0%| | 0/4 [00:00<?, ?it/s]C:\Users\dell\PycharmProjects\nuatsbot\venv\lib\site-packages\sklearn\linear_model_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
C:\Users\dell\PycharmProjects\nuatsbot\venv\lib\site-packages\sklearn\linear_model_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
C:\Users\dell\PycharmProjects\nuatsbot\venv\lib\site-packages\sklearn\linear_model_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
C:\Users\dell\PycharmProjects\nuatsbot\venv\lib\site-packages\sklearn\linear_model_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
C:\Users\dell\PycharmProjects\nuatsbot\venv\lib\site-packages\sklearn\linear_model_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
C:\Users\dell\PycharmProjects\nuatsbot\venv\lib\site-packages\sklearn\linear_model_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
C:\Users\dell\PycharmProjects\nuatsbot\venv\lib\site-packages\sklearn\linear_model_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
C:\Users\dell\PycharmProjects\nuatsbot\venv\lib\site-packages\sklearn\linear_model_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
25%|██▌ | 1/4 [00:00<00:01, 1.56it/s]
Traceback (most recent call last):
File "C:\Users\dell\PycharmProjects\nuatsbot\nuats_bot.py", line 124, in
main()
File "C:\Users\dell\PycharmProjects\nuatsbot\nuats_bot.py", line 112, in main
tickers_TA_list = list(tqdm(executor.map(TA_task, a), total=len(a)))
File "C:\Users\dell\PycharmProjects\nuatsbot\venv\lib\site-packages\tqdm\std.py", line 1195, in iter
for obj in iterable:
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.1520.0_x64__qbz5n2kfra8p0\lib\concurrent\futures_base.py", line 609, in result_iterator
yield fs.pop().result()
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.1520.0_x64__qbz5n2kfra8p0\lib\concurrent\futures_base.py", line 439, in result
return self.__get_result()
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.1520.0_x64__qbz5n2kfra8p0\lib\concurrent\futures_base.py", line 391, in __get_result
raise self._exception
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.1520.0_x64__qbz5n2kfra8p0\lib\concurrent\futures\thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "C:\Users\dell\PycharmProjects\nuatsbot\nuats_bot.py", line 49, in TA_task
klines = bclient.get_last_klines(ticker, interval, n_periods)
File "C:\Users\dell\PycharmProjects\nuatsbot\binance_client\client.py", line 289, in get_last_klines
return self.get_klines(
File "C:\Users\dell\PycharmProjects\nuatsbot\binance_client\client.py", line 197, in get_klines
return self._get('klines', data = params)
File "C:\Users\dell\PycharmProjects\nuatsbot\binance_client\client.py", line 104, in _get
return self._request_api('get', path, **kwargs)
File "C:\Users\dell\PycharmProjects\nuatsbot\binance_client\client.py", line 89, in _request_api
return self._request(method, uri, **kwargs)
File "C:\Users\dell\PycharmProjects\nuatsbot\binance_client\client.py", line 85, in _request
return self._handle_response(response)
File "C:\Users\dell\PycharmProjects\nuatsbot\binance_client\client.py", line 97, in _handle_response
raise BinanceAPIException(response)
binance_client.exceptions.BinanceAPIException: APIError(code=-1120): Invalid interval.