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Understanding the basics of algorithmic trading

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Algorithmic trading, which is also known as ‘auto-trading’ or ‘system trading’ is a trading method, where computer programs are being used to trade on financial markets. This kind of trading is done automatically without any human intervention. The increasing computing power allows traders to use advanced machine learning platforms like StrategyQuant to generate and validate algorithmic trading systems, before using them on real markets.

When it comes to algorithmic trading, a trader is able to a set few fixed trading rules, based on several factors. Including: price action, volume, indicators, timing and a mathematical model of the given market. Traders who use algorithmic trading strategies concentrate on statistics and probabilities observed during validation tests on historical market data. Therefore, it is possible to estimate if a given strategy will keep being profitable on a real market.

 

What are the benefits associated with algorithmic trading?

Algorithmic trading has received a lot of attention in the recent past, especially due to the benefits that are linked to this trading method. Here is a list of some of the most prominent benefits that are linked to algorithmic trading.

  • When it comes to algo-trading, traders can make sure that all the trades are being executed according to a fixed set of rules without intervention of human emotions.
  • The trades will be executed instantly and in a timely and fixed manner.
  • The ability of algorithmic trading to make fixed predetermined trades, gives a trader an opportunity to trade continuously 24/7.
  • Automated trading, gives an opportunity validate each system using automated simulations and checks on different market conditions, but also several different markets. This can increase chances of generating more returns.
  • The available algorithmic trading platforms like StrategyQuant make it possible to automate the strategy design and validation process. Where personal computers can perform all required steps during strategy design phase.
  • There is a reduced risk of manual errors from taking place with the trades that traders place.
  • It is possible to back test all strategies based on the historical data available. In addition, real time data can also be used to end up with better results. With the appropriate analysis of available data, you can determine if there is a viable trading strategy available to go ahead with.
  • The human traders tend to make a lot of mistakes and it is quite difficult to overcome most of those mistakes. That’s because psychological and emotional factors are playing a major role behind the mistakes. Automated trading can help human traders to overcome those mistakes.

 

On top of all these benefits, there is also the ability to periodically re-optimize your strategies. This gives the possibility to tweak the selected strategies according to current market conditions that change continuously, due to political and macro-economical factors.

 

How algo-traders find profitable strategies?

Each system trader has a well-defined workflow. It takes several steps in order to design, generate, validate and test automatic strategies.  In most cases this work flow consists of the following steps:

  1. Strategy design or strategy generation phase: in this step traders will try to define strategy rules and trading method. It can be done manually by translating a given trading strategy to computer code. Or it be done fully automatically, where computer program will self-generate interesting strategies.
  2. Validation step: In this step all generated strategies will undergo several robustness tests to verify how well a given strategy will perform under different market conditions. The most common validation tests are: OOS (out of sample test), Monte-Carlo simulations of input parameters, testing using different timeframes, market data manipulation tests and cross-market tests (testing on another markets).
  3. Forward test: during this phase all generated strategies are tested on the real market using real trades. In this period the systems are carefully monitored, to verify if their behavior is not different than observed during the validation steps.

Conclusion: due to increasing computation power of personal computers and new algorithmic learning methods like machine learning or even artificial intelligence, it is now possible for retail traders to design and implement algo-trading systems in their trading portfolios.  With the new tools like StrategyQuant, it is possible to harness the computing potential of personal computers and turn them into sophisticated algorithmic platforms. Without learning programming languages like MQL, Python or Java any trader with some basic computer skill can now design their own algo-trading systems. If you are interested in algo-trading and system trading, you can read more on the following auto-trading blog.

 

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