Discussing the article: "Forecasting exchange rates using classic machine learning methods: Logit and Probit models"
Thanks, good interesting article.
Imho, you can already try to use fundamental data on daytrips. This is not in the sense of criticising the article, but as a way of thinking. I wonder how macroeconomic data can be adequately "mixed" with price data. The problem is their rare change, for example. Probably, macroeconomics can also be used somehow in price preprocessing - transition from nominal to real exchange rates, for example.
Imho, you can already try to use fundamental data on daytrips. This is not in the sense of criticising the article, but as a way of thinking. I wonder how macroeconomic data can be adequately "mixed" with price data. The problem is their rare change, for example. Probably, macroeconomics can also be used somehow in price preprocessing - transition from nominal to real exchange rates, for example.
There is an inbuilt news calendar with macroeconomics - mix its data into predictors.
Thank you, good interesting article.
Imho, you can already try to use fundamental data on daytrips. This is not in the sense of criticising the article, but as a way of thinking. I wonder how macroeconomic data can be adequately "mixed" with price data. The problem is their rare change, for example. Probably, macroeconomics can also be used somehow in price preprocessing - transition from nominal to real exchange rates, for example.
Why ? It uses a classification predictive model that makes predictions. It counts correctly what's put into the model. What's wrong, then? That the model can't beat a naive prediction ? I didn't promise that )
"The impossibility of predicting exchange rates using classical methods..."

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Check out the new article: Forecasting exchange rates using classic machine learning methods: Logit and Probit models.
Financial market researchers will always face the difficult task of choosing a mathematical model to predict the future behavior of trading instruments. To date, a huge number of such models have been developed. So the question arises: how not to drown in all this variety of methods and approaches, where to start and which models are best to focus on, especially if you are just starting to forecast using machine learning models? If we try to reduce the forecasting task to a simple answer to the question - "will the tomorrow's closing price be higher than the ones of today?", then the logical choice would be binary classification models. Some of the simplest and most widely used are logit and probit regression. These models belong to the most common form of machine learning, so-called supervised learning.
The task of supervised learning, in turn, is to teach our model to display a set of inputs {x} (predictors or features) into a set of outputs {y} (targets or labels). Here we will predict only two market conditions - the rise or fall of the currency pair price. Therefore, we will have only two classes of labels y∊ {1,0}. Price patterns, namely standardized price increments with a certain lag, will act as predictors. This data will form our {x, y} training set to be used to estimate the parameters of our models. The predictive model based on trained classifiers is implemented as LogitExpert EA.
Author: Evgeniy Chernish