""" kpl API数据获取与处理 """ import json import os.path import time import datetime import dask import requests import constant from log_module import async_log_util from log_module.log import logger_common, logger_kpl_jingxuan_in, logger_Overall_market_strength_score, \ logger_stock_of_markets_plate # import requests from strategy import data_cache from strategy import basic_methods from strategy.kpl_data_manager import KPLStockOfMarketsPlateLogManager from strategy.market_sentiment_analysis import index_trend_expectation from trade import middle_api_protocol from utils import hx_qc_value_util, tool # 获取logger实例 logger = logger_common now = time.time() print(f"kpl_api开始运行--{now}") # 竞价 DABAN_TYPE_BIDDING = 8 # 涨停 DABAN_TYPE_LIMIT_UP = 1 # 炸板 DABAN_TYPE_OPEN_LIMIT_UP = 2 # 跌停 DABAN_TYPE_LIMIT_DOWN = 3 # 曾跌停 DABAN_TYPE_EVER_LIMIT_DOWN = 5 def __base_request(url, data, timeout=10): DELEGATE = True if not DELEGATE: headers = { "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "User-Agent": "Dalvik / 2.1.0(Linux;U;Android 6.0.1;MuMu Build/V417IR)" } # proxies={'https': '192.168.3.251:9002'} # 禁止代理，不然会走本地代理 response = requests.post(url, data=data, headers=headers, proxies={"http": None, "https": None}, timeout=timeout) if response.status_code != 200: raise Exception("请求出错") return response.text else: fdata = middle_api_protocol.load_kpl(url, data, timeout) return middle_api_protocol.request(fdata) def daBanList(pidType): data = "Order=1&a=DaBanList&st=100&c=HomeDingPan&PhoneOSNew=1&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23" \ f"&VerSion=5.8.0.2&Index=0&Is_st=1&PidType={pidType}&apiv=w32&Type=4&FilterMotherboard=0&Filter=0&FilterTIB=0" \ "&FilterGem=0 " result = __base_request("https://apphq.longhuvip.com/w1/api/index.php", data=data) return result # 市场行情-行业 主力净额由高到低排序 实时获取 def getMarketIndustryRealRankingInfo(orderJingE_DESC=True): data = f"Order={1 if orderJingE_DESC else 0}&a=RealRankingInfo&st=20&apiv=w32&Type=5&c=ZhiShuRanking&PhoneOSNew=1&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&VerSion=5.8.0.2&Index=0&ZSType=4&" result = __base_request("https://apphq.longhuvip.com/w1/api/index.php", data=data) return result # 市场行情-精选 主力净额由高到低排序 实时获取 st=20 请求地址中 st=20 代表获取前20（排序方式为主力净额降序排列） # def getMarketJingXuanRealRankingInfo(orderJingE_DESC=True): # data = f"Order={1 if orderJingE_DESC else 0}&a=RealRankingInfo&st=10&apiv=w32&Type=5&c=ZhiShuRanking&PhoneOSNew=1&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&VerSion=5.8.0.2&Index=0&ZSType=7&" # result = __base_request("https://apphq.longhuvip.com/w1/api/index.php", # data=data) # return result # def getMarketJingXuanRealRankingInfo(orderJingE_DESC=True): # data = f"Order={1 if orderJingE_DESC else 0}&a=RealRankingInfo&st=3&apiv=w32&Type=5&c=ZhiShuRanking&PhoneOSNew=1&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&VerSion=5.8.0.2&Index=0&ZSType=7&" # result = __base_request("https://apphq.longhuvip.com/w1/api/index.php", # data=data) # return result # #市场行情-精选 （排序方式为强度降序排列） st=20 请求地址中 st=20 代表获取前20 def getMarketJingXuanRealRankingInfo(): data = f"Order=1&a=RealRankingInfo&st=20&a=RealRankingInfo&apiv=w35&Type=1&Index=0&c=ZhiShuRanking&VerSion=5.13.0.0&Order=1&PhoneOSNew=1&ZSType=7&DeviceID=d6f20ce9-fa08-31c9-a493-536ebb8e9773&" result = __base_request("https://apphq.longhuvip.com/w1/api/index.php", data=data) return result # 获取代码的板块 def getStockIDPlate(code): data = f"a=GetStockIDPlate_New&apiv=w32&c=StockL2Data&StockID={code}&PhoneOSNew=1&UserID=0&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&VerSion=5.8.0.2&Token=0&" result = __base_request("https://apphq.longhuvip.com/w1/api/index.php", data=data) result = json.loads(result) if int(result["errcode"]) != 0: return None return result["ListJX"] if result["ListJX"] else result["List"] # IDPlate = getStockIDPlate(600550) # print(f"IDPlate===={IDPlate}") # 获取代码的精选板块 # 返回格式：[(板块代码,板块名称,涨幅百分比)] def getCodeJingXuanBlocks(code): data = f"a=GetStockIDPlate&apiv=w32&Type=2&c=StockL2Data&StockID={code}&PhoneOSNew=1&UserID=0&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&VerSion=5.8.0.2&Token=0&" result = __base_request("https://apphq.longhuvip.com/w1/api/index.php", data=data) result = json.loads(result) return result.get("ListJX") # 获取该概念下的个股代码及其他 def getCodesByPlate(plate_code): data = f"Order=1&a=ZhiShuStockList_W8&st=30&c=ZhiShuRanking&PhoneOSNew=1&old=1&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&VerSion=5.8.0.2&IsZZ=0&Token=0&Index=0&apiv=w32&Type=6&IsKZZType=0&UserID=0&PlateID={plate_code}&" return __base_request("https://apphq.longhuvip.com/w1/api/index.php", data=data) # 获取概念中的板块中的子板块 def getSonPlate(plate_code): data = f"a=SonPlate_Info&apiv=w32&c=ZhiShuRanking&PhoneOSNew=1&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&VerSion=5.8.0.2&PlateID={plate_code}&" return __base_request("https://apphq.longhuvip.com/w1/api/index.php", data=data) # 市场行情-行业 # def getMarketIndustryRealRankingInfo(orderJingE_DESC=True): # data = f"Order={1 if orderJingE_DESC else 0}&a=RealRankingInfo&st=80&apiv=w32&Type=5&c=ZhiShuRanking&PhoneOSNew=1&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&VerSion=5.8.0.2&Index=0&ZSType=4&" # return __base_request("https://apphq.longhuvip.com/w1/api/index.php", # data=data) # # # 市场行情-精选 # def getMarketJingXuanRealRankingInfo(orderJingE_DESC=True): # data = f"Order={1 if orderJingE_DESC else 0}&a=RealRankingInfo&st=80&apiv=w32&Type=5&c=ZhiShuRanking&PhoneOSNew=1&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&VerSion=5.8.0.2&Index=0&ZSType=7&" # return __base_request("https://apphq.longhuvip.com/w1/api/index.php", # data=data) # 获取自由流通市值 def getZYLTAmount(code): data = f"a=GetStockPanKou_Narrow&apiv=w32&c=StockL2Data&VerSion=5.8.0.2&State=1&PhoneOSNew=1&DeviceID=a38adabd-99ef-3116-8bb9-6d893c846e23&StockID={code}&" result = __base_request("https://apphq.longhuvip.com/w1/api/index.php", data=data) result = json.loads(result) if "real" in result: return result["real"].get("actualcirculation_value") return None # 获取涨停列表 def __getLimitUpInfo(pidType, page, pageSize): data = f"Order=0&a=DailyLimitPerformance&st={pageSize}&apiv=w35&Type=4&c=HomeDingPan&PhoneOSNew=1&DeviceID=a38adabb-99ef-3116-8bb9-6d893c846e24&VerSion=5.13.0.0&Index={(page - 1) * pageSize}&PidType={pidType}&" result = __base_request("https://apphq.longhuvip.com/w1/api/index.php", data=data) return result # 整理涨停列表的数据 def getLimitUpInfoNew(): pids = [(1, "首板"), (2, "2连板"), (3, "3连板"), (4, "4连板"), (5, "")] fresults = [] for pid_info in pids: results = [] for i in range(10): # start_time = time.time() result = __getLimitUpInfo(pid_info[0], i + 1, 20) # print("请求用时", time.time() - start_time) result = json.loads(result) datas = result["info"][0] results.extend(datas) # day = result["info"][1] # 连板天数？ if len(datas) < 20: break for r in results: if not r[18] and pid_info[1]: r[18] = pid_info[1] # # 替换掉板块名称 机器人概念/机器人 # for i in range(len(r)): # if type(r[i]) == str: # r[i] = kpl_util.filter_block(r[i]) fresults.extend(results) return json.dumps({"errcode": 0, "list": fresults}) # if __name__ == "__main__": # print(f"打板列表t(pidType)====={daBanList(2)}") # print(f"获取个股代码的板块==={getStockIDPlate('002766')}") # print((f"获取个股代码的精选板块==={getCodeJingXuanBlocks('002878')}")) # print(f"获取该概念下的个股代码及其他====={getCodesByPlate(885500)}") 《《《《《《《《《《 # print(f"获取概念中的板块中的子板块====={json.loads(getSonPlate(801085))}") # print(f"获取概念中的板块强度====={getSonPlate(getCodesByPlate(getCodeJingXuanBlocks('002452')[2][0]))}") # print(f"市场行情-行业板块 数==={len(getMarketIndustryRealRankingInfo(True))}") # print(f"市场行情-行业板块==={json.loads(getMarketIndustryRealRankingInfo(True))}") # 返回格式：['板块ID','板块名称','强度','涨幅','未知','成交额','''''''''强度','未知'] # print(f"市场行情-精选板块 数==={getMarketJingXuanRealRankingInfo(True)}") # print(f"市场行情-精选板块==={json.loads(getMarketJingXuanRealRankingInfo(True))}") # print(f"股票代码：{Market_situation_selected_sectors_No1[0]}") # jingxuanbankuai = json.loads(getMarketJingXuanRealRankingInfo(True)) # print(f"jingxuanbankuai==={type(jingxuanbankuai)}") # print(f"板块代码：{jingxuanbankuai['list'][0][0]}，板块名称：{jingxuanbankuai['list'][0][1]}，强度：{jingxuanbankuai['list'][0][2]}，涨幅：{jingxuanbankuai['list'][0][3]}，未知：{jingxuanbankuai['list'][0][4]}，成交额：{round(jingxuanbankuai['list'][0][5]/100000000)}亿，主力净额：{round(jingxuanbankuai['list'][0][6]/100000000,2)}亿，主买：{round(jingxuanbankuai['list'][0][7]/100000000,2)}亿，主卖：{round(jingxuanbankuai['list'][0][8]/100000000,2)}亿，未知：{jingxuanbankuai['list'][0][9]}，流通值：{round(jingxuanbankuai['list'][0][10]/100000000,2)}亿，未知/或为最大涨跌幅：{round(jingxuanbankuai['list'][0][11],2)}，未知：{round(jingxuanbankuai['list'][0][12]/100000000,2)}亿，总市值：{round(jingxuanbankuai['list'][0][13]/100000000,2)}亿，第一季度机构持仓：{round(jingxuanbankuai['list'][0][14]/100000000,2)}亿，未知：{round(jingxuanbankuai['list'][0][15],2)}，未知：{round(jingxuanbankuai['list'][0][16],2)}，强度：{round(jingxuanbankuai['list'][0][17],2)}") # # 部分板块没有子板块 # print(f"获取概念中的板块中的子板块====={json.loads(getSonPlate(801248))}") # print(f"自由流通市值==={getZYLTAmount('603319')}") # print((f"获取个股代码的精选板块列表==={getCodeJingXuanBlocks('002452')}")) # print((f"获取个股代码的精选第一板块代码==={getCodeJingXuanBlocks('002452')[0][0]}")) # print(f"获取该概念下的个股代码及其他====={json.loads(getCodesByPlate(getCodeJingXuanBlocks('002452')[0][0]))}") # print(f"获取该概念下的个股代码及其他dddddd====={json.loads(its_strongest_sector_situation)}") # print(f"涨停列表及概念板块={json.loads(getLimitUpInfoNew())['list']}") ######################################################################################################################################################################################################################## # 获取行情精选板块 强度排名 def get_market_sift_plate_its_stock_power(): @dask.delayed def batch_get_plate_codes(fs): return fs @dask.delayed def request_plate_codes(i): plate_name = i[1] log_data = None its_stock = json.loads(getCodesByPlate(i[0])) now_time_str = tool.get_now_time_str() if data_cache.OPENING_TIME < now_time_str < data_cache.NOON_MARKET_TIME: log_data = {plate_name: its_stock['list']} # 尝试过滤掉无意义的概念板块(plate_name not in ['科创板', '北交所', '次新股', '无', 'ST板块', 'ST摘帽', '并购重组', '国企改革','超跌', '壳资源', '股权转让', '送转填权']) and '增长' in plate_name if (plate_name not in ['科创板', '北交所', '次新股', '无', 'ST板块', 'ST摘帽', '并购重组', '国企改革', '超跌', '壳资源', '股权转让', '送转填权']) or ('增长' in plate_name): # print(f"{i[1]} 强度:{i[2]}") # 通过板块ID获取其下面的个股强度列表 # print(f"======={i[0]}=======") # its_stock_list_info = its_stock['list'] # logger.info(f"its_stock_list_info==={its_stock_list_info}") # 将板块强度下面对应的个股列表打印到日志中 # for i in its_stock_list_info: # if i[0] != 1: # logger.info( # f"l === 个股代码：{i[0]}，公司名称：{i[1]}，主力资金推测：{i[2]}，未知0值：{i[3]}，概念：{i[4]}，最新价：{i[5]}，当日当时涨幅：{i[6]}%，" # f"成交额：{round(i[7] / 100000000, 2)} 亿，实际换手率：{i[8]}%，未知0值：{i[9]}，实际流通：{round(i[10] / 100000000, 2)}亿，" # f"主力买：{round(i[11] / 100000000, 2)}亿，" # f"主力卖：{round(i[12] / 100000000, 2)}亿，" # f"主力净额：{round(i[13] / 10000, 2)}万，买成占比：{i[14]}%，卖成占比：{i[15]}%，净成占比：{i[16]}%，买流占比：{i[17]}%，卖流占比：{i[18]}%，净流占比：{i[19]}%，" # f"区间涨幅：{i[20]}%，量比：{i[21]}，未知0：{i[22]}，上板情况：{i[23]}，上板排名：{i[24]}，换手率：{i[25]}%，" # f"未知空值：{i[26]}，未知零值：{i[27]}，收盘封单：{i[28]}，最大封单：{i[29]}，未知空值？：{i[30]}，" # f"？：{i[30]}%，？：{i[31]}，？？：{i[32]}，振幅：{i[33]}%，未知0？？？？：{i[34]}，未知0？？？？？：{i[35]}，" # f"？=：{i[36]}，？总市值：{i[37]}，？流通市值：{i[38]}，最终归属概念（收盘后出数据？）：{i[39]}，领涨次数：{i[40]}，" # f"41未知1值：{i[41]}，第三季度机构持仓【str数据勿用运算符】：{i[42]}万，？年预测净利润：{i[43]}，上年预测净利润：{i[44]}，年内预测净利润：{i[45]}" # ) # 初始化股票强度列表 stock_power_list = [] for s in its_stock['list']: # 过滤掉涨幅大于 and s[6] < 6.5 且小于0%的 和 名称中包含ST的 和 涨速小于等于0%的 和 只要昨日未涨停 和 上证或深证的正股 and s[9] > 0.0025 if s[6] > 0 and s[1].find("ST") < 0 and s[1].find("XD") < 0 and s[23].find("板") < 0 and s[24].find("板") < 0 and (s[0].startswith('60') or s[0].startswith('00')) and s[9] > 1: # print(f"{s[1]}，个股代码：{s[0]}， 涨幅：{s[6]}% 涨速：{s[9]}% 概念：{s[4]} 主力资金推测：{s[2]} 领涨次数：{s[40]} 今日第几板：{s[23]} 是否破版{s[24]}") # 对个股强度 主要 属性列表进行装填 its_stock_power = [s[1], s[0], s[6], s[9], s[4], s[2], s[40]] # 逐个选择性添加its_stock中的元素到个股强度列表中 # print(f"its_stock_power===={its_stock_power}") # 整体将添加完善的个股强度列表添加到股票列表中 stock_power_list.append(its_stock_power) # print(f"stock_power_list===={stock_power_list}") # 过滤掉没有瞬时高强度个股的空概念 if len(stock_power_list) != 0: # 将对应板块的股票强度列表新建一个字典 stock_power_item = {i[1]: stock_power_list} # 并更新到精选板块个股字典中 market_sift_plate_stock_dict.update(stock_power_item) return log_data data = (getMarketJingXuanRealRankingInfo()) market_sift_plate = json.loads(data) # logger_kpl_jingxuan_in 打印的日志专用于开盘了数据的存储分析，不能轻易删除 # print(f"market_sift_plate 数 ======{len(market_sift_plate['list'])}") # 行情》精选板块》排名前20中》对应个股》符合条件的个股 # logger.info(f"market_sift_plate['list']======{market_sift_plate['list']}") # logger.info(f"market_sift_plate['list'][0] ======{market_sift_plate['list'][0]}") # 初始化精选板块对应个股字典 market_sift_plate_stock_dict = {} if 'list' in market_sift_plate: ds = [] for d in market_sift_plate['list']: ds.append(request_plate_codes(d)) dask_result = batch_get_plate_codes(ds) compute_results = dask_result.compute() log_datas = {} for r in compute_results: if not r: continue for b in r: log_datas[b] = r[b] now_time = tool.get_now_time_str() if data_cache.L1_DATA_START_TIME < now_time < data_cache.NOON_MARKET_TIME: # logger.info(f"精选板块股票强度数据更新 == {market_sift_plate_stock_dict}") # 只在盘中时间获取 KPLStockOfMarketsPlateLogManager().add_log(market_sift_plate['list'], log_datas) return market_sift_plate_stock_dict # 调用一下获取精选板块股票强度数据函数 【本模块内使用时调用】 # get_market_sift_plate_its_stock_power() def get_market_sift_plate_its_stock_power_process(callback): while True: try: # now = time.time() # print(f"kpl_limit_up_process开始了{now}") start_time = time.time() now_time = tool.get_now_time_str() if data_cache.L1_DATA_START_TIME < now_time < data_cache.CLOSING_TIME: its_stock_power = get_market_sift_plate_its_stock_power() time_str = datetime.datetime.now().strftime("%H%M%S") if 92900 < int(time_str) < 95000: logger_kpl_jingxuan_in.info(f"耗时：{time.time() - start_time} 数据：{its_stock_power}") callback(its_stock_power) # print(f"精选板块拉升个股更新===={its_stock_power}") except Exception as e: logger.error(f"开盘啦板块强度线程报错An error occurred: {e}") finally: time.sleep(2) # 获取涨停信息数据 def get_limit_up_info(): # 获取涨停信息列表 limit_up_info = json.loads(getLimitUpInfoNew())['list'] return limit_up_info # 获取市场行情情绪综合强度 def get_market_strong(): """ 获取市场强度 :return: """ result = __base_request("https://apphwhq.longhuvip.com/w1/api/index.php", f"a=DiskReview&apiv=w35&c=HomeDingPan&VerSion=5.13.0.0&PhoneOSNew=1&DeviceID=d6f20ce9-fa08-31c9-a493-536ebb8e9773&") data = json.loads(result) return int(data["info"]["strong"]) # market_strong = get_market_strong() # print(f"market_strong==={market_strong}") # 获取涨停板块名称列表并存储本地的函数 def get_limit_up_block_names(): # 设定当前时间点 now_time = tool.get_now_time_str() # print(f"now_time===={now_time}") if data_cache.SERVER_RESTART_TIME < now_time < data_cache.UPDATE_DATA_TIME: # print(f"在时间内使用--------------------------") # 获取涨停信息列表 limit_up_info = get_limit_up_info() # print(f"limit_up_info=={limit_up_info}") data_cache.limit_up_info = get_limit_up_info() # 提取涨停列表中的板块名称 limit_up_block_names = [] # 循环添加涨停概念 for i in limit_up_info: limit_up_block_names.append(i[5]) # print(f"limit_up_block_names==={limit_up_block_names}") # return limit_up_block_names # # 使用Counter计算每个元素的出现次数 # counter = Counter(limit_up_block_names) # # 找出出现次数最多的元素及其次数 # most_common_element, most_common_count = counter.most_common(1)[0] # # 打印出现次数最多的元素 # print(f"主线概念：{most_common_element}，出现了 {most_common_count} 次") return limit_up_block_names # 为开盘啦接口获取的涨停列表概念板块单独开一个进程 形参(callback) def kpl_limit_up_process(callback): while True: try: # now = time.time() # print(f"kpl_limit_up_process开始了{now}") limit_up_block_names = get_limit_up_block_names() callback(limit_up_block_names) # logger.info(f"涨停更新===={limit_up_block_names}") # print(f"涨停更新数量===={len(limit_up_block_names)}") # print(f"kpl_limit_up_process完成一下{now}") except Exception as e: logger.error(f"开盘啦涨停板块概念线程报错An error occurred: {e}") finally: time.sleep(1.5) # kpl_limit_up_process() # 构建涨停信息读写对象 class DailyLimitUpInfoStorageManager: # 初始化文件路径 def __init__(self, file_path=constant.KPL_LIMIT_UP_DATA_PATH): self.file_path = file_path # 添加单日涨停信息数据到文件中的一行 函数 def append_data_to_file(self, data_to_append): # print(f"data_to_append=={data_to_append}") # 读取所有行并解析为 JSON 对象列表 if os.path.exists(self.file_path): with open(self.file_path, 'r', encoding='utf-8') as file: # 获取当前日期并格式化 current_date = datetime.datetime.now().strftime('%Y-%m-%d') lines = [json.loads(line.strip()) for line in file if line.strip()] # print(f"lines type=={type(lines)}") # print(f"lines=={lines}") # 检查当前日期是否已存在于文件中 if lines: # 如果读取到的行文件列表不为空（为真） if lines[-1].get(current_date) is None: # 如果列表中的倒数最后一行获取不到当日的日期（最后一行的键 为 当日日期） # 将日期和data_to_append转换为JSON格式的字符串 json_line = json.dumps({current_date: data_to_append}, ensure_ascii=False) + '\n' # 打开文件并追加JSON行 with open(self.file_path, 'a', encoding='utf-8') as file: file.write(json_line) else: logger.info(f"（当日日期已存在于文件的最后一行了，不再重复追加写入）") else: json_line = json.dumps({current_date: data_to_append}, ensure_ascii=False) + '\n' # 打开文件并追加JSON行 with open(self.file_path, 'a', encoding='utf-8') as file: file.write(json_line) # 清理多余数据函数 def check_and_remove_oldest_entry(self, max_entries): # 读取所有行并解析为 JSON 对象列表 if os.path.exists(self.file_path): with open(self.file_path, 'r', encoding='utf-8') as file: lines = [json.loads(line.strip()) for line in file if line.strip()] else: lines = [] # 如果行数超过限制，移除最早的一些行 if len(lines) >= max_entries: # 截断列表，只保留最新的 max_entries 个对象 lines = lines[-max_entries:] # 重新打开文件以写入模式，并写入截断后的对象列表为 JSON Lines with open(self.file_path, 'w', encoding='utf-8') as file: for obj in lines: file.write(json.dumps(obj, ensure_ascii=False) + '\n') # file.write(json.dumps(obj, ensure_ascii=False)) # 隔行整理数据并合并装入一个字典数据中调用时返回这个字典数据 函数 def arrange_limit_up_info(self): limit_info = {} # 创建一个列表来存储所有解析的 JSON 对象 if os.path.exists(self.file_path): with open(self.file_path, 'r', encoding='utf-8') as file: for line in file: # 去除每行末尾的换行符（如果有的话） line = line.rstrip('\n') # 将每行解析为一个 JSON 对象 info = json.loads(line) # 假设每行都是一个字典数据，且只有一个键值对，其中键是日期 if isinstance(info, dict) and len(info) == 1: date, data = list(info.items())[0] limit_info[date] = data return limit_info # 构建一个获取读写存储本地的并整理涨停数据的函数 def get_arrange_limit_up_info(): # 实例化每日涨停信息整理方法 manager = DailyLimitUpInfoStorageManager() manager.append_data_to_file(get_limit_up_info()) manager.check_and_remove_oldest_entry(max_entries=1000) # 构建一个处理历史涨停涨停信息数据的函数 def get_handling_limit_up_info(): # 实例化每日涨停信息整理方法 history_limit_up_info = DailyLimitUpInfoStorageManager() data_cache.daily_limit_up_info = history_limit_up_info.arrange_limit_up_info() # logger.info(f"读本地的日更的历史涨停数据=={data_cache.daily_limit_up_info}") # print(f"daily_limit_up_info 类型==={type(data_cache.daily_limit_up_info)}") # 统计每日主线 daily_limit_up_info_len = len(data_cache.daily_limit_up_info) # print(f"daily_limit_up_info_len==={daily_limit_up_info_len}") historical_transaction_date_list = [] date_of_the_day = data_cache.DataCache().today_date for i in range(daily_limit_up_info_len): pre_date = hx_qc_value_util.get_previous_trading_date(date_of_the_day) # 获取前一个交易日API # target_date_str = basic_methods.pre_num_trading_day(data_cache.today_date, daily_limit_up_info_len) date_format = "%Y-%m-%d" target_date = datetime.datetime.strptime(pre_date, date_format).strftime("%Y-%m-%d") historical_transaction_date_list.append(target_date) date_of_the_day = pre_date # print(f"historical_transaction_date_list={historical_transaction_date_list}") history_sorted_plate_ranking_list = [] for key, value in data_cache.daily_limit_up_info.items(): # print(f"key=={key}") for i in historical_transaction_date_list: # print(f"i======={i}") # 找到每上一个交易日对应的本地数据的信息 if key == i: # print(f"{key}===找到了！value={value}") # plate_ranking_list = [] # 遍历交易日每一个涨停股的信息 for v in value: # print(f"v =={v}") # 将每一个涨停股的涨停概念和同班级数量 汇编为一个字典 plate_limit_up_num_dict = { v[5]: v[20] } # 将这个字典数据不重复的添加到概念排名列表中 if plate_limit_up_num_dict not in plate_ranking_list: plate_ranking_list.append(plate_limit_up_num_dict) # plate_ranking_set.add(v[20]) # print(f"plate_ranking_list={plate_ranking_list}") # 使用sorted函数和lambda表达式来根据字典的值进行排序 # 这里我们确保不修改原始字典，仅通过list(x.values())[0]来获取值 sorted_plate_ranking_list = sorted(plate_ranking_list, key=lambda x: list(x.values())[0], reverse=True) # logger.info(f"{key}=====>>>>{sorted_plate_ranking_list}") history_sorted_plate_ranking_list.append(sorted_plate_ranking_list) # print(f"history_sorted_plate_ranking_list={history_sorted_plate_ranking_list}") # for ranking_list in history_sorted_plate_ranking_list: # print(f"ranking_list={ranking_list}") # for i in ranking_list: # print(f"i={i}") # 计算历史涨停概念的连续出现次数函数 def count_key_occurrences(list_of_dicts_lists): # 创建一个字典来存储每个键的总出现次数 key_counts = {} # 遍历列表中的每个字典列表 for sublist in list_of_dicts_lists: # 遍历当前字典列表中的每个字典 for dict_item in sublist: # 遍历字典中的每个键 for key in dict_item: # 如果键不在key_counts中，则初始化计数为0 if key not in key_counts: key_counts[key] = 0 # 增加当前键的计数 key_counts[key] += 1 # 打印结果 for key, count in key_counts.items(): if count > 1: logger.info(f"'{key}' 连续出现 {count} 次") # 调用函数，传入整个列表 # count_key_occurrences(history_sorted_plate_ranking_list) # daily_limit_up_info_list = list(reversed(daily_limit_up_info_list)) # print(f"daily_limit_up_info_list==={daily_limit_up_info_list}") # 获取昨日涨停代码 （以便与K线对比） pre_trading_day_limit_up_info = data_cache.daily_limit_up_info.get(data_cache.DataCache().pre_trading_day) if pre_trading_day_limit_up_info is not None: yesterday_limit_up_code_list = [] for i in pre_trading_day_limit_up_info: symbol_code = basic_methods.format_stock_symbol(i[0]) limit_up_code = symbol_code yesterday_limit_up_code_list.append(limit_up_code) data_cache.yesterday_limit_up_code_list = yesterday_limit_up_code_list logger.info(f"昨日涨停股票数量=={len(data_cache.yesterday_limit_up_code_list)}") logger.info(f"昨日涨停代码列表=={yesterday_limit_up_code_list}") # code = pre_trading_day_limit_up_info[0][0] # logger.info(f"股票代码=={code}") # cor_name = pre_trading_day_limit_up_info[0][1] # logger.info(f"公司名称=={cor_name}") # unknown_zero_2 = pre_trading_day_limit_up_info[0][2] # logger.info(f"未知零值2=={unknown_zero_2}") # none_data = pre_trading_day_limit_up_info[0][3] # logger.info(f"空数据=={none_data}") # # 总市值(万)？ # total_market_value = round((pre_trading_day_limit_up_info[0][4] / 10000), 2) # logger.info(f"总市值=={total_market_value}(万)？") # # 最相关概念 # the_most_relevant_plate = pre_trading_day_limit_up_info[0][5] # logger.info(f"最相关概念=={the_most_relevant_plate}") # # 收盘封单金额(万) # closing_amount = round((pre_trading_day_limit_up_info[0][6] / 10000), 2) # logger.info(f"收盘封单金额=={closing_amount}(万)") # # 最大封单金额(万) # maximum_blocked_amount = round((pre_trading_day_limit_up_info[0][7] / 10000), 2) # logger.info(f"最大封单金额=={maximum_blocked_amount}(万)") # # 主力净额 # main_net_amount = round((pre_trading_day_limit_up_info[0][8] / 10000), 2) # logger.info(f"主力净额=={main_net_amount}(万)") # # 主力买 # main_buyers = round((pre_trading_day_limit_up_info[0][9] / 10000), 2) # logger.info(f"主力买=={main_buyers}(万)") # # 主力卖 # main_sellers = round((pre_trading_day_limit_up_info[0][10] / 10000), 2) # logger.info(f"主力卖=={main_sellers}(万)") # # 成交额 # transaction_amount = round((pre_trading_day_limit_up_info[0][11] / 10000), 2) # logger.info(f"成交额=={transaction_amount}(万)") # # 所属精选板块 # selected_plate = pre_trading_day_limit_up_info[0][12] # logger.info(f"所属精选板块=={selected_plate}") # # 实际流通 # actual_circulation = round((pre_trading_day_limit_up_info[0][11] / 100000000), 2) # logger.info(f"实际流通=={actual_circulation}(亿)") # # 量比？（不是，不知道是什么） # equivalent_ratio = pre_trading_day_limit_up_info[0][14] # logger.info(f"量比？=={equivalent_ratio}") # # 领涨次数 # leading_increases_times = pre_trading_day_limit_up_info[0][15] # logger.info(f"领涨次数=={leading_increases_times}") # # 未知零值 # unknown_zero_16 = pre_trading_day_limit_up_info[0][16] # logger.info(f"未知零值16=={unknown_zero_16}") # # 未知零值 # unknown_zero_17 = pre_trading_day_limit_up_info[0][17] # logger.info(f"未知零值17=={unknown_zero_17}") # # 第几板(连续涨停天数) # continuous_limit_up_days = pre_trading_day_limit_up_info[0][18] # logger.info(f"第几板=={continuous_limit_up_days}") # # 最相关概念的代码 # the_most_relevant_plate_code = pre_trading_day_limit_up_info[0][19] # logger.info(f"最相关概念的代码=={the_most_relevant_plate_code}") # # 同班级的数量(同概念涨停数量) # the_same_class_amount = pre_trading_day_limit_up_info[0][20] # logger.info(f"同概念涨停数量=={the_same_class_amount}") # get_handling_limit_up_info() # 获取全部个股的板块并存储的函数 def get_all_stocks_plate_dict(stocks_list): all_stocks_plate_dict = {} # 逐个获取个股精选板块概念和自由市值等，并整体放入一个新创建的字典中然后添加到数据中 for i in stocks_list: try: code = i.split('.')[1] # print(f"i==={i}") # 获取个股的自由市值 free_market_value = getZYLTAmount(code) # 获取个股的板块列表 selected_blocks = getStockIDPlate(code) # 提取精选板块中的板块名称 selected_plate_list = [block[1] for block in selected_blocks] # print(f"selected_block_names==={selected_block_list}") block_data = { # 添加自由市值 'free_market_value': free_market_value, # 添加精选板块 'plate': selected_plate_list } # 将code作为键，stocks_selected_block_data作为值添加到stocks_block_data字典中 all_stocks_plate_dict[code] = block_data # print(f"all_stocks_plate_dict==={all_stocks_plate_dict}") except Exception as e: print(f"获取全部个股的板块并存储的函数 An error occurred: {e}") finally: pass # return stocks_plate_data # print(f"all_stocks_plate_dict==={len(all_stocks_plate_dict)}") # 将获取到的范围票概念板块转JSON格式并存储在本地文件夹中 # 将字典转换为JSON格式的字符串 json_data = json.dumps(all_stocks_plate_dict) # 写入文件 with open(constant.ALL_STOCKS_PLATE_PATH, 'w', encoding='utf-8') as f: f.write(json_data) now_time = datetime.datetime.now() # 获取本机时间 logger.info(f"写入所有个股板块文件完成！::{now_time}") # 计算开盘啦昨日拉取的概念数据中为空的股票数量函数 def get_have_no_plate_num(): # 初始化无概念数量 have_no_plate_num = 0 plate_are_null_list = [] for k, v in data_cache.all_stocks_plate_dict.items(): pass # print(f"i==={i} T==={t}") if len(v['plate']) == 0: have_no_plate_num += 1 # print(f"{k}的概念为空") # logger.info(f"{k}的概念为空") # 股票代码格式转化为掘金格式 symbol = basic_methods.format_stock_symbol(k) sec_name = data_cache.all_stocks_all_K_line_property_dict.get(symbol) if sec_name is not None: plate_are_null_list.append(sec_name) logger.info(f"有{have_no_plate_num}只股票概念为空") logger.info(f"个股有历史K线但概念为空的有：{plate_are_null_list}") # 获取全部个股的精选板块并存储的函数 def stocks_list_selected_blocks(min_stocks): stocks_selected_block_data = [] # 逐个获取个股精选板块概念和自由市值等，并整体放入一个新创建的字典中然后添加到数据中 for i in min_stocks: try: code = i.split('.')[1] # 获取个股的自由市值 free_market_value = getZYLTAmount(code) # 获取个股的精选板块列表 # selected_blocks = getCodeJingXuanBlocks('000021') selected_blocks = getCodeJingXuanBlocks(code) # 提取精选板块中的板块名称 selected_block_list = [block[1] for block in selected_blocks] # print(f"selected_block_names==={selected_block_list}") stocks_selected_block_dict = { # 添加股票代码 'code': code, # 添加自由市值 'free_market_value': free_market_value, # 添加精选板块 'selected_block': selected_block_list } stocks_selected_block_data.append(stocks_selected_block_dict) # print(f"stocks_selected_block_data==={stocks_selected_block_dict}") except Exception as e: logger.error(f"获取全部个股的精选板块并存储的函数 An error occurred: {e}") # print(f"stocks_selected_block_data==={len(stocks_selected_block_data)}") # 将获取到的范围票概念板块转JSON格式并存储在本地文件夹中 # 将字典转换为JSON格式的字符串 json_data = json.dumps(stocks_selected_block_data) # 写入文件 with open('local_storage_data/stocks_selected_block_data.json', 'w', encoding='utf-8') as f: f.write(json_data) now_time = datetime.datetime.now() # 获取本机时间 print(f"写入精选板块文件完成！::{now_time}") # 获取实时大盘行情情绪综合强度 [分数] 函数 并 计算当日计划持仓数量 def get_real_time_market_strong(): while True: try: if data_cache.position_automatic_management_switch is True: now_time = tool.get_now_time_str() if data_cache.L1_DATA_START_TIME < now_time < data_cache.CLOSING_TIME: # 获取大盘综合强度分数 data_cache.real_time_market_strong = get_market_strong() # data_cache.time_sharing_market_strong_dirt = time_sharing_market_strong_dirt.update({now: data_cache.real_time_market_strong}) # 该logger.info的的日志不再需要打印，后续将转入到GUI客户端上直接显示，该数据的打印交由下方的打印机制异步执行单独存储，以便后续可视化呈现后进行更高效的数据分析 # logger.info(f"大盘行情情绪综合强度 [分数]==={data_cache.real_time_market_strong}分") if data_cache.MORN_MARKET_CLOSING_TIME < now_time < data_cache.NOON_MARKET_OPENING_TIME: pass logger.info(f"午间休市时间内 不打印大盘综合强度分数") else: # 大盘综合强度分数 的 异步日志 # logger_Overall_market_strength_score.info(data_cache.real_time_market_strong) async_log_util.info(logger_Overall_market_strength_score, f"{data_cache.real_time_market_strong}") usefulMoney = data_cache.account_finance_dict[0].get('usefulMoney', 0) logger.info(f"账户可用资金==={usefulMoney}元") # 低迷情绪比例 low_emotion_mood_ratio = 1 # 33分是个两级分化阶梯不好，目前不好拿捏，暂时不用 # if data_cache.real_time_market_strong <= 33: if data_cache.real_time_market_strong < 30: # 如果大盘综合强度分数小于30，将低迷情绪分数比例设置为0.01，可用资金缩小一百倍 low_emotion_mood_ratio = 0.01 if data_cache.real_time_market_strong <= 10: low_emotion_mood_ratio = 0 logger.info(f"极端低迷情绪比例===={low_emotion_mood_ratio * 100}%") data_cache.index_trend_expectation_score = index_trend_expectation() logger.info(f"大盘指数情绪预期分数==={data_cache.index_trend_expectation_score}分") # # 目前大盘指数情绪预期分数 尚不科学 强制设置为初始0值 # index_trend_expectation_score = 0 # 获取计算今天新增的持仓数量 addition_position_number = len(data_cache.addition_position_symbols_set) # 定义一个今日的剩余新增持仓数量的变量 Unfinished_opening_plan_number = 3 - addition_position_number logger.info(f"今日的剩余新增持仓数量==={Unfinished_opening_plan_number}") if Unfinished_opening_plan_number != 0: # 如果GUI看盘上没有手动设置具体的下单金额，就按照评分策略的金额下单，否则就按照GUI设置的金额下单。 if data_cache.BUY_MONEY_PER_CODE < 0: # 根据账户可用金额 计算今日计划下单金额 # 账户可用金额 默认乘以0.9，永远留一点钱，一方面也冗余一些计算误差 # ((大盘综合强度分数 + 大盘指数情绪预期分数) * 0.01) * (账户可用金额 * 0.9 * 极端低迷情绪比例 / 今日最大新增持仓票数) # data_cache.today_planned_order_amount = ((data_cache.real_time_market_strong + data_cache.index_trend_expectation_score) * 0.01) * ( # usefulMoney * 0.9 * low_emotion_mood_ratio / Unfinished_opening_plan_number) data_cache.today_planned_order_amount = (usefulMoney * 0.95 * low_emotion_mood_ratio / Unfinished_opening_plan_number) logger.info(f"采用开仓策略计算方式=》今日计划下单金额:{data_cache.today_planned_order_amount}，") else: data_cache.today_planned_order_amount = data_cache.BUY_MONEY_PER_CODE logger.info(f"采用GUI设置方式=》今日计划下单金额:{data_cache.today_planned_order_amount}") except Exception as error: logger.error(f"获取实时大盘行情情绪综合强度[分数] 函数报错: {error}") finally: time.sleep(3) # kpl_stocks_list_selected_blocks_process() #在 kpl_api.py中可以调用 # stocks_list_selected_blocks(min_stocks) #在 kpl_api.py中可以调用 # list = ['SHSE.600805','SHSE.600804'] # # all_stocks_plate_dict(list) if __name__ == "__main__": # start_time = time.time() # get_market_sift_plate_its_stock_power() # print("耗时：", time.time() - start_time) get_market_sift_plate_its_stock_power() # get_market_sift_plate_its_stock_power_process(None)