Traceback (most recent call last):
File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/window/rolling.py", line 487, in _apply_blockwise
arr = self._prep_values(arr)
^^^^^^^^^^^^^^^^^^^^^^
File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/window/rolling.py", line 360, in _prep_values
raise NotImplementedError(
NotImplementedError: ops for ExponentialMovingWindow for this dtype datetime64[ns, UTC] are not implemented
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/freqtrade/freqtrade/vendor/qtpylib/indicators.py", line 293, in rolling_weighted_mean
return series.ewm(span=window, min_periods=min_periods).mean()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/window/ewm.py", line 555, in mean
return self._apply(window_func, name="mean", numeric_only=numeric_only)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/window/rolling.py", line 617, in _apply
return self._apply_blockwise(homogeneous_func, name, numeric_only)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/window/rolling.py", line 489, in _apply_blockwise
raise DataError(
pandas.errors.DataError: Cannot aggregate non-numeric type: datetime64[ns, UTC]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/freqtrade/freqtrade/main.py", line 42, in main
return_code = args['func'](args)
^^^^^^^^^^^^^^^^^^
File "/freqtrade/freqtrade/commands/optimize_commands.py", line 58, in start_backtesting
backtesting.start()
File "/freqtrade/freqtrade/optimize/backtesting.py", line 1401, in start
min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/freqtrade/freqtrade/optimize/backtesting.py", line 1318, in backtest_one_strategy
preprocessed = self.strategy.advise_all_indicators(data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/freqtrade/freqtrade/strategy/interface.py", line 1378, in advise_all_indicators
return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/freqtrade/freqtrade/strategy/interface.py", line 1378, in
return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/freqtrade/freqtrade/strategy/interface.py", line 1410, in advise_indicators
return self.populate_indicators(dataframe, metadata)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/freqtrade/user_data/strategies/ichiV1_pro2.py", line 89, in populate_indicators
dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe, window=50)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/freqtrade/freqtrade/vendor/qtpylib/indicators.py", line 302, in hull_moving_average
ma = (2 * rolling_weighted_mean(series, window / 2, min_periods)) - \
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/freqtrade/freqtrade/vendor/qtpylib/indicators.py", line 295, in rolling_weighted_mean
return pd.ewma(series, span=window, min_periods=min_periods)
^^^^^^^
AttributeError: module 'pandas' has no attribute 'ewma'
