If you are a discretionary trader, you might have asked these questions before
- What is the future of algorithmic trading?
- Should I switch from discretionary trading to algorithmic trading?
- Is algorithmic trading taking over the financial markets?
- Who will win this war between man and machine?
In order to answer these questions, we first need to know what makes these practices stand apart from each other. This blog discusses algorithmic trading and discretionary trading, which skills you need to acquire for each and the difference between algorithmic trading and discretionary trading.
This blog covers the following:
- What is algorithmic trading?
- What is discretionary trading?
- Skills required for algorithmic trading
- Skills required for discretionary trading
- How to acquire the skills for algorithmic trading and discretionary trading?
- Difference between algorithmic trading and discretionary trading
- What is better: algorithmic trading or discretionary trading?
What is algorithmic trading?
Algorithmic trading, or simply algo trading, is the process of placing orders in the market based on a certain trading logic via online trading terminals. Algo trading is also known as black-box trading in some cases. The algo trading process includes executing the instructions generated by various trading algorithms.
In simple words, algorithmic trading is a process of converting a trading strategy into computer code which buys and sells (places 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 rapidly increasing its reach in emerging markets.
Technically, several mathematical algorithms are at play for making trading decisions based on current market data, which then send and execute the order(s) in the financial markets. This method makes trading free of all human emotional impacts (like fear, greed, etc.) since decisions to carry out each trade are systematically made by computers.
For instance, the algorithm buys shares of Apple (AAPL) if the share's current market price is less than the 200 days average price. Conversely, it would sell Apple (AAPL) shares if the current market price exceeds the 200-day’s average price.
Learn algorithmic trading basics and gain a solid foundation in this exciting field. Here is part 2 of the video series, "Algo Trading Course", which introduces you to algo trading, the industry landscape, pros and cons, building an algorithmic trading python strategy, the benefits of a quant approach, different types of data, and more
What is discretionary trading?
Discretionary trading existed before algorithmic trading came into being. In the bygone era, people used to carry out trading manually. Manual or discretionary trading required the traders to place the trade over the phone or electronically via computers.
Back in time, when the concept of automated trading was not introduced, traders would manually gather data from the market, analyse 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 more accurate method led to the invention of algorithmic trading.
Let us move to the skills needed for both algorithmic and discretionary trading.
Skills required for algorithmic trading
The following skills are needed for algorithmic trading:
- Mathematical and analytical skills
- Programming skills
- Strategy development process
- Understanding the nitty gritty of algo trading (use of large datasets, AI etc.)
Getting Started with Algorithmic Trading!
This course builds a foundation in Algorithmic Trading
Mathematical and analytical skills
The basic mathematical and analytical skills are a must for an algorithmic trader to create trading strategies. On the basis of maths and analysis, the trader creates a logical strategy.
The knowledge of a programming language such as Python Programing is an advantage in algorithmic trading. This helps you to backtest the strategy with all the available libraries. Also, you can visualise the data for better analysis and decision-making with regard to the trading strategy.
Strategy development process
The strategy development process is one of the most integral parts of algorithmic trading.
The risks and rewards of a strategy are necessary to be understood to determine whether it will help with expected returns or not.
Understanding the nitty gritty of algo trading (use of large datasets, AI etc.)
This is also an essential skill for algorithmic traders. Until and unless you know the nitty-gritty of algorithmic trading, such as dealing with large datasets, AI or Machine Learning etc., the algo trading process will remain incomplete.
Skills required for discretionary trading
The following skills are needed for discretionary trading:
- Experience in the stock market
- Analytical skill
- Attention span to monitor the market
- Emotional intelligence
Experience in the stock market
To become a discretionary trader, you must have experience in the stock market in order to understand how the market works and when to make trade-related decisions.
The skill to do analysis is a must for a successful trading journey. The analytical skills help you devise trading strategies based on complete research and understanding of the performance of the market and the financial instruments you invest in.
Attention span to monitor the market
Since discretionary trading requires regular monitoring of the market, the attention span of the trader needs to be really good. Or else the profitable opportunities can be missed. The trader must be aware of any new information relating to the market or the stocks in the portfolio.
This is important because any recent change can lead to investors buying or selling the stocks.
For example, an announcement regarding the merger of one company with another can lead to a change in investor sentiment. This can either cause the selling or buying of the particular stocks based on the reputation and trust in the company it is being merged into.
A high emotional intelligence is a must for a trader because it helps to avoid sudden decisions influenced by emotions such as fear, greed, doubt etc. Hence, any trader needs to be emotionally intelligent to think and act logically.
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How to acquire the skills for algorithmic trading and discretionary trading?
The skills required for discretionary trading depend on your personal ability to be able to keep yourself disciplined with regard to emotions, market monitoring, etc. in order to trade successfully.
When it comes to algorithmic trading, you need to be disciplined with regard to learning how the algorithms can be made to work for you to execute successful trade orders. Also, you need to learn programming to be able to program the trading strategy in a coding language such as Python, C, C++ etc.
To acquire the skills to begin your career in algorithmic trading, one must look for a course or, let us say, a programme with well-defined learning outcomes.
Let us see the skills needed in brief
- Analytical thinking - This is needed to do a proper analysis before taking decisions about entering and exiting the market.
- Mathematical knowledge - It is always better to use mathematical knowledge while analysing the market or stocks for the most accurate predictions.
- Programming - It is a must for coding your trading strategies and for executions of the same.
- Experience and knowledge of financial markets - With experience in the financial markets and knowledge of the same, you will be able to assess and predict situations with much more efficiency as compared to one with limited knowledge.
- Thorough knowledge of the strategy creation process - The strategy creation process is one of the most important parts of any trading activity. The strategy is executed to reap expected returns.
Last, but not least, you must find a course that lets you acquire skills that you do not possess but are integral to becoming a trader (be it algorithmic or discretionary trader).
You can check the skill sets mentioned in the section above and find out the ones that need to be worked on and the ones you already possess.
Difference between algorithmic trading and discretionary trading
Let us now find out the key difference between algorithmic trading and discretionary trading.
The trading strategy of discretionary traders is derived from the information gathered by learning charts, market conditions, understanding indicative signals and other related factors, which help them to draft a certain set of rules to follow before placing an order or deciding when to exit.
An algorithmic trader, on the other hand, finds it risky to depend merely on the findings gathered by examining charts. The decision to place an order or make an exit depends on the algorithm(s). The algorithms are designed based on:
- Programming Skills
- Statistics & Probability
- Risk Management Skills
- Study of Historical Data
This is done by algo professionals with the required skill set. The system studies the market and makes decisions based on the logic set for the algorithms.
Influence of human emotions
Discretionary traders are prone to be influenced by emotional factors at the time of decision-making. Traders often tend to defend their emotional bias at the time of projecting the outcome, which may lead to significant losses.
The risk of getting influenced by factors related to emotions is almost nil in algo trading. The mathematical models are purely based on the set of instructions and eliminate the intervention of any kind of emotions, be it greed, fear, false intuitions etc.
The practice of discretionary trading restricts the use of automated systems that call the shots for you. It is managed manually by the trader, and the system has little or no say in what you want to do next.
There is no need for an algorithmic trader to monitor markets and read charts, as trades are done automatically. The information fed into the system is processed by the black box, and suggestions are made for the best possible outcome. Once the trader is convinced of the outcome they can switch the algos on and just screen the progress and make changes accordingly.
There are no predefined rules for a discretionary trader. The purchase or exit is made based on the experience and the study done by the trader, which may result in multiple trading rules for each execution.
The rules in algorithmic trading are pre-defined and backtested. The backtesting of historical data increases the probability of a successful outcome. The trades are placed at predefined levels, which are governed by algorithms.
Backtesting Trading Strategies
Analysing current market conditions
An impulsive behaviour of a discretionary trader due to a sudden change in market conditions may result in a loss. This may be due to the lack of understanding or failure to read the volatility of the market.
Techniques like sentiment analysis help algos perform better in such scenarios and are able to read the fluctuations in markets based on external factors.
Indicators Observed by a Discretionary Trader
A typical set of observations made by a discretionary trader on the price chart mentioned above can be listed as follows:
- The overall trend is up
- Where should I put my stop and limit?
- Current news that can affect the upward moving trend
- The moving average is going up as well
- The current indicator signals a reversal
Indicators observed by an Algorithmic trader
Algorithmic trader indicator
The observations and conclusions made by an algorithmic trader can be listed as:
- What is the success of the algorithm?
- What does the historical data indicate?
- The future estimates of the stock based on current and historical trends
- What does the time series of a stock indicate
- What is the margin of error in the strategy that I have designed?
What is better: algorithmic trading or discretionary trading?
First of all, let us see the difference between algorithmic trading and discretionary trading with this comparative table below.
Pre-programmed rules are given to the system with the help of a computer language such as Python.
Manual trading is done on the basis of experience and judgement.
The trade orders are executed via algorithms.
Orders are placed manually to the exchange.
Speed of order execution
Orders are placed extremely fast.
The order execution is based on the manual speed which is slower than algo trading.
Order execution in different markets or of varied instruments simultaneously
Possible as trade orders are executed via computer algorithms that can work at a high speed with efficiency and accuracy.
Not possible because the human mind needs time to process information and to make a decision.
An algorithmic trading system is fully automated.
Discretionary trading can be fully or partially automated depending on the trader’s preference.
Scalability and adaptability
Highly scalable and adaptable
Less scalable and adaptable in changing market situations.
Algorithmic trading system can work all the time without any break.
A human needs breaks and hence,cannot trade all the time.
Maintenence and setup cost is high.
Maintenence and setup cost is low.
Properly backtested to increase efficiency and accuracy.
Backtest may not be as accurate as in algorithmic trading.
Although we can not say that a single perspective is enough to decide which one is better, it is obvious that technology has taken over the manual world for the betterment of tasks in all aspects.
Each of the trading types has its own advantages and disadvantages.
With the advent of the automated world, there is more logical and rational decision-making and less of an emotional impact on the decisions.
Technology is a part of evolution, and we humans have generated technologies that will define this century. Adapting to new and better means of trading is akin to moving to better results, and one cannot run away from it. Algorithms reduce the margin of error and remove the ‘human factors’ like emotions, manual trading-based errors, stale trading strategies etc.
The algorithms follow logic while making the decisions.
The benefits that algorithmic trading brings are not possible with manual or discretionary trading.
To quote Albert Hibbs: “Even though I didn't make it to the moon, my machines did”
So let your machines make money for you.
Having mentioned the difference between the two types of trading above, namely algorithmic trading and discretionary trading, you as a trader need to decide which one suits you the best. This decision depends on a trader’s circumstances, situations etc.
Hence, you can decide and adopt one after weighing the pros and cons of each.
In this 3rd and final part of video series, "Algo Trading Course" explore how Python trading bots can be used to backtest a trading strategy on the research platform such as Blueshift.
The world has transitioned from manual decision-making and task processes to automated ones. This transformation has happened to make complicated tasks involving the mathematical analysis and large data easier and faster.
With algorithmic trading, a trader can make his/her trading strategies reap better returns as compared to discretionary trading since there is logic, maths and faster execution.
If you wish to explore more and learn algorithmic trading, do check out our algo trading course.
This quantitative course is a comprehensive programme to help you in your algorithmic trading journey. After learning from the EPAT programme, you can start your own trading desk, get a new job in a financial firm or even get better opportunities in your current firm. This programme is designed to cater to the needs of professionals looking to grow in their field of algorithmic and quantitative trading. Enrol now!
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