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One of the advantages of trading forex is that the bajillion factors that move currencies make it hard for any individual or group to influence price action for prolonged periods of time. Unfortunately, this also makes it difficult for traders to predict future price action.

2. Humans move the market

At least for now. Though Robopip and mechanical trading systems in general have gained popularity over the last few years, humans still control the ebbs and flows of the forex market.

Elliot, who handles a corporate account, may hold on to a losing position rather than close a losing trade. Multiply these everyday scenarios and we get an unpredictable mix of potential price reaction.

There certainly is a Holy Grail! - Simple Trading Strategies - General - MQL5 programming forum

Those who have spent some time with markets know that, like human behavior, there are patterns that tend to repeat themselves on the charts. But what if the pattern ends and price transitions into another pattern? Stochastic, which had been reliable, is now useless while trending strategies start to make sense again. Most trading systems only work well until price shifts into another pattern.


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The continuous shifts in trading conditions and the unpredictable timing of when they occur make it difficult for traditional technical tools to be reliable all day every day. So here comes the crux of the whole AI space probably of all of modern science. What do we mean by this? Science has become sufficiently advanced that in order to gain progress on one point you lose somewhere else. The most famous trade off is probably in computers where we trade off processing speed with processor size.

The faster you want to process the bigger the CPU.

The trade off in AI is that if you want it to give you the optimal solution it will take a lot of time to process, because it has to go through each possible solution. Let us take the chess game example. Chess has roughly 7.

3 Reasons Why There’s No Holy Grail in Trading

Taking the current 8 Ghz processors it would take roughly 60 eons to calculate one move. So how do we deal with this? Mostly just minimizing the space trade off that the AI has to consider. Like for instance to only look 6 moves ahead it will take a split second.

This method of looking at a problem is called Lagrange Relaxation, which is a fancy term for reducing the complexity of a problem in order to get to a result faster. In the study of artificial intelligence as a whole this means that we are currently not building an AI that can mimic all of the human activities, rather an AI that is really really good at one thing and hopefully applying that to new fields of study.

Taking our new found knowledge, how do we apply this to AI trading algorithms? Thirdly, we need to define the trade offs we are willing to make. The space in our case is the cryptocurrency space. An actual investor would probably scan through various sources of structured and unstructured data, analyze sentiment, price movement, risk factors and other highly specialized models.