Forecasting Forex markets based on japanese candlestick and RSI, CCI, MA indicators using LSTM

In this study, the primary focus was on examining Forex market expectations for financial trading. Accurate Forex market forecasts are essential for traders and investors participating in global financial markets. Numerous forecasting methods rely on technical patterns and financial indicators. To enhance the EUR/USD forecast, an LSTM model based on candlesticks and financial variables was utilized. Japanese candlesticks rank among the most widely utilized tools for assessing financial markets. Finally, these candles, which illustrate price patterns and distinctions between buying and selling, provide crucial data for predicting future price fluctuations. A 15-minute candlestick reading was employed. Additionally, technical financial indicators like Moving Averages (MA), Commodity Channel Indices (CCI), and Relative Strength Indices (RSI) were used to generate predictive signals and estimate trends in financial markets. These indicators are vital for evaluating the model's capability to predict trends and market changes. LSTM processed data and learned from historical patterns in exchange rates. A comparison was conducted with a group of models, where the LSTM model demonstrated superiority. Various measures of accuracy and statistical performance were employed, including the achieved percentage of absolute error (MAE = 0.00043%) and the average percentage of absolute error (MAPE = 0.0407%), to assess the model's performance and measure errors. Ultimately, this study highlights the potential to enhance currency forecasts through deep learning techniques, contributing to improved decision-making for traders and investors.