面向交易员的 MQL5 编程指南第七章高级语言工具原生 Python 支持读取订单和交易的历史

在一个文件中下载:

读取订单和交易的历史

也可以使用 Python 脚本处理账户历史中的订单和交易。为了达到这些目的，提供了函数 history_orders_total、history_orders_get、history_deals_total 和 history_deals_get。

int history_orders_total(date_from, date_to)

history_orders_total函数返回指定时间间隔内交易历史中的订单数量。每个参数都由 datetime 对象设置，或者设置为自 1970.01.01 以来经历的秒数。

该函数类似于 HistoryOrdersTotal。

history_orders_get 函数有多个版本，支持按交易品种名称、订单号或位置 ID 中的子字符串进行订单筛选。所有变量都返回一个命名元组 TradeOrder 的数组（字段名称匹配 ENUM_ORDER_PROPERTY_enumerations 不带 "ORDER_" 前缀并且是小写的）。如果没有匹配的订单，数组将为空。如果出现错误，该函数将返回 None。

namedtuple[] history_orders_get(date_from, date_to, group = <"PATTERN">)

namedtuple[] history_orders_get(ticket = <ORDER_TICKET>)

namedtuple[] history_orders_get(position = <POSITION_ID>)

第一个版本选择指定时间范围内的订单（类似于 history_orders_total）。在可选的命名参数 group 中，你可以为交易品种名称的子字符串指定搜索模式（你可以在其中使用通配符 '*' 和非 '!'，参见获取有关金融工具的信息一节）。

第二个版本旨在按订单号搜索特定订单。

最后一个版本按仓位 ID（ORDER_POSITION_ID 特性）选择订单。

任一选项都相当于调用多个 MQL5 函数：HistoryOrdersTotal、 HistoryOrderSelect 和 HistoryOrderGet 函数。

这里是一个 historyordersget.py 脚本的例子，让我们看看如何获取不同条件下的历史订单的数量和列表。

from datetime import datetime
import MetaTrader5 as mt5
import pandas as pd
pd.set_option('display.max_columns', 500) # how many columns to show
pd.set_option('display.width', 1500)      # max. table width for display
...
# get the number of orders in the history for the period (total and *GBP*)
from_date = datetime(2022, 9, 1)
to_date = datetime.now()
total = mt5.history_orders_total(from_date, to_date)
history_orders=mt5.history_orders_get(from_date, to_date, group="*GBP*")
# print(history_orders)
if history_orders == None:
   print("No history orders with group=\"*GBP*\", error code={}".format(mt5.last_error()))
else :
   print("history_orders_get({}, {}, group=\"*GBP*\")={} of total {}".format(from_date,
   to_date, len(history_orders), total))

# display all canceled historical orders for ticket position 0
position_id = 0
position_history_orders = mt5.history_orders_get(position = position_id)
if position_history_orders == None:
   print("No orders with position #{}".format(position_id))
   print("error code =", mt5.last_error())
elif len(position_history_orders) > 0:
   print("Total history orders on position #{}: {}".format(position_id,
   len(position_history_orders)))
   # display received orders as is
   for position_order in position_history_orders:
      print(position_order)
   # display these orders as a table using pandas.DataFrame
   df = pd.DataFrame(list(position_history_orders),
   columns = position_history_orders[0]._asdict().keys())
   df.drop(['time_expiration', 'type_time', 'state', 'position_by_id', 'reason', 'volume_current',
   'price_stoplimit','sl','tp', 'time_setup_msc', 'time_done_msc', 'type_filling', 'external_id'],
   axis = 1, inplace = True)
   df['time_setup'] = pd.to_datetime(df['time_setup'], unit='s')
   df['time_done'] = pd.to_datetime(df['time_done'], unit='s')
   print(df)
...

脚本的结果（用缩写给出）：

history_orders_get(2022-09-01 00:00:00, 2022-09-26 21:50:04, group="*GBP*")=15 of total 44

Total history orders on position #0: 14

TradeOrder(ticket=1437318706, time_setup=1661348065, time_setup_msc=1661348065049, time_done=1661348083,

time_done_msc=1661348083632, time_expiration=0, type=2, type_time=0, type_filling=2, state=2, magic=0,

position_id=0, position_by_id=0, reason=3, volume_initial=0.01, volume_current=0.01, price_open=0.99301,

sl=0.0, tp=0.0, price_current=0.99311, price_stoplimit=0.0, symbol='EURUSD', comment='', external_id='')

TradeOrder(ticket=1437331579, time_setup=1661348545, time_setup_msc=1661348545750, time_done=1661348551,

time_done_msc=1661348551354, time_expiration=0, type=2, type_time=0, type_filling=2, state=2, magic=0,

position_id=0, position_by_id=0, reason=3, volume_initial=0.01, volume_current=0.01, price_open=0.99281,

sl=0.0, tp=0.0, price_current=0.99284, price_stoplimit=0.0, symbol='EURUSD', comment='', external_id='')

TradeOrder(ticket=1437331739, time_setup=1661348553, time_setup_msc=1661348553935, time_done=1661348563,

time_done_msc=1661348563412, time_expiration=0, type=2, type_time=0, type_filling=2, state=2, magic=0,

position_id=0, position_by_id=0, reason=3, volume_initial=0.01, volume_current=0.01, price_open=0.99285,

sl=0.0, tp=0.0, price_current=0.99286, price_stoplimit=0.0, symbol='EURUSD', comment='', external_id='')

...

ticket time_setup time_done type ..._initial price_open price_current symbol comment

0 1437318706 2022-08-24 13:34:25 2022-08-24 13:34:43 2 0.01 0.99301 0.99311 EURUSD

1 1437331579 2022-08-24 13:42:25 2022-08-24 13:42:31 2 0.01 0.99281 0.99284 EURUSD

2 1437331739 2022-08-24 13:42:33 2022-08-24 13:42:43 2 0.01 0.99285 0.99286 EURUSD

...

我们可以看到，在 9 月份，只有 44 个订单，其中 15 个订单包括英镑货币（由于未平仓仓位，这个数字是一个奇数）。历史记录包含 14 个取消的订单。

int history_deals_total(date_from, date_to)

history_deals_total 函数返回指定期间的历史交易数。

该函数类似于 HistoryDealsTotal。

history_deals_get函数有多种形式，用于选择能够按订单订单号或仓位 ID 筛选的交易。所有形式的函数都返回一个命名元组 TradeDeal的数组，其中的字段反映 ENUM_DEAL_PROPERTY_enumerations 中的特性（前缀 "DEAL_" 已从字段名中删除，并应用小写字母）。如果出错，则返回 None。

namedtuple[] history_deals_get(date_from, date_to, group = <"PATTERN">)

namedtuple[] history_deals_get(ticket = <ORDER_TICKET>)

namedtuple[] history_deals_get(position = <POSITION_ID>)

该函数的第一种形式类似于使用 history_orders_get请求历史订单。

第二种形式允许按订单号选择由特定订单生成的交易（DEAL_ORDER 特性）。

最后，第三个表单请求已经形成仓位的交易，该仓位具有给定 ID（DEAL_POSITION_ID 特性）。

该函数允许你在一次调用中获取所有事务及其特性，这类似于 HistoryDealsTotal、 HistoryDealSelect和 HistoryDealGet函数。

下面是测试脚本historydealsget.py的主要部分。

# set the time range
from_date = datetime(2020, 1, 1)
to_date = datetime.now()

# get trades for symbols whose names do not contain either "EUR" or "GBP"
deals = mt5.history_deals_get(from_date, to_date, group="*,!*EUR*,!*GBP*")
if deals == None:
   print("No deals, error code={}".format(mt5.last_error()))
elif len(deals) > 0:
   print("history_deals_get(from_date, to_date, group=\"*,!*EUR*,!*GBP*\") =",
   len(deals))
   # display all received deals as they are
   for deal in deals:
      print("  ",deal)
   # display these trades as a table using pandas.DataFrame
   df = pd.DataFrame(list(deals), columns = deals[0]._asdict().keys())
   df['time'] = pd.to_datetime(df['time'], unit='s')
   df.drop(['time_msc','commission','fee'], axis = 1, inplace = True)
   print(df)

结果示例：

history_deals_get(from_date, to_date, group="*,!*EUR*,!*GBP*") = 12

TradeDeal(ticket=1109160642, order=0, time=1632188460, time_msc=1632188460852, type=2, entry=0, magic=0, position_id=0, reason=0, volume=0.0, price=0.0, commission=0.0, swap=0.0, profit=10000.0, fee=0.0, symbol='', comment='', external_id='')

TradeDeal(ticket=1250629232, order=1268074569, time=1645709385, time_msc=1645709385815, type=0, entry=0, magic=0, position_id=1268074569, reason=0, volume=0.01, price=1970.98, commission=0.0, swap=0.0, profit=0.0, fee=0.0, symbol='XAUUSD', comment='', external_id='')

TradeDeal(ticket=1250639814, order=1268085019, time=1645709950, time_msc=1645709950618, type=1, entry=1, magic=0, position_id=1268074569, reason=0, volume=0.01, price=1970.09, commission=0.0, swap=0.0, profit=-0.89, fee=0.0, symbol='XAUUSD', comment='', external_id='')

TradeDeal(ticket=1250639928, order=1268085129, time=1645709955, time_msc=1645709955502, type=1, entry=0, magic=0, position_id=1268085129, reason=0, volume=0.01, price=1969.98, commission=0.0, swap=0.0, profit=0.0, fee=0.0, symbol='XAUUSD', comment='', external_id='')

TradeDeal(ticket=1250640111, order=1268085315, time=1645709965, time_msc=1645709965148, type=0, entry=1, magic=0, position_id=1268085129, reason=0, volume=0.01, price=1970.17, commission=0.0, swap=0.0, profit=-0.19, fee=0.0, symbol='XAUUSD', comment='', external_id='')

TradeDeal(ticket=1250640309, order=1268085512, time=1645709973, time_msc=1645709973623, type=1, entry=0, magic=0, position_id=1268085512, reason=0, volume=0.1, price=1970.09, commission=0.0, swap=0.0, profit=0.0, fee=0.0, symbol='XAUUSD', comment='', external_id='')

TradeDeal(ticket=1250640400, order=1268085611, time=1645709978, time_msc=1645709978701, type=0, entry=1, magic=0, position_id=1268085512, reason=0, volume=0.1, price=1970.22, commission=0.0, swap=0.0, profit=-1.3, fee=0.0, symbol='XAUUSD', comment='', external_id='')

TradeDeal(ticket=1250640616, order=1268085826, time=1645709988, time_msc=1645709988277, type=1, entry=0, magic=0, position_id=1268085826, reason=0, volume=1.1, price=1969.95, commission=0.0, swap=0.0, profit=0.0, fee=0.0, symbol='XAUUSD', comment='', external_id='')

TradeDeal(ticket=1250640810, order=1268086019, time=1645709996, time_msc=1645709996990, type=0, entry=1, magic=0, position_id=1268085826, reason=0, volume=1.1, price=1969.88, commission=0.0, swap=0.0, profit=7.7, fee=0.0, symbol='XAUUSD', comment='', external_id='')

TradeDeal(ticket=1445796125, order=1468026008, time=1664199450, time_msc=1664199450488, type=0, entry=0, magic=234000, position_id=1468026008, reason=3, volume=0.1, price=144.132, commission=0.0, swap=0.0, profit=0.0, fee=0.0, symbol='USDJPY', comment='python script op', external_id='')

TradeDeal(ticket=1445796155, order=1468026041, time=1664199452, time_msc=1664199452567, type=1, entry=1, magic=234000, position_id=1468026008, reason=3, volume=0.1, price=144.124, commission=0.0, swap=0.0, profit=-0.56, fee=0.0, symbol='USDJPY', comment='python script cl', external_id='')

TradeDeal(ticket=1446217804, order=1468454363, time=1664217233, time_msc=1664217233239, type=1, entry=0, magic=0, position_id=1468454363, reason=0, volume=0.01, price=0.99145, commission=0.0, swap=0.0, profit=0.0, fee=0.0, symbol='USDCHF', comment='', external_id='')

ticket order time t… e… … position_id volume price profit symbol comment external_id

0 1109160642 0 2021-09-21 01:41:00 2 0 0 0.00 0.00000 10000.00

1 1250629232 1268074569 2022-02-24 13:29:45 0 0 1268074569 0.01 1970.98000 0.00 XAUUSD

2 1250639814 1268085019 2022-02-24 13:39:10 1 1 1268074569 0.01 1970.09000 -0.89 XAUUSD

3 1250639928 1268085129 2022-02-24 13:39:15 1 0 1268085129 0.01 1969.98000 0.00 XAUUSD

4 1250640111 1268085315 2022-02-24 13:39:25 0 1 1268085129 0.01 1970.17000 -0.19 XAUUSD

5 1250640309 1268085512 2022-02-24 13:39:33 1 0 1268085512 0.10 1970.09000 0.00 XAUUSD

6 1250640400 1268085611 2022-02-24 13:39:38 0 1 1268085512 0.10 1970.22000 -1.30 XAUUSD

7 1250640616 1268085826 2022-02-24 13:39:48 1 0 1268085826 1.10 1969.95000 0.00 XAUUSD

8 1250640810 1268086019 2022-02-24 13:39:56 0 1 1268085826 1.10 1969.88000 7.70 XAUUSD

9 1445796125 1468026008 2022-09-26 13:37:30 0 0 1468026008 0.10 144.13200 0.00 USDJPY python script op

10 1445796155 1468026041 2022-09-26 13:37:32 1 1 1468026008 0.10 144.12400 -0.56 USDJPY python script cl

11 1446217804 1468454363 2022-09-26 18:33:53 1 0 1468454363 0.01 0.99145 0.00 USDCHF

获取未平仓仓位的数量和列表

内置对并行计算的支持：OpenCL