stock-trading-ml | stock trading bot that uses machine learning | Machine Learning library

by yacoubb Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | stock-trading-ml Summary

stock-trading-ml is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Neural Network applications. stock-trading-ml has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

A stock trading bot that uses machine learning to make price predictions.

Support

Quality

Security

License

Reuse

Support

stock-trading-ml has a low active ecosystem.

It has 540 star(s) with 250 fork(s). There are 58 watchers for this library.

It had no major release in the last 6 months.

There are 24 open issues and 2 have been closed. On average issues are closed in 68 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of stock-trading-ml is current.

Quality

stock-trading-ml has 0 bugs and 0 code smells.

Security

stock-trading-ml has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

stock-trading-ml code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

stock-trading-ml is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

stock-trading-ml releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

stock-trading-ml saves you 96 person hours of effort in developing the same functionality from scratch.

It has 245 lines of code, 5 functions and 5 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed stock-trading-ml and discovered the below as its top functions. This is intended to give you an instant insight into stock-trading-ml implemented functionality, and help decide if they suit your requirements.

Converts a CSV file to a dataset
Convert a csv file to a dataset
Convert a csv file into a dataset
Compute the holdings of buy and sell
Save a time series

Get all kandi verified functions for this library.

stock-trading-ml Key Features

No Key Features are available at this moment for stock-trading-ml.

stock-trading-ml Examples and Code Snippets

No Code Snippets are available at this moment for stock-trading-ml.

Community Discussions

Trending Discussions on stock-trading-ml

How to take average in a timeframe python?

QUESTION

How to take average in a timeframe python?

Asked 2021-Oct-27 at 09:07

I am beginner in Python so I kindly ask your help. I would like to have a document where I have the first column as 2011.01 and the second column is the number of ARD 'events' in that month and the third column is the average of all of the ARD displayed in that month. If not, that e.g. 2012.07 0 0

I've already tried for 3 hours and now I am getting nervous.

I really much appreciate your help

...

ANSWER

Answered 2021-Oct-25 at 21:16

I'm not quite certain I'm tracking your intent with the list_to_string function; if it's for string date comparison, let's sidestep that entirely by

Source https://stackoverflow.com/questions/69714085

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install stock-trading-ml

You can download it from GitHub.
You can use stock-trading-ml like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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