Algorithmic trading simply means that process which helps execute trade orders in an automated manner. It is more beneficial than manual trading since it provides for much more trading profits. All thanks to it being faster and more accurate!
According to “Global Algorithmic Trading Market 2018-2022” report published by Research and Markets, if data is to be believed, the global algorithmic trading market size is projected to grow from $11.1 billion in 2019 to $18.8 billion by 2024, expanding at a CAGR of 11.1 per cent. Moreover, it is being used widely and is ever-expanding its reach in emerging markets.
Here, this article is aimed to give you a thorough understanding of the following:
- What and Why of Algorithmic Trading?
- The Transformation from Manual to Algo Trading
- When did Algorithmic Trading start?
- Frequencies of Trading: HFT, MFT, LFT
- Algo Trading Strategies
- Algorithmic Trading Salaries
- What are the Rules and Regulations in India?
- How to Learn Algorithmic Trading
- The workflow of Algorithmic Trading
- How to build your own Algorithmic Trading Business?
In simple words, Algorithmic Trading is a process of converting a trading strategy into computer code which buys and sells (place the trade) the shares in an automated, fast and accurate way. Since the automated way of trading is faster and more accurate, it is preferred nowadays and is increasing its reach in emerging markets rapidly.
Technically, there are several mathematical algorithms at play for making the trading decisions on the basis of current market data, which then send and execute the order(s) in the financial markets. This method makes the trading free of all emotional human impact (like fear, greed, etc.) since decisions to carry out each trade are made by computers in a systematic manner.
For instance, the algorithm buys shares of Apple (AAPL) if the current market price of the share is less than the 200 days average price. Conversely, it would sell Apple (AAPL) shares if the current market price is more than the 200 days average price.
Okay, let us make a move ahead and understand generally, as to how the trading started and its transformation from being manual to algorithm-based.
Now, you have a fair idea of what algorithmic trading is and how it has got an upper hand over the conventional/manual trading. But what was trading like in the by-gone era when automation did not exist.
The Evolution Of Trading: Barter System To Algo Trading
Trading in the bygone era and Trading Now!
Conventional trading was what existed before algorithmic trading came into being. In the by-gone era, people used to carry out trading manually by placing the trade over the phone and also electronically with computers.
Back in time, when the concept of automated trading was not introduced, traders would gather the data from the market, analyze it and make decisions to trade based on that.
Hence, historically, there used to be human traders who would make decisions to buy or sell stocks based on market data.
Over a period of time, the need for a faster, more reliable (free of human emotions), and accurate method led to the invention of algorithmic trading.
You can read about the advantages of algorithmic trading here. And now, let us move further into understanding what all has happened post-arrival of Algorithmic trading.
Is Algo trading affecting the traditional traders?
Speaking about algorithmic trading outperforming traditional trading, it is but obvious that trading via algorithms is much faster and accurate with no human errors.
According to a finding by Economic Times in 2019, algorithmic trading is the future of financial markets and is a prerequisite for performing well in tomorrow's markets. Besides, algorithmic trading is considered to be no threat to the traditional traders. This is because human intervention will always be needed for better market-making and to ensure stability in financial markets.
Since now you know what trading was like before automation took over, ahead you will get to know when exactly manual trading started, and in what year did algorithmic trading replace it.
Going back in time, the conventional trading practice began four centuries ago, which was around 1602 when the Dutch East India Company initiated the trading practice. And, it was not until the late 1980s and 1990s that Algorithmic trading (fully electronic execution of trade) began in financial markets.
By 1998, U.S Securities and Exchange Commission (SEC) authorized electronic exchanges paving the way for computerized High-Frequency Trading (HFT). And since HFT was able to execute trades 1000 times faster than a human, it became widespread.
High Frequency Trading (HFT) is a type of Automated Trading, the explanation of which we will see ahead.
Although Algorithmic trading is one concept of executing the trade, there are different levels of frequencies (speed) at which it operates in the stock market.
Now, there is a particular level of speed at which trading (buying and selling of stocks) takes place. This speed decides the number of profits generated every second.
Below, let us go through the three types of trading, each based on its frequency or speed.
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 for increasing profitability.
An Insightful takeaway
- High-Frequency trading gained popularity as a result of the exchanges offering incentives for the firms/companies so as to add to the liquidity in the market.
- It helps to add liquidity to the market and also to eliminate small bid-ask spreads.
- In India, HFT accounts for one-third of its financial sector and is growing rapidly, making it highly possible for it to soar even further in the country.
Medium-Frequency Trading (MFT) - Medium Frequency Trading 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.
Low-Frequency Trading (LFT) - Low- Frequency Trading 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.
In the US and other developed countries, High-Frequency Trading accounted for almost 70% of the equities in 2013.
Hold on! We haven’t reached the end yet. Since algorithmic trading requires strategies for making the most profitable decision, there are various strategies, each based on different market conditions.
Algorithmic Trading Strategies
Algorithmic trading strategies are several types of ideas for conducting the most profitable algorithmic trade. Although each strategy is different, what remains the same is the procedure of conducting Algo trading. The pathway of each is ideated in a way so that it begins with retrieving real-market data feed from the exchange and with the pre-defined chunk of rules or logic, it generates a trading order. The trading order consists of all the specifications such as type, side, and quantity.
Each strategy works in its predefined manner to give the trader an accurate execution of algorithms for placing a trade.
For a better understanding, look into the list of the most popular strategies and their explanations:
- Market Making Strategies
- Arbitrage Strategies
- Statistical Strategies
- Momentum Strategies
- Sentiment Based Trading Strategies
- Machine Learning Trading Strategies
Market Making Strategies:
This strategy helps to increase the liquidity in the markets. A market maker, usually a large institution, facilitates 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.
This strategy implies taking advantage of the mispricing of the financial instrument or asset in two different markets. An example of Arbitrage Strategies is an asset which is trading in a market at a particular price but is also trading at a much higher price in another market. Hence, if you had bought the asset at a lesser price earlier, then you can sell the same in the market in which it is priced higher. This way, you will end up making a profit without having taken any risk.
Therefore, this is a scenario in which you make multiple trades simultaneously on one asset for a profit with no risk involved because of price inequalities.
Statistical Arbitrage Strategies:
Based on the mean reversion hypothesis, statistical arbitrage algorithms work mostly as a pair. 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 spread goes beyond the expected range, then you buy the stock which has gone down and sells 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 perspective.
These strategies profit from the market swings by looking at the existing trend in the market. So it seeks to buy high and sell higher for making the investment in the stocks profitable.
Now, let us learn about the relation between Value investing and Momentum investing.
Speaking of value investing, it seeks to revert to the mean or average whenever it deviates from it. This is when Momentum investing takes place since it happens in the gap in time prior to the occurrence of mean reversion. Momentum works because of the large number of emotional decisions that other traders take in the market during the time when prices are away from the mean. Hence, the gain takes place due to others’ behavioural biases and emotional mistakes.
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 investing and take actions to safeguard in case of adverse price movement.
We have talked about momentum-based trading strategies in our article about Algorithmic Trading Strategy & Paradigms.
Sentiment Based Trading Strategies:
A Sentiment trading strategy involves taking up positions in the market driven by bulls or bears. The sentiment trading strategy can be momentum-based i.e. going with the consensus opinion or market sentiment and if it’s a bull we invest high and sell higher or vice versa.
The sentiment trading strategy can even be contrarian or mean-reverting i.e. opposite to the market sentiment. A contrarian profits from the theory that when there is certain crowd behaviour regarding security, it gives rise to certain exploitable mispricing (overpricing an already prevailing rise in security) and that a great bull is followed by fall in the prices of the security due to corrections or vice versa.
Machine Learning Trading Strategies
Machine Learning implies studying algorithms and specific set of patterns that computer systems follow to make trading decisions based on market data. Originating from the study of “pattern recognition”, this puts emphasis on the fact that computers learn without being programmed specifically. Now it must be made clear that humans develop/initialize the software and then, it is upon AI (Artificial Intelligence) to improvise upon itself over a period of time. So it means that human intervention is always required. The benefit here is that Machine Learning based models analyze 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.
Here I have mentioned Bayesian Networks briefly since it is a type of Machine learning. This type can be utilized for predicting market trends. Explaining it further, it takes an event into consideration and on the basis of it, predicts/analyses the probabilities or possible causes of the event. Further, this helps to understand and learn about the possible causes of the particular event and hence, these causes can help predict market trends as mentioned earlier.
This was all about different strategies on the basis of which algorithms can be built and trade be carried. And if you are looking to make a career in the field of algorithmic trading, ahead you can take a look at the average salary each country offers a Quant.
Algorithmic trading has become a success lately, and if you are looking forward to starting your career as an Algo Trader, one of the primary questions in your mind must be “Salaries”.
Below, I have mentioned a list of Average Quant salaries which is specific to each country:
Average Base Salary
Source: Indeed, LinkedIn, Payscale, and Glassdoor
This way, you must have got a rough idea about the average Quant salary in each country. To find more on this topic, you may refer to the blog How Much Salary Does a Quant Earn?
Okay Great! Salary is fine, now how about seeing what lies for a Quant when it comes to legal concerns.
Securities and Exchange Board of India (SEBI), the regulatory body, has brought out some regulations and compliances for ensuring transparency and security in trades. Forwards Market Commission (FMC), which was the commodities market regulator, merged with SEBI in December 2015. It has listed down some extremely important compliance requirements for algorithmic trading in Indian markets.
You can read them below:
Audit Requirements - All HFT firms need to get through a half-yearly audit and auditing can only be done by Exchange empanelled system auditors (CISA certified) listed on the exchange’s website. For the audit requirement, you need to maintain logs for order, trade, control parameters, etc. of the past few years. Now you must know that the control parameters are specifically needed by Indian exchanges to understand if the strategy of the order placed is verified or not.
Execution Related - Here are certain compliances with regard to the execution of the orders. Firstly, it maintains that all the orders must be tagged with a unique identifier as specified by the exchange. Secondly, new orders can only be executed after accounting for the previous unexecuted orders. Any modifications in the algorithms are to be approved by the exchange and the system should have enough checks to terminate the execution in case of a loop or a runaway.
Commodity Markets specific - There are certain risk control measures like Daily Price Range, Maximum order size, Position limit, etc. which should be adhered to. Additionally, Market Orders and IOC (Immediate or Cancel) orders will not be placed, only Limit orders can be placed. Mini and micro contracts are not entertained by Algorithmic trading. Also, all orders should be routed through member servers located in India and from approved IDs. These systems cannot have any links with any system or ID located/linked outside India. Members must ensure that their strategy induces liquidity into the market and should submit a document explaining the same. Members shall also maintain all logs as specified above and ensure regular audits and get approvals for any changes to existing strategies.
Alright! Now as you are clear about many important tangents of Algorithmic Trading, let us move ahead and explore a few more!
Going by the number of courses available online on Algorithmic trading, there are several on display, but to find the apt one for your individual requirement is most important. Now, it is obviously in your best interest to learn from a group of market experts. To make this happen, you need to make sure that your goal is set and you look into the knowledge on the basis of the same. In short, your goal and course offered should be in complete synchronization so as to not waste even an iota of time on unnecessary information.
Furthermore, there is a well-designed platform for exercising your knowledge, so as to use the same appropriately in the live market.
Learn Algorithmic Trading: A Step By Step Guide
Coming to the list of Algo Trading courses, Quantinsti provides you with a range of courses starting from EPAT (Executive Programme in Algorithmic Trading) to a series of short (yet highly informative) Algorithmic trading courses on Algorithmic Learning Track.
Algorithmic Learning Track provides you with a list of goals to choose from. Each goal presents you with an organized set of such informative courses that should serve your purpose. Quantinsti’s learning track on the web page offers you with the courses in descending order starting from basic and ending with advanced knowledge for each goal.
Here, you can see a list of courses available on the Quantra web page for Algorithmic Trading.
EPAT gives you a more elaborative insight on Algorithmic Trading in case you are a beginner and wish to delve deeper into the understanding of each terminology.
There are several books on Algorithmic trading, which are important for understanding the details such as,” how trades/exchanges occur in markets” and delve further on market participants, trading methods, liquidity, price discovery, transaction costs, etc.
Read the blog, Essential Books on Algorithmic Trading, for a detailed synopsis of each of the relevant reads mentioned below:
- Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris
- Market Microstructure Theory by Maureen O’Hara
- Algorithmic Trading: Winning Strategies and Their Rationale by Dr. Ernest Chan
- Algorithmic Trading and DMA: An introduction to direct access trading strategies by Barry Johnson
- Schaum's Outline of Statistics and Econometrics by Dominick Salvatore, Derrick Reagle
- Analysis of Financial Time Series by Ruey Tsay
- Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications by John J. Murphy
- Options, Futures, and Other Derivatives by John C. Hull
- Dynamic Hedging: Managing Vanilla and Exotic Options by Nassim Nicholas Taleb
The courses and books mentioned above are sure to enhance your knowledge and expertise in different spheres of algorithmic trading field.
Coming to the “Understanding of the Workflow”, it is a concept that explains how each trade gets placed using algorithms behind the scenes. Simply speaking, the algorithmic system works by receiving the data from the exchange on the basis of which the trade is then placed.
Historically, manual trading used to be prevalent, in which, the trader was required to gather the data manually and place the order telephonically for the execution of the trade. That would involve a lot of time and efforts and hence, not make much of profits since not much of trading could take place
Now with Algorithmic trading coming into existence, the entire process of gathering market data till placement of the order for execution of trade has become automated.
Coming to how a quantitative analyst goes about while implementing algorithmic trade, here is a simplified diagram:
So, the image above shows how a quant implements algorithmic trade.
In the first step, you will be needing to do research or get some experience leading to a hypothesis. That is how your strategy formulation will be based on the hypothesis you set.
Then in the second step, with the help of preliminary analysis and usage of statistical tools, the rules are designed for trading.
In the third step, the strategy is formalized in coded language using one of the languages namely, Python/R/C++. This is done for the system/computerized trading platform to understand the strategy in a language that is understandable to it.
Now, in the fourth step, Testing phase 1 is done through Backtesting, in which historical price information is taken into consideration. In this, the strategy is tested using historical data to understand how well the logic would have worked if you used this in the past. This way, the performance of the strategy is tested. Also, depending on the results you get the opportunity to optimise the strategy and its parameters.
Then, the fifth step is Testing phase 2 in which the testing of strategy happens in the real environment. In this, you do not need to invest actual money but it still provides you with a very accurate and precise result. Hence, with this, one can expect to get the results which may also come about in the actual environment. The only drawback is that it is a time-consuming activity but you can do this by using the feature provided by his/her broker. Alternatively, you can also develop your framework to test the game.
The sixth step involves Deployment in the real environment, which requires multiple facets to be managed, which are generally not being considered in backtesting.
Functionally, the following aspects are required to be managed:
- Order management
- Risk Management
- Money/Fund Management
- Diversification of assets
- Portfolio management
- User Management
Technically, the following aspects are required to be managed:
- Establish Connection with the broker API.
- Passing the buy/sell orders using the broker connection
- Establish Connection with the data API (if data vendor is different from the broker)
- Accessing the real-time and historical data using a data API connection
In order to build your own algorithmic trading business, you would be needing several arrangements to make your business a success. Although seeking the knowledge to build a successful one is the first step towards your aim, there is a list of other prerequisites.
To name a few, a successful algorithmic trading business requires domain knowledge, skilled resources, technology & infrastructure in the form of hardware and software.
Besides, you will also need to ensure that compliances and regulations are done regularly so as to maintain transparency, security and smooth workflow. The aforementioned are the prerequisites for any kind of business that you may look forward to starting though.
To read further and dig deeper into the topic, please refer to the blog Setting-Up An Algo Trading Desk. This blog offers an in-depth understanding of “How to go about setting up your own successful algorithmic trading business/desk” for a successful working of the same.
Last but not least, the algorithmic trading business is sure to offer you an advanced system of trading and profit-making and has become quite a popular way of trading. Moreover, with its growing impact on emerging markets, as mentioned earlier, it is estimated by Coherent Market Insights that it will reach a CAGR of 10.1% between 2018 and 2026.
Hence, with the apt knowledge, regular compliances and regulations, an algorithmic trading platform is the fastest, secure and the most profitable.
Disclaimer: All investments and trading in the stock market involve risk. Any decisions to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.