每日股票监控器

对于非全职股民来说，很难日日盯盘，但又不想错过股市的赚钱机会，希望有个帮忙盯盘的小助手，因此开发个股票小助理

主要功能:

下载A股每日k线数据计算每只股票的M5上穿M10时间如果股票当前是M5上穿M10 并且距离M5下穿M10大于60天 则告诉我如果股票当前是M5上穿M10 并且在我设置的股票列表中，则告诉我如果被监控的股票价格在我设置的区间 则告诉我

股票源代码类似如下所示

'''每日20点 运行更新当天数据 从baostock功能列表:如果目录不存在 下载 最近数据 如果是新上市股票 则下载 最近 一年 数据计算每只股票的 最后一个M5下穿M10的日期 计算每只股票的 M5上穿M10 如果在监控列表则报警 如果被监控股票的价格在设置区间，则报警 '''import baostock as bsimport pandas as pdfrom datetime import datetime,timedeltafrom datetime import dateimport os import numpy as npfrom CorpApi import CorpApi,CORP_API_TYPEimport random import requests# 保存每个股票的数据 data_path = 'test-all'# M5下穿M10的日期列表file_path = r'm5-m10-last.csv'today_file_path = r'today_m5-m10-last.csv'nday = 60 #m5下穿m10 和m5上穿m10 间隔60天 monitor_code_list = ['601636']# 发送通知消息 def send_msg(message):return responsedef get_stocker_num(code):ret_data = {}try:url = '/list=%s' % code ret = requests.get(url)if ret.status_code == 200:data = str(ret.content,encoding='gb2312')for item in data.split('\n'):if len(item) > 10:k = item.split('=')[0].replace('var hq_str_','')shuzu = item.split(',')if k.startswith('s_'):ret_data[k] = str(shuzu[1])else:ret_data[k] = float(shuzu[3])return ret_dataexcept Exception as e:print(str(e))print_except(sys._getframe().f_code.co_name, sys.exc_info(), e)return None'''判断股票价格是否在设置的区间'''def start_monitor():# 判断今天是否是周末 dt = date.today() nw = dt.weekday()if nw == 5: # 周六 send_mssage_alert('monitor stock is running')returnelif nw == 6:return # 周一到周五 # 先判断指定股票是否达到特定值 try:list_keys = 's_sz399001,s_sh000001'data = {}data['sh601636'] = '14:30' # 旗滨集团data['sz002714'] = '45:100' # 牧原股份gplist = {}for k in data.keys():list_keys = list_keys + ',' + kgplist = get_stocker_num(list_keys)for k in data.keys():v = data[k]now_v = gplist[k]if now_v == 0:continuemin = float(v.split(':')[0])max = float(v.split(':')[1])if now_v < float(min) or now_v > max:message = '上证指数:' + gplist['s_sh000001'] + '\n' + '深证成指:' + gplist['s_sz399001']message = message + '\n\n股票:' + k + '\n当前价格:' + str(now_v) + '\n监控值:' + vmessage = message + '\n/%s.html' % str(k)send_mssage_alert(message)except Exception as e:print_except(sys._getframe().f_code.co_name, sys.exc_info(), e)'''下载 历史数据 '''def download_data(date,start_data,end_date):bs.login()# 获取指定日期的指数、股票数据stock_rs = bs.query_all_stock(date)stock_df = stock_rs.get_data()data_df = pd.DataFrame()for code in stock_df["code"]:#print("Downloading :" + code)if code.startswith('bj'):continue k_rs = bs.query_history_k_data_plus(code, "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",start_date=start_data, end_date=end_date,frequency="d", adjustflag="3")data_list = []while (k_rs.error_code == '0') & k_rs.next():# 获取一条记录，将记录合并在一起data_list.append(k_rs.get_row_data())result = pd.DataFrame(data_list, columns=k_rs.fields)result.to_csv("%s\\%s.csv" % (data_path,code), encoding="gbk", index=False)bs.logout()def download_one_stock_data(code,start,end):k_rs = bs.query_history_k_data_plus(code, "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",start_date=start, end_date=end,frequency="d", adjustflag="3")data_list = []while (k_rs.error_code == '0') & k_rs.next():# 获取一条记录，将记录合并在一起data_list.append(k_rs.get_row_data())result = pd.DataFrame(data_list, columns=k_rs.fields)return result# 更新数据 当前时间 到上次下载的日期 之间的数据 def update_data():# 今天日期today = datetime.now().strftime("%Y-%m-%d")#today = datetime.now().strftime("-12-24")print(today)# 判断是否已经存在文件bs.login()# 获取指定日期的指数、股票数据stock_rs = bs.query_all_stock(today)stock_df = stock_rs.get_data()# 没有数据则直接退出if stock_df.empty:return #print(type(stock_df))#print(stock_df)data_df = pd.DataFrame()for code in stock_df["code"]:try:save_path = "%s\\%s.csv" % (data_path,code)if not os.path.isfile(save_path):#print("it is new :" + code)start_day = (datetime.now() - timedelta(days=250)).strftime("%Y-%m-%d")new_data = download_one_stock_data(code,start_day,today)if not new_data.empty:new_data.to_csv(save_path, encoding="gbk", index=False) # 保存文件 else:old_data = pd.read_csv(save_path)# 读取文件最后一行 时间 last_date = old_data["date"].tail(1).values[0] begin_date = datetime.strptime(last_date, '%Y-%m-%d')end_date = datetime.strptime(today, '%Y-%m-%d')# 如果小于当前时间 则下载 if end_date > begin_date:begin_date += timedelta(days=1)new_data = download_one_stock_data(code,begin_date.strftime("%Y-%m-%d"),today)if not new_data.empty:# 读取原数据 old = pd.read_csv(save_path)if old.empty:return# 原数据 加上新数据new_data = pd.concat([old,new_data])new_data.to_csv(save_path, encoding="gbk", index=False) # 保存文件 except Exception as e:pass print(str(e)) bs.logout()'''生成md5 下穿m10数据表 '''def create_md5_md10():count = 0 result = []for filename in os.listdir(data_path):try:if filename.find('.') > -1:code = filename.split('.')[1]#过滤掉创业板 和科创板 # if code.startswith('300') or code.startswith('688'):#continuesave_path = os.path.join(data_path,filename)stock = pd.read_csv(save_path, encoding="gbk") #, index_col = 'date'if stock.empty:continue# 是否是ST# if stock.tail(1)['isST'].values[0] == 1:#continue# 市盈率判断 peTTM = stock.tail(1)['peTTM'].values[0]# if not (peTTM >= 5 and peTTM <= 20):# continue # 计算M5 M10 M20 M30 M60 #stock_data = stockstock['m5']=np.round(stock['close'].rolling(window=5,center=False).mean(),2)stock['m10']=np.round(stock['close'].rolling(window=10,center=False).mean(),2)stock['m20']=np.round(stock['close'].rolling(window=20,center=False).mean(),2)# M5 < M10 # if stock.tail(1)['m5'].values[0] < stock.tail(1)['m10'].values[0]:#continue# M5 向上交叉 M20stock['m5-20']=stock['m5']-stock['m20']stock['diff']=np.sign(stock['m5-20'])stock['signal'] = np.sign(stock['diff'] - stock['diff'].shift(1)) # if stock.tail(1)['signal'].values[0] != 1:#continue #M5 和M10交叉点 stock['m5-10']=stock['m5']-stock['m10']stock['diff_5_10']=np.sign(stock['m5-10'])stock['signal_5_10'] = np.sign(stock['diff_5_10'] - stock['diff_5_10'].shift(1)) # 与前一天相比 如果是正数 则向上交叉 trade = pd.concat([ pd.DataFrame({"code": stock.loc[stock["signal"] == 1, "code"],"date": stock.loc[stock["signal"] == 1, "date"],"price": stock.loc[stock["signal"] == 1, "close"],"operation": "Buy"}),pd.DataFrame({"code": stock.loc[stock["signal_5_10"] == -1, "code"],"date": stock.loc[stock["signal_5_10"] == -1, "date"],"price": stock.loc[stock["signal_5_10"] == -1, "close"],"operation": "Sell"}) ])#trade.sort_index(inplace=True)trade = trade.sort_values(by = 'date') #print(trade)# 取最后10行 反转skip_index = trade.tail(1).indexlast_date = ''trade = trade.tail(100).iloc[::-1]for index, row in trade.iterrows():if row['operation'] == 'Sell':last_date = row['date']breakif last_date == '':continue else: today = datetime.now().strftime("%Y-%m-%d")end_date = datetime.strptime(today, '%Y-%m-%d')begin_date = datetime.strptime(last_date, '%Y-%m-%d')item = {}item['code'] = (stock.tail(1)['code'].values[0]).upper()item['m5m10'] = last_dateresult.append(item)count = count + 1 except Exception as e:print(str(e))outfile = pd.DataFrame(result) # 今日数据 #outfile = outfile.sort_values(by = ['day','pe'],ascending=[0,0]) # 按板块聚合 按涨幅降序排列 #print(outfile)outfile.to_csv(file_path,encoding="gbk",index=False)'''删除股票代码前字母'''def delete_zimu_code(p):try:code = p['code'] if code.find('.') > -1:return code.split('.')[1]except Exception as e:return -1 return ''def buy_celue5_monitor(df_clear):# 挑选监控的列表 if df_clear.empty:return Falsemessage = ''for index, row in df_clear.iterrows():code = row['code']if True: # 判断行业是否已经购买过 c_data = row.to_dict()message = message + '\n'for key in c_data:if key == 'list_time':message = message + '\n上市时间:' + str(c_data[key]) + '|' + str(c_data['ssn']) # 上市几年if key == 'm5m10':message = message + '\nM5下穿M10:' + str(c_data[key]) + '|' + str(c_data['day']) # 下穿时间if key == 'plate_name':message = message + '\n行业:' + c_data[key]if key == 'code':message = message + '\n股票代码:' + c_data[key] if key == 'stock_name':message = message + '\n股票:' + c_data[key] if key == 'price':message = message + '\n价格:' + str(c_data[key]) if key == 'volume_ratio':message = message + '\n量比:' + str(c_data[key] )if key == 'cur_price_to_highest52_weeks_ratio': #print('it is here')message = message + '\n52周: %s %%' % str(100 + c_data[key] )if key == 'change_rate':message = message + '\n涨跌:' + str(c_data[key] )if key == 'return_on_equity_rate':message = message + '\nROE:' + str(c_data[key] ) if key == 'pe':message = message + '\nPE:' + str(c_data[key] ) if key == 'market_val':message = message + '\n市值:' + str(int(c_data[key] /100000000) ) + '亿' if len(message) > 0:#print(message)send_msg(message) return Truedef dingshi_send():try:filename = 'sz.002439.csv'save_path = os.path.join(data_path,filename)stock = pd.read_csv(save_path, encoding="gbk") #, index_col = 'date'if stock.empty:returnlast_date = stock.tail(1)['date'].values[0] message = '股票监控器运行正常:\n最后一条数据时间:' + str(last_date)#print(message)send_msg(message)except Exception as e:pass '''生成md5 上穿m10数据表 '''def today_md5_md10():count = 0 result = []for filename in os.listdir(data_path):try:if filename.find('.') > -1:code = filename.split('.')[1]# 过滤掉创业板 和科创板 if code.startswith('300') or code.startswith('688'):continuesave_path = os.path.join(data_path,filename)stock = pd.read_csv(save_path, encoding="gbk") #, index_col = 'date'if stock.empty:continue# 是否是STif stock.tail(1)['isST'].values[0] == 1:continue# 市盈率判断 peTTM = stock.tail(1)['peTTM'].values[0]# 跳过指数数据 if peTTM == 0:continue # if not (peTTM >= 5 and peTTM <= 20):# continue # 计算M5 M10 M20 M30 M60 #stock_data = stockstock['m5']=np.round(stock['close'].rolling(window=5,center=False).mean(),2)stock['m10']=np.round(stock['close'].rolling(window=10,center=False).mean(),2)stock['m20']=np.round(stock['close'].rolling(window=20,center=False).mean(),2)# M5 < M10 if stock.tail(1)['m5'].values[0] < stock.tail(1)['m10'].values[0]:continue# M5 向上交叉 M20# stock['m5-20']=stock['m5']-stock['m20']# stock['diff']=np.sign(stock['m5-20'])# stock['signal'] = np.sign(stock['diff'] - stock['diff'].shift(1)) #M5 和M10交叉点 stock['m5-10']=stock['m5']-stock['m10']stock['diff_5_10']=np.sign(stock['m5-10'])stock['signal_5_10'] = np.sign(stock['diff_5_10'] - stock['diff_5_10'].shift(1)) # 与前一天相比 如果是正数 则向上交叉 if stock.tail(1)['signal_5_10'].values[0] != 1: # M5 向上交叉 M10continue trade = pd.concat([ pd.DataFrame({"code": stock.loc[stock["signal_5_10"] == 1, "code"],"date": stock.loc[stock["signal_5_10"] == 1, "date"],"price": stock.loc[stock["signal_5_10"] == 1, "close"],"operation": "Buy"}),pd.DataFrame({"code": stock.loc[stock["signal_5_10"] == -1, "code"],"date": stock.loc[stock["signal_5_10"] == -1, "date"],"price": stock.loc[stock["signal_5_10"] == -1, "close"],"operation": "Sell"}) ])#trade = pd.concat([ pd.DataFrame({"code": stock.loc[stock["signal"] == 1, "code"],"date": stock.loc[stock["signal"] == 1, "date"],"price": stock.loc[stock["signal"] == 1, "close"],"operation": "Buy"}),# pd.DataFrame({"code": stock.loc[stock["signal_5_10"] == -1, "code"],"date": stock.loc[stock["signal_5_10"] == -1, "date"],"price": stock.loc[stock["signal_5_10"] == -1, "close"],"operation": "Sell"}) ])#trade.sort_index(inplace=True)trade = trade.sort_values(by = 'date') #print(trade)# 取最后10行 反转skip_index = trade.tail(1).indexlast_date = ''trade = trade.tail(100).iloc[::-1]for index, row in trade.iterrows():if row['operation'] == 'Sell':last_date = row['date']breakif last_date == '':continue else: today = datetime.now().strftime("%Y-%m-%d")end_date = datetime.strptime(today, '%Y-%m-%d')begin_date = datetime.strptime(last_date, '%Y-%m-%d')item = {}item['day'] = (end_date - begin_date).daysitem['pe'] = peTTMitem['price'] = stock.tail(1)['close'].values[0]item['code'] = (stock.tail(1)['code'].values[0]).upper()item['m5m10'] = last_date#print(item)result.append(item)# if (end_date - begin_date).days > 5:#print(last_date)#print((end_date - begin_date).days)#print(trade) count = count + 1 except Exception as e:print(str(e))outfile = pd.DataFrame(result) # 今日数据 outfile['no_zimu_code'] = outfile.apply(lambda x: delete_zimu_code(x), axis = 1) # 对每一行上市时间 统计年 df_clear = outfile[outfile['no_zimu_code'].isin(monitor_code_list)] # 监控设置的股票列表 buy_celue5_monitor(df_clear) #df_clear = outfile[outfile['no_zimu_code'].isin(monitor_code_list)]outfile = outfile.sort_values(by = ['day','pe'],ascending=[0,0]) # 按板块聚合 按涨幅降序排列 buy_celue5_monitor(outfile[outfile['day'] > nday]) # 两次间隔 大于 N天的 #print(outfile[(outfile['day'] > 10)])outfile.to_csv(today_file_path,encoding="gbk",index=False)def main():print('start')## 判断今天是否是周末 #dt = date.today() #nw = dt.weekday()#if nw == 6: # 周日# #send_msg('monitor stock is running')# dingshi_send()# return#elif nw == 5: #周六 # return ##判断路径是否存在 #if not os.path.exists(data_path):# os.mkdir(data_path)# today = datetime.now().strftime("%Y-%m-%d")# start_day = (datetime.now() - timedelta(days=365*10)).strftime("%Y-%m-%d")# download_data(today,start_day,today)#else: # update_data() # 每日更新数据 #create_md5_md10() # 生成md5数据表today_md5_md10() # 搜索m5上穿m10 start_monitor() #监控股票价格 是否在区间print('end')main()

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