The world has become quite modern in fast few years with the advancement of new technologies in both information technology field. With the great advancement that has taken place in the field of computer science and the silicon industry, people are looking forward to stock to bring some unique change. As the stock is major asserts that people count upon as this is not hidden from any other sector. As per the reports seen the majority of the people who are aware of some knowledge related to the market and the statics or maybe the gut feeling is been investing their hard-earned money into buying company stocks. This is most probably done to achieve great interest when these stocks prices will be increased.
All these people investing are mostly called as the risk-takers who prefer to have a good understanding of the commerce stream, current affairs and of course mathematics which is kind of important when some is dealing with the stuff like stocks. These people are thereby are considered as the major players in the world of intraday trading. As per many people, intraday trading is the most risk-taking trading which is often compared with the act of gambling. Nothing predicted all just based on how precise the trader makes the choice each time. Now we can have a clear overview of the problem we are dealing up with. Intraday trading is one of the trading norms of the stock market for which the vesting period is only a day. In this, the traders buy the shares when the market windows open and then sell them at the end of the day when the stock market windows are going to be closed. So, this is the fact of a day. Mainly a person makes a huge profit or just least what was with him. So unpredictable. Mainly many data set are there which can be used for this process using the technology of machine learning. Also, this a simple machine learning algorithm so that’s why even beginners will be able to grasp it quickly. The first thing that is to be done is to understand the data.
Secondly, start making the prediction using the algorithm. For your comfort, I am adding the GitHub link of a repository that has the python script mainly used to perform the part of the analysis. Then there is even the test data present in the CSV format. The third thing that the repository contains is the train data that too in the CSV format.
As of now, you have the access to all the code, test data. So, in there the things are very easy to understand kindly write to read the whole program once before carry out with using it for your prediction. This will able to make it a little bit easier if you are a beginner. Let’s try to understand the features of the data set that are present on the web with which the above-mentioned method will work. Let’s start.
Open: It is considered as the opening price of the share for the particular day when the trade is going on
High: This is the highest price that the share has touched in the whole day
Low: It is however the lowest price of the share for the particular day
Close: This is the closing price of the share for the particular day mentioned
Date: The date of the observation when the data is been recorded.
Volume: The number of share that is carried throughout the day
Out of all this one thing is considered as the resulting target:
Target:
Stock trading:
The price of the total share in the market for that particular day. As the data here is the time component it is highly advertised that our data follows the time-series pattern. But for the data to follow the time series pattern it must either qualify the trend or the seasonality. This is mainly done to check whether our data is being set as seasonal or trendy. Now after this we can perform the Dickey-Fuller analysis. If the data obtained by us is seasonal then it is quite sure that it may follow the time-series pattern. For implementing the Dickey-Fuller analysis in the python language, the initial requirement is of the variable frequency. However, in form of mathematics, it can be defined as:
Frequency = (Total time units expected until which data will show repetitive patter, in minutes) / (time gap between each observation, in minutes). Also, apart from this, there should be the presence of intraday trading data in our collection or take data set. this simply indicates the most common pattern that is being formed by the behaviour of the market on daily basis. This will make the prediction much easier.
Conclusion:
I hope the above-written information was relevant to all my readers.
