Quantitative cryptocurrency trading: exploring the use of machine learning techniques

Machine learning techniques have found application in the study and development of quantitative trading systems. These systems usually exploit supervised models trained on historical data in order to automatically generate buy/sell signals on the financial markets. Although in this context a deep exploration of the Stock, Forex, and Future exchange markets has already been made, a more limited effort has been devoted to the application of machine learning techniques to the emerging cryptocurrency exchange market. This paper explores the potential of the most established classification and time series forecasting models in cryptocurrency trading by backtesting model performance over a eight year period. The results show that, due to the heterogeneity and volatility of the underlying financial instruments, prediction models based on series forecasting perform better than classification techniques. Furthermore, trading multiple cryptocurrencies at the same time significantly increases the overall returns compared to baseline strategies exclusively based on Bitcoin trading

Currency movement forecasting using time series analysis and long short-term memory

Foreign exchange is one type of investment, which its goal is to minimize losses that could occurred. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using time series analysis called Auto Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day

Currency movement forecasting using time series analysis and long short-term memory

Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day

A Survey of Forex and Stock Price Prediction Using Deep Learning

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising

Forex Trading Signal Extraction with Deep Learning Models

The rise of AI technology has popularized deep learning models for financial trading prediction, promising substantial profits with minimal risk. Institutions like Westpac, Commonwealth Bank of Australia, Macquarie Bank, and Bloomberg invest heavily in this transformative technology. Researchers have also explored AI's potential in the exchange rate market. This thesis focuses on developing advanced deep learning models for accurate forex market prediction and AI-powered trading strategies. Three deep learning models are introduced: an event-driven LSTM model, an Attention-based VGG16 model named MHATTN-VGG16, and a pre-trained model called TradingBERT. These models aim to enhance signal extraction and price forecasting in forex trading, offering valuable insights for decision-making. The first model, an LSTM, predicts retracement points crucial for identifying trend reversals. It outperforms baseline models like GRU and RNN, thanks to noise reduction in the training data. Experiments determine the optimal number of timesteps for trend identification, showing promise for building a robotic trading platform. The second model, MHATTN-VGG16, predicts maximum and minimum price movements in forex chart images. It combines VGG16 with multi-head attention and positional encoding to effectively classify financial chart images. The third model utilizes a pre-trained BERT architecture to transform trading price data into normalized embeddings, enabling meaningful signal extraction from financial data. This study pioneers the use of pre-trained models in financial trading and introduces a method for converting continuous price data into categorized elements, leveraging the success of BERT. This thesis contributes innovative approaches to deep learning in algorithmic trading, offering traders and investors precision and confidence in navigating financial markets

Can Deep Learning Improve Technical Analysis of Forex Data to Predict Future Price Movements?

The foreign exchange market (Forex) is the world's largest market for trading foreign money, with a trading volume of over 5.1 trillion dollars per day. It is known to be very complicated and volatile. Technical analysis is the observation of past market movements with the aim of predicting future prices and dealing with the effects of market movements. A trading system is based on technical indicators derived from technical analysis. In our work, a complete trading system with a combination of trading rules on Forex time series data is developed and made available to the scientific community. The system is implemented in two phases: In the first phase, each trading rule, both the AI-based rule and the trading rules from the technical indicators, is tested for selection; in the second phase, profitable rules are selected among the qualified rules and combined. Training data is used in the training phase of the trading system. The proposed trading system was extensively trained and tested on historical data from 2010 to 2021. To determine the effectiveness of the proposed method, we also conducted experiments with datasets and methodologies used in recent work by Hernandez-Aguila et al., 2021 and by Munkhdalai et al., 2019. Our method outperforms all other methodologies for almost all Forex markets, with an average percentage gain of 20.2%. A particular focus was on training our AI-based rule with two different architectures: the first is a widely used convolutional network for image classification, i.e. ResNet50; the second is an attention-based network Vision Transformer (ViT). The results provide a clear answer to the main question that guided our research and which is the title of this paper