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3 天以前 0b0d0e790fec8c7edfdbcab5c31d625e0c2eadd6 
 strategy/all_K_line.py


@@ -3,7 +3,7 @@


from __future__ import print_function, absolute_import, unicode_literals




import copy


import logging


# import logging


import os


import time


import datetime


@@ -11,19 +11,17 @@


import json




import constant


# 引入掘金API


import utils.juejin_api


# import kpl_api


from log_module import async_log_util


from log_module.log import logger_common, logger_k_bars


from strategy import data_cache




# 引入基础算法模块


from strategy import basic_methods


# 引入本地日志模块


from strategy.logging_config import get_logger


from utils import hx_qc_value_util, tool




# 获取logger实例


logger = get_logger()


logger = logger_common




start_time = time.time()


print(f"all_K_line开始运行--{start_time}")


@@ -97,10 +95,8 @@


        results = []


        dates_list = []


        # 查询交易标的基本信息并赋值


        sec_name = data_cache.DataCache().code_name_dict.get(symbol)


        sec_name = data_cache.DataCache().code_name_dict.get(symbol, "default_name")


        # print(f"K_line_sec_name=={sec_name}")


        # TODO 查询代码名称


        # instruments = get_instruments(symbols=symbol, exchanges=None, sec_types=None, names=None, fields="sec_name", df=False)[0]


        # 将获取到的公司名称直接 生成字典放到 K线的第一个序列中


        it_K_line[0]['sec_name'] = sec_name


        for i in range(0, len(it_K_line) - 1):


@@ -165,8 +161,8 @@


                # 最高位据昨日较远 且 昨收价距历史最高价差距较小（小于5%，涨幅5%是左右是最容易把低吸骗进去的日内涨幅） 且 整体涨跌幅度较大  【当前二度突破近前高风险】


                # 【双成药业 2024-11-08 这样的二次近前高，实际数值为1.4646...，为安全起见还是设置为1.5】


                if (2 < historical_high_price_index < 15 and len(it_K_line) >= 90) and (


                        historical_high_price / round(it_K_line[0]['close'], 2) < 1.5) and (


                        historical_high_price > historical_low_price * 1.8):


                        historical_high_price / round(it_K_line[0]['close'], 2) < 1.2) and (


                        historical_high_price > historical_low_price * 2):


                    it_K_line[0]['risk_position'] = 'recent_second_break_near_high_position_risk'


                # 昨日就是最高位 且 整体涨跌幅度较大 【当前高位风险】


                if (historical_high_price_index < 2 and len(it_K_line) >= 90) and (


@@ -211,8 +207,7 @@


                current_count = 0


                start = None


            #  确保不会出现除以零的报错


            if current_yesterday_volume == 0:


                current_yesterday_volume = 1


            if current_yesterday_volume != 0:


                if round(current_today_volume / current_yesterday_volume, 2) > 1.1:


                    # print(f"i=={i} {it_K_line[i]['bob']} {round(current_today_volume/current_yesterday_volume,2)} 【放量】")


                    if current_today_growth > 0:


@@ -236,17 +231,19 @@


                    else:


                        it_K_line[i]['today_volume_shape'] = 'decreases_balance'


                        # print(f"i=={i}  {it_K_line[i]['bob']}   【缩量平收】")


            else:


                # print(f"i=={i} {it_K_line[i]['bob']} {round(current_today_volume/current_yesterday_volume,2)} 【平量】")


                if current_today_growth > 0:


                    it_K_line[i]['today_volume_shape'] = 'remained_up'


                    # print(f"i=={i}  {it_K_line[i]['bob']}   【平量上涨】")


                elif current_today_growth < 0:


                    it_K_line[i]['today_volume_shape'] = 'remained_down'


                    # print(f"i=={i}  {it_K_line[i]['bob']}   【平量下跌】")


                else:


                    it_K_line[i]['today_volume_shape'] = 'remained_balance'


                    # print(f"i=={i}  {it_K_line[i]['bob']}   【平量平收】")


                    # print(f"i=={i} {it_K_line[i]['bob']} {round(current_today_volume/current_yesterday_volume,2)} 【平量】")


                    if current_today_growth > 0:


                        it_K_line[i]['today_volume_shape'] = 'remained_up'


                        # print(f"i=={i}  {it_K_line[i]['bob']}   【平量上涨】")


                    elif current_today_growth < 0:


                        it_K_line[i]['today_volume_shape'] = 'remained_down'


                        # print(f"i=={i}  {it_K_line[i]['bob']}   【平量下跌】")


                    else:


                        it_K_line[i]['today_volume_shape'] = 'remained_balance'


                        # print(f"i=={i}  {it_K_line[i]['bob']}   【平量平收】")


            else:


                logger.info(f"{symbol} 的 昨日成交量 为 0，报错！！")




            if current_open - previous_close > 0:


                # print(f"i=={i}  {it_K_line[i]['bob']} 成交总量:{today_volume},,,成交总金额:{today_amount}")


@@ -338,12 +335,14 @@




# 实例化K线对象


k_line_history = KLineHistory()


# 实例化指数K线对象


main_index_k_line_history = KLineHistory()






# 在main.py中初始化函数里面就实例化上证A股和深证A股的历史K线方法【在本文件中调用且不写入本地文件时才需要在本文件内实例化】


# k_line_history.k_line_history_90day()




# 写入全股票90天K线


# 写入全目标标的股票90天K线


def all_stocks_all_k_line_dict_write():


    all_stocks_base_K_line_dict = k_line_history.k_line_history_90day()


    # 初始化所有个股的指标K线列表


@@ -378,11 +377,12 @@


    try:


        json_data = json.dumps(convert_datetime(all_stocks_all_K_line_property_dict), ensure_ascii=False, indent=4)


        # 将转换后的JSON字符串写入文件


        with open('local_storage_data/all_stocks_all_K_line_property_dict.json', 'w', encoding='utf-8') as f:


        with open(constant.K_BARS_PATH, 'w', encoding='utf-8') as f:


            f.write(json_data)


        async_log_util.info(logger_k_bars, json_data)


    except Exception as error:


        print(f"An error occurred while converting the data to JSON: {error}")


    print(f"历史k线写完了！{tool.get_now_time_str()}")


    logger.info(f"标的个股历史k线写完了！{tool.get_now_time_str()}")






# 用开盘啦数据检测昨日的K线中涨停属性是否有误（盘尾 集合竞价 炸开一个卖一档，但涨幅未变的）


@@ -390,8 +390,7 @@


    for key, values in data_cache.all_stocks_all_K_line_property_dict.items():


        # print(f"data_cache.limit_up_code_list==={data_cache.limit_up_code_list}")


        if 'attribute' in values[0]:


            if key not in data_cache.yesterday_limit_up_code_list and values[0][


                'attribute'] in data_cache.limit_up_type:


            if key not in data_cache.yesterday_limit_up_code_list and values[0]['attribute'] in data_cache.limit_up_type:


                logger.info(f"在K线中昨日涨停，但是在开盘啦中没有，key==={key}       公司名称==={values[0]['sec_name']} ")


                data_cache.yesterday_frying_plate_last_minute_list.append(key)


        else:


@@ -406,17 +405,17 @@


    while True:


        try:


            # 声明赋值实时时间


            now_time = datetime.datetime.now().strftime("%H:%M:%S")


            now_time = tool.get_now_time_str()




            # print(f"now_time==={now_time}")


            # if now_time > data_cache.after_closing_time and data_cache.execution is False:


            # # if now_time > data_cache.after_closing_time:


            # if now_time > data_cache.AFTER_CLOSING_TIME and data_cache.execution is False:


            # # if now_time > data_cache.AFTER_CLOSING_TIME:


            #     data_cache.execution = True


            #     # 整理当日涨停信息并写入本地管理好本地数据


            #     kpl_api.get_arrange_limit_up_info()


            #     plate_strength_analysis.get_arrange_limit_up_info()


            #     logger.info(f"整理当日涨停信息 已经运行完成")


            #     # # 获取所有个股的板块概念并写入文件【耗时较长应该放在 核心主线程 和 仓位管理 后面】


            #     kpl_api.get_all_stocks_plate_dict(data_cache.min_stocks)


            #     plate_strength_analysis.get_all_stocks_plate_dict(data_cache.min_stocks)


            #     # logger.info(f"获取所有个股的板块概念 已经运行完成")




            # 构造一个循环检测K线子带你中所有个股代码下的日期是不是和某日一致的，如果不一致则返回False


@@ -432,17 +431,18 @@


                #         data_date_and_target_date = False


                #         return data_date_and_target_date




            # 盘中不进行时间与数据的判断，一方面判断应该在开盘前完成，另一方面收盘后离最新拉取时间都还有很长时间


            # if (hour < 9 or (hour == 9 and minute < 30)) or hour >= 17:


            if data_cache.opening_time > now_time or now_time > data_cache.closing_time:


            # 盘中不进行时间与数据的判断(也不能早于服务器重启时间，因为次日凌晨拉取的K线会有错误)，一方面判断应该在开盘前完成，另一方面收盘后离最新拉取时间都还有很长时间


            # 在这个时间段内运行  9:00--9:30 或  15:00--23:00


            if data_cache.SERVER_RESTART_TIME < now_time < data_cache.OPENING_TIME or data_cache.CLOSING_TIME < now_time < data_cache.PROGRAM_SLEEP_TIME:


                # 然后判断一下，当K线字典中的任意一只个股的的第一个K线表中的第一个日期 等于 上一个交易日日期 或 今日日期  且  运行时时间未到 18:30 那么不需要新拉取直接读取已有的就行，


                # 否者还是得调用K线对象方法拉取，并重新读取赋值全局化


                # if hour < 18 or (hour == 18 and minute < 31):


                if now_time < data_cache.update_data_time:


                if now_time < data_cache.UPDATE_DATA_TIME:


                    # if now_time < data_cache.closing_time:


                    check_pre_trading_day = check_data_date(data_cache.DataCache().pre_trading_day)


                    if check_pre_trading_day is True:


                        # if hour >= 17:


                        if now_time > data_cache.checking_data_time:


                        if now_time > data_cache.CHECKING_DATA_TIME:


                            print(


                                f"未到【18:31】 但 既有数据 【是】 上个交易日数据 【不执行拉取K线】  ε=ε=ε=ε=ε=ε=ε=ε=ε=ε=ε=ε=ε(/’-‘)/")


                    else:


@@ -451,31 +451,31 @@


                        # 调用指标K线写入本地文件


                        all_stocks_all_k_line_dict_write()


                        # 读取已经获取到并存储在本地的所有票的指标K线字典，并赋值给data_cache全局缓存


                        with open('local_storage_data/all_stocks_all_K_line_property_dict.json', 'r',


                                  encoding='utf-8') as f:


                            data_cache.all_stocks_all_K_line_property_dict = json.load(f)


                            print(


                                f"data_cache.all_stocks_all_K_line_property_dict的个数{len(data_cache.all_stocks_all_K_line_property_dict)}")


                        if os.path.exists(constant.K_BARS_PATH):


                            with open(constant.K_BARS_PATH, 'r',


                                      encoding='utf-8') as f:


                                data_cache.all_stocks_all_K_line_property_dict = json.load(f)


                                # print(


                                #     f"data_cache.all_stocks_all_K_line_property_dict的个数{len(data_cache.all_stocks_all_K_line_property_dict)}")




                else:


                    check_today_date = check_data_date(data_cache.DataCache.today_date)


                    check_today_date = check_data_date(data_cache.DataCache().today_date)


                    if check_today_date is True:


                        pass


                        print(


                            f"到了【18:31】 但 既有数据 【是】 本交易日数据 【不执行拉取K线】  ε=ε=ε=ε=ε=ε=ε=ε=ε=ε=ε=ε=ε(/’-‘)/")


                        print(f"到了【18:31】 但 既有数据 【是】 本交易日数据 【不执行拉取K线】  ε=ε=ε=ε=ε=ε=ε=ε=ε=ε=ε=ε=ε(/’-‘)/")


                    else:


                        print(


                            f"到了【18:31】 且 既有数据 【不是】 本交易日数据 执行拉取K线  【既有数据日期：{data_cache.all_stocks_all_K_line_property_dict['SZSE.000034'][0]['bob'][:10]} 本交易日：{data_cache.DataCache.today_date}】")


                            f"到了【18:31】 且 既有数据 【不是】 本交易日数据 执行拉取K线  【既有数据日期：{data_cache.all_stocks_all_K_line_property_dict['SZSE.000034'][0]['bob'][:10]} 本交易日：{data_cache.DataCache().today_date}】")


                        # 调用指标K线写入本地文件


                        all_stocks_all_k_line_dict_write()


                        # 读取已经获取到并存储在本地的所有票的指标K线字典，并赋值给data_cache全局缓存


                        with open('local_storage_data/all_stocks_all_K_line_property_dict.json', 'r',


                                  encoding='utf-8') as f:


                            data_cache.all_stocks_all_K_line_property_dict = json.load(f)


                            print(


                                f"data_cache.all_stocks_all_K_line_property_dict的个数{len(data_cache.all_stocks_all_K_line_property_dict)}")


                        if os.path.exists(constant.K_BARS_PATH):


                            with open(constant.K_BARS_PATH, 'r',


                                      encoding='utf-8') as f:


                                data_cache.all_stocks_all_K_line_property_dict = json.load(f)


                                # print(


                                #     f"data_cache.all_stocks_all_K_line_property_dict的个数{len(data_cache.all_stocks_all_K_line_property_dict)}")


        except Exception as error:


            logging.exception(error)


            logger.exception(error)


            print(f"实时检测是否拉取K线线程报错An error occurred: {error}")


        finally:


            current_time = datetime.datetime.now()


@@ -491,8 +491,7 @@


    # 计算90天内涨停天数


    for i in k_line_data:


        if 'attribute' in i and (


                i['attribute'] == 'one_line_limit_up' or i['attribute'] == 'limit_down_then_limit_up' or i[


            'attribute'] == 'limit_up_then_limit_down_then_limit_up' or i['attribute'] == 'limit_up'):


                i['attribute'] == 'one_line_limit_up' or i['attribute'] == 'limit_down_then_limit_up' or i['attribute'] == 'limit_up_then_limit_down_then_limit_up' or i['attribute'] == 'limit_up'):


            limit_up_day += 1


            # print(f"涨停提日期：{i['bob']}")


        # print(f"涨停天数----limit_up_day======{limit_up_day}")