Stop Hunt Detection using Indicators and Expert Advisors in the Forex Market

Foreign exchange trading activities are one of the businesses that can generate big profits, and provide freedom for business people without the need to provide a large capital. Traders often suffer losses due to uncertainty in the market. One of them is market manipulation carried out by brokers or banks. For this reason, this research was conducted to detect any manipulation that occurred in the foreign exchange market. This research tries to combine trading systems, indicators and expert advisors that aim to help traders detect fake market price movements to minimize losses that occur due to errors in making transaction decisions. The results of the study produce an indicator that is able to detect the potential of certain patterns used by the market maker to reverse the direction of market prices, and is supported by the presence of expert advisors who are able to pinpoint potential market manipulation, so traders can avoid large losses

XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method

XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method

Dynamic portfolio rebalancing with lag-optimised trading indicators using SeroFAM and genetic algorithms

Some common technical indicators, such as moving average convergence divergence (MACD), relative strength index (RSI), and MACD histogram (MACDH) are used in technical analyses and stock trading. However, some of them are lagging indicators, affecting the effectiveness in the stock trading and portfolio management. A forecasted MACDH (fMACDH) indicator for predicting next day price by a neuro-fuzzy network, Self-reorganizing Fuzzy Associative Machine (SeroFAM) which has been reported in the prior research work. In order to further reduce the lagging effect, two trading indicators are proposed in this paper: the optimised fMACDH indicator and the fMACDH-fRSI indicator. The optimised fMACDH indicator is derived to extend price forecasting to 1-5 days ahead as the prediction depth, using 1-5 days of historical price data as the input depth. The fMACDH-fRSI indicator is derived by combining the optimized fMACDH indicator and the forecasted RSI (fRSI) indicator. A genetic algorithm (GA) and the fitness functions are designed with the SeroFAM in this paper, which are utilised for optimising parameters of these two proposed indicators. Experiments have been conducted to evaluate and benchmark of the proposed trading indicators optimised by the GA. Two rule-based portfolio rebalancing algorithms are then proposed using the optimised fMACDH trading indicator tuned by the GA: the Tactical Buy and Hold (TBH) and the Rule-Based Business Cycle (RBBC) portfolio rebalancing algorithms. The TBH algorithm takes advantage of relative differences in risk levels to perform rebalancing during trend reversals. The RBBC portfolio rebalancing algorithm takes advantage of the offsets between the business cycles of different market sectors. Experiments have been conducted to evaluate the performance of both algorithms using two sets of portfolios consisting of different assets. The TBH portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by about 26% - 27%; as well outperforms the Buy and Hold strategy by 5% - 40%. The RBBC portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by 54% - 55%; it also outperforms 12 out of the 13 assets with the Buy and Hold strategy, by an average performance of about 166%. The results are highly encouraging with consistent performances achieved in dynamic portfolio rebalancing

New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic

In this paper, two hybrid models are used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine (SVM) and Heuristic Algorithms of Imperialist Competition and Genetic. In the first model, SVM and Imperialist Competition Algorithm (ICA) are developed for stock market timing in which ICA is used to optimize the SVM parameters. In the second model, SVM is used with Genetic Algorithm (GA) where GA is used for feature selection in addition to SVM parameters optimization. Here the two approaches, Raw-based and Signal-based are devised on the basis of the literature to generate the input data of the model. For a comparison, the Hit Rate is considered as the percentage of correct predictions for periods of 1–6 day. The results show that SVM-ICA performance is better than SVM-GA and most importantly the feed-forward static neural network of the literature as the standard one

Combination of Facebook Prophet and Attention-Based LSTM with Multi- Source data for Indian Stock Market Prediction

The stock market prediction has been the subject of interest to various researchers and analysts due to its highly unpredictable nature and serves as a perfect example for time series forecasting. Over the years deep learning models such as Long-Term Short-Term Memory and statistical models such as Autoregressive Integrated Moving Average have shown promising results in predicting future stock prices. But the results from these models cannot be generalized as they fail to incorporate the dynamics of the market and influence of several external factors such as political, social, investor\u27s emotion, etc on stock markets. Recently Facebook’s creation of the Prophet model solely for time series forecasting has been successful in fitting the trends and seasonality of the data accurately compared to vanilla models. This research proposes a unique combination of the newly developed Facebook Prophet model and Attention-Based Long-Term Short-Term Memory model to predict the adjacent closing price of NIFTY 50 stocks to fit both the seasonality and non-linearity component of stock price data. Further to encompass both market and investor sentiments influencing stock prediction, data from five sources are collected from 01/01/2015 to 31/12/2019 namely historic stock price, technical indicators, news articles scraped from multiple news sources, and tweets collected from a verified Twitter account. To extract sentiments from unlabelled news and tweet data this research takes upon an unsupervised approach by implementing a pre-trained Bidirectional Encoder Representations from Transformers base uncased model. The proposed model is trained and validated on eight combinations of the dataset created by merging data from multiple sources and compared with the performance of the baseline Facebook Prophet model trained and tested with data from a single source i.e., historic stock prices. The proposed model resulted in the least Mean Absolute Percentage Error ranging from 3.3 to 7.7 for all the combinations of the data in comparison to the baseline model which achieved the highest Mean Absolute Percentage Error of 11.67

Behavior of exposed column base plate connection subjected to combined axial load and biaxial bending

Column base plate (CBP) connections are one of the most crucial structural components of steel structures that act as a transfer medium for all the forces and moments from the entire building into the foundation. Importance of this type of connection becomes significant when the structure experiences dynamic loading, such as wind or earthquake, which incorporates dynamic effects in the structure that need to be transferred to the foundation. Considerable research efforts have been made over the past few decades on CBP connections, which led to the publication of AISC Design Guide 1 (2006) for CBP design. This design guide is still widely used in the industry. All the previous studies and design guidelines considered only the uniaxial (major axis) bending moment combined with axial load for CBP connection design. However, very often the base plate experiences a bidirectional bending moment from lateral loads during any dynamic loading event. Although, the column is designed and checked under combined axial load and bi-axial bending, when it comes to the base plate connection, only the axial load and major axis bending are considered. Therefore, the objective of this research is to investigate the behavior of CBP connections subjected to combined axial load and biaxial bending through an extensive numerical parametric study, using general purpose finite element software ABAQUS. For this numerical study, an accurate nonlinear finite element (FE) model is developed, considering both geometric and material nonlinearities and validated against experimental results that are available in the literature subjected to monotonic and uniaxial cyclic loading. Validation results show that the developed FE model can effectively simulate force transfer at major contact interfaces in the connection. Concurrently, a database of CBP connection subjected to axial load and uniaxial bending, is constructed from the literature to identify the influential parameters as well as different failure modes of the CBP connection, using Machine Learning (ML) approach. Among nine different ML models, the Decision tree based ML model provides an overall accuracy of 91% for identifying the failure mode whereas base plate thickness, embedment length, and anchor rod diameter are found to be the influential parameters that govern the failure mode of CBP connections. Therefore, a total of 20 different FE models that have different base plate thicknesses and yield strengths, anchor bolt sizes and quantity as well as embedment lengths, grout thicknesses and axial load ratios are developed. Furthermore, a bidirectional symmetric lateral loading protocol is developed and applied with constant axial compressive load in the developed models. The study reveals that the thickness of base plate and anchor rod diameter are the governing parameters for different base connection behavior such as moment rotation response, maximum bolt tensile force, and yield line pattern of the base plate. Moreover, the rigidity of the base plate connection is found to be in the semi-rigid region under biaxial bending condition. Finally, this study found that the available methods for uniaxial bending overpredicts the connection rotational stiffness compared to the stiffness obtained from numerical analysis considering biaxial bending

Análisis de la efectividad y estabilidad de una combinación de indicadores de Análisis Técnico (Estocástico y el Índice de Fuerza Relativa) en el mercado accionario colombiano en el Período 2009 – 2019

El objetivo de la presente investigación es analizar el efecto de una combinación de indicadores técnicos en el mercado accionario colombiano en términos de efectividad y estabilidad durante el periodo 2009-2019. Para tal fin se utilizaron dos indicadores populares y que han demostrado en diversas investigaciones obtener buenos resultados como el Índice de Fuerza Relativa y el Indicador Estocástico para generar un solo indicador, el cual se va a llamar combinación. Las rentabilidades obtenidas fueron comparadas con la estrategia pasiva y los resultados fueron contrastados con la Hipótesis de Mercados Eficientes y la Teoría de la Caminata aleatoria mediante pruebas de robustez y simulación Bootstrapping para validar la significancia estadística de los resultados. La evidencia empírica de la investigación sugiere que, luego de incluir los costos de transacción, tanto la combinación como los indicadores técnicos por separado no superaron de manera efectiva y estable a la estrategia pasiva.The objective of this research is to analyses the effect of a combination of technical indicators on the Colombian stock market in terms of effectiveness and stability during the 2009-2019 period. For this purpose, two popular indicators were used that have been shown in many researches to obtain good results such as the Relative Strength Index and the Stochastic Indicator to generate a single indicator, this is called combination. The yields obtained were compared with the passive strategy and the results were contrasted with the Efficient-Market Hypothesis and the Theory of the Random Walk through robustness tests and Bootstrapping simulation to validate the statistical significance of the results. Empirical evidence from the research suggests that, after including transaction costs, both the combination and the separate technical indicators do not effectively and stably beat the passive strategy.Línea de Investigación: Gestión FinancieraMaestrí

Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm

A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is developed and tested on a multi-modal test problem; a modified Rastragin\u27s function. An enhanced Particle Swarm Optimization method was derived from the initial testing. Optimization of a diesel engine was carried out using the modified algorithm demonstrating an improvement of 83% compared with the unmodified PSO algorithm. Additionally, an approach to enhancing the training of ML models by leveraging Virtual Sensing as an alternative to standard multi-layer neural networks is presented. Substantial gains were made in the prediction of Particulate matter, reducing the MMSE by 50% and improving the correlation R^2 from 0.84 to 0.98. Improvements were made in models of PM, NOx, HC, CO, and Fuel Consumption using the method, while training times and convergence reliability were simultaneously improved over the traditional approach