The trading domain is as dynamic as the rest of the world. It is swiftly adapting to technological advancements and moving to the algorithmic trading domain. Considering the rapid change, it is imperative for the trader of today to upskill themselves. Estimates indicate that between 2021-2025, the algorithmic trading market is set to grow by $3.7 billion.
So, to be future-ready, let us take a look at the list of the top five skills which are essential to a trader in 2021 and beyond.
You can see the table below for a quick glance at the 5 trading skills which are covered in this blog:
Levels |
Quantitative Analysis |
Python |
Backtesting |
Machine Learning |
Risk Management |
Level 1: Learning requirements |
Quant analyst requires to learn subjects such as finance, statistics, mathematics, computer science, data science, artificial intelligence, etc. |
In order to learn Python, you need not be from a coding background. You only need to be dedicated and find a good platform for learning basics first |
For backtesting the trading strategies, you need to learn a programming language |
Machine learning requires learning programming language, data science, statistics |
Risk management requires knowledge in finance, market factors such as trends, drawdowns etc. |
Level 2: Concepts |
Implementation of concepts such as machine learning, neural networks, probability, etc.is done in trading |
Python implementation includes concepts such as functions, dataframes, loops etc. |
Implementation of performance metrics such as sharpe ratio, sortino ratio etc. to analyze the performance of the strategy |
Machine learning concepts such as regression, decision trees etc. |
Risk management includes identification, evaluation and mitigation of risks |
Level 3: Application in trading |
Quants apply their knowledge and skills to do the analysis of the market data and then create strategies which can be quantified |
Python programming helps to create trading strategies by using platforms like machine learning |
Backtesting tests the trading strategy on historical data which helps to find the effectiveness of the trading strategy |
Machine learning algorithms developed with the help of programming language are applied for algorithmic trading practices |
By studying the market factors such as trends, sharpe ratio, sortino, drawdowns etc.you can take your trading decision |
Level 4: What all can you become in the trading domain with this skill? |
As a quantitative analyst, you can apply your knowledge to become a research analyst, senior quant etc. |
Python programming helps you become a pro in various professions such as analyst, algorithmic trader, developer etc. |
Backtesting helps you to test the trading strategies both for your own trading activities as well as an analyst for some company |
With knowledge in machine learning concepts, you can become a developer, data scientist etc. |
You can engage in portfolio optimisation, position sizing, hedging etc. to help mitigate the trading risks |
Going ahead in this article, let us explore each of these key areas of expertise, understand what they mean and identify areas of improvements for ourselves.
Quantitative analysis
Quantitative analysis is a statistical/scientific study of the market based on technical indicators such as moving averages, oscillators etc. In simple words, quantitative analysts do statistical backtesting of the strategy while using the technical indicators very much like technical analysis.
Technical analysis is a study of forecasting the price of financial securities by analyzing the historical market data. Technical analysis employs models and trading rules based on price and volume. For example, technical analysis uses relative strength index, moving averages, oscillators, or chart patterns & waves.
In case of quantitative analysis, the performance of the market is found out by making use of neural networks, machine learning, and other statistical tools for developing a trading strategy which can be quantified. For quantifying the analysis, an expertise in mathematical and statistical research tools and methods is required. Usually, quantitative analysis helps the traders more than technical analysis since it provides a scientific outcome to help develop an algorithm with an entry, an exit and position sizing.
For instance, quantitative analysis uses the candlestick pattern to run a statistical test and then finds out if the strategy can be run on the basis of analysis which can be quantified.
Here is a video to help you learn about both quantitative analysis and technical analysis in detail:
The skills covered in quantitative analysis are:
- Python
- Machine learning
- Mathematical skills
- Financial skills
- Object oriented programming
- Portfolio theory
- Calculus
- Linear algebra and differential equations
- Database management
- Backtesting
Suggested read:
How to become a quantitative analyst?
Next in line is Python language. Let us find out how Python supports trading.
Python
A programming language is a prerequisite for any new aged trader who wants to make use of technological and computational advancements such as machine learning or backtesting on historical data.
Before choosing a programming language, there are several important concepts taken into consideration, such as cost, performance, resilience, modularity and various other trading strategy parameters.
On the basis of the requirements of the trading system, the choice of programming language is decided. In December 2020, Python became the second most popular language.
The image below shows the ratings received by Python are much more than the rest of the computer languages except for the language “C” which is at the top.
Source: ZDNet
Python language helps the trader with quick and easy coding for importing data and for data visualization in the form of graphs. The graphical representation makes it much easier to interpret the data for analysis. Moreover, Python has such APIs and libraries for machine learning as well as data science that make the analysis smoother as compared to other languages.
Here is a video in which the experts from the algorithmic trading domain share the reason Python is preferred in trading:
The skills covered in python are:
- Data structures
- Data types and variables
- Object Oriented Programming
- Machine learning
- Neural networks
- Natural language processing
- Analytical skills in data science
Suggested read:
Python for trading: An introduction
Another trading skill is backtesting which helps the trader to analyse the trading strategy on the historical data. Let's find out more about backtesting next.
Backtesting
Backtesting is the process of testing a trading strategy using historical data to determine the effectiveness of that strategy. Backtest results usually show the strategy’s performance in terms of popular performance metrics like sharpe ratio, sortino ratio. The performance metrics usually help to quantify the return over risk. If the results meet the necessary criteria, the strategy can be implemented with a reasonable degree of confidence. If the results are less favorable, the parameters for the strategy can be modified, adjusted and optimized to achieve a desirable result.
There are mainly two forms of backtesting systems: Vectorized backtesting and Event-Driven backtesting.
Vectorized backtesting is the initial phase of backtesting a strategy. In vectorized backtesting, all aspects of market interaction are not fully simulated. But, approximations are made to determine the potential strategy’s performance.
You can utilise vectorized backtesting to test the core strategy ideas before going in for rigorous backtesting in a more realistic environment. For instance, you can run your strategy on the backtesting platform, generate trading signals and calculate strategy returns just to find out how efficient the strategy is. For learning the vectorised backtesting in detail, you can go to this course on Python in trading.
These backtesting systems are often written in Python, R or MatLab. The speed of development is more important than the speed of execution during the initial phase of strategy creation while you are testing out crude but multiple strategy ideas.
In event-driven backtesting, the automated trading strategy is connected to a real-time market feed. The trade executions are completed on the paper/simulator, but not on the real exchange. The strategy receives the market feed and then analyses this data to trigger an event which, in turn, generates a trading signal. These systems generally run in a continuous loop to receive events and handle them appropriately.
The main advantage is that the event-driven backtesting can have sub-components such as historic data handler and broker simulator, allowing the backtesting to be performed in a manner very similar to the live execution.
With a platform like Blueshift, it is easy to backtest your trading ideas or strategies with Python language and perform algorithmic trading. Because of the simplicity of python language, backtesting is easier with Blueshift.
When you have trading ideas but no platform to backtest them, there is no assurance that the trading strategies built from these ideas will provide a beneficial outcome. The beneficial outcome, here, implies less or no losses and more gains from trading strategy.
Also, in this demo video below, you will learn about developing and backtesting your trading strategy by the experts:
Having to backtest requires following skills:
- Data analysis
- Know how of performance metrics such as drawdown, sharpe ratio
- Programming skill
Suggested reads:
- How to backtest a trading strategy
- Forex trading strategies and backtesting techniques
- Pair Trading Strategy And Backtesting Using Quantstrat [EPAT PROJECT]
Next, let us find out about machine learning.
Machine learning
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 which is based on algorithms to detect patterns in data and adjust the program actions accordingly. Basically, it is a subfield of artificial intelligence.
Machine learning algorithm uses the “learning” model. A learning model allows the algorithm to learn the input and output combination and then make its own decision with the new data.
In trading, the linear regression model, which is a machine learning model, helps to predict the price of financial securities. Also, a machine learning algorithm uses minimum human effort or intervention for getting any complex task done. Machine learning algorithms can be developed using Python which is already a key skill. Also, Python is preferred over other languages owing to its advantages which we discussed above.
You can find out about using machine learning for trading in the video here:
Further, to find out about live trading using machine learning, here is another video to give you a thorough learning into it:
Skills for machine learning include:
- Neural networks
- Natural language processing
- Applied mathematics
- Machine learning algorithms such as Random forests, logistic regression, linear regression etc.
- Programming
Suggested reads:
- Machine learning basics
- Trading using machine learning in Python
- Machine learning classification strategy in Python
- Top 10 machine learning algorithms for beginners
- Machine learning for stock price prediction using regression
Now, we will go further and find out about risk management as an important skill in trading.
Risk management
Risk management in trading is essential to control the risk of bearing the losses arising from stock market trade. Risk management involves the identification, evaluation and mitigation of risks. The risk 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 post anticipation of all the risks.
In risk management, trends are the most important factor. A trend implies the general direction or momentum of the market, asset price or other such measures.
The most popular risk management strategies and elements to make your trades successful while evading risks are as follows:
- Portfolio optimization - Portfolio optimization is the process of constructing portfolios to maximize expected return while minimizing the risk. It involves analyzing portfolios with different proportions of investments by calculating the risk and the return for each of the portfolios.
- Hedging - Hedging is an investment strategy designed to offset a potential loss. For hedging, financial instruments like insurance, future contracts, swaps, options etc. can be used to hedge.
- 1% rule and 2% investing rule - 1% and 2% rule in trading imply the maximum amount of the risk which is feasible on per trade should be either 1% or 2%. This helps you to avoid the excessive loss that may happen otherwise.
- Monitoring the trade while utilising advanced technology - Trades should be monitored using the technology such as algorithmic trading and backtesting.
- Avoiding unclear trade setups - If you are using the moving indicators like EMA, MA, etc. and one of them shows a clear trade setup but does not agree with the trade setups of other indicators, it creates confusion. In such a scenario, it is best to wait for the right trade and not make any decisions when you are not sure about it.
- Stop loss - Stop loss is a buy or sell order which gets triggered when the stock price reaches a specified price known as the stop price. This helps the trader avoid continuous monitoring of the market.
Here is a video tutorial on risk management for traders which is aimed to help with managing the portfolios:
Skills needed for risk management are:
- Experience in trading
- Programming skills
- Portfolio management
- Market monitoring
Suggested reads:
Introduction to risk management in trading
Conclusion
Since the world is moving rapidly towards advancements and improvements, we have provided an overview of the five contemporary trading skills for the traders of today. With the trading skills and their timely implementation, one can get the maximum benefits. Traders with different kinds of trading practices such as intraday trading, trend trading etc. can benefit from these trading skills.
All data and information provided in this article are for informational purposes only. QuantInsti® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.