In this article, we’re going to talk about how anyone, like us, can introduce data science to the stock market. We’ll also explain some concepts of data science with a focus on financial markets. And in the end we will try to answer / predict this as well how far can the current Bull Run go from May 2020? (with the help of an internal data scientist who is a member of our team)
PART A
Data science is a popular topic today. Everyone is talking about data. What you can do and how you can help. Dates are often represented as numbers, and those numbers can represent many different things. These numbers can be the number of sales, inventory, consumers, and last but not least, cash. That brings us to financial data, or more precisely to the stock market. Stocks, commodities, securities, and such are all very similar when it comes to trading. We buy, we sell, we hold. All this in order to make a profit
The question is, how can data science help us carry out these trades in the stock market?
Data Science Concepts for the Stock Market
When it comes to Data Science, there are a lot of words and phrases or jargon used that many do not know. We are here to solve all of that. Inherently, data science involves knowledge of statistics, math, and programming.
Algorithms
Algorithms are widely used in data science and programming. An algorithm is a set of rules used to perform a specific task. You may have heard that algorithmic trading is a popular thing on the stock market. Algorithm trading uses trading algorithms and these algorithms include, for example, rules like buying a stock only if it has dropped exactly 5% that day, or selling / stop-loss if the stock has lost 10% of its value when it was first bought, and time. These algorithms all are capable of running without human intervention. They are often referred to as trading bots because they are fundamentally mechanical in their trading methods and function without emotion.
Training
This is not your typical workout. In data science and machine learning, training involves using selected data or a portion of data to “train” a machine learning model. The complete data set is generally divided into two different parts for training and testing. The split is typically 80/20, with 80% of the total data set kept for training. This data is called training data or training set. In order for the machine learning model to make accurate predictions, it would need to learn from the past data (Training set). If we were to try to use a machine learning model to predict the future prices of a selected stock, we would give the model the stock prices of the last one Enter year to predict the following month’s rates.
Testing
After we have trained a model with the training set, we want to know how well our model is doing. This is where the other 20% of the data comes in. This data is commonly referred to as test data or test set. To validate our model’s performance, we would take the predictions from our model and compare them to our test set. For example, suppose we are training a model on annual stock price data. We will be using the January through October pricing as our training set and November and December as our testing set (this is an extremely simplified example of annual data breakdown and should not normally be used due to seasonality and the fiscal year cycle). We will have our model on JanOct prices predict the next two months by comparing these predictions with the actual November and December stock prices. The margin of error between the predictions and the actual data is what we want to reduce while playing around with our model.
Features & Target
In data science, data is typically displayed in a table format such as an Excel spreadsheet or a DataFrame. These data points can represent anything. The pillars play an important role. Let’s say we have stock prices in one column, P / B ratio, volume, and other financial data in the other columns. In this case, stock prices are our goal. The rest of the columns are features. In data science and statistics, the target variable is called the dependent variable and the characteristics are called the independent variables. The goal is what we want to predict future values ​​for, and the properties are what the machine learning model uses to make those predictions.
Modelling: Time-Series
One thing that data science uses a lot is a concept called “modeling”. Modeling generally uses a mathematical approach to using past behaviors to predict future outcomes. When it comes to financial data in the stock market, this model is usually a TimeSeries model. But what is a time series? A TimeSeries is a series of data, in our case it would be the price of a share, indexed according to a period of time, which can be monthly, daily, hourly or even by the minute. Stock charts and data are time series. In general, when it comes to modeling these stock prices, a data scientist would implement a time series model. Building a time series model involves using a machine learning or deep learning model to collect the course data. The data is then analyzed and adapted to the model. The model enables us to forecast future stock prices over a selected period of time.
Modelling: Classification
Another type of model used in machine learning and data science is called a classification model. Models that use classification receive certain data points and then predict or classify what those data points represent. For the stock market or stocks, we can enable machine learning model various financial data, such as P / E ratio, daily volume, total debt, etc., to determine if the stock is generally a good investment. The model can classify this action as buying, holding or selling according to the funding we have given.
Overfitting and Misfitting When evaluating the performance of a model, errors sometimes score “too hot” or “too cold” when searching for “just right”. Overfitting occurs when the model predicts too complexly until it loses the relationship between the target variable and the characteristic. A mismatch occurs when the model doesn’t fit the data well enough and the predictions are too easy. These are questions that data scientists need to be aware of when evaluating their models. Overfitting when the model cannot follow stock market trends and cannot adapt to the future. A mismatch is when the model basically starts by predicting the simple average price for the entire history of the stock. In other words, both Misfit and Overfit lead to a bad future price predictions and forecasts.
Conclusion The topics we cover are common key concepts in data science and machine learning. These topics and concepts are important for learning data science. There are many more concepts to cover. If you are familiar with the stock market and are interested in data science, we hope the descriptions and examples above have been helpful and understandable.
FAQ
1) Why technical analysis and fundamental analysis are not part of data science infect they fall under the data analytics category. Let us understand Data analytics focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modelling. Data science is a multi-disciplinary blend that involves algorithm development, data inference, and predictive modelling to solve analytically complex business problems. On the other hand, data analytics involves a few different branches of broader statistics and analysis ( in simple terms technical and fundamental analysis.). (Please note going forward whenever we use the term data analyst we mean both technical and fundamental analysis)
2) How the skillet and Job role of a data scientist will be different from a data analyst
A) Data Analytics person like fundamental or technical analyst will require following skillet and his role will be:
– To acquire Knowledge of Intermediate Statistics and excellent problem-solving skills along with
– Dexterity in Excel and SQL database to slice and dice data like stock prices, quarterly results, Book value, EPS, moving average, RSI etc
– He will also need Experience in working with Business Intelligence tools like Power BI for reporting and creating multiple dashboards to generate/identify various buy/sell opportunities.
– His Knowledge of Statistical tools like Python, R or SAS will enable him to identify the patterns and further assist in buy/sell opportunities. In short; To become a data analyst, one need not necessarily be from an engineering background but having strong skills in statistics, databases, modelling, and predictive analytics comes as an added advantage.
B) Data scientist person will require following skillet and his role will be:
– He needs to be well versed with topics like Mathematics, Advanced Statistics, Predictive Modelling, Machine Learning, Programming along with –
– His Proficiency in using big data tools like Hadoop and Spark will provide exploratory Data Analysis.
– His Expertise in SQL and NoSQL databases like Cassandra and MongoDB and others will help in Gleaning business insights using machine learning techniques and algorithms.
– His experience with data visualization tools like QlikView, D3.js, and Tableau will help Identifying new trends in data to make predictions for the future.
– Dexterity in programming languages like Python, R, and Scala will help in Processing, cleansing and verifying the integrity of data.
Having a practical hands-on working knowledge and expertise of various analytical and database tools is the secret success mantra to excel in Data Science.
Remember extensive training on tools like Excel and SQL to manipulate and analyze large volumes of data. Apart from learning Excel, SQL and Python, is just the start as modules on how to use Power BI and Tableau for generating dashboard and visualizations to communicate analysis results will be the key output. Anybody with minimal or no coding background can learn analytics. So, hurray if you are from a non-engineering background looking to enter the big data industry. Data scientist is one of the best career options to consider. Conclusion The exponential gains in the market have led many to question its sustainability given the apparent disconnect to the ground realities of a Covid-battered economy.
The recent market rally despite a raging second wave of the pandemic has led some to suggest that the bull market may be entering its middle or late-cycle, implying that an eventual end is near We have gathered historical data to compare various metrics of the Covid era bull market with the previous five major bull markets in India dating back to 1991. The criteria for assessing a bull market are the doubling of Sensex or Nifty50 from their most recent lowest point on a wave. Point 1 For instance, in its 64-week existence the current bull market has offered average weekly returns of 1.7 per cent, which is sharply lower than the average weekly returns of the five previous bull markets of 2.8 per cent. This data helps us to identify that the apparently rapid pace of equity returns in the current bull market is nothing unique.
The volatility of this weekly return at 3% is not different from history,” Point 2 So far the present Covid bull market has failed to beat the average weekly returns of each of the past five bull markets dating back to the one that started in 1991. Point 3 Further, in the previous five bull markets on Dalal Street, India’s average outperformance to emerging markets has stood at 52 per cent. The current bull market has so far managed an outperformance of merely 23 per cent to its emerging market peers justify more upside is yet to come Point 4 Indian equities have also managed to garner only a 55-60 per cent premium to their emerging market peers against the high of 75 per cent hit towards the end of the 2016-18’s bull run. Point 5 Since the past often provides a good indicator as to how the present may unfold even if a direct correlation can be difficult to establish given the varying economic circumstances, there is a lot for the bulls on the Street to be optimistic about. For example, the current bull market is 62 weeks long, compared with the average length of the past five bull runs at 107 weeks. That said, if one excludes the 246-week-long 2003-08 bull market, the average duration crashes down to 72 weeks.
Secondly, the average trough-to-peak absolute return of the past five bull markets was at 284 per cent against the 106 per cent gains for the Nifty50 so far. Even excluding the 630 per cent returns of the 2003-08 bull run, average returns were at 197 per cent, suggesting that there is still some way to go. Future predictions – we can witness further upside in the immediate 12 months, but definitely, the pace of gains will be slower. – there can be material upside in inflows from foreign portfolio investors going ahead. Average FPI flows-to-market-cap for the past five bull markets stood at 4.4 per cent against 1.6 per cent for the present Covid bull market. – the ongoing rush in the primary market is suggesting that the bulls may have exhausted however, evidence suggests to the contrary that we are still in the early stages of the equity issuance cycle and they could raise anywhere between 3 to 5 times the average market-cap before this bull market ends – it is true that the current bull market’s underpinnings are shallower than in the past (record money printing by global central banks, fiscal stimulus in advanced economies, and a weak domestic economy), We are with the opinion that market has some steam left and it has more legs to it.
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