-
A mean reversion trading strategy aims to identify the entry and exit points that are characterized by divergences from a defined mean. Below are the steps to formulate a mean reversion strategy with ease: 1. Determine the Mean The first task is to decide on the type of mean which may either be a simple […]
-
Mathematics occupies the central position with regard to quantitative trading, and it provides models of the market, algorithms for analyzing data and creating trading algorithms. With an extensive amount of knowledge of the particular mathematical concepts, it becomes easier for traders to make rational, analytical based decisions and to develop desirable trading models. This article […]
-
The new era of algorithmic trading has revolutionized the operation of the financial markets by extending the frontiers of speed, precision, and volume. Trading was once exclusively a face-to-face event conducted on the floor of exchanges. Now billions of trades take only microseconds, and these trades are done by lightning fast algos whipsawing through multiple […]
-
Quantitative trades have gained interest in the world for their precision and analytical tools, which trade more effectively and faster than conventional trading methods. For some who have aspirations to be a quant or for other professionals who want to expand their knowledge, there are second-to-none resources in several books detailing the strategies, math, and […]
-
Through the constant technological development and the availability of abundant numerical data, algorithmic trading has come a long way. Nowadays, trading algorithms account for most of the trade volume in securities markets with that trend expected to continue in the years to come. A few trends are outlined as global advancements which will change the […]
-
Algorithmic trading involves complex math and data analysis to make decisions, thereby limiting the capacity for emotion based decision making which usually dominates the financial world. However, this only applies to the end result. The raising and management of an algorithm gives room for human psychology to play a role even though emotions cannot be […]
-
The Simple Moving Average (SMA) Crossover is one of the most popular and accessible strategies in algorithmic trading. This approach uses moving averages of stock prices over different timeframes to identify potential buy or sell signals based on trend reversals. This case study outlines the basic steps involved in developing a simple moving average crossover […]
-
Trading through the use of algorithms has become a common practice in the last couple of decades. It has revolutionized how traders engage in executing orders by increasing the speed and volume of trades. A growing concern that has also come with the adoption of algorithmic trading is its importance, fairness, and stability in the […]
-
Alternative data applies to any dataset that a trader utilizes beyond the standard sources of ‘price, volume, earnings, and so on’. These include social media, satellite images, and many other data sets. With an array of information at their disposal, one of the challenges traders face is sorting through the data that’s relevant to them. […]
-
Predictive accuracy is the basis on which profit or a loss is weighed in algorithmic trading. Based on previous data, these algorithms are trained to anticipate patterns and relationships, allowing them to predict future trends. This paper discusses the supervision of learning for trading signal prediction using the relevant algorithms and how these models can […]