Automated trading – Pros and Cons
What is Automated trading
Automated trading systems, also known as mechanical trading systems, algorithmic trading, automated trading, or systematic trading, allow traders to set certain rules for entering and exiting trades that, once programmed, can be executed automatically by a computer. In fact, various platforms report that 70% to 80% or more of stocks traded on U.S. stock exchanges come from automated trading systems. 1
Brokers and financial backers can decipher exact passage, exit and cash the board rules into Automated trading system that permit PCs to execute and control exchanges. Probably the greatest advantage of technique mechanization is that it eliminates a portion of the feeling from exchanging as exchanges are naturally made when certain rules are met.
Exchange passage and leave rules can be founded on straightforward conditions, like moving normal intersections, or complex methodologies that require intensive information on the programming language explicit to the client’s exchanging stage. They can likewise be founded on the experience of talented developers.
Automated trading systems frequently require the utilization of programming related with direct admittance to the merchant, and a particular principles should be written in the stage’s local language. For instance, the TradeStation stage utilizes the EasyLanguage programming language. Then again, the NinjaTrader stage utilizes NinjaScript. The picture underneath shows an illustration of a mechanized system that starts three exchanges for each exchanging meeting.
Advantages of Automated Trading
There is a long list of advantages to having a computer monitor the markets for trading opportunities and execute the trades, including:
Establishing Trading “Rules”
A few exchanging stages have “System Wizards” that permit clients to look over a rundown of freely accessible specialized pointers to make a bunch of decides that can be exchanged consequently. For instance, a client can set up a long position when the 50-day moving normal crosses the 200-day moving normal on the five-minute graph of a specific exchanging instrument. The client can likewise enter the request type (like market or limit) and when to trigger the exchange (like close a bar or open the following bar) or utilize the stage’s default inputs.
Notwithstanding, numerous dealers like to compose their own pointers and systems. They as a rule work intimately with software engineers to foster the framework. While this generally requires more exertion than utilizing the stage wizard, it offers a more prominent level of adaptability and the outcomes can be more significant. As with all the other things in the exchanging scene, sadly, there is no ideal speculation technique that ensures a positive outcome.
When the standards are set up, the PC can follow the market, searching for trade open doors in view of the determinations of the exchanging system. As indicated by explicit principles, orders for any defensive stop misfortune, following stop misfortune and benefit target are consequently created once the exchange begins. In quick business sectors, right now request passage can mean the distinction between a little misfortune and a devastating one in the event that the exchange conflicts with the merchant.
Minimizing Emotions
Automated trading systems minimize emotions all through the exchanging system. Controlling feelings makes it more straightforward for merchants to adhere to an arrangement. Since exchanging orders are naturally executed subsequent to meeting the exchanging rules, merchants won’t spare a moment or uncertainty exchanging. As well as aiding merchants who fear “pulling the trigger,” computerized exchanging can control the people who will generally over-exchange – – trading at each an open door.
Back testing
Backtesting applies trading rules to historical market data to determine the viability of an idea. When developing an automated trading system, all rules should be outright and not expose to understanding. PCs can’t suppose and should be guided precisely. Dealers can take these definite rule sets and test them against recorded information prior to facing the challenge in live exchanging. Careful back-testing permits merchants to assess and tweak exchanging thoughts, just as decide framework assumptions, the normal sum a dealer can hope to win (or lose) per unit of hazard.
Preserving Discipline
Discipline is maintained even in volatile markets as trading rules are set and trades are executed automatically. Discipline is frequently lost because of passionate variables, like apprehension about assuming a misfortune or the longing to create additional gain from an exchange. Robotized exchanging assists with keeping up with discipline as the exchanging plan will be completely followed. Also, “pilot mistakes” are limited. For instance, in the event that a request to purchase 100 offers was not erroneously entered as a request to sell 1000 offers.
Perhaps the greatest test in exchanging is arranging the arrangement and exchanging plans. Regardless of whether the exchanging plan can possibly be beneficial, a merchant who disregards the guidelines will change any assumptions the framework ought to have. No exchanging plan can win 100 percent. All things considered, disappointment is essential for the game. Be that as it may, misfortunes can be mentally horrendous, so a merchant who has a few losing exchanges a line might choose to avoid the following exchange. On the off chance that the following exchange is a fruitful exchange, the merchant has broken all assumptions for the framework. Robotized exchanging frameworks permit merchants to accomplish consistency by exchanging on a timetable.
Improving Order Entry Speed
Since the computer reacts instantly to changing market conditions, automated systems can generate orders once trading criteria are met. Entering or leaving an exchange a couple of moments early can significantly affect the result of the exchange. When a position is opened, any remaining requests are naturally produced, including defensive pauses and benefit targets. The market can vary rapidly, and it very well may be baffling when an exchange hits a benefit target or breaks a stop misfortune level before a request is entered. Mechanized exchanging frameworks can forestall this.
Diversifying Trading
Automated trading systems allow users to trade multiple accounts or different strategies at the same time. This might spread the danger across various instruments, accordingly supporting the position. PCs proficiently do things that are truly challenging for people in an issue of milliseconds. PCs can check different business sectors for exchanging potential open doors, produce requests a
Drawbacks of Automated Systems
Automated trading systems boast many advantages, but there are some downfalls and realities traders should be aware of.
Pros | Cons |
Minimize emotional trading | Mechanical failures can happen |
Allows for backtesting | Requires the monitoring of functionality |
Preserves the trader’s discipline | Can perform poorly |
Allows multiple accounts |
|
Mechanical Failures
The theory behind automated trading seems simple: set up the software, write the rules, and watch the trades. In fact, auto trading is a complex method, but it is not foolproof. Depending on the platform, trade orders may be located on a computer instead of a server. This means that orders may not be sent to the market if the internet connection is interrupted. There may also be a mismatch between the “theoretical trades” generated by the strategy and the order entry platform components that translate them into actual trades. Most traders should have a learning curve when using an automated trading system, and it is generally advisable to start with small trades while refining the process.
Monitoring
While it would be great to turn on the computer and walk away for the day, systems require monitoring. This is due to potential technical failures (such as connection issues, power outages or computer failures) and system anomalies. Automated trading systems may experience anomalies that result in erroneous, missed or duplicate orders. If the system is monitored, these events can be identified and quickly resolved.
Over-Optimization
While this is not the case with automated systems, traders using backtesting techniques can create systems that look great on paper but perform poorly in the real market. Over-optimization refers to excessive curve fitting that makes trading plans unreliable in real-time. For example, you can customize a strategy to achieve excellent results on the historical data it tests. Traders sometimes mistakenly believe that a trading plan must have close to 100% winning trades or must never experience a drawdown to be a viable plan. Therefore, parameters can be adjusted to create a “near-perfect” plan that fails completely once applied to the actual market.
How it profitable
FAQ
Can automated trading be profitable?
Like many questions about profitability, the answer is far from certain. Of course, there are people and companies that make decent profits from automated trading. But even here there are pitfalls, plenty of fraud and questionable statements.
What is the most profitable method of trading?
The safest and most profitable form of trading in financial markets is company stocks. There are fewer downsides to stock trading.
Is Automated Trading possible?
Automated trading can be a beneficial and lucrative skill, but often this skill cannot be purchased online for a few dollars. Automated trading requires a lot of work and skills. In order to effectively create and maintain an Expert Advisor, traders need knowledge in the field of trading and knowledge in the field of programming.
Why Not Everyone Uses Automated Trading Systems
In the 21st century, algorithmic trading has gained momentum among both retail and institutional traders. It is widely used by investment banks, pension funds, mutual funds and hedge funds that may need to spread out larger order executions or trade too fast for human traders to react.
Algorithmic trading is often associated with HFT or high frequency trading. HFT is truly based on lightning-fast algorithms, exploiting price differences between exchanges. However, the use of computer programs is more widely used in financial markets. Algorithmics permeates almost all areas of trading and investing. Also, new methods of trading and money management are emerging thanks to new technologies.
The first automated trading system was created by a trend following fund. These funds use a mechanical method based only on price and closing data. This means that some of the original mainframes could be used to generate signals. Algorithmic trading has come a long way since then. For many funds, the entire investing process is automated, from research to stock picking, execution and risk management.
Quantitative investment funds use techniques extensively to find correlations between securities and optimize strategies. These funds combine computing power with statistical and mathematical models to maximize risk-adjusted returns, then quickly identify and execute trades.
There are some difficulties:
- A systematic trading strategy does not always work indefinitely. The benefits of the system may diminish when other traders build systems that exploit similar patterns or market inefficiencies. For strategies with lower profit margins, transaction costs can quickly exceed profits.
- Algorithmic trading systems cannot adapt to changing market conditions as well as human traders. A particular challenge for trading systems is knowing when to close them or when they may not be feasible at all. A losing streak is often followed by a winning streak, and there is always a risk that the system will shut down before the streak begins. On the other hand, if the system is no longer viable, it will only continue to incur losses.
- Large swings and sudden crashes are another problem for systematic traders. When volatility rises, the risk of slippage and large overnight gaps increases. This can be fatal to the trading system if leverage is used. At the same time, volatility tends to create better opportunities. Furthermore, automated systems cannot determine when volatility may increase due to human or system error or more persistent factors.
- Increased volatility can also disrupt the correlations on which some systems are based. This is especially true for statistical arbitrage and similar long/short strategies.
