跟新全部文件
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rm
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*.pyc
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.idea/
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*.log
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@ -4,7 +4,7 @@ from DB.model.StockDailyFreq import get_stock_daily_freq
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from DB.sqlite_db_main import SqliteDbMain, config
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from utils.tdxUtil import TdxUtil
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from utils.comm import *
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from 基本信息入库 import StockInfoMain
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from fp.基本信息入库 import StockInfoMain
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class StockDailyFreqMain:
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import sys
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from loguru import logger
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from DB.model.StockDaily import get_stock_daily
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from DB.sqlite_db_main import SqliteDbMain, config
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from utils.tdxUtil import TdxUtil
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from utils.comm import *
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from fp.基本信息入库 import StockInfoMain
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class StockDailyMain:
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def __init__(self, ts_code=None, symbol=None, restart_id=0):
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# 配置日志输出到文件和控制台
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logger.add("../log/StockDailyMain.log", rotation="500 MB", level="INFO")
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logger.add(sys.stderr, level="INFO")
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self.code_res = StockInfoMain().get_stock_basic(ts_code=ts_code, symbol=symbol, restart_id=restart_id)
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self.db_main = SqliteDbMain(config.stock_daily_db)
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self.tdx_util = TdxUtil("")
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def __filter_stock(self, code, name):
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if 'ST' in name:
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return False
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if code.startswith('30'):
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return False
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if code.startswith('68'):
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return False
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return True
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def init_data(self):
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"""
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全量入库操作
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:return:
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"""
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for result in self.code_res:
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tdx_util = TdxUtil("")
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s_type = tdx_util.get_security_type(code=result.ts_code)
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if s_type in tdx_util.SECURITY_TYPE and self.__filter_stock(result.ts_code, result.name):
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logger.info(f"{result.id}，{result.ts_code}，{result.name} 开始获取 daily 和 daily_basic_ts 数据！")
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self.save_by_code(result.ts_code)
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def save_by_code(self, ts_code):
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df, df_basic = self.get_daily_data(ts_code=ts_code)
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if df is not None and df_basic is not None:
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# 创建表
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table_name = str(ts_code).split(".")[0] + "_daily"
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new_table_class = get_stock_daily(table_name=table_name)
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self.db_main.create_table(new_table_class)
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entries = []
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for index, row in df.iterrows():
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if '停牌' != row['trade_status']:
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basic_row = df_basic[df_basic['trade_date'] == row['trade_date']]
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if len(basic_row) > 0:
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basic_row = basic_row.drop(columns=['ts_code', 'trade_date', 'close'])
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if not basic_row.empty:
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row = pd.concat([row, basic_row.iloc[0]])
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entry = new_table_class(**row)
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entries.append(entry)
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# 保存记录
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self.db_main.insert_all_entry(entries)
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def task_data(self, trade_date=datetime.now().strftime('%Y%m%d')):
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"""
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每日定时任务，补充数据操作
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:param trade_date:
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:return:
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"""
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df, df_basic = pd.DataFrame(), pd.DataFrame()
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# 日线行情数据
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for _ in range(5):
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try:
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df = xcsc_pro.daily(trade_date=trade_date)[::-1]
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break
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except Exception as e:
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logger.error(f"获取 [{trade_date}] 日线行情 出现问题：", e)
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time.sleep(1)
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continue
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else:
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logger.error("daily 重试5次依然没有解决问题，请重视~！！！")
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# 获取全部股票每日重要的基本面指标
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for _ in range(5):
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try:
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df_basic = xcsc_pro.daily_basic_ts(trade_date=trade_date)[::-1]
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break
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except Exception as e:
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logger.error(f"获取 [{trade_date}] daily_basic_ts 出现问题：", e)
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time.sleep(1)
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continue
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else:
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logger.error("daily_basic_ts 重试5次依然没有解决问题，请重视~！！！")
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df_basic = df_basic.fillna(0)
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print(f" 获取 daily 和 daily_basic_ts 数据完毕！")
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for index, row in df.iterrows():
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ts_code = row['ts_code']
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tdx_util = TdxUtil("")
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s_type = tdx_util.get_security_type(code=ts_code)
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if s_type in tdx_util.SECURITY_TYPE and '交易' == row['trade_status']:
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basic_row = df_basic[df_basic['ts_code'] == row['ts_code']]
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basic_row = basic_row.drop(columns=['ts_code', 'trade_date', 'close'])
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row = pd.concat([row, basic_row.iloc[0]])
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table_name = str(ts_code).split(".")[0] + "_daily"
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new_table_class = get_stock_daily(table_name=table_name)
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entry = new_table_class(**row)
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# 判断表是否存在
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if self.db_main.has_table(entry):
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self.db_main.insert_entry(entry)
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# 批量创建trade_date唯一索引
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def create_index(self):
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for result in self.code_res:
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tdx_util = TdxUtil("")
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s_type = tdx_util.get_security_type(code=result.ts_code)
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if s_type in tdx_util.SECURITY_TYPE and self.__filter_stock(result.ts_code, result.name):
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print(f"{result.id}，{result.ts_code}，{result.name} 开始创建索引！")
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table_name = str(result.ts_code).split(".")[0] + "_daily"
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sql = f"CREATE UNIQUE INDEX idx_unique_trade_date_{table_name} ON `{table_name}`(trade_date)"
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self.db_main.execute_sql(sql)
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# 删除指定日期的数据
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def del_data_by_date(self, trade_date):
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for result in self.code_res:
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tdx_util = TdxUtil("")
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s_type = tdx_util.get_security_type(code=result.ts_code)
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if s_type in tdx_util.SECURITY_TYPE and self.__filter_stock(result.ts_code, result.name):
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table_name = str(result.ts_code).split(".")[0] + "_daily"
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new_table_class = get_stock_daily(table_name=table_name)
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# 判断表是否存在
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if self.db_main.has_table(new_table_class):
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delete_condition = new_table_class.trade_date == trade_date
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self.db_main.delete_by_condition(new_table_class, delete_condition)
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def get_daily_data(self, ts_code):
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i = 0
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df, df_basic = None, None
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while True:
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try:
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if i > 6:
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break
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# 尝试执行请求操作
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# 如果成功，可以跳出循环
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df = xcsc_pro.daily(ts_code=ts_code)[::-1]
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df_basic = xcsc_pro.daily_basic_ts(ts_code=ts_code)[::-1]
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df_basic = df_basic.fillna(0)
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logger.info(f"{ts_code} 获取 daily 和 daily_basic_ts 数据完毕！")
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break
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except Exception as e:
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i += 1
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# 捕获超时异常
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logger.warning("请求超时，等待2分钟后重试...")
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time.sleep(120) # 休眠2分钟
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return df, df_basic
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def update_stock_basic(self):
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"""
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更新基础表，补齐股票表
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:return:
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"""
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# 1. 取 中国A股基本资料
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StockInfoMain().insert_stock_basic()
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self.code_res = StockInfoMain().get_stock_basic()
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for result in self.code_res:
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s_type = self.tdx_util.get_security_type(code=result.ts_code, name=result.name)
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if s_type not in self.tdx_util.SECURITY_TYPE:
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continue
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# 创建表
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table_name = str(result.ts_code).split(".")[0] + "_daily"
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new_table_class = get_stock_daily(table_name=table_name)
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# 2. 判断表是否存在,不存在则创建
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if not self.db_main.has_table(new_table_class):
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logger.info(f"{result.ts_code} 表不存在，开始创建！")
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# 3. 补齐数据
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self.save_by_code(result.ts_code)
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if __name__ == '__main__':
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# current_date = datetime.now()
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current_date = datetime.strptime('20231024', "%Y%m%d")
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if if_run(current_date):
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trade_date = current_date.strftime('%Y%m%d')
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main = StockDailyMain()
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# main.init_data() # 初始化全市场的数据
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main.task_data(trade_date) # 指定日期数据入库
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# main.create_index() # 创建唯一索引
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# main.del_data_by_date('20230915') # 删除指定日期数据
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# main.save_by_code(ts_code='000050.SZ') # 指定更新的股票
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# main.update_stock_basic()
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from pyecharts import options as opts
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from pyecharts.charts import Bar
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from DB.model.ConceptSector import ConceptSector
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from DB.model.IndustrySector import IndustrySector
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from DB.model.StockByConceptSector import StockByConceptSector
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from DB.model.StockByIndustrySector import StockByIndustrySector
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from DB.sqlite_db_main import SqliteDbMain, config
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from utils.comm import *
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from utils.stock_web_api import execute_stock_web_api_method, stock_web_api_industry_summary, \
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stock_web_api_concept_name
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class SectorOpt:
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def __init__(self, trade_date=datetime.now()):
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self.trade_date = trade_date.strftime('%Y%m%d')
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self.db_main = SqliteDbMain(config.stock_sector_db)
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def save_hy_sector(self):
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"""
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行业板块
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:return:
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"""
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print("开始拉取行业板块数据--->>")
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hy_sector_df = execute_stock_web_api_method(func=stock_web_api_industry_summary)
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if hy_sector_df is None:
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return None
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entries = []
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for index, row in hy_sector_df.iterrows():
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entry = IndustrySector(
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trade_date=self.trade_date,
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sector_name=row['板块'],
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pct_change=row['涨跌幅'],
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total_volume=row['总成交量'],
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total_turnover=row['总成交额'],
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net_inflows=row['净流入'],
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rising_count=row['上涨家数'],
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falling_count=row['下跌家数'],
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average_price=row['均价'],
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leading_stock=row['领涨股'],
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leading_stock_latest_price=row['领涨股-最新价'],
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leading_stock_pct_change=row['领涨股-涨跌幅']
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)
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entries.append(entry)
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self.db_main.insert_all_entry(entries)
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print("<<---行业板块数据拉取结束")
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self.db_main.get_db_size()
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def save_gn_sector(self):
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"""
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概念板块
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:return:
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"""
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print("开始拉取概念板块数据--->>")
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# 概念当日的涨幅情况
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ths_gn_df = execute_stock_web_api_method(func=stock_web_api_concept_name)
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if ths_gn_df is None:
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return None
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entries = []
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for index, row in ths_gn_df.iterrows():
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entry = ConceptSector(
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trade_date=self.trade_date,
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sector_name=row['板块名称'],
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sector_code=row['板块代码'],
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pct_change=row['涨跌幅'],
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total_market=row['总市值'],
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rising_count=row['上涨家数'],
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falling_count=row['下跌家数'],
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new_price=row['最新价'],
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leading_stock=row['领涨股票'],
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leading_stock_pct_change=row['领涨股票-涨跌幅']
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)
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entries.append(entry)
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self.db_main.insert_all_entry(entries)
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print("<<---概念板块数据拉取结束")
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self.db_main.get_db_size()
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def stock_by_gn_sector(self):
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"""
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获取概念板块个股（建议每天更新一次）
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:return:
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"""
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for _ in range(5):
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try:
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# 清空表数据
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self.db_main.delete_all_table(StockByConceptSector)
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ths_gn_df = ak.stock_board_concept_name_em()
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break
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except Exception as e:
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print("概念板块个股数据拉取错误：", e)
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time.sleep(1)
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continue
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else:
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print("概念板块个股数据拉取失败!!!")
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return None
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entries = []
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for index, row in ths_gn_df.iterrows():
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bk, bk_code = row['板块名称'], row['板块代码']
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print(f"----------- {bk} -------------")
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for _ in range(5):
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try:
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stock_gn_df = ak.stock_board_concept_cons_em(symbol=bk)
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break
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except Exception as e:
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print("概念板块个股-板块成份股 数据拉取错误：", e)
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time.sleep(1)
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continue
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else:
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print("概念板块个股-板块成份股 数据拉取失败!!!")
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return None
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print(stock_gn_df['代码'])
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for s_index, s_row in stock_gn_df.iterrows():
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entry = StockByConceptSector(
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update_date=datetime.now().strftime('%Y%m%d'),
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sector_name=bk,
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sector_code=row['板块代码'],
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stock_name=s_row['名称'],
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stock_code=s_row['代码'],
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)
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entries.append(entry)
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self.db_main.insert_all_entry(entries)
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def stock_by_hy_sector(self):
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"""
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获取行业板块个股（建议每周更新一次）
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:return:
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"""
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for _ in range(5):
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try:
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# 清空表数据
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self.db_main.delete_all_table(StockByIndustrySector)
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hy_sector_df = ak.stock_board_industry_summary_ths()
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break
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except Exception as e:
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print("行业板块个股数据拉取错误：", e)
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time.sleep(1)
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continue
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else:
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print("行业板块个股数据拉取失败!!!")
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return None
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entries = []
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for index, row in hy_sector_df.iterrows():
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bk, bk_code = row['板块'], row['序号']
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print(f"----------- {bk} -------------")
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for _ in range(5):
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try:
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stock_bk_df = ak.stock_board_industry_cons_ths(symbol=bk)
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break
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except Exception as e:
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print("行业板块个股数据拉取错误：", e)
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time.sleep(1)
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continue
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else:
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print("行业板块个股数据拉取失败!!!")
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return None
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for s_index, s_row in stock_bk_df.iterrows():
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entry = StockByIndustrySector(
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update_date=datetime.now().strftime('%Y%m%d'),
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sector_name=bk,
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sector_code=row['序号'],
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stock_name=s_row['名称'],
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stock_code=s_row['代码'],
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)
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entries.append(entry)
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self.db_main.insert_all_entry(entries)
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def get_hy_sector_by_stock_code(self, symbol):
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"""
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根据股票代码查询所属 行业板块
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:param symbol:
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:return:
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"""
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query = self.db_main.session.query(StockByIndustrySector) \
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.filter(StockByIndustrySector.stock_code == symbol).order_by(StockByIndustrySector.id)
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return self.db_main.pandas_query_by_sql(stmt=query.statement).reset_index()
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def get_gn_sector_by_stock_code(self, symbol):
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"""
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根据股票代码查询所属 概念板块
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:param symbol:
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:return:
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"""
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query = self.db_main.session.query(StockByConceptSector) \
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.filter(StockByConceptSector.stock_code == symbol).order_by(StockByConceptSector.id)
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return self.db_main.pandas_query_by_sql(stmt=query.statement).reset_index()
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def industry_generate_chart(self):
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"""
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行业板块
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:return:
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"""
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industry_num_limit_sql = f"select sector_name from stock_industry_sector where trade_date = '{self.trade_date}' ORDER BY pct_change desc limit 15"
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names_res = self.db_main.execute_sql(industry_num_limit_sql)
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# 提取结果中的单个列数据（假设你要提取 "收盘价" 这一列）
|
||||
sector_names = [result.sector_name for result in names_res]
|
||||
|
||||
data_sql = f"SELECT sec.trade_date,sec.pct_change from " \
|
||||
f"stock_industry_sector sec , " \
|
||||
f"({industry_num_limit_sql}) sec_n " \
|
||||
f"where sec.sector_name = sec_n.sector_name " \
|
||||
f"and trade_date in (select trade_date from stock_industry_sector GROUP BY trade_date order by trade_date desc LIMIT 3)"
|
||||
data_result = self.db_main.execute_sql(data_sql)
|
||||
df = pd.DataFrame(data_result, columns=['trade_date', 'pct_change'])
|
||||
# print(df)
|
||||
grouped = df.groupby('trade_date')['pct_change'].apply(list).reset_index()
|
||||
|
||||
self.__generate_chart("行业板块 —— 复盘", grouped, sector_names, 'industry_generate_chart')
|
||||
|
||||
def concept_generate_chart(self):
|
||||
"""
|
||||
概念板块
|
||||
:return:
|
||||
"""
|
||||
concept_num_limit_sql = f"select sector_name from stock_concept_sector where trade_date = '{self.trade_date}' ORDER BY pct_change desc limit 15"
|
||||
names_res = self.db_main.execute_sql(concept_num_limit_sql)
|
||||
# 提取结果中的单个列数据（假设你要提取 "收盘价" 这一列）
|
||||
sector_names = [result.sector_name for result in names_res]
|
||||
|
||||
data_sql = f"SELECT sec.trade_date,sec.pct_change from " \
|
||||
f"stock_concept_sector sec , " \
|
||||
f"({concept_num_limit_sql}) sec_n " \
|
||||
f"where sec.sector_name = sec_n.sector_name " \
|
||||
f"and trade_date in (select trade_date from stock_concept_sector GROUP BY trade_date order by trade_date desc LIMIT 3)"
|
||||
data_result = self.db_main.execute_sql(data_sql)
|
||||
df = pd.DataFrame(data_result, columns=['trade_date', 'pct_change'])
|
||||
# print(df)
|
||||
grouped = df.groupby('trade_date')['pct_change'].apply(list).reset_index()
|
||||
|
||||
self.__generate_chart("概念板块 —— 复盘", grouped, sector_names, 'concept_generate_chart')
|
||||
|
||||
def __generate_chart(self, title, grouped, sector_names, file_name):
|
||||
c = (
|
||||
Bar(init_opts=opts.InitOpts(width="100%", height="400px"))
|
||||
.add_xaxis(sector_names)
|
||||
.add_yaxis(grouped.loc[0, 'trade_date'], grouped.loc[0, 'pct_change'])
|
||||
.add_yaxis(grouped.loc[1, 'trade_date'], grouped.loc[1, 'pct_change'])
|
||||
.add_yaxis(grouped.loc[2, 'trade_date'], grouped.loc[2, 'pct_change'])
|
||||
.set_global_opts(
|
||||
title_opts=opts.TitleOpts(title=title, pos_left="center"),
|
||||
yaxis_opts=opts.AxisOpts(offset=5),
|
||||
xaxis_opts=opts.AxisOpts(offset=5, axislabel_opts=opts.LabelOpts(rotate=45, interval=0)),
|
||||
legend_opts=opts.LegendOpts(pos_top="8%")
|
||||
)
|
||||
.render(f"../html/{self.trade_date}/{file_name}.html")
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
current_date = datetime.now()
|
||||
if if_run(current_date):
|
||||
sector = SectorOpt(current_date)
|
||||
# 行业板块
|
||||
# sector.save_hy_sector()
|
||||
# 概念板块
|
||||
# sector.save_gn_sector()
|
||||
# 生成报表
|
||||
# sector.industry_generate_chart()
|
||||
# sector.concept_generate_chart()
|
||||
# -------》 一下建议一周更新一次
|
||||
# 行业板块个股
|
||||
sector.stock_by_hy_sector()
|
||||
# 感念板块个股
|
||||
sector.stock_by_gn_sector()
|
||||
|
|
@ -0,0 +1,98 @@
|
|||
from pyecharts.charts import Line
|
||||
from pyecharts import options as opts
|
||||
|
||||
from utils.comm import *
|
||||
|
||||
|
||||
class LimitList:
|
||||
|
||||
def __init__(self, trade_date):
|
||||
self.trade_date = trade_date.strftime('%Y%m%d')
|
||||
self.start_date = return_trading_day(trade_date, -7)
|
||||
self.end_date = self.trade_date
|
||||
self.limit_df = pd.DataFrame()
|
||||
for _ in range(5):
|
||||
try:
|
||||
# 涨跌停列表
|
||||
self.limit_df = xcsc_pro.limit_list_d(start_date=self.start_date, end_date=self.end_date)
|
||||
break
|
||||
except Exception as e:
|
||||
print("涨跌停列表数据获取失败，", e)
|
||||
time.sleep(1)
|
||||
continue
|
||||
else:
|
||||
print("涨跌停列表数据获取失败次数过多，请关注！！！")
|
||||
|
||||
def limit_list_chart(self):
|
||||
# 按照 trade_date 和 limit 进行分组统计
|
||||
grouped = self.limit_df.groupby(['trade_date', 'limit']).size().reset_index(name='count')
|
||||
|
||||
# 使用 pivot_table 将 limit 类型作为列，统计数量
|
||||
pivot_table = grouped.pivot_table(index='trade_date', columns='limit', values='count', fill_value=0)
|
||||
# print(pivot_table)
|
||||
# D：跌停，U：涨停，Z：炸板
|
||||
# 获取不同 limit 类型的数据
|
||||
u_list = pivot_table['U'].tolist()
|
||||
d_list = pivot_table['D'].tolist()
|
||||
z_list = pivot_table['Z'].tolist()
|
||||
trade_dates = pivot_table.index.tolist()
|
||||
|
||||
c = (
|
||||
Line(init_opts=opts.InitOpts(width="100%", height="380px"))
|
||||
.add_xaxis(trade_dates)
|
||||
.add_yaxis("跌停数", d_list)
|
||||
.add_yaxis("涨停数", u_list)
|
||||
.add_yaxis("炸板数", z_list)
|
||||
.set_global_opts(
|
||||
tooltip_opts=opts.TooltipOpts(trigger="axis"),
|
||||
title_opts=opts.TitleOpts(title="涨跌停 —— 复盘", pos_left="center"),
|
||||
yaxis_opts=opts.AxisOpts(offset=5),
|
||||
xaxis_opts=opts.AxisOpts(offset=5, axislabel_opts=opts.LabelOpts(rotate=45, interval=0)),
|
||||
legend_opts=opts.LegendOpts(pos_top="8%")
|
||||
)
|
||||
.render(f"../html/{self.trade_date}/limit_list_chart.html")
|
||||
)
|
||||
|
||||
def html_page_data(self):
|
||||
"""
|
||||
生成HTML文件
|
||||
:return:
|
||||
"""
|
||||
print(self.limit_df)
|
||||
df = self.limit_df[self.limit_df['trade_date'] == self.end_date].fillna(0).sort_values(
|
||||
by=["industry", "limit_times"],
|
||||
ascending=False).reset_index(drop=True)
|
||||
|
||||
table_content = f'<table class="table table-hover"><thead>' \
|
||||
f'<tr>' \
|
||||
f'<th scope="col">股票代码</th>' \
|
||||
f'<th scope="col">所属行业</th>' \
|
||||
f'<th scope="col">股票名称</th>' \
|
||||
f'<th scope="col">换手率</th>' \
|
||||
f'<th scope="col">连板数</th>' \
|
||||
f'<th scope="col">状态</th>' \
|
||||
f'</tr>' \
|
||||
f'</thead>' \
|
||||
f'<tbody>'
|
||||
|
||||
for index, row in df.iterrows():
|
||||
table_content += f'<tr><th scope="row">{row["ts_code"]}</th>' \
|
||||
f'<td>{row["industry"]}</td>' \
|
||||
f'<td>{row["name"]}</td>' \
|
||||
f'<td>{row["turnover_ratio"]}</td>' \
|
||||
f'<td>{row["limit_times"]}</td>'
|
||||
if row['limit'] == 'D':
|
||||
table_content += f'<td class="table-success">跌停</td>'
|
||||
elif row['limit'] == 'U':
|
||||
table_content += f'<td class="table-danger">涨停</td>'
|
||||
elif row['limit'] == 'Z':
|
||||
table_content += f'<td class="table-warning">炸板</td>'
|
||||
table_content += '</tr>'
|
||||
table_content += f'</tbody></table>'
|
||||
return table_content
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
limit = LimitList(datetime.strptime('20231108', "%Y%m%d"))
|
||||
# limit.limit_list_chart()
|
||||
limit.html_page_data()
|
||||
|
|
@ -0,0 +1,309 @@
|
|||
import sys
|
||||
|
||||
from sqlalchemy import and_
|
||||
|
||||
from DB.model.StockDaily import get_stock_daily
|
||||
from DB.sqlite_db_main import SqliteDbMain, config
|
||||
from utils.tdxUtil import TdxUtil
|
||||
from fp.基本信息入库 import StockInfoMain
|
||||
from utils.formula import *
|
||||
from loguru import logger
|
||||
|
||||
from fp.板块数据入库 import SectorOpt
|
||||
from utils.comm import *
|
||||
|
||||
|
||||
class OptionalStock:
|
||||
def __init__(self, ts_code=None, symbol=None, restart_id=0, trade_date=datetime.now()):
|
||||
self.trade_date = trade_date.strftime('%Y%m%d')
|
||||
# 配置日志输出到文件和控制台
|
||||
logger.add("../log/OptionalStock.log", rotation="500 MB", level="INFO")
|
||||
logger.add(sys.stderr, level="INFO")
|
||||
self.code_res = StockInfoMain().get_stock_basic(ts_code=ts_code, symbol=symbol, restart_id=restart_id)
|
||||
self.db_main = SqliteDbMain(config.stock_daily_db)
|
||||
self.tdx_util = TdxUtil("")
|
||||
self.sector = SectorOpt()
|
||||
|
||||
def stock_daily(self):
|
||||
rm_strategy_df, dbj_strategy_df = pd.DataFrame(), pd.DataFrame()
|
||||
for result in self.code_res:
|
||||
s_type = self.tdx_util.get_security_type(code=result.ts_code, name=result.name)
|
||||
if s_type in self.tdx_util.SECURITY_TYPE:
|
||||
table_name = str(result.ts_code).split(".")[0] + "_daily"
|
||||
new_table_class = get_stock_daily(table_name=table_name)
|
||||
df = self.db_main.pandas_query_by_model(
|
||||
model=new_table_class,
|
||||
order_col=new_table_class.id.desc(),
|
||||
page_number=1, page_size=60
|
||||
)[::-1]
|
||||
if len(df) > 0:
|
||||
logger.info(
|
||||
f"----------------<<{self.trade_date}：{result.ts_code} 开始执行 N日新高策略>>----------------")
|
||||
rm_strategy_df = rm_strategy_df.append(self.n_high_strategy(df=df))
|
||||
logger.info(
|
||||
f"----------------<<{self.trade_date}：{result.ts_code} 开始执行 连板策略>>----------------")
|
||||
dbj_strategy_df = dbj_strategy_df.append(self.conn_plate_strategy(df=df))
|
||||
return rm_strategy_df, dbj_strategy_df
|
||||
|
||||
def n_high_strategy(self, df):
|
||||
rm_strategy_df = pd.DataFrame()
|
||||
rm_kdj = RM_KDJ(df['close'], df['high'], df['low'], 9, 3, 3)
|
||||
df["KDJ_K"], df["KDJ_D"], df["KDJ_J"] = rm_kdj["KDJ_K"], rm_kdj["KDJ_D"], rm_kdj["KDJ_J"]
|
||||
total, rise, fall, tomorrow_rise, tomorrow_fall = 0, 0, 0, 0, 0
|
||||
for index, row in df.iterrows():
|
||||
trade_date, code, close = row["trade_date"], row['ts_code'], row['close']
|
||||
d, j = row['KDJ_D'], row['KDJ_J']
|
||||
# 返回所要找的数据
|
||||
if trade_date == self.trade_date:
|
||||
# TODO 这里有问题，d和J相同
|
||||
logger.info(f'{code} -- >> {trade_date} -->> d={d} --> j={j}')
|
||||
# 超买区间，未来跌幅
|
||||
if d > 80 and j > 100:
|
||||
rm_strategy_df = rm_strategy_df.append(row)
|
||||
# rise, tomorrow_rise, total = self.back_testing(df, index, rise, tomorrow_rise, total, trade_date)
|
||||
rm_strategy_df.reset_index(drop=True, inplace=True)
|
||||
# if total > 0:
|
||||
# console = f"满足策略：{total} 次，明日上涨概率：{round((tomorrow_rise / total) * 100, 2)} %，" \
|
||||
# f"三日后上涨概率：{round((rise / total) * 100, 2)} %"
|
||||
# # logger.info(console)
|
||||
# rm_strategy_df['history'] = console
|
||||
return rm_strategy_df
|
||||
|
||||
def back_testing(self, df, index, rise, tomorrow_rise, total, trade_date):
|
||||
if index > 3:
|
||||
# 明天继续下跌的概率
|
||||
tomorrow_pre = df.iloc[-index:-index + 1]['pct_chg'].values[0]
|
||||
# 未来三天最大涨幅
|
||||
max_h = df.iloc[-index:-index + 3]['pct_chg'].max().round(2)
|
||||
# 计算最大回撤
|
||||
h_max = df.iloc[-index:-index + 3]['high'].max()
|
||||
l_min = df.iloc[-index:-index + 3]['low'].min()
|
||||
mac_ret = f"{(h_max - l_min) / l_min:.2%}"
|
||||
# 平均涨幅
|
||||
mean_h = df.iloc[-index:-index + 3]['pct_chg'].mean().round(2)
|
||||
total += 1
|
||||
content = f"{trade_date} --> 明日涨幅： {tomorrow_pre}% " \
|
||||
f"--> 3日最大涨幅：{max_h}%" \
|
||||
f"--> 3日最大回撤：{mac_ret}" \
|
||||
f"--> 3日平均涨幅：{mean_h}"
|
||||
# if trade_date > "20230101":
|
||||
# logger.info(content)
|
||||
# print(content)
|
||||
if tomorrow_pre > 0:
|
||||
tomorrow_rise += 1
|
||||
if max_h > 0:
|
||||
rise += 1
|
||||
return rise, tomorrow_rise, total
|
||||
|
||||
def ma_strategy(self, df):
|
||||
# MA5:当MA5在全部MA的最上面时
|
||||
MA5, MA10, MA20, MA60 = MA(df['close'], 5), MA(df['close'], 10), MA(df['close'], 20), MA(
|
||||
df['close'], 60)
|
||||
pass
|
||||
|
||||
def conn_plate_strategy(self, df):
|
||||
dbj_strategy_df = pd.DataFrame()
|
||||
df['JXNH'] = JXNH(df['close'], df['open'], df['volume'])
|
||||
total, rise, fall, tomorrow_rise, tomorrow_fall = 0, 0, 0, 0, 0
|
||||
for index, row in df.iterrows():
|
||||
trade_date, code, close, jxnh = row["trade_date"], row['ts_code'], row['close'], row['JXNH']
|
||||
if jxnh:
|
||||
# 返回所要找的数据
|
||||
if trade_date == self.trade_date:
|
||||
dbj_strategy_df = dbj_strategy_df.append(row)
|
||||
rise, tomorrow_rise, total = self.back_testing(df, index, rise, tomorrow_rise, total, trade_date)
|
||||
dbj_strategy_df.reset_index(drop=True, inplace=True)
|
||||
if total > 0:
|
||||
console = f"满足策略：{total} 次，明日上涨概率：{round((tomorrow_rise / total) * 100, 2)} %，" \
|
||||
f"三日后上涨概率：{round((rise / total) * 100, 2)} %"
|
||||
# print(console)
|
||||
# logger.info(console)
|
||||
dbj_strategy_df['history'] = console
|
||||
return dbj_strategy_df
|
||||
|
||||
def html_page_data(self):
|
||||
rm_strategy_df, dbj_strategy_df = self.stock_daily()
|
||||
# tdx_util = TdxUtil('D:\\new_tdx')
|
||||
# tdx_util.set_zxg_file(cont=dbj_strategy_df['ts_code'])
|
||||
# 策略1
|
||||
id_name = "v-pills-rm"
|
||||
rm_strategy_nav_pills = f'<button class="nav-link active" data-target="#{id_name}" type="button">RM策略自选</button>'
|
||||
rm_strategy_nav_pills_content = self.__get_pills_content(rm_strategy_df, id_name, active=True)
|
||||
|
||||
# 策略2
|
||||
id_name = "v-pills-dbj"
|
||||
dbj_strategy_nav_pills = f'<button class="nav-link" data-target="#{id_name}" type="button">大保健策略自选</button>'
|
||||
dbj_strategy_nav_pills_content = self.__get_pills_content(dbj_strategy_df, id_name)
|
||||
|
||||
res_html = f'<div class="row border p-3"><div class="col-3"><div class="nav flex-column nav-pills" id="v-pills-tab">' \
|
||||
f'{rm_strategy_nav_pills}' \
|
||||
f'{dbj_strategy_nav_pills}' \
|
||||
f'</div></div>' \
|
||||
f'<div class="col-9"><div class="tab-content" id="v-pills-tabContent">' \
|
||||
f'{rm_strategy_nav_pills_content}' \
|
||||
f'{dbj_strategy_nav_pills_content}' \
|
||||
f'</div></div></div>'
|
||||
return res_html
|
||||
|
||||
def __get_pills_content(self, df, id_name, active=False):
|
||||
pills_content = ''
|
||||
if len(df) > 0:
|
||||
pills_content += f'<table class="table table-hover"><thead>' \
|
||||
f'<tr>' \
|
||||
f'<th scope="col">股票代码</th>' \
|
||||
f'<th scope="col">策略表现</th>' \
|
||||
f'<th scope="col">购买建议</th>' \
|
||||
f'</tr>' \
|
||||
f'</thead>' \
|
||||
f'<tbody>'
|
||||
for index, row in df.iterrows():
|
||||
pills_content += f'<tr><th scope="row">{row["ts_code"]}</th>' \
|
||||
f'<td>{row["history"]}</td>' \
|
||||
f'<td>{row["close"]}</td>'
|
||||
pills_content += '</tr>'
|
||||
pills_content += f'</tbody></table>'
|
||||
else:
|
||||
pills_content += '<p class="text-center"><span>暂无数据...</span></p>'
|
||||
pills_div = f'<div class="tab-pane fade show {"active" if active else ""}" id="{id_name}">{pills_content}</div>'
|
||||
return pills_div
|
||||
|
||||
def process_data(self, sublist):
|
||||
n = 16
|
||||
start_date = return_trading_day(self.trade_date, -n)
|
||||
res_df = pd.DataFrame(columns=["symbol"])
|
||||
# 构建子查询字符串，例如：SELECT * FROM table1 UNION ALL SELECT * FROM table2 ...
|
||||
subquery = " UNION ALL ".join(
|
||||
[f"SELECT * FROM '{table_name}' where trade_date between '{start_date}' and '{self.trade_date}'" for
|
||||
table_name in sublist])
|
||||
# 将子查询字符串添加到主查询中
|
||||
df = self.db_main.execute_sql_to_pandas(subquery)
|
||||
# print(df)
|
||||
# 使用 groupby 对数据进行分组
|
||||
grouped = df.groupby('ts_code')
|
||||
# 遍历分组后的数据
|
||||
for name, group in grouped:
|
||||
group = group.reset_index(drop=True)
|
||||
# print(f"ts_code: {name}")
|
||||
# print(group)
|
||||
selected_indexes = group[group['trade_date'] == self.trade_date].index
|
||||
if selected_indexes.empty or selected_indexes.values[0] != n:
|
||||
continue
|
||||
now_close, now_high, symbol, ts_code = group.loc[selected_indexes, 'close'].values[0], \
|
||||
group.loc[selected_indexes, 'high'].values[0], \
|
||||
group.loc[selected_indexes, 'ts_code'].values[0].split('.')[0], \
|
||||
group.loc[selected_indexes, 'ts_code'].values[0]
|
||||
max_high = group.iloc[0:n]['high'].max()
|
||||
cond = [
|
||||
now_close > max_high,
|
||||
max_high > group.loc[selected_indexes - 1, 'high'].values[0]
|
||||
]
|
||||
if not all(cond):
|
||||
continue
|
||||
# 计算倾斜角度
|
||||
skewness = round(sum(np.diff(group.iloc[0:n]['high'].tolist())), 3)
|
||||
turnover_rate, volume_ratio = group.loc[selected_indexes, 'turnover_rate'].values[0], \
|
||||
group.loc[selected_indexes, 'volume_ratio'].values[0]
|
||||
cond = [
|
||||
turnover_rate > 5,
|
||||
volume_ratio > 1.5
|
||||
]
|
||||
if not all(cond):
|
||||
continue
|
||||
logger.info("====达成条件，开始进行进一步筛选====")
|
||||
# 获取所属板块
|
||||
gn_df = self.sector.get_gn_sector_by_stock_code(symbol)
|
||||
hy_df = self.sector.get_hy_sector_by_stock_code(symbol)
|
||||
logger.info(
|
||||
f"code={symbol},now_close={now_close},last_high={max_high},"
|
||||
f"换手率={turnover_rate},量比={volume_ratio}，"
|
||||
f"趋势={skewness},"
|
||||
f"所属行业板块：{hy_df['sector_name'].to_numpy()},所属概念板块：{gn_df['sector_name'].to_numpy()}")
|
||||
|
||||
res_df = res_df.append({
|
||||
"symbol": symbol,
|
||||
"ts_code": ts_code,
|
||||
"趋势": skewness,
|
||||
"行业板块": hy_df['sector_name'].to_numpy(),
|
||||
"概念板块": gn_df['sector_name'].to_numpy()
|
||||
}, ignore_index=True)
|
||||
return res_df
|
||||
|
||||
def more_formula(self, sublist):
|
||||
# 获取删选出的结果集
|
||||
df = self.process_data(sublist=sublist)
|
||||
# 获取指标需要用到的日期数据
|
||||
start_date = return_trading_day(self.trade_date, -250)
|
||||
# 获取数据
|
||||
for index, row in df.iterrows():
|
||||
table_name = row["symbol"] + "_daily"
|
||||
# 使用 and_ 和 between 执行范围查询
|
||||
stock_daily = get_stock_daily(table_name=table_name)
|
||||
res_df = self.db_main.pandas_query_by_condition(
|
||||
model=stock_daily,
|
||||
query_condition=and_(stock_daily.trade_date >= start_date))
|
||||
selected_indexes = res_df[res_df['trade_date'] == self.trade_date].index
|
||||
c, o, v = res_df['close'], res_df['open'], res_df['volume']
|
||||
# 获取MA
|
||||
res_df['MA5'], res_df['MA10'], res_df['MA20'], res_df['MA60'], res_df['MA120'], res_df['MA250'] = \
|
||||
MA(c, 5), MA(c, 10), MA(c, 20), MA(c, 60), MA(c, 120), MA(c, 250)
|
||||
# 出现 JXNH 买点
|
||||
res_df['JXNH'] = JXNH(c, o, v)
|
||||
# 未来三天的整体表现
|
||||
|
||||
pass
|
||||
|
||||
def junit_strategy(self):
|
||||
# 测量代码块的执行时间
|
||||
start_time_total = time.time()
|
||||
table_array = []
|
||||
for result in self.code_res:
|
||||
s_type = self.tdx_util.get_security_type(code=result.ts_code, name=result.name)
|
||||
if s_type in self.tdx_util.SECURITY_TYPE:
|
||||
table_name = str(result.ts_code).split(".")[0] + "_daily"
|
||||
table_array.append(table_name)
|
||||
# 将 table_array 分成 num_sub_lists 个子列表
|
||||
num_sub_lists = 10
|
||||
chunk_size = len(table_array) // num_sub_lists
|
||||
table_sub_lists = [table_array[i:i + chunk_size] for i in range(0, len(table_array), chunk_size)]
|
||||
res_df = pd.DataFrame(columns=["symbol"])
|
||||
# 对每个子列表执行查询并合并结果
|
||||
for sublist in table_sub_lists:
|
||||
res_df = res_df.append(self.process_data(sublist))
|
||||
print(res_df.reset_index(drop=True))
|
||||
# tdx_util = TdxUtil('D:\\new_tdx')
|
||||
# tdx_util.set_zxg_file(cont=res_df['symbol'])
|
||||
# 结束时间
|
||||
logger.info(f"总执行时间: {time.time() - start_time_total / 60:.2f} 分")
|
||||
self.future(res_df)
|
||||
|
||||
def future(self, df):
|
||||
n = 3
|
||||
end_date = return_trading_day(self.trade_date, n)
|
||||
for index, row in df.iterrows():
|
||||
stock_daily = get_stock_daily(table_name=row['symbol'] + "_daily")
|
||||
future_df = self.db_main.pandas_query_by_condition(
|
||||
model=stock_daily,
|
||||
query_condition=stock_daily.trade_date.between(self.trade_date, end_date))
|
||||
selected_indexes = future_df[future_df['trade_date'] == self.trade_date].index
|
||||
print(selected_indexes)
|
||||
if selected_indexes.empty:
|
||||
continue
|
||||
now_chg = future_df.loc[selected_indexes, 'pct_chg'].values[0]
|
||||
tomorrow_chg = future_df.loc[selected_indexes + 1, 'pct_chg'].values[0]
|
||||
sum_chg = round(future_df.iloc[1:n + 1]["pct_chg"].sum(), 2)
|
||||
# todo 将结果输出到CSV文件中
|
||||
# df.to_csv(f'/Users/renmeng/work_space/python_work/qnloft-get-web-everything/股票金融/量化交易/股票数据/{code}.csv',
|
||||
# mode='a', header=False, index=False)
|
||||
logger.info(f"未来走势-----》》》》")
|
||||
logger.info(
|
||||
f'【 {row["symbol"]} 】板块：【{row["行业板块"]}】，【{row["概念板块"]}】-- > \n'
|
||||
f'当日涨幅：{now_chg}，趋势：{row["趋势"]} \n'
|
||||
f'{future_df["trade_date"][0]} 涨幅 -- > {tomorrow_chg} , 三日总涨幅：{sum_chg}'
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
date_obj = datetime.strptime('20231016', "%Y%m%d")
|
||||
opt_stack = OptionalStock(trade_date=date_obj)
|
||||
opt_stack.junit_strategy()
|
||||
# print(opt_stack.html_page_data())
|
||||
|
|
@ -0,0 +1,250 @@
|
|||
from datetime import timedelta
|
||||
|
||||
import numpy as np
|
||||
import pandas
|
||||
|
||||
from utils.comm import *
|
||||
|
||||
|
||||
class AllIndex:
|
||||
# 中国的指数
|
||||
ZH_INDEX = pd.DataFrame(
|
||||
[["上证指数", "000001.SH"], ["深证成指", "399001.SZ"], ["创业板指", "399006.SZ"], ["中证100", "000903.SH"],
|
||||
["中证100", "399903.SZ"]],
|
||||
columns=['name', 'value'])
|
||||
|
||||
# 世界的指数
|
||||
GLOBAL_INDEX = ['道琼斯', '标普500', '纳斯达克', '恒生指数', '日经225', '韩国KOSPI', '美元指数']
|
||||
|
||||
METAL_INDEX = pd.DataFrame(
|
||||
[["黄金", "Au99.99"]
|
||||
# , ["白银", "Ag99.99"]
|
||||
], columns=['name', 'value'])
|
||||
|
||||
def __init__(self, trade_date):
|
||||
self.trade_date = trade_date.strftime('%Y%m%d')
|
||||
self.start_date = return_trading_day(trade_date, -29)
|
||||
self.end_date = self.trade_date
|
||||
|
||||
def calendar_page_json(self):
|
||||
"""
|
||||
日历页面数据，只要A股指数
|
||||
获取1年内的全部数控，生成一个json文件
|
||||
:return:
|
||||
"""
|
||||
# 计算一年前的日期
|
||||
start_date = datetime.strptime(self.end_date, '%Y%m%d') - timedelta(days=365)
|
||||
start_date = start_date.strftime("%Y%m%d")
|
||||
# 在首页显示 这三个的当日最新涨跌情况
|
||||
index_page_display = ["000001.SH", "399001.SZ", "399006.SZ"]
|
||||
res_pandas = pandas.DataFrame(columns=['title', 'start', 'textColor', 'backgroundColor'])
|
||||
for stock in index_page_display:
|
||||
for _ in range(5):
|
||||
try:
|
||||
# 获取单个指数日线行情
|
||||
df = xcsc_pro.index_daily(
|
||||
ts_code=stock,
|
||||
start_date=start_date,
|
||||
end_date=self.end_date,
|
||||
fields="ts_code,trade_date,pct_chg")
|
||||
break
|
||||
except Exception as e:
|
||||
print("获取单个指数日线行情 出现问题：", e)
|
||||
time.sleep(1)
|
||||
continue
|
||||
else:
|
||||
print("重试5次依然没有解决问题，请重视~！！！")
|
||||
return None
|
||||
# 获取对应的中文名称
|
||||
name = self.ZH_INDEX.loc[self.ZH_INDEX['value'] == stock, 'name'].iloc[0]
|
||||
df['pct_chg_format'] = (round(df['pct_chg'], 2)).astype("str") + "%"
|
||||
df['title'] = name + " --> " + df['pct_chg_format']
|
||||
# 将字符串转换为日期时间对象
|
||||
df['trade_date'] = pd.to_datetime(df['trade_date'])
|
||||
# 格式化日期时间列为 "YYYY-MM-DD" 形式
|
||||
df['start'] = df['trade_date'].dt.strftime("%Y-%m-%d")
|
||||
# 设置条件
|
||||
conditions = [
|
||||
(df['pct_chg'] > 1),
|
||||
(df['pct_chg'] < -1),
|
||||
((-1 <= df['pct_chg']) & (df['pct_chg'] < 0)),
|
||||
((0 < df['pct_chg']) & (df['pct_chg'] <= 1))
|
||||
]
|
||||
# 字体颜色 当涨跌幅大于1的时候，将字体颜色设置成白色 #e60000
|
||||
text_colors = ['#ffffff', '#ffffff', '#5fcf57', '#e60000']
|
||||
df['textColor'] = np.select(conditions, text_colors)
|
||||
# 背景颜色
|
||||
background_colors = ['#e60000', '#5fcf57', '#ffffff', '#ffffff']
|
||||
df['backgroundColor'] = np.select(conditions, background_colors)
|
||||
|
||||
df = df[['title', 'start', 'textColor', 'backgroundColor']]
|
||||
res_pandas = pd.concat([res_pandas, df], ignore_index=True)
|
||||
return res_pandas
|
||||
|
||||
def html_page_data(self):
|
||||
# A股指数
|
||||
a_all_df, a_table_date, a_home_page_df = self.a_data()
|
||||
# 全球部分指数
|
||||
g_all_df, g_table_date, g_home_page_df = self.global_index()
|
||||
# 贵金属行情
|
||||
s_all_df, s_table_date, s_home_page_df = self.spot_hist_sge()
|
||||
# 生成首页的徽章
|
||||
badge = ""
|
||||
badge += self.__create_badge(a_home_page_df)
|
||||
badge += self.__create_badge(g_home_page_df)
|
||||
badge += self.__create_badge(s_home_page_df)
|
||||
|
||||
# 生成 指数数据 页
|
||||
card_deck_df = pd.concat([a_table_date, g_table_date], axis=0)
|
||||
card_deck_df = pd.concat([card_deck_df, s_table_date], axis=0)
|
||||
card_deck = self.__create_card_deck(card_deck_df)
|
||||
return badge, card_deck
|
||||
|
||||
def __create_badge(self, df):
|
||||
# 生成首页的徽章
|
||||
badge = ""
|
||||
for index, row in df.iterrows():
|
||||
badge_color = "badge-success"
|
||||
if row['pct_chg'] > 0:
|
||||
badge_color = "badge-danger"
|
||||
badge += f"<div class='col-md-auto'><span class='badge badge-pill {badge_color}'>{row['name']}：{row['pct_chg']}</span></div>"
|
||||
return badge
|
||||
|
||||
def __create_card_deck(self, df):
|
||||
grouped = df.groupby('name')
|
||||
card_deck = '<div class="card-deck">'
|
||||
row = 1
|
||||
for group_name, group_df in grouped:
|
||||
card_deck_color = "border-success"
|
||||
if group_df.head(1)["pct_chg"].values[0] > 0:
|
||||
card_deck_color = "border-danger"
|
||||
card_deck += f'<div class="card {card_deck_color} mb-3" style="max-width: 14rem;">' \
|
||||
f'<div class="card-header">{group_name}</div>' \
|
||||
f'<div class="card-body {"text-danger" if group_df.head(1)["pct_chg"].values[0] > 0 else "text-success"}">' \
|
||||
f'<h4 class="card-title">{round(group_df.head(1)["close"].values[0], 2)}</h4>' \
|
||||
f'<p class="card-text row">' \
|
||||
f'<span class="{"text-danger" if group_df.head(2)["pct_chg"].values[1] > 0 else "text-success"} ml-3">' \
|
||||
f'{round(group_df.head(2)["close"].values[1], 2)}' \
|
||||
f'</span>' \
|
||||
f'<span class="{"text-danger" if group_df.head(3)["pct_chg"].values[2] > 0 else "text-success"} ml-auto mr-3">' \
|
||||
f'{round(group_df.head(3)["close"].values[2], 2)}' \
|
||||
f'</span>' \
|
||||
f'</p>' \
|
||||
f'</div>' \
|
||||
f'</div>'
|
||||
if row % 5 == 0:
|
||||
card_deck += '</div>'
|
||||
print(f"{row - 1} -- >> {len(grouped)}")
|
||||
if row != len(grouped):
|
||||
card_deck += '<div class="card-deck">'
|
||||
if row % 5 != 0 and row == len(grouped):
|
||||
card_deck += '</div>'
|
||||
row += 1
|
||||
return card_deck
|
||||
|
||||
def a_data(self):
|
||||
"""
|
||||
A股指数数据
|
||||
返回 1个月内，3日，1日
|
||||
:return:
|
||||
"""
|
||||
# 在首页显示 这三个的当日最新涨跌情况
|
||||
index_page_display = ["000001.SH", "399001.SZ", "399006.SZ"]
|
||||
all_df, home_page_df, table_df = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
|
||||
for index, row in self.ZH_INDEX.iterrows():
|
||||
print(self.start_date, " --- >>", self.end_date)
|
||||
# 获取单个指数日线行情
|
||||
for _ in range(5):
|
||||
try:
|
||||
df = xcsc_pro.index_daily(ts_code=row['value'], start_date=self.start_date,
|
||||
end_date=self.end_date,
|
||||
fields="ts_code,trade_date,pct_chg,close")
|
||||
break
|
||||
except Exception as e:
|
||||
print("获取单个指数日线行情 出现问题：", e)
|
||||
continue
|
||||
else:
|
||||
print("重试5次依然没有解决问题，请重视~！！！")
|
||||
return None
|
||||
df['pct_chg'] = df['pct_chg'].round(2)
|
||||
df['name'] = self.ZH_INDEX.loc[self.ZH_INDEX['value'] == row['value'], 'name'].iloc[0]
|
||||
# 一个月的数据
|
||||
all_df = pd.concat([all_df, df], axis=0)
|
||||
if index_page_display.count(row['value']):
|
||||
print(df)
|
||||
# 一日的数据
|
||||
home_page_date = df.head(1)
|
||||
home_page_df = pd.concat([home_page_df, home_page_date], axis=0)
|
||||
# 三日的数据
|
||||
table_df_date = df.head(3)
|
||||
table_df = pd.concat([table_df, table_df_date], axis=0)
|
||||
return all_df, table_df, home_page_df
|
||||
|
||||
def global_index(self):
|
||||
"""
|
||||
国外部分指数数据
|
||||
3日内的涨跌幅
|
||||
1个月内的折线图
|
||||
:return:
|
||||
"""
|
||||
all_df, table_df, home_page_df = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
|
||||
for i in self.GLOBAL_INDEX:
|
||||
for _ in range(5):
|
||||
try:
|
||||
df = qs.get_data(i)
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"国外部分指数数据【{i}】拉取出现错误，", e)
|
||||
continue
|
||||
else:
|
||||
print("重试5次依然没有解决问题，请重视~！！！")
|
||||
return None
|
||||
df['pct_chg'] = round(((df['close'] - df['open']) / df['open']) * 100, 2)
|
||||
all_df_date, table_df_date, home_page_data = df.tail(30), df.tail(3), df.tail(1)
|
||||
all_df = pd.concat([all_df, all_df_date], axis=0)
|
||||
table_df = pd.concat([table_df, table_df_date], axis=0)
|
||||
home_page_df = pd.concat([home_page_df, home_page_data], axis=0)
|
||||
# pct_chg = global_df.groupby('name')['pct_chg'].apply(list).reset_index()
|
||||
# print(pct_chg)
|
||||
return all_df, table_df, home_page_df
|
||||
|
||||
def oil_index(self): # 油价
|
||||
# 调价日期
|
||||
energy_oil_hist_df = ak.energy_oil_hist()
|
||||
print(energy_oil_hist_df)
|
||||
formatted_date = energy_oil_hist_df['调整日期'].iloc[-3].strftime('%Y%m%d')
|
||||
# 地区油价
|
||||
energy_oil_detail_df = ak.energy_oil_detail(date=formatted_date)
|
||||
energy_oil_detail_df = energy_oil_detail_df[energy_oil_detail_df['地区'] == '北京']
|
||||
print(energy_oil_detail_df)
|
||||
|
||||
def spot_hist_sge(self):
|
||||
all_df, table_df, home_page_df = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
|
||||
for index, row in self.METAL_INDEX.iterrows():
|
||||
for _ in range(5):
|
||||
try:
|
||||
df = ak.spot_hist_sge(symbol=row['value'])
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"上海黄金交易所-数据资讯-行情走势-历史数据，", e)
|
||||
continue
|
||||
else:
|
||||
print("重试5次依然没有解决问题，请重视~！！！")
|
||||
return None
|
||||
df['name'] = self.METAL_INDEX.loc[self.METAL_INDEX['value'] == row['value'], 'name'].iloc[0]
|
||||
df['pct_chg'] = round(((df['close'] - df['open']) / df['open']) * 100, 2)
|
||||
all_df_date, table_df_date, home_page_data = df.tail(30), df.tail(3), df.tail(1)
|
||||
all_df = pd.concat([all_df, all_df_date], axis=0)
|
||||
table_df = pd.concat([table_df, table_df_date], axis=0)
|
||||
home_page_df = pd.concat([home_page_df, home_page_data], axis=0)
|
||||
return all_df, table_df, home_page_df
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# current_date = datetime.now().strftime('%Y%m%d')
|
||||
current_date = datetime.strptime('20230918', "%Y%m%d")
|
||||
# if if_run(current_date):
|
||||
index = AllIndex(trade_date=current_date)
|
||||
# index.a_index()
|
||||
badge, card_deck = index.html_page_data()
|
||||
print(badge)
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
from pyecharts.charts import Bar, Line
|
||||
import pyecharts.options as opts
|
||||
|
||||
from utils.stock_web_api import execute_stock_web_api_method, stock_web_api_money_flow
|
||||
from utils.comm import *
|
||||
|
||||
|
||||
class MoneyFlow:
|
||||
def __init__(self, trade_date):
|
||||
self.trade_date = trade_date.strftime('%Y%m%d')
|
||||
self.start_date = return_trading_day(trade_date, -6)
|
||||
self.end_date = self.trade_date
|
||||
print(self.start_date, "---", self.end_date)
|
||||
|
||||
def hsgt_top10(self):
|
||||
"""
|
||||
当日沪深股通十大成交股
|
||||
:return:
|
||||
"""
|
||||
df = xcsc_pro.hsgt_top10(trade_date=self.end_date)
|
||||
print(df)
|
||||
|
||||
def money_flow_chart(self):
|
||||
df = execute_stock_web_api_method(
|
||||
func=stock_web_api_money_flow,
|
||||
start_date=self.start_date, end_date=self.end_date)
|
||||
x_data = df['trade_date'].tolist()
|
||||
# 沪股通
|
||||
y_axis_hgt = (round(df['hgt'] / 100, 2)).tolist()
|
||||
# 深股通
|
||||
y_axis_sgt = (round(df['sgt'] / 100, 2)).tolist()
|
||||
# 北上资金
|
||||
y_axis_north_money = (round(df['north_money'] / 100, 2)).tolist()
|
||||
bar = (
|
||||
Bar(init_opts=opts.InitOpts(width="100%", height="400px"))
|
||||
.add_xaxis(xaxis_data=x_data)
|
||||
.add_yaxis(
|
||||
series_name="沪股通",
|
||||
y_axis=y_axis_hgt,
|
||||
label_opts=opts.LabelOpts(is_show=False),
|
||||
)
|
||||
.add_yaxis(
|
||||
series_name="深股通",
|
||||
y_axis=y_axis_sgt,
|
||||
label_opts=opts.LabelOpts(is_show=False),
|
||||
)
|
||||
.extend_axis(
|
||||
yaxis=opts.AxisOpts(
|
||||
name="金额",
|
||||
type_="value",
|
||||
interval=2000,
|
||||
axislabel_opts=opts.LabelOpts(formatter="{value} 亿元"),
|
||||
)
|
||||
)
|
||||
.set_global_opts(
|
||||
tooltip_opts=opts.TooltipOpts(
|
||||
is_show=True, trigger="axis", axis_pointer_type="cross"
|
||||
),
|
||||
xaxis_opts=opts.AxisOpts(
|
||||
type_="category",
|
||||
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
|
||||
),
|
||||
yaxis_opts=opts.AxisOpts(
|
||||
name="金额",
|
||||
type_="value",
|
||||
interval=3000,
|
||||
axislabel_opts=opts.LabelOpts(formatter="{value} 亿元"),
|
||||
axistick_opts=opts.AxisTickOpts(is_show=True),
|
||||
splitline_opts=opts.SplitLineOpts(is_show=True),
|
||||
),
|
||||
)
|
||||
)
|
||||
line = (
|
||||
Line()
|
||||
.add_xaxis(xaxis_data=x_data)
|
||||
.add_yaxis(
|
||||
series_name="北上资金",
|
||||
# yaxis_index 0：折线图使用柱状图的y轴 1：使用extend_axis中的Y轴
|
||||
yaxis_index=0,
|
||||
y_axis=y_axis_north_money,
|
||||
label_opts=opts.LabelOpts(is_show=False),
|
||||
)
|
||||
)
|
||||
bar.overlap(line).render(f"../复盘指标/html/{self.trade_date}/mixed_bar_and_line.html")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# money_flow = MoneyFlow(trade_date=datetime.now().strftime('%Y%m%d'))
|
||||
money_flow = MoneyFlow(trade_date=datetime.now())
|
||||
money_flow.money_flow_chart()
|
||||
|
|
@ -0,0 +1,130 @@
|
|||
import logging
|
||||
import sys
|
||||
|
||||
from jinja2 import Environment, FileSystemLoader
|
||||
from loguru import logger
|
||||
|
||||
from fp.新闻资讯 import News
|
||||
from fp.日线数据入库 import StockDailyMain
|
||||
from fp.板块数据入库 import SectorOpt
|
||||
from fp.涨跌停数据 import LimitList
|
||||
from fp.自选股数据 import OptionalStock
|
||||
from fp.行情指数数据 import AllIndex
|
||||
from fp.资金出入概览 import MoneyFlow
|
||||
from utils.comm import *
|
||||
|
||||
|
||||
class AppMain:
|
||||
def __init__(self, trade_date=datetime.now()):
|
||||
# 配置日志输出到文件和控制台
|
||||
logger.add("log/FPAppMain.log", rotation="500 MB", level="INFO")
|
||||
logger.add(sys.stderr, level="INFO")
|
||||
self.trade_date = trade_date.strftime('%Y%m%d')
|
||||
if if_run(trade_date):
|
||||
# 创建一个文件夹
|
||||
self.file_name = f'html/{self.trade_date}'
|
||||
create_file(self.file_name)
|
||||
# 各大指数数据
|
||||
self.index = AllIndex(trade_date=trade_date)
|
||||
# 板块数据
|
||||
self.sector_opt = SectorOpt(trade_date=trade_date)
|
||||
# 涨跌停 炸板
|
||||
self.limit_list = LimitList(trade_date=trade_date)
|
||||
# 资金流入流出
|
||||
self.money_flow = MoneyFlow(trade_date=trade_date)
|
||||
# 日线数据
|
||||
self.stock_daily = StockDailyMain()
|
||||
# 新闻咨询
|
||||
self.news = News(trade_date=trade_date)
|
||||
# 我的自选
|
||||
self.optional_stock = OptionalStock(trade_date=trade_date)
|
||||
|
||||
def calendar_page_data(self):
|
||||
logger.info(f"{self.trade_date} start--> 开始 JSON 文件生成！")
|
||||
res_df = self.index.calendar_page_json()
|
||||
# 将 DataFrame 转换为 JSON，并写入文件
|
||||
res_df.to_json('html/json/events.json', orient='records')
|
||||
logger.info(f"{self.trade_date} --> JSON 文件已更新")
|
||||
|
||||
def init_data(self):
|
||||
"""
|
||||
每日数据初始化
|
||||
:return:
|
||||
"""
|
||||
logger.info(f"{self.trade_date} start--> 开始数据初始化！")
|
||||
# 日线数据入库
|
||||
self.stock_daily.task_data(self.trade_date)
|
||||
# 行业板块 入库
|
||||
self.sector_opt.save_hy_sector()
|
||||
# 概念板块 入库
|
||||
self.sector_opt.save_gn_sector()
|
||||
# 行业板块个股
|
||||
self.sector_opt.stock_by_hy_sector()
|
||||
# 概念板块个股
|
||||
self.sector_opt.stock_by_gn_sector()
|
||||
logger.info(f"{self.trade_date} end--> 数据初始化完毕！")
|
||||
|
||||
def create_chart(self):
|
||||
logger.info(f"{self.trade_date} start--> 开始图表页面生成！")
|
||||
# 板块图表
|
||||
self.sector_opt.industry_generate_chart()
|
||||
self.sector_opt.concept_generate_chart()
|
||||
# 涨跌停 炸板 图表
|
||||
self.limit_list.limit_list_chart()
|
||||
# 资金出入图表
|
||||
self.money_flow.money_flow_chart()
|
||||
logger.info(f"{self.trade_date} end--> 图表页面生成完毕！")
|
||||
|
||||
def create_page(self):
|
||||
logger.info(f"{self.trade_date} start--> 开始数据静态页面生成！")
|
||||
badge, card_deck = self.index.html_page_data()
|
||||
limit_list_table_content = self.limit_list.html_page_data()
|
||||
# 新闻数据
|
||||
cctv_content, cls_content = self.news.html_page_data()
|
||||
# 首页数据 页面
|
||||
self.__write_html(template_name="index_template.html", write_html_name="index.html",
|
||||
badge=badge, news_cctv=cctv_content, news_cls=cls_content)
|
||||
# 指数行情 页面
|
||||
self.__write_html(template_name="zs_data_template.html", write_html_name="zs_data.html", card_deck=card_deck)
|
||||
# 涨跌停 炸板 页面
|
||||
self.__write_html(template_name="limit_list_template.html", write_html_name="limit_list.html",
|
||||
limit_table=limit_list_table_content)
|
||||
# 我的自选 页面 (这个时间会很长)
|
||||
# optional_stock_pills_tab_content = self.optional_stock.html_page_data()
|
||||
# self.__write_html(template_name="zx_data_template.html", write_html_name="zx_data.html",
|
||||
# pills_tabContent=optional_stock_pills_tab_content)
|
||||
logger.info(f"{self.trade_date} --> 数据静态页面生成完毕！")
|
||||
|
||||
def __write_html(self, template_name, write_html_name, *args, **kwargs):
|
||||
print(template_name, "---", write_html_name)
|
||||
print(dict(*args, **kwargs))
|
||||
# 设置 Jinja2 环境
|
||||
env = Environment(loader=FileSystemLoader('template/'))
|
||||
template = env.get_template(template_name)
|
||||
# 将 DataFrame 数据传递给模板并渲染
|
||||
rendered_output = template.render(dict(*args, **kwargs))
|
||||
# 将渲染后的内容写入 HTML 文件
|
||||
with open(self.file_name + f'/{write_html_name}', 'w', encoding='utf-8') as f:
|
||||
f.write(rendered_output)
|
||||
print("HTML rendered and saved as 'output.html'")
|
||||
|
||||
def upload_ftp(self):
|
||||
logger.info(f"{self.trade_date} start--> 开始FTP上传！")
|
||||
# ftp上传
|
||||
# 上传json文件
|
||||
upload_files_to_ftp(
|
||||
'html/json', '/htdocs/stock/json', "events.json")
|
||||
# 上传html文件
|
||||
upload_files_to_ftp(
|
||||
f'html/{self.trade_date}', f'/htdocs/stock/{self.trade_date}')
|
||||
logger.info(f"{self.trade_date} end--> FTP上传完毕！")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# date_obj = datetime.strptime('20231110', "%Y%m%d")
|
||||
app = AppMain()
|
||||
# app.calendar_page_data()
|
||||
# app.init_data()
|
||||
# app.create_chart()
|
||||
# app.create_page()
|
||||
# app.upload_ftp()
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
## 元素颜色
|
||||
|
||||
| 颜色代码 | 说明 |
|
||||
|-----------|-----|
|
||||
| primary | 蓝色 |
|
||||
| secondary | 灰色 |
|
||||
| success | 绿色 |
|
||||
| danger | 红色 |
|
||||
| warning | 黄色 |
|
||||
| info | 浅蓝色 |
|
||||
| light | 白色 |
|
||||
| dark | 黑色 |
|
||||
|
||||
## 表格添加颜色
|
||||
|
||||
<!-- On cells (`tr` or `td`) -->
|
||||
| 颜色代码 | 说明 |
|
||||
|-----------------|-------|
|
||||
| table-active | 选中的浅灰 |
|
||||
| table-primary ||
|
||||
| table-secondary ||
|
||||
| table-success ||
|
||||
| table-danger ||
|
||||
| table-warning ||
|
||||
| table-info ||
|
||||
| table-light ||
|
||||
| table-dark ||
|
||||
|
||||
|
||||
## 元素之间添加间隔
|
||||
|
||||
| 间隔代码 | 说明 |
|
||||
|--------------------------|--------|
|
||||
| mb-*（margin-bottom） | 内部下边距 |
|
||||
| mt-*（margin-top） | 内部上边距 |
|
||||
| ml-*（margin-left） | 内部左边距 |
|
||||
| mr-*（margin-right） | 内部右边距 |
|
||||
| px-*（padding-horizontal） | 外部水平边距 |
|
||||
| py-*（padding-vertical） | 外部垂直边距 |
|
||||
|
||||
## 元素位置
|
||||
|
||||
`ml-auto` 和 `mr-auto`： 分别用于使元素在左侧和右侧具有自动外边距，从而在容器中实现水平居左或居右。
|
||||
|
||||
`mx-auto`： 用于使元素在水平方向上具有自动外边距，从而实现水平居中对齐。
|
||||
|
||||
`my-auto`： 用于使元素在垂直方向上具有自动外边距，从而实现垂直居中对齐。
|
||||
|
||||
`text-center`： 用于文本元素，使文本水平居中对齐。
|
||||
|
||||
`text-left` 和 `text-right`： 用于文本元素，分别用于使文本左对齐或右对齐。
|
||||
|
||||
justify-content-start、justify-content-center 和 justify-content-end： 用于 Flexbox 布局中的容器，分别用于在水平方向上使内容开始、居中或结束对齐。
|
||||
|
||||
align-items-start、align-items-center 和 align-items-end： 用于 Flexbox 布局中的容器，分别用于在垂直方向上使内容顶部、居中或底部对齐。
|
||||
|
||||
align-self-start、align-self-center 和 align-self-end： 用于 Flexbox 布局中的项目，分别用于在垂直方向上使单个项目的内容顶部、居中或底部对齐。
|
||||
|
||||
## 字体大小
|
||||
|
||||
`.display-1` 到 `.display-4`：这些类用于设置显示文本的不同字体大小，其中 `.display-1` 是最大的，`.display-4` 是最小的。
|
||||
|
||||
`.h1` 到 `.h6`：这些类用于设置标题的字体大小，其中 `.h1` 是最大的，`.h6`是最小的。
|
||||
|
||||
`.lead`：这个类用于设置段落或文本的字体大小，使其稍微大一些，适合用作引导文本。
|
||||
|
||||
`.small`：这个类用于设置文本的较小字体大小。
|
||||
|
||||
`.text-[size]`：这是一个自定义类，你可以用具体的字体大小名称替换 `[size]`。例如，`.text-sm` 用于设置小号字体，`.text-lg` 用于设置大号字体。
|
||||
|
||||
```html
|
||||
<p class="display-1">这是display-1文本</p>
|
||||
<p class="display-2">这是display-2文本</p>
|
||||
<p class="display-3">这是display-3文本</p>
|
||||
<p class="display-4">这是display-4文本</p>
|
||||
<h1 class="h1">这是标题</h1>
|
||||
<p class="lead">这是引导文本</p>
|
||||
<p class="small">这是small号文本</p>
|
||||
<p class="text-lg">这是lg号文本</p>
|
||||
```
|
||||
|
|
@ -0,0 +1,71 @@
|
|||
<!doctype html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<!-- 必须的 meta 标签 -->
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
|
||||
<!-- Bootstrap 的 CSS 文件 -->
|
||||
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/css/bootstrap.min.css"
|
||||
crossorigin="anonymous">
|
||||
<link href='https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@5.13.1/css/all.css' rel='stylesheet'>
|
||||
|
||||
<title>日历</title>
|
||||
</head>
|
||||
<body>
|
||||
<div id="head"></div>
|
||||
<div class="container">
|
||||
<div id="calendar"></div>
|
||||
</div>
|
||||
|
||||
<!-- 选项 1：jQuery 和 Bootstrap 集成包（集成了 Popper） -->
|
||||
<!-- jQuery and JavaScript Bundle with Popper -->
|
||||
<script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
|
||||
<script src="https://cdn.bootcdn.net/ajax/libs/twitter-bootstrap/4.6.2/js/bootstrap.bundle.min.js"
|
||||
crossorigin="anonymous"></script>
|
||||
<script src='https://cdn.jsdelivr.net/npm/fullcalendar@6.1.8/index.global.min.js'></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/fullcalendar@5.10.1/locales/zh-cn.js"></script>
|
||||
<script>
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
var calendarEl = document.getElementById('calendar');
|
||||
$.getJSON('json/events.json?nocache=' + new Date().getTime(), function(data) {
|
||||
var events = data;
|
||||
var calendar = new FullCalendar.Calendar(calendarEl, {
|
||||
initialView: 'dayGridMonth',
|
||||
themeSystem: 'bootstrap',
|
||||
locale: 'zh-cn',
|
||||
selectable: true, // 允许选择日期
|
||||
select: function(info) {
|
||||
// 处理点击日期的事件
|
||||
var clickedDate = info.start;
|
||||
// 创建一个日期对象
|
||||
var date = new Date(clickedDate);
|
||||
// 转换为北京时间
|
||||
date.setMinutes(date.getMinutes() - date.getTimezoneOffset() + 480); // 480 分钟是北京时区的偏移量
|
||||
// 格式化日期
|
||||
var year = date.getFullYear();
|
||||
var month = (date.getMonth() + 1).toString().padStart(2, '0'); // 月份从 0 开始，所以要加 1
|
||||
var day = date.getDate().toString().padStart(2, '0');
|
||||
// 格式化后的日期字符串
|
||||
var formattedDate = `${year}${month}${day}`;
|
||||
console.log(formattedDate); // 输出格式化后的日期
|
||||
|
||||
// 进行页面跳转，例如跳转到指定日期的详情页面
|
||||
window.location.href = formattedDate + '/index.html';
|
||||
},
|
||||
// textColor 在全天时间中才生效
|
||||
events: events
|
||||
});
|
||||
|
||||
calendar.render();
|
||||
});
|
||||
});
|
||||
</script>
|
||||
<!-- 选项 2：Popper 和 Bootstrap 的 JS 插件各自独立 -->
|
||||
<!--
|
||||
<script src="https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.slim.min.js" integrity="sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/popper.js@1.16.1/dist/umd/popper.min.js" integrity="sha384-9/reFTGAW83EW2RDu2S0VKaIzap3H66lZH81PoYlFhbGU+6BZp6G7niu735Sk7lN" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/js/bootstrap.min.js" integrity="sha384-Lge2E2XotzMiwH69/MXB72yLpwyENMiOKX8zS8Qo7LDCvaBIWGL+GlRQEKIpYR04" crossorigin="anonymous"></script>
|
||||
-->
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
<nav class="navbar navbar-expand-lg navbar-light bg-light">
|
||||
<div class="container collapse navbar-collapse">
|
||||
<a class="navbar-brand" href="#">
|
||||
<img src="http://qnloft.com/img/logo.png" width="80" height="35" class="d-inline-block align-top" alt="">
|
||||
</a>
|
||||
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav"
|
||||
aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
|
||||
<span class="navbar-toggler-icon"></span>
|
||||
</button>
|
||||
<ul class="navbar-nav ml-auto">
|
||||
<li class="nav-item">
|
||||
<a class="nav-link" href="index.html">首页</span></a>
|
||||
</li>
|
||||
<li class="nav-item">
|
||||
<a class="nav-link" href="zs_data.html">指数复盘</a>
|
||||
</li>
|
||||
<li class="nav-item">
|
||||
<a class="nav-link" href="#">资金复盘</a>
|
||||
</li>
|
||||
<li class="nav-item">
|
||||
<a class="nav-link" href="limit_list.html">板块复盘</a>
|
||||
</li>
|
||||
<li class="nav-item">
|
||||
<a class="nav-link" href="zx_data.html">我的自选</a>
|
||||
</li>
|
||||
<li class="nav-item">
|
||||
<a class="nav-link" href="http://page.tdx.com.cn:7615/site/kggx/tk_yzlhb_yz.html?color=#" target="_blank">龙虎榜</a>
|
||||
</li>
|
||||
<li class="nav-item">
|
||||
<a class="nav-link" href="http://pro.gw.com.cn/newfpsq/index.html#/" target="_blank">盘中情况</a>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</nav>
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
<!doctype html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<!-- 必须的 meta 标签 -->
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
|
||||
<!-- Bootstrap 的 CSS 文件 -->
|
||||
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/css/bootstrap.min.css" integrity="sha384-xOolHFLEh07PJGoPkLv1IbcEPTNtaed2xpHsD9ESMhqIYd0nLMwNLD69Npy4HI+N" crossorigin="anonymous">
|
||||
|
||||
<title>测试页面</title>
|
||||
</head>
|
||||
<body>
|
||||
<p class="display-1">这是display-1文本</p>
|
||||
<p class="display-2">这是display-2文本</p>
|
||||
<p class="display-3">这是display-3文本</p>
|
||||
<p class="display-4">这是display-4文本</p>
|
||||
<h1 class="h1">这是标题</h1>
|
||||
<p class="lead">这是引导文本</p>
|
||||
<p class="small">这是small号文本</p>
|
||||
<p class="text-lg">这是lg号文本</p>
|
||||
|
||||
<!-- 选项 1：jQuery 和 Bootstrap 集成包（集成了 Popper） -->
|
||||
<script src="https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.slim.min.js" integrity="sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj" crossorigin="anonymous">
|
||||
</script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/js/bootstrap.bundle.min.js" integrity="sha384-7ymO4nGrkm372HoSbq1OY2DP4pEZnMiA+E0F3zPr+JQQtQ82gQ1HPY3QIVtztVua" crossorigin="anonymous">
|
||||
</script>
|
||||
|
||||
<script>
|
||||
// 使用 jQuery 来实现卡片切换和内容变换
|
||||
$(document).ready(function() {
|
||||
|
||||
|
||||
});
|
||||
</script>
|
||||
<!-- 选项 2：Popper 和 Bootstrap 的 JS 插件各自独立 -->
|
||||
<!--
|
||||
<script src="https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.slim.min.js" integrity="sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/popper.js@1.16.1/dist/umd/popper.min.js" integrity="sha384-9/reFTGAW83EW2RDu2S0VKaIzap3H66lZH81PoYlFhbGU+6BZp6G7niu735Sk7lN" crossorigin="anonymous"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/js/bootstrap.min.js" integrity="sha384-Lge2E2XotzMiwH69/MXB72yLpwyENMiOKX8zS8Qo7LDCvaBIWGL+GlRQEKIpYR04" crossorigin="anonymous"></script>
|
||||
-->
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
from utils.comm import *
|
||||
|
||||
|
||||
def stock_web_api_method_decorator(func):
|
||||
def wrapper(*args, **kwargs):
|
||||
return func(*args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
@stock_web_api_method_decorator
|
||||
def stock_web_api_money_flow(start_date=None, end_date=None):
|
||||
# 沪深港通资金流向
|
||||
df = xcsc_pro.moneyflow_hsgt(start_date=start_date, end_date=end_date)[::-1]
|
||||
if df[df["trade_date"] == end_date].empty:
|
||||
# df 如果里面不包含当天日期的数据，则使用备用接口
|
||||
ak_df = ak.stock_hsgt_fund_flow_summary_em()
|
||||
northward = ak_df[ak_df['资金方向'] == '北向']
|
||||
hgt = round(northward[northward['板块'] == '沪股通']['成交净买额'].sum(), 2) * 100
|
||||
sgt = round(northward[northward['板块'] == '深股通']['成交净买额'].sum(), 2) * 100
|
||||
north_money = round(hgt + sgt, 2)
|
||||
substitute_df = pd.DataFrame(
|
||||
[{
|
||||
"trade_date": northward["交易日"].iloc[0].strftime('%Y%m%d'),
|
||||
"hgt": hgt,
|
||||
"sgt": sgt,
|
||||
"north_money": north_money,
|
||||
}],
|
||||
index=[0],
|
||||
)
|
||||
result = pd.concat([df, substitute_df], ignore_index=True)
|
||||
return result
|
||||
if df is not None and not df.empty:
|
||||
return df
|
||||
raise Exception("沪深港通资金流向数据拉取失败！")
|
||||
|
||||
|
||||
@stock_web_api_method_decorator
|
||||
def stock_web_api_industry_summary(): # 同花顺 - 行业板块数据
|
||||
df = ak.stock_board_industry_summary_ths()
|
||||
if df is not None and not df.empty:
|
||||
return df
|
||||
raise Exception("同花顺 - 行业板块数据 数据拉取失败！")
|
||||
|
||||
|
||||
@stock_web_api_method_decorator
|
||||
def stock_web_api_concept_name(): # 同花顺 - 概念板块数据
|
||||
df = ak.stock_board_concept_name_em()
|
||||
if df is not None and not df.empty:
|
||||
return df
|
||||
raise Exception("同花顺 - 概念板块数据 数据拉取失败！")
|
||||
|
||||
|
||||
def execute_stock_web_api_method(func, *args, **kwargs):
|
||||
for _ in range(5):
|
||||
try:
|
||||
return func(*args, **kwargs)
|
||||
except Exception as e:
|
||||
print(f"【{func}】 -- 拉取出现问题 【{e}】, 开始进行重试！")
|
||||
time.sleep(1)
|
||||
else:
|
||||
print(f"【{func}】 5次出现错误，请关注！！！")
|
||||
return None
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
print(execute_stock_web_api_method(func=stock_web_api_money_flow, start_date='20231123', end_date='20231123'))
|
||||
Loading…
Reference in New Issue