Algorithmic Trading Strategies: Basics to Advanced Algo Trading Strategies

By Chainika Thakar, Viraj Bhagat & Apoorva Singh

Algorithmic trading strategies are simply strategies that are coded in a computer language such as Python for executing trade orders. The trader codes these strategies to use the processing capabilities of a computer for taking trades in a more efficient manner with no to minimum intervention.

Let us find out more about algo trading strategies with this blog that covers:

• What are algorithmic trading strategies?
• Classification of algorithmic trading strategies, paradigms & modelling ideas
• Momentum-based strategies
• Arbitrage
• Market making
• Machine Learning in trading
• Options trading and options trading strategies
• Building and implementing algorithmic trading strategies
• Steps to build algorithmic trading strategies
• Where are algo trading strategies used?
• How to learn algo trading strategies?
• FAQs about algorithmic trading strategies

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What are algorithmic trading strategies?

“Algorithmic trading” is a term that may sound complicated but can be implemented easily if you have the dedication to learn and the grit for learning.

An algorithm is, basically, a set of instructions or rules for making the computer take a step on behalf of the programmer (the one who creates the algorithm). The programmer, in the trading domain, is the trader having knowledge of at least one of the computer programming languages known as C, C++, Java, Python etc.). Learn how algorithmic trading uses python to help develop sophisticated statistical models with ease.

This knowledge of programming language is required since the trader needs to code the set of instructions in the language that computer understands.

In short, in trading, the set of instructions or rules is given to the computer (by the trader) to automate the execution of trade orders via the stock exchange with minimal human intervention. This is called algorithmic trading.

Coming to “algorithmic trading strategies”, trading strategies are devised by a trader having knowledge of the financial market with regard to

• Entering the market (buying when the prices are favourable)
• Exiting the market (selling when the prices begin going way below expectations)

These strategies are coded as the programmed set of instructions to make way for favourable returns for the trader. The set of instructions to the computer is given in programming languages (such as C, C++, Java, Python). Following which, the computer can generate signals and take the trading position accordingly.

Before moving ahead, take a quick look at the 15 most popular algo trading strategies, used by traders and investors to automate their trading decisions.

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Classification of algorithmic trading strategies, paradigms & modelling ideas

All the algorithmic trading strategies that are being used today can be classified broadly into the following categories:

• Momentum-based strategies or trend following strategy
• Arbitrage algorithmic trading strategies
• Market making algorithmic trading strategies
• Machine learning in trading
• Options trading and options trading strategies

We will be throwing some light on the strategy paradigms and modelling ideas pertaining to each algorithmic trading strategy below.

Momentum-based strategies or trend-following algorithmic trading strategies

Assume that there is a particular trend in the market. As an algo trader, you are following that trend.

Further to our assumption, the markets fall within the week. Now, you can use statistics to determine if this trend is going to continue. Or if it will change in the coming weeks. Accordingly, you will make your next move.

You have based your algorithmic trading strategy on the market trends which you determined by using statistics.

This method of following trends is called momentum trading strategies.

There are numerous ways to implement this algorithmic trading strategy and it has been discussed in detail in one of our previous articles called “Methodology of Quantifying News for Automated Trading”.

Strategy paradigms of momentum-based strategies

Momentum strategies seek to profit from the continuance of the existing trend by taking advantage of market swings.

“In simple words, buy high and sell higher and vice versa.”

And how do we achieve this?

• Short-term positions: In this particular algorithmic trading strategy we will take short-term positions in stocks that are going up or down until they show signs of reversal. It is counter-intuitive to almost all other well-known strategies.
• Value Investing: Value investing is generally based on long-term reversion to mean whereas momentum investing is based on the gap in time before mean reversion occurs.
• Momentum: Momentum is chasing performance, but systematically by taking advantage of other performance chasers who are making emotional decisions.

Explanations:

There are usually two explanations given for any strategy that have  proven to work historically,

• Either the strategy has compensated for the extra risk that it takes, or
• There are behavioural factors due to which premium exists.

Why Momentum works?

There is a long list of behavioral biases and emotional mistakes that investors exhibit due to which momentum works.

However, this is easier said than done as trends don’t last forever and can exhibit swift reversals when they peak and come to an end.

Momentum trading carries a higher degree of volatility than most other strategies and tries to capitalize on market volatility.

It is important to time the buys and sells correctly to avoid losses by using proper risk management techniques and stop-losses. Momentum investing requires proper monitoring and appropriate diversification to safeguard against such severe crashes.

Modelling ideas of momentum-based strategies

First of all, you should know how to detect price momentum or trends. As you are already into trading, you know that trends can be detected by following stocks and ETFs that have been continuously going up for days, weeks or even several months in a row.

For instance, identify the stocks trading within 10% of their 52-week high or look at the percentage price change over the last 12 or 24 weeks. Similarly to spot a shorter trend, include a shorter-term price change.

For instance, back in 2008, the oil and energy sector was continuously ranked as one of the top sectors even while it was collapsing.

Types of momentum trading strategies

We can also look at earnings to understand the movements in stock prices. Strategies based on either past returns (price momentum strategies) or earnings surprise (known as earnings momentum strategies) exploit market under-reaction to different pieces of information.

• Earnings Momentum Strategies: An earnings momentum strategy may profit from the under-reaction to information related to short-term earnings.
• Price Momentum Strategies: A price momentum strategy may profit from the market’s slow response to a broader set of information including longer-term profitability.

Take a look at  useful read below:

• Using Quadratic Discriminant Analysis To Optimize An Intraday Momentum Strategy

Arbitrage algorithmic trading strategies

Let’s understand arbitrage with an example. If we assume that a pharma corp is to be bought by another company, then the stock price of that corp could go up.

This is triggered by the acquisition which is a corporate event. If you are planning to invest based on the pricing inefficiencies that may happen during a corporate event (before or after), then you are using an event-driven strategy.

Bankruptcy, acquisition, merger, spin-offs etc. could be the event that drives such kind of an investment strategy. These arbitrage trading strategies can be market neutral and used by hedge funds and proprietary traders widely.

Statistical arbitrage

When an arbitrage opportunity arises because of misquoting in prices, it can be very advantageous to the algorithmic trading strategy. Although, such opportunities exist for a very short duration as the prices in the market get adjusted quickly. And that’s why this is the best use of algorithmic trading strategies, as an automated machine can track such changes instantly.

For instance, assume that each time that Apple‘s stock prices fall by $1, Microsoft’s prices too fall by $0.5. Now, given the case that Microsoft has not fallen yet, you can go ahead and sell Microsoft to make a profit.

Strategy paradigms of statistical arbitrage

If market making is the strategy that makes use of the bid-ask spread, statistical arbitrage seeks to profit from the statistical mispricing of one or more assets based on the expected value of these assets.

A more academic way to explain statistical arbitrage is to distribute the risk between a thousand to a few million trades in a very short holding span with the expectation of gaining profit from the law of large numbers. Statistical arbitrage Algorithms are based on the mean reversion hypothesis, mostly as a pair. You can enroll for our course on Statistical Arbitrage Trading.

Modelling ideas of statistical arbitrage

Pairs trading is one of the several strategies collectively referred to as Statistical Arbitrage Strategies. In a pairs trade strategy, stocks that exhibit historical co-movement in prices are paired using fundamental or market-based similarities.

The strategy builds upon the notion that the relative prices in a market are in equilibrium, and that deviations from this equilibrium eventually will be corrected.

When one stock outperforms the other, the outperformer is sold short and the other stock is bought long, with the expectation that the short-term diversion will end in convergence. This often hedges market risk from adverse market movements i.e. makes the strategy beta neutral.

However, the total market risk of a position depends on the amount of capital invested in each stock and the sensitivity of stocks to such risk.

Below, I have mentioned a useful read that you may like:

• Kalman Filter Techniques And Statistical Arbitrage In China’s Futures Market In Python

Stay tuned for Part II to learn about other algorithmic trading strategies.

Originally posted on QuantInsti blog.