记录
http://blog.sina.com.cn/s/blog_73b339390102yoio.html PE:市盈率 = 股价 / 每股盈利 PEG:(市盈率相对盈利增长比率/市盈增长比率) PEG=PE/(企业年盈利增长率*100) PB:市净率=股价 / 每股净资产 PS:市销率=股价 / 每股收入=总市值 / 销售收入 ROE:净资产收益率=报告期净利润/报告期末净资产 EPS:每股盈余=盈余 / 流通在外股数 beta值:(贝塔系数):每股收益=期末净利润 / 期末总股本
import math 年均投资收益率 = (pow(终值/本金, 1/年限) -1)*100
投资收益本息 = pow((1+预期年收益率),年限)*本金
投资目标年限 = math.log(终值/本金)/math.log(1+预期年收益率)
时间转换
import time a = '2020-03-06 19:18:00' a1 = time.strptime(a,'%Y-%m-%d %H:%M:%S') #格式化str为time格式 print(time.strftime('%Y%m%d',a1)) #格式化time格式为str print(time.asctime(time.localtime(time.time()))) #格式化当前时间为 Thu Apr 7 10:29:13 2016 print (time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) # 格式化成2016-03-20 11:45:39形式 print (time.strftime("%a %b %d %H:%M:%S %Y", time.localtime())) # 格式化成Sat Mar 28 22:24:24 2016形式 a = "Sat Mar 28 22:24:24 2016" print (time.mktime(time.strptime(a,"%a %b %d %H:%M:%S %Y"))) # 将格式字符串转换为时间戳 import calendar calendar.month(2016, 1) #输出2016年1月份的日历 import pandas as pd pd.to_datetime('2016-03-20').strftime('%Y%m%d') #pandas 格式化str输出 from datetime import datetime,timedelta datetime.today() # 返回当前时间时分秒都为0 now.isoweekday() # 返回的1-7代表周一--周日 now.weekday() # 返回的0-6代表周一--到周日
datetime.strptime('20150101', "%Y%m%d") # 格式化字符串成datetime格式 (pd.to_datetime('20200130')+timedelta(days=3)).strftime('%Y%m%d') #格式化后三天的日期 now = datetime.now()+timedelta(days=3) print(now.strftime('%Y-%m-%d')) #格式化当天后三天的日期
列表排序
import operator,json aa = [{"key": "780", "A": ["01", "03", "05", "07", "09"], "T": "1"}, {"key": "781", "A": ["01", "03", "05", "07", "09"], "T": "3"}, {"key": "782", "A": ["01", "03", "05", "07", "09"], "T": "9"}] print(json.dumps(aa,indent=2, ensure_ascii=False)) b = sorted(aa,key=operator.itemgetter('key')) # 列表或json数据排序 #虽说loads是转回json 但是像这样key是单引号不能直接转 需要先dumps data ="[{'a':1,'b':2,'c':3,'d':4,'e':5}]" json1 = json.dumps(data) print(json.loads(json1)) print(type(json1),json1) with open('222.txt','r') as f2: a = json.load(f2) json.dump(aa,open('111.txt','w'),indent=4) json.loads() #str转json json.load() #读取文本str格式转json json.dumps() #输出成字符串 json.dump() #将json写入文本 a = ''.join(str(i)+',' for i in df1['cod'].tolist())[:-1] #list转换str
[i,v for i,v in enumerate(list)]
a = ['e', 'a', 'u', 'o', 'i'] a.sort() #升序 正序 a.sort(reverse=True) # 降序 逆序 不能存变量 a.sort(key= lambda x : x[1]) # 根据第二个字母排序 默认根据第一个字母排序 sorted(a) # 可存变量 保留原list 可传参数 reverse = True 降序 , reverse = False 升序(默认) sorted([[6,7,8],[2,3,4],[1,2,3]], key=lambda x: x[2]) #多维列表 根据元素排序 sorted(lis,key=lambda x:cod.index(x[0])) #多维列表 根据单维列表进行指定排序 lis为多维 cod是单列表 [[k,v] for k,v in dict(new).items() if k not in dict(B1).keys()] #二维列表转化成dict,比较两个列表i[0]的差集 ['别墅' if '别墅' in i else '车位' if '车位' in i else '高层' for i in a] #列表推导示例 [[i[0],i[2]] for i in old for v in new if i[0] == v[0] and i[2] != '0'] d = {'lily':25, 'wangjun':22, 'John':25, 'Mary':19} sorted_keys = sorted(d) # 对字典而言,默认是对keys进行排序 print(sorted_keys) sorted_keys1 = sorted(d, key=lambda x : x[1]) print(d_new2) d_new = sorted(d.items(), key=lambda x: x[1], reverse=True) # 根据年龄排序,返回列表形式 print(d_new) d_new = dict(d_new) # 使用内置函数把嵌套列表转换成字典 print(d_new) sorted_values = sorted(d.values(), key=lambda x:x, reverse=False) # 排序值 print(sorted_values) 输出: ['John', 'Mary', 'lily', 'wangjun'] ['wangjun', 'Mary', 'lily', 'John'] [('lily', 25), ('John', 25), ('wangjun', 22), ('Mary', 19)] {'lily': 25, 'John': 25, 'wangjun': 22, 'Mary': 19} [19, 22, 25, 25] #互换dick的key和value d = {'lily':25, 'wangjun':22, 'John':25, 'Mary':19} d_new = {v:key for key,v in d.items()} print(d_new) 输出:{25: 'John', 22: 'wangjun', 19: 'Mary'}
编码转换
df.to_csv('abdata.csv', mode='a', encoding='utf_8_sig') # pandas导出csv 要指定编码 #python2 指定utf8 #coding:utf-8 import sys reload(sys) sys.setdefaultencoding("utf-8") f.write(unicode('%s-日期 成交:%s万 成交额:%s亿'%(i[0],i[1],i[2]),"utf-8")+ '\n') #py2写入中文也有毛病要加unicode
pandas操作
from sqlalchemy import create_engine from datetime import datetime,timedelta import numpy as np import pandas as pd import tushare as ts import matplotlib.pyplot as plt from matplotlib import colors from pylab import mpl #正常显示画图时出现的中文和符号 import time ts.set_token("123") pro = ts.pro_api() pd.set_option('display.unicode.ambiguous_as_wide', True) #设置中文列名对齐 pd.set_option('display.unicode.east_asian_width', True) #设置列名对齐 pd.set_option('display.max_rows',None) #显示所有行 pd.set_option('display.max_columns',None) #显示所有列 pd.set_option('expand_frame_repr', False) #设置不换行 pd.set_option('max_colwidth',100) #设置显示最大字符 np.set_printoptions(suppress=True) # 非科学计数法 mpl.rcParams['font.sans-serif']=['SimHei'] mpl.rcParams['axes.unicode_minus']=False pd.options.mode.chained_assignment = None %matplotlib inline #jupyter画图用
df['aa'].astype('float') #转换整列格式 df.reset_index(drop=True) #重置index索引 并删除原索引 dfs.drop_duplicates() #去除完全相同的行保留第一行 .loc['a1']#根据index获取行 .iloc[0].name #根据行号获取行的某个值 # loc和iloc 可以更换单行、单列、多行、多列的值 df1.loc[0,'age']=25 # 思路:先用loc找到要更改的值,再用赋值(=)的方法实现更换值 df1.iloc[0,2]=25 # iloc:用索引位置来查找 # at 、iat只能更换单个值 df1.at[0,'age']=25 # iat 用来取某个单值,参数只能用数字索引 df1.iat[0,2]=25 # at 用来取某个单值,参数只能用index和columns索引名称 #pivot()和unstack()实现行转列 dfcod = counts[['cod','key','日期1','日期2']].set_index(['key','日期1','日期2','cod']).unstack() df1 , df2 = df[['日期1','日期2','key']] , df.pivot('日期2','cod',v) #行转列 列转行参考 https://www.cnblogs.com/leohahah/p/9778304.html #新增一行 用append 但必须要得先创建DataFrame才行 df1=df1.append(new,ignore_index=True) # ignore_index=True,表示不按原来的索引,从0开始自动递增 #新增一列 tabsdetail['SH'] = sh.append([sh,sh,sh]).tolist() #sh是Series tabs.insert(0, '总金额', [m,m*2,m*3,m*4],allow_duplicates=True) #指定位置添加一列 np.array(df0[['name','key']]).tolist() #dataframe转化list dfdata = pd.DataFrame() dfdata = dfdata.append(df1,ignore_index=True) #pandas append必须存入变量 否则不生效 pd.DataFrame([[1,2,3],[1,2,3]],columns=['a','b','c'],index=df0.index) #创建dataframe df0 = pd.DataFrame({'id':[3,4,5,6,7,3,4,5], 'name':[10.54,11.11,12.80,10.05,11.21,10.98,11.12,10.55]}, index=('a1','a2','a3','a4','a5','a6','a7','a8')) df0.loc[df0['id'] == 3 ,'key'] = 1 df0.loc[df0['id'] == 5 ,'key'] = 0 # 进行布尔值判断 输出符合条件 df0['key'] = np.where(df0['id'] == 3 ,1,0) pd.concat([df0, df1], axis=1) #合并两个dataframe df.index=pd.to_datetime(df.date) # 将index 改为时间 df=df.sort_index() #排序index df['ret']=df.close/df.close.shift(1)-1 # .shift(1) 获取下一个 .shift(-1) 获取上一个 data.sort_values(by=['标记','时间'],ascending=[False,True]) #多列排序指定升降序 df['当天'].fillna(method='ffill',inplace=True) #根据一列nan值填充上一个不为nan的值 df['a'] = (df_new.ret+1.0).cumprod() #计算当前值并累计连乘 .cumsum()累积连加 df1['ret'].diff() # 比较上一值与当前值的差 [i for i in df["close"].rolling(k).mean()] # 移动窗口list的均值 df['c'].rolling(window=10, min_periods=1, center=False).mean() #Series中计算均值 #dataframe行转列 - 只能根据相同列名不同行名数据转置 适合matplotlib用 单index日期画图 比如多个日期 每个日期中需要转置的行名不得重复 df1 = df[['cod','盈亏','日期2']].pivot('日期2','cod','盈亏').rename_axis([None], axis=1) # pivot 指定列名 行名 数据 只能固定这三个参数 df1 = df1.rename_axis(None, axis=1).reset_index() # 取消第一个columns 将其拍平 df1.index=pd.to_datetime(df1.日期2) #dataframe行转列 - 整合统计用 可以根据多个指定的index 但是set_index必须是前面列表-1的列 不然会乱 前面列表剩下的一个元素就是数据其他为index dfcod = counts[['cod','key','盈亏','日期2','日期1']].set_index(['key','日期1','日期2','cod']).unstack() dfcod.columns = [s1 +'_'+ str(s2) for (s1,s2) in dfcod.columns.tolist()] # 将其拍平 # dfcod.reset_index(inplace=True) # 重置index 转成正常的dataframe dfcod.loc[['前10']] # 根据key分组显示index和数据 dfcod a1.index = a1.index.droplevel() #删除一个多索引的index-names # series 根据list 判断是否存在 df0[df0['id'].isin([3,4])] #根据list获取列表信息 df0[~df0['id'].isin([3,4])] #根据list获取列表信息 取反 # series 根据list 排序 df['words'] = df['words'].astype('category') #必须转换成这个格式 df['words'].cat.reorder_categories([1,2,3], inplace=True) # list长度相等用这个 df['words'].cat.set_categories([1,2,3], inplace=True) # list多 用这个 df['words'].cat.set_categories([1,2,3], inplace=True) # list少 用这个 df.sort_values('words', inplace=True) #pandas 读写mysql from sqlalchemy import create_engine mysq = create_engine('mysql+pymysql://root:mysql.123@localhost/abdata?charset=utf8') df.to_sql('coun',mysq,if_exists='append',index=False) # 追加数据 df.to_sql('counts',mysq,if_exists='replace',index=False) #删除并写入表 df = pd.read_sql_query('select * from cod1',mysq) # 查询mysql表 #pymysql读写mysql import pymysql conn = pymysql.connect('127.0.0.1', 'root', 'mysql.123', 'data',charset='utf8') cur = conn.cursor() sql1 = "SELECT * from (SELECT * from data1 ORDER BY id DESC LIMIT %s ) aa order by id" %sum cur.execute(sql1) c1 = cur.fetchall() #读取mysql conn.commit() #写入mysql cur.close() conn.close()
DataFrame样式设置
def show(v): col = 'black' if v > 0 else 'green' return 'color:%s'%col def background_gradient(s, m, M, cmap='PuBu', low=0, high=0.8): rng = M - m norm = colors.Normalize(m - (rng * low),M + (rng * high)) normed = norm(s.values) c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)] return ['" style="color: rgb(128, 0, 0);">' % color for color in c] def highlight_max(s,m): is_max = s == m return ['" style="color: rgb(128, 0, 0);">' if v else '' for v in is_max] tabs.style.applymap(show).background_gradient(cmap='Reds',axis = 1,low = 0,high = 1,subset = set1).\ apply(background_gradient,cmap='Purples',m=tabs[set2].min().min(),M=tabs[set2].max().max(),low=0,high=1,subset = set2).\ apply(highlight_max,m=tabs[set2].max().max()).background_gradient(cmap='Wistia',axis = 1,subset=['总金额']) accdeteil.style.applymap(show).background_gradient(cmap='Reds',axis = 1,low = 0,high = 1).\ background_gradient(cmap='Reds',axis = 1,low = 0,high = 1 ,subset=set2).\ background_gradient(cmap='Purples',axis = 1,low = 0,high = 1,subset = pd.IndexSlice['前10',:'9']).\ background_gradient(cmap='Purples',axis = 1,low = 0,high = 1,subset = pd.IndexSlice['前20',:'9']).\ background_gradient(cmap='Purples',axis = 1,low = 0,high = 1,subset = pd.IndexSlice['前05','1_':]).\ background_gradient(cmap='Purples',axis = 1,low = 0,high = 1,subset = pd.IndexSlice['前15','1_':]).\ background_gradient(cmap='GnBu',axis = 0,low = 0,high = 1 ,subset=['SH_']).\ apply(highlight_max,m=tabs[set2].max().max()) #可参考 https://blog.csdn.net/xiaodongxiexie/article/details/71202279 #颜色样式 https://matplotlib.org/tutorials/colors/colormaps.html
pandas作图
import matplotlib.pyplot as plt ax1 = df1[['策略净值','指数净值']].plot(figsize=(15,8)) #dataframe折线图 ax1 = ax1.axhline(y=1,ls=":",c="r"),ax1.legend(loc = 'upper right') #标记0线和指定图例位置 plt.title('策略简单回测%s'%x,size=15) plt.xlabel('') for i in range(len(df1)): if df1['当天仓位'][i]==0 and df1['当天仓位'].shift(-1)[i]==1: plt.annotate('买',xy=(df1.index[i],df1.策略净值[i]),arrowprops=dict(facecolor='r',shrink=0.05)) #标记买卖点 if df1['当天仓位'][i]==0 and df1['当天仓位'].shift(1)[i]==1: plt.annotate('卖',xy=(df1.index[i],df1.策略净值[i]),arrowprops=dict(facecolor='g',shrink=0.1)) bbox = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.9) #指定文字边框样式 t = f'累计收益率:策略{TA1}%,指数{TA2}%;\n年化收益率:策略{AR1}%,指数{AR2}%;'+\ f'\n最大回撤: 策略{MD1}%,指数{MD2}%;\n策略alpha: {round(alpha,2)},策略beta:{round(beta,2)}; \n夏普比率: {S}' plt.text(df1.index[0], df1['指数净值'].min(),text,size=13,bbox=bbox) #指定位置加文字框 ax=plt.gca() #设置图形样式 ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') plt.show()
爬虫
from bs4 import BeautifulSoup import requests headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36' } htm = requests.get(url=url,headers=headers,timeout=30,stream=False).text soup = BeautifulSoup(htm, 'html.parser') txt = soup.find_all('div', class_='lax-s') #txt = soup.find('div', class_='qi').children #etree方式获取 原文 https://mp.weixin.qq.com/s/c2Sg_LVTjOokePY2lxCGSA import requests import pandas as pd from pprint import pprint from lxml import etree import time import warnings warnings.filterwarnings("ignore") for i in range(1,15): print("正在爬取第" + str(i) + "页的数据") url = "https://search.51job.com/list/000000,000000,0000,00,9,99,%25E6%2595%25B0%25E6%258D%25AE,2,"+str(i)+'.html?' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36' } web = requests.get(url, headers=headers) web.encoding = "gbk" dom = etree.HTML(web.text) #print(etree.tostring(dom, encoding="utf-8", pretty_print=True).decode("utf-8")) #打印整个html 不能直接print # 1、岗位名称 job_name = dom.xpath('//div[@class="dw_table"]/div[@class="el"]//p/span/a[@target="_blank"]/@title') # 2、公司名称 company_name = dom.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t2"]/a[@target="_blank"]/@title') # 3、工作地点 address = dom.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t3"]/text()') # 4、工资:工资这一列有空值,为了保证数据框的一致性。采取以下方式进行数据的获取 salary_mid = dom.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t4"]') salary = [i.text for i in salary_mid] #这里None也占一个元素 保持长度一致 # 5、发布日期 release_time = dom.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t5"]/text()') #----------------------------------------------------------------------------------------------# # 下面获取二级网址的信息。为了获取二级网址的信息,首先需要获取二级网址的url # 6、获取二级网址url deep_url = dom.xpath('//div[@class="dw_table"]/div[@class="el"]//p/span/a[@target="_blank"]/@href') RandomAll = [] JobDescribe = [] CompanyType = [] CompanySize = [] Industry = [] for i in range(len(deep_url)): web_test = requests.get(deep_url[i], headers=headers) web_test.encoding = "gbk" dom_test = etree.HTML(web_test.text) # 7、爬取经验、学历信息,先合在一个字段里面,以后再做数据清洗。命名为random_all random_all = dom_test.xpath('//div[@class="tHeader tHjob"]//div[@class="cn"]/p[@class="msg ltype"]/text()') # 8、岗位描述性息 job_describe = dom_test.xpath('//div[@class="tBorderTop_box"]//div[@class="bmsg job_msg inbox"]/p/text()') # 9、公司类型 company_type = dom_test.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[1]/@title') # 10、公司规模(人数) company_size = dom_test.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[2]/@title') # 11、所属行业(公司) industry = dom_test.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[3]/@title') # 将上述信息保存到各自的列表中 RandomAll.append(random_all) JobDescribe.append(job_describe) CompanyType.append(company_type) CompanySize.append(company_size) Industry.append(industry) # 为了反爬,设置睡眠时间 time.sleep(1) # 由于我们需要爬取很多页,为了防止最后一次性保存所有数据出现的错误,因此,我们每获取一夜的数据,就进行一次数据存取。 df = pd.DataFrame() df["岗位名称"] = job_name df["公司名称"] = company_name df["工作地点"] = address df["工资"] = salary df["发布日期"] = release_time df["经验、学历"] = RandomAll df["公司类型"] = CompanyType df["公司规模"] = CompanySize df["所属行业"] = Industry df["岗位描述"] = JobDescribe # 这里在写出过程中,有可能会写入失败,为了解决这个问题,我们使用异常处理。 try: df.to_csv("job_info.csv", mode="a+", header=None, index=None, encoding="gbk") except: print("当页数据写入失败") time.sleep(1) print("完毕")
OCR图片识别
#需要安装 tesseract-ocr(需要环境变量) 、chi_sim.traineddata 、 pytesseract-0.2.4 from PIL import Image import pytesseract,os,re png = r'D:\123\111.png' pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract.exe' img = Image.open(png) tim = os.stat(png).st_mtime img1 = img.size aa = pytesseract.image_to_string(img, lang='chi_sim') print(img1,tim) print(aa)
webdriver自动化测试
#需要安装 chromedriver-v69 、ChromeSetup_64_69.exe from selenium import webdriver from selenium.webdriver.common.keys import Keys try: driver = webdriver.Chrome() driver.get("http://user/login") time.sleep(1) driver.find_element_by_id('username').send_keys('123123') driver.find_element_by_id('password').send_keys('123123') driver.find_element_by_id('login').click() time.sleep(2) driver.find_element_by_xpath('//*[@id="header"]/div[7]/div/div[1]/ul/li[4]/a').click() time.sleep(2) driver.find_elements_by_class_name('content')[2].click() time.sleep(2) s1 = driver.find_element_by_class_name('i1').text s2 = s1[3:6] s3 = driver.find_element_by_id('pre-kanjiang').text s4 = driver.find_element_by_xpath('//*[@id="money"]/strong').text s5 = driver.find_element_by_xpath('//*[@id="money"]/em').text print('key=', s2, 'time=', s3, s5 + '=', s4) fs.write('key=' + s2 + '\n' + 'time=' + s3 + '\n' + s5 + '=' + s4 + '\n') time.sleep(2) if int(s2) == int(s.get('key')): elements = driver.find_elements_by_class_name('code') if 'A' in s.keys(): data_values = s.get('A') for i in data_values: a_button_index = int(i) - 1 elements[a_button_index].click() print('a_button_index = ', a_button_index) fs.write('a_button_index = ' + str(a_button_index) + '\n') if 'B' in s.keys(): data_values = s.get('B') for j in data_values: b_button_index = int(j) + 9 elements[b_button_index].click() print('b_button_index = ', b_button_index) fs.write('b_button_index = ' + str(b_button_index) + '\n') if 'C' in s.keys(): data_values = s.get('C') for k in data_values: c_button_index = int(k) + 19 elements[c_button_index].click() print('c_button_index = ', c_button_index) fs.write('c_button_index = ' + str(c_button_index) + '\n') time.sleep(1) driver.find_elements_by_name('danwei')[1].click() driver.find_element_by_class_name('txt').clear() driver.find_element_by_class_name('txt').send_keys(int(s.get('T')) * 1) driver.find_element_by_class_name('tztj-hover').click() time.sleep(2) driver.find_element_by_class_name('tz-true-hover').click() time.sleep(2) driver.find_element_by_xpath("/html/body/div[2]/div[3]/div/button[1]").send_keys(Keys.ENTER) time.sleep(2) driver.quit() except Exception as e: print(e)
cs客户端自动化测试
import os,sys,time import pywinauto import pywinauto.clipboard import pywinauto.application import win32clipboard as wincb import win32con def winmax(): #窗口最大化 if main_window.get_show_state() != 3: main_window.maximize() main_window.set_focus() def winmin(): #窗口最小化 if main_window.GetShowState() != 2: main_window.Minimize() def closepopup(): #关闭弹窗 popup_hwnd = main_window.PopupWindow() if popup_hwnd: popup_window = app.window_(handle=popup_hwnd) popup_window.SetFocus() popup_window.Button.Click() return True return False def pos(): #获取持仓并刷新复制到剪切板 dialog_window.CCustomTabCtrl.ClickInput(coords=(30, 8)) #点击持仓 dialog_window.Button5.click() time.sleep(0.5) dialog_window.Button5.click() # time.sleep(0.2) # dialog_window.CVirtualGridCtrl.RightClick(coords=(100, 70)) # 右击持仓 # main_window.TypeKeys('C') #如果能复制了 就把这些打开 def copypos(): #获取剪切板信息 wincb.OpenClipboard() t = wincb.GetClipboardData(win32con.CF_TEXT) wincb.CloseClipboard() return t def copyposition(): #导出持仓并读取 dialog_window.CVirtualGridCtrl.RightClick(coords=(100, 70)) # 右击持仓 main_window.TypeKeys('S') time.sleep(0.1) closepopup() closepopup() with open('C:/Users/Administrator/Desktop/table.xls','r') as f: return [[i.split('\t')[1],i.split('\t')[3],i.split('\t')[4]] for i in f.readlines()[1:]] def order(x): #B是买 S是卖 开始下单 dialog_window.TypeKeys("{F6}") if x == 'B': for i in Blis: # dialog_window.window(title_re='重填').click() time.sleep(0.1) dialog_window.Edit1.set_focus() dialog_window.Edit1.set_edit_text(i[0]) dialog_window.Edit3.set_edit_text(i[1]) time.sleep(0.2) dialog_window.Button1.click() if x == 'S': for i in Slis: time.sleep(0.1) dialog_window.Edit4.set_focus() dialog_window.Edit4.set_edit_text(i[0]) dialog_window.Edit6.set_edit_text(i[1]) time.sleep(0.2) dialog_window.Button2.click() def cancel(x): #撤单 B:撤买 S:撤卖 all:全撤 dialog_window.CCustomTabCtrl.ClickInput(coords=(140, 8)) #点击委托 try: dialog_window.Button6.Click() time.sleep(0.1) dialog_window.Button6.Click() except Exception as e: pass if x == 'B': dialog_window.Button8.Click() if x == 'S': dialog_window.Button9.Click() if x == 'all': dialog_window.Button7.Click() time.sleep(0.1) closepopup() def BSlist(x): #返回买卖剩余量 B是买 S是卖 global Blis global Slis pos() #可以复制了就打开old # old = [[i.split('\t')[1],i.split('\t')[3],i.split('\t')[4]] for i in copypos().decode("gb2312").split('\r\n')[1:]] old = copyposition() new = [[i[0],'0'] for i in Slis if int(i[1]) > 0 ]+Blis if x == 'B': B1 = [[v[0],str(int(i[1])-int(v[1]))] for i in [i for i in new if i[1] != '0'] for v in old if i[0] == v[0]] B2 = [[k,v] for k,v in dict([i for i in new if i[1] != '0']).items() if k not in dict(B1).keys()] Blis = [i for i in B1 if i[1] != '0']+B2 return Blis if x == 'S': Slis = [[i[0],i[2]] for i in old for v in [i for i in new if i[1] == '0'] if i[0] == v[0] and i[2] != '0'] return Slis if __name__ == '__main__': files = [i for i in os.listdir('D:/abdata/csv/') if 'cod' in i] Blis = [] Slis = [] with open('D:/abdata/csv/'+sorted(files)[-1],'r',encoding='utf-8') as f: for i in f: i = i.strip().split(',') if i[4] == '0' and int(i[2]) >0:Blis.append([i[0],i[2]]) if i[4] == '1' and int(i[2]) >0:Slis.append([i[0],i[2]]) ''' order(x): # 需要传参 B是买 S是卖 cancel(x): # 撤单 B:撤买 S:撤卖 all:全撤 BSlist(x): # 返回买卖剩余量 B是买 S是卖 winmax(): # 窗口最大化 winmin(): # 窗口最小化 pos(): # 获取持仓并刷新复制到剪切板 copypos(): # 获取剪切板信息 closepopup(): #关闭弹窗 copyposition(): #导出持仓并读取 ''' title = '网上股票交易系统5.0' app = pywinauto.application.Application() app.connect(title=title) top_hwnd = pywinauto.findwindows.find_window(title=title) dialog_hwnd = pywinauto.findwindows.find_windows(top_level_only=False, class_name=u'#32770', parent=top_hwnd)[0] wanted_hwnds = pywinauto.findwindows.find_windows(top_level_only=False, parent=dialog_hwnd) main_window = app.window(handle=top_hwnd) dialog_window = app.window(handle=dialog_hwnd) winmax() #窗口最大 # pos() #获取复制持仓 # old = [[i.split('\t')[1],i.split('\t')[3]] for i in copypos().decode("gb2312").split('\r\n')[1:]] # new = [[i[0],'0'] for i in Slis if int(i[1]) > 0 ]+Blis B = 1 S = 1 while S > 0 : closepopup() time.sleep(0.5) # pos() #获取复制持仓 Slis = BSlist('S') S = len(Slis) if S > 0: closepopup() order('S') closepopup() time.sleep(2) cancel('all') time.sleep(2) while B > 0 : time.sleep(0.5) closepopup() # pos() #获取复制持仓 Blis = BSlist('B') B = len(Blis) if B > 0: closepopup() order('B') closepopup() time.sleep(2) cancel('all') time.sleep(2)
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