import datetime import json import os import threading import time import dask import constant from log_module import async_log_util from log_module.log import logger_common, logger_kpl_jingxuan_in, logger_debug, logger_kpl_market_sift_plate, \ logger_kpl_limit_up, logger_kpl_code_plates from strategy import kpl_api, data_cache, basic_methods, trading_dates_manager from strategy.kpl_data_manager import KPLMarketStockHeatLogManager from strategy.trading_dates_manager import TradingDatesManager from utils import tool, hx_qc_value_util # 获取行情精选板块 强度排名 def get_market_sift_plate_its_stock_power(): """ :return: {板块:[代码信息]}, 精选流入板块 """ @dask.delayed def batch_get_plate_codes(fs): return fs @dask.delayed def request_plate_codes(i): plate_name = i[1] its_stock = json.loads(kpl_api.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 constant.BLACK_CONCEPT_VALUELESS_PLATE_LIST 默认拉黑（无意义板块）的常量【代表着有无强度可能】 if (plate_name not in constant.BLACK_CONCEPT_VALUELESS_PLATE_LIST) or ('次新' in plate_name or 'ST' in plate_name or '破净股' 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]}，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]}" # ) # 初始化股票强度列表 stock_power_list = [] filtered_stock_info_list = [] for s in its_stock['list']: # 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] > 1(s[9]=涨速) 上季度机构持仓 >0 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) if int(s[42]) < 0: # 加入策略时间段限制 if data_cache.OPENING_TIME < now_time < data_cache.NOON_MARKET_TIME: # logger_common.info(f"【{s[1]}】，上季度机构持仓{int(s[42])} 小于0 被过滤掉") filtered_stock_info = { "code": s[0], "sec_name": s[1], # "increase": s[6], "institutional_holdings": int(s[42]) } # logger_common.info( # f"filtered_stock_info=={filtered_stock_info}") data_cache.filtered_stock_info_list.append(filtered_stock_info) # logger_common.info(f"data_cache.filtered_stock_info_list=={data_cache.filtered_stock_info_list}") # 每次得到新增的结果后转给data_cache.filtered_stock_info_list存储 # data_cache.filtered_stock_info_list = filtered_stock_info_list # 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 # 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。 now_time = tool.get_now_time_str() if '11:30:10' < now_time < '12:59:50': return data = (kpl_api.getMarketJingXuanRealRankingInfo()) market_sift_plate = json.loads(data) # print(f"market_sift_plate 数 ======{len(market_sift_plate['list'])}") # 精选板块【前20】 market_sift_plate['list'] ====== if data_cache.OPEN_BIDDING_TIME < now_time < data_cache.AFTER_CLOSING_TIME: logger_kpl_market_sift_plate.info(f"{market_sift_plate['list']}") # 总控制时间段 if not (data_cache.OPEN_BIDDING_TIME < now_time < data_cache.AFTER_CLOSING_TIME): return # ['801235', '化工', 6996, 0.027, 2.43, 117836347690, -122548038, 8105997595, -8228545633, 0.92, 8595377775454, 0.09, 332297449, 9954902621130, -192457252, 24.0487, 17.1809, 6996, 0.027] # market_sift_plate['list'][0] = ['801062', '军工', 3520, -0.49, 0.666, 139133934669, 383864272, 9077352839, -8693488567, 1.183, 6129448037490,-0.12, 168245858, 7088854452019, -290614763, 50.2408, 30.3672, 3520, 0] # 行情精选板块列表 前20 中的 第一个板块列表数据 = 【代码，板块名称，强度，涨幅？，量比？，成交额？，现额？，主买，主卖，1.183？，流通值？，-0.12？，300W大单净额？，总市值？，上季度机构增仓，今年平均PE，次年平均PE，强度，未知0值】 # 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] # 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 KPLMarketStockHeatLogManager().add_log(market_sift_plate['list'], log_datas) # 行情》精选板块》排名前20中》对应个股》符合条件的个股 return market_sift_plate_stock_dict, market_sift_plate.get("list", []) # 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() # 精选流入字典 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 打印的日志专用于开盘了数据的存储分析，不能轻易删除 logger_kpl_jingxuan_in.info(f"耗时：{time.time() - start_time} 数据：{its_stock_power[0]}") callback(its_stock_power) # print(f"精选板块拉升个股更新===={its_stock_power}") except Exception as e: logger_debug.exception(f"开盘啦板块强度线程报错An error occurred: {e}") finally: time.sleep(1) # 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.OPEN_BIDDING_TIME < now_time < data_cache.AFTER_CLOSING_TIME: # print(f"在时间内使用--------------------------") # 获取涨停信息列表 limit_up_info = kpl_api.get_limit_up_info() # print(f"limit_up_info=={limit_up_info}") # [股票代码，股票名称，未知布尔值，未知空值，涨停时间戳，涨停原因，封单金额，最大封单金额，主力净额，主力买，主力卖，成交额，最匹配概念，实际流通，实际换手，连板次数，未知的布尔值，振幅，封板状态，所属板块代码，所属板块封板数量] # ['002217', '合力泰', 0, '', 1750728300, 'ST摘帽', 181201440, 305027648, -11357517, 100591704, -111949221,139796123, '面板、汽车零部件', 6857734517, 2.04, 1, 0, 0, '首板', '801082', 1] # ['002703', '浙江世宝', 1, '', 1750728300, '无人驾驶', 315849152, 626532830, 139152274, 196166314, -57014040,203671488, '一季报增长、汽车零部件', 3707643911, 5.49, 1, 0, 0, '首板', '801064', 9] data_cache.limit_up_info = 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") async_log_util.info(logger_kpl_limit_up, f"{limit_up_info}") 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_debug.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() # 构建每日信息读写对象 class DailyInfoDataEntryStorageManager: # 初始化文件路径 # def __init__(self, file_path=constant.KPL_LIMIT_UP_DATA_PATH): # self.file_path = file_path def __init__(self, file_path): # 移除默认值，要求调用时必须提供 self.file_path = file_path # print(f"实例构建完成") # 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) # print(f"已写入数据1") else: 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）") 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) # print(f"已写入数据2") # 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 = DailyInfoDataEntryStorageManager(constant.KPL_LIMIT_UP_DATA_PATH) manager.append_data_to_file(kpl_api.get_limit_up_info()) manager.check_and_remove_oldest_entry(max_entries=1000) # 构建一个获取读写存储本地的并整理kpl精选流入强度数据的函数 def get_arrange_plate_strength_info(): # 实例化每日kpl精选流入强度数据整理方法 manager = DailyInfoDataEntryStorageManager(constant.KPL_PLATE_STRENGTH_DATA_PATH) data = (kpl_api.getMarketJingXuanRealRankingInfo()) market_sift_plate = json.loads(data)['list'] manager.append_data_to_file(market_sift_plate) manager.check_and_remove_oldest_entry(max_entries=1000) # get_arrange_plate_strength_info() # 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 = DailyInfoDataEntryStorageManager(constant.KPL_LIMIT_UP_DATA_PATH) 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}") # 制备历史交易日历 historical_transaction_date_list = [] date_of_the_day = data_cache.DataCache().today_date print(f"date_of_the_day = {date_of_the_day}") 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 # print(f"pre_date ==== {pre_date}") # pre_date = '2025-07-30' # 测试用 # target_date_str = basic_methods.pre_num_trading_day(data_cache.today_date, daily_limit_up_info_len) # target_date = datetime.datetime.strptime(pre_date, "%Y-%m-%d").strftime("%Y-%m-%d") historical_transaction_date_list.append(pre_date) date_of_the_day = pre_date # 历史板块排名列表 （意义不明，基本无效）【】【】【（意义不明，基本无效，后续可能另寻他法）】【】【】 # 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"key===={key}") # 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}") # 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}") # def get_yesterday_limit_up_code_list(): # history_limit_up_info = DailyInfoDataEntryStorageManager(constant.KPL_LIMIT_UP_DATA_PATH) # data_cache.daily_limit_up_info = history_limit_up_info.arrange_limit_up_info() # 获取昨日涨停代码 （以便与K线对比） '2025-07-30' pre_trading_day_limit_up_info = data_cache.daily_limit_up_info.get(data_cache.DataCache().pre_trading_day) # pre_trading_day_limit_up_info = data_cache.daily_limit_up_info.get('2025-07-31') 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 = i[0] yesterday_limit_up_code_list.append(limit_up_code) data_cache.yesterday_limit_up_code_list = yesterday_limit_up_code_list print(f"data_cache.yesterday_limit_up_code_list=={data_cache.yesterday_limit_up_code_list}") logger_common.info(f"昨日涨停股票数量=={len(data_cache.yesterday_limit_up_code_list)}") logger_common.info(f"昨日涨停代码列表=={yesterday_limit_up_code_list}") # 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_common.info(f"'{key}' 连续出现 {count} 次") # 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) # 记录到日志 async_log_util.info(logger_kpl_code_plates, 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}") # 构建一个获取读写存储本地的并整理kpl精选流入强度数据的函数 def get_arrange_plate_strength_info(): # 实例化每日kpl精选流入强度数据整理方法 manager = DailyInfoDataEntryStorageManager(constant.KPL_PLATE_STRENGTH_DATA_PATH) data = (kpl_api.getMarketJingXuanRealRankingInfo()) market_sift_plate = json.loads(data)['list'] manager.append_data_to_file(market_sift_plate) manager.check_and_remove_oldest_entry(max_entries=1000) # 构建每日板块强度历史对象 class DailyHistoricalPlateIntensity: # 初始化 def __init__(self, file_path): # 移除默认值，要求调用时必须提供 self.file_path = file_path # 需要构建一个每日板块强度列表的函数 def get_data_list(self): # 读取每日板块强度文件 data_list = [] with open(constant.KPL_PLATE_STRENGTH_DATA_PATH, 'r', encoding='utf-8') as file: for line in file: if line.strip(): # 跳过空行 # print(f"line=={line}") data_list.append(json.loads(line)) # print(f"data_list={data_list}") return data_list # 返回数据 # 构建每日板块强度检索函数 def find_plate_name_in_data(self, data_list, plate_name): # data = [] data_list.reverse() daily_net_amount_dict = {} # 以日期进行检索 for i in data_list: # print(f"I={i}") for key, value in i.items(): # if key == '2025-07-17': for i in value: if i[1] == plate_name: print(f"{key} 板块:{i[1]} 主力净额:{round(i[6]/100000000,2)}亿") i_net_amount = round(i[6] / 100000000, 2) # data.append(i_net_amount) date_net_amount_dict = { key: i[6] } daily_net_amount_dict.update(date_net_amount_dict) # print(f"daily_net_amount_dict={daily_net_amount_dict}") return daily_net_amount_dict def calculate_plate_trend(plate_name): # 获取前7个交易日期列表 today_date = datetime.datetime.now().strftime("%Y-%m-%d") print(f"today_date={today_date}") TradingDatesManager = trading_dates_manager.TradingDatesManager() pre_trading_day = TradingDatesManager.get_pre_trading_day(today_date) print(f"pre_trading_day={pre_trading_day}") # 统计交易日数量 count = 7 pres_trading_date_list = TradingDatesManager.get_pres_trading_days(today_date, count = 7) logger_common.info(f"pres_trading_date_list=={pres_trading_date_list}") print(f"pres_trading_date_list=={pres_trading_date_list}") # 创建实例（传入 file_path） plate_intensity = DailyHistoricalPlateIntensity(file_path=constant.KPL_PLATE_STRENGTH_DATA_PATH) # 调用方法 data_list = plate_intensity.get_data_list() # 获取kpl板块强度历史数据 # 整理出板块主力净额 target_plate_net_amount = plate_intensity.find_plate_name_in_data(data_list, plate_name) # 查找板块 # print(f"target_plate_net_amount=={target_plate_net_amount}") target_plate_net_amount_list = [] for i in pres_trading_date_list: target_date_plate_net_amount = target_plate_net_amount.get(i) if target_date_plate_net_amount is not None: # print(f"{i}:{target_date_plate_net_amount}") target_date_plate_net_amount = round(target_date_plate_net_amount/100000000, 2) it_date_plate_net_amount_dict = { 'date': i, 'net_amount': target_date_plate_net_amount } target_plate_net_amount_list.append(target_date_plate_net_amount) # print(f"target_plate_net_amount_list=={target_plate_net_amount_list}") amount_list = target_plate_net_amount_list # 2. 遍历列表，检查每个字典的值是否符合要求（例如：值不为None或满足其他条件） # 统计None值数量 # none_count = sum(1 for item in amount_list if item['net_amount'] is None) # print(f"none_count=={none_count}") return target_plate_net_amount_list """主力净额形态分析器""" class MainForceAnalyzer: """主力净额形态分析器""" def __init__(self, window=5): """ 初始化分析器 参数: window: 分析窗口大小(默认5) """ self.window = window def analyze(self, net_amounts): """ 分析主力净额形态 参数: net_amounts: 主力净额列表(时间倒序)，元素为float或None 返回: dict: 包含综合评分、形态描述和详细特征的字典 """ # 数据校验 if not net_amounts or len(net_amounts) < self.window: return { "score": 0.0, "description": "数据不足", "details": {} } # 1. 数据预处理 processed, none_positions = self._preprocess_data(net_amounts) # 2. 分析窗口数据 window_data = processed[:self.window] # 3. 计算核心指标 strength = self._calculate_strength(window_data) trend = self._calculate_trend(window_data) reversal, reversal_type = self._detect_reversal(window_data, none_positions) volatility = self._calculate_volatility(window_data) valid_ratio = 1.0 - len(none_positions) / len(net_amounts) # 4. 综合评分计算 score = self._calculate_composite_score( strength=strength, trend=trend, reversal=reversal, volatility=volatility, valid_ratio=valid_ratio ) # 5. 生成形态描述 description, details = self._generate_description( score=score, strength=strength, trend=trend, reversal_type=reversal_type, volatility=volatility ) return { "score": score, "description": description, "details": details } def _preprocess_data(self, net_amounts): """数据预处理：处理None值并记录位置""" processed = [] none_positions = [] moving_avg = [] for i, amount in enumerate(net_amounts): if amount is None: none_positions.append(i) if moving_avg: fill_value = sum(moving_avg) / len(moving_avg) processed.append(fill_value) else: processed.append(0) else: processed.append(amount) # 保持最近3个有效值 if len(moving_avg) < 3: moving_avg.append(amount) else: moving_avg = moving_avg[1:] + [amount] return processed, none_positions def _calculate_strength(self, data): """计算主力强度指标""" inflow = sum(v for v in data if v > 0) outflow = sum(-v for v in data if v < 0) total = inflow + outflow if total > 0: return (inflow - outflow) / total return 0 def _calculate_trend(self, data): """计算趋势指标""" if len(data) >= 2: return data[0] - data[1] return 0 def _calculate_volatility(self, data): """计算波动性指标""" if len(data) < 2: return 0 mean = sum(data) / len(data) variance = sum((x - mean) ** 2 for x in data) / len(data) return variance ** 0.5 def _detect_reversal(self, data, none_positions): """检测反转信号""" reversal_score = 0 reversal_type = "无反转信号" # 检查三日反转模式 if len(data) >= 3: if all(i not in none_positions for i in range(3)): # 反转加强：连续两日流出后大幅流入 if data[0] > 0 and data[1] < 0 and data[2] < 0: avg_outflow = (abs(data[1]) + abs(data[2])) / 2 reversal_strength = data[0] / avg_outflow reversal_score = min(1.0, reversal_strength * 0.5) reversal_type = "反转加强" # 反转走弱：连续两日流入后大幅流出 elif data[0] < 0 and data[1] > 0 and data[2] > 0: avg_inflow = (data[1] + data[2]) / 2 reversal_strength = abs(data[0]) / avg_inflow reversal_score = -min(1.0, reversal_strength * 0.5) reversal_type = "反转走弱" # 检查两日反转模式 if len(data) >= 2 and reversal_type == "无反转信号": if 0 not in none_positions and 1 not in none_positions: # 流入转流出 if data[0] < 0 and data[1] > 0: reversal_score = -0.3 reversal_type = "流入转流出" # 流出转流入 elif data[0] > 0 and data[1] < 0: reversal_score = 0.3 reversal_type = "流出转流入" return reversal_score, reversal_type def _calculate_composite_score(self, strength, trend, reversal, volatility, valid_ratio): """计算综合评分""" # 标准化趋势值 normalized_trend = trend / 10.0 if abs(trend) > 0 else 0 # 波动性因子（波动性越高，评分越低） volatility_factor = 1 - min(volatility / 5.0, 1) # 综合评分公式 score = ( 0.4 * strength + 0.3 * normalized_trend + 0.2 * reversal + 0.1 * volatility_factor ) * valid_ratio # 确保在-1到1范围内 return max(-1.0, min(1.0, score)) def _generate_description(self, score, strength, trend, reversal_type, volatility): """生成形态描述和详细特征""" # 基础形态描述 if score > 0.8: base_desc = "强势流入加速" elif score > 0.6: base_desc = "持续流入加强" elif score > 0.4: base_desc = "温和流入" elif score > 0.2: base_desc = "小幅净流入" elif score > -0.2: base_desc = "震荡调整" elif score > -0.4: base_desc = "小幅净流出" elif score > -0.6: base_desc = "温和流出" elif score > -0.8: base_desc = "持续流出" else: base_desc = "强势流出" # 添加反转描述 if reversal_type != "无反转信号": # 组合反转描述和基础描述 description = f"{reversal_type}后{base_desc}" else: description = base_desc # 添加波动性描述 if volatility > 5: description += " (高波动)" elif volatility > 2: description += " (中波动)" # 创建详细特征字典 details = { "主力强度": f"{strength:.2%}", "短期趋势": f"{trend:.2f}亿", "波动率": f"{volatility:.2f}", "反转信号": reversal_type, "分析窗口": self.window } return description, details # 策略中分析板块趋势 def analyze_plate_trend(plate_name): target_data = calculate_plate_trend(plate_name) print(f"target_data = {target_data}") print(f"target_data type ={type(target_data)}") # logger_common.info(f"主力净额数据【{plate_name}】:") # for i, amount in enumerate(target_data[:5]): # 只显示最近5日 # if amount is None: # logger_common.info(f"第{i + 1}日: N/A") # else: # logger_common.info(f"第{i + 1}日: {amount:.2f}亿") # 创建分析器实例 analyzer = MainForceAnalyzer(window=7) # 执行分析 result = analyzer.analyze(target_data) result["plate_name"] = "plate_name" # 打印结果 # print("\n分析结果:") # logger_common.info(f"综合评分: {result['score']:.4f}") # logger_common.info(f"形态描述: {result['description']}") # print(f"形态描述: {result['description']}") # logger_common.info("详细特征:") # for k, v in result['details'].items(): # logger_common.info(f" - {k}: {v}") # 示例2：使用不同窗口大小分析 # print("\n不同窗口大小比较:") # for window in [3, 5, 7]: # analyzer = MainForceAnalyzer(window=window) # result = analyzer.analyze(target_data) # print(f"窗口={window}天: 评分={result['score']:.2f}, 描述={result['description']}") # # 示例3：分析多个板块 # sectors = { # "医药": target_data, # "科技": [15.2, -4.5, -3.8, 12.1, None, -5.2, 18.3], # "新能源": [8.5, 7.2, -2.5, 6.8, 5.5, None, 9.2] # } # # print("\n板块分析比较:") # analyzer = MainForceAnalyzer(window=5) # for sector, data in sectors.items(): # result = analyzer.analyze(data) # print(f"{sector}板块: 评分={result['score']:.2f}, 描述={result['description']}") # calculate_plate_trend('机器人概念') return result # 将积累好的过滤文件写入本地存储 def write_filtered_file_local_storage(): with open(constant.FILTERED_STOCK_PATH, "w", encoding="utf-8") as file: json.dump(list(data_cache.filtered_stock_info_list), file, ensure_ascii=False, indent=4) logger_common.info(f"filtered_stock_info_list===》JSON文件已写入: {data_cache.filtered_stock_info_list}") print(f"filtered_stock_info_list===》JSON文件已写入: {data_cache.filtered_stock_info_list}") # 将写好的过滤文件本地文件读取出来 def read_filtered_file_local_storage(): """ 从本地存储读取过滤后的股票信息文件 返回: list: 包含股票信息的列表（原本可能是set，但JSON会转为list） """ with open(constant.FILTERED_STOCK_PATH, "r", encoding="utf-8") as file: return json.load(file) # 字典元素的列表去重函数 def deduplicate_dict_list(dict_list): seen = set() unique_list = [] for d in dict_list: # 将字典转换为可哈希的元组（按字段排序确保一致性） tuple_repr = tuple(sorted(d.items())) if tuple_repr not in seen: seen.add(tuple_repr) unique_list.append(d) return unique_list # 检查因强度中的过滤因素而被错过的涨停函数【建立在策略13:00之前不重启的基础上】 def check_intensity_missing_limit_up_stock(): filtered_stock_info_list = [] try: filtered_stock_info = read_filtered_file_local_storage() logger_common.info(f"filtered_stock_info_list 成功读取到 {len(filtered_stock_info)} 条股票记录") print(f"filtered_stock_info_list 成功读取到 {len(filtered_stock_info)} 条股票记录") # 集合后去重重回列表格式 filtered_stock_info_list = deduplicate_dict_list(filtered_stock_info) except Exception as e: logger_common.info(f"读取过滤上季度机构持仓信息本地文件失败: {str(e)}") print(f"读取过滤上季度机构持仓信息本地文件失败: {str(e)}") # 实例化每日涨停信息整理方法 today_limit_up_info = kpl_api.get_limit_up_info() # print(today_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}") today_limit_up_code_list = [] # 整理出当日的涨停列表 if today_limit_up_info: for i in today_limit_up_info: limit_up_code = i[0] today_limit_up_code_list.append(limit_up_code) print(f"today_limit_up_code_list=={today_limit_up_code_list}") logger_common.info(f"当日截止收盘 涨停股票数量=={len(today_limit_up_code_list)}") logger_common.info(f"当日截止收盘 涨停代码列表=={today_limit_up_code_list}") filtered_stock_is_in_limit_up = False for code in today_limit_up_code_list: for filtered_stock_info in filtered_stock_info_list: logger_common.info(f"逐个打印过滤个股信息: {filtered_stock_info}") if filtered_stock_info["code"] == code: print(f"错过的涨停股票信息: {filtered_stock_info}") logger_common.info(f"错过的涨停股票信息: {filtered_stock_info}") filtered_stock_is_in_limit_up = True logger_common.info(f"涨停之中有被过滤的吗? 答:{filtered_stock_is_in_limit_up}") print(f"涨停之中会被过滤的吗? 答:{filtered_stock_is_in_limit_up}") if __name__ == '__main__': # analyze_plate_trend('人工智能') # 每日收盘后把 写好的日志下载到本地 就可以使用以下函数检测了 check_intensity_missing_limit_up_stock()