An Examination of Alternative Trading Techniques Using Intraday EUR/USD Currency Prices

Global financial institutions provide a mechanism for multinational corporations to hedge against exchange rate risk via currency futures contracts and spot exchange rates. Currency managers working at these global financial institutions overseeing EUR/USD spot currency traders lack adequate data to determine if alternative trading tools could increase net gains for their respective firms. The purpose of this quantitative study is to examine the net gains from alternative trading techniques that can be utilized by currency managers working for international banks and hedge funds when trading the EUR/USD currency on an intraday basis. A buy and hold strategy, sell and hold strategy, and a Bollinger Band strategy was applied to tick level sample data gathered from 2009 to 2016 to determine net gains from each strategy. The results of an ANOVA test indicate that there is a statistically significant difference, however the Bollinger Band strategy produced an overall net loss from trading. The findings suggest that using an alternative trading strategy, Bollinger Bands, on an intraday basis does not increase net gains from trading activity

Comparative study of central decision makers versus groups of evolved agents trading in equity markets

This paper investigates the process of deriving a single decision solely based on the decisions made by a population of experts. Four different amalgamation processes are studied and compared among one another, collectively referred to as central decision makers. The expert, also referred to as reference, population is trained using a simple genetic algorithm using crossover, elitism and immigration using historical equity market data to make trading decisions. Performance of the trained agent population’s elite, as determined by results from testing in an out-of-sample data set, is also compared to that of the centralized decision makers to determine which displays the better performance. Performance was measured as the area under their total assets graph over the out-of-sample testing period to avoid biasing results to the cut off date using the more traditional measure of profit. Results showed that none of the implemented methods of deriving a centralized decision in this investigation outperformed the evolved and optimized agent population. Further, no difference in performance was found between the four central decision makersAgents, Decision Making, Equity Market Trading, Genetic Algorithms, Technical Indicators

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

Multi Indicator based Hierarchical Strategies for Technical Analysis of Crypto market Paradigm

The usage of technical analysis in the crypto market is very popular among algorithmic traders. This involves the application of strategies based on technical indicators, which shoot BUY and SELL signals to help the investors to take trading decisions. However, instead of depending on the popular myths of the market, a proper empirical analysis can be helpful in lucrative endeavors in trading cryptocurrencies. In this work, four technical indicators namely Exponential Moving Averages (EMA), Bollinger Bands (BB), Relative Strength Index (RSI), and Parabolic Stop And Reverse (PSAR) are used individually to devise strategies that are implemented, and their performance is validated using the price data of Bitcoin from yahoo finance for 2018-22, individually for each year and all the five years consolidated to compute the performance metrics including Profit percentage, Net profitability percentage, and Number of total transactions. The results show that the performance of strategies based on trend indicators is better than that of momentum indicators where the EMA strategy provided the best result with a profit percentage of 394.13%. Further, the performance of these strategies is analyzed in three different market scenarios namely Uptrend/Bullish trend, Downtrend/Bearish trend, and Fluctuating/oscillating markets to analyze the applicability of each of these smart strategies in the three scenarios. Based on the insights obtained from the analysis, Hybrid strategies using multiple indicators with a hierarchical approach are developed whose performance is further improved by imposing constraints in a Downtrend market scenario. The novelty of these algorithms is that they identify the scenario in the market using multiple indicators in a hierarchal approach, and utilize appropriate indicators as per the market scenario. Four strategies namely, Multi indicator based Hierarchical Strategy (MIHS) with EMA9, Multi indicator based Hierarchical Strategy (MIHS) with EMA7, Multi-Indicator based Hierarchical Constrained Strategy (MIHCS) with EMA9, and Multi-Indicator based Hierarchical Constrained Strategy (MIHCS) with EMA7 are developed which give profit percentage of 154.45%, 437.48%, 256.31%, and 701.77% respectively when applied on the Bitcoin price data during 2018-22

Maximizing information on the environment by dynamically controlled qubit probes

We explore the ability of a qubit probe to characterize unknown parameters of its environment. By resorting to quantum estimation theory, we analytically find the ultimate bound on the precision of estimating key parameters of a broad class of ubiquitous environmental noises ("baths") which the qubit may probe. These include the probe-bath coupling strength, the correlation time of generic bath spectra, the power laws governing these spectra, as well as their dephasing times T2. Our central result is that by optimizing the dynamical control on the probe under realistic constraints one may attain the maximal accuracy bound on the estimation of these parameters by the least number of measurements possible. Applications of this protocol that combines dynamical control and estimation theory tools to quantum sensing are illustrated for a nitrogen-vacancy center in diamond used as a probe.Comment: 8 pages + 6 pages (appendix), 3 Figure

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

Supervised Learning Models to Predict Stock Direction Within Different Sectors in a Bull and Bear Market

Forecasting stock market price movement is a well researched and an alluring topic within the machine learning and financial realm. Supervised machine learning algorithms such as Random Forest (RF) and Support Vector Machines (SVM) have been used independently to gain insight on the market. With such volatility in the market the scope of this study will utilized the RF and SVM in a very volatility market to determine if these models will perform at a high level or outperform each other in both markets. This relative study is performed on 16 stocks in 4 different sectors over the bear market ”housing crash” of 2008 . The model utilized technical indicators as the respective parameters to assist in predicting the stock price movement when determining the performance of each model. Despite the No Free Lunch Theorem stating one model can not out perform another model, the study displayed higher accuracy for the RF model. Each model was evaluated using the confusion metrics to calculate the precision, recall, and F1 score

Case based reasoning approach for transaction outcomes prediction on currency markets

This paper presents a case based reasoning approach for making profit in the foreign exchange (forex) market with controlled risk using k nearest neighbour (kNN) and improving on the results with neural networks (NNs) and a combination of both. Although many professionals have proven that exchange rates can be forecast using neural networks for example, poor trading strategies and unpredictable market fluctuation can inevitably still result in substantial loss. As a result, the method proposed in this paper will focus on predicting the outcome of potential trades with fixed stop loss (ST) and take profit (TP) positions1, in terms of a win or loss. With the help of the Monte Carlo method, randomly generated trades together with different traditional technical indicators are fed into the models, resulting in a win or lose output. This is clearly a case based reasoning approach, in terms of searching similar past trade setups for selecting successful trades. There are several advantages over classical forecasting associated with such an approach, and the technique presented in this paper brings a novel perspective to problem of exchange trades predictability. The strategies implemented have not been empirically investigated with such wide a range of time granularities as is done in this paper, in any to the authors known academic literature. The profitability of this approach is back-tested at the end of this paper and highly encouraging results are reported

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