MQL5 ReferencePython Integrationcopy_rates_from

copy_rates_from

Get bars from the MetaTrader 5 terminal starting from the specified date.

copy_rates_from(
   symbol,       // symbol name
   timeframe,    // timeframe
   date_from,    // initial bar open date
   count         // number of bars
   )

Parameters

symbol

[in] Financial instrument name, for example, "EURUSD". Required unnamed parameter.

timeframe

[in] Timeframe the bars are requested for. Set by a value from the TIMEFRAME enumeration. Required unnamed parameter.

date_from

[in] Date of opening of the first bar from the requested sample. Set by the 'datetime' object or as a number of seconds elapsed since 1970.01.01. Required unnamed parameter.

count

[in] Number of bars to receive. Required unnamed parameter.

Return Value

Returns bars as the numpy array with the named time, open, high, low, close, tick_volume, spread and real_volume columns. Return None in case of an error. The info on the error can be obtained using last_error().

Note

See the CopyRates() function for more information.

Only data whose date is less than (earlier) or equal to the date specified will be returned. It means, the open time of any bar is always less or equal to the specified one.

MetaTrader 5 terminal provides bars only within a history available to a user on charts. The number of bars available to users is set in the "Max. bars in chart" parameter.

When creating the 'datetime' object, Python uses the local time zone, while MetaTrader 5 stores tick and bar open time in UTC time zone (without the shift). Therefore, 'datetime' should be created in UTC time for executing functions that use time. Data received from the MetaTrader 5 terminal has UTC time.

TIMEFRAME is an enumeration with possible chart period values

ID	Description
TIMEFRAME_M1	1 minute
TIMEFRAME_M2	2 minutes
TIMEFRAME_M3	3 minutes
TIMEFRAME_M4	4 minutes
TIMEFRAME_M5	5 minutes
TIMEFRAME_M6	6 minutes
TIMEFRAME_M10	10 minutes
TIMEFRAME_M12	12 minutes
TIMEFRAME_M12	15 minutes
TIMEFRAME_M20	20 minutes
TIMEFRAME_M30	30 minutes
TIMEFRAME_H1	1 hour
TIMEFRAME_H2	2 hours
TIMEFRAME_H3	3 hours
TIMEFRAME_H4	4 hours
TIMEFRAME_H6	6 hours
TIMEFRAME_H8	8 hours
TIMEFRAME_H12	12 hours
TIMEFRAME_D1	1 day
TIMEFRAME_W1	1 week
TIMEFRAME_MN1	1 month

Example:

from datetime import datetime
import MetaTrader5 as mt5
# display data on the MetaTrader 5 package
print("MetaTrader5 package author: ",mt5.__author__)
print("MetaTrader5 package version: ",mt5.__version__)

# import the 'pandas' module for displaying data obtained in the tabular form
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
# import pytz module for working with time zone
import pytz

# establish connection to MetaTrader 5 terminal
if not mt5.initialize():
    print("initialize() failed, error code =",mt5.last_error())
    quit()

# set time zone to UTC
timezone = pytz.timezone("Etc/UTC")
# create 'datetime' object in UTC time zone to avoid the implementation of a local time zone offset
utc_from = datetime(2020, 1, 10, tzinfo=timezone)
# get 10 EURUSD H4 bars starting from 01.10.2020 in UTC time zone
rates = mt5.copy_rates_from("EURUSD", mt5.TIMEFRAME_H4, utc_from, 10)

# shut down connection to the MetaTrader 5 terminal
mt5.shutdown()
# display each element of obtained data in a new line
print("Display obtained data 'as is'")
for rate in rates:
    print(rate)

# create DataFrame out of the obtained data
rates_frame = pd.DataFrame(rates)
# convert time in seconds into the datetime format
rates_frame['time']=pd.to_datetime(rates_frame['time'], unit='s')

# display data
print("\nDisplay dataframe with data")
print(rates_frame)

Result:
MetaTrader5 package author:  MetaQuotes Software Corp.
MetaTrader5 package version:  5.0.29

Display obtained data 'as is'
(1578484800, 1.11382, 1.11385, 1.1111, 1.11199, 9354, 1, 0)
(1578499200, 1.11199, 1.11308, 1.11086, 1.11179, 10641, 1, 0)
(1578513600, 1.11178, 1.11178, 1.11016, 1.11053, 4806, 1, 0)
(1578528000, 1.11053, 1.11193, 1.11033, 1.11173, 3480, 1, 0)
(1578542400, 1.11173, 1.11189, 1.11126, 1.11182, 2236, 1, 0)
(1578556800, 1.11181, 1.11203, 1.10983, 1.10993, 7984, 1, 0)
(1578571200, 1.10994, 1.11173, 1.10965, 1.11148, 7406, 1, 0)
(1578585600, 1.11149, 1.11149, 1.10923, 1.11046, 7468, 1, 0)
(1578600000, 1.11046, 1.11097, 1.11033, 1.11051, 3450, 1, 0)
(1578614400, 1.11051, 1.11093, 1.11017, 1.11041, 2448, 1, 0)

Display dataframe with data
                 time     open     high      low    close  tick_volume  spread  real_volume
0 2020-01-08 12:00:00  1.11382  1.11385  1.11110  1.11199         9354       1            0
1 2020-01-08 16:00:00  1.11199  1.11308  1.11086  1.11179        10641       1            0
2 2020-01-08 20:00:00  1.11178  1.11178  1.11016  1.11053         4806       1            0
3 2020-01-09 00:00:00  1.11053  1.11193  1.11033  1.11173         3480       1            0
4 2020-01-09 04:00:00  1.11173  1.11189  1.11126  1.11182         2236       1            0
5 2020-01-09 08:00:00  1.11181  1.11203  1.10983  1.10993         7984       1            0
6 2020-01-09 12:00:00  1.10994  1.11173  1.10965  1.11148         7406       1            0
7 2020-01-09 16:00:00  1.11149  1.11149  1.10923  1.11046         7468       1            0
8 2020-01-09 20:00:00  1.11046  1.11097  1.11033  1.11051         3450       1            0
9 2020-01-10 00:00:00  1.11051  1.11093  1.11017  1.11041         2448       1            0