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

Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques with a Novelty Feature Engineering Scheme

Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment strategy is constructed according to the ensemble machine learning techniques. Empirical results from 2000 to 2017 of China’s stock market confirm that our feature engineering has effective predictive power, with a prediction accuracy of more than 60% for some trend patterns. Various measures such as big data, feature standardization, and elimination of abnormal data can effectively solve data noise. An investment strategy based on our forecasting framework excels in both individual stock and portfolio performance theoretically. However, transaction costs have a significant impact on investment. Additional technical indicators can improve the forecast accuracy to varying degrees. Technical indicators, especially momentum indicators, can improve forecasting accuracy in most cases

Improving Stock Trading Decisions Based on Pattern Recognition Using Machine Learning Technology

PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from Jan 1, 2000 to Dec 31, 2014 is used as the training data set, and the data set from Jan 1, 2015 to Oct 30, 2020 is used to verify the forecasting effect. Empirical results show that the two-day candlestick patterns after filtering have the best prediction effect when forecasting one day ahead; these patterns obtain an average annual return, an annual Sharpe ratio, and an information ratio as high as 36.73%, 0.81, and 2.37, respectively. After screening, three-day candlestick patterns also present a beneficial effect when forecasting one day ahead in that these patterns show stable characteristics. Two other popular machine learning methods, multilayer perceptron network and long short-term memory neural networks, are applied to the pattern recognition framework to evaluate the dependency of the prediction model. A transaction cost of 0.2% is considered on the two-day patterns predicting one day ahead, thus confirming the profitability. Empirical results show that applying different machine learning methods to two-day and three-day patterns for one-day-ahead forecasts can be profitable

Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model

In the world of stock investment, one of the things that commonly happens is stock price fluctuations or the ups and downs of stock prices. As a result of these fluctuations, many novice investors are afraid to play stocks. However, on the other hand, stocks are a type of investment that can be relied upon during disasters or economic turmoil, such as in 2019, namely the Covid-19 pandemic. For stock price fluctuations to be estimated by investors, it is necessary to carry out a forecasting activity. This study builds stock price forecasting using the Light Gradient Boosting Machine (LightGBM) algorithm, which has high accuracy and efficiency. To forecast stock price time series, the model used is the LightGBM ensemble. At the same time, they were optimizing the determination of hyperparameters using Grid Search Cross Validation (GSCV). This study will also compare the LGBM algorithm with other algorithms to see which model is optimal in forecasting price stock data. In this study, the test used the RMSE metric by comparing the original data (testing data) with the predicted results. The experimental results show that the LightGBM model can compete with and outperform boosting-based forecasting models like XGBoost, AdaBoost, and CatBoost. In comparing forecasting models, the same dataset is used so that the results are accurate, and the comparisons are equivalent. In future research, paying attention to the data during pre-processing is necessary because it has many outliers. In addition, it is necessary to include exogenous variables and external variables, which are determined to involve many parties

Technical Analysis Based Automatic Trading Prediction System for Stock Exchange using Support Vector Machine

Stock exchange trading has been utilized to gain profit by constantly buying and selling best-performing stocks in a short term. Deep knowledge, time dedication, and experience are essential for optimizing profit if stock price fluctuations are analyzed manually. This research proposes a new trading prediction system that has the ability to automatically predict the accurate time for buying and selling stock using a combination of technical analysis and support vector machine (SVM). Technical analysis is used to analyze stock price fluctuation based on historical data by utilizing technical indicators such as moving average, Bollinger bands, relative strength index, stochastic oscillator, and Aroon oscillator. SVM maps inputs into higher dimensional spaces using non-linear kernel functions, making it suitable for various technical indicators implementation as inputs in stock trading prediction. Experimentation on five Indonesian stocks reveals that the combination of technical analysis and support vector machine is best suited for continuously fluctuated stocks, with the highest accuracy of 77.8%

Dynamic Feature Engineering and model selection methods for temporal tabular datasets with regime changes

The application of deep learning algorithms to temporal panel datasets is difficult due to heavy non-stationarities which can lead to over-fitted models that under-perform under regime changes. In this work we propose a new machine learning pipeline for ranking predictions on temporal panel datasets which is robust under regime changes of data. Different machine-learning models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks with and without simple feature engineering are evaluated in the pipeline with different settings. We find that GBDT models with dropout display high performance, robustness and generalisability with relatively low complexity and reduced computational cost. We then show that online learning techniques can be used in post-prediction processing to enhance the results. In particular, dynamic feature neutralisation, an efficient procedure that requires no retraining of models and can be applied post-prediction to any machine learning model, improves robustness by reducing drawdown in regime changes. Furthermore, we demonstrate that the creation of model ensembles through dynamic model selection based on recent model performance leads to improved performance over baseline by improving the Sharpe and Calmar ratios of out-of-sample prediction performances. We also evaluate the robustness of our pipeline across different data splits and random seeds with good reproducibility of results

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection

Explorando a forma fraca da (in)eficiência de mercado por meio de algoritmos de inteligência artificial

A pesquisa apresentada neste trabalho visou analisar o desempenho de diferentes algoritmos de inteligência artificial (IA) para previsão de movimentos dos principais índices das maiores bolsas de valores ao redor do mundo. Para tanto, foram coletados dados diários de 34 índices, entre os anos de 2010 e 2019, e estimados os movimentos desses índices com o uso de quatro dos principais algoritmos de IA: Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Naive Bayes (NB) e Random Forest (RF). Tais algoritmos foram treinados com base em nove indicadores técnicos amplamente empregados na análise de ativos financeiros. De forma geral, evidenciou-se a possibilidade de se obter retornos superiores à média de mercado a partir dos algoritmos selecionados e treinados com base em indicadores técnicos. Destaca-se, portanto, o potencial de exploração de ineficiências de diferentes mercados de capitais ao redor do mundo em sua forma fraca a partir de algoritmos de IA. De forma específica, constatou-se que o desempenho dos algoritmos variou de acordo com a medida de desempenho utilizada. Quando se considerou a acurácia como medida de desempenho, o algoritmo ANN obteve desempenhos superiores aos dos demais; ao passo que o algoritmo NB apresentou os piores desempenhos independentemente das medidas empregadas para mensurá-lo. O estudo desenvolvido traz uma série de contribuições à pesquisa sobre o emprego desses algoritmos para previsão do movimento de índices de ativos financeiros nos mercados de capitais ao redor do mundo: (i)  obtiveram-se evidências robustas da utilidade e relevância de algoritmos de IA para prever movimentos de preços nas principais bolsas de valores do mundo; (ii) verificou-se que a medida empregada para mensurar o desempenho dos algoritmos influencia de forma significativa sua avaliação; e (iii) constatou-se que os indicadores técnicos podem auxiliar em decisões que agregam valor ao serem conjugados com técnicas de IA

Análisis y predicción de las tendencias de venta en el mercado usando árboles de regresión

The latest advances in artificial intelligence techniques are increasingly related to deep learning, which are more sophisticated and expensive in terms of computational and structural aspects. These techniques have been successful in various areas such as medical, food, industry, banks, and others, helping in data processing and related predictions, which creates a competitive advantage against other industries...Los recientes avances de las técnicas de inteligencia artificial cada vez se encuentran más relacionadas al aprendizaje profundo, los cuales a su vez son más sofisticados y costosos con respecto al costo computacional y a la infraestructura. Estas técnicas han demostrado ser exitosas en diversas áreas como la médica, industria alimentaria, bancos, entre otras, ayudando en tareas de procesamiento de datos y predicciones relacionadas a estos, lo cual genera una ventaja competitiva..

Analítica de variables asociadas a la generación de reclamos en la distribución directa

52 páginasUno de los actores principales en el modelo de venta directa son las promotoras comerciales. Las empresas dedicadas a este modelo prestan especial atención a las reclamaciones por productos faltantes posterior al proceso de entrega de sus órdenes de pedido. Identificar las variables que ocasionan estas reclamaciones por parte de la promotora permiten obtener un activo valioso y competitivo, de donde se podrán generar análisis y acciones de analítica predictiva para evitar el reclamo y mejorar el nivel de servicio. Esta investigación presenta un panorama general de la logística del proceso de entrega de los productos a la promotora comercial en una empresa de venta directa y propone un modelo predictivo de clasificación supervisada para encontrar las futuras promotoras reclamantes. La implementación de este modelo permitió identificar que los días disponibles para venta es la variable protagonista en el comportamiento de la promotora y genera información beneficiosa para mitigar los reclamos y los costos que estos conllevan.One of the main actors in the direct sales model is commercial promoters. The companies dedicated to this model take special attention to claims for missing products after the delivery process of their order forms. Identifying the variables that cause these claims allows obtaining a valuable and competitive asset, from which analyzes, and predictive analytical actions can be generated to avoid the claim and improve the level of service. This research presents an overview of the logistics of the product delivery process to the commercial promoter at the company and proposes a predictive model of supervised classification to find future claimant promoters. The implementation of this model allowed us to identify that the days available for sale is the leading variable in the behavior of the developer and generates beneficial information to mitigate the claims and the costs.Maestría en Diseño y Gestión de ProcesosMagíster en Diseño y Gestión de Proceso