如何获取 Python MetaTrader 5 的每周数据

发布于 2025-01-11 16:45:10 字数 378 浏览 1 评论 0原文

我正在开发一个脚本来获取历史数据。我正在尝试获取每周数据，但 MetaTrader 5 返回空数据框。我正在使用：

import MetaTrader5 as mt5
import pandas as pd

mt5.initialize()

ticks = mt5.copy_rates_range(
    'WIN$N', 
    mt5.TIMEFRAME_W1, 
    datetime(2022, 2, 28),
    datetime(2022, 3, 4)
)

print (pd.DataFrame(ticks))

为什么我得到一个空数据框？将此脚本与分钟或每小时数据一起使用，效果非常好。我尝试查看 MT5 文档，但没有使用每周或每月数据的示例。

原文

I'm developing a script to get historical data. I'm trying to get weekly data but MetaTrader 5 is returning an empty dataframe. I'm using:

import MetaTrader5 as mt5
import pandas as pd

mt5.initialize()

ticks = mt5.copy_rates_range(
    'WIN$N', 
    mt5.TIMEFRAME_W1, 
    datetime(2022, 2, 28),
    datetime(2022, 3, 4)
)

print (pd.DataFrame(ticks))

Why I'm getting an empty dataframe? Using this script with Minutes or Hourly data it works perfectly. I try to look at MT5 documentation but there is none example using WEEKLY or MONTLHY data.

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萌︼了一个春 2025-01-18 16:45:10

获取每周和每月数据的解决方案是（将 WIN$N 更改为您的股票）：

years = [2001, 2002, 2003, 2004, 2005,
        2006, 2007, 2008, 2009, 2010,
        2011, 2012, 2013, 2014, 2015,
        2016, 2017, 2018, 2019, 2020,
        2021]

months = [1, 2, 3, 4, 5, 6,
         7, 8, 9, 10, 11, 12]

timeframes = {
    'W1' : 1  | 0x8000,
    'MN1': 1  | 0xC000
}        

    
for year in years:
    for month in months:
        for timeframe in timeframes:
            path = day.strftime("data/" + str(timeframe) + "/%Y/%m")
            os.makedirs(path, exist_ok=True)
            # request tick data
            if month != 12:
                ticks = mt5.copy_rates_range(
                    'WIN$N', 
                    timeframes[timeframe], 
                    datetime(year, month, 1), 
                    datetime(year, month + timedelta(month=1), 1)
                )
                ticks = pd.DataFrame(ticks)
                ticks.to_csv(path + "/" + str(year) + '_' + str(month) + ".csv")
            else:
                ticks = mt5.copy_rates_range(
                    'WIN$N', 
                    timeframes[timeframe], 
                    datetime(year, month, 1), 
                    datetime(year, month, calendar.monthrange(ano,mes)[1])
                )
                ticks = pd.DataFrame(ticks)
                ticks.to_csv(path + "/" + str(year) + '_' + str(month) + ".csv")

The solution to get weekly and monthly data is (change WIN$N for your stock):

years = [2001, 2002, 2003, 2004, 2005,
        2006, 2007, 2008, 2009, 2010,
        2011, 2012, 2013, 2014, 2015,
        2016, 2017, 2018, 2019, 2020,
        2021]

months = [1, 2, 3, 4, 5, 6,
         7, 8, 9, 10, 11, 12]

timeframes = {
    'W1' : 1  | 0x8000,
    'MN1': 1  | 0xC000
}        

    
for year in years:
    for month in months:
        for timeframe in timeframes:
            path = day.strftime("data/" + str(timeframe) + "/%Y/%m")
            os.makedirs(path, exist_ok=True)
            # request tick data
            if month != 12:
                ticks = mt5.copy_rates_range(
                    'WIN$N', 
                    timeframes[timeframe], 
                    datetime(year, month, 1), 
                    datetime(year, month + timedelta(month=1), 1)
                )
                ticks = pd.DataFrame(ticks)
                ticks.to_csv(path + "/" + str(year) + '_' + str(month) + ".csv")
            else:
                ticks = mt5.copy_rates_range(
                    'WIN$N', 
                    timeframes[timeframe], 
                    datetime(year, month, 1), 
                    datetime(year, month, calendar.monthrange(ano,mes)[1])
                )
                ticks = pd.DataFrame(ticks)
                ticks.to_csv(path + "/" + str(year) + '_' + str(month) + ".csv")

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