ResNet18和34的残差块，用的是2个3x3的卷积""" 178 | expansion = 1 179 | 180 | def __init__(self, in_planes, planes, stride=1): 181 | super(BasicBlock, self).__init__() 182 | self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, 183 | stride=stride, padding=1, bias=False) 184 | self.bn1 = nn.BatchNorm2d(planes) 185 | self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, 186 | stride=1, padding=1, bias=False) 187 | self.bn2 = nn.BatchNorm2d(planes) 188 | self.shortcut = nn.Sequential() 189 | # 经过处理后的x要与x的维度相同(尺寸和深度) 190 | # 如果不相同，需要添加卷积+BN来变换为同一维度 191 | if stride != 1 or in_planes != self.expansion * planes: 192 | self.shortcut = nn.Sequential( 193 | nn.Conv2d(in_planes, self.expansion * planes, 194 | kernel_size=1, stride=stride, bias=False), 195 | nn.BatchNorm2d(self.expansion * planes) 196 | ) 197 | 198 | def forward(self, x): 199 | out = F.relu(self.bn1(self.conv1(x))) 200 | out = self.bn2(self.conv2(out)) 201 | out += self.shortcut(x) 202 | out = F.relu(out) 203 | return out 204 | 205 | 206 | class Bottleneck(nn.Module): 207 | """用于ResNet50,101和152的残差块，用的是1x1+3x3+1x1的卷积""" 208 | # 前面1x1和3x3卷积的filter个数相等，最后1x1卷积是其expansion倍 209 | expansion = 4 210 | 211 | def __init__(self, in_planes, planes, stride=1): 212 | super(Bottleneck, self).__init__() 213 | self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) 214 | self.bn1 = nn.BatchNorm2d(planes) 215 | self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, 216 | stride=stride, padding=1, bias=False) 217 | self.bn2 = nn.BatchNorm2d(planes) 218 | self.conv3 = nn.Conv2d(planes, self.expansion * planes, 219 | kernel_size=1, bias=False) 220 | self.bn3 = nn.BatchNorm2d(self.expansion * planes) 221 | 222 | self.shortcut = nn.Sequential() 223 | if stride != 1 or in_planes != self.expansion * planes: 224 | self.shortcut = nn.Sequential( 225 | nn.Conv2d(in_planes, self.expansion * planes, 226 | kernel_size=1, stride=stride, bias=False), 227 | nn.BatchNorm2d(self.expansion * planes) 228 | ) 229 | 230 | def forward(self, x): 231 | out = F.relu(self.bn1(self.conv1(x))) 232 | out = F.relu(self.bn2(self.conv2(out))) 233 | out = self.bn3(self.conv3(out)) 234 | out += self.shortcut(x) 235 | out = F.relu(out) 236 | return out 237 | 238 | 239 | class ResNet(nn.Module): 240 | """实现将ResNet迁移应用于股票预测""" 241 | def __init__(self, block, num_blocks, num_classes=2): 242 | super(ResNet, self).__init__() 243 | self.in_planes = 64 244 | 245 | self.conv1 = nn.Conv2d(1, 64, kernel_size=3, 246 | stride=1, padding=1, bias=False) 247 | self.bn1 = nn.BatchNorm2d(64) 248 | 249 | self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) 250 | self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) 251 | self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) 252 | self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) 253 | self.linear = nn.Linear(4 * 512 * block.expansion, num_classes) 254 | 255 | def _make_layer(self, block, planes, num_blocks, stride): 256 | strides = [stride] + [1] * (num_blocks - 1) 257 | layers = [] 258 | for stride in strides: 259 | layers.append(block(self.in_planes, planes, stride)) 260 | self.in_planes = planes * block.expansion 261 | return nn.Sequential(*layers) 262 | 263 | def forward(self, x): 264 | x = x.view(x.size()[0], 1, x.size()[1], x.size()[2]) 265 | out = F.relu(self.bn1(self.conv1(x))) 266 | out = self.layer1(out) 267 | out = self.layer2(out) 268 | out = self.layer3(out) 269 | out = self.layer4(out) 270 | # out = F.avg_pool2d(out, 4) 271 | out = out.view(out.size(0), -1) 272 | # print(out.size()) 273 | out = self.linear(out) 274 | return out 275 | 276 | 277 | def ResNet18(): 278 | return ResNet(BasicBlock, [2, 2, 2, 2]) 279 | 280 | 281 | def ResNet34(): 282 | return ResNet(BasicBlock, [3, 4, 6, 3]) 283 | 284 | 285 | def ResNet50(): 286 | return ResNet(Bottleneck, [3, 4, 6, 3]) 287 | 288 | 289 | def ResNet101(): 290 | return ResNet(Bottleneck, [3, 4, 23, 3]) 291 | 292 | 293 | def ResNet152(): 294 | return ResNet(Bottleneck, [3, 8, 36, 3]) 295 | 296 | 297 | class DenseLayer(nn.Sequential): 298 | def __init__(self, in_channels, growth_rate, bn_size): 299 | super(DenseLayer, self).__init__() 300 | self.add_module('norm1', nn.BatchNorm2d(in_channels)) 301 | self.add_module('relu1', nn.ReLU(inplace=True)) 302 | self.add_module('conv1', nn.Conv2d(in_channels, bn_size * growth_rate, 303 | kernel_size=1, 304 | stride=1, bias=False)) 305 | self.add_module('norm2', nn.BatchNorm2d(bn_size*growth_rate)) 306 | self.add_module('relu2', nn.ReLU(inplace=True)) 307 | self.add_module('conv2', nn.Conv2d(bn_size*growth_rate, growth_rate, 308 | kernel_size=3, 309 | stride=1, padding=1, bias=False)) 310 | 311 | # 重载forward函数 312 | def forward(self, x): 313 | new_features = super(DenseLayer, self).forward(x) 314 | return torch.cat([x, new_features], 1) 315 | 316 | 317 | class DenseBlock(nn.Sequential): 318 | def __init__(self, num_layers, in_channels, bn_size, growth_rate): 319 | super(DenseBlock, self).__init__() 320 | for i in range(num_layers): 321 | self.add_module('denselayer%d' % (i+1), 322 | DenseLayer(in_channels + growth_rate * i, 323 | growth_rate, bn_size)) 324 | 325 | 326 | class Transition(nn.Sequential): 327 | def __init__(self, in_channels, out_channels): 328 | super(Transition, self).__init__() 329 | self.add_module('norm', nn.BatchNorm2d(in_channels)) 330 | self.add_module('relu', nn.ReLU(inplace=True)) 331 | self.add_module('conv', nn.Conv2d(in_channels, out_channels, 332 | kernel_size=1, 333 | stride=1, bias=False)) 334 | self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2)) 335 | 336 | 337 | class DenseNetBC(nn.Module): 338 | def __init__(self, growth_rate=12, block_config=(6, 12, 24, 16), 339 | bn_size=4, theta=0.5, num_classes=2): 340 | super(DenseNetBC, self).__init__() 341 | 342 | # 初始的卷积为filter:2倍的growth_rate 343 | num_init_feature = 2 * growth_rate 344 | 345 | # 原DenseNet对cifar-10与ImageNet的分别初始化 346 | # if num_classes == 10: 347 | # self.features = nn.Sequential(OrderedDict([ 348 | # ('conv0', nn.Conv2d(3, num_init_feature, 349 | # kernel_size=3, stride=1, 350 | # padding=1, bias=False)), 351 | # ])) 352 | # else: 353 | # self.features = nn.Sequential(OrderedDict([ 354 | # ('conv0', nn.Conv2d(3, num_init_feature, 355 | # kernel_size=7, stride=2, 356 | # padding=3, bias=False)), 357 | # ('norm0', nn.BatchNorm2d(num_init_feature)), 358 | # ('relu0', nn.ReLU(inplace=True)), 359 | # ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) 360 | # ])) 361 | self.features = nn.Sequential(OrderedDict([ 362 | ('conv0', nn.Conv2d(1, num_init_feature, 363 | kernel_size=3, stride=1, 364 | padding=1, bias=False)), 365 | ])) 366 | 367 | num_feature = num_init_feature 368 | for i, num_layers in enumerate(block_config): 369 | self.features.add_module('denseblock%d' % (i+1), 370 | DenseBlock(num_layers, num_feature, 371 | bn_size, growth_rate)) 372 | num_feature = num_feature + growth_rate * num_layers 373 | if i != len(block_config)-1: 374 | self.features.add_module('transition%d' % (i + 1), 375 | Transition(num_feature, 376 | int(num_feature * theta))) 377 | num_feature = int(num_feature * theta) 378 | 379 | self.features.add_module('norm5', nn.BatchNorm2d(num_feature)) 380 | self.features.add_module('relu5', nn.ReLU(inplace=True)) 381 | self.features.add_module('avg_pool', nn.AdaptiveAvgPool2d((1, 1))) 382 | 383 | self.linear = nn.Linear(num_feature, num_classes) 384 | 385 | for m in self.modules(): 386 | if isinstance(m, nn.Conv2d): 387 | nn.init.kaiming_normal_(m.weight) 388 | elif isinstance(m, nn.BatchNorm2d): 389 | nn.init.constant_(m.weight, 1) 390 | nn.init.constant_(m.bias, 0) 391 | elif isinstance(m, nn.Linear): 392 | nn.init.constant_(m.bias, 0) 393 | 394 | def forward(self, x): 395 | # 增加一个1的维度(图像处理中为RGB维度) 396 | x = x.view(x.size()[0], 1, x.size()[1], x.size()[2]) 397 | features = self.features(x) 398 | out = features.view(features.size(0), -1) 399 | out = self.linear(out) 400 | return out 401 | 402 | 403 | def dense_net_BC_100(): 404 | return DenseNetBC(growth_rate=12, block_config=(16, 16, 16)) 405 | 406 | 407 | class Prediction: 408 | def __init__(self, data_days=10, batch_size=50): 409 | # 策略所需数据天数 410 | self.data_days = data_days 411 | # 指数组合 412 | self.index_name = 'hs300' 413 | self.index_code = 'sh.000300' 414 | # 存储路径 415 | self.base_data_path = './data/' 416 | self.data_path = './data/stocks/' 417 | # 指数组合内股票名称,代码数据 418 | self.stocks = pd.read_csv('{}{}_stocks.csv'.format(self.base_data_path, self.index_name)) 419 | self.stocks_codes = self.stocks['code'] 420 | # 指数日线数据 421 | self.index = pd.read_csv('{}{}.csv'.format(self.data_path, self.index_code)) 422 | # 交易日str序列 423 | self.trading_dates = self.index['date'] 424 | # 一次喂入数据批次 425 | self.batch_size = batch_size 426 | # 输入列 427 | self.input_columns = ('open', 'high', 'low', 'close', 'preclose', 428 | 'turn', 'peTTM', 'psTTM', 'pcfNcfTTM', 'pbMRQ',) 429 | input_size = len(self.input_columns) 430 | 431 | # cnn模型 432 | self.cnn = CNNModel(data_days, input_size).to(device) 433 | 434 | # RNN类型 输入大小 隐层大小 隐层数 435 | # rnn_types = 'LSTM', 'GRU', 'RNN_TANH', 'RNN_RELU' 436 | hidden_size = 20 437 | n_layers = 2 438 | # 初始化模型 439 | self.lstm = RNNModel('LSTM', input_size, hidden_size, n_layers).to(device) 440 | self.gru = RNNModel('GRU', input_size, hidden_size, n_layers).to(device) 441 | self.rnn_tanh = RNNModel('RNN_TANH', input_size, hidden_size, n_layers).to(device) 442 | self.rnn_relu = RNNModel('RNN_RELU', input_size, hidden_size, n_layers).to(device) 443 | 444 | # ResNet模型 445 | self.resnet18 = ResNet18().to(device) 446 | self.resnet34 = ResNet34().to(device) 447 | self.resnet50 = ResNet50().to(device) 448 | self.resnet101 = ResNet101().to(device) 449 | self.resnet152 = ResNet152().to(device) 450 | 451 | # DenseNet模型 452 | self.densenet = dense_net_BC_100().to(device) 453 | 454 | # 使用MSE误差 455 | self.criterion = nn.MSELoss() 456 | # 使用AdamW优化器 默认参数 457 | self.cnn_optimizer = torch.optim.AdamW(self.cnn.parameters()) 458 | self.lstm_optimizer = torch.optim.AdamW(self.lstm.parameters()) 459 | self.gru_optimizer = torch.optim.AdamW(self.gru.parameters()) 460 | self.rnn_tanh_optimizer = torch.optim.AdamW(self.rnn_tanh.parameters()) 461 | self.rnn_relu_optimizer = torch.optim.AdamW(self.rnn_relu.parameters()) 462 | self.rn18_optimizer = torch.optim.AdamW(self.resnet18.parameters()) 463 | self.rn34_optimizer = torch.optim.AdamW(self.resnet34.parameters()) 464 | self.rn50_optimizer = torch.optim.AdamW(self.resnet50.parameters()) 465 | self.rn101_optimizer = torch.optim.AdamW(self.resnet101.parameters()) 466 | self.rn152_optimizer = torch.optim.AdamW(self.resnet152.parameters()) 467 | self.densenet_optimizer = torch.optim.AdamW(self.densenet.parameters()) 468 | 469 | def __train(self, model_name, model, optim, train_dataset, epochs=2): 470 | # 生成训练数据 471 | train_data = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True) 472 | # 设置为训练模式 473 | model.train() 474 | print('*' * 20, '\n', model_name, '模型训练中') 475 | for epoch in range(epochs): 476 | for data, label in train_data: 477 | # 前向传播 计算结果 478 | output = model.forward(data) 479 | # label = label.view(label.size()[0], 1) 480 | # 计算误差 481 | loss = self.criterion(output, label) 482 | # 清除梯度记录 483 | optim.zero_grad() 484 | # 误差反向传播 485 | loss.backward() 486 | # 优化器更新参数 487 | optim.step() 488 | print('Train_loss:', loss.item(), end='\r') 489 | if loss.item() < 1e-3: 490 | break 491 | # 保存训练好的模型 492 | torch.save(model, '{}{}.pt'.format(self.base_data_path, model_name)) 493 | print('\n', model_name, '模型训练完成') 494 | 495 | def train_cnn(self, train_dataset, epochs=2, retrain=False): 496 | if not os.path.exists('{}CNN.pt'.format(self.base_data_path)): 497 | retrain = True 498 | if not retrain: 499 | # 读取训练好的模型 500 | self.cnn = torch.load('{}CNN.pt'.format(self.base_data_path, self.index_name)) 501 | return 502 | self.__train('CNN', self.cnn, self.cnn_optimizer, train_dataset, epochs) 503 | 504 | def train_lstm(self, train_dataset, epochs=2, retrain=False): 505 | if not os.path.exists('{}LSTM.pt'.format(self.base_data_path)): 506 | retrain = True 507 | if not retrain: 508 | # 读取训练好的模型 509 | self.lstm = torch.load('{}LSTM.pt'.format(self.base_data_path)) 510 | return 511 | self.__train('LSTM', self.lstm, self.lstm_optimizer, train_dataset, epochs) 512 | 513 | def train_gru(self, train_dataset, epochs=2, retrain=False): 514 | if not os.path.exists('{}GRU.pt'.format(self.base_data_path)): 515 | retrain = True 516 | if not retrain: 517 | # 读取训练好的模型 518 | self.rnn_relu = torch.load('{}GRU.pt'.format(self.base_data_path)) 519 | return 520 | self.__train('GRU', self.gru, self.gru_optimizer, train_dataset, epochs) 521 | 522 | def train_rnn_tanh(self, train_dataset, epochs=2, retrain=False): 523 | if not os.path.exists('{}RNN_tanh.pt'.format(self.base_data_path)): 524 | retrain = True 525 | if not retrain: 526 | # 读取训练好的模型 527 | self.rnn_relu = torch.load('{}RNN_tanh.pt'.format(self.base_data_path)) 528 | return 529 | self.__train('RNN_tanh', self.rnn_tanh, self.rnn_tanh_optimizer, train_dataset, epochs) 530 | 531 | def train_rnn_relu(self, train_dataset, epochs=2, retrain=False): 532 | if not os.path.exists('{}RNN_relu.pt'.format(self.base_data_path)): 533 | retrain = True 534 | if not retrain: 535 | # 读取训练好的模型 536 | self.rnn_relu = torch.load('{}RNN_relu.pt'.format(self.base_data_path)) 537 | return 538 | self.__train('RNN_relu', self.rnn_relu, self.rnn_relu_optimizer, train_dataset, epochs) 539 | 540 | def train_resnet18(self, train_dataset, epochs=2, retrain=False): 541 | if not os.path.exists('{}resnet18.pt'.format(self.base_data_path)): 542 | retrain = True 543 | if not retrain: 544 | # 读取训练好的模型 545 | self.resnet18 = torch.load('{}resnet18.pt'.format(self.base_data_path)) 546 | return 547 | self.__train('resnet18', self.resnet18, self.rn18_optimizer, train_dataset, epochs) 548 | 549 | def train_resnet34(self, train_dataset, epochs=2, retrain=False): 550 | if not os.path.exists('{}resnet34.pt'.format(self.base_data_path)): 551 | retrain = True 552 | if not retrain: 553 | # 读取训练好的模型 554 | self.resnet34 = torch.load('{}resnet34.pt'.format(self.base_data_path)) 555 | return 556 | self.__train('resnet34', self.resnet34, self.rn34_optimizer, train_dataset, epochs) 557 | 558 | def train_resnet50(self, train_dataset, epochs=2, retrain=False): 559 | if not os.path.exists('{}resnet50.pt'.format(self.base_data_path)): 560 | retrain = True 561 | if not retrain: 562 | # 读取训练好的模型 563 | self.resnet50 = torch.load('{}resnet50.pt'.format(self.base_data_path)) 564 | return 565 | self.__train('resnet50', self.resnet50, self.rn50_optimizer, train_dataset, epochs) 566 | 567 | def train_resnet101(self, train_dataset, epochs=2, retrain=False): 568 | if not os.path.exists('{}resnet101.pt'.format(self.base_data_path)): 569 | retrain = True 570 | if not retrain: 571 | # 读取训练好的模型 572 | self.resnet101 = torch.load('{}resnet101.pt'.format(self.base_data_path)) 573 | return 574 | self.__train('resnet101', self.resnet101, self.rn101_optimizer, train_dataset, epochs) 575 | 576 | def train_resnet152(self, train_dataset, epochs=2, retrain=False): 577 | if not os.path.exists('{}resnet152.pt'.format(self.base_data_path)): 578 | retrain = True 579 | if not retrain: 580 | # 读取训练好的模型 581 | self.resnet152 = torch.load('{}resnet152.pt'.format(self.base_data_path)) 582 | return 583 | self.__train('resnet152', self.resnet152, self.rn152_optimizer, train_dataset, epochs) 584 | 585 | def train_densenet(self, train_dataset, epochs=2, retrain=False): 586 | if not os.path.exists('{}densenet.pt'.format(self.base_data_path)): 587 | retrain = True 588 | if not retrain: 589 | # 读取训练好的模型 590 | self.densenet = torch.load('{}densenet.pt'.format(self.base_data_path)) 591 | return 592 | self.__train('densenet', self.densenet, self.densenet_optimizer, train_dataset, epochs) 593 | 594 | def __predict_data(self, stock_code: str, today: tuple, abs_date=False): 595 | stock_data = pd.read_csv('{}{}.csv'.format(self.data_path, stock_code)) 596 | # 当前日期在数据集中序号 597 | date_index = today[1] if abs_date else len(stock_data) - len(self.trading_dates) + today[1] 598 | # 数据不足时返回0 599 | if date_index < self.data_days: 600 | return 0 601 | # 生成预测数据 602 | stock_data = pd.DataFrame(stock_data, columns=self.input_columns) 603 | # 将0替换为上一行数据 604 | stock_data = stock_data.replace(0, None) 605 | stock_data = stock_data[date_index - self.data_days:date_index] 606 | stock_data = np.reshape(stock_data.values, (1, self.data_days, len(self.input_columns))) 607 | stock_data = torch.tensor(stock_data, dtype=torch.float32, device=device) 608 | return stock_data 609 | 610 | def __predict(self, model, stock_code: str, today: tuple): 611 | # 设置为预测模式 612 | model.eval() 613 | stock_data = self.__predict_data(stock_code, today) 614 | if type(stock_data) == int: 615 | return 0 616 | with torch.no_grad(): 617 | # 前向传播 输出结果 618 | output = model.forward(stock_data) 619 | return output 620 | 621 | def predict_cnn(self, stock_code: str, today: tuple): 622 | return self.__predict(self.cnn, stock_code, today) 623 | 624 | def predict_lstm(self, stock_code: str, today: tuple): 625 | return self.__predict(self.lstm, stock_code, today) 626 | 627 | def predict_gru(self, stock_code: str, today: tuple): 628 | return self.__predict(self.gru, stock_code, today) 629 | 630 | def predict_rnn_tanh(self, stock_code: str, today: tuple): 631 | return self.__predict(self.rnn_tanh, stock_code, today) 632 | 633 | def predict_rnn_relu(self, stock_code: str, today: tuple): 634 | return self.__predict(self.rnn_relu, stock_code, today) 635 | 636 | def predict_resnet18(self, stock_code: str, today: tuple): 637 | return self.__predict(self.resnet18, stock_code, today) 638 | 639 | def predict_resnet34(self, stock_code: str, today: tuple): 640 | return self.__predict(self.resnet34, stock_code, today) 641 | 642 | def predict_resnet50(self, stock_code: str, today: tuple): 643 | return self.__predict(self.resnet50, stock_code, today) 644 | 645 | def predict_resnet101(self, stock_code: str, today: tuple): 646 | return self.__predict(self.resnet101, stock_code, today) 647 | 648 | def predict_resnet152(self, stock_code: str, today: tuple): 649 | return self.__predict(self.resnet152, stock_code, today) 650 | 651 | def predict_densenet(self, stock_code: str, today: tuple): 652 | return self.__predict(self.densenet, stock_code, today) 653 | 654 | 655 | if __name__ == '__main__': 656 | dataset = StockDataset(data_days=10, remake_data=False) 657 | print('训练集大小:', len(dataset)) 658 | 659 | prediction = Prediction(data_days=10, batch_size=200) 660 | 661 | # p2 = t_data.loc[trading_day1[1], 'high'] / t_data.loc[trading_day1[1] - 1, 'high'] - 1 662 | # p3 = t_data.loc[trading_day1[1], 'low'] / t_data.loc[trading_day1[1] - 1, 'low'] - 1 663 | # p4 = t_data.loc[trading_day1[1], 'close'] / t_data.loc[trading_day1[1] - 1, 'close'] - 1 664 | # print(return_rate_transform(p1), p2, p3, p4) 665 | 666 | prediction.train_cnn(dataset, retrain=False, epochs=1) 667 | prediction.train_lstm(dataset, retrain=False, epochs=1) 668 | # GRU与tanhRNN效果不佳 抛弃 669 | # prediction.train_gru(dataset, retrain=False, epochs=1) 670 | # prediction.train_rnn_tanh(dataset, retrain=False, epochs=1) 671 | prediction.train_rnn_relu(dataset, retrain=False, epochs=1) 672 | prediction.train_resnet18(dataset, retrain=False, epochs=1) 673 | prediction.train_resnet34(dataset, retrain=False, epochs=1) 674 | prediction.train_resnet50(dataset, retrain=False, epochs=1) 675 | prediction.train_resnet101(dataset, retrain=False, epochs=1) 676 | prediction.train_resnet152(dataset, retrain=False, epochs=1) 677 | prediction.train_densenet(dataset, retrain=False, epochs=1) 678 | 679 | plt.rcParams['font.sans-serif'] = ['SimHei'] 680 | plt.rcParams['axes.unicode_minus'] = False 681 | plt.figure(figsize=[30, 15], dpi=160) 682 | for code in dataset.stocks_codes[:5]: 683 | print('正在绘制'+code+'预测图像') 684 | plt.clf() 685 | df = pd.read_csv('./data/stocks/' + code + '.csv') 686 | trading_dates = df['date'] 687 | x_r = range(0, len(trading_dates)) 688 | x_ticks = list(x_r[::100]) 689 | x_ticks.append(x_r[-1]) 690 | x_labels = [trading_dates[i] for i in x_ticks] 691 | true_close = df['close'].values 692 | 693 | def close_p(x): 694 | if type(x) == int: 695 | return x 696 | x = x[0, 1].item() 697 | return x if 0.2 > x > -0.2 else 0.0 698 | 699 | print('计算CNN') 700 | cnn_close = [true_close[j]*(1+close_p(prediction.predict_cnn(code, (0, j)))) 701 | for j in range(len(trading_dates))] 702 | print('计算LSTM') 703 | lstm_close = [true_close[j]*(1+close_p(prediction.predict_lstm(code, (0, j)))) 704 | for j in range(len(trading_dates))] 705 | # print('计算GRU') 706 | # gru_close = [true_close[j]*(1+close_p(prediction.predict_gru(code, (0, j)))) 707 | # for j in range(len(trading_dates))] 708 | # print('计算RNN_tanh') 709 | # rnn_tanh_close = [true_close[j] * (1 + close_p(prediction.predict_rnn_tanh(code, (0, j)))) 710 | # for j in range(len(trading_dates))] 711 | print('计算RNN_relu') 712 | rnn_relu_close = [true_close[j] * (1 + close_p(prediction.predict_rnn_relu(code, (0, j)))) 713 | for j in range(len(trading_dates))] 714 | print('计算ResNet18') 715 | rn18_close = [true_close[j]*(1+close_p(prediction.predict_resnet18(code, (0, j)))) 716 | for j in range(len(trading_dates))] 717 | print('计算ResNet34') 718 | rn34_close = [true_close[j]*(1+close_p(prediction.predict_resnet34(code, (0, j)))) 719 | for j in range(len(trading_dates))] 720 | print('计算ResNet50') 721 | rn50_close = [true_close[j]*(1+close_p(prediction.predict_resnet50(code, (0, j)))) 722 | for j in range(len(trading_dates))] 723 | print('计算ResNet101') 724 | rn101_close = [true_close[j]*(1+close_p(prediction.predict_resnet101(code, (0, j)))) 725 | for j in range(len(trading_dates))] 726 | print('计算ResNet152') 727 | rn152_close = [true_close[j]*(1+close_p(prediction.predict_resnet152(code, (0, j)))) 728 | for j in range(len(trading_dates))] 729 | print('计算DenseNet') 730 | densenet_close = [true_close[j]*(1+close_p(prediction.predict_densenet(code, (0, j)))) 731 | for j in range(len(trading_dates))] 732 | 733 | def sp(i, predict_close, label_name): 734 | plt.subplot(3, 3, i) 735 | plt.plot(x_r, true_close, label='真实值') 736 | plt.plot(x_r, predict_close, label=label_name) 737 | plt.ylabel('收盘价') 738 | plt.xticks(x_ticks, x_labels) 739 | plt.legend() 740 | 741 | sp(1, cnn_close, 'CNN模型预测值') 742 | sp(2, lstm_close, 'LSTM模型预测值') 743 | # sp(3, gru_close, 'GRU模型预测值') 744 | # sp(4, rnn_tanh_close, 'RNN_tanh模型预测值') 745 | sp(3, rnn_relu_close, 'RNN_relu模型预测值') 746 | sp(4, rn18_close, 'ResNet18模型预测值') 747 | sp(5, rn34_close, 'ResNet34模型预测值') 748 | sp(6, rn34_close, 'ResNet50模型预测值') 749 | sp(7, rn101_close, 'ResNet101模型预测值') 750 | sp(8, rn101_close, 'ResNet152模型预测值') 751 | sp(9, densenet_close, 'DenseNet模型预测值') 752 | 753 | plt.savefig(code+'_predict.jpg') 754 | 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /stock_indicator.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | @version: 3.8.3 4 | @author: Yamisora 5 | @file: stock_indicator.py 6 | 股票技术指标接口 7 | Created on 2018/07/26 8 | @original author: Wangzili 9 | @contact: 446406177@qq.com 10 | """ 11 | import pandas as pd 12 | import numpy as np 13 | import itertools 14 | 15 | 16 | def ma(df, n=10): 17 | """ 18 | 移动平均线 Moving Average 19 | MA（N）=（第1日收盘价+第2日收盘价—+……+第N日收盘价）/N 20 | """ 21 | pv = pd.DataFrame() 22 | pv['date'] = df['date'] 23 | pv['ma'] = df.close.rolling(n).mean() 24 | return pv 25 | 26 | 27 | def _ma(series, n): 28 | """ 29 | 移动平均 30 | """ 31 | return series.rolling(n).mean() 32 | 33 | 34 | def md(df, n=10): 35 | """ 36 | 移动标准差 37 | STD=S（CLOSE,N）=[∑（CLOSE-MA(CLOSE，N)）^2/N]^0.5 38 | """ 39 | _md = pd.DataFrame() 40 | _md['date'] = df.date 41 | _md["md"] = df.close.rolling(n).std(ddof=0) 42 | return _md 43 | 44 | 45 | def _md(series, n): 46 | """ 47 | 标准差MD 48 | """ 49 | return series.rolling(n).std(ddof=0) # 有时候会用ddof=1 50 | 51 | 52 | def ema(df, n=12): 53 | """ 54 | 指数平均数指标 Exponential Moving Average 55 | 今日EMA（N）=2/（N+1）×今日收盘价+(N-1)/（N+1）×昨日EMA（N） 56 | EMA(X,N)=[2×X+(N-1)×EMA(ref(X),N]/(N+1) 57 | """ 58 | _ema = pd.DataFrame() 59 | _ema['date'] = df['date'] 60 | _ema['ema'] = df.close.ewm(ignore_na=False, span=n, min_periods=0, adjust=False).mean() 61 | return _ema 62 | 63 | 64 | def _ema(series, n): 65 | """ 66 | 指数平均数 67 | """ 68 | return series.ewm(ignore_na=False, span=n, min_periods=0, adjust=False).mean() 69 | 70 | 71 | def dma(df, n=10, m=50, k=10): 72 | """ 73 | 平均线差 DMA(10,50,10) 74 | DDD=MA（N1）-MA(N2) 75 | AMA= MA（DDD,M） 76 | 其中N1表示短期天数，N2表示长期天数，M表示AMA的天数 77 | """ 78 | _dma = pd.DataFrame() 79 | _dma['date'] = df['date'] 80 | _dma['ddd'] = _ma(df.close, n) - _ma(df.close, m) 81 | _dma['ama'] = _ma(_dma.ddd, k) 82 | del _dma['ddd'] 83 | return _dma 84 | 85 | 86 | def dmi(df, n=14, m=6): 87 | """ 88 | # 通达信计算方法 89 | TRZ: = EMA(MAX(MAX(HIGH - LOW, ABS(HIGH - REF(CLOSE, 1))), ABS(REF(CLOSE, 1) - LOW)), 7); 90 | HD: = HIGH - REF(HIGH, 1); 91 | LD: = REF(LOW, 1) - LOW; 92 | DMP: = EMA(IF(HD > 0 AND HD > LD, HD, 0), 7); 93 | DMM: = EMA(IF(LD > 0 AND LD > HD, LD, 0), 7); 94 | PDI: DMP * 100 / TRZ; 95 | MDI: DMM * 100 / TRZ; 96 | ADX: EMA(ABS(MDI - PDI) / (MDI + PDI) * 100, 7); 97 | ADXR: EMA(ADX, 7); 98 | tr = pd.Series(np.vstack([df.high - df.low, (df.high - df.close.shift()).abs(), (df.low - df.close.shift()).abs()]).max(axis=0)) 99 | trz = _ema(tr, n) 100 | _m = pd.DataFrame() 101 | _m['hd'] = df.high - df.high.shift() 102 | _m['ld'] = df.low.shift() - df.low 103 | _m['mp'] = _m.apply(lambda x: x.hd if x.hd > 0 and x.hd > x.ld else 0, axis=1) 104 | _m['mm'] = _m.apply(lambda x: x.ld if x.ld > 0 and x.hd < x.ld else 0, axis=1) 105 | _m['dmp'] = _ema(_m.mp, n) 106 | _m['dmm'] = _ema(_m.mm, n) 107 | _dmi = pd.DataFrame() 108 | _dmi['date'] = df.date 109 | _dmi['pdi'] = _m.dmp * 100 / trz 110 | _dmi['mdi'] = _m.dmm * 100 / trz 111 | _dmi['adx'] = _ema((_dmi.mdi - _dmi.pdi).abs() / (_dmi.mdi + _dmi.pdi) * 100, m) 112 | _dmi['adxr'] = _ema(_dmi.adx, m) 113 | return _dmi 114 | """ 115 | """ 116 | # 同花顺计算方式 117 | TR := SUM(MAX(MAX(HIGH-LOW,ABS(HIGH-REF(CLOSE,1))),ABS(LOW-REF(CLOSE,1))),N); 118 | HD: = HIGH - REF(HIGH, 1); 119 | LD: = REF(LOW, 1) - LOW; 120 | DMP:= SUM(IF(HD>0 AND HD>LD,HD,0),N); 121 | DMM:= SUM(IF(LD>0 AND LD>HD,LD,0),N); 122 | ＋DI: DMP*100/TR; 123 | －DI: DMM*100/TR; 124 | ADX: MA(ABS(－DI-＋DI)/(－DI+＋DI)*100,M); 125 | ADXR:(ADX+REF(ADX,M))/2; 126 | """ 127 | tr = pd.Series(np.vstack([df.high - df.low, (df.high - df.close.shift()).abs(), (df.low - df.close.shift()).abs()]).max(axis=0)) 128 | trz = tr.rolling(n).sum() 129 | _m = pd.DataFrame() 130 | _m['hd'] = df.high - df.high.shift() 131 | _m['ld'] = df.low.shift() - df.low 132 | _m['mp'] = _m.apply(lambda x: x.hd if x.hd > 0 and x.hd > x.ld else 0, axis=1) 133 | _m['mm'] = _m.apply(lambda x: x.ld if x.ld > 0 and x.hd < x.ld else 0, axis=1) 134 | _m['dmp'] = _m.mp.rolling(n).sum() 135 | _m['dmm'] = _m.mm.rolling(n).sum() 136 | _dmi = pd.DataFrame() 137 | _dmi['date'] = df.date 138 | _dmi['pdi'] = _m.dmp * 100 / trz 139 | _dmi['mdi'] = _m.dmm * 100 / trz 140 | _dmi['adx'] = _ma((_dmi.mdi - _dmi.pdi).abs() / (_dmi.mdi + _dmi.pdi) * 100, m) 141 | _dmi['adxr'] = (_dmi.adx + _dmi.adx.shift(m)) / 2 142 | return _dmi 143 | 144 | 145 | def macd(df, n=12, m=26, k=9): 146 | """ 147 | 平滑异同移动平均线(Moving Average Convergence Divergence) 148 | 今日EMA（N）=2/（N+1）×今日收盘价+(N-1)/（N+1）×昨日EMA（N） 149 | DIFF= EMA（N1）- EMA（N2） 150 | DEA(DIF,M)= 2/(M+1)×DIF +[1-2/(M+1)]×DEA(REF(DIF,1),M) 151 | MACD（BAR）=2×（DIF-DEA） 152 | return: 153 | osc: MACD bar / OSC 差值柱形图 DIFF - DEM 154 | diff: 差离值 155 | dea: 讯号线 156 | """ 157 | _macd = pd.DataFrame() 158 | _macd['date'] = df['date'] 159 | _macd['diff'] = _ema(df.close, n) - _ema(df.close, m) 160 | _macd['dea'] = _ema(_macd['diff'], k) 161 | _macd['macd'] = _macd['diff'] - _macd['dea'] 162 | del _macd['diff'], _macd['dea'] 163 | return _macd 164 | 165 | 166 | def kdj(df, n=9): 167 | """ 168 | 随机指标KDJ 169 | N日RSV=（第N日收盘价-N日内最低价）/（N日内最高价-N日内最低价）×100% 170 | 当日K值=2/3前1日K值+1/3×当日RSV=SMA（RSV,M1） 171 | 当日D值=2/3前1日D值+1/3×当日K= SMA（K,M2） 172 | 当日J值=3 ×当日K值-2×当日D值 173 | """ 174 | _kdj = pd.DataFrame() 175 | _kdj['date'] = df['date'] 176 | rsv = (df.close - df.low.rolling(n).min()) / (df.high.rolling(n).max() - df.low.rolling(n).min()) * 100 177 | _kdj['k'] = sma(rsv, 3) 178 | _kdj['d'] = sma(_kdj.k, 3) 179 | _kdj['j'] = 3 * _kdj.k - 2 * _kdj.d 180 | return _kdj 181 | 182 | 183 | def rsi(df, n=6): 184 | """ 185 | 相对强弱指标（Relative Strength Index，简称RSI n=6、12和24 186 | LC= REF(CLOSE,1) 187 | RSI=SMA(MAX(CLOSE-LC,0),N,1)/SMA(ABS(CLOSE-LC),N1,1)×100 188 | SMA（C,N,M）=M/N×今日收盘价+(N-M)/N×昨日SMA（N） 189 | """ 190 | # pd.set_option('display.max_rows', 1000) 191 | _rsi = pd.DataFrame() 192 | _rsi['date'] = df['date'] 193 | px = df.close - df.close.shift() 194 | px[px < 0] = 0 195 | _rsi['rsi'] = sma(px, n) / sma((df['close'] - df['close'].shift()).abs(), n) * 100 196 | # def tmax(x): 197 | # if x < 0: 198 | # x = 0 199 | # return x 200 | # _rsi['rsi'] = sma((df['close'] - df['close'].shift(1)).apply(tmax), n) / sma((df['close'] - df['close'].shift(1)).abs(), n) * 100 201 | return _rsi 202 | 203 | 204 | def stochrsi(df, n=12): 205 | """ 206 | 随机强弱指标（Relative Strength Index，简称RSI n=6、12和24 207 | Stochastic Relative Strength Index（Stoch RSI） 208 | https://zhuanlan.zhihu.com/p/55070261 209 | """ 210 | _stochrsi = pd.DataFrame() 211 | _stochrsi['date'] = df.date 212 | px = df.close - df.close.shift() 213 | px[px < 0] = 0 214 | rsi = sma(px, n) / sma((df.close - df.close.shift()).abs(), n) * 100 215 | lrsi = rsi.rolling(n).min() 216 | _stochrsi['stochrsi'] = (rsi - lrsi) / (rsi.rolling(n).max() - lrsi) * 100 217 | _stochrsi['fastk'] = sma(_stochrsi.stochrsi, 3) # or _ma ?????? 218 | _stochrsi['fastd'] = sma(_stochrsi.fastk, 3) # or _ma ?????？ 219 | return _stochrsi 220 | 221 | 222 | def vrsi(df, n=6): 223 | """ 224 | 量相对强弱指标 225 | VRSI=SMA（最大值（成交量-REF（成交量，1），0），N,1）/SMA（ABS（（成交量-REF（成交量，1），N，1）×100% 226 | """ 227 | _vrsi = pd.DataFrame() 228 | _vrsi['date'] = df['date'] 229 | px = df['volume'] - df['volume'].shift(1) 230 | px[px < 0] = 0 231 | _vrsi['vrsi'] = sma(px, n) / sma((df['volume'] - df['volume'].shift(1)).abs(), n) * 100 232 | return _vrsi 233 | 234 | 235 | def boll(df, n=26, k=2): 236 | """ 237 | 布林线指标BOLL boll(26,2) MID=MA(N) 238 | 标准差MD=根号[∑（CLOSE-MA(CLOSE，N)）^2/N] 239 | UPPER=MID＋k×MD 240 | LOWER=MID－k×MD 241 | """ 242 | _boll = pd.DataFrame() 243 | _boll['date'] = df.date 244 | _boll['boll_mid'] = _ma(df.close, n) 245 | _mdd = _md(df.close, n) 246 | _boll['boll_up'] = _boll.boll_mid + k * _mdd 247 | _boll['boll_low'] = _boll.boll_mid - k * _mdd 248 | return _boll 249 | 250 | 251 | def bbiboll(df, n=10, k=3): 252 | """ 253 | BBI多空布林线 bbiboll(10,3) 254 | BBI={MA(3)+ MA(6)+ MA(12)+ MA(24)}/4 255 | 标准差MD=根号[∑（BBI-MA(BBI，N)）^2/N] 256 | UPR= BBI＋k×MD 257 | DWN= BBI－k×MD 258 | """ 259 | # pd.set_option('display.max_rows', 1000) 260 | _bbiboll = pd.DataFrame() 261 | _bbiboll['date'] = df.date 262 | _bbiboll['bbi'] = (_ma(df.close, 3) + _ma(df.close, 6) + _ma(df.close, 12) + _ma(df.close, 24)) / 4 263 | _bbiboll['md'] = _md(_bbiboll.bbi, n) 264 | _bbiboll['upr'] = _bbiboll.bbi + k * _bbiboll.md 265 | _bbiboll['dwn'] = _bbiboll.bbi - k * _bbiboll.md 266 | del _bbiboll['md'] 267 | return _bbiboll 268 | 269 | 270 | def wr(df, n=14): 271 | """ 272 | 威廉指标 w&r 273 | WR=[最高值（最高价，N）-收盘价]/[最高值（最高价，N）-最低值（最低价，N）]×100% 274 | """ 275 | 276 | _wr = pd.DataFrame() 277 | _wr['date'] = df['date'] 278 | higest = df.high.rolling(n).max() 279 | _wr['w_r'] = (higest - df.close) / (higest - df.low.rolling(n).min()) * 100 280 | return _wr 281 | 282 | 283 | def bias(df, n=12): 284 | """ 285 | 乖离率 bias 286 | bias=[(当日收盘价-12日平均价)/12日平均价]×100% 287 | """ 288 | _bias = pd.DataFrame() 289 | _bias['date'] = df.date 290 | _mav = df.close.rolling(n).mean() 291 | _bias['bias'] = (np.true_divide((df.close - _mav), _mav)) * 100 292 | # _bias["bias"] = np.vectorize(lambda x: round(Decimal(x), 4))(BIAS) 293 | return _bias 294 | 295 | 296 | def asi(df, n=5): 297 | """ 298 | 振动升降指标(累计震动升降因子) ASI # 同花顺给出的公式不完整就不贴出来了 299 | """ 300 | _asi = pd.DataFrame() 301 | _asi['date'] = df.date 302 | _m = pd.DataFrame() 303 | _m['a'] = (df.high - df.close.shift()).abs() 304 | _m['b'] = (df.low - df.close.shift()).abs() 305 | _m['c'] = (df.high - df.low.shift()).abs() 306 | _m['d'] = (df.close.shift() - df.open.shift()).abs() 307 | _m['r'] = _m.apply(lambda x: x.a + 0.5 * x.b + 0.25 * x.d if max(x.a, x.b, x.c) == x.a else ( 308 | x.b + 0.5 * x.a + 0.25 * x.d if max(x.a, x.b, x.c) == x.b else x.c + 0.25 * x.d 309 | ), axis=1) 310 | _m['x'] = df.close - df.close.shift() + 0.5 * (df.close - df.open) + df.close.shift() - df.open.shift() 311 | _m['k'] = np.maximum(_m.a, _m.b) 312 | _asi['si'] = 16 * (_m.x / _m.r) * _m.k 313 | _asi["asi"] = _ma(_asi.si, n) 314 | return _asi 315 | 316 | 317 | def vr_rate(df, n=26): 318 | """ 319 | 成交量变异率 vr or vr_rate 320 | VR=（AVS+1/2CVS）/（BVS+1/2CVS）×100 321 | 其中： 322 | AVS：表示N日内股价上涨成交量之和 323 | BVS：表示N日内股价下跌成交量之和 324 | CVS：表示N日内股价不涨不跌成交量之和 325 | """ 326 | _vr = pd.DataFrame() 327 | _vr['date'] = df['date'] 328 | _m = pd.DataFrame() 329 | _m['volume'] = df.volume 330 | _m['cs'] = df.close - df.close.shift(1) 331 | _m['avs'] = _m.apply(lambda x: x.volume if x.cs > 0 else 0, axis=1) 332 | _m['bvs'] = _m.apply(lambda x: x.volume if x.cs < 0 else 0, axis=1) 333 | _m['cvs'] = _m.apply(lambda x: x.volume if x.cs == 0 else 0, axis=1) 334 | _vr['vr_rate'] = (_m.avs.rolling(n).sum() + 1 / 2 * _m.cvs.rolling(n).sum() 335 | ) / (_m.bvs.rolling(n).sum() + 1 / 2 * _m.cvs.rolling(n).sum()) * 100 336 | return _vr 337 | 338 | 339 | def vr(df, n=5): 340 | """ 341 | 开市后平均每分钟的成交量与过去5个交易日平均每分钟成交量之比 342 | 量比:=V/REF(MA(V,5),1); 343 | 涨幅:=(C-REF(C,1))/REF(C,1)*100; 344 | 1)量比大于1.8，涨幅小于2%，现价涨幅在0—2%之间，在盘中选股的 345 | 选股:量比>1.8 AND 涨幅>0 AND 涨幅<2; 346 | """ 347 | _vr = pd.DataFrame() 348 | _vr['date'] = df.date 349 | _vr['vr'] = df.volume / _ma(df.volume, n).shift(1) 350 | _vr['rr'] = (df.close - df.close.shift(1)) / df.close.shift(1) * 100 351 | return _vr 352 | 353 | 354 | def arbr(df, n=26): 355 | """ 356 | 人气意愿指标 arbr(26) 357 | N日AR=N日内（H－O）之和除以N日内（O－L）之和 358 | 其中，H为当日最高价，L为当日最低价，O为当日开盘价，N为设定的时间参数，一般原始参数日设定为26日 359 | N日BR=N日内（H－CY）之和除以N日内（CY－L）之和 360 | 其中，H为当日最高价，L为当日最低价，CY为前一交易日的收盘价，N为设定的时间参数，一般原始参数日设定为26日。 361 | """ 362 | _arbr = pd.DataFrame() 363 | _arbr['date'] = df.date 364 | _arbr['ar'] = (df.high - df.open).rolling(n).sum() / (df.open - df.low).rolling(n).sum() * 100 365 | _arbr['br'] = (df.high - df.close.shift(1)).rolling(n).sum() / (df.close.shift() - df.low).rolling(n).sum() * 100 366 | return _arbr 367 | 368 | 369 | def dpo(df, n=20, m=6): 370 | """ 371 | 区间震荡线指标 dpo(20,6) 372 | DPO=CLOSE-MA（CLOSE, N/2+1） 373 | MADPO=MA（DPO,M） 374 | """ 375 | _dpo = pd.DataFrame() 376 | _dpo['date'] = df['date'] 377 | _dpo['dpo'] = df.close - _ma(df.close, int(n / 2 + 1)) 378 | _dpo['dopma'] = _ma(_dpo.dpo, m) 379 | return _dpo 380 | 381 | 382 | def tema(df, n=20): 383 | """ 384 | h 一般取20或60 385 | TEMA三重指数移动平均线是帕特里克马洛于1994年开发的一个更平滑更快速的移动均线 386 | """ 387 | _tema = pd.DataFrame() 388 | _tema['date'] = df.date 389 | a1 = _ema(df.close, n) 390 | a2 = _ema(a1, n) 391 | a3 = _ema(a2, n) 392 | _tema['tema'] = (3 * a1 - 3 * a2 + a3) / _ma(df.close, n) 393 | return _tema 394 | 395 | 396 | def trix(df, n=12, m=20): 397 | """ 398 | 三重指数平滑平均 TRIX(12) 399 | TR= EMA(EMA(EMA(CLOSE,N),N),N)，即进行三次平滑处理 400 | TRIX=(TR-昨日TR)/ 昨日TR×100 401 | TRMA=MA（TRIX，M） 402 | """ 403 | _trix = pd.DataFrame() 404 | _trix['date'] = df.date 405 | tr = _ema(_ema(_ema(df.close, n), n), n) 406 | _trix['trix'] = (tr - tr.shift()) / tr.shift() * 100 407 | _trix['trma'] = _ma(_trix.trix, m) 408 | return _trix 409 | 410 | 411 | def bbi(df): 412 | """ 413 | 多空指数 BBI(3,6,12,24) 414 | BBI=（3日均价+6日均价+12日均价+24日均价）/4 415 | """ 416 | _bbi = pd.DataFrame() 417 | _bbi['date'] = df['date'] 418 | _bbi['bbi'] = (_ma(df.close, 3) + _ma(df.close, 6) + _ma(df.close, 12) + _ma(df.close, 24)) / 4 419 | return _bbi 420 | 421 | 422 | def mtm(df, n=6, m=5): 423 | """ 424 | 动力指标 MTM(6,5) 425 | MTM（N日）=C-REF(C,N)式中，C=当日的收盘价，REF(C,N)=N日前的收盘价；N日是只计算交易日期，剔除掉节假日。 426 | MTMMA（MTM，N1）= MA（MTM，N1） 427 | N表示间隔天数，N1表示天数 428 | """ 429 | _mtm = pd.DataFrame() 430 | _mtm['date'] = df.date 431 | _mtm['mtm'] = df.close - df.close.shift(n) 432 | _mtm['mtmma'] = _ma(_mtm.mtm, m) 433 | return _mtm 434 | 435 | 436 | def obv(df): 437 | """ 438 | 能量潮 On Balance Volume 439 | 多空比率净额= [（收盘价－最低价）－（最高价-收盘价）] ÷（ 最高价－最低价）×V # 同花顺貌似用的下面公式 440 | 主公式：当日OBV=前一日OBV+今日成交量 441 | 1.基期OBV值为0，即该股上市的第一天，OBV值为0 442 | 2.若当日收盘价＞上日收盘价，则当日OBV=前一日OBV＋今日成交量 443 | 3.若当日收盘价＜上日收盘价，则当日OBV=前一日OBV－今日成交量 444 | 4.若当日收盘价＝上日收盘价，则当日OBV=前一日OBV 445 | """ 446 | _obv = pd.DataFrame() 447 | _obv["date"] = df['date'] 448 | # tmp = np.true_divide(((df.close - df.low) - (df.high - df.close)), (df.high - df.low)) 449 | # _obv['obvv'] = tmp * df.volume 450 | # _obv["obv"] = _obv.obvv.expanding(1).sum() / 100 451 | _m = pd.DataFrame() 452 | _m['date'] = df.date 453 | _m['cs'] = df.close - df.close.shift() 454 | _m['v'] = df.volume 455 | _m['vv'] = _m.apply(lambda x: x.v if x.cs > 0 else (-x.v if x.cs < 0 else 0), axis=1) 456 | _obv['obv'] = _m.vv.expanding(1).sum() 457 | return _obv 458 | 459 | 460 | def cci(df, n=14): 461 | """ 462 | 顺势指标 463 | TYP:=(HIGH+LOW+CLOSE)/3 464 | CCI:=(TYP-MA(TYP,N))/(0.015×AVEDEV(TYP,N)) 465 | """ 466 | _cci = pd.DataFrame() 467 | _cci["date"] = df['date'] 468 | typ = (df.high + df.low + df.close) / 3 469 | _cci['cci'] = ((typ - typ.rolling(n).mean()) / 470 | (0.015 * typ.rolling(min_periods=1, center=False, window=n).apply( 471 | lambda x: np.fabs(x - x.mean()).mean()))) 472 | return _cci 473 | 474 | 475 | def priceosc(df, n=12, m=26): 476 | """ 477 | 价格振动指数 478 | PRICEOSC=(MA(C,12)-MA(C,26))/MA(C,12) * 100 479 | """ 480 | _c = pd.DataFrame() 481 | _c['date'] = df['date'] 482 | man = _ma(df.close, n) 483 | _c['osc'] = (man - _ma(df.close, m)) / man * 100 484 | return _c 485 | 486 | 487 | def sma(a, n, m=1): 488 | """ 489 | 平滑移动指标 Smooth Moving Average 490 | """ 491 | ''' # 方法一，此方法有缺陷 492 | _sma = [] 493 | for index, value in enumerate(a): 494 | if index == 0 or pd.isna(value) or np.isnan(value): 495 | tsma = 0 496 | else: 497 | # Y=(M*X+(N-M)*Y')/N 498 | tsma = (m * value + (n - m) * tsma) / n 499 | _sma.append(tsma) 500 | return pd.Series(_sma) 501 | ''' 502 | ''' # 方法二 503 | 504 | results = np.nan_to_num(a).copy() 505 | # FIXME this is very slow 506 | for i in range(1, len(a)): 507 | results[i] = (m * results[i] + (n - m) * results[i - 1]) / n 508 | # results[i] = ((n - 1) * results[i - 1] + results[i]) / n 509 | # return results 510 | ''' 511 | # b = np.nan_to_num(a).copy() 512 | # return ((n - m) * a.shift(1) + m * a) / n 513 | 514 | a = a.fillna(0) 515 | b = a.ewm(min_periods=0, ignore_na=False, adjust=False, alpha=m/n).mean() 516 | return b 517 | 518 | 519 | def dbcd(df, n=5, m=16, t=76): 520 | """ 521 | 异同离差乖离率 dbcd(5,16,76) 522 | BIAS=(C-MA(C,N))/MA(C,N) 523 | DIF=(BIAS-REF(BIAS,M)) 524 | DBCD=SMA(DIF,T,1) =（1-1/T）×SMA(REF(DIF,1),T,1)+ 1/T×DIF 525 | MM=MA(DBCD,5) 526 | """ 527 | _dbcd = pd.DataFrame() 528 | _dbcd['date'] = df.date 529 | man = _ma(df.close, n) 530 | _bias = (df.close - man) / man 531 | _dif = _bias - _bias.shift(m) 532 | _dbcd['dbcd'] = sma(_dif, t) 533 | _dbcd['mm'] = _ma(_dbcd.dbcd, n) 534 | return _dbcd 535 | 536 | 537 | def roc(df, n=12, m=6): 538 | """ 539 | 变动速率 roc(12,6) 540 | ROC=(今日收盘价-N日前的收盘价)/ N日前的收盘价×100% 541 | ROCMA=MA（ROC，M） 542 | ROC:(CLOSE-REF(CLOSE,N))/REF(CLOSE,N)×100 543 | ROCMA:MA(ROC,M) 544 | """ 545 | _roc = pd.DataFrame() 546 | _roc['date'] = df['date'] 547 | _roc['roc'] = (df.close - df.close.shift(n))/df.close.shift(n) * 100 548 | _roc['rocma'] = _ma(_roc.roc, m) 549 | return _roc 550 | 551 | 552 | def vroc(df, n=12): 553 | """ 554 | 量变动速率 555 | VROC=(当日成交量-N日前的成交量)/ N日前的成交量×100% 556 | """ 557 | _vroc = pd.DataFrame() 558 | _vroc['date'] = df['date'] 559 | _vroc['vroc'] = (df.volume - df.volume.shift(n)) / df.volume.shift(n) * 100 560 | return _vroc 561 | 562 | 563 | def cr(df, n=26): 564 | """ 能量指标 565 | CR=∑（H-PM）/∑（PM-L）×100 566 | PM:上一交易日中价（(最高、最低、收盘价的均值) 567 | H：当天最高价 568 | L：当天最低价 569 | """ 570 | _cr = pd.DataFrame() 571 | _cr['date'] = df.date 572 | # pm = ((df['high'] + df['low'] + df['close']) / 3).shift(1) 573 | pm = (df[['high', 'low', 'close']]).mean(axis=1).shift(1) 574 | _cr['cr'] = (df.high - pm).rolling(n).sum()/(pm - df.low).rolling(n).sum() * 100 575 | return _cr 576 | 577 | 578 | def psy(df, n=12): 579 | """ 580 | 心理指标 PSY(12) 581 | PSY=N日内上涨天数/N×100 582 | PSY:COUNT(CLOSE>REF(CLOSE,1),N)/N×100 583 | MAPSY=PSY的M日简单移动平均 584 | """ 585 | _psy = pd.DataFrame() 586 | _psy['date'] = df.date 587 | p = df.close - df.close.shift() 588 | p[p <= 0] = np.nan 589 | _psy['psy'] = p.rolling(n).count() / n * 100 590 | return _psy 591 | 592 | 593 | def wad(df, n=30): 594 | """ 595 | 威廉聚散指标 WAD(30) 596 | TRL=昨日收盘价与今日最低价中价格最低者；TRH=昨日收盘价与今日最高价中价格最高者 597 | 如果今日的收盘价>昨日的收盘价，则今日的A/D=今日的收盘价－今日的TRL 598 | 如果今日的收盘价<昨日的收盘价，则今日的A/D=今日的收盘价－今日的TRH 599 | 如果今日的收盘价=昨日的收盘价，则今日的A/D=0 600 | WAD=今日的A/D+昨日的WAD；MAWAD=WAD的M日简单移动平均 601 | """ 602 | def dmd(x): 603 | if x.c > 0: 604 | y = x.close - x.trl 605 | elif x.c < 0: 606 | y = x.close - x.trh 607 | else: 608 | y = 0 609 | return y 610 | 611 | _wad = pd.DataFrame() 612 | _wad['date'] = df['date'] 613 | _ad = pd.DataFrame() 614 | _ad['trl'] = np.minimum(df.low, df.close.shift(1)) 615 | _ad['trh'] = np.maximum(df.high, df.close.shift(1)) 616 | _ad['c'] = df.close - df.close.shift() 617 | _ad['close'] = df.close 618 | _ad['ad'] = _ad.apply(dmd, axis=1) 619 | _wad['wad'] = _ad.ad.expanding(1).sum() 620 | _wad['mawad'] = _ma(_wad.wad, n) 621 | return _wad 622 | 623 | 624 | def mfi(df, n=14): 625 | """ 626 | 资金流向指标 mfi(14) 627 | MF＝TYP×成交量；TYP:当日中价（(最高、最低、收盘价的均值) 628 | 如果当日TYP>昨日TYP，则将当日的MF值视为当日PMF值。而当日NMF值＝0 629 | 如果当日TYP<=昨日TYP，则将当日的MF值视为当日NMF值。而当日PMF值=0 630 | MR=∑PMF/∑NMF 631 | MFI＝100-（100÷(1＋MR)） 632 | """ 633 | _mfi = pd.DataFrame() 634 | _mfi['date'] = df.date 635 | _m = pd.DataFrame() 636 | _m['typ'] = df[['high', 'low', 'close']].mean(axis=1) 637 | _m['mf'] = _m.typ * df.volume 638 | _m['typ_shift'] = _m.typ - _m.typ.shift(1) 639 | _m['pmf'] = _m.apply(lambda x: x.mf if x.typ_shift > 0 else 0, axis=1) 640 | _m['nmf'] = _m.apply(lambda x: x.mf if x.typ_shift <= 0 else 0, axis=1) 641 | # _mfi['mfi'] = 100 - (100 / (1 + _m.pmf.rolling(n).sum() / _m.nmf.rolling(n).sum())) 642 | _m['mr'] = _m.pmf.rolling(n).sum() / _m.nmf.rolling(n).sum() 643 | _mfi['mfi'] = 100 * _m.mr / (1 + _m.mr) # 同花顺自己给出的公式和实际用的公式不一样，真操蛋，浪费两个小时时间 644 | return _mfi 645 | 646 | 647 | def pvt(df): 648 | """ 649 | pvt 量价趋势指标 pvt 650 | 如果设x=(今日收盘价—昨日收盘价)/昨日收盘价×当日成交量， 651 | 那么当日PVT指标值则为从第一个交易日起每日X值的累加。 652 | """ 653 | _pvt = pd.DataFrame() 654 | _pvt['date'] = df.date 655 | 656 | x = (df.close - df.close.shift(1)) / df.close.shift(1) * df.volume 657 | _pvt['pvt'] = x.expanding(1).sum() 658 | return _pvt 659 | 660 | 661 | def wvad(df, n=24, m=6): 662 | """ # 算法是对的，同花顺计算wvad用的n=6 663 | 威廉变异离散量 wvad(24,6) 664 | WVAD=N1日的∑ {(当日收盘价－当日开盘价)/(当日最高价－当日最低价)×成交量} 665 | MAWVAD=MA（WVAD，N2） 666 | """ 667 | _wvad = pd.DataFrame() 668 | _wvad['date'] = df.date 669 | # _wvad['wvad'] = (np.true_divide((df.close - df.open), (df.high - df.low)) * df.volume).rolling(n).sum() 670 | _wvad['wvad'] = (np.true_divide((df.close - df.open), (df.high - df.low)) * df.volume).rolling(n).sum() 671 | _wvad['mawvad'] = _ma(_wvad.wvad, m) 672 | return _wvad 673 | 674 | 675 | def cdp(df): 676 | """ 677 | 逆势操作 cdp 678 | CDP=(最高价+最低价+收盘价)/3 # 同花顺实际用的(H+L+2*c)/4 679 | AH=CDP+(前日最高价-前日最低价) 680 | NH=CDP×2-最低价 681 | NL=CDP×2-最高价 682 | AL=CDP-(前日最高价-前日最低价) 683 | """ 684 | _cdp = pd.DataFrame() 685 | _cdp['date'] = df.date 686 | # _cdp['cdp'] = (df.high + df.low + df.close * 2).shift(1) / 4 687 | _cdp['cdp'] = df[['high', 'low', 'close', 'close']].shift().mean(axis=1) 688 | _cdp['ah'] = _cdp.cdp + (df.high.shift(1) - df.low.shift()) 689 | _cdp['al'] = _cdp.cdp - (df.high.shift(1) - df.low.shift()) 690 | _cdp['nh'] = _cdp.cdp * 2 - df.low.shift(1) 691 | _cdp['nl'] = _cdp.cdp * 2 - df.high.shift(1) 692 | return _cdp 693 | 694 | 695 | def env(df, n=14): 696 | """ 697 | ENV指标 ENV(14) 698 | Upper=MA(CLOSE，N)×1.06 699 | LOWER= MA(CLOSE，N)×0.94 700 | """ 701 | _env = pd.DataFrame() 702 | _env['date'] = df.date 703 | _env['env_up'] = df.close.rolling(n).mean() * 1.06 704 | _env['env_low'] = df.close.rolling(n).mean() * 0.94 705 | return _env 706 | 707 | 708 | def mike(df, n=12): 709 | """ 710 | 麦克指标 mike(12) 711 | 初始价（TYP）=（当日最高价＋当日最低价＋当日收盘价）/3 712 | HV=N日内区间最高价 713 | LV=N日内区间最低价 714 | 初级压力线（WR）=TYP×2-LV 715 | 中级压力线（MR）=TYP+HV-LV 716 | 强力压力线（SR）=2×HV-LV 717 | 初级支撑线（WS）=TYP×2-HV 718 | 中级支撑线（MS）=TYP-HV+LV 719 | 强力支撑线（SS）=2×LV-HV 720 | """ 721 | _mike = pd.DataFrame() 722 | _mike['date'] = df.date 723 | typ = df[['high', 'low', 'close']].mean(axis=1) 724 | hv = df.high.rolling(n).max() 725 | lv = df.low.rolling(n).min() 726 | _mike['wr'] = typ * 2 - lv 727 | _mike['mr'] = typ + hv - lv 728 | _mike['sr'] = 2 * hv - lv 729 | _mike['ws'] = typ * 2 - hv 730 | _mike['ms'] = typ - hv + lv 731 | _mike['ss'] = 2 * lv - hv 732 | return _mike 733 | 734 | 735 | def vma(df, n=5): 736 | """ 737 | 量简单移动平均 VMA(5) VMA=MA(volume,N) 738 | VOLUME表示成交量；N表示天数 739 | """ 740 | _vma = pd.DataFrame() 741 | _vma['date'] = df.date 742 | _vma['vma'] = _ma(df.volume, n) 743 | return _vma 744 | 745 | 746 | def vmacd(df, qn=12, sn=26, m=9): 747 | """ 748 | 量指数平滑异同平均 vmacd(12,26,9) 749 | 今日EMA（N）=2/（N+1）×今日成交量+(N-1)/（N+1）×昨日EMA（N） 750 | DIFF= EMA（N1）- EMA（N2） 751 | DEA(DIF,M)= 2/(M+1)×DIF +[1-2/(M+1)]×DEA(REF(DIF,1),M) 752 | MACD（BAR）=2×（DIF-DEA） 753 | """ 754 | _vmacd = pd.DataFrame() 755 | _vmacd['date'] = df.date 756 | _vmacd['diff'] = _ema(df.volume, qn) - _ema(df.volume, sn) 757 | _vmacd['dea'] = _ema(_vmacd['diff'], m) # TODO: 不能用_vmacd.diff, 不知道为什么 758 | _vmacd['vmacd'] = (_vmacd['diff'] - _vmacd['dea']) 759 | del _vmacd['diff'], _vmacd['dea'] 760 | return _vmacd 761 | 762 | 763 | def vosc(df, n=12, m=26): 764 | """ 765 | 成交量震荡 vosc(12,26) 766 | VOSC=（MA（VOLUME,SHORT）- MA（VOLUME,LONG））/MA（VOLUME,SHORT）×100 767 | """ 768 | _c = pd.DataFrame() 769 | _c['date'] = df['date'] 770 | _c['vosc'] = (_ma(df.volume, n) - _ma(df.volume, m)) / _ma(df.volume, n) * 100 771 | return _c 772 | 773 | 774 | def tapi(df, n=6): 775 | """ 776 | 加权指数成交值 tapi(6) 777 | TAPI=每日成交总值/当日加权指数=a/PI；A表示每日的成交金额，PI表示当天的股价指数即指收盘价 778 | """ 779 | _tapi = pd.DataFrame() 780 | _tapi['date'] = df.date 781 | _tapi['tapi'] = df.amount / df.close 782 | _tapi['matapi'] = _ma(_tapi.tapi, n) 783 | return _tapi 784 | 785 | 786 | def vstd(df, n=10): 787 | """ 788 | 成交量标准差 vstd(10) 789 | VSTD=STD（Volume,N）=[∑（Volume-MA(Volume，N)）^2/N]^0.5 790 | """ 791 | _vstd = pd.DataFrame() 792 | _vstd['date'] = df.date 793 | _vstd['vstd'] = df.volume.rolling(n).std(ddof=1) 794 | return _vstd 795 | 796 | 797 | def adtm(df, n=23, m=8): 798 | """ 799 | 动态买卖气指标 adtm(23,8) 800 | 如果开盘价≤昨日开盘价，DTM=0 801 | 如果开盘价＞昨日开盘价，DTM=(最高价-开盘价)和(开盘价-昨日开盘价)的较大值 802 | 如果开盘价≥昨日开盘价，DBM=0 803 | 如果开盘价＜昨日开盘价，DBM=(开盘价-最低价) 804 | STM=DTM在N日内的和 805 | SBM=DBM在N日内的和 806 | 如果STM > SBM,ADTM=(STM-SBM)/STM 807 | 如果STM < SBM , ADTM = (STM-SBM)/SBM 808 | 如果STM = SBM,ADTM=0 809 | ADTMMA=MA(ADTM,M) 810 | """ 811 | _adtm = pd.DataFrame() 812 | _adtm['date'] = df.date 813 | _m = pd.DataFrame() 814 | _m['cc'] = df.open - df.open.shift(1) 815 | _m['ho'] = df.high - df.open 816 | _m['ol'] = df.open - df.low 817 | _m['dtm'] = _m.apply(lambda x: max(x.ho, x.cc) if x.cc > 0 else 0, axis=1) 818 | _m['dbm'] = _m.apply(lambda x: x.ol if x.cc < 0 else 0, axis=1) 819 | _m['stm'] = _m.dtm.rolling(n).sum() 820 | _m['sbm'] = _m.dbm.rolling(n).sum() 821 | _m['ss'] = _m.stm - _m.sbm 822 | _adtm['adtm'] = _m.apply(lambda x: x.ss / x.stm if x.ss > 0 else (x.ss / x.sbm if x.ss < 0 else 0), axis=1) 823 | _adtm['adtmma'] = _ma(_adtm.adtm, m) 824 | return _adtm 825 | 826 | 827 | def mi(df, n=12): 828 | """ 829 | 动量指标 mi(12) 830 | A=CLOSE-REF(CLOSE,N) 831 | MI=SMA(A,N,1) 832 | """ 833 | _mi = pd.DataFrame() 834 | _mi['date'] = df.date 835 | _mi['mi'] = sma(df.close - df.close.shift(n), n) 836 | return _mi 837 | 838 | 839 | def micd(df, n=3, m=10, k=20): 840 | """ 841 | 异同离差动力指数 micd(3,10,20) 842 | MI=CLOSE-ref(CLOSE,1)AMI=SMA(MI,N1,1) 843 | DIF=MA(ref(AMI,1),N2)-MA(ref(AMI,1),N3) 844 | MICD=SMA(DIF,10,1) 845 | """ 846 | _micd = pd.DataFrame() 847 | _micd['date'] = df.date 848 | mi = df.close - df.close.shift(1) 849 | ami = sma(mi, n) 850 | dif = _ma(ami.shift(1), m) - _ma(ami.shift(1), k) 851 | _micd['micd'] = sma(dif, m) 852 | return _micd 853 | 854 | 855 | def rc(df, n=50): 856 | """ 857 | 变化率指数 rc(50) 858 | RC=收盘价/REF（收盘价，N）×100 859 | ARC=EMA（REF（RC，1），N，1） 860 | """ 861 | _rc = pd.DataFrame() 862 | _rc['date'] = df.date 863 | _rc['rc'] = df.close / df.close.shift(n) * 100 864 | _rc['arc'] = sma(_rc.rc.shift(), n) 865 | return _rc 866 | 867 | 868 | def rccd(df, n=59, m=21, k=28): 869 | """ # TODO: 计算结果错误和同花顺不同，检查不出来为什么 870 | 异同离差变化率指数 rate of change convergence divergence rccd(59,21,28) 871 | RC=收盘价/REF（收盘价，N）×100% 872 | ARC=EMA(REF(RC,1),N,1) 873 | DIF=MA(ref(ARC,1),N1)-MA MA(ref(ARC,1),N2) 874 | RCCD=SMA(DIF,N,1) 875 | """ 876 | _rccd = pd.DataFrame() 877 | _rccd['date'] = df.date 878 | rc = df.close / df.close.shift(n) * 100 879 | arc = sma(rc.shift(), n) 880 | dif = _ma(arc.shift(), m) - _ma(arc.shift(), k) 881 | _rccd['rccd'] = sma(dif, n) 882 | return _rccd 883 | 884 | 885 | def srmi(df, n=9): 886 | """ 887 | SRMIMI修正指标 srmi(9) 888 | 如果收盘价>N日前的收盘价，SRMI就等于（收盘价-N日前的收盘价）/收盘价 889 | 如果收盘价 0 else (x.cs/x.cp if x.cs < 0 else 0), axis=1) 899 | return _srmi 900 | 901 | 902 | # def dptb(df, n=7): 903 | # """ 904 | # 大盘同步指标 dptb(7) 905 | # DPTB=（统计N天中个股收盘价>开盘价，且指数收盘价>开盘价的天数或者个股收盘价<开盘价，且指数收盘价<开盘价）/N 906 | # """ 907 | # ind = ts.get_k_data("sh000001", start=df.date.iloc[0], end=df.date.iloc[-1]) 908 | # sd = df.copy() 909 | # sd.set_index('date', inplace=True) # 可能出现停盘等情况，所以将date设为index 910 | # ind.set_index('date', inplace=True) 911 | # _dptb = pd.DataFrame(index=df.date) 912 | # q = ind.close - ind.open 913 | # _dptb['p'] = sd.close - sd.open 914 | # _dptb['q'] = q 915 | # _dptb['m'] = _dptb.apply(lambda x: 1 if (x.p > 0 and x.q > 0) or (x.p < 0 and x.q < 0) else np.nan, axis=1) 916 | # _dptb['jdrs'] = _dptb.m.rolling(n).count() / n 917 | # _dptb.drop(columns=['p', 'q', 'm'], inplace=True) 918 | # _dptb.reset_index(inplace=True) 919 | # return _dptb 920 | 921 | 922 | # def jdqs(df, n=20): 923 | # """ 924 | # 阶段强势指标 jdqs(20) 925 | # JDQS=（统计N天中个股收盘价>开盘价，且指数收盘价<开盘价的天数）/（统计N天中指数收盘价<开盘价的天数） 926 | # """ 927 | # ind = ts.get_k_data("sh000001", start=df.date.iloc[0], end=df.date.iloc[-1]) 928 | # sd = df.copy() 929 | # sd.set_index('date', inplace=True) # 可能出现停盘等情况，所以将date设为index 930 | # ind.set_index('date', inplace=True) 931 | # _jdrs = pd.DataFrame(index=df.date) 932 | # q = ind.close - ind.open 933 | # _jdrs['p'] = sd.close - sd.open 934 | # _jdrs['q'] = q 935 | # _jdrs['m'] = _jdrs.apply(lambda x: 1 if (x.p > 0 and x.q < 0) else np.nan, axis=1) 936 | # q[q > 0] = np.nan 937 | # _jdrs['t'] = q 938 | # _jdrs['jdrs'] = _jdrs.m.rolling(n).count() / _jdrs.t.rolling(n).count() 939 | # _jdrs.drop(columns=['p', 'q', 'm', 't'], inplace=True) 940 | # _jdrs.reset_index(inplace=True) 941 | # return _jdrs 942 | 943 | 944 | # def jdrs(df, n=20): 945 | # """ 946 | # 阶段弱势指标 jdrs(20) 947 | # JDRS=（统计N天中个股收盘价<开盘价，且指数收盘价>开盘价的天数）/（统计N天中指数收盘价>开盘价的天数） 948 | # """ 949 | # ind = ts.get_k_data("sh000001", start=df.date.iloc[0], end=df.date.iloc[-1]) 950 | # sd = df.copy() 951 | # sd.set_index('date', inplace=True) 952 | # ind.set_index('date', inplace=True) 953 | # _jdrs = pd.DataFrame(index=df.date) 954 | # q = ind.close - ind.open 955 | # _jdrs['p'] = sd.close - sd.open 956 | # _jdrs['q'] = q 957 | # _jdrs['m'] = _jdrs.apply(lambda x: 1 if (x.p < 0 and x.q > 0) else np.nan, axis=1) 958 | # q[q < 0] = np.nan 959 | # _jdrs['t'] = q 960 | # _jdrs['jdrs'] = _jdrs.m.rolling(n).count() / _jdrs.t.rolling(n).count() 961 | # _jdrs.drop(columns=['p', 'q', 'm', 't'], inplace=True) 962 | # _jdrs.reset_index(inplace=True) 963 | # return _jdrs 964 | 965 | 966 | def zdzb(df, n=125, m=5, k=20): 967 | """ 968 | 筑底指标 zdzb(125,5,20) 969 | A=（统计N1日内收盘价>=前收盘价的天数）/（统计N1日内收盘价<前收盘价的天数） 970 | B=MA（A,N2） 971 | D=MA（A，N3） 972 | """ 973 | _zdzb = pd.DataFrame() 974 | _zdzb['date'] = df.date 975 | p = df.close - df.close.shift(1) 976 | q = p.copy() 977 | p[p < 0] = np.nan 978 | q[q >= 0] = np.nan 979 | _zdzb['zdzb_a'] = p.rolling(n).count() / q.rolling(n).count() 980 | _zdzb['zdzb_b'] = _zdzb.zdzb_a.rolling(m).mean() 981 | _zdzb['zdzb_d'] = _zdzb.zdzb_a.rolling(k).mean() 982 | return _zdzb 983 | 984 | 985 | def atr(df, n=14): 986 | """ 987 | 真实波幅 atr(14) 988 | TR:MAX(MAX((HIGH-LOW),ABS(REF(CLOSE,1)-HIGH)),ABS(REF(CLOSE,1)-LOW)) 989 | ATR:MA(TR,N) 990 | """ 991 | _atr = pd.DataFrame() 992 | _atr['date'] = df.date 993 | # _atr['tr'] = np.maximum(df.high - df.low, (df.close.shift(1) - df.low).abs()) 994 | # _atr['tr'] = np.maximum.reduce([df.high - df.low, (df.close.shift(1) - df.high).abs(), (df.close.shift(1) - df.low).abs()]) 995 | _atr['tr'] = np.vstack([df.high - df.low, (df.close.shift(1) - df.high).abs(), (df.close.shift(1) - df.low).abs()]).max(axis=0) 996 | _atr['atr'] = _atr.tr.rolling(n).mean() 997 | return _atr 998 | 999 | 1000 | def mass(df, n=9, m=25): 1001 | """ 1002 | 梅丝线 mass(9,25) 1003 | AHL=MA(（H-L）,N1) 1004 | BHL= MA（AHL，N1） 1005 | MASS=SUM（AHL/BHL，N2） 1006 | H：表示最高价；L：表示最低价 1007 | """ 1008 | _mass = pd.DataFrame() 1009 | _mass['date'] = df.date 1010 | ahl = _ma((df.high - df.low), n) 1011 | bhl = _ma(ahl, n) 1012 | _mass['mass'] = (ahl / bhl).rolling(m).sum() 1013 | return _mass 1014 | 1015 | 1016 | def vhf(df, n=28): 1017 | """ 1018 | 纵横指标 vhf(28) 1019 | VHF=（N日内最大收盘价与N日内最小收盘价之前的差）/（N日收盘价与前收盘价差的绝对值之和） 1020 | """ 1021 | _vhf = pd.DataFrame() 1022 | _vhf['date'] = df.date 1023 | _vhf['vhf'] = (df.close.rolling(n).max() - df.close.rolling(n).min()) / (df.close - df.close.shift(1)).abs().rolling(n).sum() 1024 | return _vhf 1025 | 1026 | 1027 | def cvlt(df, n=10): 1028 | """ 1029 | 佳庆离散指标 cvlt(10) 1030 | cvlt=（最高价与最低价的差的指数移动平均-前N日的最高价与最低价的差的指数移动平均）/前N日的最高价与最低价的差的指数移动平均 1031 | """ 1032 | _cvlt = pd.DataFrame() 1033 | _cvlt['date'] = df.date 1034 | p = _ema(df.high.shift(n) - df.low.shift(n), n) 1035 | _cvlt['cvlt'] = (_ema(df.high - df.low, n) - p) / p * 100 1036 | return _cvlt 1037 | 1038 | 1039 | def up_n(df): 1040 | """ 1041 | 连涨天数 up_n 连续上涨天数，当天收盘价大于开盘价即为上涨一天 # 同花顺实际结果用收盘价-前一天收盘价 1042 | """ 1043 | _up = pd.DataFrame() 1044 | _up['date'] = df.date 1045 | p = df.close - df.close.shift() 1046 | p[p > 0] = 1 1047 | p[p < 0] = 0 1048 | m = [] 1049 | for k, g in itertools.groupby(p): 1050 | t = 0 1051 | for i in g: 1052 | if k == 0: 1053 | m.append(0) 1054 | else: 1055 | t += 1 1056 | m.append(t) 1057 | # _up['p'] = p 1058 | _up['up'] = m 1059 | return _up 1060 | 1061 | 1062 | def down_n(df): 1063 | """ 1064 | 连跌天数 down_n 连续下跌天数，当天收盘价小于开盘价即为下跌一天 1065 | """ 1066 | _down = pd.DataFrame() 1067 | _down['date'] = df.date 1068 | p = df.close - df.close.shift() 1069 | p[p > 0] = 0 1070 | p[p < 0] = 1 1071 | m = [] 1072 | for k, g in itertools.groupby(p): 1073 | t = 0 1074 | for i in g: 1075 | if k == 0: 1076 | m.append(0) 1077 | else: 1078 | t += 1 1079 | m.append(t) 1080 | _down['down'] = m 1081 | return _down 1082 | 1083 | 1084 | def elder(df, n=20): 1085 | """ 1086 | 艾达透视因子 观察多控指标 1087 | A = H - EMA(C, N) 1088 | B = L - EMA(C, N) 1089 | Elder = (A-B)/C 1090 | """ 1091 | _elder = pd.DataFrame() 1092 | _elder['date'] = df.date 1093 | a = df.high - _ema(df.close, n) 1094 | b = df.low - _ema(df.close, n) 1095 | _elder['elder'] = (a - b) / df.close 1096 | return _elder 1097 | 1098 | 1099 | """ --------------------------------------- 趋势性因子 -------------------------------------------""" 1100 | 1101 | 1102 | def acd(df, n=6): 1103 | """ 1104 | 收集派发因子 n=6或20 累积/派发线（Accumulation/Distribution Line，A/D或AC） 1105 | ACD指标将市场分为两股收集(买入)及派发(估出)的力量 1106 | """ 1107 | _acd = pd.DataFrame() 1108 | _acd['date'] = df.date 1109 | _m = pd.DataFrame() 1110 | _m['dif'] = df.close - df.close.shift() 1111 | _m['close'] = df.close 1112 | _m['low'] = np.minimum(df.low, df.low.shift()) 1113 | _m['high'] = np.maximum(df.high, df.high.shift()) 1114 | _m['cd'] = _m.apply(lambda x: x.close - x.low if x.dif > 0 else (x.close - x.high if x.dif < 0 else 0), axis=1) 1115 | _acd['acd'] = _m.cd.rolling(n).sum() 1116 | return _acd 1117 | 1118 | 1119 | def cop(df, n=11, m=14, h=10): 1120 | """ 1121 | 估波指标(Coppock 1122 | Curve)又称“估波曲线”，通过计算月度价格的变化速率的加权平均值来测量市场的动量，属于长线指标。 1123 | 1124 | 估波指标由Edwin 1125 | Sedgwick 1126 | Coppock于1962年提出，主要用于判断牛市的到来。该指标只能产生买进讯号。依估波指标买进股票后，应另外寻求其他指标来辅助卖出讯号。 1127 | """ 1128 | _cop = pd.DataFrame() 1129 | _cop['date'] = df.date 1130 | rc = (df.close - df.close.shift(n)) / df.close.shift(n) * 100 + (df.close - df.close.shift(m)) / df.close.shift(m) * 100 1131 | _cop['cop'] = _ema(rc, h) 1132 | return _cop 1133 | 1134 | 1135 | def join_frame(d1, d2, column='date'): 1136 | # 将两个DataFrame 按照datetime合并 1137 | return d1.join(d2.set_index(column), on=column) 1138 | 1139 | 1140 | # if __name__ == "__main__": 1141 | # import tushare as ts 1142 | # data = ts.get_k_data("000063", start="2017-05-01") 1143 | # data = ts.get_k_data("002264", start="2017-05-01") 1144 | # 使用tushare数据api 1145 | # apikey = '' 1146 | # if os.path.exists('tushare_apikey.txt'): 1147 | # with open('tushare_apikey.txt', 'r') as f: 1148 | # apikey = f.read() 1149 | # pro = ts.pro_api(apikey) 1150 | # data = pro.daily(ts_code='000001.SZ', start_date='20211101', end_date='20211128') 1151 | # data.rename(columns={'trade_date': 'date'}, inplace=True) 1152 | # print(data) 1153 | # maf = ma(data, n=10) 1154 | # 将均线合并到data中 1155 | # print(join_frame(data, maf)) 1156 | 1157 | # data = pd.DataFrame({"close": [1,2,3,4,5,6,7,8,9,0]}) 1158 | # print(ma(data)) 1159 | # mdf = md(data) 1160 | # print(md(data, n=26)) 1161 | # print(join_frame(data, mdf)) 1162 | # emaf = ema(data) 1163 | # print(ema(data, 5)) 1164 | # print(join_frame(data, emaf)) 1165 | # print(dma(data)) 1166 | # print(dmi(data)) 1167 | # print(macd(data)) 1168 | # print(kdj(data)) 1169 | # print(vrsi(data, 6)) 1170 | # print(boll(data)) 1171 | # print(bbiboll(data)) 1172 | # print(wr(data)) 1173 | # print(bias(data)) 1174 | # print(rsi(data)) 1175 | # print(asi(data)) 1176 | # print(vr_rate(data)) 1177 | # print(vr(data)) 1178 | # print(arbr(data)) 1179 | # print(dpo(data)) 1180 | # print(trix(data)) 1181 | # print(bbi(data)) 1182 | # print(ts.top_list(date="2019-01-17")) 1183 | # print(mtm(data)) 1184 | # print(obv(data)) 1185 | # print(cci(data)) 1186 | # print(priceosc(data)) 1187 | # print(dbcd(data)) 1188 | # print(roc(data)) 1189 | # print(vroc(data)) 1190 | # print(cr(data)) 1191 | # print(psy(data)) 1192 | # print(wad(data)) 1193 | # print(mfi(data)) 1194 | # print(pvt(data)) 1195 | # print(wvad(data)) 1196 | # print(cdp(data)) 1197 | # print(env(data)) 1198 | # print(mike(data)) 1199 | # print(vr(data)) 1200 | # print(vma(data)) 1201 | # print(vmacd(data)) 1202 | # print(vosc(data)) 1203 | # print(tapi(data)) 1204 | # print(vstd(data)) 1205 | # print(adtm(data)) 1206 | # print(mi(data)) 1207 | # print(micd(data)) 1208 | # print(rc(data)) 1209 | # print(rccd(data)) 1210 | # print(srmi(data)) 1211 | # print(dptb(data)) 1212 | # print(jdqs(data)) 1213 | # pd.set_option('display.max_rows', 1000) 1214 | # print(jdrs(data)) 1215 | # print(join_frame(data, jdrs(data))) 1216 | # print(data) 1217 | # print(zdzb(data)) 1218 | # print(atr(data)) 1219 | # print(mass(data)) 1220 | # print(vhf(data)) 1221 | # print(cvlt(data)) 1222 | # print(up_n(data)) 1223 | # print(down_n(data)) 1224 | # --------------------------------- 以下函数没有同花顺验证,慎重使用 -------------------------------- 1225 | # print(elder(data)) 1226 | # print(acd(data)) 1227 | # print(cop(data)) 1228 | # print(tema(data, n=20)) 1229 | # print(stochrsi(data)) 1230 | # print(rsi(data)) 1231 | -------------------------------------------------------------------------------- /data/stocks/sh.000300.csv: -------------------------------------------------------------------------------- 1 | ,date,code,open,high,low,close,preclose,volume,amount,pctChg 2 | 0,2019-01-02,sh.000300,3017.0670,3018.7750,2958.4880,2969.5350,3010.6530,6866302208,76105572352.0000,-1.365800 3 | 1,2019-01-03,sh.000300,2963.0180,3000.4410,2953.2540,2964.8420,2969.5350,7086710272,76664799232.0000,-0.158000 4 | 2,2019-01-04,sh.000300,2940.1910,3036.8140,2935.8290,3035.8740,2964.8420,10331897088,107141038080.0000,2.395800 5 | 3,2019-01-07,sh.000300,3055.1530,3061.7490,3035.9120,3054.3030,3035.8740,10116435968,105703940096.0000,0.607000 6 | 4,2019-01-08,sh.000300,3049.8740,3055.5100,3038.5330,3047.7030,3054.3030,8617058560,79628881920.0000,-0.216100 7 | 5,2019-01-09,sh.000300,3065.2300,3117.9610,3062.8470,3078.4750,3047.7030,10736434944,117129678848.0000,1.009700 8 | 6,2019-01-10,sh.000300,3077.4810,3092.5410,3062.5720,3072.6860,3078.4750,8527175680,90206871552.0000,-0.188000 9 | 7,2019-01-11,sh.000300,3082.2610,3102.8330,3071.0260,3094.7780,3072.6860,7178642432,77420240896.0000,0.719000 10 | 8,2019-01-14,sh.000300,3092.4980,3097.4060,3064.3820,3067.7840,3094.7780,6612170240,68115969024.0000,-0.872200 11 | 9,2019-01-15,sh.000300,3071.5180,3129.3700,3064.6700,3127.9900,3067.7840,8877296128,99976892416.0000,1.962500 12 | 10,2019-01-16,sh.000300,3126.1340,3135.0110,3117.2920,3128.6510,3127.9900,8021434112,85781590016.0000,0.021100 13 | 11,2019-01-17,sh.000300,3132.6280,3141.7090,3110.0600,3111.4160,3128.6510,8017487104,82888208384.0000,-0.550900 14 | 12,2019-01-18,sh.000300,3124.5020,3171.6860,3120.3450,3168.1730,3111.4160,10396383488,106780659712.0000,1.824200 15 | 13,2019-01-21,sh.000300,3170.8770,3199.2130,3170.8770,3185.6360,3168.1730,9391502336,105536987136.0000,0.551200 16 | 14,2019-01-22,sh.000300,3181.9490,3181.9490,3135.3370,3143.3170,3185.6360,7299052800,78150877184.0000,-1.328400 17 | 15,2019-01-23,sh.000300,3136.0960,3157.6260,3131.8670,3141.0530,3143.3170,5987047936,62669651968.0000,-0.072000 18 | 16,2019-01-24,sh.000300,3146.6040,3165.6290,3125.3000,3158.7810,3141.0530,7293463552,80963878912.0000,0.564400 19 | 17,2019-01-25,sh.000300,3165.1810,3203.1740,3164.4080,3184.4690,3158.7810,8396772864,93207953408.0000,0.813200 20 | 18,2019-01-28,sh.000300,3203.1180,3228.8670,3174.7240,3183.7770,3184.4690,8262243072,97371496448.0000,-0.021700 21 | 19,2019-01-29,sh.000300,3177.2610,3200.9890,3148.2340,3193.9690,3183.7770,7765833472,85943910400.0000,0.320100 22 | 20,2019-01-30,sh.000300,3181.6550,3202.8960,3167.8930,3168.4820,3193.9690,6374651648,73005121536.0000,-0.798000 23 | 21,2019-01-31,sh.000300,3182.6980,3216.0670,3178.0100,3201.6330,3168.4820,8624783872,97509924864.0000,1.046300 24 | 22,2019-02-01,sh.000300,3225.7330,3247.4950,3207.7160,3247.3970,3201.6330,8201964032,94766411776.0000,1.429400 25 | 23,2019-02-11,sh.000300,3239.8070,3307.0630,3239.8070,3306.4720,3247.3970,9881843456,114752356352.0000,1.819100 26 | 24,2019-02-12,sh.000300,3306.3520,3335.3600,3298.6330,3330.3350,3306.4720,12223246336,122834100224.0000,0.721700 27 | 25,2019-02-13,sh.000300,3337.7790,3405.5770,3329.1430,3397.0270,3330.3350,16382833152,168866263040.0000,2.002600 28 | 26,2019-02-14,sh.000300,3392.8540,3415.3990,3381.9820,3402.1400,3397.0270,15112729088,138649288704.0000,0.150500 29 | 27,2019-02-15,sh.000300,3391.2420,3395.0290,3335.8700,3338.7040,3402.1400,14366860288,141555060736.0000,-1.864600 30 | 28,2019-02-18,sh.000300,3366.4230,3445.7440,3366.4230,3445.7440,3338.7040,18288609024,180449742848.0000,3.206000 31 | 29,2019-02-19,sh.000300,3454.7210,3480.7930,3419.0730,3439.6070,3445.7440,17908378624,185762746368.0000,-0.178100 32 | 30,2019-02-20,sh.000300,3449.9320,3463.7730,3421.5050,3451.9270,3439.6070,14739031040,145570263040.0000,0.358200 33 | 31,2019-02-21,sh.000300,3450.1410,3500.2510,3431.6310,3442.7050,3451.9270,18014328832,185197154304.0000,-0.267200 34 | 32,2019-02-22,sh.000300,3439.3780,3520.1180,3417.9780,3520.1180,3442.7050,18723797504,202869288960.0000,2.248600 35 | 33,2019-02-25,sh.000300,3575.8520,3729.4830,3574.6880,3729.4830,3520.1180,34255712256,376382394368.0000,5.947700 36 | 34,2019-02-26,sh.000300,3734.7050,3762.1120,3679.2700,3684.6930,3729.4830,35803869696,387632861184.0000,-1.201000 37 | 35,2019-02-27,sh.000300,3682.8110,3737.6310,3638.3330,3678.3920,3684.6930,28623600640,307306147840.0000,-0.171000 38 | 36,2019-02-28,sh.000300,3675.2640,3695.4640,3650.0530,3669.3700,3678.3920,18481021952,206010957824.0000,-0.245300 39 | 37,2019-03-01,sh.000300,3699.2150,3749.7140,3668.7650,3749.7140,3669.3700,20471100416,235620622336.0000,2.189600 40 | 38,2019-03-04,sh.000300,3783.0360,3886.5790,3764.7760,3794.1040,3749.7140,32349012736,376397283328.0000,1.183800 41 | 39,2019-03-05,sh.000300,3785.3760,3816.1620,3770.7850,3816.0130,3794.1040,23085316608,261464121344.0000,0.577400 42 | 40,2019-03-06,sh.000300,3822.8580,3855.1550,3787.5220,3848.0900,3816.0130,31807737088,331155615744.0000,0.840600 43 | 41,2019-03-07,sh.000300,3841.1450,3845.1820,3778.8000,3808.8490,3848.0900,32984654848,359339151360.0000,-1.019800 44 | 42,2019-03-08,sh.000300,3721.4010,3774.5430,3656.1910,3657.5790,3808.8490,31799007744,349296693248.0000,-3.971500 45 | 43,2019-03-11,sh.000300,3663.9770,3731.9090,3650.8120,3729.9540,3657.5790,23091286528,268718460928.0000,1.978800 46 | 44,2019-03-12,sh.000300,3758.3150,3804.7200,3723.6780,3755.3510,3729.9540,27492638208,325180620800.0000,0.680900 47 | 45,2019-03-13,sh.000300,3758.7130,3763.0920,3708.8140,3724.1940,3755.3510,23368224768,286171049984.0000,-0.829700 48 | 46,2019-03-14,sh.000300,3710.2930,3747.8190,3673.8500,3698.4850,3724.1940,19081723648,231820148736.0000,-0.690300 49 | 47,2019-03-15,sh.000300,3713.9360,3782.2360,3713.9360,3745.0050,3698.4850,17517454336,224194363392.0000,1.257800 50 | 48,2019-03-18,sh.000300,3753.6430,3851.7470,3733.6780,3851.7470,3745.0050,21692805888,276473450496.0000,2.850300 51 | 49,2019-03-19,sh.000300,3856.5880,3868.3350,3815.2180,3833.9620,3851.7470,18172710912,224096890880.0000,-0.461700 52 | 50,2019-03-20,sh.000300,3825.3350,3855.1460,3790.5110,3835.4390,3833.9620,17563208960,215406993408.0000,0.038500 53 | 51,2019-03-21,sh.000300,3840.2780,3864.0690,3823.4660,3836.8910,3835.4390,19631031040,231993446400.0000,0.037900 54 | 52,2019-03-22,sh.000300,3838.1250,3845.5800,3786.5910,3833.8010,3836.8910,17135699200,198518386688.0000,-0.080500 55 | 53,2019-03-25,sh.000300,3776.4420,3807.1790,3741.6060,3742.8250,3833.8010,19858632960,223396564992.0000,-2.373000 56 | 54,2019-03-26,sh.000300,3766.5280,3770.8430,3687.5470,3700.4380,3742.8250,17304657920,196547809280.0000,-1.132500 57 | 55,2019-03-27,sh.000300,3724.4050,3750.2640,3703.4500,3743.3870,3700.4380,13875845376,177507528704.0000,1.160600 58 | 56,2019-03-28,sh.000300,3731.0610,3755.7160,3710.0810,3728.3950,3743.3870,13327212800,172604391424.0000,-0.400500 59 | 57,2019-03-29,sh.000300,3739.7730,3876.3790,3739.7730,3872.3410,3728.3950,21563910400,283022299136.0000,3.860800 60 | 58,2019-04-01,sh.000300,3901.1690,3982.9090,3901.1690,3973.9280,3872.3410,27138270464,340799401984.0000,2.623400 61 | 59,2019-04-02,sh.000300,3986.3440,3988.0970,3956.5050,3971.2850,3973.9280,22600034048,281892057088.0000,-0.066500 62 | 60,2019-04-03,sh.000300,3947.8690,4026.4970,3939.7830,4022.1560,3971.2850,23191736832,284826497024.0000,1.281000 63 | 61,2019-04-04,sh.000300,4039.7410,4072.1130,4021.7050,4062.2300,4022.1560,26041093888,313539432448.0000,0.996300 64 | 62,2019-04-08,sh.000300,4097.1020,4122.6030,4017.4880,4057.2280,4062.2300,26750557696,324670451712.0000,-0.123100 65 | 63,2019-04-09,sh.000300,4063.8130,4084.2430,4038.2820,4075.4300,4057.2280,18295680768,243187912704.0000,0.448600 66 | 64,2019-04-10,sh.000300,4062.4420,4115.3780,4035.4740,4085.8470,4075.4300,19468761344,278282027008.0000,0.255600 67 | 65,2019-04-11,sh.000300,4083.4610,4097.1240,3990.2390,3997.5770,4085.8470,16904964096,236259155968.0000,-2.160400 68 | 66,2019-04-12,sh.000300,3986.7820,4006.9330,3961.4640,3988.6160,3997.5770,13586211840,178795937792.0000,-0.224200 69 | 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147,2019-08-09,sh.000300,3686.7490,3689.1770,3628.1160,3633.5290,3669.2930,8529558016,116355690496.0000,-0.974700 150 | 148,2019-08-12,sh.000300,3647.8660,3699.1040,3643.3160,3699.1040,3633.5290,8208305664,121129345024.0000,1.804700 151 | 149,2019-08-13,sh.000300,3675.5860,3686.4300,3658.0120,3665.7510,3699.1040,7650030336,103764320256.0000,-0.901700 152 | 150,2019-08-14,sh.000300,3712.0650,3717.5570,3680.6080,3682.4010,3665.7510,9120259072,128301670400.0000,0.454200 153 | 151,2019-08-15,sh.000300,3618.0090,3694.2980,3612.1100,3694.0000,3682.4010,8859883264,119241080832.0000,0.315000 154 | 152,2019-08-16,sh.000300,3697.9400,3738.0960,3687.0750,3710.5380,3694.0000,8831916800,132696911872.0000,0.447700 155 | 153,2019-08-19,sh.000300,3736.0340,3791.0940,3720.6890,3791.0940,3710.5380,13932701184,195448938496.0000,2.171000 156 | 154,2019-08-20,sh.000300,3786.0260,3799.8970,3778.7300,3787.7320,3791.0940,10860208896,149115342848.0000,-0.088700 157 | 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363,2020-07-03,sh.000300,4353.1937,4419.5955,4348.4535,4419.5955,4335.8445,29048553900,442335024266.1000,1.931596 366 | 364,2020-07-06,sh.000300,4465.8081,4678.1121,4465.8081,4670.0949,4419.5955,40582102500,597438217716.0000,5.667926 367 | 365,2020-07-07,sh.000300,4739.6104,4796.3155,4688.0574,4698.1264,4670.0949,40600855300,635254742706.0000,0.600234 368 | 366,2020-07-08,sh.000300,4692.8621,4803.3643,4670.6501,4774.0042,4698.1264,34807484700,532849825964.5000,1.615065 369 | 367,2020-07-09,sh.000300,4770.0769,4854.1201,4757.2943,4840.7712,4774.0042,35104689300,556718732098.9000,1.398553 370 | 368,2020-07-10,sh.000300,4796.0873,4821.3745,4734.4725,4753.1333,4840.7712,31694965100,510472055095.9000,-1.810412 371 | 369,2020-07-13,sh.000300,4742.3738,4878.0773,4741.8743,4852.9612,4753.1333,32164544600,541105098721.0000,2.100255 372 | 370,2020-07-14,sh.000300,4836.1655,4860.4404,4729.9258,4806.6902,4852.9612,29724198500,511211048184.9000,-0.953459 373 | 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387,2020-08-06,sh.000300,4779.2377,4791.8565,4691.1569,4762.7642,4777.1089,22785440600,404323404006.9000,-0.300280 390 | 388,2020-08-07,sh.000300,4742.2939,4749.0217,4637.8427,4707.9262,4762.7642,21351821600,397041212825.0000,-1.151390 391 | 389,2020-08-10,sh.000300,4681.7553,4757.6634,4646.2885,4724.8697,4707.9262,20378883200,371297560017.6000,0.359893 392 | 390,2020-08-11,sh.000300,4730.3748,4791.5337,4674.8642,4681.7837,4724.8697,20554651100,350828586241.1000,-0.911898 393 | 391,2020-08-12,sh.000300,4668.8899,4681.6206,4566.2736,4647.6446,4681.7837,20288723900,347298673202.0000,-0.729190 394 | 392,2020-08-13,sh.000300,4666.4901,4671.7689,4626.1033,4635.7126,4647.6446,15255009200,250945872578.9000,-0.256732 395 | 393,2020-08-14,sh.000300,4627.6100,4708.0709,4617.6320,4704.6288,4635.7126,14729292100,254343934256.2000,1.486637 396 | 394,2020-08-17,sh.000300,4727.1204,4838.0864,4721.3589,4815.2261,4704.6288,26800155600,416469154934.0000,2.350819 397 | 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411,2020-09-09,sh.000300,4634.2470,4645.1972,4558.0304,4584.5887,4694.3894,16227710700,244905191934.1000,-2.338977 414 | 412,2020-09-10,sh.000300,4632.0994,4647.7505,4572.2058,4581.9775,4584.5887,15065287800,226146354804.6000,-0.056956 415 | 413,2020-09-11,sh.000300,4572.2572,4630.8842,4563.1061,4627.2826,4581.9775,11530242400,198240927301.5000,0.988767 416 | 414,2020-09-14,sh.000300,4652.0539,4666.9862,4625.9877,4651.0507,4627.2826,10723517900,211653411810.9000,0.513651 417 | 415,2020-09-15,sh.000300,4649.4472,4691.4997,4630.5374,4688.4826,4651.0507,10191592500,194573684119.3000,0.804805 418 | 416,2020-09-16,sh.000300,4683.0060,4690.6815,4638.4586,4657.3584,4688.4826,10875364100,196120184850.9000,-0.663844 419 | 417,2020-09-17,sh.000300,4642.0542,4664.9491,4601.6390,4632.7142,4657.3584,10598363100,214146647269.6000,-0.529145 420 | 418,2020-09-18,sh.000300,4636.6422,4737.8039,4631.7045,4737.0887,4632.7142,16042647500,281885944229.0000,2.252988 421 | 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