rsi about machine-learning-for-trading HOT 1 OPEN

prices['rsi'] = prices.groupby(level='symbol').close.apply(RSI)

ValueError Traceback (most recent call last) File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\frame.py:11610, in _reindex_for_setitem(value, index) 11609 try:

11610 reindexed_value = value.reindex(index)._values
11611 except ValueError as err:
11612 # raised in MultiIndex.from_tuples, see test_insert_error_msmgs

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\series.py:4918, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance) 4901 @doc( 4902 NDFrame.reindex, # type: ignore[has-type] 4903 klass=_shared_doc_kwargs["klass"], (...) 4916 tolerance=None, 4917 ) -> Series: -> 4918 return super().reindex( 4919 index=index, 4920 method=method, 4921 copy=copy, 4922 level=level, 4923 fill_value=fill_value, 4924 limit=limit, 4925 tolerance=tolerance, 4926 )

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\generic.py:5360, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance) 5359 # perform the reindex on the axes -> 5360 return self._reindex_axes( 5361 axes, level, limit, tolerance, method, fill_value, copy 5362 ).finalize(self, method="reindex")

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\generic.py:5375, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy) 5374 ax = self._get_axis(a) -> 5375 new_index, indexer = ax.reindex( 5376 labels, level=level, limit=limit, tolerance=tolerance, method=method 5377 ) 5379 axis = self._get_axis_number(a)

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\indexes\base.py:4268, in Index.reindex(self, target, method, level, limit, tolerance) 4267 if self._index_as_unique: -> 4268 indexer = self.get_indexer( 4269 target, method=method, limit=limit, tolerance=tolerance 4270 ) 4271 elif self._is_multi:

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\indexes\base.py:3802, in Index.get_indexer(self, target, method, limit, tolerance) 3798 return this._get_indexer( 3799 target, method=method, limit=limit, tolerance=tolerance 3800 ) -> 3802 return self._get_indexer(target, method, limit, tolerance)

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\indexes\base.py:3823, in Index._get_indexer(self, target, method, limit, tolerance) 3821 # error: Item "IndexEngine" of "Union[IndexEngine, ExtensionEngine]" 3822 # has no attribute "_extract_level_codes" -> 3823 tgt_values = engine._extract_level_codes( # type: ignore[union-attr] 3824 target 3825 ) 3826 else:

File ~\anaconda3\envs\baclass\lib\site-packages\pandas_libs\index.pyx:714, in pandas._libs.index.BaseMultiIndexCodesEngine._extract_level_codes()

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\indexes\multi.py:143, in MultiIndexUIntEngine._codes_to_ints(self, codes) 141 # Shift the representation of each level by the pre-calculated number 142 # of bits: --> 143 codes <<= self.offsets 145 # Now sum and OR are in fact interchangeable. This is a simple 146 # composition of the (disjunct) significant bits of each level (i.e. 147 # each column in "codes") in a single positive integer:

ValueError: operands could not be broadcast together with shapes (2004775,2) (3,) (2004775,2)

The above exception was the direct cause of the following exception:

TypeError Traceback (most recent call last) Cell In[44], line 1 ----> 1 prices['rsi'] = prices.groupby(level='symbol').close.apply(RSI)

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\frame.py:3950, in DataFrame.setitem(self, key, value) 3947 self._setitem_array([key], value) 3948 else: 3949 # set column -> 3950 self._set_item(key, value)

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\frame.py:4143, in DataFrame._set_item(self, key, value) 4133 def _set_item(self, key, value) -> None: 4134 """ 4135 Add series to DataFrame in specified column. 4136 (...) 4141 ensure homogeneity. 4142 """ -> 4143 value = self._sanitize_column(value) 4145 if ( 4146 key in self.columns 4147 and value.ndim == 1 4148 and not is_extension_array_dtype(value) 4149 ): 4150 # broadcast across multiple columns if necessary 4151 if not self.columns.is_unique or isinstance(self.columns, MultiIndex):

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\frame.py:4867, in DataFrame._sanitize_column(self, value) 4865 return _reindex_for_setitem(value, self.index) 4866 elif is_dict_like(value): -> 4867 return _reindex_for_setitem(Series(value), self.index) 4869 if is_list_like(value): 4870 com.require_length_match(value, self.index)

File ~\anaconda3\envs\baclass\lib\site-packages\pandas\core\frame.py:11617, in _reindex_for_setitem(value, index) 11613 if not value.index.is_unique: 11614 # duplicate axis 11615 raise err

11617 raise TypeError(
11618 "incompatible index of inserted column with frame index"
11619 ) from err
11620 return reindexed_value

TypeError: incompatible index of inserted column with frame index

4. See error

Expected behavior A clear and concise description of what you expected to happen.

Screenshots If applicable, add screenshots to help explain your problem.

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• OS: [e.g. MacOSX, Windows, Ubuntu]
• Version [e.g. Big Sur, 10, 20.04]

Additional context Add any other context about the problem here.

def calculate_rsi(group):
rsi = RSI(group['close'])
return pd.Series(rsi, index=group.index)

rsi_values = prices.groupby(level='symbol').apply(calculate_rsi)
rsi_df = pd.DataFrame(rsi_values, columns=['rsi'])
rsi_df = rsi_df.reset_index(level=0).drop(columns=['symbol'])

prices = prices.merge(rsi_df, left_index=True, right_index=True)

from machine-learning-for-trading.