ML Research Article Implementation

Completed Posted 4 years ago Paid on delivery
Completed Paid on delivery

BACKGROUND

I run a small Proprietary Trading firm in London - UK trading financial securities through automated trading systems. We are currently implementing algorithms embedding Machine Learning processses.

We are used to work with freelancers and always looking for new talents to work with throughout the company's growth.

THE JOB

This job is to precisely implement algorithms as described in these 2 research articles:

- A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine

The implementation must be in Python and use GPU hardware computation (if applicable).

This algorithm will have to be implemented following a standardised protocol as it has to be integrated into a bigger system (see enclosed file)

The successful candidate will have to show good communication skills (English or French), a positive attitude turned to "make things happen"/problem solving, ability to meet deadlines/deliver ontime.

Ideally, the timeline for completing the job will be set to a week from the date of acceptation of the job. Timeline will be a crucial point.

Payment will be made after checking of the code.

Looking forward to seeing your proposals!

Many thanks!

Machine Learning (ML) Python Algorithm

Project ID: #21488455

About the project

3 proposals Remote project Active 4 years ago

Awarded to:

AItechnology

Experienced data scientist who has extensively worked on machine learning projects particularly in financial services domain including implementation of academic papers. Looking forward to working with you again !

£550 GBP in 7 days
(53 Reviews)
6.6

3 freelancers are bidding on average £600 for this job

AlexanderPGR

Hi, there. I have read your description carefully. I am really interested in your project. I have high Python skill and also ML knowledge. Also, I have experience with ML project such as CNN modeling, FR, NLP , etc. Pl More

£750 GBP in 7 days
(21 Reviews)
5.3