Data Science is a most popular subject in today’s era.Many times data is also represented as numbers and these numbers many times represent many different things. These numbers can be the number of sales, inventory, consumers, and may be cash.It can be also used in stock market.
This takes us to financial data or towards stock market. Stocks, securities, commodities, and such are mostly similar when it comes to trading. We can buy, we can sell, we can hold a share for making a profit. so now question is:
How can we make use of Data Science to help us when it comes to making trades on the stock market?
Data Science Different Concepts for the Stock Market
When we talks about Data Science, there are many number of words,phrases or jargon are used that we may don’t know. Data science involves information of statistics, math, and programming.
Let’s explain some different data science concepts about finance and the stock market.
An algorithm is a set of rules which is used to perform a specific task. You may aware of algorithmic trading in the stock market. Algorithmic trading uses different trading algorithms which involve rules such as buying a stock only after stock goes down exactly 5% that day or selling if the stock has lost 10% of the value when it was first bought.
These algorithms all are capable to work without human intervention. They are mainly referred as trading bots they do trade without emotion.
This is not your simple training. Here training includes using selected data or a part of the data to train a machine learning model. The whole dataset is mainly split in two different parts for training and testing. This split is mainly 80/20 ratio with 80% of the dataset held for only training. This data is mainly known as training data or training set.
If we want to use a machine learning model to predict the future prices of a selected stocks, then we have to provide stock prices from the past year for predicting future prices of stock.
After completing training period of model, we have to know that our model is performing good or not. Here the other 20% of the data comes in the scene. This data is mainly known as testing data or testing set.
For example, if we have train a model on one year’s worth of stock price data. We will use the prices from February to September as our training set and October and November will be our testing set . After completing training our model on Feb-Sep prices, we have to predict the next two months. These predictions will then be compared to the actual prices from oct and nov. The number of error between the predictions and the real data is what we have to reduce.
Features & Target
In data science, data is mainly displayed in format like a Excel sheet.The columns play an vital role. Let us consider that we have stock prices in one single column, P/B Ratio, Volume, and other financial data in the other columns.
If the stock prices will be our Target then the rest of the columns will be the Features. In data science & statistics the target variable is mainly known as dependent variable.
Data science Mainly and heavily uses a concept called Modeling. Modeling mainly uses a mathematical approach taking part in past behaviors for forecasting future outcomes. When we talk about financial data in the stock market, that model is mainly known as Time-Series model.
A Time-Series is a series of data,here in case of stock market it would be price value of a stock and it is indexed in order by a period of different time which can be monthly, daily, hourly, or even minutely. Most of the stock charts and data is a time-series.
Second model in machine learning and data science is mainly known as Classification Model. Models which uses classification are provided different points of data and then it predicts.
In the Case of stock market or stocks, we can provide a machine learning model different types of financial data’s such as the P/E Ratio, Daily Volume, Total Debt, etc for determining if stock is fundamentally a good investment or not. The model also classifies this stock as a Buy, Hold, or Sell.
Overfitting & Underfitting
Overfitting takes place when the model are predicting too complexly to the point where it misses relationship between the target variable and the feature. Underfitting takes place when the model does not able to fit the data enough and the predictions are too simple.