MQL5 Programming for TradersAdvanced language toolsNative python supportReading the history of orders and deals

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Reading the history of orders and deals

Working with orders and deals in the account history using Python scripts is also possible. For these purposes, there are functions history_orders_total, history_orders_get, history_deals_total, and history_deals_get.

int history_orders_total(date_from, date_to)

The history_orders_total function returns the number of orders in the trading history in the specified time interval. Each of the parameters is set by the datetime object or as the number of seconds since 1970.01.01.

The function is an analog of HistoryOrdersTotal.

The history_orders_get function is available in several versions and supports order filtering by substring in symbol name, ticket, or position ID. All variants return an array of named tuples TradeOrder (field names match ENUM_ORDER_PROPERTY_enumerations without the "ORDER_" prefix and in lowercase). If there are no matching orders, the array will be empty. In case of an error, the function will return 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>)

The first version selects orders within the specified time range (similar to history_orders_total). In the optional named parameter group, you can specify a search pattern for a substring of the symbol name (you can use the wildcard characters '*' and negation '!' in it, see the section Getting information about financial instruments).

The second version is designed to search for a specific order by its ticket.

The last version selects orders by position ID (ORDER_POSITION_ID property).

Either option is equivalent to calling several MQL5 functions: HistoryOrdersTotal, HistoryOrderSelect, and HistoryOrderGet-functions.

Let's see on an example of the script historyordersget.py how to get the number and list of historical orders for different conditions.

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)
...

The result of the script (given with abbreviations):

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

...

We can see that in September, there were only 44 orders, 15 of which included the GBP currency (an odd number due to the open position). The history contains 14 canceled orders.

int history_deals_total(date_from, date_to)

The history_deals_total function returns the number of deals in history for the specified period.

The function is an analog of HistoryDealsTotal.

The history_deals_get function has several forms and is designed to select trades with the ability to filter by order ticket or position ID. All forms of the function return an array of named tuples TradeDeal, with fields reflecting properties from the ENUM_DEAL_PROPERTY_enumerations (the prefix "DEAL_" has been removed from the field names and lowercase has been applied). In case of an error, we get 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>)

The first form of the function is similar to requesting historical orders using history_orders_get.

The second form allows the selection of deals generated by a specific order by its ticket (the DEAL_ORDER property).

Finally, the third form requests deals that have formed a position with a given ID (the DEAL_POSITION_ID property).

The function allows you to get all transactions together with their properties in one call, which is analogous to the bunch of HistoryDealsTotal, HistoryDealSelect, and HistoryDealGet-functions.

Here is the main part of the test script historydealsget.py.

# 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)

An example of result:

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

Getting the number and list of open positions

Built-in support for parallel computing: OpenCL