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 […]

## Difference Between Model and Algorithm

One common problem while working with beginners in data science is the confusion about what is a model and what is an algorithm. In this article, I will try to explain the difference between a model and algorithm in simple words. In simple words, an algorithm is a set of rules to follow to solve […]

## What is Regularization in Data Science – Lasso, Ridge and Elastic Net

While building a model in data science, our goal is to fit the model to our data in such a way that the model learns the general pattern/trend in the data. However, this doesn’t always happen. In some cases, the model will very closely follow the training data to the nose rather than just learning […]

## New Course – Credit Risk Modelling in R

We are pleased to announce the addition of a new course – Credit Risk Modelling in R – to our growing library of courses on Data Science for Finance Professionals. Course: Credit Risk Modelling Learn to model credit risk using statistical models such as logistic regression and decision trees with real-life data. In this course, our objective is […]

## Predictive Modelling: Splitting Data into Training and Test Set

One of the most important jobs of a data scientist is to build predictive models for specific business problems. For example, what is the probability that a consumer will default on its loan payment in the next month or its credit card payment. This is a typical example of a predictive model where the data […]

## New Course – Fixed Income Markets

We are pleased to announce the addition of a new course – Fixed Income Markets – to our library of courses for Finance Professionals. Course: Fixed Income Markets Learn the characteristics, analysis, and valuation of fixed income securities. This course provides a thorough understanding of fixed income markets and the analysis and valuation of fixed […]

## CFA Exam Adds Fintech, Big Data, and Data Analysis to 2019 Curriculum

Come 2019, the wealth managers and financial analysts aspiring to add the Chartered Financial Analyst designation to their credentials have one more subject to deal with. The CFA Institute has decided to add fintech to its 2019 exam curriculum. The new curriculum contains a section called fintech and adds study material on hot industry topics […]