What to Consider When Choosing a Trading Algorithm
Traders and investors are always looking for the best and most reliable ways to make gains in the stock market. One of the most popular ways of trading used by traders and investors is algorithmic trading.
Are you wondering what to consider when choosing a trading algorithm? Have you started to invest but aren't sure which direction to go? Trading algorithms can help give you the edge you need but you don't want to jump into a bad one.
Read on as we outline the steps that quantitative traders and traders who use algorithms follow in order to create their algorithms.
Are you wondering what to consider when choosing a trading algorithm? Have you started to invest but aren't sure which direction to go? Trading algorithms can help give you the edge you need but you don't want to jump into a bad one.
Read on as we outline the steps that quantitative traders and traders who use algorithms follow in order to create their algorithms.
What Is A Trading Algorithm?
Let's start with the definition of an algorithm.
An algorithm is a process or set of instructions followed in mathematical calculations. In most practical applications, computers use algorithms to carry out problem-solving techniques.
Programmers and engineers use algorithms for image recognition, product recommendation, and content curation. Algorithms are the backbone of search engines like Google, Bing, and DuckDuckGo.
In the financial context, trading algorithms are computer programs written by quantitative traders. Trading algorithms execute trades in tenths of a second. These programs are often based on complex mathematical models.
An algorithm is a process or set of instructions followed in mathematical calculations. In most practical applications, computers use algorithms to carry out problem-solving techniques.
Programmers and engineers use algorithms for image recognition, product recommendation, and content curation. Algorithms are the backbone of search engines like Google, Bing, and DuckDuckGo.
In the financial context, trading algorithms are computer programs written by quantitative traders. Trading algorithms execute trades in tenths of a second. These programs are often based on complex mathematical models.
Who Uses Trading Algorithms?
Algorithmic trading is very popular with trading firms because of its high utility. Companies like Credit Suisse, Hudson River Trading, and Citadel Securities use automated trading as a main part of their investment strategies.
These companies hire quantitative traders to develop algorithms and implement them as part of their trading strategy. Quantitative traders have strong mathematical and programming skills. They must also have comprehensive understanding of the financial markets.
High Frequency Trading
Large firms use an automated trading strategy called High Frequency Trading (HFT). HFT is the use of sophisticated algorithms to execute high numbers of trades. These trades happen simultaneously across several different markets.
Market Liquidity
The sheer volume of trades executed using HFT has a strong effect on mark liquidity. HFT increases market liquidity by reducing price movements and bid-ask spreads. The reduction in price movements also reduces trading costs, which in-turn increases trading volume (or at least it can).
The Advantage of HFT
Prices fluctuate too fast and too often for human traders to take advantage. This means that many opportunities to profit are left on the table. The speed and precision of High Frequency Trading allows firms to take advantage of opportunities human traders can't.
HFT Risks
High Frequency trading is not without its risks.
Firms put a lot of trust in the powerful algorithms used for HFT, but sometimes that trust is misplaced. The deployment of a faulty algorithm cost Knight Capital almost $450 million. This major loss happened in less than an hour and took years to recover from.
HFT also poses potential risk to the financial market system as a whole. The sheer volume of trades executed by trading algorithms creates market volatility. This volatility can result in major losses across several markets all at once.
On May 6, 2010, the financial markets experienced a flash crash. In less than 20 minutes, $1 trillion in market value was temporarily lost. Had this loss been permanent, millions of people with money invested in the markets would have lost everything. An investigation from the SEC revealed that a very large order, placed by a trading algorithm, was the culprit. Despite this near miss High Frequency Trading is still widely used in the industry.
These companies hire quantitative traders to develop algorithms and implement them as part of their trading strategy. Quantitative traders have strong mathematical and programming skills. They must also have comprehensive understanding of the financial markets.
High Frequency Trading
Large firms use an automated trading strategy called High Frequency Trading (HFT). HFT is the use of sophisticated algorithms to execute high numbers of trades. These trades happen simultaneously across several different markets.
Market Liquidity
The sheer volume of trades executed using HFT has a strong effect on mark liquidity. HFT increases market liquidity by reducing price movements and bid-ask spreads. The reduction in price movements also reduces trading costs, which in-turn increases trading volume (or at least it can).
The Advantage of HFT
Prices fluctuate too fast and too often for human traders to take advantage. This means that many opportunities to profit are left on the table. The speed and precision of High Frequency Trading allows firms to take advantage of opportunities human traders can't.
HFT Risks
High Frequency trading is not without its risks.
Firms put a lot of trust in the powerful algorithms used for HFT, but sometimes that trust is misplaced. The deployment of a faulty algorithm cost Knight Capital almost $450 million. This major loss happened in less than an hour and took years to recover from.
HFT also poses potential risk to the financial market system as a whole. The sheer volume of trades executed by trading algorithms creates market volatility. This volatility can result in major losses across several markets all at once.
On May 6, 2010, the financial markets experienced a flash crash. In less than 20 minutes, $1 trillion in market value was temporarily lost. Had this loss been permanent, millions of people with money invested in the markets would have lost everything. An investigation from the SEC revealed that a very large order, placed by a trading algorithm, was the culprit. Despite this near miss High Frequency Trading is still widely used in the industry.
The Benefits of Using Algorithmic Trading
There are many benefits that come with using a trading algorithm. Let's consider a few of them.
Lightning Quick Execution
Trading algorithms execute trades at a speed much faster than humans possibly can. The speed of trading algorithms means your trades are placed nearly instantly and at the best prices possible. This precision also means a reduction in both transaction costs and large price changes.
Reduced Human Error
Human traders have a tendency to make mistakes while trading due to their emotional or psychological biases.
Automated trading removes these human biases from trading. Trading algorithms are strictly sets of executable instructions. They do not have feelings, so they will place trades exactly as instructed.
Human traders are also prone to making manual trading errors. Algorithmic trading removes the possibility for these errors with pre-set, automatic trade execution.
Risk Reduction
Perhaps the biggest benefit of using a trading algorithm is risk reduction. With algorithmic trading you have the ability to automatically set limits. Setting limits is important because no one wants to spend or risk more capital than they can afford. Automated trading gives you several different ways to set limits on your trading with the use of limit orders and market orders.
Your algorithms can also be used for back-testing. Back-testing is the process of testing trading strategies on past market data. Using your algorithm to perform back-testing can determine if your strategy is viable or needs to be replaced.
Lightning Quick Execution
Trading algorithms execute trades at a speed much faster than humans possibly can. The speed of trading algorithms means your trades are placed nearly instantly and at the best prices possible. This precision also means a reduction in both transaction costs and large price changes.
Reduced Human Error
Human traders have a tendency to make mistakes while trading due to their emotional or psychological biases.
Automated trading removes these human biases from trading. Trading algorithms are strictly sets of executable instructions. They do not have feelings, so they will place trades exactly as instructed.
Human traders are also prone to making manual trading errors. Algorithmic trading removes the possibility for these errors with pre-set, automatic trade execution.
Risk Reduction
Perhaps the biggest benefit of using a trading algorithm is risk reduction. With algorithmic trading you have the ability to automatically set limits. Setting limits is important because no one wants to spend or risk more capital than they can afford. Automated trading gives you several different ways to set limits on your trading with the use of limit orders and market orders.
Your algorithms can also be used for back-testing. Back-testing is the process of testing trading strategies on past market data. Using your algorithm to perform back-testing can determine if your strategy is viable or needs to be replaced.
Possible Disadvantages to Consider
Using algorithmic trading can come with many benefits. But there a few potential disadvantages to consider before implementing a trading algorithm.
Technical Failure
Algo trading is dependent on computer hardware and software. Thus, it's important to consider the risk of technical failure. Technical failure is the sudden malfunction of any technology-based system.
Algo trading requires a ton of processing power. This is because a trading program must constantly be fed a large amount of real-time data. You will need a powerful PC to keep up with the demands of algorithmic trading.
Algorithmic trading relies on real-time market data to keep programs going. This means your computer must have constant, uninterrupted access to the internet. Loss of connection to the internet at any point during trading hours can disrupt the implementation of your algorithm and cost you money.
Technical Expertise
Besides powerful hardware, algorithmic trading requires technical know-how. This means you must be able to turn your trading strategy into programmable instructions.
If you don't have any knowledge of programming and aren't willing to learn, you can opt for using algorithmic trading software.
Technical Failure
Algo trading is dependent on computer hardware and software. Thus, it's important to consider the risk of technical failure. Technical failure is the sudden malfunction of any technology-based system.
Algo trading requires a ton of processing power. This is because a trading program must constantly be fed a large amount of real-time data. You will need a powerful PC to keep up with the demands of algorithmic trading.
Algorithmic trading relies on real-time market data to keep programs going. This means your computer must have constant, uninterrupted access to the internet. Loss of connection to the internet at any point during trading hours can disrupt the implementation of your algorithm and cost you money.
Technical Expertise
Besides powerful hardware, algorithmic trading requires technical know-how. This means you must be able to turn your trading strategy into programmable instructions.
If you don't have any knowledge of programming and aren't willing to learn, you can opt for using algorithmic trading software.
The Most Popular Algorithmic Trading Strategies
Just like formal trading strategies, there are an infinite number of possible trading algorithms. Let's outline the most commonly used strategies by algorithmic traders and investors.
Mathematical Model-Based Strategies
Quantitative traders frequently use mathematical models as part of their trading strategies. These models often use calculus, statistics, and probability to leverage maximize returns. Top trading firms like Citadel, Virtu Financial, and Tower Research use mathematical models as staples of their trading strategy.
Trend-Following Strategies
Successful traders have to understand the trends of the market. They use moving averages, price action, breakouts, and other indicators to make gains.
Algorithmic traders turn these technical indicators into an algorithm, which they use as a complete trading strategy. Trend-following strategies are very common because no forecasting or prediction is required for successful implementation.
Mean Reversion
Mean Reversion is a statistical tool used to take advantage of large price discrepancies on a particular security. Traders use mean reversion to profit on large increases and decreases in price. Mean reversion can be seen as the statistical version of "buy low, sell high."
Time-Weighted Average Price (TWAP)
Algorithmic trading often executes large market orders all at one time. These large orders have a sizeable effect on the market by suddenly increasing the price of a security. Using the Time-Weighted Average Price strategy breaks large orders up in to small orders and executes them on a set time interval.
Volume-Weighted Average Price (VWAP)
The Volume-Weighted Average Price strategy is like the TWAP strategy. VWAP takes large orders and executes them in smaller chunks. Trade execution is determined by the individual historical volume traded for each security.
The biggest difference between TWAP and VWAP is that VWAP is determined over a series of days, and TWAP is single-day indicator.
Index Fund Rebalancing
Index Fund Rebalancing is an opportunistic trading strategy. Index funds go through periods of modification in order to stay balanced and diversified. This looks something like buying and selling certain shares, or adding and removing certain titles.
Maintaining balance over time is essential to having a successful index fund. Algorithmic traders are aware of this and execute timed trades in order to profit from the expected movements of an index fund.
How Do I Implement an Algorithmic Trading Strategy?
There are several components to developing a successful algorithmic trading strategy. Let's take a look at the essential components.
Understand The Fundamentals
First thing's first, you need to understand the fundamentals of the market. You must have basic understanding of technical analysis, fundamental analysis, and sentimental analysis. Without proper understanding of these three components you will not be able to develop a profitable trading strategy.
Acquire Programming Skills/Software
Trading algorithms are built-in programming environments. If you don't know how to program, it's essential that you learn.
You will have to constantly tweak and update your algorithms. You can only do this if you know your way around the command-line. Python is the most preferred programming language for quantitative traders.
If you're new to programming, developing programming skills will take time. It is important to understand that this is a difficult skill to master and will not happen overnight. If you are patient and persistent, learning how to program will pay dividends.
Alternatively, you can purchase pre-built algorithmic trading software. This can be a costly alternative, but it will allow you to streamline the process of creating and implementing trading algorithms.
Develop Models/Trading Strategies
Once you have a solid understanding of trading fundamentals and programming skills/software, it's time to develop models.
Your models will need many hours of research to develop. The most basic models are made using technical analysis, while some of the most advanced models use stochastic calculus and Bayesian probability. Ultimately, the best model to use is one that produces results.
Understand Your Strategy
Whether you are using basic technical analysis or advanced mathematical models as your strategy, it is important to understand how and why your strategy works.
If something goes wrong with your model or you run into an unexpected error, it's up to you to figure out how to fix it. Having intimate knowledge of how your models work will make problem-solving much easier.
Perform Back-Testing
Before testing them on current market data, it's important to back-test your models. Back-testing will tell you how well your models would have worked with past historical data.
If your models perform poorly with historical data, they are likely not worth implementing. If your models perform well with past data, then they are likely worth implementing.
Back-testing will reveal any weaknesses in your trading strategy. Implementing your models before back-testing them is a recipe for failure. Don't skip this step!
Long-Term Success
Having a good trading strategy isn't enough to have long-term success. Defining your long-term goals is equally as important.
Before diving in ask yourself these questions:
- How much do I plan to make with algorithmic trading?
- How much time do I have to dedicate to learning?
- How much risk am I willing to handle?
- How long am I willing to trade?
- What is my exit strategy if things go bad?
Success in trading is a marathon, not a sprint. Think long and hard about your answers to these questions before embarking on your journey.
The Bottom-Line on Algorithmic Trading
Remember, algorithmic trading is not a get-rich-quick scheme!
Building a profitable trading algorithm requires skill, patience, and willingness to constantly learn. Master the fundamentals, research and test your strategies deeply, and stay consistent. Success will come.
Are you interested in developing your own automated trading strategy? Check out our guide on how to get started.
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About The Author: Kevin Davey is an award winning private futures, forex and commodities trader. He has been trading for over 25 years.Three consecutive years, Kevin achieved over 100% annual returns in a real time, real money, year long trading contest, finishing in first or second place each of those years.
Kevin is the author of 5 highly acclaimed books, including "Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading" (Wiley 2014). Kevin provides a wealth of trading information at his website: https://www.kjtradingsystems.com
Copyright, Kevin Davey and KJ Trading Systems. All Rights Reserved. Reprint of above article is permitted, as long as the About The Author information is included.
Kevin is the author of 5 highly acclaimed books, including "Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading" (Wiley 2014). Kevin provides a wealth of trading information at his website: https://www.kjtradingsystems.com
Copyright, Kevin Davey and KJ Trading Systems. All Rights Reserved. Reprint of above article is permitted, as long as the About The Author information is included.