# 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
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.

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