Tendencias líderes de investigación sobre estrategias de trading

[EN] Trading strategies have attracted the attention of academic researchers and practitioners for a long time, but most specially in recent years due to the explosion of high-quality databases and computation capacity. Numerous studies are devoted to the analysis and proposal of trading strategies which cover aspects such as trend prediction, variables selection, technical analysis, pattern recognition etc. and apply many di erent methodologies. This paper conducts a meta-literature review which covers 1187 research articles from 1984 to 2020. The aim of this paper is to show the increasing importance of the topic and present a systematic study of the leading research areas, countries, institutions and authors contributing to this field. Moreover, a network analysis to identify the main research streams and future research opportunities is conducted.[ES] La creación de estrategias de inversión siempre ha atraído la atención de los académicos y de los inversores profesionales, pero, indudablemente, esta popularidad ha aumentado en los últimos años, con la aparición de bases de datos más completas y mayor potencia de cálculo de las computadoras. Son numerosos los estudios que analizan y proponen estrategias de inversión y que tratan aspectos como la predicción de la tendencia, la selección de variables, el análisis técnico, el reconocimiento de patrones etc. aplicando diferentes metodologías. En este trabajo se realiza un estudio bibliográfico que abarca 1187 artículos de investigación desde 1984 hasta 2020. El objetivo es mostrar la creciente importancia de este campo de investigación y presentar un análisis sistemático de los países, instituciones y autores que más están contribuyendo al avance del conocimiento. Además, se realiza un análisis de redes para identificar las principales áreas de investigación y las tendencias futuras.Oliver-Muncharaz, J.; García García, F. (2020). Leading research trends on trading strategies. Finance, Markets and Valuation. 6(2):27-54. https://doi.org/10.46503/LHTP1113S27546

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

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

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

I Don't Care Whose Fault It Is! Or, An Introduction to the Short-Term Forecasting Theory, Implementing Fuzzy-logic and Neural Networks

The Independent Studies program closed in 2016. This thesis was one of 25 accepted by Library for long-term preservation and presentation in UWSpace.In contradiction with much conventional economic theory, this thesis argues that successful short-term forecasting is both possible and practicable. Beginning with the assumption, and widely-held belief, that there are patterns to be discovered in the stock market, the thesis develops the Short-Term Forecasting Theory (STFT) to demonstrate how useful and accurate short-term forecasts might be achieved. In short, this thesis posits that, if short-term financial forecasting of an equity can be broken down to a mechanical procedure, the problem of short-term forecasting is reduced to the question of finding the proper tools for this procedure. This thesis presents two computing methods – fuzzy logic and neural networks – that, when combined, could serve as an appropriate tool for implementation

Machine Learning-Driven Decision Making based on Financial Time Series

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