In our past many years of research, we can give some conclusions regarding forex trading: 1.If your algorithm or trading system doesn't use machine learning or regular repetitive optimisation , then you are simply wasting your time. Because forex is probably the only industry where machine learning has been used for decades even when there was not much computational power available. 2.Already most of the banks, liquidity providers and hedge funds are using highly sophisticated machine learning algos and hence, if you are trying to make money out of the forex market using traditional trading systems, then you are simply trying to make a fool out of yourself and wasting your time. So think about it carefully before even putting any money to any system which doesn't use machine learning.
So now we are mainly working on 'Deep Neural Network' and 'Machine learning' algorithms. This is an example of test results of 'Deep Neural Network'.
If anyone is interested in these algorithms can share his feedback and comments to improve the algo. In near future, we may publish more accounts using machine learning and neural networks.
Or if you are interested to subscribe to the signals of the system, then also you can comment here.
Neural Network trading systems are usually considered as black box trading systems which imitate the behaviour of human brain and it makes trading decisions by identifying certain patterns which are very difficult to identify for a normal human being.
But we will try to explain in details about our system in all of our future posts for those who are interested in Neural network and machine learning algorithms.
Features of our System: ******************* 1. Our current implementation of the Neural network uses optimised weights for specific indicator values used inside the code which are used for making trading decisions by the network. 2.Currently, we are using a 5 layer NN out of which one is the input layer, 3 are hidden layer and 1 is the output layer for making trading decisions. We may add more layers to the system if required. 3.In future versions, we may exclude weights out of the code so that the weights can be optimised by the EA user as well as it might be possible to apply the weights directly to the EA after the optimisation is complete.
Usually NN and Machine learning algos are so complex that there can be an endless discussion on it and still most part of the output produced by the systems are not completely understood by the developers. But as we progress further, we will try to understand more and also, we will update here for others who want to learn Machine learning and neural networks in Forex trading.
Features of the System: -------------------------------- 1.The system uses multiple indicator values as input to the multi-layer neural network. 2.After the inputs are fed to the first layer, it goes through multiple hidden layers to produce output equivalents of the indicator values. 3.Finally, the outputs are used for making trading decisions for buy or sell or trade close signal. 4.The above process runs continuously on every couple of hours.
Though the above process is just a brief summary of what is going on inside the network, but it is very difficult to know exactly how the entries and exits are decided by the network.
Multi-layer neural network works like a human brain where each neuron holds a certain value usually between 0 to 1 and based on the input value a specific neuron is fired up which triggers the next neuron and so on.
In our EA, currently we are using stochastic and RSI indicators and few other indicators as input values to the neural network and the values are further processed in each layer until it reaches the final layer which gives a buy or sell signal.
We are trying to iterate the whole process of training the EA, but it has not been implemented yet. So it uses fixed weights which are optimised through training. We will explain what are weights and further regarding the improvement in our future posts.
New features added to the EA: ------------------------------------------ 1.A time filter was added to the EA to restrict the trading of the EA during specific hours of the day 2.Also, Spread filter is added to check the widening of the spread during new events and stop trading during those times 3.Additional hidden layers were added to the network as well as the activation function for individual neurons was modified
In this post, I will further expand on neural networks and about our system improvements so far.
The main advantage of the neural networks is that the it can identify many hidden patterns in the market which usually a normal human being even an experienced trader can't identify.
So the main challenge is to feed the neural network the right kind of input parameters and sufficient number of parameters for classification of BUY and SELL signals at the output for entering trades.
But it again leads to another problem if the number of input parameters become very large, then it takes a lot of processing power or resources to calculate the output value. Also, it takes a lot of time for optimising the network with many combination of inputs.
Another important criteria is choosing the right kind of neuron activation function suitable for the specific algorithm for trade entry and exit. We are partially changing the neuron activation functions in the code to see which is best suitable for our trading system.
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레버리지는 추가적인 위험 및 손실 노출을 만듭니다. 외환 거래를 결정하기 전에 투자 목표, 경험 수준 및 위험 허용 오차를 신중하게 고려하십시오.
초기 투자의 일부 또는 전부를 잃을 수 있습니다. 잃을 여유가 없는 돈을 투자하지 마십시오. 외환 거래와 관련된 위험에 대해 스스로 교육하고 궁금한 점이 있으면 독립 금융 또는 세무사에게 조언을 구하십시오.
모든 데이터 및 정보는 정보 제공 목적으로만 있는 그대로 제공되며 거래 목적이나 조언을 위한 것이 아닙니다.
과거의 성과는 미래의 결과를 나타내는 것이 아닙니다.