Let’s look at a few commands that we will frequently use while exploring time series data. length() The length() function tells us the number of elements in out time series dataset. head() The head() function displays the top n elements of the dataset. This is useful while exploring large datasets. tail() The tail() function displays the last n elements of the […]

# Data Science

## Financial Time Series Data

Welcome to this course on financial time series analysis using R. In this course, we will learn about financial time series data analysis in R. You will learn about how to explore and build time series data, calculate its key statistics, and plot time series charts. You will also learn about how to use the […]

## Check If Data Is Normally Distributed Using R – QQ Plots

The first step to check if your data is normally distributed is to plot a histogram and observe its shape. If it looks bell-shaped and symmetric around the mean you can assume that your data is normally distributed. However, using histograms to assess normality of data can be problematic especially if you have small dataset. […]

## Settlement Price of Futures Contracts

While looking at the historical price dataset of a Futures contract, you will see some important columns such as Open, High, Low, Last, Change, Settle, Volume, and Previous Open Day Interest for each trading day. The Last column is the price of the last trade on the day. The Settle column shows the settlement price […]

## Calmar Ratio: Definition, Calculation, and Importance

Calmar ratio is a popular risk-adjusted measure used by investors in their investment selection process. The Calmar ratio is calculated by dividing the compounded annual rate of return for period and dividing it by the maximum drawdown for the same period. The calculations are done using absolute values. Calmar ratios are generally calculated using 36 […]

## New Course – Quantitative Trading Strategies with R

We are pleased to announce the addition of a new course – Quantitative Trading Strategies with R – to our growing library of courses on Data Science for Finance Professionals. Course: Quantitative Trading Strategies with R A step-by-step approach to building solid quantitative trading strategies using R Quantitative and algorithmic trading now accounts for over one-third of all […]

## Backtesting Quantitative Trading Strategies

Backtesting is one of the most important steps in building a successful quantitative trading strategy. It is in fact a key step that differentiates algorithmic trading from discretionary trading. Quants use their computational finance and programming skills to build complex trading strategies. However, before these strategies are executed in the live market, they are tested […]

## How to Build Your Own Quantitative Trading Strategy

As we know, quantitative trading involves developing and executing trading strategies based on quantitative research. The quants traders start with a hypothesis and then conduct extensive data crunching and mathematical computations to identify profitable trading opportunities in the market. The most common inputs to these mathematical models are the price and the volume data, though […]

## Why Financial Traders Should Learn R

If you work in the finance industry, especially as a trader, then I bet you can’t live a day without Excel spreadsheets. Excel is one of the most important tools for traders and investors. However, with time, the nature of financial data has become quite complex. The traders need to deal with much larger amounts […]

## Quantitative Trading Vs. Algorithmic Trading

While talking about quants and trading desks, you will often come across terms such as quantitative trading and algorithmic trading. So, what is quantitative trading and how does it differ from algorithmic trading. Let’s take a look. Quantitative trading involves the development of trading strategies with the help of advanced mathematical models. It involves conducting […]