Using a tradermade api, evaluate for foreign exchange prediction.
Data Product:
There are ways to predict the FOREX market, but ARIMA is not able to take into account abstract or complex data. The exogenous feature is comparable to what physicists call “dark matter”. For the lack of concrete calculations that could be derived from particular data, we are essentially asking ARIMA to calculate a effective quantity from the shape of what is not present. These types of factors that change the course of FOREX trading must be able to customize a solid trade algorithm so the basic “banking” components like stop-loss, positioning and leverage parameters and geopolitical information, will be able to decrease the risk of live trading. An application that uses a Recursive Neural Network (RNN) is the next step to explore.
Conclusion:
Although RNNs are less explainable step by step as ARIMA is, they deal with the abstract components because they can look at the effective dynamics and imply explainability. This isn’t exactly the concrete logic that a board of directors like to base large decisions on, but if the results are better than standard statistical method results, consistently, then the effort of this type of data product will pay off.