Algorithmic Trading: What it is, How to Start, Strategies, and More – Part I

Algorithmic trading (or simply algo-trading) is a method of trading where we use computer programs to follow a defined set of instructions or rules to calculate the price, quantity, timing and other characteristics of the orders. ⁽¹⁾

So, if you are curious about how technology is revolutionising the financial world and boosting trades, stick around—algorithmic trading might just be the game-changer you’ve been looking for!

Also, algorithmic trading market size was valued at USD 15.76 Bn. in 2023 and the total algorithmic trading revenue is expected to grow by 10.6 % annually from 2024 to 2030, reaching nearly USD 31.90 Bn. ⁽²⁾

Let us learn more about algorithmic trading with this blog that covers:

• Brief of Algorithmic Trading
• The Transformation from Manual to Algo Trading
• When did Algorithmic Trading start?
• Frequencies of Trading: HFT, MFT, LFT
• Algo Trading Strategies
• How to Learn Algorithmic Trading?
• The workflow of Algorithmic Trading
• How to build your own Algorithmic Trading Business or Desk?
• Advantages of algorithmic trading
• Disadvantages of algorithmic trading
• Recent developments and potential future trends in algorithmic trading

Brief of Algorithmic Trading

In algorithmic trading, the trading signals (buy/sell decisions) are generated based on a set of instructions. ⁽¹⁾

Let’s dive deeper into the evolution of trading, from its manual beginnings to the sophisticated algorithm-based systems we have today.

Visit QuantInsti blog to watch the videos in the “Algo Trading Course.”
This segment covers the basics of algorithmic trading, the industry landscape, pros and cons, how to build an algo trading strategy with Python, the benefits of a quant approach, and much more!

Further, let us find out the transformation of trading from a manual to an algorithmic approach.

The Transformation from Manual to Algo Trading

So, what was trading like in the bygone era when automation did not exist?

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The Evolution Of Trading: Barter System To Algo Trading

Why you should be doing algorithmic trading?

Trading in the bygone era and Trading Now!

Conventional trading was what existed before algorithmic trading came into being. Looking back, conventional trading dates back to around 1602 with the Dutch East India Company, which marked the beginning of organised trading practices. Back in time, when the concept of automated trading was not introduced, traders would execute the trades manually without having any other option.

Over a period of time, the need for a faster, more reliable (free of human emotions), and accurate method led to the beginning of algorithmic trading.

And now, let us move further into understanding what has happened post-arrival of Algorithmic trading.

Is algo trading affecting the traditional traders?

Speaking about algorithmic trading outperforming traditional trading, it is obvious that trading via algorithms is much faster with no human errors. Besides, algorithmic trading is considered to be no threat to traditional traders. This is because human intervention will always be needed for better market-making and to ensure stability in financial markets. ⁽²⁾

Since now you know what trading was like before automation took over, next you will get to know when exactly manual trading started, and when algorithmic trading came into the picture.

When did Algorithmic Trading start?

It wasn’t until the late 1980s and 1990s that algorithmic trading, involving fully electronic trade execution, made its debut in financial markets.

By 1998, the U.S. Securities and Exchange Commission (SEC) had approved electronic exchanges, paving the way for computerised High-Frequency Trading (HFT). Since HFT can execute trades up to 1,000 times faster than humans, it quickly became widespread.

Now we will discuss the various types of trading frequencies which are adopted by the traders.

Frequencies of Trading: HFT, MFT, LFT

Now, there is a particular level of speed at which trading (buying and selling of stocks) takes place.

Below, let us go through the three types of trading, each based on its frequency or speed.

1. High-Frequency Trading (HFT): This type of trading leads to high-speed trade, i.e., large numbers of orders are executed within seconds. Hence, it makes the trading of securities possible in the market every millisecond, making it highly profitable. This type of trading is a low-latency trading practice which means that the trading happens much faster than the competition in response to market events.
2. Medium-Frequency Trading (MFT): Takes a few minutes to a day to place the trade, and hence, is slower than high-frequency trading. Its latency (time taken to place the trade) is higher than HFT.
3. Low-Frequency Trading (LFT): Takes place in a day to a couple of weeks and is the slowest type of trading. Hence, the latency time (time taken to place the trade) is much higher than HFT and MFT.

Hold on! We haven’t reached the end yet. Since algorithmic trading requires strategies for making the most profitable decisions, there are various strategies, each based on different market conditions.

Let us check out the algorithmic trading strategies now.

Algo Trading Strategies

Here’s a  list of the most popular strategies and their explanations:

Market Making Strategies

This strategy helps to increase the liquidity in the markets. A market maker, usually a large institution, facilitates a large volume of trade orders for buying and selling. The reason behind the market makers being large institutions is that there are a huge amount of securities involved in the same. Hence, it may not be feasible for an individual intermediary to facilitate the kind of volume required.

In this process, the market makers buy and sell the securities of a particular set of firms. Every market maker functions by displaying buy and sell quotations for a specific number of securities. As soon as an order is received from a buyer, the market maker sells the shares from its own inventory and completes the order. Hence, it ensures liquidity in the financial markets which makes it simpler for investors as well as traders to buy and sell. This sums up that market makers are extremely important for sufficing trade.

Statistical Arbitrage Strategies

Statistical arbitrage strategies are based on the mean reversion hypothesis. Such strategies expect to gain from the statistical mispricing of one or more than one asset on the basis of the expected value of assets.

One of the examples of Statistical Arbitrage is pair trading where we look at a ratio or spread between the pair of stocks’ prices, which are cointegrated. If the value of the spread goes beyond the expected range, then you buy the stock which has gone down and sell the stock which has outperformed in the expectation that the spread will go back to its normal level. Statistical arbitrage can work with a hundred or more stocks in its portfolio which are classified according to a number of factors and can be fully automated from both analysis & execution perspectives.

Momentum Strategies

The momentum trading strategies profit from the market swings by looking at the existing trends in the market. So it seeks to buy high and sell higher to make the investment in the stocks profitable.

Momentum works because of the large number of emotional decisions that other traders make in the market during the time when prices are away from the mean. Hence, the gain takes place due to others’ behavioural biases.

The only tricky part here is that trends may swiftly reverse and disrupt the momentum gains, which makes these strategies highly volatile. So it is extremely imperative to schedule the buys and sells correctly and avoid losses. This can be done with appropriate risk management techniques that can properly monitor the investment and take actions to safeguard in case of adverse price movement.

Mean reversion strategies

Financial markets are a dynamic ecosystem, constantly shifting and adapting. Amidst this volatility, the mean reversion principle emerges as a strategic beacon. At its core, mean reversion trading hinges on a simple yet profound notion: what goes up must come down, and what falls too far is likely to bounce back. This foundation is built upon the idea that asset prices, amidst short-term fluctuations, possess an inherent tendency to gravitate back towards their historical averages over time. Note that when you look at one asset, this mean reversion principle could be a short term phenomenon.

Recommended read:

Quantitative Value Investing Strategy in Python

Value investors often use this approach to buy stocks for long-term investments. Similarly, mean reversion principles can be used with technical indicators to develop short-term trading strategies based on the expectation that prices will revert to their mean.

Sentiment-Based Trading Strategies

Sentiment-Based Trading Strategies involve making trading decisions based on the analysis of market sentiment, that is, the collective mood or attitude of investors towards a particular asset or market. The sentiment of the market is usually ascertained by social media, news articles, financial reports, etc. These sources help to find out whether the sentiment is bullish, bearish, or neutral, on the basis of which the trades are executed accordingly.

Recommended read:

Sentiment analysis for trading

Machine Learning Trading Strategies

Machine learning, as the name suggests is the ability of a machine to learn, even without programming it explicitly. It is a type of Artificial Intelligence or AI which is based on algorithms to detect patterns in data and adjust the program actions accordingly.

Example:

Facebook’s News feed personalises each of its members’ feeds using machine learning. The software uses statistical and predictive analytics to identify patterns in the user’s data and uses it to populate the user’s Newsfeed. If a user reads and comments on a particular friend’s posts then the news feed will be designed in a way that more activities of that particular friend will be visible to the user in his feed. The advertisements are also shown in the feed according to the data based on user’s interests, likes, and comments on Facebook pages.

So it means that human intervention is always required. The benefit here is that Machine Learning based models analyse huge amounts of data at a high speed and indulge in improvements themselves. This is much simpler than a conventional basic computer model built by data scientists or quants.

This was all about different strategies on the basis of which algorithms can be built for trading.

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Algorithmic trading strategies and paradigms

How much salary does a quant earn?

Stay tuned for Part II to learn about algorithmic trading educational resources.

Originally posted on QuantInsti blog.

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