Stock Market Prediction using Deep Learning

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The field of the stock market is almost unpredicted. This is the place where the person can earn the fortune only if he or she can crack down the mantra with which the irregular up and downs of the stock market can be predicted easily. That is the reason according to a lot of people if you are someone new to the stock market then the chances of you earning a profit become low while I would not support or neglect that completely because in this field the one thing that matters a lot is a sharp mind and proper planning before who invest your hard earn money in this dangerous known sector.

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Many a time it’s very hard to predictive the stock market price correctly as it never follows the same pattern that can be cracked up easily. So here one question that is there in all the minds mostly that if humans can’t predict these prices will the machine would also not able to do that too. As in today’s world where a lot of new technology has helped human to understand various unsolved prediction maybe the stock prices pattern or prediction can be known by us too. There are many scientists, analysts and even researchers across the world who are trying hard to drive the answer to all these questions for a long time now. In this article, we are going to see how the concept of deep learning can help us predict the stock market prices and much more. Before that let us try to understand what, do we mean by deep learning?

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What is deep learning?

Deep learning can be defined as one of the most popular techniques under the concept of machine learning. This technology mainly teaches the computer all the activities naturally humans can do by themselves. Such as learning, predicting and much more. This concept has become very popular over the past few years and is the main concept behind various technology such as driverless cars, voice controlling, image processing and many more things like that. This technology is getting a lot of attention by the scientist as well as researchers so that can be improved and used more often in all most everything going around which is something magical to explore in simple words.

The approach of machine learning that we are going to discuss today is called algorithmic trading. This concept is still under research and yet many other things are to be added with this technology so that can precisely predict the stock market. First of all, we know that the stock market is dependent on many factors. All these factors are acting as the wheel of this very car known as the stock market. But the most important concept that is been taken into account for this method of algorithmic trading. The two main factors that will be used here are:

  1. The impact as well as a correlation that is been caused on the stock prices of many other companies. In this, we can predict how the stock market prices are increased and decreased of many other companies based on the target company.
  2. The other thing will be the records and performance of the target company.
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By considering both these important factors and using three important deep learning concepts that are. Firstly, the concept that is used is the neural network-based regressor model which is used to take the account of the impact which is being caused due to other companies to the targeted company. The second concept is a recurrent neural network model that will be used here to study all the important past behaviour of the targeted company and will provide the result accordingly.

To make this thing work as per the demand the LSTM Layer is being used. The third thing that is used is an artificial intelligence neural network which is capable to take both the above predictions and will also help us to reach a firm result that will be meeting all the required statics.

The concept will also be using a dataset and in here we are going to use the dataset of 500 companies database. The S&P list is being set up here precisely. To scrap the tickets of over 500 companies there are many codes that is being available will help you for sure. I am going to attach one here for reference:

Example:

import bs4 as bs
import pickle
import requestsdef save_tickers():
resp=requests.get('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
soup=bs.BeautifulSoup(resp.text)
table=soup.find('table',{'class':'wikitable sortable'})
tickers=[]
for row in table.findAll('tr')[1:]:
ticker=row.findAll('td')[0].text[:-1]
tickers.append(ticker)with open("tickers.pickle",'wb') as f:
pickle.dump(tickers, f)return tickerssave_tickers()   

Conclusion:

Many methods are being used with technology like deep learning to predictive the sensitive data related to the stock market. I hope the article was helpful for you.  There are separate set of codes that is available for visualizing and prediction. Many references can be easily found out on the web.