5 Qualities the Best People in the Algorithmic Trading Industry Tend to Have

10 min read

By Chainika Thakar

Algorithmic trading industry is full of determined, motivated and enthusiastic people. Adding to these personality traits, the best people in the algorithmic trading industry have the following 5 qualities:

  1. Good observer and listener
  2. Realist
  3. Optimist but self aware
  4. Courageous and persistent
  5. Lifelong learner

Good observer and listener

In the algorithmic trading industry, the most successful are those who remain alert about even the slightest of the changes in the financial markets. With the help of a focused attitude and observational skills, the traders are able to strengthen their trading strategies. Hence, the traders are better able to maximize their returns. Financial markets are impacted by the minutest of the details taking place in the economy. World politics, economic trends, and even weather hold the power to change the market trends. By learning about the financial markets’ scenario in the past and present, a trader is best prepared to face volatility in the future (because of both planned and unplanned circumstances).


Realist

As an algorithmic trader, you need to be a realist so that you do not make wrong decisions. For instance, over-trading in excitement in case of an uptrend, fear of missing out on making more money, or guilt of losing funds in the past.

Below, you can see an example of overconfidence and overtading:

Source: Yahoo!

In the image above, it is clearly visible that the trader jumped too quickly from one stock to another. Not only is changing the mind regarding trades too risky because of instability but it also leads to a lot of transaction cost and commission expenditure. In algorithmic trading, the emotions of the trader can lead to such risky strategy creation. Hence, it is extremely important to be a realist.


Optimist but self aware

While trading, as much as it is risky to be overconfident, optimism and self-awareness are the real gems. A successful algorithmic trader maintains balanced emotions and remains positive about the next step. While trading, an algorithmic trader takes calculated risks and believes in his/her abilities to create the trading strategies after proper backtesting.

You can see in the image above that a successful algorithmic trader’s step is always a wise one by maintaining a balanced optimistic approach. With a bit of humour, the image shows that the pet of a professional trader knows how to assess risk vs reward and to believe in his decision!

While it is highly important to take calculated risks, the need for identification, evaluation and mitigation of risks usually arises when the market moves in the opposite direction from the expectations. So, it is really important to set your expectations on the basis of a thorough analysis of the market and after anticipating all the risks.

Let us take a look at this table which is an example of how crucial it is to take calculated risks because with every 5% extra loss, your percentage of gain needed to recover the losses increases. This simply implies greater the loss, the harder it is to recover:

Source: Medium

Here, ​portfolio optimisation is an important process that involves analyzing portfolios with different proportions of investments. This optimisation process is done by calculating the risk and the return for each of the portfolios and selecting the mix of investments that achieves the desired returns against risks.

Recommended read:

Portfolio & Risk Management


Courageous and persistent

An algorithmic trader is always open to taking calculated risks and is courageous enough to go ahead with his/her decisions. Persistent attitude is another best friend of an algorithmic trader since it helps the trader to learn in the live markets. While paper trading is much better to begin with, the trader learns the practical approach to trading in the live market.

This also implies that one should avoid making a trading decision when the financial markets are expected to not perform well. An algorithmic trader does not keep any guilt or fear to take a decision and remains persistent.

Source: Quotes on stock market


Lifelong learner

Constantly working on an idea and improving it to ultimately make it work is the key to success in any field. As an algorithmic trader, one must learn as much as is possible with regard to algorithmic trading. There are some free resources available for keeping the learning going on and some essential books on algorithmic trading (free and paid).


While the above mentioned were the 5 qualities of an algorithmic trader, below you can see some of the other key personality traits or the discipline maintained by algorithmic traders:

  • They constantly study financial markets to gain best experience
  • They know at least one programming language
  • They have quantitative skills
  • They acquire data management abilities

They constantly follow financial markets to gain the best experience

The financial markets are various platforms where the trade takes place.

Essential components of financial markets

  • Exchanges - It is the exchange that runs the entire process of buying, selling and trading instruments smoothly
  • Regulatory authority - The regulatory boards or agencies are the authorities that supervise the activities of the exchanges. Examples of regulatory authorities pertaining to securities market include:
  • U.S. Securities and Exchange Commission (SEC) - Regulates U.S stock exchanges/market
  • Securities and Exchange Board of India (SEBI) - Regulates indian stock exchanges/market
  • Analysis- Analysis are of three types and the goal is to find out the correct value of a particular security. These are fundamental, technical and quantitative analysis.

Fundamental analysis includes the factors such as cash flow, profitability, balance sheet size etc.

Technical analysis uses charting software to visualise price patterns, volume and movement.

Quantitative analysis involves the study of financial events through mathematical and statistical modelling. Trading opportunities are identified using statistical techniques.

  • Traded volume - ​​Traded Volume is the total number of shares or contracts traded for a security during a specific period of time. While average volume is the volume traded per unit of time. Higher the volume of a stock higher will be its liquidity in the market.
  • OHLC - It is an abbreviation to Open, High, Low, and Close (OHLC). It refers to the dataset of stock prices where there are four prices for each data point. For example, having a daily price dataset of a particular stock from 2019 to 2020, each data point (each day) would have four prices referring to Open price, High price, Low price and Close price.
  • Trend - Trend is the direction in which the market moves based on the prices of the security. If the prices move in the upward direction, it is known as the upward trend and on the contrary, if the prices move in the downward direction, it is the downward trend.
  • Order - An order is an instruction by an investor to a broker or a brokerage firm or directly at the trading venue to buy or sell securities like stocks, bonds or derivatives. Orders can be placed over the phone or online manually/through the algorithms.
  • Spread - In finance, the spread can be defined as the difference between any two prices. It is also defined as the difference between the current bid and current ask prices for a given security (bid-ask spread).
  • Liquidity - Liquidity refers to the ability and ease with which assets can be converted into cash without affecting the current asset price in the market to a great extent. Market liquidity refers to the extent to which a market allows assets such as stocks, bonds, or derivative products, to be bought and sold without paying a huge bid-ask spread. Cash is the most liquid asset as compared to other assets.

How should you follow the stock market?

In order to follow the stock markets’ performance, the best websites are Google and Economic Times Market. You can also learn how to follow the stock market with help of Economic Times Market.


They know at least one programming language well

While there are a lot of programming languages, the best people in algorithmic trading are familiar with at least one of them. It is an incorrect belief that programming can be skipped if you are aiming to grab the role of a risk manager or an investment/asset manager in the algorithmic trading domain. A programming language is extremely important in the algorithmic trading sector since you require programming skills from the validation of strategy hypothesis to backtesting and execution of the strategy.

Popular programming languages

There are a lot of computer programming languages that you can learn. Yet there are some of the popular ones which are Python, C, C++, Java and HTML. Learning any one of the popular programming languages helps throughout the algorithmic trading journey.

Ways to learn programming languages

The best way to learn a programming language is via courses available online. With the availability of various courses online, you can find the one which best suits your level of knowledge.

Recommended courses:

Python is known to be a preferred language for developing trading strategies by programmers/developers since it provides benefits such as:

  • Python has certain APIs and libraries for machine learning as well as data science that make the analysis smoother as compared to other languages.
  • It helps the trader with quick and easy coding for importing data and for data visualization in the form of graphs.
  • Most quant traders prefer Python as it helps them build their own data connectors, execution mechanisms, backtesting, risk and order management, walk forward analysis and optimization testing modules.
  • First updates to Python trading libraries are a regular occurrence in the developer community.

They have quantitative skills

What are quantitative skills?

Quantitative skills consist of the knowledge of statistics, mathematical models and statistical research methods. Also, your quantitative skills make you efficient in finding suitable models to define randomness, to calculate asset price movements and to examine the statistical properties of market data, etc.

How to acquire these imperative quantitative skills?

To acquire quantitative skills, there are some short online courses that are self-paced and interactive. Moreover, you can select the specific aspects which you want to learn offered by some well-known platforms.

In the algorithmic trading industry, you require quantitative skills for all the job roles. The job roles in algorithmic trading include quantitative analyst, quantitative developer, risk analyst etc. By possessing quantitative skills you can excel in each of the job roles in the algorithmic trading domain. Some of the courses offered by Quantra are:

Trading with Machine Learning: Regression

This course is perfect to create your first trading strategy using machine learning algorithms. Learn in a step-by-step fashion: acquire data, pre-process it, train and test the machine learning regression model, and predict the stock prices. Hands-on coding assistance is provided.

Trading with Machine Learning: Classification and SVM (Support Vector Machine)

Learn to use SVM on financial markets data and create your own prediction algorithm. The course covers classification algorithms, performance measures in machine learning, hyper-parameters tuning and building of supervised classifiers.

Neural Networks in Trading

This course is highly recommended for programmers and quants to implement neural networks and deep learning in financial markets. Offered by Dr. Ernest Chan, learn to use advanced techniques such as LSTM, RNN in live trading.

Decision Trees in Trading

With this course, you will learn to predict markets and find trading opportunities using AI techniques. Also, you will be able to train the algorithm to go through hundreds of technical indicators to decide which indicator performs best in predicting the correct market trend. Further, optimize these AI models and learn how to use them in live trading.

Executive Programmes

With the executive programmes, you get to learn the practical aspects of the industry from various professionals, industry stalwarts etc.

One such programme is the Executive Programme in Algorithmic Trading (EPAT) which is a comprehensive 6 months’ virtual classroom programme  covering essential modules of Algorithmic Trading, such as:

  • Market microstructure
  • Financial instruments
  • Statistics
  • Data analysis
  • Portfolio management
  • Basics of coding in Python/Matlab/Excel
  • Use of machine learning
  • Trading, tech, infra and operations
  • Live trading strategy building

The course inspires traditional traders towards a successful algorithmic trading career, by focusing on derivatives, quantitative trading, electronic market-making or trading related technology and risk management.


They acquire data management abilities

What is data management?

Data management is usually done to clean the raw data which is extracted directly from the source and can consist of several issues such as duplicates, non-stationarity etc. Clean and usable data means the data which is ready for being used for various purposes in trading such as backtesting, analysis and forecasting the trades etc. Data management does not end here, it is needed to make sure that there are no further errors in data, it is easily accessible and reliable.

How is data management done?

Data management is usually done by data engineers in the algorithmic trading industry. There are three types of data engineers:

  • Generalist - They do the entire work of creating the data pipeline such as retrieving the data from the sources to processing it and doing the final analysis. This procedure takes up the entire skillset of a data scientist and is required by small companies or teams which do not have much of the staff for specialization.
  • Pipeline-centric - They are required in the mid-sized companies which have complex data needs and need the data team to conduct a lot of work that requires the background of distributed systems and computer science.
  • Database-centric - They are usually found in large companies with their data distributed across the databases. There are various data analysts in such companies and the data engineers are required to pull the information from the main application of the database into the analytics database.

Conclusion

Although the best people in the algorithmic trading industry keep making themselves better with advanced knowledge and expertise, these qualities will contribute in developing you in your career.. This will contribute to your efforts in gaining the best job roles and positions in the algorithmic trading industry.

Hope you liked the article and it served its purpose well! We’d like to know your thoughts on the same, do share your comments below.

You too can start your quest to upgrade your knowledge of Algorithmic Trading with the Executive Programme in Algorithmic Trading (EPAT) - a comprehensive course covering topics ranging from Statistics & Econometrics to Financial Computing & Technology including Machine Learning and more. Enroll now!


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