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Efficiency metrics help to measure the degree of success or the failure of any algorithmic trading strategy. This study outlines how algorithmic traders and investors measure performance in terms of returns, risks, and efficiencies, among other value points that assist them in understanding when to adjust their strategies to better meet differing financial objectives. These metrics are key for everyone...

Backtesting is a key procedure in algorithmic (algo) trading which relies on historical data to assess the effectiveness of a particular trading strategy. It enables traders to put a strategy to the test ,perfect it and verify its usefulness without putting any capital at risk. In a way, backtesting allows traders to imagine how their strategy might have fared if...

Heffernan’s approach, momentum trading, involves riding on the coat-tails of existing trends, which in effect captures more return as the market is already moving in the direction of the position. During a strong market, an investor can simply buy any security and, due to momentum getting pushed in the direction of the traction of the market trends, the deployed collateral...

Quantitative trading is a self-explanatory term often called “quant trading.” As the name suggests, this method focuses on mathematical and statistical models as a basis framework to make all trading decisions. To the fresh faces, quant trading may seem like an advanced unemotional computer-reliant approach comprising of algorithms, models, and formulations. Nevertheless, once you grasp the fundamentals, you unlock a...

Algorithmic Trading: What Is It? The advent of algorithmic trading, most often referred to as ‘algo’ trading, has changed the face of financial markets, allowing traders to place orders in a highly efficient manner. Today, however, automated trading has become an integral part of a world’s major markets controlled by vast systems that execute trades based on preset conditions. Such...