by Daniel Stepnicka
In the recent years, technology, computing power and data processing moved forward very much. A few years ago, decisions made by market analysts, researchers and traders was almost only way how to process market and economic data. Nowadays, these people in banks, research firms and other corporations are eliminated by applying computers and related technologies. Partly, it is still the future, but in bank sector for example, Goldman Sachs is one of the most progressive firms in industry. Usual trader in retail sector will not have such capacity as traders and researchers in banks like Goldman Sachs, but there is still something what can be learned from these firms.
Still privileging opinion between retail traders in automated trading on Forex or other financial markets is to automate some idea what they uses in present, for their manual trading. This approach is the old way to algorithmic trading. It is only tool for their manual trading, not real algorithmic trading which is different mainly in process of development and evaluation. Other approach which has been until the recent years probably the most common is to find logic in the market, test it and optimize. Between retail community, this is still the most used approach to algorithmic trading from developing to trading and evaluation. And at the same time, this is the reason why they mostly fail in algorithmic trading.
Financial markets are very complex environment with great dynamic and changes. It is not surprising that to compete in this environment can only algorithmic traders or developers with approaches to process big information blocks. Skepticism about algorithmic or automated trading as separate discipline of trading on financial markets was right until we gradually discovered and we are still discovering, how to use today’s technology and computing power to process these big data.
The methods for data processing like machine learning starts to be more commonly mentioned in public as a way how to process data with time effectiveness against manual or computerized brute force method. Every developer as well as banks, uses different approach and process to achieve their goals. But in general, with these methods you can develop and evaluate millions of trading concepts in short time. What these developers did weeks to test one trading concept, now can be done with computing power within a few seconds. Big players like banks are far more forward. This is the biggest game changer to algorithmic trading on financial markets, where is the whole process only about evaluation of data.
Algorithmic trading is about objective edge in the market, which can be measured and evaluated. From developer perspective, it is very difficult to learn and understand how to develop and evaluate automated trading systems with predictive capabilities. Mainly for this reason we can see so little successful developers. For retail traders is today’s environment good to catch the wave of this trading of objective edge and not subjective emotions. Moreover, it is not necessary to become algorithmic trader and developer at the same time. There are solutions how to implement algorithmic trading in trader’s portfolio without need of developing them. Where traders paid for market research, now they can pay for algorithmic solution to balance their portfolio.
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