akurgat / automating-technical-analysis Goto Github PK

Hey there 👋 Just wanted to let you know that your app on Streamlit Cloud deployed from this repo has gone over its resource limits. Access to the app is temporarily limited. Visit the app to see more details and possible solutions.

Hey there 👋 Just wanted to let you know that your app on Streamlit Cloud deployed from this repo has gone over its resource limits. Access to the app is temporarily limited. Visit the app to see more details and possible solutions.

Hey there 👋 Just wanted to let you know that your app on Streamlit Cloud deployed from this repo has gone over its resource limits. Access to the app is temporarily limited. Visit the app to see more details and possible solutions.

Hey there 👋 Just wanted to let you know that your app on Streamlit Cloud deployed from this repo has gone over its resource limits. Access to the app is temporarily limited. Visit the app to see more details and possible solutions.

File "/home/appuser/venv/lib/python3.9/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 564, in _run_script
exec(code, module.dict)
File "/app/automating-technical-analysis/Trade.py", line 9, in
data_update()
File "/app/automating-technical-analysis/app/data_sourcing.py", line 26, in data_update
update_market_data()
File "/app/automating-technical-analysis/app/update_market_data.py", line 9, in update_market_data
df_binance = pd.DataFrame(data['symbols'])[pd.DataFrame(data['symbols'])['status'] == 'TRADING'][['symbol', 'baseAsset', 'quoteAsset']]

TypeError: '<' not supported between instances of 'float' and 'str'
Traceback:
File "C:\Users\qbl\AppData\Local\Programs\Python\Python39\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 541, in _run_script
exec(code, module.dict)
File "C:\Users\qbl\Desktop\automating-technical-analysis\Trade.py", line 167, in
main(app_data = app_data)
File "C:\Users\qbl\Desktop\automating-technical-analysis\Trade.py", line 22, in main
app_data.exchange_data(exchange)
File "c:\Users\qbl\Desktop\automating-technical-analysis\app\data_sourcing.py", line 51, in exchange_data
self.indexes = np.sort(self.df_indexes['Indexes'].unique())
File "<array_function internals>", line 200, in sort
File "C:\Users\qbl\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\core\fromnumeric.py", line 1030, in sort
a.sort(axis=axis, kind=kind, order=order)

?

Hello, are you open to turning this into a cli;
then later on an Api;

if so, can you create 2 branches?

1. cli
2. docker

I don't want to touch the master branch.

ranaroussi/yfinance#1592

ranaroussi/yfinance#1610

Hey there 👋 Just wanted to let you know that your app on Streamlit Cloud deployed from this repo has gone over its resource limits. Access to the app is temporarily limited. Visit the app to see more details and possible solutions.

Hi, Thanks for sharing this great project !

You've provided the model file but I cannot find the details for training the model.
Can I know more details on the training process ?

Thanks.

Hey there 👋 Just wanted to let you know that your app on Streamlit Cloud deployed from this repo has gone over its resource limits. Access to the app is temporarily limited. Visit the app to see more details and possible solutions.

Thank you very much if you can

is it possible to show the results from backtesting along side with the percentage chance of being correct?
i feel that this is an important way to show the accuracy of the predictions.

the backtest could be done on 95% of the historical data within the time frame selected.