Using Recurrent Neural Networks To Forecasting of Forex

This paper reports empirical evidence that a neural networks model is applicable to the statistically reliable prediction of foreign exchange rates. Time series data and technical indicators such as moving average, are fed to neural nets to capture the underlying "rules" of the movement in currency exchange rates. The trained recurrent neural networks forecast the exchange rates between American Dollar and four other major currencies, Japanese Yen, Swiss Frank, British Pound and EURO. Various statistical estimates of forecast quality have been carried out. Obtained results show, that neural networks are able to give forecast with coefficient of multiple determination not worse then 0.65. Linear and nonlinear statistical data preprocessing, such as Kolmogorov-Smirnov test and Hurst exponents for each currency were calculated and analyzed.Comment: 23 pages, 13 figure

Can social microblogging be used to forecast intraday exchange rates?

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Towards this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter's social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The final publication is available at http://www.springer.com/economics/economic+theory/journal/1106

Forecast foreign exchange with both linear and non-linear models coupled with trading rules for selected currencies

The importance of forecasting exchange rate is evident both academically and practically, but it is not an easy task to perform as the foreign exchange market has long been considered complex, erratic and exhibits apparently random behavior. The challenge is posited in a number of studies that highlight the poor out-of-sample forecasting performance of a variety of structural exchange rate models and conclude that none of these models could significantly outperform a simple random-walk model in both short- and medium-terms. An extensive subsequent literature using non-linear econometric techniques, different currencies, data periodicity and samples also draw similar conclusion that exchange rates, just like other financial time series, can be well modeled using a random walk model. In this paper we attempt to employ a “hybrid” model to investigate the effectiveness of monetary fundamentals and other macroeconomic variables in predicting the bull and bear market longer-term trends (macro-cycles) and in forecasting the exchange rate movements. In particular, we intend to use a combined model of both parametric Markov Logistic model and a nonparametric multilayer feedforward neural network coupled with technical trading rules to predict the macro-cycles of the selected currencies by using the macroeconomic fundamental variables as inputs. When applying the linear models, most existing studies seem to use the same specification for estimation and forecasting, but the dynamic impact of the concerned variables is ignored. In this study we allow for variations in model specification throughout the forecasting period to address this stylized fact, and, furthermore, we combine the linear model and nonlinear neural network model by adopting both an equal weighted approach and a profit weighted approach to capture both the linear and nonlinear components of the exchange rate mechanism. It is expected that the combined hybrid models will outperform those single models in terms of predicting power and trading advantage in different market condition. We choose three pairs of currencies including the US dollar (USD), the Japanese yen (JPY) and Canadian dollar (CAD) in this study. The USD and JPY are one of the mostly traded currencies in the world, and the Canadian dollar is chosen because of its close economic ties to the United States. The bilateral exchange rate of CAD and JPY is studied as it is interesting to see if our model works for the less traded currencies, and also to complete the “triangle” of the three currencies. The results confirm that the combination models have a significant predictive and market timing ability and outperform the benchmark models in terms of returns, even although their advantage diminishes in the periods of central bank intervention

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 prediction engine: framework, modelling techniques and implementations

Having accurate prediction in foreign exchange (Forex) market is useful because it provides intelligent information for investment strategy. This paper studies extracted repeating patterns of historical Forex time series, so to predict future trend direction by matching the forming trend with a repeating pattern. In the proposed Forex prediction engine, global pattern movements over a period of time are extracted using a linear regression line (LRL) enhanced technique, and then further segmented into what we called up and down curves. Subsequently, the artificial neural network (ANN) is applied to classify or group the uptrend and downtrend patterns. Finally, the dynamic time warping (DTW) is used through brute force to identify a trend pattern similar to the current trend at least for the beginning part. The remaining part of the matched pattern can provide predictive clues about next day trend movement. The experimental results generated on the dataset of AUD–USD and EUR–USD currencies between 2012 and 2013 demonstrate reliable accuracy performance of 72%

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

Robust FOREX Trading with Deep Q Network (DQN)

Financial trading is one of the most attractive areas in finance. Trading systems development is not an easy task because it requires extensive knowledge in several areas such as quantitative analysis, financial skills, and computer programming. A trading systems expert, as a human, also brings in their own bias when developing the system. There should be another, more effective way to develop the system using artificial intelligence. The aim of this study was to compare the performance of AI agents to the performance of the buy-and-hold strategy and the expert trader. The tested market consisted of 15 years of the Forex data market, from two currency pairs (EURUSD, USDJPY) obtained from Dukascopy Bank SA Switzerland. Both hypotheses were tested with a paired t-Test at the 0.05 significance level. The findings showed that AI can beat the buy & hold strategy with significant superiority, in FOREX for both currency pairs (EURUSD, USDJPY), and that AI can also significantly outperform CTA (experienced trader) for trading in EURUSD. However, the AI could not significantly outperform CTA for USDJPY trading. Limitations, contributions, and further research were recommended