import tushare as ts import db import history import my_thread import time # 初始化ts pro = ts.pro_api(token='233302841e61938a0e1b77e74dd6fe86703e0a5ddece3544569c9017') t1 = time.time() # 排序参数，取第2位排序 def take_second(elem): return elem[1] # 排序参数，取第4位排序 def take_four(elem): return elem[3] # 排序参数，取第4位排序 def take_five(elem): return elem[4] # 判断今天是否交易日 def check_trade_day(): toDay = time.strftime('%Y%m%d', time.localtime()) h = int(time.strftime('%H', time.localtime())) m = int(time.strftime('%M', time.localtime())) cal = pro.query('trade_cal', start_date=toDay, end_date=toDay) for i in cal.index: if cal.loc[i][2] == 1: # 过滤9点半之前 if h <= 9 and m < 30: return False # 过滤11点半之后 if h == 11 and m > 30: return False return True else: return False # 获取正常运行的行业分类列表 def get_running_list(): list = pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date') mysql.save_cate_table(list) return list # 获取实时行情 def get_now_price(): # 实时行情API today = ts.get_today_all() # 保存到实时行情表 mysql.save_now_table(today) # 取出涨幅接近涨停的票 for i in today.index: d = today.loc[i] if d['changepercent'] > 9.6: # print(d['code'] + " " + d['name']) v = {'code': d['code'], 'name': d['name'], 'percent': d['changepercent']} maxData.append(v) # 给接近涨停的票加上[行业分类]参数 for i in range(len(maxData)): for j in category.index: if maxData[i]['code'] == category.loc[j]['symbol']: maxData[i]['cate'] = category.loc[j]['industry'] print(maxData) # 取出接近涨停票中的cate分类 def get_hot_cate(): # 取出接近涨停票中的cate分类 # 并计算出现每个分类的次数 # 目的是取出[热门行业] cates = {} for i in range(len(maxData)): key = maxData[i].get('cate') if key in cates: num = cates.get(key) + 1 else: num = 1 cates[key] = num # 分类排序并取出排名 sorts = [] cate_list = list(cates.items()) cate_list.sort(key=take_second, reverse=True) # 取行业排名前6 for i in range(0, 6): if cate_list[i][0] is not None: sorts.append(cate_list[i][0]) print('\n') print(sorts) query_sort_history(sorts) # 查询同一行业里最近180天的高位数据 def query_sort_history(sorts): # 清空历史表 mysql.clear_history() tt = [] # 处理每个行业的数据 for i in range(len(sorts)): thread1 = my_thread.MyThread('Thread-'+str(i), sorts[i]) thread1.start() tt.append(thread1) # 取出同行业全部票 # stocks = mysql.getListForCate(sorts[i]) # stockPercent = [] # for i in range(len(stocks)): # code = stocks[i][0] # # 查询票当前涨幅 # p = mysql.getPercent(code) # for j in p: # s = (float(j[0]),) # a = stocks[i] + s # stockPercent.append(a) # # 根据涨幅排序 # stockPercent.sort(key=take_four, reverse=True) # # print(stockPercent) # # for z in range(len(stockPercent)): # if 3 <= float(stockPercent[z][3]): # # print(stockPercent[z]) # # 查询单个的180天历史高位数据，并保存到数据库 # toDay = time.strftime('%Y-%m-%d', time.localtime()) # history.query_stock_history(mysql, toDay, stockPercent[z][0], # stockPercent[z][1], # stockPercent[z][2]) # 等待所有线程执行完成 for i in tt: i.join() compile_stock(sorts) # 生成比对结果 def compile_stock(sorts): current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) # 准备写文件 f = open('./code.txt', 'a+') f.write("\t\n\n") f.write(str(sorts)) f.write("\t\n") f.write(current_time) # 处理每个行业的数据 for x in range(len(sorts)): stocks = mysql.getListForCate(sorts[x]) stockPercent = [] for i in range(len(stocks)): code = stocks[i][0] p = mysql.getPercent(code) for j in p: s = (float(j[0]),) a = stocks[i] + s # print(a) stockPercent.append(a) # 根据涨幅排序 stockPercent.sort(key=take_four, reverse=True) # print(stockPercent) # 今日龙头 maxPercent = [] for i in range(len(stockPercent)): # 取出今天行业的领涨龙头 if 9.8 <= float(stockPercent[i][3]): maxPercent.append(stockPercent[i]) # print(maxPercent) allStr = '' for i in maxPercent: # 查询龙头历史数据 days = mysql.selectDay(i[0]) for j in days: result = mysql.selectRelationStocks(j[4], i[2]) # print(str(result)) allStr += str(result) # print(allStr) # 处理字符 allStr = allStr.replace("(", "") allStr = allStr.replace(")", "") allStr = allStr.replace("'", "") allStr = allStr.replace(",,", ",") allStr = allStr.replace(" ", "") # print(allStr) # 移除重复字符 sortList = [] for i in allStr.split(','): if i != '': d = (i, allStr.count(i)) if sortList.count(d) <= 0: sortList.append(d) sortList.sort(key=take_second, reverse=True) # print(sortList) result = [] for i in sortList: # 查询历史中高位出现的次数 num = mysql.selectCount(i[0])[0] if num >= 3: p = 0 price = 0 n = '' # 从保存的实时信息中取出 df = mysql.get_now(i[0]) for j in df: p = j[3] price = j[4] n = j[2] # 过滤百分比太高和太低的数据 if 1 <= float(p) <= 7: # 查询15天内的上升趋势 toDay = time.strftime('%Y-%m-%d', time.localtime()) t = history.query_month_history(i[0], toDay) if t[0]: value = (i[0] + ' ' + n, price, str(p) + "%", t[1], num) result.append(value) # 保存结果到数据库 mysql.save_result(current_time, sorts[x], str(value)) result.sort(key=take_five, reverse=True) print(sorts[x]) print(result) f.write("\t\n" + sorts[x] + " => " + str(result)) f.close() if check_trade_day() is False: print('~~ 非交易时间 ~~') exit() # 初始化数据库操作类 mysql = db.MY_SQL() # 行业分类API() category = get_running_list() # 接近涨停的票数据 maxData = [] # 获取实时行情 get_now_price() # 取出接近涨停票中的cate分类 get_hot_cate() mysql.closeDB() print(time.time() - t1)