You are expected to design, code, and present to the class a programming project that has a purpose (solves a real problem) for a real life user.
Our Project is a Stock Market Prediction tool that utilises
Machine Learning algorithms in models such as
Linear Regression and
Support Vector Machine in order to forecast the Stock Market changes for a certain period of time in the future.
Users would be anyone over the age of 18, attempting to build or to improve their financial portfolio. _**_Our tool will aid them in **investing and buying shares from companies as well allowing them to maximise the profits and minimise their losses. It also allows more inexperienced users to attain an understanding of the movement of the Stock Market allowing them to make informed decisions about their future.
Our aims are to develop the tool as an Official Python Package and upload it to
PyPi as an open-source package for use all around the world. Through this, we can make our project much more accessible and also increase its development by decreasing possible bugs through user engagement with platforms such as
Github allowing users to make
Pull Requests which can aid the development of the tool. We want to allow for multiple different algorithms to be used through the use of multiple function and iteration to make the results as accurate and reliable as possible. We will release the project in iteration ranging from
Github release system and will package and make public each major update, i.e. one with a major bug fix or a new version such as
The users are people interested in finance or the economy and our tool can be easily integrated into websites using the
Flask framework for Python or in applications on desktop using C++ or mobile through Java and/or Swift. Another alternative would be to develop a
RESTful API with
Flask (which has been made) and then integrating this with a
NodeJS application on the
Electron framework using
AJAX requests in Back-End in order to transfer the data to a modern-looking GUI or application.
This may not be entirely up-to-date. For download, reference the PyPi site or the Github site.
__ARU300 released this 8 hours ago · 4 commits to master since this release
Another release for RTFD, updating the
ARU300 released this 9 hours ago · 7 commits to master since this release
.readthedocs.yml and a
mkdir.yml in order to integrate support with the ReadTheDocsSite.
Documentation is now live on Read The Docs.
ARU300 released this yesterday · 21 commits to master since this release
v1.0 we have developed a new method of Stock Market Prediction using the
API has also been updated to include these changes.
The next major update will entail document changes and maybe a deployment to
ARU300 released this on 26 Jul · 32 commits to master since this release
v0.1a, we have made a few developments in terms of software.
We have been ironing out errors in the code and making sure that the
Linear Regression and
Support Vector Machine algorithms work as expected.
We have developed an API and website in
Flask that runs on
https:\\localhost:5000. The API can be used to output the predictions.
We are going to integrate an
LSTM algorithm + some other algorithms and iteration to find the best prediction possible. This is expected to be our
v2.0 release. The
v1.X updates may mainly include website changes and documentation changes.
__ARU300 released this on 5 Jul · 32 commits to master since this release
Disclaimer: The project has only been active for around 2 days with under 12hrs of work.
Stock,pyworks with a function named
getStock. By entering the ‘companyName’ you can retrieve the stocks and a plot for the stocks from the previous year. *(To plot the results set the parameter ‘plot’ to true and ‘mavgPlot’ to true as well in order to plot the Moving Average as well.
SVM Predict.pydoes work, however in pre-release
v0.1-alphait is not yet a function however it shall be in the
Predict.pydoes not work and will need to be improved and fixed.
Our project is licensed under the GNU General Public License v3.0.
Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.