from datetime import datetime
import MetaTrader5 as mt5
# 显示有关MetaTrader 5程序包的数据
print("MetaTrader5 package author: ",mt5.__author__)
print("MetaTrader5 package version: ",mt5.__version__)
# 导入'pandas'模块,用于以表格形式显示获得的数据
import pandas as pd
pd.set_option('display.max_columns', 500) # number of columns to be displayed
pd.set_option('display.width', 1500) # max table width to display
# 建立与MetaTrader 5程序端的连接
if not mt5.initialize():
print("initialize() failed, error code =",mt5.last_error())
quit()
# 从当日获取10个GBPUSD D1柱形图
rates = mt5.copy_rates_from_pos("GBPUSD", mt5.TIMEFRAME_D1, 0, 10)
# 断开与MetaTrader 5程序端的连接
mt5.shutdown()
# 在新行显示所获得数据的每个元素
print("Display obtained data 'as is'")
for rate in rates:
print(rate)
# 从所获得的数据创建DataFrame
rates_frame = pd.DataFrame(rates)
# 将时间(以秒为单位)转换为日期时间格式
rates_frame['time']=pd.to_datetime(rates_frame['time'], unit='s')
# 显示数据
print("\nDisplay dataframe with data")
print(rates_frame)
结果:
MetaTrader5程序包作者:MetaQuotes Software Corp.
MetaTrader5程序包版本:5.0.29
将获得的数据显示为“保持原来状态”
(1581552000, 1.29568, 1.30692, 1.29441, 1.30412, 68228, 0, 0)
(1581638400, 1.30385, 1.30631, 1.3001, 1.30471, 56498, 0, 0)
(1581897600, 1.30324, 1.30536, 1.29975, 1.30039, 49400, 0, 0)
(1581984000, 1.30039, 1.30486, 1.29705, 1.29952, 62288, 0, 0)
(1582070400, 1.29952, 1.3023, 1.29075, 1.29187, 57909, 0, 0)
(1582156800, 1.29186, 1.29281, 1.28489, 1.28792, 61033, 0, 0)
(1582243200, 1.28802, 1.29805, 1.28746, 1.29566, 66386, 0, 0)
(1582502400, 1.29426, 1.29547, 1.28865, 1.29283, 66933, 0, 0)
(1582588800, 1.2929, 1.30178, 1.29142, 1.30037, 80121, 0, 0)
(1582675200, 1.30036, 1.30078, 1.29136, 1.29374, 49286, 0, 0)
显示带有数据的数据框
time open high low close tick_volume spread real_volume
0 2020-02-13 1.29568 1.30692 1.29441 1.30412 68228 0 0
1 2020-02-14 1.30385 1.30631 1.30010 1.30471 56498 0 0
2 2020-02-17 1.30324 1.30536 1.29975 1.30039 49400 0 0
3 2020-02-18 1.30039 1.30486 1.29705 1.29952 62288 0 0
4 2020-02-19 1.29952 1.30230 1.29075 1.29187 57909 0 0
5 2020-02-20 1.29186 1.29281 1.28489 1.28792 61033 0 0
6 2020-02-21 1.28802 1.29805 1.28746 1.29566 66386 0 0
7 2020-02-24 1.29426 1.29547 1.28865 1.29283 66933 0 0
8 2020-02-25 1.29290 1.30178 1.29142 1.30037 80121 0 0
9 2020-02-26 1.30036 1.30078 1.29136 1.29374 49286 0 0
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