Machine Learning is changing the world slowly by overtaking every aspect of the industry. Financial companies are now realizing eventually that the result of providing computers instructions is not that fruitful as making them learn on their own.
Financial Trading startups are slowly turning into machine learning based companies in order to be able to find the best solutions to work even more smarter. We can already notice how Machine Learning is playing an integral role in the financial ecosystem. Machine Learning has already found its way into our daily financial life.
What is Financial Trading?
First, let us know what does Financial Trading mean? It simply means the buying and selling of the financial instruments in order to make a profit. These financial instruments can be shares, forex, CFDs, etc. Financial Trading is opted by everyone from individuals to our government who are trying to make a profit out of it. The financial industry is huge and is making a great impact in the entire world presently.
Financial trading can be very risky and difficult to comprehend. It needs handwork, proper planning and lot of thought processing which will ultimately give you a reward. There is no definite scheme that makes you rich in this field.
Few people consider financial trading as a side business and some as their source of income. Opting to do it as a side business can be effective as it will give you flexible hours to work on but eventually, it depends on what is your investing style. There is a lot of risks involved as there are chances that you might lose the investment. It just requires you to be a bit analytical and observant about the current financial trends.
How can Machine Learning benefit Financial Trading?
There are a few machine learning models that help in the prediction of Financial Trading.
Following are the few models in supervised machine learning that can do the task.
What is Linear Regression?
One of the hottest approaches to Financial Trading is using Linear Regression algorithm.
Wikipedia defines Linear Regression as "Linear regression is a linear approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. The case of one explanatory variable is called simple linear regression"
This is just an example of how a linear regression graph looks like.
It is a statistical tool that has a number of uses including in predicting the stock market. It helps in quantifying the trend of a particular stock, it may be grouped or broad-based. You can also predict the risks in the stock profiles. You draw a line on the trading chart which helps in analyzing the trends in the price. It helps in minimizing the distance between the point to each of the listed prices to provide a way for you to evaluate the trends.
This is an example of how Linear Regression helps in predicting stock markets
What is Logistic Regression?
The definition of Logistic Regression is "It helps in measuring the relationship between dependent variable and one or more independent variables by estimating the different probabilities with the help of a sigmoid function". It is quite similar to Linear Regression.
Logistic Regression model helps in predicting the output with an accuracy of 52% which has incredible good returns. You just have to change parameters and create your strategy to use the model.
Logistic Regression used in predicting trade.
What is KNN?
KNN is used in predicting as it involves statistical technique to predict and classify data that is based on the similarity to the nearest neighbor. It is either used for classification or regression. There are few steps that have to be applied which eventually helps in predicting stock prices. The first task is to know which indicators you want to measure your data and then also specify the goal that you want.
In this model, for each data point, the algorithm finds the k nearest after which it classifies the data point to the rest majority present. The model can help in improving the chances of prediction than normal guessing which is useful in financial trading.
What is Decision Trees?
Decision trees are basically used for building models for classification and regression that are used in data mining as well as trading. In a decision tree algorithm, it performs a set of recursive functions before predicting the end result after you plot the actions on the screen.
Hence basically a decision tree is a type of flowchart that helps in making decisions for you and this technique is used by machine learning model to figure out the end result.
By looking at the above defined Machine Learning models we can see how they can help in financial trading business by predicting the stock market. First, you have to frame your problem, collect the required data and clean it, after splitting the data for training and validation, you have to use an appropriate model to make the predictions. By training the model you can make your stock market predictions and ultimately check the performance.