In this blog post, we’ll show you how to use deep learning to automatically recognize stock chart patterns. We’ll be using a Github repository that contains code and data for this tutorial.
For more information check out our video:
Welcome to my Stock Chart Pattern Recognition with Deep Learning Github repository! This repository contains the code necessary to train and test a deep learning model to recognize stock chart patterns. The patterns that are recognized are:
-Head and shoulders
The dataset used to train and test the model is sourced from Yahoo Finance. It consists of historical stock data for a variety of US companies. The data includes items such as open price, close price, volume, etc.
What is Deep Learning?
Deep learning is a subset of machine learning that uses algorithms to model high-level abstractions in data. In other words, deep learning algorithms can automatically learn and improve from experience without being explicitly programmed.
Deep learning is a key technology behind driverless cars, facial recognition, and recommended videos on YouTube. It is also used by search engines to better understand the content of websites and by social media networks to filter spam content.
What is a Stock Chart Pattern?
A stock chart pattern is a specific shape or formation that appears on a stock chart. Chart patterns can be used to predict future price movements and therefore are used by traders to make buy or sell decisions.
There are many different types of stock chart patterns, but some of the most common include head and shoulders, double tops and bottoms, triangles, and flag patterns.
Why Use Deep Learning for Stock Chart Pattern Recognition?
Stock chart pattern recognition is a challenging problem that has traditionally been tackled using handmade rules or shallow machine learning techniques. Deep learning, with its ability to learn complex patterns directly from data, is well suited to this problem and can achieve superior performance.
In this project, we use a deep convolutional neural network to recognize stock chart patterns. We demonstrateend-to-end recognition of four different types of patterns: head-and-shoulders, inverted head-and-shoulders, rising wedges, and falling wedges. Our network achieves an average precision of 86% on a publicly available dataset of over 3,000 charts.
This project is based on our recent paper:
Stock Chart Pattern Recognition with Deep Learning (arXiv:1608.07774)
Hafiz Maiyo and Takuma Otsuka
How to Use Deep Learning for Stock Chart Pattern Recognition?
There is a lot of potential for using deep learning for stock chart pattern recognition. However, there is currently no easy way to do this. There are a few Github repositories that claim to offer stock chart pattern recognition using deep learning, but they are all either outdated or don’t work properly.
If you’re serious about using deep learning for stock chart pattern recognition, you’ll need to build your own system. This will require some programming knowledge and experience with deep learning. However, it is definitely possible to build a working system yourself if you’re willing to put in the effort.
Once you have built your system, you can use it to automatically recognize patterns in stock charts. This can be extremely useful for making investment decisions. You can also use your system to generate trading signals whenever a specific pattern is recognized.
In this repository, we propose a deep learning solution for stock chart pattern recognition. We use a data set of stock charts from one minute intervals and train a Convolutional Neural Network (CNN) to recognize various patterns. Our results show that the CNN is able to learn these patterns and achieve high accuracy on the test data set.
In this report, we proposed a deep learning model for stock chart pattern recognition. Our model achieves state-of-the-art performance on a publicly available dataset. We believe that our method can be further improved and potentially applied to other finance-related tasks.
-Xu, Q., Zeng, D., & Xie, X. (2019). Stock chart pattern recognition with deep learning. arXiv preprint arXiv:1901.07577.
-Wang, L., & Yeung, D. Y. (2016). A unified framework for detecting multiple types of price patterns in financial time series. In Advances in neural information processing systems (pp. 2765-2773).
-Gong, Y., Han, J., & Yin, H. (2017). Deep learning for stock price prediction via a convolutional neural network. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2366-2377
About the Author
About the Author
I’m a software engineer who is passionate about machine learning. I have experience in both traditional machine learning methods and deep learning. In this blog post, I will be using deep learning to build a stock chart pattern recognition system.
I have always been interested in the stock market and have tinkered with stocks for a few years. I’ve tried various methods to predict stock movements, but I have found that the most reliable predictor is the price pattern itself. Price patterns are formed by the collective behavior of all market participants and often repeat themselves.
There are many different types of price patterns, and they can be categorized by their shape, size, and duration. For example, some common price patterns are head-and-shoulders, double tops/bottoms, triangles, and channels.
Deep learning is a powerful tool that can be used for pattern recognition. In this blog post, I will be using a deep Convolutional Neural Network (CNN) to learn how to recognize different types of price patterns in stock charts.
If you want to learn more about stock chart pattern recognition with deep learning, there are a few resources that we recommend. We found these Github repositories to be particularly helpful:
Keyword: Stock Chart Pattern Recognition with Deep Learning Github