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صفحه اصلی
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سی و سومین کنفرانس بین المللی مهندسی برق
An Integrated Technical Analysis and Machine Learning Trading Model for Noisy and Volatile Financial Markets
نویسندگان :
Arvin Esfandiari
1
Ali Doustmohammadi
2
1- دانشگاه صنعتی امیرکبیر(پلی تکنیک تهران)
2- دانشگاه صنعتی امیرکبیر(پلی تکنیک تهران)
کلمات کلیدی :
Financial Markets،xLSTM،RL،MACD Indicator Optimization
چکیده :
Financial markets are notably challenging to predict due to their complex dynamics and low signal-to-noise ratio (SNR). While machine learning methods have been widely explored for financial forecasting, many fail to deliver consistent profitability. This study introduces a novel trading framework that integrates extended long-short-term memory (xLSTM) with a wavelet denoising for improved prediction accuracy. Additionally, a Genetic Algorithm (GA) combined with an Expectation Maximization-Kalman Filter (EM-KF) is employed to optimize the coefficients of the Moving Average Convergence Divergence (MACD) indicator. The predicted outputs and optimized MACD histogram are fed into a Reinforcement Learning (RL) model, which utilizes a Q-table for decision-making. Backtesting on hourly historical trading data for the specific cryptocurrencies demonstrates the robustness of the proposed trading model in terms of profitability.
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ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.6.0
