An intelligent forex monitoring system

The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. We attempt to compare the performance of a Takagi-Sugeno, type neuro-fuzzy system and a feedforward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. We considered the exchange values of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that the neuro-fuzzy technique performed better than the neural network <br /

Can Deep Learning Improve Technical Analysis of Forex Data to Predict Future Price Movements?

The foreign exchange market (Forex) is the world's largest market for trading foreign money, with a trading volume of over 5.1 trillion dollars per day. It is known to be very complicated and volatile. Technical analysis is the observation of past market movements with the aim of predicting future prices and dealing with the effects of market movements. A trading system is based on technical indicators derived from technical analysis. In our work, a complete trading system with a combination of trading rules on Forex time series data is developed and made available to the scientific community. The system is implemented in two phases: In the first phase, each trading rule, both the AI-based rule and the trading rules from the technical indicators, is tested for selection; in the second phase, profitable rules are selected among the qualified rules and combined. Training data is used in the training phase of the trading system. The proposed trading system was extensively trained and tested on historical data from 2010 to 2021. To determine the effectiveness of the proposed method, we also conducted experiments with datasets and methodologies used in recent work by Hernandez-Aguila et al., 2021 and by Munkhdalai et al., 2019. Our method outperforms all other methodologies for almost all Forex markets, with an average percentage gain of 20.2%. A particular focus was on training our AI-based rule with two different architectures: the first is a widely used convolutional network for image classification, i.e. ResNet50; the second is an attention-based network Vision Transformer (ViT). The results provide a clear answer to the main question that guided our research and which is the title of this paper

The naturalistic turn in economics: implications for the theory of finance

Economics is increasingly adopting the methodological standards and procedures of the natural sciences. The paper analyzes this 'naturalistic turn' from the philosophical perspective on naturalism, and I discuss the implications for the field of finance. The theory of finance is an interesting case in point for the methodological issues, as it manifests a paradigmatic tension between the pure theory of finance and Behavioral Finance. I distinguish between three kinds of naturalism: mark I, the reduction of behavior on psychoneural phenomena, mark II, the transfer of patterns of causal explanations from the natural sciences to the social sciences, mark III, the enrichment of the ontology from observer-independent to observer-relative facts. Building an integrated naturalistic paradigm from these three ingredients, I show that naturalism in economics will only be completed by a simultaneous linguistic turn, with language being analyzed from the naturalistic viewpoint. I relate this proposition with recent results of research into finance, especially connecting Behavioral Finance with the sociology of finance. --Naturalism,causation in economics,neuroeconomics,behavioral finance,social ontology,sociology of finance

Evaluation of the Profitability of Technical Analysis for Asian Currencies in the Forex Spot Market for Short-Term Trading

Technical analysis has garnered an unprecedented amount of interest among short-term traders in the Forex spot market over the past couple of decades. The main purpose of this study is to examine the profitability of technical analysis as applied to three active Asian currencies in the Forex spot market for short-term trading. This study also tests the relationship between various related parameters of currency trading such as Maximum Drawdown, Time in Position, Dealt Lots, Trading Charges and profitability. It covers ten currency pairs, including ten foreign exchange rates of three active Asian currencies in the Forex spot market (the Japanese Yen, Singaporean dollar, and Hong Kong dollar), five time frames involving Intra-day timeframes, and ten technical indicators (5 leading and 5 lagging). The study covers a period of three months running from April 10, 2012 through July 10, 2012. The results indicate that technical analysis is profitable for Asian currencies as attested by the fact that all the currency pairs, time frames and indicators have yielded trading profits in the Forex spot market

Regression genetic programming for estimating trend end in foreign exchange market

Most forecasting algorithms use a physical time scale for studying price movement in financial markets, making the flow of physical time discontinuous. The use of a physical time scale can make companies oblivious to significant activities in the market, which poses a risk. Directional changes is a different and newer approach, which uses an event-based time scale. This approach summarises data into alternating trends called upward directional change and downward directional change. Each of these trends are further dismembered into directional change (DC) event and overshoot (OS) event. We present a genetic programming (GP) algorithm that evolves equations that express linear and non-linear relationships between the length of DC and OS events in a given dataset. This allows us to have an expectation when a trend will reverse, which can lead to increased profitability. This novel trend reversal estimation approach is then used as part of a DC-based trading strategy. We aim to appraise whether the new knowledge can lead to greater excess return. We assess the efficiency of the modified trading strategy on 250 different datasets from five different currency pairs, consisting of intraday data from the foreign exchange (Forex) spot market. Results show that our algorithm is able to return profitable trading strategies and statistically outperform state-of-the-art financial trading strategies, such as technical analysis, buy and hold and other DC-based trading strategies

High-low Strategy of Portfolio Composition using Evolino RNN Ensembles

trategy of investment is important tool enabling better investor's decisions in uncertain finance market. Rules of portfolio selection help investors balance accepting some risk for the expectation of higher returns. The aim of the research is to propose strategy of constructing investment portfolios based on the composition of distributions obtained by using high–low data. The ensemble of 176 Evolino recurrent neural networks (RNN) trained in parallel investigated as an artificial intelligence solution, which applied in forecasting of financial markets. Predictions made by this tool twice a day with different historical data give two distributions of expected values, which reflect future dynamic exchange rates. Constructing the portfolio, according to the shape, parameters of distribution and the current value of the exchange rate allows the optimization of trading in daily exchange-rate fluctuations. Comparison of a high-low portfolio with a close-to-close portfolio shows the efficiency of the new forecasting tool and new proposed trading strategy

Optimising Directional Changes trading strategies with different algorithms

Directional Changes (DC), a novel approach for sampling market data, allows the extraction of trends in financial time series by converting series from a time based format to an event-driven format. This paradigm has been shown to give some predictability in financial prediction, and has been used to generate profitable trading strategies on the FOREX market. In the past, a genetic algorithm was used to optimise the parameters of DC-based trading strategy. The goal of this work is to explore whereas different machine learning algorithms can be used to improve the results on the aforementioned optimisation task. For this purpose, we explore two algorithms, namely Particle Swarm Optimization and Shuffled Frog Leaping Algorithm. After comparing the performance of these two algorithms on 36 different datasets from 4 different currency pairs, we find that they statistically improve the profitability of the DC-based trading strategies

NEUROEVOLUTION AND AN APPLICATION OF AN AGENT BASED MODEL FOR FINANCIAL MARKET

Market prediction is one of the most difficult problems for the machine learning community. Even though, successful trading strategies can be found for the training data using various optimization methods, these strategies usually do not perform well on the test data as expected. Therefore, selection of the correct strategy becomes problematic. In this study, we propose an evolutionary algorithm that produces a variation of trader agents ensuring that the trading strategies they use are different. We discuss that because the selection of the correct strategy is difficult, a variety of agents can be used simultaneously in order to reduce risk. We simulate trader agents on real market data and attempt to optimize their actions. Agent decisions are based on Echo State Networks. The agents take various market indicators as inputs and produce an action such as: buy or sell. We optimize the parameters of the echo state networks using evolutionary algorithms