import datetime
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import json
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import os
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import time
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import dask
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import constant
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from log_module.log import logger_common, logger_kpl_jingxuan_in, logger_debug
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from strategy import kpl_api, data_cache, basic_methods
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from utils import tool, hx_qc_value_util
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# 获取行情精选板块 强度排名
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def get_market_sift_plate_its_stock_power():
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@dask.delayed
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def batch_get_plate_codes(fs):
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return fs
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@dask.delayed
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def request_plate_codes(i):
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plate_name = i[1]
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its_stock = json.loads(kpl_api.getCodesByPlate(i[0]))
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# now_time_str = tool.get_now_time_str()
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# if data_cache.OPENING_TIME < now_time_str < data_cache.NOON_MARKET_TIME:
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log_data = {plate_name: its_stock['list']}
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# 尝试过滤掉无意义的概念板块 【代表着有无强度可能】
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if (plate_name not in ['科创板', '北交所', '无', '并购重组', '国企改革', '超跌', '壳资源', '股权转让', '送转填权']) or ('次新' in plate_name or 'ST' in plate_name or '破净股' in plate_name):
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# print(f"{i[1]} 强度:{i[2]}")
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# 通过板块ID获取其下面的个股强度列表
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# print(f"======={i[0]}=======")
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# its_stock_list_info = its_stock['list']
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# logger.info(f"its_stock_list_info==={its_stock_list_info}")
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# 将板块强度下面对应的个股列表打印到日志中
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# for i in its_stock_list_info:
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# if i[0] != 1:
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# logger.info(
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# 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]}%，"
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# 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，"
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# f"主力买：{round(i[11] / 100000000, 2)}亿，"
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# f"主力卖：{round(i[12] / 100000000, 2)}亿，"
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# 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]}%，"
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# 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]}%，"
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# 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]}，"
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# f"？：{i[30]}%，？：{i[31]}，？？：{i[32]}，振幅：{i[33]}%，未知0？？？？：{i[34]}，未知0？？？？？：{i[35]}，"
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# 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]}，"
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# f"41未知1值：{i[41]}，上季度机构持仓【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]}"
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# )
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# 初始化股票强度列表
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stock_power_list = []
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for s in its_stock['list']:
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# 过滤掉涨幅大于 当日涨幅s[6] < 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 上季度机构持仓 >0
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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 and int(s[42]) >= 0:
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# 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]}")
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# 对个股强度 主要 属性列表进行装填
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its_stock_power = [s[1], s[0], s[6], s[9], s[4], s[2], s[40]]
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# 逐个选择性添加its_stock中的元素到个股强度列表中
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# print(f"its_stock_power===={its_stock_power}")
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# 整体将添加完善的个股强度列表添加到股票列表中
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stock_power_list.append(its_stock_power)
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# print(f"stock_power_list===={stock_power_list}")
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# 过滤掉没有瞬时高强度个股的空概念
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if len(stock_power_list) != 0:
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# 将对应板块的股票强度列表新建一个字典
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stock_power_item = {i[1]: stock_power_list}
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# 并更新到精选板块个股字典中
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market_sift_plate_stock_dict.update(stock_power_item)
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return log_data
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# 定义一个时间段，在这个时间段内才会执行下面的代码，主要就是把强度数据作为日志打印存储下来。
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now_time = tool.get_now_time_str()
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if not (data_cache.OPENING_TIME < now_time < data_cache.NOON_MARKET_TIME):
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return
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data = (kpl_api.getMarketJingXuanRealRankingInfo())
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market_sift_plate = json.loads(data)
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# 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
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# print(f"market_sift_plate 数 ======{len(market_sift_plate['list'])}")
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# 行情》精选板块》排名前20中》对应个股》符合条件的个股
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# logger.info(f"market_sift_plate['list']======{market_sift_plate['list']}")
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# logger.info(f"market_sift_plate['list'][0] ======{market_sift_plate['list'][0]}")
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# 初始化精选板块对应个股字典
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market_sift_plate_stock_dict = {}
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if 'list' in market_sift_plate:
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ds = []
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for d in market_sift_plate['list']:
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ds.append(request_plate_codes(d))
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dask_result = batch_get_plate_codes(ds)
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compute_results = dask_result.compute()
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log_datas = {}
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for r in compute_results:
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if not r:
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continue
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for b in r:
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log_datas[b] = r[b]
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# logger.info(f"精选板块股票强度数据更新 == {market_sift_plate_stock_dict}")
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# 只在盘中时间获取
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kpl_api.KPLStockOfMarketsPlateLogManager().add_log(market_sift_plate['list'], log_datas)
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return market_sift_plate_stock_dict
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# 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】
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# get_market_sift_plate_its_stock_power()
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def get_market_sift_plate_its_stock_power_process(callback):
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while True:
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try:
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# now = time.time()
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# print(f"kpl_limit_up_process开始了{now}")
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start_time = time.time()
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now_time = tool.get_now_time_str()
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if data_cache.L1_DATA_START_TIME < now_time < data_cache.CLOSING_TIME:
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its_stock_power = get_market_sift_plate_its_stock_power()
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time_str = datetime.datetime.now().strftime("%H%M%S")
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if 92900 < int(time_str) < 95000:
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logger_kpl_jingxuan_in.info(f"耗时：{time.time() - start_time} 数据：{its_stock_power}")
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callback(its_stock_power)
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# print(f"精选板块拉升个股更新===={its_stock_power}")
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except Exception as e:
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logger_debug.exception(f"开盘啦板块强度线程报错An error occurred: {e}")
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finally:
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time.sleep(2)
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# 获取涨停板块名称列表并存储本地的函数
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def get_limit_up_block_names():
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# 设定当前时间点
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now_time = tool.get_now_time_str()
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# print(f"now_time===={now_time}")
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if data_cache.SERVER_RESTART_TIME < now_time < data_cache.UPDATE_DATA_TIME:
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# print(f"在时间内使用--------------------------")
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# 获取涨停信息列表
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limit_up_info = kpl_api.get_limit_up_info()
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# print(f"limit_up_info=={limit_up_info}")
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data_cache.limit_up_info = kpl_api.get_limit_up_info()
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# 提取涨停列表中的板块名称
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limit_up_block_names = []
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# 循环添加涨停概念
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for i in limit_up_info:
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limit_up_block_names.append(i[5])
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# print(f"limit_up_block_names==={limit_up_block_names}")
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# return limit_up_block_names
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# # 使用Counter计算每个元素的出现次数
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# counter = Counter(limit_up_block_names)
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# # 找出出现次数最多的元素及其次数
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# most_common_element, most_common_count = counter.most_common(1)[0]
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# # 打印出现次数最多的元素
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# print(f"主线概念：{most_common_element}，出现了 {most_common_count} 次")
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return limit_up_block_names
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# 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)
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def kpl_limit_up_process(callback):
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while True:
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try:
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# now = time.time()
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# print(f"kpl_limit_up_process开始了{now}")
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limit_up_block_names = get_limit_up_block_names()
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callback(limit_up_block_names)
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# logger.info(f"涨停更新===={limit_up_block_names}")
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# print(f"涨停更新数量===={len(limit_up_block_names)}")
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# print(f"kpl_limit_up_process完成一下{now}")
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except Exception as e:
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logger_debug.error(f"开盘啦涨停板块概念线程报错An error occurred: {e}")
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finally:
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time.sleep(1.5)
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# kpl_limit_up_process()
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# 构建涨停信息读写对象
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class DailyLimitUpInfoStorageManager:
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# 初始化文件路径
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def __init__(self, file_path=constant.KPL_LIMIT_UP_DATA_PATH):
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self.file_path = file_path
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# 添加单日涨停信息数据到文件中的一行 函数
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def append_data_to_file(self, data_to_append):
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# print(f"data_to_append=={data_to_append}")
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# 读取所有行并解析为 JSON 对象列表
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if os.path.exists(self.file_path):
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with open(self.file_path, 'r', encoding='utf-8') as file:
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# 获取当前日期并格式化
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current_date = datetime.datetime.now().strftime('%Y-%m-%d')
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lines = [json.loads(line.strip()) for line in file if line.strip()]
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# print(f"lines type=={type(lines)}")
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# print(f"lines=={lines}")
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# 检查当前日期是否已存在于文件中
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if lines: # 如果读取到的行文件列表不为空（为真）
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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）
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# 将日期和data_to_append转换为JSON格式的字符串
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json_line = json.dumps({current_date: data_to_append}, ensure_ascii=False) + '\n'
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# 打开文件并追加JSON行
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with open(self.file_path, 'a', encoding='utf-8') as file:
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file.write(json_line)
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else:
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logger_common.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）")
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else:
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json_line = json.dumps({current_date: data_to_append}, ensure_ascii=False) + '\n'
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# 打开文件并追加JSON行
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with open(self.file_path, 'a', encoding='utf-8') as file:
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file.write(json_line)
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# 清理多余数据函数
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def check_and_remove_oldest_entry(self, max_entries):
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# 读取所有行并解析为 JSON 对象列表
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if os.path.exists(self.file_path):
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with open(self.file_path, 'r', encoding='utf-8') as file:
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lines = [json.loads(line.strip()) for line in file if line.strip()]
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else:
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lines = []
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# 如果行数超过限制，移除最早的一些行
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if len(lines) >= max_entries:
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# 截断列表，只保留最新的 max_entries 个对象
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lines = lines[-max_entries:]
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# 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
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with open(self.file_path, 'w', encoding='utf-8') as file:
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for obj in lines:
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file.write(json.dumps(obj, ensure_ascii=False) + '\n')
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# file.write(json.dumps(obj, ensure_ascii=False))
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# 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
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def arrange_limit_up_info(self):
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limit_info = {}
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# 创建一个列表来存储所有解析的 JSON 对象
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if os.path.exists(self.file_path):
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with open(self.file_path, 'r', encoding='utf-8') as file:
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for line in file:
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# 去除每行末尾的换行符（如果有的话）
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line = line.rstrip('\n')
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# 将每行解析为一个 JSON 对象
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info = json.loads(line)
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# 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
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if isinstance(info, dict) and len(info) == 1:
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date, data = list(info.items())[0]
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limit_info[date] = data
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return limit_info
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# 构建一个获取读写存储本地的并整理涨停数据的函数
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def get_arrange_limit_up_info():
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# 实例化每日涨停信息整理方法
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manager = DailyLimitUpInfoStorageManager()
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manager.append_data_to_file(kpl_api.get_limit_up_info())
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manager.check_and_remove_oldest_entry(max_entries=1000)
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# 构建一个处理历史涨停涨停信息数据的函数
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def get_handling_limit_up_info():
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# 实例化每日涨停信息整理方法
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history_limit_up_info = DailyLimitUpInfoStorageManager()
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data_cache.daily_limit_up_info = history_limit_up_info.arrange_limit_up_info()
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# logger.info(f"读本地的日更的历史涨停数据=={data_cache.daily_limit_up_info}")
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# print(f"daily_limit_up_info 类型==={type(data_cache.daily_limit_up_info)}")
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# 统计每日主线
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daily_limit_up_info_len = len(data_cache.daily_limit_up_info)
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# print(f"daily_limit_up_info_len==={daily_limit_up_info_len}")
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historical_transaction_date_list = []
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date_of_the_day = data_cache.DataCache().today_date
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for i in range(daily_limit_up_info_len):
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pre_date = hx_qc_value_util.get_previous_trading_date(date_of_the_day) # 获取前一个交易日API
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# target_date_str = basic_methods.pre_num_trading_day(data_cache.today_date, daily_limit_up_info_len)
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date_format = "%Y-%m-%d"
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target_date = datetime.datetime.strptime(pre_date, date_format).strftime("%Y-%m-%d")
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historical_transaction_date_list.append(target_date)
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date_of_the_day = pre_date
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# print(f"historical_transaction_date_list={historical_transaction_date_list}")
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history_sorted_plate_ranking_list = []
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for key, value in data_cache.daily_limit_up_info.items():
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# print(f"key=={key}")
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for i in historical_transaction_date_list:
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# print(f"i======={i}")
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# 找到每上一个交易日对应的本地数据的信息
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if key == i:
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# print(f"{key}===找到了！value={value}")
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#
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plate_ranking_list = []
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# 遍历交易日每一个涨停股的信息
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for v in value:
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# print(f"v =={v}")
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# 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
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plate_limit_up_num_dict = {
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v[5]: v[20]
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}
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# 将这个字典数据不重复的添加到概念排名列表中
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if plate_limit_up_num_dict not in plate_ranking_list:
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plate_ranking_list.append(plate_limit_up_num_dict)
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# plate_ranking_set.add(v[20])
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# print(f"plate_ranking_list={plate_ranking_list}")
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# 使用sorted函数和lambda表达式来根据字典的值进行排序
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# 这里我们确保不修改原始字典，仅通过list(x.values())[0]来获取值
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sorted_plate_ranking_list = sorted(plate_ranking_list, key=lambda x: list(x.values())[0], reverse=True)
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# logger.info(f"{key}=====>>>>{sorted_plate_ranking_list}")
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history_sorted_plate_ranking_list.append(sorted_plate_ranking_list)
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# print(f"history_sorted_plate_ranking_list={history_sorted_plate_ranking_list}")
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# for ranking_list in history_sorted_plate_ranking_list:
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# print(f"ranking_list={ranking_list}")
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# for i in ranking_list:
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# print(f"i={i}")
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# 计算历史涨停概念的连续出现次数函数
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def count_key_occurrences(list_of_dicts_lists):
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# 创建一个字典来存储每个键的总出现次数
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key_counts = {}
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# 遍历列表中的每个字典列表
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for sublist in list_of_dicts_lists:
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# 遍历当前字典列表中的每个字典
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for dict_item in sublist:
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# 遍历字典中的每个键
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for key in dict_item:
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# 如果键不在key_counts中，则初始化计数为0
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if key not in key_counts:
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key_counts[key] = 0
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# 增加当前键的计数
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key_counts[key] += 1
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# 打印结果
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for key, count in key_counts.items():
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if count > 1:
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logger_common.info(f"'{key}' 连续出现 {count} 次")
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# 调用函数，传入整个列表
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# count_key_occurrences(history_sorted_plate_ranking_list)
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# daily_limit_up_info_list = list(reversed(daily_limit_up_info_list))
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# print(f"daily_limit_up_info_list==={daily_limit_up_info_list}")
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# 获取昨日涨停代码 （以便与K线对比）
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pre_trading_day_limit_up_info = data_cache.daily_limit_up_info.get(data_cache.DataCache().pre_trading_day)
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if pre_trading_day_limit_up_info is not None:
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yesterday_limit_up_code_list = []
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for i in pre_trading_day_limit_up_info:
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symbol_code = basic_methods.format_stock_symbol(i[0])
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limit_up_code = symbol_code
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yesterday_limit_up_code_list.append(limit_up_code)
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data_cache.yesterday_limit_up_code_list = yesterday_limit_up_code_list
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logger_common.info(f"昨日涨停股票数量=={len(data_cache.yesterday_limit_up_code_list)}")
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logger_common.info(f"昨日涨停代码列表=={yesterday_limit_up_code_list}")
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# code = pre_trading_day_limit_up_info[0][0]
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# logger.info(f"股票代码=={code}")
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# cor_name = pre_trading_day_limit_up_info[0][1]
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# logger.info(f"公司名称=={cor_name}")
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# unknown_zero_2 = pre_trading_day_limit_up_info[0][2]
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# logger.info(f"未知零值2=={unknown_zero_2}")
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# none_data = pre_trading_day_limit_up_info[0][3]
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# logger.info(f"空数据=={none_data}")
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# # 总市值(万)？
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# total_market_value = round((pre_trading_day_limit_up_info[0][4] / 10000), 2)
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# logger.info(f"总市值=={total_market_value}(万)？")
|
# # 最相关概念
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# the_most_relevant_plate = pre_trading_day_limit_up_info[0][5]
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# logger.info(f"最相关概念=={the_most_relevant_plate}")
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# # 收盘封单金额(万)
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# closing_amount = round((pre_trading_day_limit_up_info[0][6] / 10000), 2)
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# logger.info(f"收盘封单金额=={closing_amount}(万)")
|
# # 最大封单金额(万)
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# maximum_blocked_amount = round((pre_trading_day_limit_up_info[0][7] / 10000), 2)
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# logger.info(f"最大封单金额=={maximum_blocked_amount}(万)")
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# # 主力净额
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# main_net_amount = round((pre_trading_day_limit_up_info[0][8] / 10000), 2)
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# logger.info(f"主力净额=={main_net_amount}(万)")
|
# # 主力买
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# 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
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# 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
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# 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
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# 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 = kpl_api.getZYLTAmount(code)
|
# 获取个股的板块列表
|
selected_blocks = kpl_api.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_common.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_common.info(f"有{have_no_plate_num}只股票概念为空")
|
print(f"有{have_no_plate_num}只股票概念为空")
|
logger_common.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 = kpl_api.getZYLTAmount(code)
|
# 获取个股的精选板块列表
|
# selected_blocks = getCodeJingXuanBlocks('000021')
|
selected_blocks = kpl_api.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_debug.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}")
|
|
|
if __name__ == '__main__':
|
# get_have_no_plate_num()
|
pass
|
