Search

A trading system in any stock market is built on long-term, intermediate-term, and short-term indicators. Some 'lagging' indicators, such as the simple and exponential moving averages, can be used to determine the direction of a medium- to long-term trend. Some 'leading' oscillators, on the other hand, can tell a trader whether or not a trend is losing momentum. This paper examines how well moving average envelopes and Bollinger Bands measure stock price volatility, and how useful these technical analysis tools are for short-term horizons. The paper then attempts to evaluate the speed of these indicators in order to explain the sensitivity and response time of data collected from a secondary survey in the Indian capital market. The article concludes that moving average envelopes outperform Bollinger Bands in real trading settings, since technical trading rules are generally designed for short-term investments. Bollinger Bands can detect abrupt price fluctuations, however they are not more effective than moving average envelopes to measure profitability. [ABSTRACT FROM AUTHOR]

Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS's oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization. [ABSTRACT FROM AUTHOR]

Quantitative investment trading is becoming more and more popular due to the gradual integration of computer technology, mathematics, and statistics. It is of great practical significance to develop a multi-species portfolio investment model that takes into account various transaction costs and conforms to live trading. In this paper, we use the free software R to program the Bollinger Bands trading strategy and test it on the historical data of the Chinese futures market. Through in-sample optimization, out-of-sample testing and correlation test, the varieties with good back testing effect are selected for risky investment portfolio to provide investors involved in the Chinese futures market with specific trading strategies that can be used for reference, and at the same time to provide investors with a way of thinking to develop quantitative investment portfolio models. [ABSTRACT FROM AUTHOR]

Technical analysis helps investors to better time their entry and exit from financial asset positions. This methodology relies solely on past information on financial assets price and volumes to predict a financial asset's future price trend. Modern research has established that combined with other sentiment measures such as social media, it can outperform the standard buy and hold strategy. Moreover, it has been documented that novice and professional investors technical analysis in their investing strategy. An experienced investor should combine fundamental analysis and technical analysis for better trading results. Programmers use technical analysis to create algorithmic trading systems that learn and adapt to the changing trading environments and perform trading accordingly without human involvement. There are hundreds of technical tools offered by known trading platforms. investors must use specific tools that fit their trading style and risk adoption. Moreover, different financial assets such as stocks, exchange trade funds (ETFs), cryptocurrency, futures, and commodities demand different sets of tools. Furthermore, investors should use these tools according to the time frame they use for trading. This paper will discuss different technical tools that are used to help traders of different time frames and different financial assets to achieve better returns over the traditional buy and hold strategy. [ABSTRACT FROM AUTHOR]

Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS’s oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization.

The article "Envelopes, Bollinger Bands, and Ichimoku Clouds in Cryptoasset Trading" presents a technical explanation of three financial indicators used in cryptoasset trading: Envelopes, Bollinger Bands, and Ichimoku Clouds. These indicators are used by traders to develop trading strategies in the cryptoasset market. The article describes the trading signals that can be obtained using these indicators and mentions other indicators that complement their analysis. The article focuses on the "Ichimoku Clouds" indicator, which uses multiple lines and clouds to identify momentum signals, support and resistance areas, and bullish or bearish trends. The importance of using other indicators to confirm trends and minimize trading risks is highlighted. It is concluded that while these indicators are useful, none of them are infallible, and it is necessary to compare them with other indicators. [Extracted from the article]

An investor uses the graphical presentation of Bollinger Bands to get signals of the ups and downs, as well the volatility of the market from the expansion and tightening of the UBB and LBB, reflecting higher and lower volatility. The percent (%) b helps determine the opportunities during extreme periods from the market, looking at the concentration of line graph at the value "0" or "1" reflecting the bearish and bullish trend, respectively. The Bandwidth Index was able to picture out the bullish trend with a squeeze at the upper band. The positive unimodality of Q for NEPSE daily return for the period of the fiscal year 1998–1999 to the fiscal year 2019–2020 indicated normality for the market return. Nevertheless, the results for the trading signals based on the Bollinger bands are seen as useful for an investor by giving a clear signal to "buy" or "sell". At the same time, relying only on Bollinger Bands with a specific period MA, i.e. the Bollinger Bands with a shorter moving average (MA) shows higher fluctuations and vice-versa, hence, could show false signals while choosing inappropriate MA, therefore, help of other technical analysis tools should be taken while going for an investment decision.

Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty etc. Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guided Deep Learning (DL) models has been developed on nonlinear and nonstationary price data of agricultural commodities for forecasting by considering technical indicators viz., Moving Average (MA), Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), Exponential MA (EMA) and Fast Fourier Transformation (FFT). HM Models (HMMs) can effectively handle the sequential dependencies and hidden states, while DL approach can learn complex patterns and relationships within the price series and thus the drawback of lack of generalization capability in the DL model has been overcome by HMM. In this study, the Potato price data of the Champadanga district of West Bengal, India has been utilized to assess the performance of the proposed technique. HMM has been combined with six baseline DL models viz., Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) for forecast modeling. Performance evaluation metrics viz., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and the insightful Diebold–Mariano (DM) test revealed that Hidden Markov hybridized with DL models surpassed baseline DL models in forecasting accuracy for 1-week, 4-week, 8-week and 12-week ahead DL predictions. The proposed approach holds significant promise for enhancing the precision of agricultural commodity price forecasting with far-reaching implications for various stakeholders such as farmers and planners. • A novel Hidden Markov based Deep Learning (DL) models for accurate agricultural commodity price forecasting. • Addresses DL model generalization challenges by integrating sequential dependencies and hidden states obtained from HMMs. • Proposed models outperform baseline models - RNN, CNN, LSTM, GRU, BiLSTM, and BiGRU for price predictions of agriculture commodities. • Evaluated using RMSE, MAPE, MAE and Diebold Mariano test, for accuracy and reliability of the proposed approach. • Offers precision in commodity price forecasts, benefiting stakeholders such as farmers, planners and policymakers. [ABSTRACT FROM AUTHOR]

Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns. [ABSTRACT FROM AUTHOR]

We use mixture of percentile functions to model credit spread evolution, which allows to obtain a flexible description of indices and their components at the same time. We show regularity results in order to extend mixture percentile to the dynamic case. We characterize the stochastic differential equation of the flow of cumulative distribution function and we link it with the ordered list of the components of the credit index. The main financial goal is to introduce a functional version of Bollinger bands. The crossing of bands by the spread is associated with a trading signal. Finally, we show the richness of the signals produced by functional Bollinger bands compared with standard one with a practical example in credit asset. [ABSTRACT FROM AUTHOR]

In the financial market, commodity prices change over time, yielding profit opportunities. Various trading strategies have been proposed to yield good earnings. Pairs trading is one such critical, widely-used strategy with good effect. Given two highly correlated paired target stocks, the strategy suggests buying one when its price falls behind, selling it when its stock price converges, and operating the other stock inversely. In the existing approach, the genetic Bollinger Bands and correlation-coefficient-based pairs trading strategy (GBCPT) utilizes optimization technology to determine the parameters for correlation-based candidate pairs and discover Bollinger Bands-based trading signals. The correlation coefficients are used to calculate the relationship between two stocks through their historical stock prices, and the Bollinger Bands are indicators composed of the moving averages and standard deviations of the stocks. In this paper, to achieve more robust and reliable trading performance, AGBCPT, an advanced GBCPT algorithm, is proposed to take into account volatility and more critical parameters that influence profitability. It encodes six critical parameters into a chromosome. To evaluate the fitness of a chromosome, the encoded parameters are utilized to observe the trading pairs and their trading signals generated from Bollinger Bands. The fitness value is then calculated by the average return and volatility of the long and short trading pairs. The genetic process is repeated to find suitable parameters until the termination condition is met. Experiments on 44 stocks selected from the Taiwan 50 Index are conducted, showing the merits and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.

In the financial market, commodity prices change over time, yielding profit opportunities. Various trading strategies have been proposed to yield good earnings. Pairs trading is one such critical, widely-used strategy with good effect. Given two highly correlated paired target stocks, the strategy suggests buying one when its price falls behind, selling it when its stock price converges, and operating the other stock inversely. In the existing approach, the genetic Bollinger Bands and correlation-coefficient-based pairs trading strategy (GBCPT) utilizes optimization technology to determine the parameters for correlation-based candidate pairs and discover Bollinger Bands-based trading signals. The correlation coefficients are used to calculate the relationship between two stocks through their historical stock prices, and the Bollinger Bands are indicators composed of the moving averages and standard deviations of the stocks. In this paper, to achieve more robust and reliable trading performance, AGBCPT, an advanced GBCPT algorithm, is proposed to take into account volatility and more critical parameters that influence profitability. It encodes six critical parameters into a chromosome. To evaluate the fitness of a chromosome, the encoded parameters are utilized to observe the trading pairs and their trading signals generated from Bollinger Bands. The fitness value is then calculated by the average return and volatility of the long and short trading pairs. The genetic process is repeated to find suitable parameters until the termination condition is met. Experiments on 44 stocks selected from the Taiwan 50 Index are conducted, showing the merits and effectiveness of the proposed approach.

Copyright of Economic & Political Thought / Myśl Ekonomiczna & Polityczna is the property of Lazarski University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sale and purchase (S&P) of secondhand vessels is a key source of profit for dry bulk shipowners, and profitability of such transaction depends on timing decisions. Using a sample of ship prices from June 1986 to 2014, this study applies four technical indicators to identify optimal trading timing for dry bulk ships. Some simulated results are found in the study. Firstly, among four technical indicators, Bollinger bands were found to be optimal for capsize and handysize vessels. Moreover, technical strategies outperform a benchmark buy-and-hold strategy, and they could be more effective in the markets for larger ships (implying lower market efficiency). Thirdly, the investment performance of technical strategies in most vessel sizes is superior to that of the stock market. Finally, delaying a transaction due to market illiquidity can result in shipowners missing ideal timing, thus incurring significant losses, particularly in the case of smaller vessels. [ABSTRACT FROM AUTHOR]

Statistical arbitrage is based on pairs trading of mean-reverting returns. We used cointegration approach and ECM-DCC-GARCH to construct 98 pairs of 152 stocks of 3 currencies. Stocks trading is done by Contract for Difference (CFD), a financial derivative product which facilitates short selling and provides a leverage up to 25 times. To measure the performance of a leveraged strategy, we introduced the profit factor which is the annualized return rate per unit risk. And the historical risk is measured by maximum drawdown. We compared three main strategies: percentage, standard deviation of cointegration long-term residuals and Bollinger Bands (dynamic standard deviation), with and without double confirmation of short-term standard deviation modelled by ECM-DCC-GARCH. Each of the three main strategies is optimized by two optimizers: absolute profit and profit factor. The optimization period goes from 2012-01-01 to 2014-12-31, and validation period is from 2015-01-01 to 2016-06-01. Our results showed that the USD Bollinger Bands strategy without double confirmation and optimized by profit factor, outperformed other strategies and provided the highest annualized return rate per unit risk; 32% of our sample pairs ended up in loss, and 94% of which are explained by a cointegration break during the testing period. [ABSTRACT FROM AUTHOR]

Technical analysis is an important part of financial industry, research, and teaching. The methodology has two parts: i) calculation of the individual tools and ii) visual representations. In this article, I provide a community-contributed command, candlechart, to draw the most common technical analysis charts. My intent is to draw these charts similarly to industry examples. The popular candle price chart is combined with charts for volume, moving-average convergence divergence, relative strength index, and Bollinger bands. [ABSTRACT FROM AUTHOR]

Binary options are a new instrument of the financial market. The aim of this paper is to analyze the use of binary options with trading and to illustrate this on the practical example of trades based on Bollinger bands indicator. Currency pair EUR/USD and 6912 time series values of this instrument will be put to analysis. The contribution will be evaluated 8 strategies based on Bollinger Bands. There will be used a backtesting method. From the results follows the most trades could have been realized with the use of Bollinger bands with a double deviation. This strategy, however, also showed the greatest percentage of failed trades. On the contrary the fewest transactions could have been carried out with Bollinger bands with a triple deviation and the MACD filter.

Monitoring the performance of a photovoltaic (PV) system when environmental parameters are not available is very difficult. Comparing the energy datasets of the arrays belonging to the same PV plant is one strategy. If the extension of a PV plant is limited, all the arrays are subjected to the same environmental conditions. Therefore, identical arrays produce the same energy amount, whatever the solar radiation and cell temperature. This is valid for small- to medium-rated power PV plants (3–50 kWp) and, moreover, this typology of PV plants sometimes is not equipped with a meteorological sensor system. This paper presents a supervision methodology based on comparing the average energy of each array and the average energy of the whole PV plant. To detect low-intensity anomalies before they become failures, the variability of the energy produced by each array is monitored by using the Bollinger Bands (BB) method. This is a statistical tool developed in the financial field to evaluate the stock price volatility. This paper introduces two modifications in the standard BB method: the exponential moving average (EMA) instead of the simple moving average (SMA), and the size of the width of BB, set to three times the standard deviation instead of four times. Until the produced energy of each array is contained in the BB, a serious anomaly is not present. A case study based on a real operating 19.8 kWp PV plant is discussed.

This paper introduces a new protection system for solar photovoltaic generator (SPVG)-connected networks. The system is a combination of voltage-restrained overcurrent relays (VROCRs) and directional overcurrent relays (DOCRs). The DOCRs are implemented to sense high fault current on the grid side, and VROCRs are deployed to sense low fault current supplied by the SPVG. Furthermore, a novel challenge for the optimal coordination of DOCRs-DOCRs and DOCRs-VROCRs is formulated. Due to the inclusion of additional constraints of VROCR, the relay coordination problem becomes more complicated. To solve this complex problem, a hybrid Harmony Search Algorithm-Bollinger Bands (HSA-BB) method is proposed. Also, the lower and upper bands in BB are dynamically adjusted with the generation number to assist the HSA in the exploration and exploitation stages. The proposed method is implemented on three different SPVG-connected networks. To exhibit the effectiveness of the proposed method, the obtained results are compared with the genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search algorithm (CSA), HSA and hybrid GA-nonlinear programming (GA-NLP) method. Also, the superiority of the proposed method is evaluated using descriptive and nonparametric statistical tests.

This article describes an algorithm for stock pairs trading using financial multipliers of underlying companies. This algorithm has been tested on historical data and compared with classical Bollinger bands strategy. The results of tests were presented for two financial sectors of US stock market.

In this study, a new algorithm that will improve the performance and the solution quality of the ABC (artificial bee colony) algorithm, a swarm intelligence based optimization algorithm is proposed. ABC updates one parameter of the individuals before the fitness evaluation. Bollinger bands is a powerful statistical indicator which is used to predict future stock price trends. By the proposed method an additional update equation for all ABC-based optimization algorithms is developed to speed up the convergence utilizing the statistical power of the Bollinger bands. The proposed algorithm was tested against classical ABC algorithm and recent ABC variants. The results of the proposed method show better performance in comparison with ABC-based algorithm with one parameter update in convergence speed and solution quality. [ABSTRACT FROM AUTHOR]

The main subject of this paper is to find the right approach to the evaluation of stock and predicting the moment in which investors should take action by using different approaches and methods during a certain period of time. This analysis has been made by using stock prices quoted on the Zagreb Stock Exchange (ZSE). This paper identifies how fast and to what extent turbulences in the global economy may have an impact on Croatian stock market. In the paper are estimated situations in which it is better to rely more on indicators of fundamental analysis than on technical analysis and vice-versa. It is also estimated which indicators should be used as the support to these two analyses. In the paper were explored the movements of the stock prices and CROBEX index and whether they can be used as the signal of a “cyclone” in the economy or presented as the result of a slow down in the economy.

This study compares returns from using two different strategies­ technical analysis and Buy-Hold strategy- of investment in Iranian capital market. For this purpose, we calculate the returns from active strategy arising from Bollinger Bands and Relative Strength Index (RSI) model and compare with the return from using the Buy-Hold as a passive strategy, for the period of 1376-1386. Our results show that during of our study period, the returns from active strategy by using Bollinger Bands and Relative Strength Index (RSI) model do not exceed the return from using the Buy-Hold as a passive strategy in the TSE. However, Bollinger Bands and Relative Strength Index (RSI) model has a lower return variance relative to Buy-Hold strategy. Therefore, in point of view of risk, the active strategy is more advantageous than the passive strategy.

A directional Bollinger bands (BB) method for the detection of defects in plain and twill fabric is presented, whereas a previous BB method was for patterned Jacquard fabric. BB are constructed using the moving average and standard deviation to characterize any irregularities (i.e., defects) in a patterned texture. Every patterned texture constitutes a primitive unit that can be used to generate the texture by a translational rule. The regularity property for a patterned texture can be implicitly regarded as the periodic signals on the rows and columns of an image. To utilize such a regularity property, an embedded shift-invariant characteristic of BB is explored. The original BB method is further developed using directional rotation iterations, which enables the detection of directional defects in plain and twill fabric. The directional BB method is an efficient, fast, and shift-invariant approach that enables defective regions to be clearly outlined. This approach is also immune to the alignment problem that often arises in the original method. The detection accuracies for 77 defective images and 100 defect-free images are 96.1% and 96%, respectively. In a pixel-to-pixel evaluation comparing the detection results of the defective images with the ground-truth images, a 93.51% detection success rate is achieved. [ABSTRACT FROM AUTHOR]