To begin with, what exactly is Algorithmic Trading?

Automated and pre-programmed trading instructions are used in algorithmic trading to consider variables such as price, timing, and volume while executing orders. A set of instructions for resolving a problem is known as an algorithm. Over a few seconds, computer algorithms deliver a small fraction of the entire order to the market.

Trading decisions on a stock market are made using algorithms, sophisticated calculations, mathematical models, and human supervision, all of which are automated. In many cases, algorithmic traders use high-frequency trading technology, which allows a corporation to conduct tens of thousands of transactions every second. A few examples of the various applications of algorithmic trading include order execution, arbitrage, and trend trading approaches, to name a few.

1. Reduce the impact on the market as much as possible.

The price of a stock may be affected by a major transaction. Discriminatory trades disrupt the market price, so they are referred to as such. As a result, traders open huge positions that can impact the market incrementally to avoid this.

An investor can buy one million shares of Apple stock in batches of 1,000 shares. Every five minutes, the investor might buy 1,000 shares and then assess the impact of the trade on Apple stock prices. The investor will continue to buy if the price does not change. It’s possible to buy Apple stock at a lower price by employing this technique. However, there are two major drawbacks to this strategy:

• The investor must pay a predetermined charge for every transaction he makes for the technique to work.
• It takes a long time to implement. To complete this trade, buying 1,000 shares every five minutes would take the investor just over 83 hours (more than three days).

This can be solved by using a algo trading that buys shares and immediately checks whether it has affected the market price. Trades can be completed in less time and with a lower number of transactions if this is implemented.

1. Rule-based decision-making is ensured.

Because market participants are typically swayed by their own emotions and opinions, the public frequently despised their algo trading strategies. Market indicators such as financial markets, for example, provided warnings of the imminent calamity in the run-up to the global financial crisis of 2008. Because of the “bull market frenzy” that gripped the financial markets in the mid-2000s, many investors failed to see these warning signs, assuming that a crisis was improbable. For the algorithmic solution to work, all algo trades must adhere to a set of rules that have been established in advance.