b-fg / nuatsbot Goto Github PK

Hello!
I have a problem with receiving signals.
When I launch the bot, it only writes the word Signal to me but does not write other data.
What should I do?
Thank you!

After running for only a short while, the app crashes due to a timeout reaching binance api. Here's the output when the app stops:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 603, in urlopen
    chunked=chunked)
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 344, in _make_request
    self._validate_conn(conn)
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 843, in _validate_conn
    conn.connect()
  File "/usr/local/lib/python3.7/site-packages/urllib3/connection.py", line 316, in connect
    conn = self._new_conn()
  File "/usr/local/lib/python3.7/site-packages/urllib3/connection.py", line 165, in _new_conn
    (self.host, self.timeout))
urllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x120b78d68>, 'Connection to api.binance.com timed out. (connect timeout=10)')

having issue with importing moves , six is installed yet moves isnt ?

Import "six.moves" could not be resolved from source

had to use # pyright: ignore but not sure if or how it might effect the project!

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.

Dear team,

I'm having issues when using 1h, 4h, 12h,1d on the max 1200 req per minute.
any method to make a sort of timeout between?

so i can pull more?

thanks and cheers.

The bot stops after sending the signal how to make it work every 2 mins?

I set up the config.py added my telegram bit token and channel ID
But it doesn't work...

My config.py

https://user-images.githubusercontent.com/41206183/46945089-8a10eb00-d091-11e8-8b16-59c34cea5272.jpeg

It says Telegram Broadcasting ['channelid'] and stops but nothing happens see below code

https://user-images.githubusercontent.com/41206183/46945102-9432e980-d091-11e8-9483-6a030eeb2a9b.png

https://user-images.githubusercontent.com/41206183/46945112-9a28ca80-d091-11e8-8801-dbaf2f566d6b.jpeg

What's the prob?please help

I'm trying to send the queries drawn from the API to telegram via a ready-made this code. However, when more than one query is received, it sends it to telegram as a single post. How can we solve this incoming multiple queries being sent separately? I added example output from this way.

Hello, I've got such an error. Don't know how to fix)
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(
Traceback (most recent call last):
File "c:/Users/Admin/Desktop/nuatsbot-master/nuatsbot-master/nuats_bot.py", line 124, in
main()
File "c:/Users/Admin/Desktop/nuatsbot-master/nuatsbot-master/nuats_bot.py", line 112, in main
tickers_TA_list = list(tqdm(executor.map(TA_task, a), total=len(a)))
File "C:\Users\Admin\AppData\Local\Programs\Python\Python38\lib\site-packages\tqdm\std.py", line 1195, in iter
for obj in iterable:
File "C:\Users\Admin\AppData\Local\Programs\Python\Python38\lib\concurrent\futures_base.py", line 611, in result_iterator
yield fs.pop().result()
File "C:\Users\Admin\AppData\Local\Programs\Python\Python38\lib\concurrent\futures_base.py", line 434, in result
self._condition.wait(timeout)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python38\lib\threading.py", line 302, in wait
waiter.acquire()

Hello

The requirements.txt is not updated and there are 2-3 python modules which are not installable coz the pip command cannot find any module of that name..
The code exame is below when I ran pip3 install -r requirements.txt

Help...

Hi ;)
I was searching for examples of little bots using free api's rather than binance or any exchange where kyc is needed.
I would love to ask you, how many lines would it take to write a script just printing a signal when BTCUSDT weekly RSI reached a specific level ? I'm a beginner and I would love to start with the most minimalistic script possible using Ta-lib or open API.
Would you be able to help ? I'd be ok to pay you for the writing of those lines... Please keep in touch ;)
thanks in advance

Traceback (most recent call last):
File "nuats_bot.py", line 124, in
main()
File "nuats_bot.py", line 96, in main
if (len(notifications_list) > 0): broadcast_signals(notifications_list)
File "nuats_bot.py", line 28, in broadcast_signals
all_notifications_list = list(filter(None.ne, tickers_TA_list)) # Filter out tickers with no bull/bear notifications
AttributeError: 'NoneType' object has no attribute 'ne'

I’m using your bot but when i want load only the BTC pairing on Binance the scanner did not work. I use the example on Github like this ['BTCUSDT', 'ETHBTC'].