Algorithmic Trading Strategies – 1

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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
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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.