Leveraging Machine Learning for Optimal Trade Entry Point Identification in the Cryptocurrency Market
In the domain of financial forecasting, machine learning (ML) models have garnered significant attention in recent years. One prominent application lies in the cryptocurrency market, where intelligent trading bots facilitate a substantial portion of daily transactions.
This paper investigates the efficacy of ML-based methods for identifying optimal trade entry points. Specifically, we employ the Relative Strength Index (RSI) and Simple Moving Average (SMA) indicators to extract a set of trend and crossover features. Subsequently, these features are utilized to train multilayer neural network, support vector machine, and nearest neighbor learning models. The performance of each model is then evaluated using digital currency market data encompassing the first seven months of 2023 for a variety of cryptocurrencies.
Our findings demonstrate that, firstly, the extracted features exhibit promising efficacy. Secondly, the nearest neighbor model achieved the highest profitability during the evaluation period compared to the other investigated models.
In the research conducted in this field, technical indicators are often used directly in market forecasting but in the proposed method of this article, instead of directly using the values of the indicators as the input of the classification methods, trend behaviors and their intersections have been used. In the continuation of this research, it is possible to determine the best exit points from the transaction with the help of machine learning and the use of volume indicators in the learning process.
پرداخت حق اشتراک به معنای پذیرش "شرایط خدمات" پایگاه مگیران از سوی شماست.
اگر عضو مگیران هستید:
اگر مقاله ای از شما در مگیران نمایه شده، برای استفاده از اعتبار اهدایی سامانه نویسندگان با ایمیل منتشرشده ثبت نام کنید. ثبت نام
- حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران میشود.
- پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانههای چاپی و دیجیتال را به کاربر نمیدهد.