Deep Candlestick Mining

A data mining process we name Deep Candlestick Mining (DCM) is developed using Randomised Decision Trees, Long Short Term Memory Recurrent Neural Networks and k-means++, and is shown to discover candlestick patterns significantly outperforming traditional ones. A test for the predictive ability of novel versus traditional candlestick patterns is devised using all significant candlestick patterns within the traditional or deep mined categories. The deep mined candlestick system demonstrates a remarkable ability to outperform the traditional system by 75.2% and 92.6% on the German Bund 10-year futures contract and EURUSD hourly data

Machine Learning Methods to Exploit the Predictive Power of Open, High, Low, Close (OHLC) Data

Novel machine learning techniques are developed for the prediction of financial markets, with a combination of supervised, unsupervised and Bayesian optimisation machine learning methods shown able to give a predictive power rarely previously observed. A new data mining technique named Deep Candlestick Mining (DCM) is proposed that is able to discover highly predictive dataset specific candlestick patterns (arrangements of open, high, low, close (OHLC) aggregated price data structures) which significantly outperform traditional candlestick patterns. The power that OHLC features can provide is further investigated, using LSTM RNNs and XGBoost trees, in the prediction of a mid-price directional change, defined here as the mid-point between either the open and close or high and low of an OHLC bar. This target variable has been overlooked in the literature, which is surprising given the relative ease of predicting it, significantly in excess of noisier financial quantities. However, the true value of this quantity is only known upon the period's ending – i.e. it is an after-the-fact observation. To make use of and enhance the remarkable predictability of the mid-price directional change, multi-period predictions are investigated by training many LSTM RNNs (XGBoost trees being used to identify powerful OHLC input feature combinations), over different time horizons, to construct a Bayesian optimised trend prediction ensemble. This fusion of long-, medium- and short-term information results in a model capable of predicting market trend direction to greater than 70% better than random. A trading strategy is constructed to demonstrate how this predictive power can be used by exploiting an artefact of the LSTM RNN training process which allows the trading system to size and place trades in accordance with the ensemble's predictive certainty

Machine Learning-Driven Decision Making based on Financial Time Series

L'abstract è presente nell'allegato / the abstract is in the attachmen

Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange

Foreign Exchange, commonly called Forex, is a form of investment in the non-real sector in great demand. Forex is a marketplace that specializes in foreign exchange trading. Technology advancements have made it easy to monitor investment conditions in real time and present them in an easyto - understand graphical form. As a result, predictions are closely related to investment, starting from market sentiment and economic conditions to technical matters. One of the Artificial Intelligence methods that can be used in classifying is the Support Vector Machine (SVM). SVM is a machine learning classification method based on the Structural Risk Minimization (SRM) principle to find the best hyperplane that separates two classes in the input space that determines the classification decision function by minimizing empirical risk. This study used candlestick patterns to predict foreign exchange chart movements using the Support Vector Machine (SVM) classification method. The purpose of this study was to measure the accuracy of the Support Vector Machine method in making predictions using candlestick patterns so that it can assist traders in making decisions in forex trading. The accuracy level obtained from the data classification results reached 90.72% with a precision of 87.69%. With a relatively good level of accuracy, the Support Vector Machine (SVM) method can be used to predict chart movements in foreign exchange using candlesticks to indicate the current trend’s direction

Estimating the upper limit of prehistoric peak ground acceleration using an in situ, intact and vulnerable stalagmite from Plavecka priepast cave (Detrekoi-zsomboly), Little Carpathians, Slovakia-first results

Earthquakes hit urban centres in Europe infrequently, but occasionally with disastrous effects. Obtaining an unbiased view of seismic hazard (and risk) is therefore very important. In principle, the best way to test probabilistic seismic hazard assessments (PSHAs) is to compare them with observations that are entirely independent of the procedure used to produce PSHA models. Arguably, the most valuable information in this context should be information on long-term hazard, namely maximum intensities (or magnitudes) occurring over time intervals that are at least as long as a seismic cycle. The new observations can provide information of maximum intensity (or magnitude) for long timescale as an input data for PSHA studies as well. Long-term information can be gained from intact stalagmites in natural caves. These formations survived all earthquakes that have occurred over thousands of years, depending on the age of the stalagmite. Their 'survival' requires that the horizontal ground acceleration (HGA) has never exceeded a certain critical value within that time period. Here, we present such a stalagmite-based case study from the Little Carpathians of Slovakia. A specially shaped, intact and vulnerable stalagmite in the Plavecka priepast cave was examined in 2013. This stalagmite is suitable for estimating the upper limit of horizontal peak ground acceleration generated by prehistoric earthquakes. The critical HGA values as a function of time going back into the past determined from the stalagmite that we investigated are presented. For example, at the time of Joko event (1906), the critical HGA value cannot have been higher than 1 and 1.3 m/s(2) at the time of the assumed Carnuntum event (similar to 340 AD), and 3000 years ago, it must have been lower than 1.7 m/s(2). We claimed that the effect of Joko earthquake (1906) on the location of the Plavecka priepast cave is consistent with the critical HGA value provided by the stalagmite we investigated. The approach used in this study yields significant new constraints on the seismic hazard, as tectonic structures close to Plavecka priepast cave did not generate strong earthquakes in the last few thousand years. The results of this study are highly relevant given that the two capitals, Vienna and Bratislava, are located within 40 and 70 km of the cave, respectively.Web of Science2151130111

Spatio-Temporal Sentiment Hotspot Detection Using Geotagged Photos

We perform spatio-temporal analysis of public sentiment using geotagged photo collections. We develop a deep learning-based classifier that predicts the emotion conveyed by an image. This allows us to associate sentiment with place. We perform spatial hotspot detection and show that different emotions have distinct spatial distributions that match expectations. We also perform temporal analysis using the capture time of the photos. Our spatio-temporal hotspot detection correctly identifies emerging concentrations of specific emotions and year-by-year analyses of select locations show there are strong temporal correlations between the predicted emotions and known events.Comment: To appear in ACM SIGSPATIAL 201

Technical Analysis-Based Data Mining Strategies for Stock Market Trend Observation

This study introduces a comprehensive approach that utilizes technical analysis-based data mining strategies to observe and predict stock market trends, by leveraging historical trading data, technical indicators such as moving averages, RSI, and MACD, to systematically analyze and interpret market behavior, thereby providing investors and traders with actionable insights for making informed decisions in the volatile environment of stock trading. By integrating quantitative analysis with predictive modeling, the methodology aims to enhance the accuracy of trend forecasts and identify profitable trading opportunities. Through the application of cross-validation and backtesting techniques, the effectiveness of these strategies is rigorously evaluated against actual market movements, offering a robust framework for risk management and portfolio optimization. This interdisciplinary approach not only demystifies the complexities of the stock market but also opens new avenues for research and development in financial technology, promising a significant contribution to the field of economic forecasting and investment strategy

Predicting stock price direction using machine learning models

The present work aims to evaluate the efficiency of machine learning models in predicting next-day stock price direction, using technical indicators and candlestick patterns. This study considered 440 stocks from the Standard & Poor's 500 index and for each one of them, three different binary models were developed based on the following machine learning algorithms: Deep Neural Network, Support Vector Machine, and Random Forest. Additionally, a naive predictor was created to help compare the models results. The models were judged based on their accuracy and financial returns. The results showed that the machine learning models achieved similar results to the naive model and failed to accurately predict the next-day stock price direction using the selected features, indicating that there is no apparent relationship between them.O presente trabalho visa avaliar a eficiência dos modelos de aprendizagem automática na previsão da direção do preço de ações no dia seguinte, utilizando indicadores técnicos e padrões de vela. Este estudo considerou 440 ações do índice Standard & Poor's 500 e, para cada uma delas, foram desenvolvidos três modelos binários diferentes com base nos seguintes algoritmos de aprendizagem automática: Deep Neural Network, Support Vector Machine, e Random Forest. Além disso, foi criado um naive predictor para ajudar a comparar os resultados dos modelos. Os modelos foram julgados com base na sua precisão e retorno financeiro. Os resultados mostraram que os modelos de aprendizagem automática alcançaram resultados semelhantes aos do modelo naive e não conseguiram prever com precisão a direção do preço das ações no dia seguinte, utilizando as características selecionadas, indicando que não existe uma relação aparente entre os mesmos