VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning

Volatility-based trading strategies have attracted a lot of attention in financial markets due to their ability to capture opportunities for profit from market dynamics. In this article, we propose a new volatility-based trading strategy that combines statistical analysis with machine learning techniques to forecast stock markets trend. The method consists of several steps including, data exploration, correlation and autocorrelation analysis, technical indicator use, application of hypothesis tests and statistical models, and use of variable selection algorithms. In particular, we use the k-means++ clustering algorithm to group the mean volatility of the nine largest stocks in the NYSE and NasdaqGS markets. The resulting clusters are the basis for identifying relationships between stocks based on their volatility behaviour. Next, we use the Granger Causality Test on the clustered dataset with mid-volatility to determine the predictive power of a stock over another stock. By identifying stocks with strong predictive relationships, we establish a trading strategy in which the stock acting as a reliable predictor becomes a trend indicator to determine the buy, sell, and hold of target stock trades. Through extensive backtesting and performance evaluation, we find the reliability and robustness of our volatility-based trading strategy. The results suggest that our approach effectively captures profitable trading opportunities by leveraging the predictive power of volatility clusters, and Granger causality relationships between stocks. The proposed strategy offers valuable insights and practical implications to investors and market participants who seek to improve their trading decisions and capitalize on market trends. It provides valuable insights and practical implications for market participants looking to

Buy/Sell Signal Detection in Stock Trading with Bollinger Bands and Parabolic SAR

Technical analysis for stock market with its technical indicators is helpful for traders/investors to predict correct timings either for buying or selling stocks. Trading strategy can be realized with the use of selected trading indicators to know when accumulation or distribution of stocks occurs. This paper proposes trading strategies employing Bollinger Bands and Parabolic SAR indicators. A web-based application is developed to help testing the performance of the proposed strategies. Historical end-of-day (EOD) stock price for a sufficient period of time was used to back-test the proposed strategy performance. Several stocks from LQ-45 were selected to represent up-, down-, and sideway-trends. The results of the research are: the best strategy for an up-trending stock contributes to 17.06% profit and 1.19% for sideway market trend. Whilst when in down-trend, strategy #4 can minimize loss up to 2.62% than the only Bollinger Bands strategy

Stock Market Super-System: Buy and Hold, Trend Following, and Day Trading

This Interactive Qualifying Project is a stock market simulation exploring the reliability of three stock trading strategies: buy and hold, day trading, and trend following. Each strategy was tested in a separate simulation, each using the same budget and executed simultaneously over a seven week period. Ten companies were selected for use in all three simulations and general corporate histories were reviewed. The goal of the project was to produce a final super-system containing percentages of the three basic strategies. The conclusions reached were based on careful analysis of the results. As expected, the final super-system contains primarily buy and hold, followed by trend following, and day trading takes the lowest priority. Overall, this project was successful and informative

Use of Genetic Algorithm in Algorithmic Trading to Optimize Technical Analysis in the International Stock Market (Forex)

Recent studies on financial markets have demonstrated that technical analysis can help us effectively predict the stock market index trend. Business systems are widely used for stock market analysis. This paper uses a genetic algorithm (GA) to develop a stock market trading optimization system. Our proposed system can generate a decision-making strategy for buying, holding, and selling stocks for each day and generate high returns for each stock. The system consists of two stages: removing restricted stocks and producing a stock trading strategy. Accordingly, evolutionary computation, like GA, is highly promising because of its intelligence, flexibility, and search strength (fast and efficient). The multiple-objective nature of the utilized algorithm can be regarded as the center of gravity of the research question. The proper functioning or malfunctioning of the resulting portfolio management can be employed as a benchmark for selecting or discarding the algorithm. On the other hand, the research question is focused on the application of technical analysis indicators. Therefore, both aspects of the research question, namely the multiple-objective nature of the algorithm in terms of the analysis method and technical indicators in terms of features selected for analysis, must be taken into account

Evaluation of the pairs-trading strategy on the Toronto Stock Exchange : 2001-2010

1 online resource (30 p.) : col. ill.Includes abstract.Includes bibliographical references (p. 29-30).This paper focuses on evaluating the return characteristics of pairs-trading strategy on the Toronto Stock Exchange. Through analysis of trading results, we found a significant evidence to indicate that pairs trading strategy is consistent in achieving profitability on Toronto Stock Exchange. Moreover, profitability has a decreasing trend during the sample period of 2001 to 2010 and it is largely distorted by the global financial crisis from 2007-2009

Portfolio Management Using Artificial Trading Systems Based on Technical Analysis

Evolutionary algorithms consist of several heuristics able to solve optimization tasks by imitating some aspects of natural evolution. In the \ufb01eld of computational \ufb01nance, this type of procedures, combined with neural networks, swarm intelligence, fuzzy systems and machine learning has been successfully applied to a variety of problems, such as the prediction of stock price movements and the optimal allocation of funds in a portfolio. Nowadays, there is an increasing interest among computer scientists to solve these issues concurrently by de\ufb01ning automatic trading strategies based on arti\ufb01cial expert systems, technical analysis and fundamental and economic information. The objective is to develop procedures able, from one hand, to mimic the practitioners behavior and, from the other, to beat the market. In this sense, Fernandez-Rodr\uedguez et al. (2005) investigate the pro\ufb01tability of the generalized moving average trading rule for the General Index of Madrid Stock Market by optimizing parameter values with a genetic algorithm. They conclude that the optimized trading rules are superior to a risk-adjusted buy-and-hold strategy if the transaction costs are reasonable. Similarly, Papadamou & Stephanides (2007) present the GATradeTool, a parameter optimization tool based on genetic algorithms for technical trading rules. In the description of this software, they compare it with other commonly used, non-adaptive tools in terms of stability of the returns and computational costs. Results of the tests on the historical data of a UBS fund show that GATradeTool outperforms the other tools. Fern\ue1ndez-Blanco et al. (2008) propose to use the moving average convergence divergence technical indicator to predict stock indices by optimizing its parameters with a genetic algorithm. Experimental results for the Dow Jones Industrial Average index con\ufb01rm the capability of evolutionary algorithms to improve technical indicators with respect to the classical con\ufb01gurations adopted by practitioners. An alternative approach to generate technical trading systems for stock timing that combines machine learning paradigms and a variable length string multi-objective genetic algorithm is proposed in Kaucic (2010). The most informative technical indicators are selected by the genetic algorithm and combined into a unique trading signal by a learning method. A static single-position automated day trading strategy between the S&P 500 Composite Index and the 3-months Treasury Bill is analyzed in three market phases, up-trend, down-trend and sideways-movements, covering the period 2000-2006. The results indicate that the near-optimal set of rules varies among market phases but presents stable results and is able to reduce or eliminate losses in down-trend periods. As a natural consequence of these studies, evolutionary algorithms may constitute a promising tool also for portfolio strategies involving more than two stocks. In the \ufb01eld of portfolio selection, Markowitz and Sharpe models are frequently used as a task for genetic algorithm optimization. For instance, the problem of \ufb01nding the ef\ufb01cient frontier associated with the standard mean-variance portfolio is tackled by Chang et al. (2000). They extend the standard model to include cardinality and composition constraints by applying three heuristic algorithms based upon genetic algorithms, tabu search and simulated annealing. Computational results are presented for \ufb01ve data sets involving up to 225 assets. Wilding (2003) proposes a hybrid procedure for portfolio management based on factor models, allowing constraints on the number of trades and securities. A genetic algorithm is responsible for selecting the best subset of securities that appears in the \ufb01nal solution, while a quadratic programming routine determines the utility value for that subset. Experiments show the ability of this approach to generate portfolios highly able to track an index. The \u3b2 12 G genetic portfolio algorithm proposed by Oh et al. (2006) selects stocks based on their market capitalization and optimizes their weights in terms of portfolio \u3b2\u2019s standard deviation. The performance of this procedure depends on market volatility and tends to register outstanding performance for short-term applications. The approach I consider for portfolio management is quite different from the previous models and is based on technical analysis. In general, portfolio optimizations using technical analysis are modular procedures where a module employs a set of rules based on technical indicators in order to classify the assets in the market, while another module concentrates on generating and managing portfolio over time (for a detailed presentation of the subject, the interested reader may refer to Jasemi et al. (2011)). An interesting application in this context is the approach developed by Korczak & Lipinski (2003) that leads to the optimization of portfolio structures by making use of arti\ufb01cial trading experts, previously discovered by a genetic algorithm (see Korczak & Roger (2002)), and evolutionary strategies. The approach has been tested using data from the Paris Stock Exchange. The pro\ufb01ts obtained by this algorithm are higher than those of the buy-and-hold strategy. Recently, Ghandar et al. (2009) describe a two-modules interacting procedure where a genetic algorithm optimizes a set of fuzzy technical trading rules according to market conditions and interacts with a portfolio strategy based on stock ranking and cardinality constraints. They introduce several performance metrics to compare their portfolios with the Australian Stock Exchange index, showing greater returns and lower volatility. An alternative multi-modular approach has been developed by Gorgulho et al. (2011) that aims to manage a \ufb01nancial portfolio by using technical analysis indicators optimized by a genetic algorithm. In order to validate the solutions, authors compare the designed strategy against the market itself, the buy-and-hold and a purely random strategy, under distinct market conditions. The results are promising since the approach outperforms the competitors. As the previous examples demonstrate, the technical module occupies, in general, a subordinate position relative to the management component. Since transaction costs, cardinality and composition constraints are of primary importance for the rebalancing purpose, the effective impact of technical signals in the development of optimal portfolios is not clear. To highlight the bene\ufb01ts of using technical analysis in portfolio management, I propose an alternative genetic optimization heuristic, based on an equally weighted zero investment strategy, where funds are equally divided among the stocks of a long portfolio and the stocks of a short one. Doing so, the trading signals directly in\ufb02uence the portfolio construction. Moreover, I implement three types of portfolio generation models according to the risk-adjusted measure considered as the objective, in order to study the relation between portfolio risk and market condition changes. The remainder of the chapter is organized as follows. Section 2 explains in detail the proposed method, focusing on the investment strategy, the de\ufb01nitions of the technical indicators and the evolutionary learning algorithm adopted. Section 3 presents the experimental results and discussions. Finally, Section 4 concludes the chapter with some remarks and ideas for future improvements

Is Technical Analysis Profitable on Renewable Energy Stocks? Evidence from Trend-Reinforcing, Mean-Reverting and Hybrid Fractal Trading Systems

Demand for power sources is gradually shifting from ozone-depleting-substances towards renewable and sustainable energy resources. The growth prospects of the renewable energy industry coupled with improved cost efficiency means that renewable energy companies offer potential returns for traders in stock markets. Nonetheless, there have been no studies investigating technical trading rules in renewable energy stocks by amalgamating fractal geometry with technical indicators that focus on different market phases. In this paper, we explore the profitability of technical analysis using a portfolio of 20 component stocks from the NASDAQ OMX Renewable Energy Generation Index using fractal dimension together with trend-reinforcing and mean-reverting (contrarian) indicators. Using daily prices for the period 1 July 2012 to 30 June 2022, we apply several tests to measure trading performance and risk-return dynamics of each form of technical trading system—both in isolation and simultaneously. Overall, trend (contrarian) trading system outperforms (underperforms) the naïve buy-and-hold policy on a risk-adjusted basis, while the outcome is further enhanced (reduced) by the fractal-reinforced strategy. Simultaneous use of both trend-reinforcing and mean-reverting indicators strengthened by fractal geometry generates the best risk-return trade-off, significantly outperforming the benchmark. Our findings suggest that renewable energy stock prices do not fully capture historical price patterns, allowing traders to earn significant profits from the weak form market inefficiency

Fractal Profit Landscape of the Stock Market

We investigate the structure of the profit landscape obtained from the most basic, fluctuation based, trading strategy applied for the daily stock price data. The strategy is parameterized by only two variables, p and q. Stocks are sold and bought if the log return is bigger than p and less than -q, respectively. Repetition of this simple strategy for a long time gives the profit defined in the underlying two-dimensional parameter space of p and q. It is revealed that the local maxima in the profit landscape are spread in the form of a fractal structure. The fractal structure implies that successful strategies are not localized to any region of the profit landscape and are neither spaced evenly throughout the profit landscape, which makes the optimization notoriously hard and hypersensitive for partial or limited information. The concrete implication of this property is demonstrated by showing that optimization of one stock for future values or other stocks renders worse profit than a strategy that ignores fluctuations, i.e., a long-term buy-and-hold strategy.Comment: 12 pages, 4 figure