Algorithmic trading using deep learning is garbage?

Name: Algorithmic trading using deep learning is garbage?
Uploaded: 2022-02-25T16:07:26.000Z
Duration: 6 min 22 s

Introduction

In this video, Lucas from Country discusses the mistake to avoid when creating a deep learning algorithmic trading strategy.

Creating a Deep Learning Algorithmic Trading Strategy

The financial data contains a lot of noise, making it difficult for the algorithm to find the global minimum.

When training a neural network using stochastic gradient descent, the initial weights are randomly initialized and can lead to different performance results.

Solutions to Fix the Issue

One solution is shown in Lucas' course "Deep Learning for Algorithmic Trading."

Other solutions can be discussed in future videos upon request.

Overall, it's important to be careful when using deep learning for algorithmic trading due to these issues with financial data and stochastic initialization weight.

Video description

Today, I will show you if algorithmic trading using deep learning is garbage. And I will show you how to fix the major issue of the deep learning algorithm. 💰 Join our community: https://discord.gg/wXjNPAc5BH 📚Read our book 2ND EDITION (with MT5 live trading): https://www.amazon.com/dp/B0BB5DDB1Q 🖥Our Udemy courses: https://www.quantreo.com Disclaimer: I am not authorized by any financial authority to give investment advice. This book is for educational purposes only. I disclaim all responsibility for any loss of capital on your part. Moreover, 78.18% of private investors lose money trading CFD. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technologies, this work contains or describes are subject to open-source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. This video is not intended as financial advice. Please consult a qualified professional if you require financial advice. Past performance is no indication of future performance.