CNN

Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms ArXiv ID: 2412.18202 “View on arXiv” Authors: Unknown Abstract This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key information. The filtered and dimensionality-reduced price data is fed into a GANs network, and its output serve as input of a fully connected network. Through cross-validation, a model is trained to capture features that precede large price fluctuations. The model predicts the likelihood and direction of significant price changes in real-time price sequences, placing trades at moments of high prediction accuracy. Empirical results demonstrate that using autoencoders and convolution to filter and denoise financial data, combined with GANs, achieves a certain level of predictive performance, validating the capabilities of machine learning algorithms to discover underlying patterns in financial sequences. Keywords - CNN;GANs; Cryptocurrency; Prediction. ...

Multimodal Deep Reinforcement Learning for Portfolio Optimization ArXiv ID: 2412.17293 “View on arXiv” Authors: Unknown Abstract We propose a reinforcement learning (RL) framework that leverages multimodal data including historical stock prices, sentiment analysis, and topic embeddings from news articles, to optimize trading strategies for SP100 stocks. Building upon recent advancements in financial reinforcement learning, we aim to enhance the state space representation by integrating financial sentiment data from SEC filings and news headlines and refining the reward function to better align with portfolio performance metrics. Our methodology includes deep reinforcement learning with state tensors comprising price data, sentiment scores, and news embeddings, processed through advanced feature extraction models like CNNs and RNNs. By benchmarking against traditional portfolio optimization techniques and advanced strategies, we demonstrate the efficacy of our approach in delivering superior portfolio performance. Empirical results showcase the potential of our agent to outperform standard benchmarks, especially when utilizing combined data sources under profit-based reward functions. ...

A Deep Learning Approach to Predict the Fall [“of Price”] of Cryptocurrency Long Before its Actual Fall ArXiv ID: 2411.13615 “View on arXiv” Authors: Unknown Abstract In modern times, the cryptocurrency market is one of the world’s most rapidly rising financial markets. The cryptocurrency market is regarded to be more volatile and illiquid than traditional markets such as equities, foreign exchange, and commodities. The risk of this market creates an uncertain condition among the investors. The purpose of this research is to predict the magnitude of the risk factor of the cryptocurrency market. Risk factor is also called volatility. Our approach will assist people who invest in the cryptocurrency market by overcoming the problems and difficulties they experience. Our approach starts with calculating the risk factor of the cryptocurrency market from the existing parameters. In twenty elements of the cryptocurrency market, the risk factor has been predicted using different machine learning algorithms such as CNN, LSTM, BiLSTM, and GRU. All of the models have been applied to the calculated risk factor parameter. A new model has been developed to predict better than the existing models. Our proposed model gives the highest RMSE value of 1.3229 and the lowest RMSE value of 0.0089. Following our model, it will be easier for investors to trade in complicated and challenging financial assets like bitcoin, Ethereum, dogecoin, etc. Where the other existing models, the highest RMSE was 14.5092, and the lower was 0.02769. So, the proposed model performs much better than models with proper generalization. Using our approach, it will be easier for investors to trade in complicated and challenging financial assets like Bitcoin, Ethereum, and Dogecoin. ...