Algorithmic Trading Strategies – 1

1. Algorithmic Trading Strategies – 1 Algorithm trading requires various strategies to work successfully. In this article, we break out several common ones you can use as a trader for yourself or your clients. Just like Alphabot automatically places a trade-in your account and has in-built risk management capabilities, you can decide your strategies in conjunction with your relationship manager. Different bots are offered on Alphabot to traders based on different risk-reward profiles. Some of the strategies are as follows - Mean reversion The mean reversion strategy works on the proposition that the price of security tends to converge to an average or mean in due course of time. Hence, if the price of a security is appreciably high or low compared to its mean, it will tend to reverse course and head towards its mean value at some point. Apart from its primary name, this strategy is also known as a reversal or counter-trend strategy. The way this strategy works is that the algo trading strategies use the historical price movement of a security to determine its mean value. It also assesses the upper and lower price level of the security and uses the combination of these data to determine when to execute a trade. When the prices of security are at the upper or lower bound, the algorithm intraday trading strategies trades with the idea that they will go back to their mean level. This strategy can prove very beneficial when the price of a security is exceptionally high or low because in such a case, a reversion is nearly guaranteed. Thus, if the 30-day moving average of security is higher than its 120-day moving average, the algorithm will expect the price to decline towards the mean because it is too high. One aspect of being careful of while using this strategy is when the prices are not too far away from the mean. In such cases, it may so happen that the moving average may catch up to the mean value of the security before the price can revert, thus negating any possible benefit from the trade. Statistical Arbitrage Similar to an arbitrage strategy, the statistical arbitrage strategy makes use of inefficiencies in prices of securities. It can be used when the price of securities is incorrectly quoted. Also similar to an arbitrage strategy, these inefficiencies in securities prices do not last long. Hence, it needs to be executed quickly, which is where automated algorithmic trading comes in handy. But where this strategy is different from an arbitrage strategy is that while arbitrage refers to the price arbitrage available for security listed across different platforms, the statistical arbitrage strategy works when two securities are involved. These securities could be related to companies in the same industry or securities which behave similarly in a particular market. So while arbitrage

2. strategy is adopted in the mispricing of one security listed across different platforms, statistical arbitrage makes use of price inefficiencies between two relatable securities. Let's consider two companies from the information technology sector. Being of similar nature and from the same industry, their prices may behave similarly; in essence, they may be correlated in a precise manner. The algorithm studies the behaviour of these securities over some time. Once it finds inefficiencies between these prices, it can execute a trade before the price of one security has the chance to correct and maintain its movement with the other security's price. The level of inefficiency may be low, but a large enough trade can be quite profitable using this strategy. Sentiment-based trading The sentiment-based Algo trading strategies make trading decisions based on news. There are several kinds of data being released daily. This data ranges from economical to corporate announcements. Market participants put forth their views on this data. Algorithmic trading systems based on sentiment assess whether the data point that has been released overwhelms or underwhelms the prevailing opinion. These systems even use websites like Twitter to analyse the prevailing sentiment. Opinions expressed on that and similar platforms can help these systems arrive at a consensus. Using this information, these systems aim to predict the movements in prices of securities based on how the actual data turns out. Thus, the use of intraday trading strategies. Across the two articles, we have provided you with details of different algorithmic trading strategies and how they work. There are other strategies too, but these are the ones which work as an excellent foundation for you as you explore the complex yet intriguing world of automated algorithmic trading.