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@@ -1,10 +1,16 @@
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+# coding:utf-8
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from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
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from xtquant.xttype import StockAccount
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from xtquant import xtdata, xtconstant
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from datetime import datetime as dt
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+import pandas as pd
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+import time
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+import datetime
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+import sys
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+import os
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-
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-
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+pd.set_option('display.max_columns', None) # 设置显示最大行
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+"""
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def run():
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'''阻塞线程接收行情回调'''
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import time
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@@ -12,15 +18,376 @@ def run():
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while True:
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time.sleep(3)
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now_date = dt.now()
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- if not client.is_connected() or dt.now() > now_date.replace(hour=11, minute=30, second=0):
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+ if not client.is_connected() :
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raise Exception('行情服务连接断开')
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break
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return
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+
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def trader(data):
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- print(dt.now(), len(data.keys()), data.keys())
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+ # print('callback',os.getpid())
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+ print(f'{dt.now()},len={len(data.keys())}')
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+ print(data)
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+
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+
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+# stocks = xtdata.get_stock_list_in_sector("沪深债券")
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+# print(len(stocks))
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+stocks = ['110043.SH', '110044.SH', '128075.SZ', '128079.SZ', '113017.SH', '128119.SZ']
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+# # stocks = ['110055.SZ', '110061.SZ']
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+stock_code = '127045.SZ'
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+# seq2 = xtdata.subscribe_quote(stock_code, period='tick', start_time='20230101', end_time='20230407', count=10000, callback=trader)
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+# # print(seq2)
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+# seq = xtdata.subscribe_whole_quote(['沪深债券'], callback=trader)
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+sss = xtdata.subscribe_whole_quote(stocks, callback=trader)
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+# aaa = xtdata.subscribe_quote('128130.SZ', period='tick', start_time='', end_time='', count=0, callback=trader)
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+#
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+xtdata.run()
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+exit()
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+"""
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+
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+
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+class MyXtQuantTraderCallback(XtQuantTraderCallback):
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+ def on_disconnected(self):
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+ """
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+ 连接断开
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+ :return:
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+ """
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+ print(datetime.datetime.now(), '连接断开回调')
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+
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+ def on_stock_order(self, order):
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+ """
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+ 委托回报推送
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+ :param order: XtOrder对象
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+ :return:
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+ """
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+ print(datetime.datetime.now(), '委托回调', order.order_remark)
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+
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+ def on_stock_trade(self, trade):
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+ """
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+ 成交变动推送
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+ :param trade: XtTrade对象
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+ :return:
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+ """
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+ print(datetime.datetime.now(), '成交回调', trade.order_remark)
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+
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+ def on_order_error(self, order_error):
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+ """
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+ 委托失败推送
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+ :param order_error:XtOrderError 对象
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+ :return:
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+ """
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+ # print("on order_error callback")
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+ # print(order_error.order_id, order_error.error_id, order_error.error_msg)
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+ print(f"委托报错回调 {order_error.order_remark} {order_error.error_msg}")
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+
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+ def on_cancel_error(self, cancel_error):
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+ """
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+ 撤单失败推送
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+ :param cancel_error: XtCancelError 对象
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+ :return:
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+ """
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+ print(datetime.datetime.now(), sys._getframe().f_code.co_name)
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+
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+ def on_order_stock_async_response(self, response):
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+ """
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+ 异步下单回报推送
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+ :param response: XtOrderResponse 对象
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+ :return:
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+ """
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+ print(f"异步委托回调 {response.order_remark}")
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+
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+ def on_cancel_order_stock_async_response(self, response):
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+ """
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+ :param response: XtCancelOrderResponse 对象
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+ :return:
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+ """
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+ print(datetime.datetime.now(), sys._getframe().f_code.co_name)
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+
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+ def on_account_status(self, status):
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+ """
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+ :param response: XtAccountStatus 对象
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+ :return:
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+ """
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+ print(datetime.datetime.now(), sys._getframe().f_code.co_name)
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+
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+
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+def get_tick(code, start_time, end_time, period='tick'):
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+ from xtquant import xtdata
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+
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+ xtdata.download_history_data(code, period=period, start_time=start_time, end_time=end_time)
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+ data = xtdata.get_local_data(field_list=[], stock_code=[code], period=period)
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+ result_list = data[code]
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+ df = pd.DataFrame(result_list)
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+
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+ df['time'] = df['time'].apply(lambda x: datetime.datetime.fromtimestamp(x / 1000.0))
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+ return df.iloc[-20:].loc[:, ['time', 'lastPrice', 'open', 'high','low','amount', 'volume']]
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+ # return df.iloc[-20:,:]
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+
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+
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+def process_timestamp(df, filename):
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+ df = df.set_index('time_str')
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+ result = df.resample('3S').first().ffill()
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+ # result = result[(result.index >= '2022-07-20 09:30') & (result.index <= '2022-07-20 15:00')]
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+ result = result.reset_index()
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+ # result.to_csv(filename + '.csv')
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+ print(result)
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+
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+
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+def get_macd_data(data, short=0, long1=0, mid=0):
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+ if short == 0:
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+ short = 12
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+ if long1 == 0:
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+ long1 = 26
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+ if mid == 0:
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+ mid = 9
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+ data['sema'] = pd.Series(data['close']).ewm(span=short).mean()
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+ data['lema'] = pd.Series(data['close']).ewm(span=long1).mean()
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+ data.fillna(0, inplace=True)
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+ data['dif'] = data['sema'] - data['lema']
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+ data['dea'] = pd.Series(data['dif']).ewm(span=mid).mean()
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+ data['macd'] = 2 * (data['dif'] - data['dea'])
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+ data.fillna(0, inplace=True)
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+ return data
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+
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+
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+def get_hlfx(data):
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+ Trading_signals = 0
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+ data_temp = data[['time', 'open', 'close', 'high', 'low', 'dif', 'dea', 'macd']]
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+ data_temp.columns = ['time', 'open', 'close', 'high', 'low', 'dif', 'dea', 'macd']
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+ df_day = pd.DataFrame(columns=['time', 'open', 'close', 'high', 'low', 'volume', 'money', 'HL'])
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+ # 先处理去包含
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+ for i in data_temp.index:
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+ if i == 0 or i == 1:
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+ df_day = pd.concat([df_day, data_temp.iloc[[i]]], ignore_index=True)
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+ # 不包含
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+ elif (df_day.iloc[-1, 3] > data_temp.loc[i, 'high']
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+ and df_day.iloc[-1, 4] > data_temp.loc[i, 'low']) \
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+ or (df_day.iloc[-1, 3] < data_temp.loc[i, 'high']
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+ and df_day.iloc[-1, 4] < data_temp.loc[i, 'low']):
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+ df_day = pd.concat([df_day, data_temp.loc[[i]]], ignore_index=True)
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+ # 包含
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+ else:
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+ # 左高，下降
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+ if df_day.iloc[-2, 3] > df_day.iloc[-1, 3]:
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+ df_day.iloc[-1, 3] = min(df_day.iloc[-1, 3], data_temp.loc[i, 'high'])
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+ df_day.iloc[-1, 4] = min(df_day.iloc[-1, 4], data_temp.loc[i, 'low'])
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+ else:
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+ # 右高，上升
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+ df_day.iloc[-1, 3] = max(df_day.iloc[-1, 3], data_temp.loc[i, 'high'])
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+ df_day.iloc[-1, 4] = max(df_day.iloc[-1, 4], data_temp.loc[i, 'low'])
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+ # print('111', df_day, data_temp)
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+
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+ if len(df_day.index) > 2:
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+ # 寻找顶底分型
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+ for x in range(2, len(df_day.index)):
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+ m = x - 1
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+ # 底
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+ # 符合底分型形态，且第2、3根k线是阳线
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+ if ((df_day.loc[x, 'high'] > df_day.loc[x - 1, 'high']) and
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+ (df_day.loc[x - 2, 'high'] > df_day.loc[x - 1, 'high'])) and \
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+ df_day.loc[x, 'close'] > df_day.loc[x, 'open'] and \
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+ df_day.loc[x - 1, 'close'] > df_day.loc[x - 1, 'open']:
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+
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+ df_day.loc[x, 'HL'] = 'L*'
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+ while m:
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+ if df_day.loc[m, 'HL'] in ['H', 'HH', 'H*']:
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+ if (x - m) > 3:
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+ # 成笔——>L
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+ df_day.loc[x, 'HL'] = 'L'
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+ # 产生信号，进入hlfx_pool
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+ if x == len(df_day.index) - 1:
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+ Trading_signals = 1
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+
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+ elif df_day.loc[m, 'HL'] == 'L':
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+ if df_day.loc[m - 1, 'low'] > df_day.loc[x - 1, 'low']:
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+ # 前一个为底更高，且中间不存在更低的底
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+ df_day.loc[x, 'HL'] = 'L'
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+
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+ # 产生信号，进入hlfx_pool
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+ if x == len(df_day.index) - 1:
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+ Trading_signals = 1
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+
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+ # 获得MACD，判断MACD判断背驰
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+ x_macd_dif, x_macd_dea, x_macd_macd = data_temp.loc[x, 'dif'], data_temp.loc[x, 'dea'], \
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+ data_temp.loc[x, 'macd']
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+ m_macd_dif, m_macd_dea, m_macd_macd = data_temp.loc[m, 'dif'], data_temp.loc[m, 'dea'], \
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+ data_temp.loc[m, 'macd']
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+
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+ # MACD底背驰
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+ if m_macd_dif < x_macd_dif:
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+ # 背驰底->LL
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+ df_day.loc[x, 'HL'] = 'LL'
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+ break
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+ break
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+ m = m - 1
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+ if m == 0:
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+ df_day.loc[x, 'HL'] = 'L'
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+
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+ # 顶
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+ elif ((df_day.loc[x, 'high'] < df_day.loc[x - 1, 'high']) and (
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+ df_day.loc[x - 2, 'high'] < df_day.loc[x - 1, 'high'])):
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+
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+ df_day.loc[x, 'HL'] = 'H*'
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+ while m:
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+ if df_day.loc[m, 'HL'] in ['L', 'LL', 'L*']:
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+ if x - m > 3:
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+ # 成笔->H
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+ df_day.loc[x, 'HL'] = 'H'
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+ # 产生信号，进入hlfx_pool
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+ if x == len(df_day.index) - 1:
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+ Trading_signals = 2
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+
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+ elif df_day.loc[m, 'HL'] == 'H':
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+ if df_day.loc[x - 1, 'high'] > df_day.loc[m - 1, 'high']:
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+ # 前一个为顶，且中间存在不包含 or 更高的顶
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+ df_day.loc[x, 'HL'] = 'H'
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+ # 产生信号，进入hlfx_pool
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+ if x == len(df_day.index) - 1:
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+ Trading_signals = 2
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+
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+ # 获得MACD，判断MACD判断背驰
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+ x_macd_dif, x_macd_dea, x_macd_macd = data_temp.loc[x, 'dif'], data_temp.loc[x, 'dea'], \
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+ data_temp.loc[x, 'macd']
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+ m_macd_dif, m_macd_dea, m_macd_macd = data_temp.loc[m, 'dif'], data_temp.loc[m, 'dea'], \
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+ data_temp.loc[m, 'macd']
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+
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+ # MACD顶背驰
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+ if x_macd_dif < m_macd_dif:
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+ df_day.loc[x, 'HL'] = 'HH'
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+ break
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+ break
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+ m = m - 1
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+ if m == 0:
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+ df_day.loc[x, 'HL'] = 'H'
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+
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+ else:
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+ df_day.loc[x, 'HL'] = '-'
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+ df_temp = df_day[['time', 'HL']]
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+
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+ return df_temp, Trading_signals
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+
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+
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+def ma(df, num):
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+ i = -1 * (5 + num)
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+ try:
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+ if i == -5:
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+ ma_num = sum(df['close'][i:])/5
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+ else:
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+ ma_num = (sum(df['close'][i:(i+5)]))/5
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+ # print(df['close'][i:(i+5)])
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+ except:
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+ return 9999999
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+ else:
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+ return ma_num
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+
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+
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+def cont(data):
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+ code = '127045.SZ'
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+ dd = xtdata.get_market_data(field_list=[], stock_list=[code], period='tick', start_time='20230101', end_time='',
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+ count=-1, dividend_type='none', fill_data=True)
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+ # print(dt.now(), type(dd), dd[code].shape)
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+ # print(dd[code]['time'][-10:-1], '\n', dd[code]['lastPrice'][-10:-1], '\n', dd[code]['open'][-10:-1], '\n', dd[code]['high'][-10:-1])
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+ # print(dd[code], '\n'*2)
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+ dic = {'time': dd[code]['time'],
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+ 'open': dd[code]['open'],
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+ 'close': dd[code]['lastPrice'],
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+ 'high': dd[code]['high'],
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+ 'low': dd[code]['low']}
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+ df_day = pd.DataFrame(dic)
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+ df_day['time'] = df_day['time'].apply(lambda x: dt.fromtimestamp(x / 1000.0))
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+ ma_now = ma(df_day, 0)
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+ ma_1 = ma(df_day, 1)
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+ ma_2 = ma(df_day, 2)
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+ ma_3 = ma(df_day,3)
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+ # print(ma_now,ma_1,ma_2)
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+
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+ positions = xt_trader.query_stock_positions(acc)
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+ positions_dict = {positions[x].stock_code: positions[x].can_use_volume for x in range(0, len(positions))}
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+ # print(positions_dict)
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+
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+ if code in positions_dict and positions_dict[code] != 0:
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+ if df_day['close'].iloc[-1] < ma_now or ma_now < ma_1:
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+ order_id = xt_trader.order_stock(acc, code, xtconstant.STOCK_SELL, positions_dict[code],
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+ xtconstant.LATEST_PRICE, 0, '可转债', '滞涨')
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+ print(f'可转债卖出_{order_id}，成交价格：{xtconstant.LATEST_PRICE}')
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+ if df_day['close'].iloc[-1] > ma_now > ma_1 and ma_1 < ma_2 <= ma_3 and df_day['close'].iloc[-2] < ma_1:
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+ print('buy:', df_day['time'].iloc[-1], df_day['close'].iloc[-1], ma_now)
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+ order_id = xt_trader.order_stock(acc, code, xtconstant.STOCK_BUY, 10, xtconstant.LATEST_PRICE,
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+ 0, '可转债', '跳多MA5')
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+ print(f'可转债Buy——{order_id},成交价格：{xtconstant.LATEST_PRICE}')
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+ elif df_day['close'].iloc[-1] > df_day['close'].iloc[-2] and df_day['close'].iloc[-4] >df_day['close'].iloc[-3] > df_day['close'].iloc[-2]:
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+ print('buy:', df_day['time'].iloc[-1], df_day['close'].iloc[-1], ma_now)
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+ order_id = xt_trader.order_stock(acc, code, xtconstant.STOCK_BUY, 10, xtconstant.LATEST_PRICE, 0, '可转债', 'tick底分型')
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+ print(f'可转债Buy——{order_id},成交价格：{xtconstant.LATEST_PRICE}')
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+ # df_day = get_macd_data(df_day)
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+ # df_day, Trading_signals= get_hlfx(df_day)
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+ # print(df_day)
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+ if dic ==1:
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+ pass
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+ elif dic ==1:
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+ pass
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+
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+
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+def dump_single_code_tick():
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+ # 导出单个转债的tick数据
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+ code='127045'
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+ start_date = '20230101'
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+ end_date = '202304010'
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+
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+ post_fix = 'SZ' if code.startswith('12') else 'SH'
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+ code = '{}.{}'.format(code,post_fix)
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+ print(code)
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+ filename = '{}'.format(code)
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+ df = get_tick(code, start_date, end_date)
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+ print(df)
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+
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+
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+if __name__ == '__main__':
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+ # 指定客户端所在路径
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+ path = r'c:\\qmt\\userdata_mini'
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+ # 生成session id 整数类型 同时运行的策略不能重复
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+ session_id = int(time.time())
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+ xt_trader = XtQuantTrader(path, session_id)
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+ # 创建资金账号为 800068 的证券账号对象
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+ acc = StockAccount('920000207040', 'SECURITY')
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+ # 创建交易回调类对象，并声明接收回调
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+ callback = MyXtQuantTraderCallback()
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+ xt_trader.register_callback(callback)
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+ # 启动交易线程
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+ xt_trader.start()
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+ # 建立交易连接，返回0表示连接成功
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+ connect_result = xt_trader.connect()
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+ print('建立交易连接，返回0表示连接成功', connect_result)
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+ # 对交易回调进行订阅，订阅后可以收到交易主推，返回0表示订阅成功
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+ subscribe_result = xt_trader.subscribe(acc)
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+ print('对交易回调进行订阅，订阅后可以收到交易主推，返回0表示订阅成功', subscribe_result)
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+
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+ code = '127045.SZ'
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+ st = dt.now()
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+ stocks = xtdata.get_stock_list_in_sector('沪深A股')
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+ xtdata.subscribe_quote(code, 'tick', start_time='', end_time='', count=1000, callback=cont)
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+ # xtdata.subscribe_whole_quote(stocks,callback=cont)
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+ # dd = xtdata.get_market_data(field_list=[], stock_list=[code], period='tick', start_time='20230101', end_time='',
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+ # count=-1,
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+ # dividend_type='none', fill_data=True)
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+
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+ # print(positions_dict[code])
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+ # print(type(dd), type(dd[code]), type(dd[code][-1]), dd[code], dd)
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+ # print(dd[code]['lastPrice'])
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+ # dd[code]['time'] = dd[code]['time'].apply(lambda x: dt.fromtimestamp(x / 1000.0))
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+ # print(type(dd[code]['time']), dd[code]['time'])
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+ # print('________','\n')
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+ # print(type(list(dd[code]['time'])))
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+ # print('________')
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+ # print(dd[code])
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+ xtdata.run()
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+ # dump_single_code_tick()
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+
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+ et = dt.now()
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+ print(et-st)
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+
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+
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+
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+
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+
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-# stocks = stocks = xtdata.get_stock_list_in_sector('沪深A股')
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-stocks = ['000001.SZ', '600000.SH', '300389.SZ', '001229.SZ', '600674.SH', '000895.SZ']
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-xtdata.subscribe_whole_quote(stocks, callback=trader)
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-run()
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