Trying to add a Class for data from metatrader

[andydoc]

I am trying to add a class to download and update data from metatrader.
So far I have done this
globally:

import time
import warnings
from functools import wraps

import numpy as np
import pandas as pd
from tqdm.auto import tqdm

import MetaTrader5 as mt

from vectorbt import _typing as tp
from vectorbt.data.base import Data
from vectorbt.utils.config import merge_dicts, get_func_kwargs
from vectorbt.utils.datetime_ import (
    get_utc_tz,
    get_local_tz,
    to_tzaware_datetime,
    datetime_to_ms
)

then:

class MetaTrader5Data(Data):
    """`Data` for data coming from `metatrader-trade-api`.
    
    Usage:
        * Fetch the 1-minute data of the last 2 hours, wait 1 minute, and update:
        ```pycon
        >>> import vectorbt as vbt
        >>> mt_data = vbt.MetaTrader5Data.download(
        ...     "EURUSD",
        ...     start='2 hours ago UTC',
        ...     end='15 minutes ago UTC',
        ...     interval='M1'
        ... )
        >>> mt_data.get()
                                    Open      High       Low     Close      Volume
        timestamp
        2021-12-27 14:04:00+00:00  177.0500  177.0500  177.0500  177.0500    1967
        2021-12-27 14:05:00+00:00  177.0500  177.0500  177.0300  177.0500    3218
        2021-12-27 14:06:00+00:00  177.0400  177.0400  177.0400  177.0400     873
        ...                             ...       ...       ...       ...     ...
        2021-12-27 15:46:00+00:00  177.9500  178.0000  177.8289  177.8850  162778
        2021-12-27 15:47:00+00:00  177.8810  177.9600  177.8400  177.9515  123284
        2021-12-27 15:48:00+00:00  177.9600  178.0500  177.9600  178.0100  159700
        [105 rows x 5 columns]
        >>> import time
        >>> time.sleep(60)
        >>> mt_data = mt_data.update()
        >>> mt_data.get()
                                    Open      High       Low     Close      Volume
        timestamp
        2021-12-27 14:04:00+00:00  177.0500  177.0500  177.0500  177.0500    1967
        2021-12-27 14:05:00+00:00  177.0500  177.0500  177.0300  177.0500    3218
        2021-12-27 14:06:00+00:00  177.0400  177.0400  177.0400  177.0400     873
        ...                             ...       ...       ...       ...     ...
        2021-12-27 15:47:00+00:00  177.8810  177.9600  177.8400  177.9515  123284
        2021-12-27 15:48:00+00:00  177.9600  178.0500  177.9600  178.0100  159700
        2021-12-27 15:49:00+00:00  178.0100  178.0700  177.9700  178.0650  185037
        [106 rows x 5 columns]
        ```
    """

    @classmethod
    def download_symbol(cls,
                        symbol: str,
                        timeframe: str = 'D1',
                        start: tp.DatetimeLike = 0,
                        end: tp.DatetimeLike = 'now UTC',
                        limit: int = 500,
                        **kwargs) -> tp.Frame:
        """Download the symbol.
        Args:
            symbol (str): Symbol.
            timeframe (str): Timeframe of data.
                Must be one of the shortened names of MT5 timeframes i.e.
                    M1, M2, M3... H1, H2... D1, W1, MN1
            start (any): Start datetime.
                See `vectorbt.utils.datetime_.to_tzaware_datetime`.
            end (any): End datetime.
                See `vectorbt.utils.datetime_.to_tzaware_datetime`.
            limit (int): The maximum number of returned items restricted in the code to the attribute maxbars.
        """
        from datetime import datetime

        limit = min(limit,mt.terminal_info()._asdict()['maxbars'])
        
        _timeframe = "TIMEFRAME_"+timeframe

        if not hasattr(mt, _timeframe):
            raise ValueError("invalid timeframe")

        start_ts = to_tzaware_datetime(start, tz=get_utc_tz())#.isoformat()
        end_ts = to_tzaware_datetime(end, tz=get_utc_tz())#.isoformat()

        def _is_forex_symbol(symbol):
            return 'Forex' in mt.symbol_info("EURJPY")._asdict()['path']
        
        import sys
        try:
            my_array = mt.copy_rates_range(symbol, _timeframe, start_ts, end_ts)
        except:
            print("Oops!", sys.exc_info()[0], "occurred.")
            print(symbol, _timeframe, start_ts, end_ts)
            return
        
        df = pd.DataFrame(my_array, columns = ['time', 'open', 'high', 'low', 'close', 'tick_volume'])
        df['time']=pd.to_datetime(df['time'], unit='s')
        df = df.rename(columns={"time": "timestamp", "open": "Open", "high": "High", "low": "Low", "close": "Close", "tick_volume": "Volume"})
        df.set_index('timestamp')
        
        
        df['Open'] = df['Open'].astype(float)
        df['High'] = df['High'].astype(float)
        df['Low'] = df['Low'].astype(float)
        df['Close'] = df['Close'].astype(float)
        df['Volume'] = df['Volume'].astype(float)

        return df

    def update_symbol(self, symbol: str) -> tp.Frame:
        """Update the symbol.
        `**kwargs` will override keyword arguments passed to `AlpacaData.download_symbol`."""
        download_kwargs = self.select_symbol_kwargs(symbol, self.download_kwargs)
        download_kwargs['start'] = self.data[symbol].index[-1]
        download_kwargs['show_progress'] = False
        kwargs = merge_dicts(download_kwargs, kwargs)
        s = self.download_symbol(symbol, **kwargs)
        return s

The code in def download_symbol() works standalone but then:

import vectorbt as vbt
mt.initialize(login=xxx, server="yyy",password="zzz")

mt_data = MetaTrader5Data.download("EURUSD", start='5 days ago UTC', end='3 days ago UTC', timeframe='M1')
mt_data.get()
mt.shutdown()

leads to:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In [146], line 4
      1 import vectorbt as vbt
      2 mt.initialize(login=1051365440, server="FTMO-Demo",password="2TB7LAC2XH")
----> 4 mt_data = MetaTrader5Data.download("EURUSD", start='5 days ago UTC', end='3 days ago UTC', timeframe='M1')
      5 mt_data.get()
      6 mt.shutdown()

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\vectorbt\data\base.py:587, in Data.download(cls, symbols, tz_localize, tz_convert, missing_index, missing_columns, wrapper_kwargs, **kwargs)
    584     data[s] = cls.download_symbol(s, **_kwargs)
    586 # Create new instance from data
--> 587 return cls.from_data(
    588     data,
    589     tz_localize=tz_localize,
    590     tz_convert=tz_convert,
    591     missing_index=missing_index,
    592     missing_columns=missing_columns,
    593     wrapper_kwargs=wrapper_kwargs,
    594     download_kwargs=kwargs
    595 )

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\vectorbt\data\base.py:514, in Data.from_data(cls, data, tz_localize, tz_convert, missing_index, missing_columns, wrapper_kwargs, **kwargs)
    512         v = pd.Series(v)
    513     else:
--> 514         v = pd.DataFrame(v)
    516 # Perform operations with datetime-like index
    517 if isinstance(v.index, pd.DatetimeIndex):

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\frame.py:720, in DataFrame.__init__(self, data, index, columns, dtype, copy)
    710         mgr = dict_to_mgr(
    711             # error: Item "ndarray" of "Union[ndarray, Series, Index]" has no
    712             # attribute "name"
   (...)
    717             typ=manager,
    718         )
    719     else:
--> 720         mgr = ndarray_to_mgr(
    721             data,
    722             index,
    723             columns,
    724             dtype=dtype,
    725             copy=copy,
    726             typ=manager,
    727         )
    729 # For data is list-like, or Iterable (will consume into list)
    730 elif is_list_like(data):

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\internals\construction.py:329, in ndarray_to_mgr(values, index, columns, dtype, copy, typ)
    324         values = values.reshape(-1, 1)
    326 else:
    327     # by definition an array here
    328     # the dtypes will be coerced to a single dtype
--> 329     values = _prep_ndarraylike(values, copy=copy_on_sanitize)
    331 if dtype is not None and not is_dtype_equal(values.dtype, dtype):
    332     # GH#40110 see similar check inside sanitize_array
    333     rcf = not (is_integer_dtype(dtype) and values.dtype.kind == "f")

File ~\AppData\Roaming\Python\Python310\site-packages\pandas\core\internals\construction.py:583, in _prep_ndarraylike(values, copy)
    581     values = values.reshape((values.shape[0], 1))
    582 elif values.ndim != 2:
--> 583     raise ValueError(f"Must pass 2-d input. shape={values.shape}")
    585 return values

ValueError: Must pass 2-d input. shape=()

Does anyone have any ideas where I have gone wrong?

I took out the mt.initialise() and mt.shutdown() calls from inside the class definition and placed them around the call but with no difference...

[andydoc]

sorry ignore

        import sys
        try:
            my_array = mt.copy_rates_range(symbol, _timeframe, start_ts, end_ts)
        except:
            print("Oops!", sys.exc_info()[0], "occurred.")
            print(symbol, _timeframe, start_ts, end_ts)
            return

i had forgotten to remove the .isoformat() from the datetime definitions earlier then, having done that, failed to remove the exception trapper from my_array = mt.copy_rates_range(symbol, _timeframe, start_ts, end_ts)
it should now just read my_array = mt.copy_rates_range(symbol, _timeframe, start_ts, end_ts)