Moving Average Convergence Divergence Research Articles

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions

IntroductionThe cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations.MethodsIn this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly.Results and discussionTo make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices

Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty etc. Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guided Deep Learning (DL) models has been developed on nonlinear and nonstationary price data of agricultural commodities for forecasting by considering technical indicators viz., Moving Average (MA), Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), Exponential MA (EMA) and Fast Fourier Transformation (FFT). HM Models (HMMs) can effectively handle the sequential dependencies and hidden states, while DL approach can learn complex patterns and relationships within the price series and thus the drawback of lack of generalization capability in the DL model has been overcome by HMM. In this study, the Potato price data of the Champadanga district of West Bengal, India has been utilized to assess the performance of the proposed technique. HMM has been combined with six baseline DL models viz., Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) for forecast modeling. Performance evaluation metrics viz., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and the insightful Diebold–Mariano (DM) test revealed that Hidden Markov hybridized with DL models surpassed baseline DL models in forecasting accuracy for 1-week, 4-week, 8-week and 12-week ahead DL predictions. The proposed approach holds significant promise for enhancing the precision of agricultural commodity price forecasting with far-reaching implications for various stakeholders such as farmers and planners.

MACHINE LEARNING FOR STOCK MARKET FORECASTING: A REVIEW OF MODELS AND ACCURACY

As financial markets become increasingly complex and dynamic, the application of machine learning (ML) techniques for stock market forecasting has garnered significant attention. This paper presents a comprehensive review of various ML models employed in the realm of stock market forecasting, focusing on their methodologies and the accuracy achieved in predicting market trends. The review begins by examining traditional time-series models such as autoregressive integrated moving average (ARIMA) and moving average convergence divergence (MACD) and their limitations in capturing the intricate patterns present in financial data. Subsequently, the discussion transitions to more advanced ML models, including support vector machines (SVM), artificial neural networks (ANN), and ensemble methods like random forests and gradient boosting. Each model's strengths and weaknesses are scrutinized in the context of stock market forecasting. The paper explores the pivotal role of feature selection and engineering in enhancing the predictive power of ML models. Feature sets encompassing financial indicators, macroeconomic variables, sentiment analysis from news articles, and social media data are analyzed for their impact on forecasting accuracy. Additionally, the incorporation of technical indicators and alternative data sources is explored as potential avenues to improve model robustness. A critical aspect of this review is the assessment of accuracy in predicting stock market movements. The evaluation is conducted through a comparative analysis of model performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and accuracy rates. The study also addresses the challenge of model overfitting and proposes strategies to mitigate this issue for more reliable predictions. This review provides a nuanced understanding of the landscape of ML models for stock market forecasting, highlighting the diverse approaches, challenges, and opportunities in the quest for improved accuracy. It contributes valuable insights for researchers, practitioners, and investors seeking to leverage the potential of ML in navigating the complexities of financial markets.
 Keywords:  Machine Learning, Stock Market, Forecasting, Models, Review.

Hybrid model for rainfall prediction with statistical and technical indicator feature set

As excessive rain may cause numerous disasters, rainfall prediction is very crucial and the prediction should be realistic since it encourages individuals to take preventative steps. This work proposes a new rainfall prediction model by following three major phases: Preprocessing, Extraction of Features and Prediction. The Improved Data Normalization technique is carried out for preprocessing the data. Then, it extracts the technical Indicator features like Average Directional Movement (ADX), Moving Average Convergence Divergence (MACD), RSI, Welles Wilder's Smoothing Average (WWS), statistical & higher order statistical features. According to these features, prediction takes place by a hybrid model that includes the Improved DBN & LSTM methods. By fine-tuning the best weights, the training will be carried out optimally to increase the prediction model's efficacy. For this, the Brownian Motion-based Pelican Optimization Algorithm (BMPOA) is implemented. Finally, the proposed technique has contrasted over existing models concerning different metrics.

Trend-Enhanced Improved Bollinger Bands Trend-Following High-Frequency Trading Strategy for Futures Market

The application of digital scientific technologies in the global financial markets has become widespread, which has led to the prevalence of the concept of quantitative trading. Among various quantitative trading strategies, high-frequency trading has gained significant popularity. In this study, we propose an improved Bollinger Bands trend-following high-frequency trading strategy based on trend enhancement. Instead of using the simple moving average (SMA), we replace it with the exponential moving average (EMA). Furthermore, we introduce a 3-times average true range (ATR) limit price rebound exit system to achieve timely profit-taking and stop-loss. By incorporating trend measurement indicators such as the average directional index (ADX), moving average convergence divergence (MACD), and commodity channel index (CCI), we determine the futures trend. Subsequently, this study formulates suitable parameters for the high-frequency trading strategy and conduct backtesting on the 2022 CSI 300 stock index futures, resulting in a remarkably high annualized return. These results present a novel approach to high-frequency trading strategy and provide valuable insights for the market.

How to Become Rich Using Quantitative Trading?

Bitcoin has become a very common investment product, also known as 'digital gold' because of its global discussion and rising prices. This paper develops a model that can only rely on these data to predict future price fluctuations and plan trading decisions. The trading decision planning model consists of three sub-models that are mutually undertaken. For model 1, in order to judge the price trend reliably, this paper uses the Long Short-Term Memory (LSTM) neural network, which is sensitive to the time series, to learn all the time series price data up to the trading day, and to predict the future price data of them respectively, which is provided to another sub-models for subsequent work. For Model 2, after obtaining the follow-up price data, all the technical indicators needed are calculated in this model, and then the moving average convergence divergence (MACD) rule is used to determine whether the transaction is carried out for the first time, and then the predicted price trend is used for the second judgment. For model 3, the model begins to make decision- making planning. This model mainly solves the bi-objective programming problem with the goal of maximizing returns and minimizing risks. In the range of controllable risks, the model will give the optimal solution of maximizing returns. Finally, using the model, the sensitivity of the model to transaction cost is studied by changing the transaction commission, and it is found that the final investment income is more sensitive to the transaction commission of bitcoin.

Technical Indicators of Major Healthcare Companies: A Comprehensive Analysis

This research aims to provide a comprehensive analysis of major healthcare stocks along with the nifty health care index within the healthcare sector. Employing technical indicators such as the Moving Average Convergence Divergence (MACD), Rate of Change (ROC) and Relative Strength Index (RSI) we conduct a detailed trend analysis to identify historical patterns and potential turning points in stock prices. Furthermore, advanced time series forecasting models and predictive analytics are utilized to forecast future share prices for the selected three companies, offering valuable insights for investors and market participants.
 In addition, we investigate the relationship between the stock prices of Apollo Hospital, Fortis Healthcare & Narayana Hruda and the Nifty Healthcare Index. This analysis sheds light on how individual stock prices are influenced by broader sectoral performance. We also explore the impact of MACD, ROC, and RSI technical indicators on the prices of all healthcare stocks through rigorous statistical analysis. Our findings provide healthcare sector stakeholders with a deeper understanding of stock behaviour, potential future movements, correlations with market indices and risk assessment.
 The results of this study enhance the decision-making process for investors and market participants in the healthcare sector. By examining historical trends, forecasting price dynamics, assessing sectoral correlations, evaluating technical indicator effects and risk measurement, this research contributes to a more comprehensive view of these stocks and their positioning within the healthcare industry.

Mechanisms of Stock Selection and Its Capital Weighing in the Portfolio Design Based on the MACD-K-Means-Mean-VaR Model

When designing a stock portfolio, investors must select stocks with different characteristics and increasing price trends and weigh each capital. Both are fundamental to diversifying loss and profit. Therefore, the mechanisms that accommodate both are needed. Based on this, this research aims to design a stock selection and capital weighing mechanism using the MACD-K-means-Mean-VaR model. The moving average convergence–divergence (MACD) is used to analyze stock buying time, providing trend, momentum, and potential price reversal insights. Then, stocks with increasing price trends are clustered using K-means, a grouping simple pattern data method based on specific characteristics. The best stocks from each cluster are capital weighted using the mean value at risk (mean-VaR), a portfolio optimization model adjusting loss possibility to the investor’s acceptance tolerance. The mechanism is then applied to Indonesia’s 100 stock index data to analyze variable sensitivities and compare it with another model. The application reveals that all variables significantly impact portfolio return mean and VaR, suggesting the need for clustering and analyzing stock price movements in stock portfolio design. This research academically develops a portfolio design mechanism by clustering stocks and analyzing price movement trends. It enables investors to practically diversify and choose stocks with increasing price trends, reducing losses and increasing profit opportunities.

Learning and processing framework using Fuzzy Deep Neural Network for trading and portfolio rebalancing

Trading strategies are an interesting topic of financial research. Moving Average Convergence Divergence (MACD) indicator is susceptible to performing worse than expected in unstable financial markets. This paper first presents a data-driven Interpretable Fuzzy Deep Neural Network (IFDNN) that provides insight into neural network inferences using fuzzy logic. Fuzzy rules are induced from the inference process of Neural Networks. Next, a learning and processing framework is proposed using IFDNN to detect trend reversals by forecasting look-ahead prices. IFDNN not only learns the drifts and shifts in market patterns, but also provides traders an option to dive into the reasoning behind why Neural Networks predict certain values. Genetic Algorithms are used to optimise trading parameters of the proposed framework. The proposed framework can perform portfolio rebalancing. The effectiveness of the framework is evaluated on three financial market indexes. The whipsaw effects cause frequent entrances and exits from the market. In this paper, a custom percentage oscillator is implemented to avoid this issue. The performances of the proposed framework using f-MACD are compared with those of the vanilla MACD. Two types of Reinforcement Learning models, Advantage Actor Critic and Deep Deterministic Policy Gradient are incorporated into the proposed framework with results compared.

Optimal and Non-Optimal MACD Parameter Values and Their Ranges for Stock-Index Futures: A Comparative Study of Nikkei, Dow Jones, and Nasdaq

This study investigates the optimal and non-optimal parameter values of the MACD (Moving Average Convergence Divergence) technical analysis indicator for three major stock market index futures: the Nikkei 225, the Dow Jones, and the Nasdaq. Using a recently developed methodology, it reveals the existence of specific ranges of optimal and non-optimal values for each of the three parameters of the MACD indicator in these indices. Sample models employing the optimal parameter values in the three index futures generated significantly higher returns, outperforming both a non-technical buy-and-hold strategy and a random strategy that did not incorporate any market information. This discovery suggests that the three market indices may not be weak-form efficient. Therefore, this study contributes to the research on market efficiency by verifying inefficiency using a new approach. The highlight of this study is identifying that the ranges of optimal parameter values for the three indices are different from each other, but the optimal parameter value combinations for each of the three indices share a unique characteristic form. This issue and its finding have not been explored in the existing literature. Several interesting findings and valuable insights for market participants and researchers arise from this study. The new methodology is unique in finding optimal and non-optimal parameter values through the analysis of parameter sets used in well-performing and poorly performing sample models. Its validity and reliability have been confirmed by this study, making a useful contribution to the field of technical analysis research, particularly in parameter optimization insight.

Beating the market with a simple proposed portfolio and technical trading rules

This study presents evidence that well-known technical trading rules (TTRs) applied to a proposed simple exchange-traded fund (ETF) portfolio provide better risk-adjusted performance than the ‘market’ and top-ranked balanced funds (BFs). We form a portfolio of two well-known ETFs and apply some well-known TTRs like Moving Average (MA), Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) techniques to this simple proposed portfolio from January 31, 2007 to August 31, 2021. We design two strategies: (1) Long/Money and (2) Leverage/Money. We compare the risk-adjusted performance of some well-known trading rules applied to our proposed portfolio with strategy (1), Long/Money, which is a more conservative investment strategy, to the risk-adjusted performance of 14 top-ranked BFs. The best trading rules with strategy (1) applied to our portfolio outperform all the top-ranked BFs for the entire period and over two equal sub-periods. Similarly, the best trading rules applied to our portfolio with strategy (2) outperform the ‘market’, a Buy and Hold (B&H) strategy for the entire period and over two equal sub-periods. This paper contributes to the literature examining the common assumptions of efficient markets and could have important practical implications for many fund managers. In addition, the social impact of our paper could affect thousands of retail traders who are trying to ‘beat’ the market.

Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach

The Philippine stock market, just like most of its neighbors in the region, was seriously impacted by the global pandemic COVID-19. Investors remain hopeful while continuing to seek great ones in the damaged market. This paper developed a methodology for portfolio selection and optimization with the use of technical analysis, machine learning techniques, and portfolio optimization model. The combined methods of technical analysis, K-means clustering algorithm, and mean-variance portfolio optimization model will result in the development of the proposed TAKMV method. The study aims to integrate these three important analyses to identify portfolio investments. This paper uses the average annual risk and annual rate of return data for the years 2018 and 2020 to form the clusters and assessed the stocks that correspond to the investor's technical strategy such as Moving Average Convergence/Divergence (MACD) and Hybrid MACD with Arnaud Legoux Moving Average (ALMA). This paper solved the risk minimization problem on selected shares of the companies, based on the mean-variance portfolio optimization model. There are 230 and 239 companies for 2018 and 2020, respectively, listed in Philippine Stock Market, and all simulations were performed in MATLAB environment platform. Results showed that MACD strategy dominates the MACD-ALMA strategy in terms of the number of assets with a positive annual rate of return. The MACD works efficiently in the pre-COVID-19 condition while MACD-ALMA works efficiently during-COVID-19 condition, regardless of the number of assets with a positive annual rate of return. The results also show that the maximum expected portfolio return (RP) can be achieved using the MACD and MACD-ALMA in the pre-and during-COVID-19 conditions, respectively. The MACD-ALMA shows an advantage during high-risk market conditions and can also provide maximum RP. The performance of the TAKMV method was validated by applying its results and comparing it to the next year's historical price. The 2018 results were compared to 2019 data and the 2020 results were compared to 2021 data. For consistency, the comparison was applied to the same company per portfolio. Simulation results show that the MACD strategy is more effective compared to MACD-ALMA.

THEORETICAL METHODOLOGICAL JUSTIFICATION IN TECHNICAL ANALYSIS OF FINANCIAL MARKETS

We live in strange times times of change, crises, pandemics, conflicts and even wars. Technology is developing at an incredible speed, and our ability to predict or react is narrowed by rapidly changing trends, market changes and more. Throughout millennia of written and unwritten history, humanity has learned to change, evolve and survive. Economists and marketers have long recognised the cyclical nature of events and their repeatability, thus creating theories and tools. In our attempt to predict future events, we often refer to past experience and historical lessons. In the field of finance, technical analysis is a methodology for analysing financial markets, the purpose of which is to predict likely price commute based on regularities deploy on similar price changes in the past under similar circumstances. For this purpose, historical market data such as prices and volume are used (Kirkpatrick, Dahlquist, 2006). At its core, technical analysis has many tools: time charts, support and resistance charts, Fibonacci analysis, Japanese candlestick chart and many other theoretical methods and methodologies with greater or lesser practical success. But while there are many instruments, the indicators that help correctly start the future investment or withdrawal are a few looking for changes in time and matching trends by comparing the lengths of their intervals: there are indicators reporting the involvement of the instrument in the trend Indicator ADL, McClellan Oscillator, the Index of McClellan Sherman (McClellan Summation Index) and indicators follow the change in prices Indicator %C (Percent Contraction Index), Indicator ADX (Average directional index), Index of the Commodity Channel (Commodity Channel Index), Indicator MACD (Moving Average Convergence / Divergence), Relative strength index (RSI), Relative vigor index (RVI), Stochastic oscillator and indicators based on market volume Accumulation/distribution index, Cash flow and Balance sheet volume. The purpose of the research is to draw attention to a theoretical hypothesis for the beginning of the trend wave itself, the beginning of the considered period supporting future investment or withdrawal from a given industry, the development of a trending product or service. The chosen methodology and corresponding tool formula are entirely author’s and would help to visualise the time intervals for investment or withdrawal from the market.

Prerequisites and scenarios for the EU economy transformation inlight of Industry 4.0 development

The purpose of this study is to verify the condition of European Union economy and its ability to effectively implement the concept of sustainable development in the era of the fourth industrial revolution. To achieve the goal settled by this research, we used the technical indicator MACD (Moving Average Convergence / Divergence), based on the assessment of the convergence and divergence of moving averages of macroeconomic indicators. The studies carried out made it possible to identify three scenarios for the state of the EU economy after 2022. All these scenarios pose a high risk of a crisis phenomena in this economic system. According to the optimistic scenario, after 2022, a downward correctional wave is predicted in the EU-27 economy with the emergence of a situation similar to the situation in 2008. The search for a new economic model based on the active implementation of Industry 4.0 technologies can become a way for the EU economy to recover from the crisis and move to a qualitatively new level of socio-economic development.

Application of Machine Learning With News Sentiment in Stock Trading Strategies

This study empirically tested the feasibility of machine learning in trading strategies using technical indicators and news information as the feature variables for machine learning. Six indicators were adopted in this study, including moving average (MA), moving average convergence/divergence (MACD), relative strength index (RSI), stochastic oscillator (KD), and on-balance volume (OBV), and news sentiment ratio (SR) developed in this study via text mining. Selected machine learning models, including support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), recurrent neural network (RNN), and long short-term memory (LSTM), were also employed for investigation. This study backtested the daily historical data of the constituent stocks in the Taiwan Top 50 ETF from January 1, 2003, to December 31, 2018, using three categories of trading strategies along with conventional and countertrend operations. The following conclusions were drawn after analyzing the performance of these trading strategies via various means: 1. Technical indicators such as MA, MACD, and RSI performed poorly in most cases. 2. Specific parameters were of relative importance to several technical indicators, including MA, MACD, RSI, and OBV. 3. OBV was a technical indicator with a positive impact on trading strategies. 4. The machine learning-based XGBoost models were able to outperform trading strategies with technical indicators under specific scenarios. 5. SR, the news sentiment ratio developed in this study, could not significantly improve the performance of machine learning models. The empirical results of this study suggest that these machine-learning models are capable of analyzing long-term stock price movements to some extent.

A new index for assessing the coastal wind disasters based on the HY-2 satellite data

Tropical cyclone disasters have a substantial impact on human activity in coastal zones. The traditional methods used to classify wind disasters include the Beaufort Scale, Saffir-Simpson Scale, and Chicago Mercantile Exchange Hurricane Index (CMEHI). This research established a new index, the Haiyang Cyclone Index (HYCI), which considers spatial distributions and sea level anomalies (SLA) using HY-2 satellite data. The HYCI of three mega coastal cities, Qingdao, Shanghai, and Guangzhou, was calculated. A brief analysis was then performed using Moving Average Convergence Divergence (MACD) and Long Short-Term Memory (LSTM). The proposed HYCI can accurately evaluate the impact of cyclone disasters, thereby confirming its financial application potential.

Trading Strategies Using Technical Indicators: An Active Portfolio Management Approach

The study aims to evaluate the effectiveness of active trading strategies developed using technical indicators against the buy-and-hold passive investment strategy. It uses the simple moving average (SMA), Bollinger band (BB), and moving average convergence divergence (MACD) as technical indicators to generate the buy and sell signals. The study uses the data of treasury bills as a proxy for a risk-free portfolio and the Nifty Total Return Index (TRI) as a proxy for a risky equity portfolio in daily frequency from June 30, 1999 to March 31, 2021. It creates the cumulative wealth index for the sample period using active portfolio management based on the buy and sell signals generated using various technical indicators. The findings suggest that SMA and MACD trading strategies provide better risk-adjusted returns than the buy-and-hold and Bollinger band trading strategies. Also, the shorter days moving average outperforms the longer days moving average trading strategy. The trading strategies perform better during the bear market than the bull market.

The Relationship between VIX and Technical Indicator: The Analysis of Shared-Frailty Model

Abstract This article uses a shared-fragility model for the recurrent spell length for the volatility index (VIX). Ten future VIXs have been chosen. The article examines the impacts of the top five market technical indicators, including the Moving Average Convergence–Divergence (MACD), Relative Strength Index (RSI), Triple Exponential Average (TRIX), Historical Volatility (HV), and Vertical Horizontal Filter (VHF). On the spell time of upward, downward, and overall trends, our findings supported the hypothesis that investors can better understand the pattern and trend of volatility risk by impacting the relevant technical index on various futures VIXs. We examined how technical indicators affect the volatility index. In the Weibull distribution, the upward trend data has the most significant values. The positive values provided by MACD and TRIX imply a strong positive relationship with VIX and a longer survival time. The results also show that the greater the value of historical volatility, the greater the risk to commercial investments. As a result, the possibility of VIX in the trading pattern has a shorter time because the selling signals are coming out. JEL classification numbers: G15. Keywords: VIX Index, Technical Indicator, Shared-frailty Model.

RETRACTED: Machine learning-based probabilistic profitable model in algorithmic trading

Machine learning models are nowadays becoming ubiquitous in algorithmic trading and investment management. These models are mostly used in the pre-trade analysis phase to determine the buy or sell decisions using various machine learning techniques. We aim to implement a machine learning-driven approach using various technical indicators to predict stock market prices and then accordingly make a decision about buying or selling. First, an effective trading strategy is discussed that selects the potentially profitable stocks, and then the technical indicators such as simple moving average (SMA), exponential moving average (EMA), relative strength index (RSI), and moving average convergence divergence (MACD) are calculated for those potentially profitable stocks. Then supervised machine learning algorithms such as multiple linear regression, support vector machine regression, and decision tree regression are applied, where the close price of the stock is predicted using technical indicators for the next day, and based on that buy or sell signals are generated. The model is then tested on 12 different SNP500 stocks, one for every month in 2018, with the mean squared error (MSE) varying between 30.33 and 48.16 and the root MSE varying between 5.51 and 6.93, where the error is calculated on the difference in the number of days when the stock price actually increases and the predicted number of days for various models.

Research on Multistage Dynamic Trading Model Based on Gray Model and Auto-Regressive Integrated Moving Average Model

Quantitative portfolio investment mainly depends on historical data analysis and market trend prediction to make appropriate decisions, which is an important mean to reduce risks and increase returns. Based on summarizing the existing traditional single forecasting models and multiobjective dynamic programming models, this paper puts forward a new quantitative portfolio model to improve the accuracy of asset price forecasting results and the appropriateness of investment trading strategies, to better realize the maximization of investment returns. This model analyzes and forecasts daily price data by establishing a combination forecasting model of the gray GM (1,1) model and the ARIMA time series model and establishes a multiobjective dynamic programming model with moving average convergence divergence (MACD) and Sharpe ratio indicators as risk constraints to formulate appropriate investment trading strategies. The results show that by solving the quantitative portfolio trading model established in this paper and analyzing the sensitivity and robustness of the model, the price of gold and Bitcoin, two volatile assets, can be accurately predicted, and the best investment portfolio trading strategy can be effectively worked out on the premise of considering the risk level.