Ever since the advent of the use of robots in forex trading, there has been a radical change in the outlook of things in the forex market. Most forex traders now prefer to use a robot in order not to attach much feeling when trading on the forex platform. However, the use of robots in forex trading started to increase after the advent of the Meta 4 trader, an app that helps traders to easily trade forex. Right now, we have many robot expert advisors in the world, that help and advice traders on the best way to trade and make a profit in the forex market. Moreover, the ability to choose the best EA for your trading can be a herculean task to make, since there are so many of them out there.

The foreign exchange or forex market is the biggest financial market in the world. It is even bigger than the stock market. Moreover, the forex market continues to grow, and one major reason for this is the use of the latest technologies, including artificial intelligence. In fact, it wouldn't be wrong to say that AI is revolutionizing forex trading. According to the 2019 Triennial Central Bank Survey of FX and OTC Derivatives Markets, the forex market has a daily volume of $6.6 trillion. When so much money is at stake, it is natural for market participants to demand and develop tools that help to reduce their risk and increase their accuracy.

With so many advances in technology and analysis tools, it's getting hard for traders to keep up. One of the highly discussed topics is machine learning. If you want to know where these two fields intersect, let's first clarify what each of the terms means. Foreign exchange, or Forex, is the process of converting one currency into another. The value of every specific currency is determined by market factors such as trade, investment, tourism, and geopolitical risk.

Landing a good job is generally considered the purpose of education today. But not everyone subscribes to that. "Your economic security does not lie in your job; it lies in your own power to produce--to think, to learn, to create, to adapt. It's not having wealth; it's having the power to produce wealth." As today's foreign exchange or "forex" market shows, this is indeed true.

Now we want to use this model for trading under a commercial trading platform and see if it is going to generate a profit. The techniques used in this story are focusing on the model in my previous story, but they can be tweaked to fit another model. The intention here is to make the model usable by other systems, e.g. a trading platform. You can find the source code for this story in directories "LSTM-FX-CTrader-Client" and "LSTM-FX-Prediction-Server" here: In the previous story, we have trained and tested a model and saved the resulting model as a directory and the scaler used for the data as a file. The model and the scaler are the only items that we need, in addition to understanding the input and the output parameters.

Many research papers cover the prediction of financial time series but only a small number of them speak about the application in a real trading strategy. Most of the time the research only gives the performance metrics of the model (accuracy, RMSE, …) but without trying to transform it into a profitable strategy. When we talk about financial time series, we talk about stochastic processes, meaning it deals with a lot of randomness. For this reason, it's unrealistic to expect getting an accuracy similar to the ones obtained in many other applications of Deep Learning. So, don't expect getting a 80% accuracy when predicting the market to go up or down for a given time horizon.

One of the great advantages of Forex Trading over that of other asset classes is the ability to trade in a 24 hour market. This has given rise for the need for many Forex Traders to automate their trading systems. The process of creating and testing an automated trading system can be a long one and even if a trader takes the time necessary they can never be sure of the results. One of the factors that many traders don't take into consideration are the constant changes that are happening in the market. The current market conditions are very different from those just a few short months ago.