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

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

Assessing Manual Dataset Creation For Xauusd Market Prediction : A Comparative Study Logistic Regression And Decision Tree Model

This study aims to develop a simplified dataset for more effective market prediction, focusing on the Forex trading of XAUUSD (Gold/USD). The dataset was gathered from the TradingView platform, covering the period from March 4, 2023, to December 21, 2023. The data collection method involved intensive observation of daily and weekly charts, utilizing Daily and Weekly Moving Average (MA) indicators and the concept of breakout. The analysis focused on measuring the distance between the Daily MA at the beginning and end of the period (start and stop), and utilizing this data for entry strategy in the following three time periods. The trading strategy adopted involves the simultaneous use of Buy and Sell orders, with a Stop Loss (SL) to Take Profit (TP) ratio of 1:2. TP was adjusted to accommodate aggressive price movements, while SL remained constant. The collected data was meticulously recorded and stored in Excel format for further analysis.With the prepared dataset, this research applies two AI models, Logistic Regression and Decision Tree, to predict the best trading decision – Buy or Sell. The study aims not only to create a useful dataset for market prediction but also to compare the effectiveness of two different AI methods in the context of Forex trading of XAUUSD. The results are expected to provide insights into which model is more accurate and efficient in analyzing and predicting market trends, with practical implications for traders and market analysts

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

Evaluation of the Profitability of Technical Analysis for Asian Currencies in the Forex Spot Market for Short-Term Trading

Technical analysis has garnered an unprecedented amount of interest among short-term traders in the Forex spot market over the past couple of decades. The main purpose of this study is to examine the profitability of technical analysis as applied to three active Asian currencies in the Forex spot market for short-term trading. This study also tests the relationship between various related parameters of currency trading such as Maximum Drawdown, Time in Position, Dealt Lots, Trading Charges and profitability. It covers ten currency pairs, including ten foreign exchange rates of three active Asian currencies in the Forex spot market (the Japanese Yen, Singaporean dollar, and Hong Kong dollar), five time frames involving Intra-day timeframes, and ten technical indicators (5 leading and 5 lagging). The study covers a period of three months running from April 10, 2012 through July 10, 2012. The results indicate that technical analysis is profitable for Asian currencies as attested by the fact that all the currency pairs, time frames and indicators have yielded trading profits in the Forex spot market

Reinforcement Learning Applied to Trading Systems: A Survey

Financial domain tasks, such as trading in market exchanges, are challenging and have long attracted researchers. The recent achievements and the consequent notoriety of Reinforcement Learning (RL) have also increased its adoption in trading tasks. RL uses a framework with well-established formal concepts, which raises its attractiveness in learning profitable trading strategies. However, RL use without due attention in the financial area can prevent new researchers from following standards or failing to adopt relevant conceptual guidelines. In this work, we embrace the seminal RL technical fundamentals, concepts, and recommendations to perform a unified, theoretically-grounded examination and comparison of previous research that could serve as a structuring guide for the field of study. A selection of twenty-nine articles was reviewed under our classification that considers RL's most common formulations and design patterns from a large volume of available studies. This classification allowed for precise inspection of the most relevant aspects regarding data input, preprocessing, state and action composition, adopted RL techniques, evaluation setups, and overall results. Our analysis approach organized around fundamental RL concepts allowed for a clear identification of current system design best practices, gaps that require further investigation, and promising research opportunities. Finally, this review attempts to promote the development of this field of study by facilitating researchers' commitment to standards adherence and helping them to avoid straying away from the RL constructs' firm ground.Comment: 38 page