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Automated Trading Systems – Build Scale, Leverage, And Profits

Introduction to Automated Trading Systems

Automated Trading Systems (abbreviated ATS) are computer programs that execute buy and sell orders in financial markets automatically, following predetermined algorithms. Such systems bring precision, discipline, and efficiency to trading by mostly eliminating emotional biases, ensuring timely execution, and allowing for 24/7 trading.

ATS also offer the benefits of backtesting, enabling traders to refine strategies, and diversification by managing multiple assets simultaneously. You can leverage and build scale. These systems reduce labor costs and provide a powerful tool for traders and investors seeking to optimize their trading strategies.

What percentage of trading is automated and algorithmicAccording to a report, algorithmic trading contributed nearly 60-73% of all U.S. equity trading in 2018. 

This article is partly written by AI and partly by us. The article is edited and fact checked. 

What Is Automated Trading?

Automated trading is the use of computer algorithms to execute financial transactions in assets, for example, stocks and forex.

How Has Automated Trading Evolved?

Automated trading has evolved over time form being a manual process to being automated.

Historically, trading was a manual process. However, in the 1970s, electronic trading platforms emerged, speeding up order execution. For example, Ed Seykota was one of the pioneers in automated trading

The real shift came in the 1980s and 1990s with the rise of algorithmic trading, where computers could analyze market data and execute trades autonomously. This was also when S&P 500 futures started trading (1982) and changed the stock markets. 

Technology has been instrumental in this evolution. High-speed internet, powerful computers, and advanced data tools have fueled the development of sophisticated algorithms. Machine learning and AI have further enhanced automated trading during the latter years.

Today, automated trading systems are prevalent, offering speed and efficiency benefits, but also sometimes raising concerns about market stability. In essence, automated trading’s history reflects a shift from manual to algorithmic methods, shaped by ongoing technological advancements. Even small retail traders can use automated and mechanical trading to their advantage. 

How Do Automated Trading Systems Work?

Automated Trading Systems work with minimal human intervention because computer programs execute the trading orders. They rely on algorithms to make trading decisions, execute trades, and manage portfolios. Here’s a brief overview:

  1. Explanation of Algorithmic Trading: Algorithmic trading, also known as algo trading,  uses predefined mathematical models and rules to automate the process of buying and selling financial assets, such as stocks, currencies, or commodities, normally based on a lot of backtesting beforehand. Algorithms analyze market data, identify trading opportunities, and execute orders at optimal prices and speeds. This approach aims to remove human emotions from trading, increase efficiency, and improve risk management.
  2. Components of an Automated Trading System: a. Data Feed: Automated trading systems rely on real-time market data, including price quotes, order book data, and news feeds. This information is essential for making informed trading decisions. The good thing is that most of these products are very cheap.

b. Algorithm: The core of an ATS is the trading algorithm, which is a set of rules and logic programmed to determine when to enter or exit trades. Algorithms can be based on various strategies, such as trend-following, mean reversion, or statistical arbitrage. As mentioned, these rules are not taken from thin air or from the a**, but based on many hours (months!) of backtesting. 

c. Order Execution: ATS send trading orders to the market electronically through direct market access (DMA) or via brokers. The system’s speed and efficiency in executing orders are critical for achieving desired outcomes.

d. Risk Management: ATS incorporate risk management parameters to control trade size, limit losses, and adjust strategies in response to changing market conditions. Again, this is based on backtesting. 

e. Backtesting and Optimization: Before deploying an ATS in live markets, it is essential to backtest and optimize the algorithm using historical data. This process helps fine-tune the strategy for better performance. 95% of the work is backtesting. After you have backtested you just push buttons. 

f. Monitoring and Oversight: Traders or portfolio managers monitor the automated trading system to ensure it operates as intended and intervene if necessary. Personally, we never leave the computer as long as our programs are running. 

g. Connectivity: ATS need robust connectivity to market exchanges and trading platforms, ensuring fast and reliable execution.

In summary, automated trading systems leverage algorithms to execute trades based on predefined rules and real-time market data. They consist of components like data feeds, algorithms, order execution mechanisms, risk management tools, and ongoing monitoring to facilitate efficient and systematic trading in financial markets.

What Are The Types of Automated Trading Strategies?

There are five types of automated trading strategies:

  1. Trend-following strategies: Trend-following strategies are automated trading approaches that aim to profit from market trends. They involve analyzing historical price data to identify prevailing trends, whether bullish (upward) or bearish (downward), and then executing trades in the direction of the trend. These strategies typically rely on technical indicators like moving averages to make trading decisions. Trend strategies typically have low win rate, many small winners, and occasional big winners. 
  2. Mean-reversion strategies: Mean-reversion strategies are designed to capitalize on the idea that asset prices tend to revert to their historical average or mean over time. Automated systems employing this strategy buy when an asset’s price is significantly below its historical average and sell when it is above. These strategies often use statistical measures to identify potential reversions to the mean. Typically, win rate is high, but tend to see infrequent big losers that can wipe out many winners. 
  3. High-frequency trading strategies: High-frequency trading (HFT) strategies involve executing a large number of trades in extremely short timeframes, often within milliseconds or microseconds. HFT algorithms rely on speed and technology to exploit market inefficiencies, such as price disparities between different exchanges or asset classes. These strategies require powerful hardware and low-latency connectivity to execute trades swiftly. These types of strategies are not for the retail trader. You need low latency for this, and retail traders can’t compete with bigger players further up in the food chain. 
  4. Arbitrage strategies: Arbitrage strategies seek to profit from price discrepancies of the same asset across different markets or exchanges, often they are market-neutral trading strategies. Automated arbitrage systems simultaneously buy low and sell high to capture the price difference, making risk-free profits. This type of strategy requires fast execution and may involve triangular arbitrage, statistical arbitrage, or spatial arbitrage, among others.
  5. Machine learning and AI-based strategies: Machine learning and AI-based trading strategies leverage advanced algorithms and models to analyze vast amounts of data and make trading decisions. These strategies can adapt to changing market conditions and learn from historical patterns. They often incorporate techniques like neural networks, deep learning, natural language processing, and reinforcement learning to optimize trading outcomes.

What Are The Advantages of Automated Trading Systems?

The advantages of automated trading systems are efficiency and speed, elimination of emotional bias, and diversification and risk.

  1. Efficiency and Speed: Automated trading systems excel in executing trades swiftly and efficiently. They can analyze market conditions, execute orders, and manage positions with lightning speed, ensuring traders capitalize on opportunities before manual traders can react. This efficiency minimizes slippage and maximizes profit potential. However, for the average retail trader, this is not a critical factor. You should focus on “slow frequency” trading.
  2. Elimination of Emotional Bias: One of the foremost benefits of automated trading is the removal (more likely reduction) of emotional bias from trading decisions. Emotions like fear and greed can cloud judgment and lead to impulsive actions. Automated systems follow pre-defined rules, executing trades solely based on data and algorithms, preventing emotional reactions that can result in costly mistakes. 
  3. Diversification and Risk Management: Automated trading allows for the simultaneous management of multiple strategies and assets, facilitating diversification. This diversification spreads risk and minimizes exposure to individual market fluctuations. Moreover, automated systems can implement risk management parameters and position sizing rules consistently, safeguarding capital and ensuring disciplined trading. We have written an article that shows how two trading strategies enhance returns when the complement each other.

What Are The Risks and Challenges of Automated Trading?

The risks and challenges of automated trading are systematic risks, execution risks, regulatory risks, and fat finger risks.

Automated trading involves using computer algorithms to execute trades in financial markets. While it offers numerous benefits, it also comes with several risks and challenges:

  1. Systematic Risks: These are market-wide risks that can impact automated trading strategies. Factors like economic downturns, geopolitical events, or sudden market crashes can trigger significant losses for automated systems. Systematic risks are challenging to predict and can lead to substantial portfolio drawdowns.
  2. Data and Execution Risks: The accuracy and timeliness of data are critical in automated trading. Data inaccuracies or delays can result in incorrect trading decisions, leading to losses. Execution risks pertain to the reliability and speed of trade execution by brokers or exchanges. Slow execution or slippage can adversely affect trading outcomes. This is not a primary risk for retail traders, though. You should not trade strategies that rely on fast data!
  3. Regulatory and Ethical Concerns: Automated trading is subject to various regulations aimed at maintaining market integrity and investor protection. Compliance with these rules is crucial to avoid legal issues and penalties. Additionally, ethical concerns arise from the potential for high-frequency trading (HFT) strategies to exploit market conditions unfairly, potentially harming market participants and causing market instability. Ethical dilemmas also emerge when considering the impact of automated trading on job displacement in the financial industry.
  4. Fat Finger risks: You have probably read about it, but fat fingers have made firms go belly up, and it probably applies to individual traders as well. If you accidentally start the program when you shouldn’t, etc. 

How To Get Started with Automated Trading?

Getting Started with Automated Trading involves the process of developing or selecting an automated trading system and choosing the appropriate trading platform or software. We have partially covered this in our take on Amibroker vs Tradestation review

To begin, traders need to define their trading strategy and rules, considering factors like risk tolerance, asset classes, and trading frequency. This is based on backtesting. If you are new to backtesting, we recommend our backtesting course with a trading strategy.

Next, you must select a suitable trading platform or software that supports automation. Personally, we use Amibroker and Tradestation, and we have also dabbled a little with Python (we have plenty of Python trading strategies). This platform should offer essential features such as real-time data feeds, order execution capabilities, and backtesting tools.

Building or customizing a trading system can be complex, involving programming skills and thorough testing. Alternatively, traders can choose from various pre-built trading systems and strategies available in the market. However, if you are serious about trading, we recommend putting in the time and resources to learn a platform. 

Backtesting and Optimization

Backtesting is a crucial process of automated trading systems. It involves assessing the performance of a trading strategy by applying it to historical market data to simulate past trading decisions. This helps traders and developers understand how the strategy would have performed in the past, allowing them to gauge its potential effectiveness. Nevertheless, it only provides what has happened, and not what is going to happen. This is why you want to have strategies with a large sample size. We believe backtesting is the best tool you have. 

Most traders have no idea if they have a positive expectancy in the first place, and thus backtesting is a fantastic tool. Please read or article about how to instantly improve your trading: 5 key steps.

The importance of backtesting lies in its ability to validate and refine trading strategies. By analyzing past results, traders can identify flaws, optimize parameters, and ensure their strategies are robust and reliable. It provides a level of confidence in the system’s viability before risking real capital.

Optimization, on the other hand, involves fine-tuning the parameters and rules of an automated trading system to enhance its performance. Optimization aims to maximize returns while minimizing risk. Strategies for optimization include adjusting parameters like entry and exit criteria, risk management rules, and position sizing. Additionally, optimization may involve conducting sensitivity analysis to understand how changes in various factors affect strategy outcomes.

Successful optimization is a continual process, as market conditions evolve. Traders need to adapt their strategies to changing environments to maintain profitability.

In summary, backtesting is a vital tool for assessing strategy performance, and optimization is the ongoing refinement process that ensures trading systems remain effective and competitive markets.

Real-Life Examples of Successful Automated Trading

Real-Life Examples of Successful Automated Trading refer to specific instances where automated trading strategies have yielded remarkable results in financial markets. These instances often serve as insightful case studies, showcasing the effectiveness of automated trading systems.

One notable example is Renaissance Technologies, a hedge fund founded by mathematician James Simons. Their Medallion Fund has consistently outperformed traditional investment strategies, largely attributed to sophisticated automated trading algorithms. 

Another success story is the Quantitative Investment Management (QIM) firm, which manages the Global Program, a trend-following commodity trading system. Over the years, QIM’s automated approach has generated substantial returns, attracting the attention of investors seeking consistent profits.

These real-life examples emphasize key lessons for aspiring traders. Firstly, automated trading systems excel in analyzing vast datasets. Secondly, risk management and robust backtesting are crucial to mitigate potential losses. Thirdly, automation makes you scale and trade almost unlimited number of trading systems. This is the key lesson from the Medallion Fund. Lastly, adaptability is vital as market conditions change, requiring constant optimization of trading algorithms.

In conclusion, successful automated trading cases like Renaissance Technologies and Quantitative Investment Management demonstrate the potential for substantial financial gains through automated strategies, scale, and strategy diversification. 

What Are The Common Mistakes to Avoid?

The common mistakes to avoid are technical glitches, behavioral mistakes, and fat-finger errors. Common mistakes in any endeavor can hinder progress and lead to unfavorable outcomes.

In the context of automated trading, these mistakes often manifest as pitfalls that traders should be vigilant about. Pitfalls in automated trading refer to the errors and oversights that can undermine the effectiveness and profitability of automated trading strategies. These pitfalls can range from technical glitches and algorithmic errors to behavioral biases and inadequate risk management.

To mitigate common errors in automated trading, traders need to adopt a proactive and systematic approach:

One key step is to thoroughly test and backtest trading algorithms to identify and rectify any flaws or vulnerabilities. Additionally, traders should implement robust risk management protocols to protect their capital from significant losses. We recommend to ALWAYS trade smaller than you’d like to, and be very careful about leverage. You need many years of experience to become a good trader. 

It’s essential to monitor the automated systems regularly. Personally, we never leave the computer while our programs are running. 

Furthermore, avoiding over-optimization, which can lead to curve-fitting and poor real-world performance, is crucial. Emotion-driven decisions should be eliminated by relying on data-driven analysis and statistics. 

Pitfalls of automated trading – a real-life example

Automated trading gives you power and leverage but can also lead to disastrous results. Let’s give you an example from the real trading world in August 2012. This is what we wrote on our personal blog in 2012:

In one of my current strategies, I was filled en masse today. Usually, that strategy gives me on average 4 fills a day, but today I got 59 fills. Quite unusual!

I stopped the program because I suspected something was wrong. Had I continued, I theoretically would have received over 100 fills/stocks. When I do automation, I always see the black swan in the back of my head, so I stopped it. Better safe than sorry, I thought.

A quick look at the P/L showed something could be wrong: I was down about 6,000 USD in seconds, and I feared this was just the tip of the iceberg. After having a quick look at some of my fills, I discovered an insane volume, and this was in stocks with no news. Looking at those incredible moves, I suspected some automated trading program must have gone completely wrong, and my strategy actually will turn very profitable when this selling stops.

So I sent the program again to buy some more. And yes, after some 30 minutes, everything cooled down, and the P/L just ticked upwards, securing a very profitable day. This was one of the rare moments in a trading year when the opportunity to make really good money suddenly came out of nowhere. And yet again, I sit here kicking myself for not trading bigger sizes. There are good opportunities for us who provide liquidity, but you have to go for the jugular!

I haven’t seen anything on the news except for some rumors that an algo order that should have been executed over five days got executed in 5 minutes. I guess someone will be fired….. Automated trading gives power but can turn almost lethal when executed incorrectly. And don’t even think about if the quotes feeding the program are wrong.

Look at BRK/B, PL, and DDD today, to mention a few. DDD went from 38 to 34 USD in just a few minutes. PL had a range of about 27 to 25.5 once every minute.  PL went about 5% after selling abated, and DDD rose about 9% from the bottom!

What happened on this day?

For those with a good memory, you’ll probably remember that a fat finger error at the Knight market maker on the 1st of August led to a 460 million dollar loss, and the Knight was forced to be acquired. In hindsight, it was easy to spot but hard to know in advance. The losses for Knight was prey for small traders like us.

Regulation and Compliance

Regulation refers to the set of rules, laws, and guidelines established by government authorities or industry bodies to govern and control various aspects of business operations, financial activities, and specific industries.

Compliance, on the other hand, involves adhering to these regulations and ensuring that an organization conducts its activities in a manner that aligns with the prescribed legal and ethical standards. It encompasses the processes, systems, and actions taken by businesses to meet these regulatory requirements and avoid violations, penalties, or legal issues.

Overview of Regulatory Requirements for Automated Trading: Automated trading systems, often used in financial markets, are subject to specific regulatory requirements. These regulations aim to maintain market integrity, transparency, and investor protection.

Key aspects include disclosing trading strategies, risk management protocols, and ensuring fair access for all participants. Regulators like the SEC in the United States and ESMA in Europe impose stringent rules on algorithmic trading to prevent market manipulation and excessive risk-taking.

Compliance Best Practices: Compliance best practices involve implementing effective strategies and procedures to meet regulatory obligations efficiently and mitigate associated risks.

Key recommendations include thorough documentation of trading algorithms, robust risk management measures, continuous monitoring, and regular compliance audits.

Additionally, staying informed about evolving regulations and industry standards is crucial for maintaining compliance over time. Ultimately, compliance best practices are essential for safeguarding an organization’s reputation and legal standing in a highly regulated environment.

What Are The Future Trends in Automated Trading?

Future Trends in automated trading refer to the evolving landscape of financial markets characterized by the integration of cutting-edge technologies and the growing significance of artificial intelligence and machine learning.

One prominent trend is the adoption of emerging technologies such as blockchain and quantum computing. Blockchain, with its decentralized ledger system, enhances transparency and security in financial transactions, reducing fraud and errors.

Quantum computing, on the other hand, offers the potential for lightning-fast data analysis and complex risk assessment, revolutionizing trading strategies and risk management.

Another critical aspect is the increasing role of artificial intelligence (AI) and machine learning (ML) in automated trading systems. AI and ML algorithms analyze vast datasets with precision, identifying trading patterns and market anomalies that human traders might overlook.

These technologies enable trading algorithms to adapt and optimize their strategies in real-time, making split-second decisions to maximize returns while managing risk.

Moreover, AI-driven sentiment analysis and natural language processing enable traders to monitor news and social media sentiment, incorporating external factors into trading strategies. These technologies also facilitate the development of robo-advisors, which offer personalized investment advice based on individual risk profiles and financial goals.

In summary, Future Trends in Automated Trading encompass the integration of emerging technologies like blockchain and quantum computing, as well as the increasing prominence of artificial intelligence and machine learning. These innovations are poised to reshape the financial landscape, improving efficiency, reducing risk, and enabling more sophisticated trading strategies.

What Are The Best Tips for Risk Management?

The best tips for risk management in automated trading involve implementing strategies to minimize potential losses and protect your capital. On rule overrules everything: always trade smaller than you’d like to avoid making human errors like selling in a panic and buying in FOMO mood

It entails identifying and assessing various risks associated with trading algorithms and systems. Key aspects include diversifying your portfolio to spread risk, and regularly monitoring and adjusting your trading strategies to adapt to changing market conditions.

Strategies for mitigating risks in automated trading focus on minimizing exposure to potential threats. This can involve implementing risk control mechanisms such as setting maximum drawdown limits, using risk-adjusted performance metrics, and employing robust risk models to analyze market data and anticipate adverse events.

Position sizing and capital allocation are crucial elements of risk management. Properly sizing positions means determining the optimal quantity of a security or contract to trade, taking into account factors like account size, risk, market volatility, and a margin of safety. Effective capital allocation ensures that you distribute your capital wisely across various trades to minimize the impact of losses on your overall portfolio, helping to preserve capital for future opportunities. You need strategy diversification!

Conclusion and Final Thoughts:

Automated trading systems allows you to build scale because you can trade almost an unlimited number of trades. Even as a retail trader there is no problem in trading 5-6 different asset classes and each having 4-5 different strategies spanning different market directions (long and short), different time frames (daily, weekly), or different types of strategies (mean reversion, trend, momentum).

A trading program like Amibroker costs 300 for a lifetime license, and Tradestation is free. Likewise, you find plenty of resources in Python for free on the internet.

At the end of the day, the best way to learn is by trial and error. Just start, get some ideas, and backtest. Be patience, and you’ll learn a lot over the coming months (and years). However, automated trading strategies are not likely to make you instantly rich. You should like the method and the work, and the results will inevitably come if you are patient.    

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